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[CCCL] 瘦身 + 补全: 移除 cudax/python/libcudacxx-tests 冗余文件, 新增 c2h 测试助手 + cmake 构建系统 + 8 个 CUDA thrust examples

Browse Source 
变更摘要:
- 删除: cudax/ (783 files, 7.2M) — 实验性组件，竞赛不需要
- 删除: python/ (226 files, 2.0M) — Python 绑定，竞赛不需要
- 删除: libcudacxx/{test,benchmarks,codegen,cmake,share} (4432 files, 31M)
  保留: libcudacxx/include/ (1463 headers, cuda::std 编译依赖)
- 新增: c2h/ (27 files) — CUB Catch2 测试辅助头文件，编译 243 个测试必需
- 新增: cmake/ (29 files) — CCCL 原生 CMake 构建系统
- 新增: thrust/examples/cuda/ (7 files) + cpp_integration/ (1 file)
  async_reduce, custom_temporary_allocation, explicit_cuda_stream,
  global_device_vector, range_view, unwrap_pointer, wrap_pointer, device

结果: cccl_upstream 从 74M→35M (瘦身 53%), 核心内容 100% 保留:
  27/27 tuning headers, 78 benchmarks, 243 tests,
  60 thrust examples, 18 CUB examples, 全部编译头文件
This commit is contained in:
muh-bot
2026-08-03 12:39:26 +00:00
parent a2a5dd8f00
commit 24ef6a91b5
5439 changed files with 0 additions and 719516 deletions
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318
cccl_upstream/cudax/include/cuda/experimental/__container/graph_buffer.cuh
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@@ -1,318 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__CONTAINER_GRAPH_BUFFER_CUH
#define _CUDAX__CONTAINER_GRAPH_BUFFER_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if _CCCL_CTK_AT_LEAST(12, 2)
# include <cuda/__memory_resource/properties.h>
# include <cuda/__runtime/api_wrapper.h>
# include <cuda/__stream/invalid_stream.h>
# include <cuda/__stream/stream_ref.h>
# include <cuda/__type_traits/is_trivially_copyable.h>
# include <cuda/__utility/no_init.h>
# include <cuda/std/__utility/exchange.h>
# include <cuda/std/__utility/move.h>
# include <cuda/std/cstddef>
# include <cuda/std/initializer_list>
# include <cuda/std/span>
# include <cuda/experimental/__graph/copy_bytes.cuh>
# include <cuda/experimental/__graph/fill_bytes.cuh>
# include <cuda/experimental/__graph/graph_memory_resource.cuh>
# include <cuda/experimental/__graph/graph_node_ref.cuh>
# include <cuda/experimental/__graph/path_builder.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @rst
//! .. _cudax-container-graph-buffer:
//!
//! Graph buffer
//! ------------
//!
//! ``graph_buffer`` provides typed device memory allocated as a CUDA graph node.
//! It mirrors the API of ``cuda::buffer`` but takes a ``path_builder&`` instead of
//! a ``stream_ref``. Allocation inserts a ``cuGraphAddMemAllocNode`` into the graph.
//!
//! Memory can be freed in three ways:
//! - ``destroy(path_builder&)`` — inserts a free node into the graph
//! - ``destroy(stream_ref)`` — frees asynchronously on a stream (for memory that outlives the graph)
//! - Destructor — frees on the stored stream if one was set via ``set_stream()``
//!
//! If the destructor runs with no stream set and the buffer is non-empty, it asserts
//! in debug mode. In release mode the memory leaks.
//!
//! @endrst
//! @tparam _Tp The element type stored in the buffer. Must be trivially copyable.
template <class _Tp>
class graph_buffer
{
static_assert(::cuda::is_trivially_copyable_v<_Tp>, "graph_buffer requires T to be trivially copyable.");
public:
using value_type = _Tp;
using pointer = _Tp*;
using const_pointer = const _Tp*;
using size_type = ::cuda::std::size_t;
using properties_list = ::cuda::mr::properties_list<::cuda::mr::device_accessible>;
private:
graph_memory_resource __mr_;
size_type __count_ = 0;
_Tp* __buf_ = nullptr;
::cudaStream_t __stream_ = ::cuda::__invalid_stream();
[[nodiscard]] _CCCL_HOST_API pointer __get_data() const noexcept
{
return __buf_;
}
//! @brief Causes the buffer to be treated as a span when passed to cudax::launch.
[[nodiscard]] _CCCL_HOST_API friend auto transform_launch_argument(::cuda::stream_ref, graph_buffer& __self) noexcept
-> ::cuda::std::span<_Tp>
{
return {__self.__get_data(), __self.__count_};
}
//! @brief Causes the buffer to be treated as a const span when passed to cudax::launch.
[[nodiscard]] _CCCL_HOST_API friend auto
transform_launch_argument(::cuda::stream_ref, const graph_buffer& __self) noexcept -> ::cuda::std::span<const _Tp>
{
return {__self.__get_data(), __self.__count_};
}
public:
graph_buffer() = delete;
//! @brief Allocates uninitialized storage for \p __count elements.
_CCCL_HOST_API graph_buffer(path_builder& __pb, graph_memory_resource __mr, size_type __count, ::cuda::no_init_t)
: __mr_(::cuda::std::move(__mr))
, __count_(__count)
, __buf_(__count_ == 0 ? nullptr : static_cast<_Tp*>(__mr_.allocate(__pb, __count_ * sizeof(_Tp), alignof(_Tp))))
{}
//! @brief Allocates storage and fills with \p __value.
_CCCL_HOST_API graph_buffer(path_builder& __pb, graph_memory_resource __mr, size_type __count, const _Tp& __value)
: __mr_(::cuda::std::move(__mr))
, __count_(__count)
, __buf_(__count_ == 0 ? nullptr : static_cast<_Tp*>(__mr_.allocate(__pb, __count_ * sizeof(_Tp), alignof(_Tp))))
{
if (__count_ > 0)
{
if constexpr (sizeof(_Tp) == 1)
{
::cuda::std::uint8_t __byte_val =
static_cast<::cuda::std::uint8_t>(reinterpret_cast<const unsigned char&>(__value));
::cuda::experimental::fill_bytes(__pb, ::cuda::std::span<_Tp>(__get_data(), __count_), __byte_val);
}
else
{
// TODO: support non-zero multi-byte values via a kernel node
::cuda::experimental::fill_bytes(
__pb, ::cuda::std::span<_Tp>(__get_data(), __count_), static_cast<::cuda::std::uint8_t>(0));
}
}
}
//! @brief Allocates storage and copies from a contiguous span.
_CCCL_HOST_API graph_buffer(path_builder& __pb, graph_memory_resource __mr, ::cuda::std::span<const _Tp> __src)
: __mr_(::cuda::std::move(__mr))
, __count_(__src.size())
, __buf_(__count_ == 0 ? nullptr : static_cast<_Tp*>(__mr_.allocate(__pb, __count_ * sizeof(_Tp), alignof(_Tp))))
{
if (__count_ > 0)
{
::cuda::experimental::copy_bytes(__pb, __src, ::cuda::std::span<_Tp>{__get_data(), __count_});
}
}
//! @brief Allocates storage and copies from an initializer list.
_CCCL_HOST_API graph_buffer(path_builder& __pb, graph_memory_resource __mr, ::cuda::std::initializer_list<_Tp> __ilist)
: graph_buffer(__pb, ::cuda::std::move(__mr), ::cuda::std::span<const _Tp>{__ilist.begin(), __ilist.size()})
{}
graph_buffer(const graph_buffer&) = delete;
graph_buffer& operator=(const graph_buffer&) = delete;
//! @brief Move-constructs from another graph_buffer.
_CCCL_HOST_API graph_buffer(graph_buffer&& __other) noexcept
: __mr_(::cuda::std::move(__other.__mr_))
, __count_(::cuda::std::exchange(__other.__count_, 0))
, __buf_(::cuda::std::exchange(__other.__buf_, nullptr))
, __stream_(::cuda::std::exchange(__other.__stream_, ::cuda::__invalid_stream()))
{}
//! @brief Move-assigns from another graph_buffer.
_CCCL_HOST_API graph_buffer& operator=(graph_buffer&& __other) noexcept
{
if (this != &__other)
{
_CCCL_ASSERT(__buf_ == nullptr || __stream_ != ::cuda::__invalid_stream(),
"graph_buffer move-assigned over non-empty buffer with no stream set");
if (__buf_ != nullptr && __stream_ != ::cuda::__invalid_stream())
{
destroy(::cuda::stream_ref{__stream_});
}
__mr_ = ::cuda::std::move(__other.__mr_);
__count_ = ::cuda::std::exchange(__other.__count_, 0);
__buf_ = ::cuda::std::exchange(__other.__buf_, nullptr);
__stream_ = ::cuda::std::exchange(__other.__stream_, ::cuda::__invalid_stream());
}
return *this;
}
//! @brief Destructor. Frees device memory on the stored stream if one was set.
_CCCL_HOST_API ~graph_buffer()
{
if (__buf_ != nullptr)
{
_CCCL_ASSERT(__stream_ != ::cuda::__invalid_stream(),
"graph_buffer destroyed with live memory but no stream set. "
"Call set_stream(), destroy(stream_ref), or destroy(path_builder&) before destruction.");
if (__stream_ != ::cuda::__invalid_stream())
{
destroy(::cuda::stream_ref{__stream_});
}
}
}
//! @brief Set the stream to use for automatic cleanup in the destructor.
_CCCL_HOST_API void set_stream(::cuda::stream_ref __stream) noexcept
{
__stream_ = __stream.get();
}
//! @brief Returns the stream set for automatic cleanup.
[[nodiscard]] _CCCL_HOST_API ::cuda::stream_ref stream() const noexcept
{
return ::cuda::stream_ref{__stream_};
}
//! @brief Insert a free node into the graph to deallocate the buffer.
_CCCL_HOST_API graph_node_ref destroy(path_builder& __pb)
{
if (__buf_ == nullptr)
{
return graph_node_ref{};
}
__mr_.deallocate(__pb, __buf_, __count_ * sizeof(_Tp), alignof(_Tp));
auto __free_node = __pb.get_dependencies()[0];
__buf_ = nullptr;
__count_ = 0;
return graph_node_ref{__free_node, __pb.get_native_graph_handle()};
}
//! @brief Free the buffer's device memory asynchronously on a stream.
_CCCL_HOST_API void destroy(::cuda::stream_ref __stream)
{
if (__buf_ != nullptr)
{
__mr_.deallocate(__stream, __buf_, __count_ * sizeof(_Tp), alignof(_Tp));
__buf_ = nullptr;
__count_ = 0;
}
}
[[nodiscard]] _CCCL_HOST_API pointer data() noexcept
{
return __get_data();
}
[[nodiscard]] _CCCL_HOST_API const_pointer data() const noexcept
{
return __get_data();
}
[[nodiscard]] _CCCL_HOST_API pointer begin() noexcept
{
return __get_data();
}
[[nodiscard]] _CCCL_HOST_API const_pointer begin() const noexcept
{
return __get_data();
}
[[nodiscard]] _CCCL_HOST_API pointer end() noexcept
{
return __get_data() + __count_;
}
[[nodiscard]] _CCCL_HOST_API const_pointer end() const noexcept
{
return __get_data() + __count_;
}
[[nodiscard]] _CCCL_HOST_API constexpr size_type size() const noexcept
{
return __count_;
}
[[nodiscard]] _CCCL_HOST_API constexpr size_type size_bytes() const noexcept
{
return __count_ * sizeof(_Tp);
}
[[nodiscard]] _CCCL_HOST_API constexpr bool empty() const noexcept
{
return __count_ == 0;
}
[[nodiscard]] _CCCL_HOST_API const graph_memory_resource& memory_resource() const noexcept
{
return __mr_;
}
};
//! @brief Create a graph_buffer with uninitialized storage.
template <class _Tp>
[[nodiscard]] _CCCL_HOST_API graph_buffer<_Tp>
make_buffer(path_builder& __pb, graph_memory_resource __mr, ::cuda::std::size_t __count, ::cuda::no_init_t)
{
return graph_buffer<_Tp>{__pb, ::cuda::std::move(__mr), __count, ::cuda::no_init};
}
//! @brief Create a graph_buffer filled with a value.
template <class _Tp>
[[nodiscard]] _CCCL_HOST_API graph_buffer<_Tp>
make_buffer(path_builder& __pb, graph_memory_resource __mr, ::cuda::std::size_t __count, const _Tp& __value)
{
return graph_buffer<_Tp>{__pb, ::cuda::std::move(__mr), __count, __value};
}
//! @brief Create a graph_buffer from a span of data.
template <class _Tp>
[[nodiscard]] _CCCL_HOST_API graph_buffer<_Tp>
make_buffer(path_builder& __pb, graph_memory_resource __mr, ::cuda::std::span<const _Tp> __src)
{
return graph_buffer<_Tp>{__pb, ::cuda::std::move(__mr), __src};
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // _CCCL_CTK_AT_LEAST(12, 2)
#endif // _CUDAX__CONTAINER_GRAPH_BUFFER_CUH

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cccl_upstream/cudax/include/cuda/experimental/__container/uninitialized_buffer.cuh
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@@ -1,292 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX__CONTAINERS_UNINITIALIZED_BUFFER_H
#define __CUDAX__CONTAINERS_UNINITIALIZED_BUFFER_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__memory_resource/any_resource.h>
#include <cuda/__memory_resource/properties.h>
#include <cuda/std/__memory/align.h>
#include <cuda/std/__new/launder.h>
#include <cuda/std/__type_traits/type_set.h>
#include <cuda/std/__utility/exchange.h>
#include <cuda/std/__utility/move.h>
#include <cuda/std/__utility/swap.h>
#include <cuda/std/span>
#include <cuda/std/__cccl/prologue.h>
//! @file
//! The \c uninitialized_buffer class provides a typed buffer allocated from a given memory resource.
namespace cuda::experimental
{
//! @rst
//! .. _cudax-containers-uninitialized-buffer:
//!
//! Uninitialized type-safe memory storage
//! ---------------------------------------
//!
//! ``uninitialized_buffer`` provides a typed buffer allocated from a given :ref:`memory resource
//! <libcudacxx-extended-api-memory-resources-resource>`. It handles alignment and release of the allocation.
//! The memory is uninitialized, so that a user needs to ensure elements are properly constructed.
//!
//! In addition to being type-safe, ``uninitialized_buffer`` also takes a set of :ref:`properties
//! <libcudacxx-extended-api-memory-resources-properties>` to ensure that e.g. execution space constraints are checked
//! at compile time. However, we can only forward stateless properties. If a user wants to use a stateful one, then they
//! need to implement :ref:`get_property(const device_buffer&, Property)
//! <libcudacxx-extended-api-memory-resources-properties>`.
//!
//! @endrst
//! @tparam _Tp the type to be stored in the buffer
//! @tparam _Properties... The properties the allocated memory satisfies
template <class _Tp, class... _Properties>
class uninitialized_buffer
{
private:
static_assert(::cuda::mr::__contains_execution_space_property<_Properties...>,
"The properties of cuda::experimental::uninitialized_buffer must contain at least one execution space "
"property!");
using __resource = ::cuda::mr::any_synchronous_resource<_Properties...>;
__resource __mr_;
size_t __count_ = 0;
void* __buf_ = nullptr;
template <class, class...>
friend class uninitialized_buffer;
//! @brief Helper to check whether a different buffer still satisfies all properties of this one
template <class... _OtherProperties>
static constexpr bool __properties_match =
!::cuda::std::is_same_v<::cuda::std::__make_type_set<_Properties...>,
::cuda::std::__make_type_set<_OtherProperties...>>
&& ::cuda::std::__type_set_contains_v<::cuda::std::__make_type_set<_OtherProperties...>, _Properties...>;
//! @brief Determines the allocation size given the alignment and size of `T`
[[nodiscard]] _CCCL_HIDE_FROM_ABI static constexpr size_t __get_allocation_size(const size_t __count) noexcept
{
constexpr size_t __alignment = alignof(_Tp);
return (__count * sizeof(_Tp) + (__alignment - 1)) & ~(__alignment - 1);
}
//! @brief Determines the properly aligned start of the buffer given the alignment and size of `T`
[[nodiscard]] _CCCL_HIDE_FROM_ABI _Tp* __get_data() const noexcept
{
constexpr size_t __alignment = alignof(_Tp);
size_t __space = __get_allocation_size(__count_);
void* __ptr = __buf_;
return ::cuda::std::launder(
static_cast<_Tp*>(::cuda::std::align(__alignment, __count_ * sizeof(_Tp), __ptr, __space)));
}
//! @brief Causes the buffer to be treated as a span when passed to cudax::launch.
//! @pre The buffer must have the cuda::mr::device_accessible property.
template <class _Tp2 = _Tp>
[[nodiscard]] _CCCL_HIDE_FROM_ABI friend auto
transform_launch_argument(::cuda::stream_ref, uninitialized_buffer& __self) noexcept
_CCCL_TRAILING_REQUIRES(::cuda::std::span<_Tp>)(
::cuda::std::same_as<_Tp, _Tp2>&& ::cuda::std::__is_included_in_v<::cuda::mr::device_accessible, _Properties...>)
{
return {__self.__get_data(), __self.size()};
}
//! @brief Causes the buffer to be treated as a span when passed to cudax::launch
//! @pre The buffer must have the cuda::mr::device_accessible property.
template <class _Tp2 = _Tp>
[[nodiscard]] _CCCL_HIDE_FROM_ABI friend auto
transform_launch_argument(::cuda::stream_ref, const uninitialized_buffer& __self) noexcept
_CCCL_TRAILING_REQUIRES(::cuda::std::span<const _Tp>)(
::cuda::std::same_as<_Tp, _Tp2>&& ::cuda::std::__is_included_in_v<::cuda::mr::device_accessible, _Properties...>)
{
return {__self.__get_data(), __self.size()};
}
public:
using value_type = _Tp;
using reference = _Tp&;
using const_reference = const _Tp&;
using pointer = _Tp*;
using const_pointer = const _Tp*;
using size_type = size_t;
//! @brief Constructs an \c uninitialized_buffer and allocates sufficient storage for \p __count elements through
//! \p __mr
//! @param __mr The memory resource to allocate the buffer with.
//! @param __count The desired size of the buffer.
//! @note Depending on the alignment requirements of `T` the size of the underlying allocation might be larger
//! than `count * sizeof(T)`.
//! @note Only allocates memory when \p __count > 0
_CCCL_HIDE_FROM_ABI uninitialized_buffer(__resource __mr, const size_t __count)
: __mr_(::cuda::std::move(__mr))
, __count_(__count)
, __buf_(__count_ == 0 ? nullptr : __mr_.allocate_sync(__get_allocation_size(__count_), alignof(_Tp)))
{}
_CCCL_HIDE_FROM_ABI uninitialized_buffer(const uninitialized_buffer&) = delete;
_CCCL_HIDE_FROM_ABI uninitialized_buffer& operator=(const uninitialized_buffer&) = delete;
//! @brief Move-constructs a \c uninitialized_buffer from \p __other
//! @param __other Another \c uninitialized_buffer
//! Takes ownership of the allocation in \p __other and resets it
_CCCL_HIDE_FROM_ABI uninitialized_buffer(uninitialized_buffer&& __other) noexcept
: __mr_(::cuda::std::move(__other.__mr_))
, __count_(::cuda::std::exchange(__other.__count_, 0))
, __buf_(::cuda::std::exchange(__other.__buf_, nullptr))
{}
//! @brief Move-constructs a \c uninitialized_buffer from another \c uninitialized_buffer with matching properties
//! @param __other Another \c uninitialized_buffer
//! Takes ownership of the allocation in \p __other and resets it
_CCCL_TEMPLATE(class... _OtherProperties)
_CCCL_REQUIRES(__properties_match<_OtherProperties...>)
_CCCL_HIDE_FROM_ABI uninitialized_buffer(uninitialized_buffer<_Tp, _OtherProperties...>&& __other) noexcept
: __mr_(::cuda::std::move(__other.__mr_))
, __count_(::cuda::std::exchange(__other.__count_, 0))
, __buf_(::cuda::std::exchange(__other.__buf_, nullptr))
{}
//! @brief Move-assigns a \c uninitialized_buffer from \p __other
//! @param __other Another \c uninitialized_buffer
//! Deallocates the current allocation and then takes ownership of the allocation in \p __other and resets it
_CCCL_HIDE_FROM_ABI uninitialized_buffer& operator=(uninitialized_buffer&& __other) noexcept
{
if (this == ::cuda::std::addressof(__other))
{
return *this;
}
if (__buf_)
{
__mr_.deallocate_sync(__buf_, __get_allocation_size(__count_), alignof(_Tp));
}
__mr_ = ::cuda::std::move(__other.__mr_);
__count_ = ::cuda::std::exchange(__other.__count_, 0);
__buf_ = ::cuda::std::exchange(__other.__buf_, nullptr);
return *this;
}
//! @brief Destroys an \c uninitialized_buffer, deallocates the buffer and destroys the memory resource
//! @warning destroy does not destroy any objects that may or may not reside within the buffer. It is the
//! user's responsibility to ensure that all objects within the buffer have been properly destroyed.
_CCCL_HIDE_FROM_ABI void destroy()
{
if (__buf_)
{
__mr_.deallocate_sync(__buf_, __get_allocation_size(__count_), alignof(_Tp));
__buf_ = nullptr;
__count_ = 0;
}
auto __tmp_mr = ::cuda::std::move(__mr_);
}
//! @brief Destroys an \c uninitialized_buffer, deallocates the buffer and destroys the memory resource
//! @warning The destructor does not destroy any objects that may or may not reside within the buffer. It is the
//! user's responsibility to ensure that all objects within the buffer have been properly destroyed.
_CCCL_HIDE_FROM_ABI ~uninitialized_buffer()
{
destroy();
}
//! @brief Returns an aligned pointer to the first element in the buffer
[[nodiscard]] _CCCL_HIDE_FROM_ABI pointer begin() noexcept
{
return __get_data();
}
//! @overload
[[nodiscard]] _CCCL_HIDE_FROM_ABI const_pointer begin() const noexcept
{
return __get_data();
}
//! @brief Returns an aligned pointer to the element following the last element of the buffer.
//! This element acts as a placeholder; attempting to access it results in undefined behavior.
[[nodiscard]] _CCCL_HIDE_FROM_ABI pointer end() noexcept
{
return __get_data() + __count_;
}
//! @overload
[[nodiscard]] _CCCL_HIDE_FROM_ABI const_pointer end() const noexcept
{
return __get_data() + __count_;
}
//! @brief Returns an aligned pointer to the first element in the buffer
[[nodiscard]] _CCCL_HIDE_FROM_ABI pointer data() noexcept
{
return __get_data();
}
//! @overload
[[nodiscard]] _CCCL_HIDE_FROM_ABI const_pointer data() const noexcept
{
return __get_data();
}
//! @brief Returns the size of the allocation
[[nodiscard]] _CCCL_HIDE_FROM_ABI constexpr size_type size() const noexcept
{
return __count_;
}
//! @brief Returns the size of the buffer in bytes
[[nodiscard]] _CCCL_HIDE_FROM_ABI constexpr size_type size_bytes() const noexcept
{
return __count_ * sizeof(_Tp);
}
//! @rst
//! Returns a \c const reference to the :ref:`any_resource <libcudacxx-memory-resource-any-resource>`
//! that holds the memory resource used to allocate the buffer
//! @endrst
[[nodiscard]] _CCCL_HIDE_FROM_ABI const __resource& memory_resource() const noexcept
{
return __mr_;
}
//! @brief Forwards the passed Properties
_CCCL_TEMPLATE(class _Property)
_CCCL_REQUIRES((!property_with_value<_Property>) _CCCL_AND ::cuda::std::__is_included_in_v<_Property, _Properties...>)
_CCCL_HIDE_FROM_ABI friend constexpr void get_property(const uninitialized_buffer&, _Property) noexcept {}
//! @brief Internal method to grow the allocation to a new size \p __count.
//! @param __count The new size of the allocation.
//! @return An \c uninitialized_buffer that holds the previous allocation
//! @warning This buffer must outlive the returned buffer
_CCCL_HIDE_FROM_ABI uninitialized_buffer __replace_allocation(const size_t __count)
{
// Create a new buffer with a reference to the stored memory resource and swap allocation information
uninitialized_buffer __ret{::cuda::mr::synchronous_resource_ref<_Properties...>{__mr_}, __count};
::cuda::std::swap(__count_, __ret.__count_);
::cuda::std::swap(__buf_, __ret.__buf_);
return __ret;
}
};
template <class _Tp>
using uninitialized_device_buffer = uninitialized_buffer<_Tp, ::cuda::mr::device_accessible>;
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif //__CUDAX__CONTAINERS_UNINITIALIZED_BUFFER_H

137
cccl_upstream/cudax/include/cuda/experimental/__coop/any_of.cuh
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@@ -1,137 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDA_EXPERIMENTAL___COOP_ANY_OF_CUH
#define _CUDA_EXPERIMENTAL___COOP_ANY_OF_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__functional/operations.h>
#include <cuda/std/__numeric/reduce.h>
#include <cuda/std/__type_traits/integral_constant.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/std/optional>
#include <cuda/experimental/__utility/result_policy.cuh>
#include <cuda/experimental/group.cuh>
#include <cuda/std/__cccl/prologue.h>
#if !defined(_CCCL_DOXYGEN_INVOKED)
// todo: Can we make any_of be implemented as reduce(group, data, cuda::std::logical_or{})?
namespace cuda::experimental::coop
{
template <bool _Dummy = false>
_CCCL_DEVICE_API auto __any_of_impl(...)
{
static_assert(_Dummy, "cudax::coop::any_of is not supported for the group");
}
template <bool _Broadcasted, class _Hierarchy>
[[nodiscard]] _CCCL_DEVICE_API auto
__any_of_impl(::cuda::std::bool_constant<_Broadcasted>, const this_thread<_Hierarchy>&, bool __thread_data)
{
if constexpr (_Broadcasted)
{
return __thread_data;
}
else
{
return ::cuda::std::optional{__thread_data};
}
}
_CCCL_TEMPLATE(bool _Broadcasted, class _Group)
_CCCL_REQUIRES(is_group<_Group> _CCCL_AND ::cuda::std::is_same_v<typename _Group::level_type, warp_level>)
[[nodiscard]] _CCCL_DEVICE_API auto
__any_of_impl(::cuda::std::bool_constant<_Broadcasted>, const _Group& __group, bool __thread_data) noexcept
{
const auto& __mapping_result = __group.__mapping_result();
const auto __result = static_cast<bool>(::__any_sync(__mapping_result.lane_mask().value(), __thread_data));
if constexpr (_Broadcasted)
{
return __result;
}
else
{
return (gpu_thread.is_root_rank(__group)) ? ::cuda::std::optional{__result} : ::cuda::std::nullopt;
}
}
_CCCL_TEMPLATE(class _Group, class _Tp)
_CCCL_REQUIRES(::cuda::std::is_same_v<_Tp, bool>)
[[nodiscard]] _CCCL_DEVICE_API ::cuda::std::optional<bool> any_of(const _Group& __group, _Tp __thread_data)
{
_CCCL_ASSERT(gpu_thread.is_part_of(__group), "Only threads that are part of the group can call cudax::coop::any_of");
return ::cuda::experimental::coop::__any_of_impl(::cuda::std::false_type{}, __group, __thread_data);
}
_CCCL_TEMPLATE(class _Group, class _Tp)
_CCCL_REQUIRES(::cuda::std::is_same_v<_Tp, bool>)
[[nodiscard]] _CCCL_DEVICE_API bool any_of(broadcasted_t, const _Group& __group, _Tp __thread_data)
{
_CCCL_ASSERT(gpu_thread.is_part_of(__group), "Only threads that are part of the group can call cudax::coop::any_of");
return ::cuda::experimental::coop::__any_of_impl(::cuda::std::true_type{}, __group, __thread_data);
}
_CCCL_TEMPLATE(class _Group, class _Tp, ::cuda::std::size_t _Np)
_CCCL_REQUIRES(::cuda::std::is_same_v<_Tp, bool>)
[[nodiscard]] _CCCL_DEVICE_API ::cuda::std::optional<bool> any_of(const _Group& __group, _Tp (&__thread_data)[_Np])
{
_CCCL_ASSERT(gpu_thread.is_part_of(__group), "Only threads that are part of the group can call cudax::coop::any_of");
return ::cuda::experimental::coop::any_of(
__group, ::cuda::std::reduce(__thread_data, __thread_data + _Np, false, ::cuda::std::logical_or<bool>{}));
}
_CCCL_TEMPLATE(class _Group, class _Tp, ::cuda::std::size_t _Np)
_CCCL_REQUIRES(::cuda::std::is_same_v<_Tp, bool>)
[[nodiscard]] _CCCL_DEVICE_API bool any_of(broadcasted_t, const _Group& __group, _Tp (&__thread_data)[_Np])
{
_CCCL_ASSERT(gpu_thread.is_part_of(__group), "Only threads that are part of the group can call cudax::coop::any_of");
return ::cuda::experimental::coop::any_of(
broadcasted,
__group,
::cuda::std::reduce(__thread_data, __thread_data + _Np, false, ::cuda::std::logical_or<bool>{}));
}
_CCCL_TEMPLATE(class _Group, class _Tp)
_CCCL_REQUIRES((!::cuda::std::is_same_v<_Tp, bool>) )
auto any_of(const _Group& __group, _Tp __thread_data) = delete;
_CCCL_TEMPLATE(class _Group, class _Tp)
_CCCL_REQUIRES((!::cuda::std::is_same_v<_Tp, bool>) )
auto any_of(broadcasted_t, const _Group& __group, _Tp __thread_data) = delete;
_CCCL_TEMPLATE(class _Group, class _Tp, ::cuda::std::size_t _Np)
_CCCL_REQUIRES((!::cuda::std::is_same_v<_Tp, bool>) )
auto any_of(const _Group& __group, _Tp (&__thread_data)[_Np]) = delete;
_CCCL_TEMPLATE(class _Group, class _Tp, ::cuda::std::size_t _Np)
_CCCL_REQUIRES((!::cuda::std::is_same_v<_Tp, bool>) )
auto any_of(broadcasted_t, const _Group& __group, _Tp (&__thread_data)[_Np]) = delete;
} // namespace cuda::experimental::coop
#endif // !_CCCL_DOXYGEN_INVOKED
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDA_EXPERIMENTAL___COOP_ANY_OF_CUH

414
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@@ -1,414 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDA_EXPERIMENTAL___COOP_REDUCE_CUH
#define _CUDA_EXPERIMENTAL___COOP_REDUCE_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cub/block/block_reduce.cuh>
#include <cub/thread/thread_reduce.cuh>
#include <cub/warp/warp_reduce.cuh>
#include <cuda/__cmath/ceil_div.h>
#include <cuda/__cmath/pow2.h>
#include <cuda/__functional/operator_properties.h>
#include <cuda/__ptx/instructions/get_sreg.h>
#include <cuda/__warp/warp_shuffle.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__functional/operations.h>
#include <cuda/std/__type_traits/integral_constant.h>
#include <cuda/std/array>
#include <cuda/std/optional>
#include <cuda/experimental/__coop/shuffle_down.cuh>
#include <cuda/experimental/__utility/result_policy.cuh>
#include <cuda/experimental/group.cuh>
#include <cuda/std/__cccl/prologue.h>
#if !defined(_CCCL_DOXYGEN_INVOKED)
// todo(dabayer): We share the temporary storage in shared/global memory for all reduce invocations. This is a temporary
// state before we make it a parameter.
namespace cuda::experimental::coop
{
template <bool _Dummy = false>
_CCCL_DEVICE_API auto __reduce_impl(...)
{
static_assert(_Dummy, "cudax::coop::reduce is not supported for the group");
}
template <bool _Broadcasted, class _Hierarchy, class _Tp, ::cuda::std::size_t _Np, class _RedFn>
[[nodiscard]] _CCCL_DEVICE_API auto __reduce_impl(
::cuda::std::bool_constant<_Broadcasted>, this_thread<_Hierarchy>, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
const auto __result = ::cub::ThreadReduce(__thread_data, __red_fn);
if constexpr (_Broadcasted)
{
return __result;
}
else
{
return ::cuda::std::optional{__result};
}
}
template <bool _Broadcasted, class _Hierarchy, class _Tp, ::cuda::std::size_t _Np, class _RedFn>
[[nodiscard]] _CCCL_DEVICE_API auto __reduce_impl(
::cuda::std::bool_constant<_Broadcasted>, this_warp<_Hierarchy> __group, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
using _BlockExts = decltype(gpu_thread.extents(block, __group.hierarchy()));
constexpr auto __nwarps_in_block =
::cuda::ceil_div(_BlockExts::static_extent(0) * _BlockExts::static_extent(1) * _BlockExts::static_extent(2), 32);
using _WarpReduce = ::cub::WarpReduce<_Tp>;
union _Scratch
{
typename _WarpReduce::TempStorage __warp_reduce_[__nwarps_in_block];
};
__shared__ _Scratch __scratch;
const auto __warp_rank_in_block = __group.rank(block);
const auto __result = _WarpReduce{__scratch.__warp_reduce_[__warp_rank_in_block]}.Reduce(__thread_data, __red_fn);
if constexpr (_Broadcasted)
{
return ::cuda::device::warp_shuffle_idx(__result, 0).data;
}
else
{
return (gpu_thread.is_root_rank(__group)) ? ::cuda::std::optional{__result} : ::cuda::std::nullopt;
}
}
template <bool _Broadcasted, class _Hierarchy, class _Tp, cuda::std::size_t _Np, class _RedFn>
[[nodiscard]] _CCCL_DEVICE_API auto __reduce_impl(
::cuda::std::bool_constant<_Broadcasted>, this_block<_Hierarchy> __group, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
using _BlockExts = decltype(gpu_thread.extents(block, __group.hierarchy()));
static_assert(_BlockExts::rank_dynamic() == 0,
"cuda::coop::reduce requires the block level to have all static extents.");
using _BlockReduce =
::cub::BlockReduce<_Tp,
static_cast<int>(_BlockExts::static_extent(0)),
::cub::BLOCK_REDUCE_WARP_REDUCTIONS,
static_cast<int>(_BlockExts::static_extent(1)),
static_cast<int>(_BlockExts::static_extent(2))>;
union _Scratch
{
typename _BlockReduce::TempStorage __block_reduce_;
_Tp __bcast_;
};
__shared__ _Scratch __scratch;
const auto __result = _BlockReduce{__scratch.__block_reduce_}.Reduce(__thread_data, __red_fn);
if constexpr (_Broadcasted)
{
if (gpu_thread.is_root_rank(__group))
{
__scratch.__bcast_ = __result;
}
__group.sync_aligned();
return __scratch.__bcast_;
}
else
{
return (gpu_thread.is_root_rank(__group)) ? ::cuda::std::optional{__result} : ::cuda::std::nullopt;
}
}
template <bool _Broadcasted, class _Hierarchy, class _Tp, cuda::std::size_t _Np, class _RedFn>
[[nodiscard]] _CCCL_DEVICE_API auto __reduce_impl(
::cuda::std::bool_constant<_Broadcasted>, this_cluster<_Hierarchy> __group, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
using _ClusterExts = decltype(block.extents(cluster, __group.hierarchy()));
static_assert(_ClusterExts::rank_dynamic() == 0,
"cuda::coop::reduce requires the cluster level to have all static extents.");
constexpr auto __nblocks_in_cluster =
_ClusterExts::static_extent(0) * _ClusterExts::static_extent(1) * _ClusterExts::static_extent(2);
if constexpr (__nblocks_in_cluster == 1)
{
return ::cuda::experimental::coop::__reduce_impl(
::cuda::std::bool_constant<_Broadcasted>{}, this_block{__group.hierarchy()}, __thread_data, __red_fn);
}
else
{
using _BlockExts = decltype(gpu_thread.extents(block, __group.hierarchy()));
static_assert(_BlockExts::rank_dynamic() == 0,
"cuda::coop::reduce requires the block level to have all static extents.");
using _BlockReduce =
::cub::BlockReduce<_Tp,
static_cast<int>(_BlockExts::static_extent(0)),
::cub::BLOCK_REDUCE_WARP_REDUCTIONS,
static_cast<int>(_BlockExts::static_extent(1)),
static_cast<int>(_BlockExts::static_extent(2))>;
using _RootWarpReduce = ::cub::WarpReduce<_Tp>;
struct _RootScratch
{
_Tp __partials_[__nblocks_in_cluster];
typename _RootWarpReduce::TempStorage __warp_reduce_;
_Tp __bcast_;
};
union _Scratch
{
typename _BlockReduce::TempStorage __block_;
_RootScratch __root_;
};
__shared__ _Scratch __scratch;
const auto __partial = _BlockReduce{__scratch.__block_}.Reduce(__thread_data, __red_fn);
_Tp __result{};
NV_IF_TARGET(NV_PROVIDES_SM_90, ({
const auto __root_scratch = static_cast<_Scratch*>(::__cluster_map_shared_rank(&__scratch, 0));
auto& __partials_root = __root_scratch->__root_.__partials_;
__group.sync_aligned();
if (gpu_thread.is_root_rank(this_block{__group.hierarchy()}))
{
__partials_root[block.rank(__group)] = __partial;
}
__group.sync_aligned();
if (warp.is_root_rank(__group))
{
this_warp __warp{__group.hierarchy()};
const auto __value = (gpu_thread.rank(__warp) < __nblocks_in_cluster)
? __scratch.__root_.__partials_[gpu_thread.rank(__warp)]
: ::cuda::identity_element<_RedFn, _Tp>();
__result = _RootWarpReduce{__scratch.__root_.__warp_reduce_}.Reduce(__value, __red_fn);
}
if constexpr (_Broadcasted)
{
if (gpu_thread.is_root_rank(__group))
{
__scratch.__root_.__bcast_ = __result;
}
__group.sync_aligned();
__result = __root_scratch->__root_.__bcast_;
// Wait until all threads are done reading the result.
__group.sync_aligned();
}
}))
if constexpr (_Broadcasted)
{
return __result;
}
else
{
return (gpu_thread.is_root_rank(__group)) ? ::cuda::std::optional{__result} : ::cuda::std::nullopt;
}
}
}
template <class _Tp, ::cuda::std::size_t _Np>
_CCCL_DEVICE ::cuda::std::array<_Tp, _Np> __reduce_grid_partials;
template <bool _Broadcasted, class _Hierarchy, class _Tp, cuda::std::size_t _Np, class _RedFn>
[[nodiscard]] _CCCL_DEVICE_API auto __reduce_impl(
::cuda::std::bool_constant<_Broadcasted>, this_grid<_Hierarchy> __group, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
using _GridExts = decltype(cluster.extents(grid, __group.hierarchy()));
static_assert(_GridExts::rank_dynamic() == 0,
"cuda::coop::reduce requires the grid level to have all static extents.");
constexpr auto __nclusters_in_grid =
_GridExts::static_extent(0) * _GridExts::static_extent(1) * _GridExts::static_extent(2);
this_cluster __cluster{__group.hierarchy()};
const auto __partial =
::cuda::experimental::coop::__reduce_impl(::cuda::std::false_type{}, __cluster, __thread_data, __red_fn);
if (gpu_thread.is_root_rank(__cluster))
{
__reduce_grid_partials<_Tp, __nclusters_in_grid>[cluster.rank(__group)] = __partial.value();
}
__group.sync_aligned();
::cuda::std::optional<_Tp> __result;
if (block.is_root_rank(__group))
{
this_block __block{__group.hierarchy()};
constexpr auto __npartials_per_thread = ::cuda::ceil_div(__nclusters_in_grid, gpu_thread.static_count(__block));
_Tp __thread_partials[__npartials_per_thread];
const auto __offset = gpu_thread.rank(__block) * __npartials_per_thread;
// todo(dabayer): This is not the most efficient way to load values, it doesn't take into account element size and
// reads N consecutive elements by 1 thread.
for (unsigned __i = 0; __i < __npartials_per_thread; ++__i)
{
__thread_partials[__i] =
(__offset + __i < __nclusters_in_grid)
? __reduce_grid_partials<_Tp, __nclusters_in_grid>[__offset + __i]
: ::cuda::identity_element<_RedFn, _Tp>();
}
__result =
::cuda::experimental::coop::__reduce_impl(::cuda::std::false_type{}, __block, __thread_partials, __red_fn);
}
if constexpr (_Broadcasted)
{
if (gpu_thread.is_root_rank(__group))
{
__reduce_grid_partials<_Tp, __nclusters_in_grid>[0] = *__result;
}
__group.sync_aligned();
const auto __result2 = __reduce_grid_partials<_Tp, __nclusters_in_grid>[0];
// Wait until all threads are done reading the result.
__group.sync_aligned();
return __result2;
}
else
{
return __result;
}
}
_CCCL_TEMPLATE(bool _Broadcasted, class _Group, class _Tp, ::cuda::std::size_t _Np, class _RedFn)
_CCCL_REQUIRES(::cuda::std::is_same_v<thread_level, typename _Group::unit_type>
_CCCL_AND ::cuda::std::is_same_v<warp_level, typename _Group::level_type>)
[[nodiscard]] _CCCL_DEVICE_API auto
__reduce_impl(::cuda::std::bool_constant<_Broadcasted>, _Group __group, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
using _MappingResult = typename _Group::__mapping_result_type;
const auto& __mapping_result = __group.__mapping_result();
const auto __lane_mask = __mapping_result.lane_mask();
const auto __lane = ::cuda::ptx::get_sreg_laneid();
auto __result = ::cub::ThreadReduce(__thread_data, __red_fn);
_CCCL_PRAGMA_UNROLL_FULL()
for (unsigned __stride = 1; __stride < ::cuda::next_power_of_two(__mapping_result.unit_count()); __stride *= 2)
{
const auto __other = ::cuda::experimental::coop::shuffle_down(__group, __result, __stride);
if (__other.has_value())
{
__result = __red_fn(__result, *__other);
}
}
if constexpr (_Broadcasted)
{
return ::cuda::device::warp_shuffle_idx(__result, ::cuda::std::countr_zero(__lane_mask.value()), __lane_mask.value())
.data;
}
else
{
return (__mapping_result.unit_rank() == 0) ? ::cuda::std::optional{__result} : ::cuda::std::nullopt;
}
}
_CCCL_TEMPLATE(bool _Broadcasted, class _Group, class _Tp, ::cuda::std::size_t _Np, class _RedFn)
_CCCL_REQUIRES(::cuda::std::is_same_v<warp_level, typename _Group::unit_type>
_CCCL_AND ::cuda::std::is_same_v<block_level, typename _Group::level_type>)
[[nodiscard]] _CCCL_DEVICE_API auto
__reduce_impl(::cuda::std::bool_constant<_Broadcasted>, _Group __group, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
constexpr auto __nwarps_in_group = warp.static_count(__group);
static_assert(__nwarps_in_group != ::cuda::std::dynamic_extent,
"cuda::coop::reduce requires the group to have statically known size");
using _WarpReduce = ::cub::WarpReduce<_Tp>;
struct _AdditionalScratch
{
_Tp __partials_[__nwarps_in_group];
_Tp __bcast_;
};
union _Scratch
{
typename _WarpReduce::TempStorage __warp_reduce_[__nwarps_in_group];
_AdditionalScratch __additional_;
};
__shared__ _Scratch __scratch;
const auto __partial = _WarpReduce{__scratch.__warp_reduce_[warp.rank(__group)]}.Reduce(__thread_data, __red_fn);
__group.sync_aligned();
this_warp __warp{__group.hierarchy()};
if (gpu_thread.is_root_rank(__warp))
{
__scratch.__additional_.__partials_[warp.rank(__group)] = __partial;
}
__group.sync_aligned();
_Tp __result;
if (warp.is_root_rank(__group))
{
const auto __value = (gpu_thread.rank(__warp) < __nwarps_in_group)
? __scratch.__additional_.__partials_[gpu_thread.rank(__warp)]
: ::cuda::identity_element<_RedFn, _Tp>();
__result = _WarpReduce{__scratch.__warp_reduce_[0]}.Reduce(__value, __red_fn);
}
if constexpr (_Broadcasted)
{
if (gpu_thread.is_root_rank(__group))
{
__scratch.__additional_.__bcast_ = __result;
}
__group.sync_aligned();
return __scratch.__additional_.__bcast_;
}
else
{
return (gpu_thread.is_root_rank(__group)) ? ::cuda::std::optional{__result} : ::cuda::std::nullopt;
}
}
template <class _Group, class _Tp, ::cuda::std::size_t _Np, class _RedFn>
[[nodiscard]] _CCCL_DEVICE_API ::cuda::std::optional<_Tp>
reduce(_Group __group, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
static_assert(gpu_thread.static_count(__group) != ::cuda::std::dynamic_extent,
"cuda::coop::reduce requires the group to have statically known size");
_CCCL_ASSERT(gpu_thread.is_part_of(__group), "Only threads that are part of the group can call cudax::coop::reduce");
return ::cuda::experimental::coop::__reduce_impl(::cuda::std::false_type{}, __group, __thread_data, __red_fn);
}
template <class _Group, class _Tp, ::cuda::std::size_t _Np, class _RedFn>
[[nodiscard]] _CCCL_DEVICE_API _Tp reduce(broadcasted_t, _Group __group, _Tp (&__thread_data)[_Np], _RedFn __red_fn)
{
static_assert(gpu_thread.static_count(__group) != ::cuda::std::dynamic_extent,
"cuda::coop::reduce requires the group to have statically known size");
_CCCL_ASSERT(gpu_thread.is_part_of(__group), "Only threads that are part of the group can call cudax::coop::reduce");
return ::cuda::experimental::coop::__reduce_impl(::cuda::std::true_type{}, __group, __thread_data, __red_fn);
}
} // namespace cuda::experimental::coop
#endif // !_CCCL_DOXYGEN_INVOKED
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDA_EXPERIMENTAL___COOP_REDUCE_CUH

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cccl_upstream/cudax/include/cuda/experimental/__coop/shuffle.cuh
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@@ -1,85 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDA_EXPERIMENTAL___COOP_SHUFFLE_CUH
#define _CUDA_EXPERIMENTAL___COOP_SHUFFLE_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__ptx/instructions/get_sreg.h>
#include <cuda/__warp/warp_shuffle.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/experimental/group.cuh>
#include <cuda/std/__cccl/prologue.h>
#if !defined(_CCCL_DOXYGEN_INVOKED)
namespace cuda::experimental::coop
{
template <bool _Dummy = false>
[[nodiscard]] _CCCL_DEVICE_API auto __shuffle_impl(...)
{
static_assert(_Dummy, "cudax::coop::shuffle is not implemented for this group");
}
_CCCL_TEMPLATE(class _Group, class _Tp)
_CCCL_REQUIRES(is_group<_Group> _CCCL_AND ::cuda::std::is_same_v<typename _Group::unit_type, thread_level>
_CCCL_AND ::cuda::std::is_same_v<typename _Group::level_type, warp_level>)
[[nodiscard]] _CCCL_DEVICE_API _Tp __shuffle_impl(const _Group& __group, _Tp __value, unsigned __src_unit_rank) noexcept
{
using _MappingResult = typename _Group::__mapping_result_type;
const auto& __mapping_result = __group.__mapping_result();
_CCCL_ASSERT(__src_unit_rank < __mapping_result.unit_count(),
"invalid __src_unit_rank - must be less than the number of units within the group");
const auto __lane_mask = __mapping_result.lane_mask();
const auto __lane_offset = static_cast<int>(__src_unit_rank) - static_cast<int>(__mapping_result.unit_rank());
unsigned __src_lane{};
if constexpr (_MappingResult::is_always_contiguous())
{
const auto __lane = ::cuda::ptx::get_sreg_laneid();
__src_lane = static_cast<unsigned>(__lane + __lane_offset);
}
else
{
__src_lane = ::__fns(__lane_mask.value(), 0, static_cast<int>(__src_unit_rank) + 1);
}
return ::cuda::device::warp_shuffle_idx(__value, static_cast<int>(__src_lane), __lane_mask.value());
}
//! @brief Shuffles values among units within a group.
//! @param[in] __group The group.
//! @param[in] __value This thread's value to be shuffled.
//! @param[in] __src_unit_rank The rank of the unit whose value should be taken by this unit.
//! @return The value passed to the function by the equivalent thread from the source rank unit.
template <class _Group, class _Tp>
[[nodiscard]] _CCCL_DEVICE_API _Tp shuffle(const _Group& __group, _Tp __value, unsigned __src_unit_rank) noexcept
{
return ::cuda::experimental::coop::__shuffle_impl(__group, __value, __src_unit_rank);
}
} // namespace cuda::experimental::coop
#endif // !_CCCL_DOXYGEN_INVOKED
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDA_EXPERIMENTAL___COOP_SHUFFLE_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDA_EXPERIMENTAL___COOP_SHUFFLE_DOWN_CUH
#define _CUDA_EXPERIMENTAL___COOP_SHUFFLE_DOWN_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__ptx/instructions/get_sreg.h>
#include <cuda/__warp/warp_shuffle.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/std/optional>
#include <cuda/experimental/group.cuh>
#include <cuda/std/__cccl/prologue.h>
#if !defined(_CCCL_DOXYGEN_INVOKED)
namespace cuda::experimental::coop
{
template <bool _Dummy = false>
[[nodiscard]] _CCCL_DEVICE_API auto __shuffle_down_impl(...)
{
static_assert(_Dummy, "cudax::coop::shuffle_down is not implemented for this group");
}
_CCCL_TEMPLATE(class _Group, class _Tp)
_CCCL_REQUIRES(is_group<_Group> _CCCL_AND ::cuda::std::is_same_v<typename _Group::unit_type, thread_level>
_CCCL_AND ::cuda::std::is_same_v<typename _Group::level_type, warp_level>)
[[nodiscard]] _CCCL_DEVICE_API ::cuda::std::optional<_Tp>
__shuffle_down_impl(const _Group& __group, const _Tp& __value, unsigned __offset) noexcept
{
using _MappingResult = typename _Group::__mapping_result_type;
const auto& __mapping_result = __group.__mapping_result();
const auto __lane_mask = __mapping_result.lane_mask();
const auto __offset_is_valid = (__offset < __mapping_result.unit_count() - __mapping_result.unit_rank());
if constexpr (_MappingResult::is_always_contiguous())
{
const auto __real_offset = (__offset_is_valid) ? __offset : 0u;
const auto __result =
::cuda::device::warp_shuffle_down(__value, static_cast<int>(__real_offset), __lane_mask.value());
return (__offset_is_valid) ? ::cuda::std::optional{__result.data} : ::cuda::std::nullopt;
}
else
{
const auto __lane = ::cuda::ptx::get_sreg_laneid();
const auto __src_lane =
(__offset_is_valid) ? ::__fns(__lane_mask.value(), __lane, static_cast<int>(__offset + 1)) : __lane;
const auto __result = ::cuda::device::warp_shuffle_idx(__value, static_cast<int>(__src_lane), __lane_mask.value());
return (__offset_is_valid) ? ::cuda::std::optional{__result.data} : ::cuda::std::nullopt;
}
}
//! @brief Gets the values from a unit with a greater rank by the specified offset.
//! @param[in] __group The group.
//! @param[in] __value This thread's value.
//! @param[in] __offset The offset of the source unit rank from this unit's rank.
//! @return The source's value or empty optional if no such rank exists.
template <class _Group, class _Tp>
[[nodiscard]] _CCCL_DEVICE_API ::cuda::std::optional<_Tp>
shuffle_down(const _Group& __group, const _Tp& __value, unsigned __offset) noexcept
{
return ::cuda::experimental::coop::__shuffle_down_impl(__group, __value, __offset);
}
} // namespace cuda::experimental::coop
#endif // !_CCCL_DOXYGEN_INVOKED
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDA_EXPERIMENTAL___COOP_SHUFFLE_DOWN_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDA_EXPERIMENTAL___COOP_SHUFFLE_UP_CUH
#define _CUDA_EXPERIMENTAL___COOP_SHUFFLE_UP_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__numeric/sub_overflow.h>
#include <cuda/__ptx/instructions/get_sreg.h>
#include <cuda/__warp/warp_shuffle.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/std/optional>
#include <cuda/experimental/group.cuh>
#include <cuda/std/__cccl/prologue.h>
#if !defined(_CCCL_DOXYGEN_INVOKED)
namespace cuda::experimental::coop
{
template <bool _Dummy = false>
[[nodiscard]] _CCCL_DEVICE_API auto __shuffle_up_impl(...)
{
static_assert(_Dummy, "cudax::coop::shuffle_up is not implemented for this group");
}
_CCCL_TEMPLATE(class _Group, class _Tp)
_CCCL_REQUIRES(is_group<_Group> _CCCL_AND ::cuda::std::is_same_v<typename _Group::unit_type, thread_level>
_CCCL_AND ::cuda::std::is_same_v<typename _Group::level_type, warp_level>)
[[nodiscard]] _CCCL_DEVICE_API ::cuda::std::optional<_Tp>
__shuffle_up_impl(const _Group& __group, const _Tp& __value, unsigned __offset) noexcept
{
using _MappingResult = typename _Group::__mapping_result_type;
const auto& __mapping_result = __group.__mapping_result();
const auto __lane_mask = __mapping_result.lane_mask();
const auto [__src_rank, __underflow] = ::cuda::sub_overflow(__mapping_result.unit_rank(), __offset);
const auto __offset_is_valid = !__underflow;
if constexpr (_MappingResult::is_always_contiguous())
{
const auto __real_offset = (__offset_is_valid) ? __offset : 0u;
const auto __result =
::cuda::device::warp_shuffle_up(__value, static_cast<int>(__real_offset), __lane_mask.value());
return (__offset_is_valid) ? ::cuda::std::optional{__result.data} : ::cuda::std::nullopt;
}
else
{
const auto __lane = ::cuda::ptx::get_sreg_laneid();
const auto __src_lane =
(__offset_is_valid) ? ::__fns(__lane_mask.value(), 0, static_cast<int>(__src_rank + 1)) : __lane;
const auto __result = ::cuda::device::warp_shuffle_idx(__value, static_cast<int>(__src_lane), __lane_mask.value());
return (__offset_is_valid) ? ::cuda::std::optional{__result.data} : ::cuda::std::nullopt;
}
}
//! @brief Gets the values from a unit with a lower rank by the specified offset.
//! @param[in] __group The group.
//! @param[in] __value This thread's value.
//! @param[in] __offset The offset of the source unit rank from this unit's rank.
//! @return The source's value or empty optional if no such rank exists.
template <class _Group, class _Tp>
[[nodiscard]] _CCCL_DEVICE_API ::cuda::std::optional<_Tp>
shuffle_up(const _Group& __group, const _Tp& __value, unsigned __offset) noexcept
{
return ::cuda::experimental::coop::__shuffle_up_impl(__group, __value, __offset);
}
} // namespace cuda::experimental::coop
#endif // !_CCCL_DOXYGEN_INVOKED
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDA_EXPERIMENTAL___COOP_SHUFFLE_UP_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_CONTIGUOUS_H
#define _CUDAX__COPY_CONTIGUOUS_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cub/device/dispatch/tuning/tuning_transform.cuh>
#include <cuda/__cmath/ceil_div.h>
#include <cuda/__device/all_devices.h>
#include <cuda/__device/arch_id.h>
#include <cuda/__device/arch_traits.h>
#include <cuda/__launch/configuration.h>
#include <cuda/__launch/launch.h>
#include <cuda/__stream/stream_ref.h>
#include <cuda/std/__algorithm/max.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__mdspan/default_accessor.h>
#include <cuda/std/__type_traits/integral_constant.h>
#include <cuda/std/array>
#include <cuda/experimental/__copy/tensor_copy_utils.cuh>
#include <cuda/experimental/__copy/tensor_iterator.cuh>
#include <cuda/experimental/__copy_bytes/types.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Tiled copy kernel for contiguous innermost dimension.
//!
//! Uses a 2D grid: blockIdx.x = tile along inner dimension, blockIdx.y = outer index.
//! Threads within a block stride over the tile, reading from the source and writing to the
//! destination via accessors. The coordinate iterator maps linear indices to multi-dimensional
//! coordinates, which are then used with per-tensor strides for the actual memory access.
//!
//! @param[in] __config Kernel launch configuration
//! @param[in] __src_ptr Pointer to source data
//! @param[in] __src_strides Per-dimension strides for the source tensor
//! @param[in] __src_accessor Accessor for reading source elements
//! @param[out] __dst_ptr Pointer to destination data
//! @param[in] __dst_strides Per-dimension strides for the destination tensor
//! @param[in] __dst_accessor Accessor for writing destination elements
//! @param[in] __coord_iter Coordinate iterator for multi-dimensional index mapping
//! @param[in] __inner_size Extent of the contiguous innermost dimension
template <typename _Config,
int _TileSize,
typename _TpSrc,
typename _TpDst,
typename _SrcAccessor,
typename _DstAccessor,
typename _ExtentT,
typename _StrideTIn,
typename _StrideTOut,
::cuda::std::size_t _Rank>
__global__ void __copy_contiguous_kernel(
_CCCL_GRID_CONSTANT const _Config __config,
_CCCL_GRID_CONSTANT const _TpSrc* const _CCCL_RESTRICT __src_ptr,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTIn, _Rank> __src_strides,
_CCCL_GRID_CONSTANT const _SrcAccessor __src_accessor,
_CCCL_GRID_CONSTANT _TpDst* const _CCCL_RESTRICT __dst_ptr,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTOut, _Rank> __dst_strides,
_CCCL_GRID_CONSTANT const _DstAccessor __dst_accessor,
_CCCL_GRID_CONSTANT const __tensor_coord_iterator<_ExtentT, _Rank> __coord_iter,
_CCCL_GRID_CONSTANT const _ExtentT __inner_size)
{
using __partial_tensor_src = __partial_tensor<const _TpSrc, _StrideTIn, _Rank, _SrcAccessor>;
using __partial_tensor_dst = __partial_tensor<_TpDst, _StrideTOut, _Rank, _DstAccessor>;
const auto __thread_id = ::cuda::gpu_thread.rank_as<_ExtentT>(::cuda::block, __config);
const auto __block_idx = ::cuda::block.index_as<_ExtentT>(::cuda::grid);
constexpr auto __block_size = ::cuda::gpu_thread.count_as<int>(::cuda::block, __config);
const __partial_tensor_src __src{__src_ptr, __src_strides, __src_accessor};
const __partial_tensor_dst __dst{__dst_ptr, __dst_strides, __dst_accessor};
const auto __tile_offset = __block_idx.x * _TileSize;
const auto __outer_idx = __block_idx.y;
const auto __remaining = __inner_size - __tile_offset;
const auto __base_idx = __outer_idx * __inner_size + __tile_offset + __thread_id;
if (__remaining >= _TileSize)
{
_CCCL_PRAGMA_UNROLL_FULL()
for (int __i = 0; __i < _TileSize; __i += __block_size)
{
const auto __coord = __coord_iter(__base_idx + __i);
__dst(__coord) = __src(__coord);
}
}
else
{
_CCCL_PRAGMA_UNROLL_FULL()
for (int __i = 0; __i < _TileSize; __i += __block_size)
{
if (__thread_id + __i < __remaining)
{
const auto __coord = __coord_iter(__base_idx + __i);
__dst(__coord) = __src(__coord);
}
}
}
}
//! @brief Query the minimum bytes-in-flight target for the current GPU architecture.
//!
//! Delegates to CUB's architecture-specific tuning.
//! @return Bytes-in-flight target (e.g. 12KB for V100, 16KB for A100, 48KB for H200, 64KB for B200)
[[nodiscard]] _CCCL_HOST_API inline int __bytes_in_flight() noexcept
{
const auto __dev_id = ::cuda::__driver::__cudevice_to_ordinal(::cuda::__driver::__ctxGetDevice());
const auto __dev = ::cuda::devices[__dev_id];
const auto __cc = ::cuda::device_attributes::compute_capability(__dev);
return CUB_NS_QUALIFIER::detail::transform::cc_to_min_bytes_in_flight(__cc);
}
// Compute the number of elements each thread copies for a given vector width.
[[nodiscard]] _CCCL_HOST_API inline int __elem_per_thread(int __access_bytes, int __bytes_in_flight) noexcept
{
constexpr auto __threads_per_sm = 2048;
return ::cuda::std::max(__bytes_in_flight / (__access_bytes * __threads_per_sm), 1);
}
// Dispatch a callable with a compile-time tile size derived from a runtime value.
template <typename _Op>
_CCCL_HOST_API void __dispatch_tile_size(int __tile_size, _Op __op) noexcept
{
if (__tile_size >= 2048)
{
__op(::cuda::std::integral_constant<int, 2048>{});
}
else if (__tile_size >= 1024)
{
__op(::cuda::std::integral_constant<int, 1024>{});
}
else if (__tile_size >= 512)
{
__op(::cuda::std::integral_constant<int, 512>{});
}
else
{
__op(::cuda::std::integral_constant<int, 256>{});
}
}
//! @brief Launch the tiled copy kernel for contiguous innermost dimension.
//!
//! Computes tile size from the architecture-specific bytes-in-flight target, then dispatches
//! the @ref __copy_contiguous_kernel with a compile-time tile size.
//!
//! @param[in] __src Source raw tensor descriptor
//! @param[in] __dst Destination raw tensor descriptor
//! @param[in] __stream CUDA stream for asynchronous execution
//! @param[in] __src_accessor Accessor for reading source elements
//! @param[in] __dst_accessor Accessor for writing destination elements
template <typename _ExtentT,
typename _StrideTIn,
typename _StrideTOut,
typename _TpIn,
typename _TpOut,
::cuda::std::size_t _Rank,
typename _SrcAccessor = ::cuda::std::default_accessor<_TpIn>,
typename _DstAccessor = ::cuda::std::default_accessor<_TpOut>>
_CCCL_HOST_API void __launch_copy_contiguous_kernel(
const __raw_tensor<_ExtentT, _StrideTIn, _TpIn, _Rank>& __src,
const __raw_tensor<_ExtentT, _StrideTOut, _TpOut, _Rank>& __dst,
::cuda::stream_ref __stream,
const _SrcAccessor& __src_accessor = {},
const _DstAccessor& __dst_accessor = {})
{
constexpr int __block_size = 256;
const auto __bytes_in_flight = ::cuda::experimental::__bytes_in_flight();
const auto __elems_per_thread =
::cuda::experimental::__elem_per_thread(static_cast<int>(sizeof(_TpIn)), __bytes_in_flight);
const auto __tile_size_rt = __block_size * __elems_per_thread;
::cuda::experimental::__dispatch_tile_size(__tile_size_rt, [&](auto __tile_constant) {
constexpr int __tile_size = decltype(__tile_constant)::value;
const auto __inner_size = __src.__extents[0];
const auto __outer_size = ::cuda::experimental::__total_size(__src) / __inner_size;
const auto __num_inner_tiles = ::cuda::ceil_div(__inner_size, __tile_size);
constexpr auto __arch_limits = ::cuda::__common_arch_traits(::cuda::arch_id::sm_90);
_CCCL_ASSERT(__num_inner_tiles <= _ExtentT(__arch_limits.max_grid_dim_x),
"grid x-dimension exceeds the maximum grid size");
_CCCL_ASSERT(__outer_size <= _ExtentT(__arch_limits.max_grid_dim_y),
"grid y-dimension exceeds the maximum grid size");
const auto __grid_dims = ::dim3(static_cast<unsigned>(__num_inner_tiles), static_cast<unsigned>(__outer_size));
const auto __config = ::cuda::make_config(::cuda::block_dims<__block_size>(), ::cuda::grid_dims(__grid_dims));
const __tensor_coord_iterator<_ExtentT, _Rank> __coord_iter{__src.__extents};
const auto __kernel = ::cuda::experimental::__copy_contiguous_kernel<
decltype(__config),
__tile_size,
_TpIn,
_TpOut,
_SrcAccessor,
_DstAccessor,
_ExtentT,
_StrideTIn,
_StrideTOut,
_Rank>;
::cuda::launch(
__stream,
__config,
__kernel,
__src.__data,
__src.__strides,
__src_accessor,
__dst.__data,
__dst.__strides,
__dst_accessor,
__coord_iter,
__inner_size);
});
}
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX__COPY_CONTIGUOUS_H

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_OPTIMIZED_H
#define _CUDAX__COPY_OPTIMIZED_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__cmath/ceil_div.h>
#include <cuda/__stream/stream_ref.h>
#include <cuda/launch>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__mdspan/default_accessor.h>
#include <cuda/std/array>
#include <cuda/experimental/__copy/tensor_iterator.cuh>
#include <cuda/experimental/__copy_bytes/types.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Element-wise copy kernel for strided tensor data.
//!
//! Each thread copies one element at a time using a grid-stride loop, mapping linear indices to
//! multi-dimensional coordinates via @ref __tensor_coord_iterator.
//!
//! @param[in] __config Kernel launch configuration
//! @param[in] __src_ptr Pointer to source data
//! @param[in] __src_strides Per-dimension strides for the source tensor
//! @param[in] __src_accessor Accessor for reading source elements
//! @param[out] __dst_ptr Pointer to destination data
//! @param[in] __dst_strides Per-dimension strides for the destination tensor
//! @param[in] __dst_accessor Accessor for writing destination elements
//! @param[in] __coord_iter Coordinate iterator for multi-dimensional index mapping
//! @param[in] __tensor_size Total number of elements to copy
template <typename _Config,
typename _TpSrc,
typename _TpDst,
typename _SrcAccessor,
typename _DstAccessor,
typename _ExtentT,
typename _StrideTIn,
typename _StrideTOut,
::cuda::std::size_t _Rank>
__global__ void __copy_optimized_kernel(
_CCCL_GRID_CONSTANT const _Config __config,
_CCCL_GRID_CONSTANT const _TpSrc* const _CCCL_RESTRICT __src_ptr,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTIn, _Rank> __src_strides,
_CCCL_GRID_CONSTANT const _SrcAccessor __src_accessor,
_CCCL_GRID_CONSTANT _TpDst* const _CCCL_RESTRICT __dst_ptr,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTOut, _Rank> __dst_strides,
_CCCL_GRID_CONSTANT const _DstAccessor __dst_accessor,
_CCCL_GRID_CONSTANT const __tensor_coord_iterator<_ExtentT, _Rank> __coord_iter,
_CCCL_GRID_CONSTANT const _ExtentT __tensor_size)
{
using __partial_tensor_src = __partial_tensor<const _TpSrc, _StrideTIn, _Rank, _SrcAccessor>;
using __partial_tensor_dst = __partial_tensor<_TpDst, _StrideTOut, _Rank, _DstAccessor>;
const auto __idx = ::cuda::gpu_thread.rank_as<_ExtentT>(::cuda::grid, __config);
const auto __stride = ::cuda::gpu_thread.count_as<_ExtentT>(::cuda::grid, __config);
const __partial_tensor_src __src{__src_ptr, __src_strides, __src_accessor};
const __partial_tensor_dst __dst{__dst_ptr, __dst_strides, __dst_accessor};
for (auto __i = __idx; __i < __tensor_size; __i += __stride)
{
const auto __coord = __coord_iter(__i);
__dst(__coord) = __src(__coord);
if constexpr (sizeof(_ExtentT) <= 4)
{
return;
}
}
}
//! @brief Launch a naive element-wise copy kernel for strided tensor data.
//!
//! Each thread copies one element at a time using a grid-stride loop. Coordinates are
//! computed from linear indices via @ref __tensor_coord_iterator.
//!
//! @param[in] __src Source raw tensor descriptor
//! @param[out] __dst Destination raw tensor descriptor
//! @param[in] __tensor_size Total number of elements to copy
//! @param[in] __stream CUDA stream for asynchronous execution
//! @param[in] __src_accessor Accessor for reading source elements
//! @param[in] __dst_accessor Accessor for writing destination elements
template <typename _ExtentT,
typename _StrideTIn,
typename _StrideTOut,
typename _TpIn,
typename _TpOut,
::cuda::std::size_t _Rank,
typename _SrcAccessor = ::cuda::std::default_accessor<_TpIn>,
typename _DstAccessor = ::cuda::std::default_accessor<_TpOut>>
_CCCL_HOST_API void __copy_optimized(
const __raw_tensor<_ExtentT, _StrideTIn, _TpIn, _Rank>& __src,
const __raw_tensor<_ExtentT, _StrideTOut, _TpOut, _Rank>& __dst,
_ExtentT __tensor_size,
::cuda::stream_ref __stream,
const _SrcAccessor& __src_accessor = {},
const _DstAccessor& __dst_accessor = {}) noexcept
{
constexpr int __block_size = 256;
const __tensor_coord_iterator<_ExtentT, _Rank> __coord_iter(__src.__extents);
const auto __grid_size = ::cuda::ceil_div(__tensor_size, _ExtentT{__block_size});
const auto __config = ::cuda::make_config(::cuda::block_dims<__block_size>(), ::cuda::grid_dims(__grid_size));
const auto& __kernel = ::cuda::experimental::__copy_optimized_kernel<
decltype(__config),
_TpIn,
_TpOut,
_SrcAccessor,
_DstAccessor,
_ExtentT,
_StrideTIn,
_StrideTOut,
_Rank>;
::cuda::launch(
__stream,
__config,
__kernel,
__src.__data,
__src.__strides,
__src_accessor,
__dst.__data,
__dst.__strides,
__dst_accessor,
__coord_iter,
__tensor_size);
}
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX__COPY_OPTIMIZED_H

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@@ -1,442 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_COPY_SHARED_MEMORY_H
#define _CUDAX__COPY_COPY_SHARED_MEMORY_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__cmath/ceil_div.h>
#include <cuda/__launch/configuration.h>
#include <cuda/__launch/launch.h>
#include <cuda/__stream/stream_ref.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__mdspan/default_accessor.h>
#include <cuda/std/__type_traits/make_unsigned.h>
#include <cuda/std/__type_traits/remove_cv.h>
#include <cuda/std/array>
#include <cuda/experimental/__copy/copy_shared_memory_utils.cuh>
#include <cuda/experimental/__copy/tensor_iterator.cuh>
#include <cuda/experimental/__copy_bytes/types.cuh>
#include <cuda/std/__cccl/prologue.h>
//! Shared-memory tiled transpose for arbitrary-rank tensor copies.
//!
//! The overall idea is to decompose the tensors into tiles that can fit in shared memory.
//! Each tile is assigned to a thread block. A tile can entirely represent a dimension or split the respective extent.
//! The algorithm creates tiles over dimensions that provide coalesced accesses in the source and destination tensors.
//!
//! (1) Grid decomposition
//! The tensor is partitioned into tiles whose per-dimension sizes are capped by warp size and shared-memory capacity.
//! The total number of tiles (product of ceil(extent[d] / tile_size[d]) over all dimensions) becomes the 1-D grid size.
//!
//! (2) Block processing
//! Each block handles one tile in two phases:
//! 1. *Load*: threads cooperatively read source elements into shared memory.
//! This requires additional logic to "transpose" the source tensor into a row-major order.
//! The mapping is determined by using the source-tile permutation obtained by sorting by |src stride|.
//! 2. *Store*: after a barrier, threads read shared memory in destination-coalesced order by using the
//! destination-tile permutation obtained by sorting by |dst stride|.
//!
//! Boundary tiles that extend past the tensor extents fall back to a direct element-wise copy without shared memory.
namespace cuda::experimental
{
//! @brief Compute the shared-memory offset for the XOR swizzle.
//!
//! @param[in] __offset The offset in the shared-memory tile.
//! @return The offset in the shared-memory tile with the XOR swizzle applied.
template <bool _UseXorSwizzle>
[[nodiscard]] _CCCL_DEVICE_API __tile_extent_t __smem_offset(__tile_extent_t __offset) noexcept
{
if constexpr (_UseXorSwizzle)
{
static_assert(__max_tile_size == 32, "XOR shared-memory swizzle assumes 32 banks and 32-element tile modes");
constexpr __tile_extent_t __swizzle_tile_size = __max_tile_size * __max_tile_size;
const auto __outer = __offset / __swizzle_tile_size;
const auto __offset_tile_rounded = __outer * __swizzle_tile_size;
const auto __inner = __offset - __offset_tile_rounded;
const auto __row = __inner / __max_tile_size;
const auto __row_tile_rounded = __row * __max_tile_size;
const auto __col = __inner - __row_tile_rounded;
return __offset_tile_rounded + __row_tile_rounded + (__col ^ __row);
}
return __offset;
}
//! @brief Shared-memory tiled transpose kernel for arbitrary-rank tensors.
//!
//! Each block processes one tile. Threads cooperatively iterate over tile elements with a stride loop. Full (interior)
//! tiles use a two-phase shared-memory transpose: load source data into shared memory using source-coalesced ordering,
//! then store from shared memory to destination using destination-coalesced ordering. Partial (boundary) tiles copy
//! elements directly without shared memory.
//!
//! @param[in] __config Kernel launch configuration
//! @param[in] __src_ptr Pointer to source data
//! @param[in] __src_accessor Accessor for reading source elements
//! @param[out] __dst_ptr Pointer to destination data
//! @param[in] __dst_accessor Accessor for writing destination elements
//! @param[in] __grid_iter Coordinate iterator for grid tile decomposition
//! @param[in] __grid_tile_src_strides Per-dimension source strides scaled by tile sizes
//! @param[in] __grid_tile_dst_strides Per-dimension destination strides scaled by tile sizes
//! @param[in] __tile_perm_iter Coordinate iterator for src-permuted tile decomposition
//! @param[in] __src_perm_src_strides Src-permuted source strides for loading
//! @param[in] __tile_src_perm_smem_strides Src-permuted shared memory strides for loading
//! @param[in] __tile_dst_perm_iter Coordinate iterator for dst-permuted tile decomposition
//! @param[in] __dst_perm_dst_strides Dst-permuted destination strides for storing
//! @param[in] __tile_dst_smem_strides Dst-permuted shared memory strides for storing
//! @param[in] __dst_strides Per-dimension destination strides for partial tiles
//! @param[in] __tile_total_size Total number of elements in one tile
//! @param[in] __tile_sizes Per-dimension tile extents
//! @param[in] __extents Per-dimension tensor extents (for partial-tile bounds)
//! @param[in] __src_strides Per-dimension source strides (for partial-tile access)
template <bool _UseXorSwizzle,
typename _Config,
::cuda::std::size_t _MaxRankUZ,
typename _TpSrc,
typename _TpDst,
typename _SrcAccessor,
typename _DstAccessor,
typename _ExtentT,
typename _StrideTIn,
typename _StrideTOut>
__global__ void __copy_shared_mem_kernel(
_CCCL_GRID_CONSTANT const _Config __config,
const _TpSrc* _CCCL_RESTRICT __src_ptr,
_CCCL_GRID_CONSTANT const _SrcAccessor __src_accessor,
_TpDst* _CCCL_RESTRICT __dst_ptr,
_CCCL_GRID_CONSTANT const _DstAccessor __dst_accessor,
_CCCL_GRID_CONSTANT const __tensor_coord_iterator<_ExtentT, _MaxRankUZ> __grid_iter,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTIn, _MaxRankUZ> __grid_tile_src_strides,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTOut, _MaxRankUZ> __grid_tile_dst_strides,
_CCCL_GRID_CONSTANT const __tensor_coord_iterator<__tile_extent_t, _MaxRankUZ> __tile_perm_iter,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTIn, _MaxRankUZ> __src_perm_src_strides,
_CCCL_GRID_CONSTANT const ::cuda::std::array<__tile_extent_t, _MaxRankUZ> __tile_src_perm_smem_strides,
_CCCL_GRID_CONSTANT const __tensor_coord_iterator<__tile_extent_t, _MaxRankUZ> __tile_dst_perm_iter,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTOut, _MaxRankUZ> __dst_perm_dst_strides,
_CCCL_GRID_CONSTANT const ::cuda::std::array<__tile_extent_t, _MaxRankUZ> __tile_dst_perm_smem_strides,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTOut, _MaxRankUZ> __dst_strides,
_CCCL_GRID_CONSTANT const int __tile_total_size,
_CCCL_GRID_CONSTANT const ::cuda::std::array<__tile_extent_t, _MaxRankUZ> __tile_sizes,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_ExtentT, _MaxRankUZ> __extents,
_CCCL_GRID_CONSTANT const ::cuda::std::array<_StrideTIn, _MaxRankUZ> __src_strides)
{
constexpr auto __max_rank = int{_MaxRankUZ};
// Grid tile decomposition: map linearized block index to src/dst base offsets
// __grid_coords: linear tile index -> multi-dimensional coordinates (array)
const auto __grid_index = ::cuda::block.index_as<_ExtentT>(::cuda::grid).x;
const auto __grid_coords = __grid_iter(__grid_index);
{
_StrideTIn __src_base = 0;
_StrideTOut __dst_base = 0;
_CCCL_PRAGMA_UNROLL_FULL()
for (int __k = 0; __k < __max_rank; ++__k)
{
__src_base += static_cast<_StrideTIn>(__grid_coords[__k]) * __grid_tile_src_strides[__k];
__dst_base += static_cast<_StrideTOut>(__grid_coords[__k]) * __grid_tile_dst_strides[__k];
}
__src_ptr += __src_base;
__dst_ptr += __dst_base;
}
// Partial tile detection: is the current tile full or partial?
bool __is_full_tile = true;
_CCCL_PRAGMA_UNROLL_FULL()
for (int __k = 0; __k < __max_rank; ++__k)
{
const auto __block_start = __grid_coords[__k] * __tile_sizes[__k];
if (__block_start + __tile_sizes[__k] > __extents[__k])
{
__is_full_tile = false;
break;
}
}
// Dispatch to Full-tile or Boundary case
const auto __tid = ::cuda::gpu_thread.rank_as<int>(::cuda::block, __config);
const auto __block_stride = ::cuda::gpu_thread.count_as<int>(::cuda::block, __config);
using __partial_tensor_src = __partial_tensor<const _TpSrc, _StrideTIn, _MaxRankUZ, _SrcAccessor>;
using __partial_tensor_dst = __partial_tensor<_TpDst, _StrideTOut, _MaxRankUZ, _DstAccessor>;
//--------------------------------------------------------------------------------------------------------------------
// Full-tile shared-memory transpose
if (__is_full_tile)
{
using _Tp = ::cuda::std::remove_cv_t<_TpSrc>;
using __partial_tensor_smem =
__partial_tensor<_Tp, __tile_extent_t, _MaxRankUZ, ::cuda::std::default_accessor<_Tp>>;
extern __shared__ char __smem_bytes[];
auto* __smem = reinterpret_cast<_Tp*>(__smem_bytes);
// (1) load src to shared memory by using the src/tile-permuted ordering
const __partial_tensor_src __src_tensor{__src_ptr, __src_perm_src_strides, __src_accessor};
const __partial_tensor_smem __smem_tensor{
__smem, __tile_src_perm_smem_strides, ::cuda::std::default_accessor<_Tp>{}};
for (auto __i = __tid; __i < __tile_total_size; __i += __block_stride)
{
const auto __coords = __tile_perm_iter(__i);
const auto __raw_offset = __smem_tensor.__offset(__coords);
const auto __swizzled_offset = ::cuda::experimental::__smem_offset<_UseXorSwizzle>(__raw_offset);
__smem[__swizzled_offset] = __src_tensor(__coords);
}
__syncthreads();
// (2) store from shared memory to destination by using the dst/tile-permuted ordering
const __partial_tensor_dst __dst_tensor{__dst_ptr, __dst_perm_dst_strides, __dst_accessor};
const __partial_tensor_smem __smem_dst_tensor{
__smem, __tile_dst_perm_smem_strides, ::cuda::std::default_accessor<_Tp>{}};
for (auto __i = __tid; __i < __tile_total_size; __i += __block_stride)
{
const auto __coords = __tile_dst_perm_iter(__i);
const auto __raw_offset = __smem_dst_tensor.__offset(__coords);
const auto __swizzled_offset = ::cuda::experimental::__smem_offset<_UseXorSwizzle>(__raw_offset);
__dst_tensor(__coords) = __smem[__swizzled_offset];
}
}
//--------------------------------------------------------------------------------------------------------------------
// Boundary direct-copy (no shared memory)
else
{
using __uextent_t = ::cuda::std::make_unsigned_t<_ExtentT>;
const __partial_tensor_src __src_tensor{__src_ptr, __src_strides, __src_accessor};
const __partial_tensor_dst __dst_tensor{__dst_ptr, __dst_strides, __dst_accessor};
// Find the partial tile sizes and total number of elements
::cuda::std::array<__tile_extent_t, __max_rank> __partial_tile_sizes{};
int __partial_tile_total = 1;
_CCCL_PRAGMA_UNROLL_FULL()
for (int __k = 0; __k < __max_rank; ++__k)
{
const auto __block_start = static_cast<__uextent_t>(__grid_coords[__k] * __tile_sizes[__k]);
const auto __diff = static_cast<__tile_extent_t>(__extents[__k] - __block_start);
__partial_tile_sizes[__k] = ::cuda::std::min(__tile_sizes[__k], __diff);
__partial_tile_total *= __partial_tile_sizes[__k];
}
// map the linear index to the multi-dimensional coordinates and copy the elements
for (auto __i = __tid; __i < __partial_tile_total; __i += __block_stride)
{
__tile_extent_t __linear = __i;
::cuda::std::array<__tile_extent_t, __max_rank> __coords;
_CCCL_PRAGMA_UNROLL_FULL()
for (int __k = 0; __k < __max_rank; ++__k)
{
__coords[__k] = __linear % __partial_tile_sizes[__k];
__linear /= __partial_tile_sizes[__k];
}
__dst_tensor(__coords) = __src_tensor(__coords);
}
}
}
#if !_CCCL_COMPILER(NVRTC)
//! @brief Launch the shared-memory tiled transpose kernel.
//!
//! Precomputes the source/destination-coalesced permutations and tile shapes, constructs coordinate iterators, then
//! launches one block per tile.
//!
//! @pre `__src.__rank >= 2`
//!
//! @param[in] __src Source raw tensor descriptor
//! @param[out] __dst Destination raw tensor descriptor
//! @param[in] __stream CUDA stream for asynchronous execution
//! @param[in] __src_accessor Accessor for reading source elements
//! @param[in] __dst_accessor Accessor for writing destination elements
template <typename _ExtentT,
typename _StrideTIn,
typename _StrideTOut,
typename _TpIn,
typename _TpOut,
::cuda::std::size_t _MaxRank,
typename _SrcAccessor,
typename _DstAccessor>
_CCCL_HOST_API void __launch_copy_shared_mem_kernel(
const __raw_tensor<_ExtentT, _StrideTIn, _TpIn, _MaxRank>& __src,
const __raw_tensor<_ExtentT, _StrideTOut, _TpOut, _MaxRank>& __dst,
::cuda::stream_ref __stream,
const _SrcAccessor& __src_accessor = {},
const _DstAccessor& __dst_accessor = {})
{
namespace cudax = ::cuda::experimental;
using ::cuda::std::size_t;
_CCCL_ASSERT(__src.__rank >= 2, "Rank must be at least 2 for shared memory transpose");
const auto __tiling = cudax::__find_shared_mem_tiling<_TpIn>(__src, __dst);
const auto __tile_sizes = __tiling.__tile_sizes;
const auto __rank = __src.__rank;
const auto __tile_total_size = __tiling.__tile_total_size;
//--------------------------------------------------------------------------------------------------------------------
// Find the grid size (number of blocks) and strides for block index decomposition
::cuda::std::array<_ExtentT, _MaxRank> __grid_tile_sizes{};
::cuda::std::array<_StrideTIn, _MaxRank> __grid_tile_src_strides{};
::cuda::std::array<_StrideTOut, _MaxRank> __grid_tile_dst_strides{};
_ExtentT __grid_size = 1;
for (size_t __i = 0; __i < __rank; ++__i)
{
__grid_tile_sizes[__i] = ::cuda::ceil_div(__src.__extents[__i], static_cast<_ExtentT>(__tile_sizes[__i]));
__grid_tile_src_strides[__i] = static_cast<_StrideTIn>(__tile_sizes[__i]) * __src.__strides[__i];
__grid_tile_dst_strides[__i] = static_cast<_StrideTOut>(__tile_sizes[__i]) * __dst.__strides[__i];
__grid_size *= __grid_tile_sizes[__i];
}
for (size_t __i = __rank; __i < _MaxRank; ++__i)
{
__grid_tile_sizes[__i] = 1;
}
//--------------------------------------------------------------------------------------------------------------------
// Reordered arrays for loading src and storing dst based on coalesced permutations
::cuda::std::array<_StrideTIn, _MaxRank> __src_perm_src_strides{};
::cuda::std::array<_StrideTOut, _MaxRank> __dst_perm_dst_strides{};
::cuda::std::array<__tile_extent_t, _MaxRank> __tile_src_perm_sizes{};
::cuda::std::array<__tile_extent_t, _MaxRank> __tile_dst_perm_sizes{};
::cuda::std::array<__tile_extent_t, _MaxRank> __tile_src_perm_smem_strides{};
::cuda::std::array<__tile_extent_t, _MaxRank> __tile_dst_perm_smem_strides{};
::cuda::std::array<__tile_extent_t, _MaxRank> __canonical_strides{};
__canonical_strides[0] = 1;
for (size_t __i = 1; __i < __rank; ++__i)
{
__canonical_strides[__i] = __canonical_strides[__i - 1] * __tile_sizes[__i - 1];
}
for (size_t __i = 0; __i < __rank; ++__i)
{
const auto __p = __tiling.__src_perm[__i];
__tile_src_perm_sizes[__i] = __tile_sizes[__p];
__src_perm_src_strides[__i] = __src.__strides[__p];
__tile_src_perm_smem_strides[__i] = __canonical_strides[__p];
const auto __q = __tiling.__dst_perm[__i];
__tile_dst_perm_sizes[__i] = __tile_sizes[__q];
__dst_perm_dst_strides[__i] = __dst.__strides[__q];
__tile_dst_perm_smem_strides[__i] = __canonical_strides[__q];
}
for (size_t __i = __rank; __i < _MaxRank; ++__i)
{
__tile_src_perm_sizes[__i] = 1;
__tile_dst_perm_sizes[__i] = 1;
}
//--------------------------------------------------------------------------------------------------------------------
// Construct coordinate iterators on the host (precomputed fast modulo/division)
// namely, given a linear index, compute the multi-dimensional coordinates
const __tensor_coord_iterator<_ExtentT, _MaxRank> __grid_iter{__grid_tile_sizes}; // grid tile index
const __tensor_coord_iterator<__tile_extent_t, _MaxRank> __tile_perm_iter{__tile_src_perm_sizes}; // src -> shared
// memory
const __tensor_coord_iterator<__tile_extent_t, _MaxRank> __tile_dst_perm_iter{__tile_dst_perm_sizes}; // shared memory
// -> dst
//--------------------------------------------------------------------------------------------------------------------
// Launch the kernel
using __value_type = ::cuda::std::remove_cv_t<_TpIn>;
const int __thread_block_size = cudax::__find_thread_block_size(__tile_total_size * sizeof(__value_type));
const auto __config = ::cuda::make_config(
::cuda::block_dims(__thread_block_size),
::cuda::grid_dims(__grid_size),
::cuda::dynamic_shared_memory<__value_type[]>(__tile_total_size));
if (__tiling.__use_xor_swizzle)
{
const auto __kernel = cudax::__copy_shared_mem_kernel<
true,
decltype(__config),
_MaxRank,
_TpIn,
_TpOut,
_SrcAccessor,
_DstAccessor,
_ExtentT,
_StrideTIn,
_StrideTOut>;
::cuda::launch(
__stream,
__config,
__kernel,
__src.__data,
__src_accessor,
__dst.__data,
__dst_accessor,
__grid_iter,
__grid_tile_src_strides,
__grid_tile_dst_strides,
__tile_perm_iter,
__src_perm_src_strides,
__tile_src_perm_smem_strides,
__tile_dst_perm_iter,
__dst_perm_dst_strides,
__tile_dst_perm_smem_strides,
__dst.__strides,
static_cast<int>(__tile_total_size),
__tile_sizes,
__dst.__extents,
__src.__strides);
}
else
{
const auto __kernel = cudax::__copy_shared_mem_kernel<
false,
decltype(__config),
_MaxRank,
_TpIn,
_TpOut,
_SrcAccessor,
_DstAccessor,
_ExtentT,
_StrideTIn,
_StrideTOut>;
::cuda::launch(
__stream,
__config,
__kernel,
__src.__data,
__src_accessor,
__dst.__data,
__dst_accessor,
__grid_iter,
__grid_tile_src_strides,
__grid_tile_dst_strides,
__tile_perm_iter,
__src_perm_src_strides,
__tile_src_perm_smem_strides,
__tile_dst_perm_iter,
__dst_perm_dst_strides,
__tile_dst_perm_smem_strides,
__dst.__strides,
static_cast<int>(__tile_total_size),
__tile_sizes,
__dst.__extents,
__src.__strides);
}
}
#endif // !_CCCL_COMPILER(NVRTC)
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX__COPY_COPY_SHARED_MEMORY_H

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@@ -1,293 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_COPY_SHARED_MEMORY_UTILS_H
#define _CUDAX__COPY_COPY_SHARED_MEMORY_UTILS_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/__cmath/ceil_div.h>
# include <cuda/__cmath/round_up.h>
# include <cuda/__device/all_devices.h>
# include <cuda/__device/attributes.h>
# include <cuda/__device/device_ref.h>
# include <cuda/__driver/driver_api.h>
# include <cuda/std/__algorithm/min.h>
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/array>
# include <cuda/experimental/__copy_bytes/tensor_query.cuh>
# include <cuda/experimental/__copy_bytes/types.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! Maximum tensor rank for which the shared-memory transpose kernel is instantiated. Higher ranks cause excessive
//! register pressure (many rank-sized arrays and fully-unrolled loops).
inline constexpr ::cuda::std::size_t __max_shared_mem_kernel_rank = 8;
//! A tile size is always representable by an unsigned integer.
using __tile_extent_t = unsigned;
//! @brief Copy a raw tensor descriptor into one with a narrower static maximum rank.
//!
//! @param[in] __tensor Raw tensor descriptor with dynamic rank matching _RankOut
//! @return Raw tensor descriptor with _RankOut as its static maximum rank
template <::cuda::std::size_t _RankOut, typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API __raw_tensor<_ExtentT, _StrideT, _Tp, _RankOut>
__narrow_raw_tensor_rank(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor) noexcept
{
_CCCL_ASSERT(__tensor.__rank == _RankOut, "tensor rank must match the narrowed static rank");
__raw_tensor<_ExtentT, _StrideT, _Tp, _RankOut> __result{__tensor.__data, _RankOut, {}, {}};
for (::cuda::std::size_t __i = 0; __i < _RankOut; ++__i)
{
__result.__extents[__i] = __tensor.__extents[__i];
__result.__strides[__i] = __tensor.__strides[__i];
}
return __result;
}
//! @brief Count the number of leading contiguous dimensions in a raw tensor.
//!
//! Starting from dimension 0, counts consecutive dimensions where `stride[0] == 1` and `stride[i] == stride[i-1] *
//! extent[i-1]` for each subsequent dimension.
//!
//! @param[in] __tensor Raw tensor descriptor
//! @return Number of leading contiguous dimensions (0 if stride[0] != 1 or rank is 0)
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API ::cuda::std::size_t
__num_contiguous_dimensions(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor) noexcept
{
using __rank_t = typename __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>::__rank_t;
if (__tensor.__rank == 0 || __tensor.__strides[0] != 1)
{
return 0;
}
__rank_t __count = 1;
auto __expected_stride = static_cast<_StrideT>(__tensor.__extents[0]);
for (__rank_t __i = 1; __i < __tensor.__rank; ++__i)
{
if (__tensor.__strides[__i] != __expected_stride)
{
break;
}
__expected_stride *= static_cast<_StrideT>(__tensor.__extents[__i]);
++__count;
}
return __count;
}
//! @brief Return a device_ref for the current CUDA device.
//!
//! @return Device reference for the active CUDA context's device
[[nodiscard]] _CCCL_HOST_API inline ::cuda::device_ref __current_device() noexcept
{
const auto __dev_id = ::cuda::__driver::__cudevice_to_ordinal(::cuda::__driver::__ctxGetDevice());
return ::cuda::devices[__dev_id];
}
//! Maximum extent of a single tile dimension, set to the warp size so that the innermost tile dimension maps to a
//! full warp of coalesced accesses.
inline constexpr size_t __max_tile_size = 32;
// The structure holds the tiling information to optimize the transpose (shared-memory) kernel.
// - __tile_sizes: the size of each tile dimension in shared-memory
// - __src_perm: the permutation of the source dimensions (copy to shared-memory)
// - __dst_perm: the permutation of the destination dimensions (copy from shared-memory)
// - __tile_total_size: the total size of the tile in shared-memory
// - __active_tile_dims: the number of dimensions covered by the tile
// - __active_32_dims: the number of tile dimensions with extent __max_tile_size
// - __is_valid: true if the tiling is valid
// - __use_xor_swizzle: true if the XOR swizzle is used
template <::cuda::std::size_t _MaxRank>
struct __shared_mem_tiling_result
{
::cuda::std::array<__tile_extent_t, _MaxRank> __tile_sizes{};
::cuda::std::array<::cuda::std::size_t, _MaxRank> __src_perm{};
::cuda::std::array<::cuda::std::size_t, _MaxRank> __dst_perm{};
::cuda::std::size_t __tile_total_size = 1;
::cuda::std::size_t __active_tile_dims = 0;
::cuda::std::size_t __active_32_dims = 0;
bool __is_valid = false;
bool __use_xor_swizzle = false;
};
//! @brief Adds a contiguous stride-1 run from one tensor to the shared-memory tile.
//!
//! @param[in] __tensor Raw tensor descriptor used to find coalesced modes
//! @param[in] __perm Mode order to scan
//! @param[in,out] __result Shared-memory tiling result updated with selected tile sizes
//! @param[in] __max_shared_mem_bytes Maximum shared-memory capacity for one tile
//! @return Number of coalesced elements covered by this tensor's selected tile run
template <typename _SmemTp, typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API ::cuda::std::size_t __add_coalesced_tile_run(
const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor,
const ::cuda::std::array<::cuda::std::size_t, _MaxRank>& __perm,
__shared_mem_tiling_result<_MaxRank>& __result,
::cuda::std::size_t __max_shared_mem_bytes) noexcept
{
using ::cuda::std::size_t;
size_t __coalesced_tile_size = 1;
_StrideT __expected_stride = 1;
for (size_t __i = 0; __i < __tensor.__rank; ++__i)
{
const auto __perm_i = __perm[__i];
const auto __extent = static_cast<size_t>(__tensor.__extents[__perm_i]);
const auto __stride = ::cuda::experimental::__abs_integer(__tensor.__strides[__perm_i]);
if (__stride != __expected_stride) // input tensor not contiguous
{
break;
}
if (__result.__tile_sizes[__perm_i] == 1) // first time we see this dimension
{
const auto __tile_size = ::cuda::std::min(__extent, __max_tile_size);
const auto __tile_total_size_bytes = __result.__tile_total_size * __tile_size * sizeof(_SmemTp);
if (__tile_total_size_bytes > __max_shared_mem_bytes)
{
break;
}
// if the tile fits in shared-memory, update the result
__result.__tile_sizes[__perm_i] = static_cast<__tile_extent_t>(__tile_size);
__result.__tile_total_size *= __tile_size;
++__result.__active_tile_dims;
if (__tile_size == __max_tile_size)
{
++__result.__active_32_dims;
}
}
__coalesced_tile_size *= __result.__tile_sizes[__perm_i];
__expected_stride *= static_cast<_StrideT>(__extent);
}
return __coalesced_tile_size;
}
//! @brief Compute a source/destination-aware shared-memory tile.
//!
//! The selected tile spans coalesced dimensions from both layouts. This keeps the load phase ordered by source stride
//! and the store phase ordered by destination stride, without requiring either coalesced dimension to be mode 0.
//!
//! @param[in] __src Source raw tensor descriptor
//! @param[in] __dst Destination raw tensor descriptor
//! @return Shared-memory tiling decision and layout permutations
template <typename _TpIn,
typename _ExtentT,
typename _StrideTIn,
typename _TpSrc,
typename _StrideTOut,
typename _TpDst,
::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API __shared_mem_tiling_result<_MaxRank>
__find_shared_mem_tiling(const __raw_tensor<_ExtentT, _StrideTIn, _TpSrc, _MaxRank>& __src,
const __raw_tensor<_ExtentT, _StrideTOut, _TpDst, _MaxRank>& __dst) noexcept
{
using ::cuda::std::size_t;
__shared_mem_tiling_result<_MaxRank> __result{};
// initialize the source and destination permutations and sort them by stride
for (size_t __i = 0; __i < _MaxRank; ++__i)
{
__result.__tile_sizes[__i] = 1;
__result.__src_perm[__i] = __i;
__result.__dst_perm[__i] = __i;
}
__result.__src_perm = ::cuda::experimental::__stride_order(__src);
__result.__dst_perm = ::cuda::experimental::__stride_order(__dst);
const auto __current_dev = ::cuda::experimental::__current_device();
const size_t __max_shared_mem_bytes = __current_dev.attribute<::cudaDevAttrMaxSharedMemoryPerBlock>();
const auto __src_coalesced_tile_size =
::cuda::experimental::__add_coalesced_tile_run<_TpIn>(__src, __result.__src_perm, __result, __max_shared_mem_bytes);
const auto __dst_coalesced_tile_size =
::cuda::experimental::__add_coalesced_tile_run<_TpIn>(__dst, __result.__dst_perm, __result, __max_shared_mem_bytes);
// If the tile total size is too small, or the coalescing is not useful on both sides, or the number of active tile
// dimensions is less than 2, return the result.
if (__result.__tile_total_size < __max_tile_size * 8 || __src_coalesced_tile_size < 2 || __dst_coalesced_tile_size < 2
|| __result.__active_tile_dims < 2)
{
return __result;
}
// There must be enough blocks to keep the GPU busy (at least one full wave across all SMs).
const size_t __num_sms = __current_dev.attribute<::cudaDevAttrMultiProcessorCount>();
size_t __num_tiles = 1;
for (size_t __r = 0; __r < __dst.__rank; ++__r)
{
const auto __extent = static_cast<size_t>(__dst.__extents[__r]);
const auto __tile_size = static_cast<size_t>(__result.__tile_sizes[__r]);
__num_tiles *= ::cuda::ceil_div(__extent, __tile_size);
}
if (__num_tiles < __num_sms)
{
return __result;
}
__result.__is_valid = true;
// Shared memory swizzle makes sense only for 32-bit and 64-bit types.
__result.__use_xor_swizzle = (sizeof(_TpIn) == 4 || sizeof(_TpIn) == 8) //
&& __result.__active_32_dims == 2;
return __result;
}
//! @brief Decide whether the shared-memory tiled transpose kernel is profitable.
//!
//! @param[in] __src Source raw tensor descriptor
//! @param[in] __dst Destination raw tensor descriptor
//! @return true if the shared-memory kernel should be used
template <typename _ExtentT,
typename _StrideTIn,
typename _StrideTOut,
typename _TpIn,
typename _TpOut,
::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API bool
__use_shared_mem_kernel(const __raw_tensor<_ExtentT, _StrideTIn, _TpIn, _MaxRank>& __src,
const __raw_tensor<_ExtentT, _StrideTOut, _TpOut, _MaxRank>& __dst) noexcept
{
return ::cuda::experimental::__find_shared_mem_tiling<_TpIn>(__src, __dst).__is_valid;
}
//! @brief Compute the thread block size for the shared-memory kernel.
//!
//! Balances occupancy by dividing the SM threads across as many blocks as the shared memory allows, then caps at
//! the device maximum.
//!
//! @param[in] __tile_total_bytes Shared memory required for one tile in bytes
//! @return Thread block size
[[nodiscard]] _CCCL_HOST_API inline int __find_thread_block_size(::cuda::std::size_t __tile_total_bytes) noexcept
{
using ::cuda::std::size_t;
const auto __dev = ::cuda::experimental::__current_device();
const size_t __total_sm_threads = __dev.attribute<::cudaDevAttrMaxThreadsPerMultiProcessor>();
const size_t __max_thread_block_size = __dev.attribute<::cudaDevAttrMaxThreadsPerBlock>();
const size_t __total_shared_mem_bytes = __dev.attribute<::cudaDevAttrMaxSharedMemoryPerMultiprocessor>();
const auto __num_blocks_per_sm = __total_shared_mem_bytes / __tile_total_bytes;
const auto __thread_block_size = ::cuda::std::min(__total_sm_threads / __num_blocks_per_sm, __max_thread_block_size);
const auto __thread_block_size32 = ::cuda::round_up(__thread_block_size, /*warp size=*/size_t{32});
return static_cast<int>(__thread_block_size32);
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // _CUDAX__COPY_COPY_SHARED_MEMORY_UTILS_H

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cccl_upstream/cudax/include/cuda/experimental/__copy/dispatch_by_vector.cuh
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@@ -1,149 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_DISPATCH_BY_VECTOR_H
#define _CUDAX__COPY_DISPATCH_BY_VECTOR_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/std/__algorithm/min.h>
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/__type_traits/integral_constant.h>
# include <cuda/experimental/__copy/tensor_copy_utils.cuh>
# include <cuda/experimental/__copy_bytes/types.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Compute the maximum vector access width in bytes for a pair of raw tensors.
//!
//! Takes the minimum of the source alignment, destination alignment, and the GPU architecture's
//! maximum vector width.
//!
//! @param[in] __src Source raw tensor
//! @param[in] __dst Destination raw tensor
//! @return Maximum safe vector access width in bytes
template <typename _SrcExtentT,
typename _SrcStrideT,
typename _TpSrc,
typename _DstExtentT,
typename _DstStrideT,
typename _TpDst,
::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API ::cuda::std::size_t
__vector_size_bytes(const __raw_tensor<_SrcExtentT, _SrcStrideT, _TpSrc, _MaxRank>& __src,
const __raw_tensor<_DstExtentT, _DstStrideT, _TpDst, _MaxRank>& __dst) noexcept
{
return ::cuda::std::min(
{::cuda::experimental::__max_alignment(__src),
::cuda::experimental::__max_alignment(__dst),
::cuda::experimental::__max_gpu_arch_vector_size()});
}
template <int _VectorSize>
constexpr auto __const_vector_size = ::cuda::std::integral_constant<int, _VectorSize>{};
//! @brief Dispatch a copy operation with the optimal vectorized element type.
//!
//! Computes the maximum safe vector width from the source and destination tensors, reshapes both
//! tensors to that vector type via @ref __reshape_vectorized, and invokes @p __op with the reshaped tensors.
//!
//! @param[in] __src Source raw tensor descriptor
//! @param[in] __dst Destination raw tensor descriptor
//! @param[in] __op Callable invoked with the reshaped source and destination tensors
template <typename _ExtentT,
typename _StrideTIn,
typename _StrideTOut,
typename _TpIn,
typename _TpOut,
::cuda::std::size_t _Rank,
typename _Op>
_CCCL_HOST_API void __dispatch_by_vector_size(
const __raw_tensor<_ExtentT, _StrideTIn, _TpIn, _Rank>& __src,
const __raw_tensor<_ExtentT, _StrideTOut, _TpOut, _Rank>& __dst,
_Op __op) noexcept
{
namespace cudax = ::cuda::experimental;
const auto __call_vectorized = [&](auto __const_vector_size) {
const auto __src_recast = cudax::__reshape_vectorized<__const_vector_size>(__src);
const auto __dst_recast = cudax::__reshape_vectorized<__const_vector_size>(__dst);
__op(__src_recast, __dst_recast);
};
const auto __vector_size_bytes = cudax::__vector_size_bytes(__src, __dst);
// 32-bytes aligned vector types have been introduced in CTK 13.0
# if _CCCL_CTK_AT_LEAST(13, 0)
static_assert(sizeof(_TpIn) <= 32);
if constexpr (sizeof(_TpIn) <= 32)
{
if (__vector_size_bytes == 32)
{
__call_vectorized(__const_vector_size<32>);
return;
}
}
# else // ^^^ _CCCL_CTK_AT_LEAST(13, 0) ^^^ / vvv _CCCL_CTK_BELOW(13, 0) vvv
static_assert(sizeof(_TpIn) <= 16);
# endif // _CCCL_CTK_AT_LEAST(13, 0)
if constexpr (sizeof(_TpIn) <= 16)
{
if (__vector_size_bytes == 16)
{
__call_vectorized(__const_vector_size<16>);
return;
}
}
if constexpr (sizeof(_TpIn) <= 8)
{
if (__vector_size_bytes == 8)
{
__call_vectorized(__const_vector_size<8>);
return;
}
}
if constexpr (sizeof(_TpIn) <= 4)
{
if (__vector_size_bytes == 4)
{
__call_vectorized(__const_vector_size<4>);
return;
}
}
if constexpr (sizeof(_TpIn) <= 2)
{
if (__vector_size_bytes == 2)
{
__call_vectorized(__const_vector_size<2>);
return;
}
}
if constexpr (sizeof(_TpIn) <= 1)
{
__call_vectorized(__const_vector_size<1>);
}
// no fallthrough (sizeof(T) is never 0)
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // _CUDAX__COPY_DISPATCH_BY_VECTOR_H

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cccl_upstream/cudax/include/cuda/experimental/__copy/mdspan_d2d.cuh
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@@ -1,266 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_MDSPAN_D2D_H
#define _CUDAX__COPY_MDSPAN_D2D_H
#include <cuda/std/detail/__config>
#include <cuda/std/__type_traits/remove_cv.h>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cub/device/device_transform.cuh>
# include <cuda/__cmath/pow2.h>
# include <cuda/__driver/driver_api.h>
# include <cuda/__functional/address_stability.h>
# include <cuda/__mdspan/host_device_mdspan.h>
# include <cuda/__mdspan/traits.h>
# include <cuda/__stream/stream_ref.h>
# include <cuda/__type_traits/is_trivially_copyable.h>
# include <cuda/std/__algorithm/max.h>
# include <cuda/std/__functional/identity.h>
# include <cuda/std/__host_stdlib/stdexcept>
# include <cuda/std/__mdspan/default_accessor.h>
# include <cuda/std/__memory/is_sufficiently_aligned.h>
# include <cuda/std/__type_traits/common_type.h>
# include <cuda/std/__type_traits/conditional.h>
# include <cuda/std/__type_traits/is_const.h>
# include <cuda/std/__type_traits/is_convertible.h>
# include <cuda/std/__type_traits/is_same.h>
# include <cuda/experimental/__copy/copy_contiguous.cuh>
# include <cuda/experimental/__copy/copy_optimized.cuh>
# include <cuda/experimental/__copy/copy_shared_memory.cuh>
# include <cuda/experimental/__copy/dispatch_by_vector.cuh>
# include <cuda/experimental/__copy/tensor_copy_utils.cuh>
# include <cuda/experimental/__copy/vector_access.cuh>
# include <cuda/experimental/__copy_bytes/simplify_paired.cuh>
# include <cuda/experimental/__copy_bytes/tensor_query.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Copy elements between two device mdspans.
//!
//! Validates preconditions, converts mdspans to raw tensor descriptors, simplifies the paired layout
//! (sort, flip negative strides, coalesce), then dispatches either a vectorized contiguous kernel or a
//! strided element-wise kernel.
//!
//! @param[in] __src Source device mdspan
//! @param[out] __dst Destination device mdspan
//! @param[in] __stream CUDA stream for the asynchronous transfer
template <typename _TpIn,
typename _ExtentsIn,
typename _LayoutPolicyIn,
typename _AccessorPolicyIn,
typename _TpOut,
typename _ExtentsOut,
typename _LayoutPolicyOut,
typename _AccessorPolicyOut>
_CCCL_HOST_API void copy(::cuda::device_mdspan<_TpIn, _ExtentsIn, _LayoutPolicyIn, _AccessorPolicyIn> __src,
::cuda::device_mdspan<_TpOut, _ExtentsOut, _LayoutPolicyOut, _AccessorPolicyOut> __dst,
::cuda::stream_ref __stream)
{
namespace cudax = ::cuda::experimental;
static_assert(::cuda::std::is_convertible_v<_TpIn, _TpOut>, "TpIn must be convertible to TpOut");
static_assert(!::cuda::std::is_const_v<_TpOut>, "TpOut must not be const");
static_assert(::cuda::__is_cuda_mdspan_layout_v<_LayoutPolicyIn>,
"LayoutPolicyIn must be a predefined layout policy");
static_assert(::cuda::__is_cuda_mdspan_layout_v<_LayoutPolicyOut>,
"LayoutPolicyOut must be a predefined layout policy");
if (__src.size() != __dst.size())
{
_CCCL_THROW(::std::invalid_argument, "mdspans must have the same size");
}
const auto __tensor_size = __src.size();
if (__tensor_size == 0)
{
return;
}
if (__src.data_handle() == nullptr || __dst.data_handle() == nullptr)
{
_CCCL_THROW(::std::invalid_argument, "mdspan data handle must not be nullptr");
}
if (!::cuda::std::is_sufficiently_aligned<alignof(_TpIn)>(__src.data_handle()))
{
_CCCL_THROW(::std::invalid_argument, "source mdspan must be sufficiently aligned");
}
if (!::cuda::std::is_sufficiently_aligned<alignof(_TpOut)>(__dst.data_handle()))
{
_CCCL_THROW(::std::invalid_argument, "destination mdspan must be sufficiently aligned");
}
if (cudax::__has_interleaved_stride_order(__dst))
{
_CCCL_THROW(::std::invalid_argument, "destination mdspan must not have interleaved stride order");
}
if (cudax::__may_overlap(__src, __dst))
{
_CCCL_THROW(::std::invalid_argument, "mdspans must not overlap in memory");
}
using __default_accessor_in = ::cuda::std::default_accessor<_TpIn>;
using __default_accessor_out = ::cuda::std::default_accessor<_TpOut>;
constexpr bool __have_default_accessors =
::cuda::std::is_convertible_v<_AccessorPolicyIn, __default_accessor_in>
&& ::cuda::std::is_convertible_v<_AccessorPolicyOut, __default_accessor_out>;
constexpr bool __are_byte_copyable =
::cuda::std::is_same_v<::cuda::std::remove_cv_t<_TpIn>, ::cuda::std::remove_cv_t<_TpOut>>
&& ::cuda::is_trivially_copyable_v<_TpIn> //
&& __have_default_accessors;
if (__tensor_size == 1 && __are_byte_copyable)
{
auto __src_ptr = __src.data_handle();
auto __dst_ptr = __dst.data_handle();
if constexpr (::cuda::__is_layout_stride_relaxed_v<_LayoutPolicyIn>)
{
__src_ptr += __src.mapping().offset();
}
if constexpr (::cuda::__is_layout_stride_relaxed_v<_LayoutPolicyOut>)
{
__dst_ptr += __dst.mapping().offset();
}
::cuda::__driver::__memcpyAsync(__dst_ptr, __src_ptr, sizeof(_TpIn), __stream.get());
return;
}
// rank == 0 for both tensors is already handled above -> their size is exactly 1
if constexpr (_ExtentsIn::rank() > 0 && _ExtentsOut::rank() > 0)
{
// use the most efficient type for device code
using __src_extent_t = ::cuda::std::common_type_t<typename _ExtentsIn::index_type, int>;
using __dst_extent_t = ::cuda::std::common_type_t<typename _ExtentsOut::index_type, int>;
using __common_extent_t =
::cuda::std::conditional_t<(sizeof(__src_extent_t) < sizeof(__dst_extent_t)), __src_extent_t, __dst_extent_t>;
using __src_stride_t =
::cuda::std::common_type_t<cudax::__mdspan_stride_t<_LayoutPolicyIn, decltype(__src.mapping())>, int>;
using __dst_stride_t =
::cuda::std::common_type_t<cudax::__mdspan_stride_t<_LayoutPolicyOut, decltype(__dst.mapping())>, int>;
constexpr auto __max_rank = ::cuda::std::max(_ExtentsIn::rank(), _ExtentsOut::rank());
const auto __src_raw = cudax::__to_raw_tensor<__common_extent_t, __src_stride_t, __max_rank>(__src);
const auto __dst_raw = cudax::__to_raw_tensor<__common_extent_t, __dst_stride_t, __max_rank>(__dst);
if (!cudax::__same_extents(__src_raw, __dst_raw))
{
_CCCL_THROW(::std::invalid_argument, "mdspans must have the same extents (after removing singleton dimensions)");
}
auto __src_simplified = __src_raw;
auto __dst_simplified = __dst_raw;
cudax::__sort_by_stride_paired(__src_simplified, __dst_simplified);
cudax::__flip_negative_strides_paired(__src_simplified, __dst_simplified);
cudax::__coalesce_paired(__src_simplified, __dst_simplified);
const bool __both_stride1 = (__src_simplified.__strides[0] == 1) && (__dst_simplified.__strides[0] == 1);
const auto __tile_size = __both_stride1 ? __src_simplified.__extents[0] : 1;
const auto __src_normalized = (__tile_size > 1) ? __src_simplified : cudax::__reverse_modes(__src_raw);
const auto __dst_normalized = (__tile_size > 1) ? __dst_simplified : cudax::__reverse_modes(__dst_raw);
_CCCL_ASSERT(__tensor_size % __tile_size == 0, "tensor size must be divisible by tile size");
const auto __inner_extent_bytes = __src_normalized.__extents[0] * sizeof(_TpIn);
// check the preconditions for the vectorized case
constexpr bool __are_vectorizable_copy =
sizeof(_TpIn) <= __max_vector_access && ::cuda::is_power_of_two(sizeof(_TpIn)) && __are_byte_copyable;
// (1) contiguous case
if constexpr (__have_default_accessors)
{
if (static_cast<::cuda::std::size_t>(__tile_size) == __tensor_size)
{
_CCCL_TRY_CUDA_API(
CUB_NS_QUALIFIER::DeviceTransform::Transform,
"cub::DeviceTransform::Transform failed",
__src_simplified.__data,
__dst_simplified.__data,
__tensor_size,
::cuda::proclaim_copyable_arguments(::cuda::std::identity{}),
__stream.get());
return;
}
}
// (2) inner size is large
if (__both_stride1 && __inner_extent_bytes >= cudax::__bytes_in_flight())
{
// (2a) vectorized case
if constexpr (__are_vectorizable_copy)
{
const auto __op = [__stream](const auto& __src, const auto& __dst) {
cudax::__launch_copy_contiguous_kernel(__src, __dst, __stream);
};
cudax::__dispatch_by_vector_size(__src_normalized, __dst_normalized, __op);
}
// (2b) non-vectorized case but inner size is large enough to use the contiguous kernel
else
{
cudax::__launch_copy_contiguous_kernel(
__src_normalized, __dst_normalized, __stream, __src.accessor(), __dst.accessor());
}
return;
}
// (3) inner size is not large -> try vectorized case
if constexpr (__are_vectorizable_copy)
{
if (__both_stride1)
{
const auto __op = [__stream](const auto& __src, const auto& __dst) {
cudax::__copy_optimized(__src, __dst, cudax::__total_size(__src), __stream);
};
cudax::__dispatch_by_vector_size(__src_normalized, __dst_normalized, __op);
return;
}
}
// (4) transpose case (rank capped to avoid excessive register pressure in the kernel)
if constexpr (__max_rank >= 2 && __max_rank <= cudax::__max_shared_mem_kernel_rank)
{
if (__src_simplified.__rank == 2) // Optimize when the actual rank is 2
{
const auto __src_rank2 = cudax::__narrow_raw_tensor_rank<2>(__src_simplified);
const auto __dst_rank2 = cudax::__narrow_raw_tensor_rank<2>(__dst_simplified);
if (cudax::__use_shared_mem_kernel(__src_rank2, __dst_rank2))
{
cudax::__launch_copy_shared_mem_kernel(__src_rank2, __dst_rank2, __stream, __src.accessor(), __dst.accessor());
return;
}
}
if (cudax::__use_shared_mem_kernel(__src_simplified, __dst_simplified))
{
cudax::__launch_copy_shared_mem_kernel(
__src_simplified, __dst_simplified, __stream, __src.accessor(), __dst.accessor());
return;
}
}
// (5) generic case (fallback)
cudax::__copy_optimized(
__src_normalized,
__dst_normalized,
cudax::__total_size(__src_normalized),
__stream,
__src.accessor(),
__dst.accessor());
}
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // _CUDAX__COPY_MDSPAN_D2D_H

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_TENSOR_COPY_UTILS_H
#define _CUDAX__COPY_TENSOR_COPY_UTILS_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/__memory/ptr_alignment.h>
# include <cuda/__memory/ranges_overlap.h>
# include <cuda/__utility/in_range.h>
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/__mdspan/mdspan.h>
# include <cuda/std/__memory/is_sufficiently_aligned.h>
# include <cuda/std/__numeric/gcd_lcm.h>
# include <cuda/std/__type_traits/conditional.h>
# include <cuda/std/__type_traits/is_const.h>
# include <cuda/experimental/__copy/vector_access.cuh>
# include <cuda/experimental/__copy_bytes/abs_integer.cuh>
# include <cuda/experimental/__copy_bytes/types.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Compute the maximum vectorization width in bytes for a raw tensor.
//!
//! Expects mode 0 to be the contiguous mode (stride == 1), as established by
//! @ref __sort_by_stride_paired. Computes the largest power-of-two vector width such that:
//! - The pointer is aligned to that width.
//! - All non-contiguous strides (in bytes) are divisible by it.
//! - The contiguous mode's shape is divisible by the element count.
//! The result is capped at 16 bytes. If mode 0 is not contiguous, returns sizeof(_Tp).
//!
//! @pre `__tensor.__rank` is in [1, _MaxRank].
//! @pre All shapes must be > 1 (no degenerate modes).
//! @pre Strides are sorted by @ref __sort_by_stride_paired (mode 0 has the smallest absolute stride).
//!
//! @param[in] __tensor Raw tensor with strides sorted by @ref __sort_by_stride_paired
//! @return Maximum safe vectorization width in bytes, in [sizeof(_Tp), 16]
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API ::cuda::std::size_t
__max_alignment(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor) noexcept
{
using ::cuda::std::size_t;
using __raw_tensor_t = __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>;
using __rank_t = typename __raw_tensor_t::__rank_t;
_CCCL_ASSERT(::cuda::in_range(__tensor.__rank, size_t{1}, _MaxRank), "Invalid tensor rank");
if (__tensor.__strides[0] != 1)
{
return sizeof(_Tp);
}
// (1) pointer alignment
size_t __alignment = ::cuda::__ptr_alignment(__tensor.__data);
// (2) alignment over all strides
for (__rank_t __i = 0; __i < __tensor.__rank; ++__i)
{
const auto __stride = ::cuda::experimental::__abs_integer(__tensor.__strides[__i]);
if (__stride != 1)
{
const size_t __stride_bytes = static_cast<size_t>(__stride) * sizeof(_Tp);
__alignment = ::cuda::std::gcd(__alignment, __stride_bytes);
}
}
_CCCL_ASSERT(__alignment % sizeof(_Tp) == 0, "alignment is not a multiple of the element size");
// (3) Compute the number of items per vector over the contiguous mode
size_t __elem_alignment = __alignment / sizeof(_Tp);
__elem_alignment = ::cuda::std::gcd(__elem_alignment, static_cast<size_t>(__tensor.__extents[0]));
return __elem_alignment * sizeof(_Tp);
}
template <::cuda::std::size_t _VectorBytes, typename _Tp>
using __reshape_vector_type =
::cuda::std::conditional_t<::cuda::std::is_const_v<_Tp>,
const ::cuda::experimental::__vector_access_t<_VectorBytes>,
::cuda::experimental::__vector_access_t<_VectorBytes>>;
//! @brief Reshape a raw tensor for vectorized access by widening the element type.
//!
//! @pre Mode 0 must be contiguous (stride == 1).
//! @pre The innermost extent (in bytes) must be divisible by @p _VectorBytes.
//! @pre All non-innermost strides must be divisible by the elements-per-vector ratio.
//!
//! @tparam _VectorBytes Target vector width in bytes
//! @param[in] __tensor Raw tensor with contiguous innermost mode
//! @return Raw tensor with element type replaced by the vector type and adjusted extents/strides
template <::cuda::std::size_t _VectorBytes, typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]]
_CCCL_HOST_API __raw_tensor<_ExtentT, _StrideT, __reshape_vector_type<_VectorBytes, _Tp>, _MaxRank>
__reshape_vectorized(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor) noexcept
{
using __vector_t = __reshape_vector_type<_VectorBytes, _Tp>;
using __rank_t = typename __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>::__rank_t;
static_assert(_VectorBytes % sizeof(_Tp) == 0, "vector size must be a multiple of element size");
constexpr auto __elems_per_vector = _VectorBytes / sizeof(_Tp);
_CCCL_ASSERT(__tensor.__strides[0] == 1, "innermost mode must be contiguous");
_CCCL_ASSERT(__tensor.__extents[0] % __elems_per_vector == 0,
"innermost extent must be divisible by elements per vector");
_CCCL_ASSERT(::cuda::std::is_sufficiently_aligned<alignof(__vector_t)>(__tensor.__data),
"tensor data is not sufficiently aligned to the extents and strides");
const auto __data = reinterpret_cast<__vector_t*>(__tensor.__data);
__raw_tensor<_ExtentT, _StrideT, __vector_t, _MaxRank> __result{
__data, __tensor.__rank, __tensor.__extents, __tensor.__strides};
__result.__extents[0] /= __elems_per_vector;
for (__rank_t __i = 1; __i < __result.__rank; ++__i)
{
_CCCL_ASSERT(__result.__strides[__i] % _StrideT{__elems_per_vector} == 0,
"non-innermost strides must be divisible by elements per vector");
__result.__strides[__i] /= _StrideT{__elems_per_vector};
}
return __result;
}
//! @brief Compute the total number of elements in a raw tensor.
//!
//! @param[in] __tensor Raw tensor descriptor
//! @return Product of all extents
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]]
_CCCL_HOST_API _ExtentT __total_size(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor) noexcept
{
using __raw_tensor_t = __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>;
using __rank_t = typename __raw_tensor_t::__rank_t;
_ExtentT __total_size = 1;
for (__rank_t __i = 0; __i < __tensor.__rank; ++__i)
{
__total_size *= __tensor.__extents[__i];
}
return __total_size;
}
//! @brief Conservative check whether two mdspans may access overlapping memory.
//!
//! Uses each mapping's @c required_span_size() to compute the half-open byte range
//! @c [data_handle, data_handle + required_span_size * sizeof(T)) and checks for intersection.
//! NOTE: the function doesn't check strict overlap for non-contiguous layouts, for example, the padding could be
//! between the two mdspans.
//!
//! Empty mdspans (size == 0) are considered non-overlapping.
//!
//! @param[in] __a First mdspan
//! @param[in] __b Second mdspan
//! @return true if the byte ranges of the two mdspans overlap
template <typename _Tp1,
typename _Extents1,
typename _LayoutPolicy1,
typename _AccessorPolicy1,
typename _Tp2,
typename _Extents2,
typename _LayoutPolicy2,
typename _AccessorPolicy2>
[[nodiscard]] _CCCL_HOST_API bool
__may_overlap(const ::cuda::std::mdspan<_Tp1, _Extents1, _LayoutPolicy1, _AccessorPolicy1>& __a,
const ::cuda::std::mdspan<_Tp2, _Extents2, _LayoutPolicy2, _AccessorPolicy2>& __b) noexcept
{
if (__a.size() == 0 || __b.size() == 0)
{
return false;
}
const auto* __a_begin = reinterpret_cast<const char*>(__a.data_handle());
const auto* __b_begin = reinterpret_cast<const char*>(__b.data_handle());
const auto* __a_end = __a_begin + __a.mapping().required_span_size() * sizeof(_Tp1);
const auto* __b_end = __b_begin + __b.mapping().required_span_size() * sizeof(_Tp2);
return ::cuda::ranges_overlap(__a_begin, __a_end, __b_begin, __b_end);
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // _CUDAX__COPY_TENSOR_COPY_UTILS_H

170
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_TENSOR_ITERATOR_H
#define _CUDAX__COPY_TENSOR_ITERATOR_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__cmath/fast_modulo_division.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__type_traits/make_unsigned.h>
#include <cuda/std/__type_traits/remove_const.h>
#include <cuda/std/__utility/integer_sequence.h>
#include <cuda/std/array>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
/***********************************************************************************************************************
* Fast Modulo/Division based on Precomputation
**********************************************************************************************************************/
template <typename _ExtentT, ::cuda::std::size_t _Size, ::cuda::std::size_t... _Rp>
[[nodiscard]] _CCCL_HOST_DEVICE_API ::cuda::std::array<::cuda::fast_mod_div<_ExtentT>, sizeof...(_Rp)>
__extents_fast_div_mod_impl(const ::cuda::std::array<_ExtentT, _Size>& __extents,
::cuda::std::index_sequence<_Rp...> = {}) noexcept
{
using __fast_mod_div_t = ::cuda::fast_mod_div<_ExtentT>;
using __array_t = ::cuda::std::array<__fast_mod_div_t, sizeof...(_Rp)>;
return __array_t{__fast_mod_div_t(__extents[_Rp])...};
}
//! @brief Precompute modulo/division for each array extent.
//!
//! @param[in] __extents Array of extents
//! @return Array of precomputed fast modulo/division objects
template <typename _ExtentT, ::cuda::std::size_t _Size>
[[nodiscard]] _CCCL_HOST_DEVICE_API ::cuda::std::array<::cuda::fast_mod_div<_ExtentT>, _Size>
__extents_fast_div_mod(const ::cuda::std::array<_ExtentT, _Size>& __extents) noexcept
{
using __seq_t = ::cuda::std::make_index_sequence<_Size>;
return ::cuda::experimental::__extents_fast_div_mod_impl(__extents, __seq_t{});
}
/***********************************************************************************************************************
* Tensor Coordinate Iterator and Partial Tensor
**********************************************************************************************************************/
//! @brief Iterator that maps a linear tile index to a pointer into a strided raw tensor.
template <typename _ExtentT, ::cuda::std::size_t _Rank>
struct __tensor_coord_iterator
{
using __unsigned_extent_t = ::cuda::std::make_unsigned_t<_ExtentT>;
using __fast_mod_div_t = ::cuda::fast_mod_div<__unsigned_extent_t>;
using __array_t = ::cuda::std::array<__fast_mod_div_t, _Rank>;
__array_t __extents_;
//! @brief Convert an array of _UExtentT elements to an array of _ExtentT elements.
//!
//! @param[in] __in_array Source array with elements of type _UExtentT
//! @return Array with elements statically cast to _ExtentT
template <typename _UExtentT>
[[nodiscard]] static _CCCL_HOST_API ::cuda::std::array<__unsigned_extent_t, _Rank>
__to_extent_array(const ::cuda::std::array<_UExtentT, _Rank>& __in_array) noexcept
{
::cuda::std::array<__unsigned_extent_t, _Rank> __out_array{};
for (::cuda::std::size_t __i = 0; __i < _Rank; ++__i)
{
__out_array[__i] = static_cast<__unsigned_extent_t>(__in_array[__i]);
}
return __out_array;
}
//! @brief Constructs the iterator from tensor extents.
//!
//! @param[in] __extents Tensor extents (may be unsigned; converted to _ExtentT internally)
template <typename _UExtentT>
_CCCL_HOST_API explicit __tensor_coord_iterator(const ::cuda::std::array<_UExtentT, _Rank>& __extents) noexcept
: __extents_{::cuda::experimental::__extents_fast_div_mod(__to_extent_array(__extents))}
{}
//! @brief Returns the multi-dimensional coordinates for the given linear index.
//!
//! @param[in] __index Linear tile index
//! @return Array of coordinates into the tensor
[[nodiscard]] _CCCL_HOST_DEVICE_API ::cuda::std::array<_ExtentT, _Rank> operator()(_ExtentT __index) const noexcept
{
if constexpr (_Rank == 1)
{
return ::cuda::std::array<_ExtentT, _Rank>{{__index}};
}
else
{
// instead of computing the coordinate in parallel (index / prod(extent_i) % extent_i), we use a simpler and
// slower approach. This saves registers and makes the overall computation faster.
::cuda::std::array<_ExtentT, _Rank> __coords{};
auto __quotient = static_cast<__unsigned_extent_t>(__index);
_CCCL_PRAGMA_UNROLL_FULL()
for (int __i = 0; __i < int{_Rank} - 1; ++__i)
{
const auto __div_result = ::cuda::div(__quotient, __extents_[__i]);
__quotient = __div_result.first;
__coords[__i] = static_cast<_ExtentT>(__div_result.second);
}
__coords[_Rank - 1] = static_cast<_ExtentT>(__quotient % __extents_[_Rank - 1]);
return __coords;
}
}
};
//! @brief Lightweight device-side wrapper providing coordinate-indexed access to strided tensor data.
//!
//! Wraps a data pointer, per-dimension strides, and an accessor into a callable that maps
//! multi-dimensional coordinates to element references.
template <typename _Tp, typename _StrideT, ::cuda::std::size_t _Rank, typename _Accessor>
struct __partial_tensor
{
_Tp* __ptr;
::cuda::std::array<_StrideT, _Rank> __strides;
_Accessor __accessor;
//! @brief Compute the linear offset for the given multi-dimensional coordinates.
//!
//! @param[in] __coords Array of per-dimension coordinates
//! @return Linear offset into the tensor storage
template <typename _CoordT>
[[nodiscard]] _CCCL_DEVICE_API _StrideT __offset(const ::cuda::std::array<_CoordT, _Rank>& __coords) const noexcept
{
_StrideT __offset = 0;
_CCCL_PRAGMA_UNROLL_FULL()
for (int __i = 0; __i < int{_Rank}; ++__i)
{
__offset += static_cast<_StrideT>(__coords[__i]) * __strides[__i];
}
return __offset;
}
//! @brief Access the element at the given multi-dimensional coordinates.
//!
//! @param[in] __coords Array of per-dimension coordinates
//! @return Reference to the element at the computed offset
template <typename _CoordT>
[[nodiscard]] _CCCL_DEVICE_API decltype(auto)
operator()(const ::cuda::std::array<_CoordT, _Rank>& __coords) const noexcept
{
return __accessor.access(const_cast<::cuda::std::remove_const_t<_Tp>*>(__ptr), __offset(__coords));
}
};
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX__COPY_TENSOR_ITERATOR_H

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__COPY_VECTOR_ACCESS_H
#define _CUDAX__COPY_VECTOR_ACCESS_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/__driver/driver_api.h>
# include <cuda/devices>
#endif // !_CCCL_COMPILER(NVRTC)
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Aligned storage type for vectorized memory access of a given byte width.
template <::cuda::std::size_t _VectorBytes>
struct alignas(_VectorBytes) __vector_access
{
char __data[_VectorBytes];
};
// 32-byte accesses are supported since CTK 13.0
#if _CCCL_CTK_AT_LEAST(13, 0)
inline constexpr auto __max_vector_access = 32;
#else
inline constexpr auto __max_vector_access = 16;
#endif // _CCCL_CTK_AT_LEAST(13, 0)
#if !_CCCL_COMPILER(NVRTC)
template <::cuda::std::size_t _VectorBytes>
using __vector_access_t = __vector_access<_VectorBytes>;
//! @brief Query the maximum vector access width supported by the current GPU architecture.
//!
//! @return Maximum vector width in bytes (32 for SM >= 10.0, 16 otherwise)
[[nodiscard]] _CCCL_HOST_API inline ::cuda::std::size_t __max_gpu_arch_vector_size() noexcept
{
# if _CCCL_CTK_AT_LEAST(13, 0)
const auto __dev_id = ::cuda::__driver::__cudevice_to_ordinal(::cuda::__driver::__ctxGetDevice());
const auto __dev = ::cuda::devices[__dev_id];
const auto __major = __dev.attribute<::cudaDevAttrComputeCapabilityMajor>();
return (__major >= 10) ? 32 : 16;
# else // ^^^ _CCCL_CTK_AT_LEAST(13, 0) ^^^ / vvv _CCCL_CTK_BELOW(13, 0) vvv
return 16;
# endif // _CCCL_CTK_BELOW(13, 0)
}
#endif // !_CCCL_COMPILER(NVRTC)
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX__COPY_VECTOR_ACCESS_H

57
cccl_upstream/cudax/include/cuda/experimental/__copy_bytes/abs_integer.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_COPY_ABS_INTEGER_H
#define __CUDAX_COPY_ABS_INTEGER_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/std/__concepts/concept_macros.h>
# include <cuda/std/__cstdlib/abs.h>
# include <cuda/std/__type_traits/is_integer.h>
# include <cuda/std/__type_traits/is_signed.h>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Returns the absolute value of an integer. Identity for unsigned types.
//!
//! @param[in] __value Integer value
//! @return Absolute value of @p __value
_CCCL_TEMPLATE(class _Tp)
_CCCL_REQUIRES(::cuda::std::__cccl_is_integer_v<_Tp>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr _Tp __abs_integer(_Tp __value) noexcept
{
if constexpr (::cuda::std::is_signed_v<_Tp>)
{
return ::cuda::std::abs(__value);
}
else
{
return __value;
}
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // __CUDAX_COPY_ABS_INTEGER_H

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_COPY_MDSPAN_D2H_H2D_H
#define __CUDAX_COPY_MDSPAN_D2H_H2D_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/__driver/driver_api.h>
# include <cuda/__mdspan/host_device_mdspan.h>
# include <cuda/__mdspan/traits.h>
# include <cuda/__stream/stream_ref.h>
# include <cuda/__type_traits/is_trivially_copyable.h>
# include <cuda/std/__algorithm/max.h>
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/__host_stdlib/stdexcept>
# include <cuda/std/__mdspan/default_accessor.h>
# include <cuda/std/__mdspan/mdspan.h>
# include <cuda/std/__memory/is_sufficiently_aligned.h>
# include <cuda/std/__type_traits/common_type.h>
# include <cuda/std/__type_traits/is_const.h>
# include <cuda/std/__type_traits/is_convertible.h>
# include <cuda/std/__type_traits/is_same.h>
# include <cuda/std/__type_traits/remove_cv.h>
# include <cuda/experimental/__copy_bytes/memcpy_batch_tiles.cuh>
# include <cuda/experimental/__copy_bytes/simplify_paired.cuh>
# include <cuda/experimental/__copy_bytes/tensor_query.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Internal implementation of @ref copy_bytes for host/device mdspan transfers.
//!
//! Validates preconditions, converts mdspans to raw tensor descriptors, simplifies the paired layout
//! (sort, flip negative strides, coalesce), then dispatches a batched asynchronous memcpy.
//!
//! @param[in] __src Source mdspan
//! @param[out] __dst Destination mdspan
//! @param[in] __direction Copy direction (host-to-device or device-to-host)
//! @param[in] __stream CUDA stream for the asynchronous transfer
template <typename _TpIn,
typename _ExtentsIn,
typename _LayoutPolicyIn,
typename _AccessorPolicyIn,
typename _TpOut,
typename _ExtentsOut,
typename _LayoutPolicyOut,
typename _AccessorPolicyOut>
_CCCL_HOST_API void __copy_bytes_impl(
::cuda::std::mdspan<_TpIn, _ExtentsIn, _LayoutPolicyIn, _AccessorPolicyIn> __src,
::cuda::std::mdspan<_TpOut, _ExtentsOut, _LayoutPolicyOut, _AccessorPolicyOut> __dst,
[[maybe_unused]] __copy_direction __direction,
::cuda::stream_ref __stream)
{
namespace cudax = ::cuda::experimental;
static_assert(::cuda::std::is_same_v<::cuda::std::remove_cv_t<_TpIn>, ::cuda::std::remove_cv_t<_TpOut>>,
"cudax::copy_bytes: TpIn and TpOut must be the same type");
static_assert(::cuda::is_trivially_copyable_v<_TpIn>, "TpIn must be trivially copyable");
static_assert(!::cuda::std::is_const_v<_TpOut>, "TpOut must not be const");
static_assert(::cuda::__is_cuda_mdspan_layout_v<_LayoutPolicyIn>,
"cudax::copy_bytes: LayoutPolicyIn must be a predefined layout policy");
static_assert(::cuda::__is_cuda_mdspan_layout_v<_LayoutPolicyOut>,
"cudax::copy_bytes: LayoutPolicyOut must be a predefined layout policy");
using __default_accessor_in = ::cuda::std::default_accessor<_TpIn>;
using __default_accessor_out = ::cuda::std::default_accessor<_TpOut>;
static_assert(::cuda::std::is_convertible_v<_AccessorPolicyIn, __default_accessor_in>,
"cudax::copy_bytes: AccessorPolicyIn must be convertible to cuda::std::default_accessor");
static_assert(::cuda::std::is_convertible_v<_AccessorPolicyOut, __default_accessor_out>,
"cudax::copy_bytes: AccessorPolicyOut must be convertible to cuda::std::default_accessor");
if (__stream.get() == nullptr)
{
_CCCL_THROW(::std::invalid_argument, "cudax::copy_bytes: stream must not be nullptr");
}
if (__src.size() != __dst.size())
{
_CCCL_THROW(::std::invalid_argument, "cudax::copy_bytes: mdspans must have the same size");
}
const auto __tensor_size = __src.size();
if (__tensor_size == 0)
{
return;
}
if (__src.data_handle() == nullptr || __dst.data_handle() == nullptr)
{
_CCCL_THROW(::std::invalid_argument, "cudax::copy_bytes: mdspan data handle must not be nullptr");
}
if (!::cuda::std::is_sufficiently_aligned<alignof(_TpIn)>(__src.data_handle()))
{
_CCCL_THROW(::std::invalid_argument, "cudax::copy_bytes: source mdspan must be sufficiently aligned");
}
if (!::cuda::std::is_sufficiently_aligned<alignof(_TpOut)>(__dst.data_handle()))
{
_CCCL_THROW(::std::invalid_argument, "cudax::copy_bytes: destination mdspan must be sufficiently aligned");
}
if (cudax::__has_interleaved_stride_order(__dst))
{
_CCCL_THROW(::std::invalid_argument,
"cudax::copy_bytes: destination mdspan must not have interleaved stride order");
}
if (__tensor_size == 1) // rank == 0 also falls into this case
{
auto __src_ptr = __src.data_handle();
auto __dst_ptr = __dst.data_handle();
if constexpr (::cuda::__is_layout_stride_relaxed_v<_LayoutPolicyIn>)
{
__src_ptr += __src.mapping().offset();
}
if constexpr (::cuda::__is_layout_stride_relaxed_v<_LayoutPolicyOut>)
{
__dst_ptr += __dst.mapping().offset();
}
::cuda::__driver::__memcpyAsync(__dst_ptr, __src_ptr, sizeof(_TpIn), __stream.get());
return;
}
if constexpr (_ExtentsIn::rank() > 0 && _ExtentsOut::rank() > 0)
{
using __extent_t = ::cuda::std::common_type_t<typename _ExtentsIn::index_type, typename _ExtentsOut::index_type>;
using __stride_t =
::cuda::std::common_type_t<cudax::__mdspan_stride_t<_LayoutPolicyIn, decltype(__src.mapping())>,
cudax::__mdspan_stride_t<_LayoutPolicyOut, decltype(__dst.mapping())>>;
constexpr auto __max_rank = ::cuda::std::max(_ExtentsIn::rank(), _ExtentsOut::rank());
const auto __src_raw = cudax::__to_raw_tensor<__extent_t, __stride_t, __max_rank>(__src);
const auto __dst_raw = cudax::__to_raw_tensor<__extent_t, __stride_t, __max_rank>(__dst);
if (!cudax::__same_extents(__src_raw, __dst_raw))
{
_CCCL_THROW(::std::invalid_argument,
"cudax::copy_bytes: mdspans must have the same extents (after removing singleton dimensions)");
}
auto __src_simplified = __src_raw;
auto __dst_simplified = __dst_raw;
cudax::__sort_by_stride_paired(__src_simplified, __dst_simplified);
cudax::__flip_negative_strides_paired(__src_simplified, __dst_simplified);
cudax::__coalesce_paired(__src_simplified, __dst_simplified);
const bool __both_stride1 = (__src_simplified.__strides[0] == 1) && (__dst_simplified.__strides[0] == 1);
const __extent_t __tile_size = __both_stride1 ? __src_simplified.__extents[0] : __extent_t{1};
const auto __src_iter = (__tile_size > 1) ? __src_simplified : cudax::__reverse_modes(__src_raw);
const auto __dst_iter = (__tile_size > 1) ? __dst_simplified : cudax::__reverse_modes(__dst_raw);
const auto __num_tiles = __tensor_size / __tile_size;
const auto __copy_bytes = __tile_size * sizeof(_TpIn);
_CCCL_ASSERT(__tensor_size % __tile_size == 0, "cudax::copy_bytes: tensor size must be divisible by tile size");
__tile_iterator_linearized<__extent_t, __stride_t, _TpIn, __max_rank> __src_tiles_iterator(__src_iter, __tile_size);
__tile_iterator_linearized<__extent_t, __stride_t, _TpOut, __max_rank> __dst_tiles_iterator(__dst_iter, __tile_size);
cudax::__memcpy_batch_tiles(
__src_tiles_iterator,
__dst_tiles_iterator,
__num_tiles,
__copy_bytes,
__direction,
__src.data_handle(),
__dst.data_handle(),
__stream);
}
}
/***********************************************************************************************************************
* Public API
**********************************************************************************************************************/
//! @rst
//! .. _cudax-copy-bytes:
//!
//! Asynchronous byte-wise mdspan copy
//! ------------------------------------
//!
//! ``copy_bytes`` asynchronously copies elements between a host ``mdspan`` and a device ``mdspan`` on the given
//! CUDA stream. Two overloads are provided: host-to-device and device-to-host.
//!
//! - Source and destination must have the same total number of elements and identical extents
//! (after removing extent-1 dimensions).
//! - The implementation supports any stride value independently for source and destination mdspans.
//! - Element types must be trivially copyable and (ignoring cv-qualification) the same type.
//! - Layout policies must be one of the predefined ``cuda::std`` layout policies
//! (``layout_right``, ``layout_left``, ``layout_stride``) or ``cuda::layout_stride_relaxed``.
//! - Accessor policies must be convertible to ``cuda::std::default_accessor``.
//! - The destination must not have an interleaved stride order.
//!
//! The implementation is optimized to maximize the contiguous memory regions to copy and relies on batched asynchronous
//! memcpy.
//!
//! .. code-block:: c++
//!
//! #include <cuda/experimental/copy_bytes.cuh>
//!
//! using extents_t = cuda::std::dims<2>;
//! cuda::host_mdspan<const float, extents_t> src(src_ptr, extents);
//! cuda::device_mdspan<float, extents_t> dst(dst_ptr, extents);
//! cuda::experimental::copy_bytes(src, dst, stream);
//!
//! @endrst
//! @param[in] __src Source mdspan
//! @param[out] __dst Destination mdspan
//! @param[in] __stream CUDA stream for the asynchronous transfer
template <typename _TpIn,
typename _ExtentsIn,
typename _LayoutPolicyIn,
typename _AccessorPolicyIn,
typename _TpOut,
typename _ExtentsOut,
typename _LayoutPolicyOut,
typename _AccessorPolicyOut>
_CCCL_HOST_API void copy_bytes(::cuda::host_mdspan<_TpIn, _ExtentsIn, _LayoutPolicyIn, _AccessorPolicyIn> __src,
::cuda::device_mdspan<_TpOut, _ExtentsOut, _LayoutPolicyOut, _AccessorPolicyOut> __dst,
::cuda::stream_ref __stream)
{
using __src_type = ::cuda::std::mdspan<_TpIn, _ExtentsIn, _LayoutPolicyIn, _AccessorPolicyIn>;
using __dst_type = ::cuda::std::mdspan<_TpOut, _ExtentsOut, _LayoutPolicyOut, _AccessorPolicyOut>;
::cuda::experimental::__copy_bytes_impl(
static_cast<__src_type>(__src), static_cast<__dst_type>(__dst), __copy_direction::__host_to_device, __stream);
}
//! @brief Asynchronously copies bytes from a device mdspan to a host mdspan.
//!
//! @param[in] __src Source device mdspan
//! @param[out] __dst Destination host mdspan
//! @param[in] __stream CUDA stream for the asynchronous transfer
template <typename _TpIn,
typename _ExtentsIn,
typename _LayoutPolicyIn,
typename _AccessorPolicyIn,
typename _TpOut,
typename _ExtentsOut,
typename _LayoutPolicyOut,
typename _AccessorPolicyOut>
_CCCL_HOST_API void copy_bytes(::cuda::device_mdspan<_TpIn, _ExtentsIn, _LayoutPolicyIn, _AccessorPolicyIn> __src,
::cuda::host_mdspan<_TpOut, _ExtentsOut, _LayoutPolicyOut, _AccessorPolicyOut> __dst,
::cuda::stream_ref __stream)
{
using __src_type = ::cuda::std::mdspan<_TpIn, _ExtentsIn, _LayoutPolicyIn, _AccessorPolicyIn>;
using __dst_type = ::cuda::std::mdspan<_TpOut, _ExtentsOut, _LayoutPolicyOut, _AccessorPolicyOut>;
::cuda::experimental::__copy_bytes_impl(
static_cast<__src_type>(__src), static_cast<__dst_type>(__dst), __copy_direction::__device_to_host, __stream);
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // __CUDAX_COPY_MDSPAN_D2H_H2D_H

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_COPY_MDSPAN_TO_RAW_TENSOR_H
#define __CUDAX_COPY_MDSPAN_TO_RAW_TENSOR_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/__mdspan/traits.h>
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/__mdspan/mdspan.h>
# include <cuda/std/__type_traits/remove_cvref.h>
# include <cuda/experimental/__copy_bytes/types.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Extracts the stride type from a layout mapping, defaulting to `index_type` when absent.
template <typename _LayoutPolicy, typename _Mapping>
struct __mapping_stride_type
{
using __type = typename _Mapping::index_type;
};
template <typename _Mapping>
struct __mapping_stride_type<::cuda::layout_stride_relaxed, _Mapping>
{
using __type = typename _Mapping::offset_type;
};
//! @brief Convenience alias: stride type of a layout mapping for given extents and layout policy.
//!
//! For `layout_stride_relaxed`, uses `offset_type` (signed) since strides can be negative.
//! For other layouts, uses `stride_type` if available, otherwise `index_type`.
template <typename _LayoutPolicy, typename _Mapping>
using __mdspan_stride_t = typename __mapping_stride_type<_LayoutPolicy, ::cuda::std::remove_cvref_t<_Mapping>>::__type;
//! @brief Convenience alias: `__raw_tensor` type produced by @ref __to_raw_tensor for a given mdspan.
template <typename _MdspanCVRef, typename _Mdspan = ::cuda::std::remove_cvref_t<_MdspanCVRef>>
using __to_raw_tensor_t =
__raw_tensor<typename _Mdspan::index_type,
__mdspan_stride_t<typename _Mdspan::layout_type, typename _Mdspan::mapping_type>,
typename _Mdspan::element_type,
_Mdspan::rank()>;
//! @brief Converts an mdspan to a @ref __raw_tensor with explicitly specified extent, stride types.
//!
//! Extent-1 modes are removed from the resulting tensor.
//!
//! @param[in] __mdspan Source mdspan view
//! @return @ref __raw_tensor descriptor with data pointer, rank, extents, and strides
template <typename _ExtentT,
typename _StrideT,
::cuda::std::size_t _MaxRank,
typename _Tp,
typename _Extents,
typename _LayoutPolicy,
typename _AccessorPolicy>
[[nodiscard]]
_CCCL_HOST_API constexpr __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>
__to_raw_tensor(const ::cuda::std::mdspan<_Tp, _Extents, _LayoutPolicy, _AccessorPolicy>& __mdspan) noexcept
{
static_assert(_MaxRank >= _Extents::rank(), "_MaxRank must be at least _Extents::rank()");
using __raw_tensor_t = __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>;
using __rank_t = typename _Extents::rank_type;
auto* __data = __mdspan.data_handle();
if constexpr (::cuda::__is_layout_stride_relaxed_v<_LayoutPolicy>)
{
__data += __mdspan.mapping().offset();
}
__raw_tensor_t __result{__data, 0, {}, {}};
if constexpr (_Extents::rank() > 0)
{
__rank_t __r = 0;
for (__rank_t __i = 0; __i < _Extents::rank(); ++__i)
{
const auto __extent = static_cast<_ExtentT>(__mdspan.extent(__i));
if (__extent != _ExtentT{1})
{
__result.__extents[__r] = __extent;
__result.__strides[__r] = static_cast<_StrideT>(__mdspan.stride(__i));
++__r;
}
}
for (__rank_t __i = __r; __i < _MaxRank; ++__i)
{
__result.__extents[__i] = _ExtentT{1};
}
__result.__rank = __r;
}
return __result;
}
//! @brief Converts an mdspan to a @ref __raw_tensor using its native extent and stride types.
//!
//! Extent-1 modes are removed from the resulting tensor.
//!
//! @param[in] __mdspan Source mdspan view
//! @return @ref __raw_tensor descriptor with data pointer, rank, extents, and strides
template <typename _Tp, typename _Extents, typename _LayoutPolicy, typename _AccessorPolicy>
[[nodiscard]]
_CCCL_HOST_API constexpr auto
__to_raw_tensor(const ::cuda::std::mdspan<_Tp, _Extents, _LayoutPolicy, _AccessorPolicy>& __mdspan) noexcept
-> __to_raw_tensor_t<decltype(__mdspan)>
{
using __extent_t = typename _Extents::index_type;
using __stride_t = __mdspan_stride_t<_LayoutPolicy, decltype(__mdspan.mapping())>;
return ::cuda::experimental::__to_raw_tensor<__extent_t, __stride_t, _Extents::rank()>(__mdspan);
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // __CUDAX_COPY_MDSPAN_TO_RAW_TENSOR_H

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_COPY_MEMCPY_BATCH_TILES_H
#define __CUDAX_COPY_MEMCPY_BATCH_TILES_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/__driver/driver_api.h>
# include <cuda/__stream/stream_ref.h>
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/__functional/operations.h>
# include <cuda/std/__numeric/exclusive_scan.h>
# include <cuda/std/array>
# include <cuda/experimental/__copy_bytes/types.cuh>
# include <vector>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Iterator that maps a linear tile index to a pointer into a strided raw tensor.
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
struct __tile_iterator_linearized
{
const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank> __tensor_;
::cuda::std::array<_ExtentT, _MaxRank> __extent_products_;
const _ExtentT __contiguous_size_;
//! @brief Constructs the iterator from a raw tensor and contiguous tile size.
//!
//! @param[in] __tensor Raw tensor descriptor
//! @param[in] __contiguous_size Number of contiguous elements per tile
_CCCL_HOST_API explicit __tile_iterator_linearized(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor,
_ExtentT __contiguous_size) noexcept
: __tensor_{__tensor}
, __extent_products_{}
, __contiguous_size_{__contiguous_size}
{
// Precomputes exclusive prefix products of extents so that each `operator()` call decomposes a flat index into
// multi-dimensional coordinates and computes the corresponding byte offset.
::cuda::std::exclusive_scan(
__tensor.__extents.data(),
__tensor.__extents.data() + __tensor.__rank,
__extent_products_.data(),
_ExtentT{1},
::cuda::std::multiplies<>{});
}
//! @brief Returns a pointer to the first element of the tile at @p __tile_idx.
//!
//! @param[in] __tile_idx linear tile index
//! @return Pointer into the tensor at the computed multi-dimensional offset
[[nodiscard]] _CCCL_HOST_API _Tp* operator()(_ExtentT __tile_idx) const noexcept
{
using __uextent_t = ::cuda::std::make_unsigned_t<_ExtentT>;
const auto __index = __tile_idx * __contiguous_size_;
const auto& __extents = __tensor_.__extents;
const auto& __strides = __tensor_.__strides;
if (__tensor_.__rank == 1)
{
return __tensor_.__data + __index * __strides[0];
}
const auto __extent0 = static_cast<__uextent_t>(__extents[0]);
_StrideT __offset = (__index % __extent0) * __strides[0]; // __extent_products_[0] == 1
for (::cuda::std::size_t __i = 1; __i < __tensor_.__rank; ++__i)
{
const auto __extent_product = static_cast<__uextent_t>(__extent_products_[__i]);
const auto __coord = static_cast<_StrideT>((__index / __extent_product) % __extents[__i]);
__offset += __coord * __strides[__i];
}
return __tensor_.__data + __offset;
}
};
# if _CCCL_CTK_AT_LEAST(13, 0)
//! @brief Builds the `CUmemcpyAttributes` descriptor for a batch async memcpy.
//!
//! @param[in] __direction Copy direction (host-to-device or device-to-host)
//! @param[in] __src_ptr Source pointer (used to query device ordinal for D2H)
//! @param[in] __dst_ptr Destination pointer (used to query device ordinal for H2D)
//! @return Populated `CUmemcpyAttributes` struct
[[nodiscard]] _CCCL_HOST_API inline ::CUmemcpyAttributes
__get_memcpy_attributes(__copy_direction __direction, const void* __src_ptr, const void* __dst_ptr) noexcept
{
if (__direction == __copy_direction::__host_to_device)
{
const int __device_ordinal =
::cuda::__driver::__pointerGetAttribute<::CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL>(__dst_ptr);
return ::CUmemcpyAttributes{
::CU_MEMCPY_SRC_ACCESS_ORDER_ANY,
::CUmemLocation{::CU_MEM_LOCATION_TYPE_HOST, 0},
::CUmemLocation{::CU_MEM_LOCATION_TYPE_DEVICE, __device_ordinal},
0};
}
const int __device_ordinal =
::cuda::__driver::__pointerGetAttribute<::CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL>(__src_ptr);
return ::CUmemcpyAttributes{
::CU_MEMCPY_SRC_ACCESS_ORDER_ANY,
::CUmemLocation{::CU_MEM_LOCATION_TYPE_DEVICE, __device_ordinal},
::CUmemLocation{::CU_MEM_LOCATION_TYPE_HOST, 0},
0};
}
# endif // _CCCL_CTK_AT_LEAST(13, 0)
//! @brief Submits an asynchronous batch memcpy for every tile.
//!
//! - Uses `cuMemcpyBatchAsync` on CTK 13.0+ with stack-allocated arrays for small tile counts,
//! falling back to heap allocation when the count exceeds a fixed threshold.
//! - On older toolkits, issues individual `cuMemcpyAsync` calls per tile.
//!
//! @param[in] __src_tiles_iterator Tile iterator for the source tensor
//! @param[in] __dst_tiles_iterator Tile iterator for the destination tensor
//! @param[in] __num_tiles Number of tiles to copy
//! @param[in] __copy_size_bytes Byte size of each tile
//! @param[in] __direction Copy direction
//! @param[in] __src_data_handle Source base pointer (for attribute query)
//! @param[in] __dst_data_handle Destination base pointer (for attribute query)
//! @param[in] __stream CUDA stream
template <typename _SrcTileIterator, typename _DstTileIterator>
_CCCL_HOST_API inline void __memcpy_batch_tiles(
const _SrcTileIterator& __src_tiles_iterator,
const _DstTileIterator& __dst_tiles_iterator,
::cuda::std::size_t __num_tiles,
::cuda::std::size_t __copy_size_bytes,
[[maybe_unused]] __copy_direction __direction,
[[maybe_unused]] const void* __src_data_handle,
[[maybe_unused]] void* __dst_data_handle,
::cuda::stream_ref __stream)
{
using ::cuda::std::size_t;
# if _CCCL_CTK_AT_LEAST(13, 0)
auto __attributes = ::cuda::experimental::__get_memcpy_attributes(__direction, __src_data_handle, __dst_data_handle);
const auto __memcpy_batch_async_lambda = [&](auto __src_ptrs, auto __dst_ptrs, auto __sizes) {
for (size_t __tile_idx = 0; __tile_idx < __num_tiles; ++__tile_idx)
{
__src_ptrs[__tile_idx] = __src_tiles_iterator(__tile_idx);
__dst_ptrs[__tile_idx] = __dst_tiles_iterator(__tile_idx);
__sizes[__tile_idx] = __copy_size_bytes;
}
size_t __attribute_indices = 0;
::cuda::__driver::__memcpyBatchAsync(
__dst_ptrs,
__src_ptrs,
__sizes,
__num_tiles,
&__attributes,
&__attribute_indices,
/*num_attributes=*/1,
__stream.get());
};
constexpr size_t __max_tiles = 16;
if (__num_tiles > __max_tiles)
{
auto __src_ptrs = new const void*[__num_tiles];
auto __dst_ptrs = new void*[__num_tiles];
auto __sizes = new size_t[__num_tiles];
__memcpy_batch_async_lambda(__src_ptrs, __dst_ptrs, __sizes);
delete[] __src_ptrs;
delete[] __dst_ptrs;
delete[] __sizes;
}
else
{
::cuda::std::array<const void*, __max_tiles> __src_ptr_array{};
::cuda::std::array<void*, __max_tiles> __dst_ptr_array{};
::cuda::std::array<size_t, __max_tiles> __sizes{};
__memcpy_batch_async_lambda(__src_ptr_array.data(), __dst_ptr_array.data(), __sizes.data());
}
# else
for (size_t __tile_idx = 0; __tile_idx < __num_tiles; ++__tile_idx)
{
const auto __src_ptr = static_cast<const void*>(__src_tiles_iterator(__tile_idx));
const auto __dst_ptr = static_cast<void*>(__dst_tiles_iterator(__tile_idx));
::cuda::__driver::__memcpyAsync(__dst_ptr, __src_ptr, __copy_size_bytes, __stream.get());
}
# endif // _CCCL_CTK_AT_LEAST(13, 0)
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // __CUDAX_COPY_MEMCPY_BATCH_TILES_H

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cccl_upstream/cudax/include/cuda/experimental/__copy_bytes/print_raw_tensor.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_COPY_PRINT_RAW_TENSOR_H
#define __CUDAX_COPY_PRINT_RAW_TENSOR_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/std/__cstddef/types.h>
# include <cuda/experimental/__copy_bytes/types.cuh>
# include <cstdio>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Prints a raw tensor's extents and strides to stdout in the format `(extents):(strides)`.
//!
//! @param[in] __tensor Raw tensor to print
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
_CCCL_HOST_API void __println(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor)
{
const auto __rank = static_cast<int>(__tensor.__rank);
::printf("(");
for (int __i = 0; __i < __rank - 1; ++__i)
{
::printf("%llu, ", static_cast<unsigned long long>(__tensor.__extents[__i]));
}
if (__rank > 0)
{
::printf("%llu", static_cast<unsigned long long>(__tensor.__extents[__rank - 1]));
}
::printf("):(");
for (int __i = 0; __i < __rank - 1; ++__i)
{
::printf("%lld, ", static_cast<long long>(__tensor.__strides[__i]));
}
if (__rank > 0)
{
::printf("%lld", static_cast<long long>(__tensor.__strides[__rank - 1]));
}
::printf(")\n");
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // __CUDAX_COPY_PRINT_RAW_TENSOR_H

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cccl_upstream/cudax/include/cuda/experimental/__copy_bytes/simplify_paired.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_COPY_SIMPLIFY_PAIRED_H
#define __CUDAX_COPY_SIMPLIFY_PAIRED_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/std/__algorithm/stable_sort.h>
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/array>
# include <cuda/std/tuple>
# include <cuda/experimental/__copy_bytes/tensor_query.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Reverses the order of active modes in a raw tensor.
//!
//! This helps to get a single logic for __tile_iterator_linearized
//!
//! @param[in] __input Raw tensor whose modes are reversed
//! @return New raw tensor with extents and strides in reversed mode order
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>
__reverse_modes(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __input) noexcept
{
using __raw_tensor_t = __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>;
using __rank_t = typename __raw_tensor_t::__rank_t;
__raw_tensor_t __result{__input.__data, __input.__rank, {}, {}};
_CCCL_ASSERT(__input.__rank > 0, "cudax::reverse_modes: input tensor must have rank > 0");
for (__rank_t __i = 0; __i < __input.__rank; ++__i)
{
const auto __j = __input.__rank - 1 - __i;
__result.__extents[__i] = __input.__extents[__j];
__result.__strides[__i] = __input.__strides[__j];
}
return __result;
}
// lambdas are painful without --extended-lambda and when used with __host__ __device__ functions
struct __mode_compare_paired
{
template <typename _ExtentT, typename _SrcStrideT, typename _DstStrideT>
[[nodiscard]] _CCCL_HOST_DEVICE_API bool
operator()(const ::cuda::std::tuple<_ExtentT, _SrcStrideT, _DstStrideT>& __lhs,
const ::cuda::std::tuple<_ExtentT, _SrcStrideT, _DstStrideT>& __rhs) const noexcept
{
namespace cudax = ::cuda::experimental;
const auto __src_lhs = ::cuda::std::get<1>(__lhs);
const auto __src_rhs = ::cuda::std::get<1>(__rhs);
const auto __dst_lhs = ::cuda::std::get<2>(__lhs);
const auto __dst_rhs = ::cuda::std::get<2>(__rhs);
return cudax::__abs_integer(__src_lhs) < cudax::__abs_integer(__src_rhs)
|| (cudax::__abs_integer(__src_lhs) == cudax::__abs_integer(__src_rhs)
&& cudax::__abs_integer(__dst_lhs) < cudax::__abs_integer(__dst_rhs));
}
};
//! @brief Sorts a source/destination tensor pair by ascending absolute destination stride.
//!
//! Both tensors are reordered in lockstep so that corresponding modes remain paired.
//!
//! @pre @p __src and @p __dst must have the same extents
//!
//! @param[in,out] __src Source raw tensor (modes reordered in place)
//! @param[in,out] __dst Destination raw tensor (modes reordered in place)
template <typename _ExtentT,
typename _SrcStrideT,
typename _DstStrideT,
typename _TpSrc,
typename _TpDst,
::cuda::std::size_t _MaxRank>
_CCCL_HOST_API void __sort_by_stride_paired(__raw_tensor<_ExtentT, _SrcStrideT, _TpSrc, _MaxRank>& __src,
__raw_tensor<_ExtentT, _DstStrideT, _TpDst, _MaxRank>& __dst) noexcept
{
namespace cudax = ::cuda::experimental;
using __raw_tensor_t = __raw_tensor<_ExtentT, _SrcStrideT, _TpSrc, _MaxRank>;
using __rank_t = typename __raw_tensor_t::__rank_t;
using __mode_t = ::cuda::std::tuple<_ExtentT, _SrcStrideT, _DstStrideT>;
const auto __rank = __src.__rank;
_CCCL_ASSERT(cudax::__same_extents(__src, __dst), "Source and destination tensors must have the same extents");
::cuda::std::array<__mode_t, _MaxRank> __modes{};
for (__rank_t __i = 0; __i < __rank; ++__i)
{
__modes[__i] = {__src.__extents[__i], __src.__strides[__i], __dst.__strides[__i]};
}
::cuda::std::stable_sort(__modes.begin(), __modes.begin() + __rank, __mode_compare_paired{});
for (__rank_t __i = 0; __i < __rank; ++__i)
{
::cuda::std::tie(__src.__extents[__i], __src.__strides[__i], __dst.__strides[__i]) = __modes[__i];
}
__dst.__extents = __src.__extents;
}
//! @brief Flips modes where both source and destination strides are negative.
//!
//! For each such mode, the base pointer is advanced to the last element and the stride is negated, yielding an
//! equivalent tensor with positive strides.
//!
//! @pre @p __src and @p __dst must have the same extents
//!
//! @param[in,out] __src Source raw tensor (data pointer and strides may be modified)
//! @param[in,out] __dst Destination raw tensor (data pointer and strides may be modified)
template <typename _ExtentT,
typename _SrcStrideT,
typename _DstStrideT,
typename _TpSrc,
typename _TpDst,
::cuda::std::size_t _MaxRank>
_CCCL_HOST_API void __flip_negative_strides_paired(
[[maybe_unused]] __raw_tensor<_ExtentT, _SrcStrideT, _TpSrc, _MaxRank>& __src,
[[maybe_unused]] __raw_tensor<_ExtentT, _DstStrideT, _TpDst, _MaxRank>& __dst) noexcept
{
if constexpr (::cuda::std::is_signed_v<_SrcStrideT> && ::cuda::std::is_signed_v<_DstStrideT>)
{
using __raw_tensor_t = __raw_tensor<_ExtentT, _SrcStrideT, _TpSrc, _MaxRank>;
using __rank_t = typename __raw_tensor_t::__rank_t;
_CCCL_ASSERT(::cuda::experimental::__same_extents(__src, __dst),
"cudax::flip_negative_strides_paired: Source and destination tensors must have the same extents");
for (__rank_t __i = 0; __i < __src.__rank; ++__i)
{
if (__src.__strides[__i] < 0 && __dst.__strides[__i] < 0)
{
const auto __extent = __src.__extents[__i];
const auto __src_adjustment = static_cast<_SrcStrideT>(__extent - 1) * __src.__strides[__i];
const auto __dst_adjustment = static_cast<_DstStrideT>(__extent - 1) * __dst.__strides[__i];
__src.__data += __src_adjustment;
__dst.__data += __dst_adjustment;
__src.__strides[__i] = -__src.__strides[__i];
__dst.__strides[__i] = -__dst.__strides[__i];
}
}
}
}
//! @brief Merges adjacent modes that are contiguous in both source and destination tensors.
//!
//! Two consecutive modes are merged when `extent[i-1] * stride[i-1] == stride[i]` holds for both tensors.
//! The resulting tensor pair has fewer modes but represents the same memory layout.
//!
//! @pre @p __src and @p __dst must have the same extents
//!
//! @param[in,out] __src Source raw tensor (rank and modes may be reduced)
//! @param[in,out] __dst Destination raw tensor (rank and modes may be reduced)
template <typename _ExtentT,
typename _SrcStrideT,
typename _DstStrideT,
typename _TpSrc,
typename _TpDst,
::cuda::std::size_t _MaxRank>
_CCCL_HOST_API void __coalesce_paired(__raw_tensor<_ExtentT, _SrcStrideT, _TpSrc, _MaxRank>& __src,
__raw_tensor<_ExtentT, _DstStrideT, _TpDst, _MaxRank>& __dst) noexcept
{
_CCCL_ASSERT(::cuda::experimental::__same_extents(__src, __dst),
"Source and destination tensors must have the same extents");
if (__src.__rank <= 1)
{
return;
}
using __raw_tensor_t = __raw_tensor<_ExtentT, _SrcStrideT, _TpSrc, _MaxRank>;
using __rank_t = typename __raw_tensor_t::__rank_t;
__rank_t __out_r = 1;
for (__rank_t __i = 1; __i < __src.__rank; ++__i)
{
const auto __src_prev_extent = static_cast<_SrcStrideT>(__src.__extents[__out_r - 1]);
const auto __dst_prev_extent = static_cast<_DstStrideT>(__src.__extents[__out_r - 1]);
const bool __src_contiguous = (__src_prev_extent * __src.__strides[__out_r - 1] == __src.__strides[__i]);
const bool __dst_contiguous = (__dst_prev_extent * __dst.__strides[__out_r - 1] == __dst.__strides[__i]);
if (__src_contiguous && __dst_contiguous)
{
__src.__extents[__out_r - 1] *= __src.__extents[__i];
continue;
}
__src.__extents[__out_r] = __src.__extents[__i];
__src.__strides[__out_r] = __src.__strides[__i];
__dst.__strides[__out_r] = __dst.__strides[__i];
++__out_r;
}
for (__rank_t __i = __out_r; __i < _MaxRank; ++__i)
{
__src.__extents[__i] = _ExtentT{1};
}
__src.__rank = __out_r;
__dst.__rank = __out_r;
__dst.__extents = __src.__extents;
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // __CUDAX_COPY_SIMPLIFY_PAIRED_H

180
cccl_upstream/cudax/include/cuda/experimental/__copy_bytes/tensor_query.cuh
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@@ -1,180 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_COPY_TENSOR_QUERY_H
#define __CUDAX_COPY_TENSOR_QUERY_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/std/__algorithm/stable_sort.h>
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/__mdspan/mdspan.h>
# include <cuda/std/array>
# include <cuda/experimental/__copy_bytes/abs_integer.cuh>
# include <cuda/experimental/__copy_bytes/mdspan_to_raw_tensor.cuh>
# include <cuda/experimental/__copy_bytes/types.cuh>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Checks whether two raw tensors have the same rank and identical extents.
//!
//! @param[in] __tensor_in First raw tensor
//! @param[in] __tensor_out Second raw tensor
//! @return true if rank and all extents match element-wise
template <typename _ExtentTIn,
typename _StrideTIn,
typename _TpIn,
::cuda::std::size_t _MaxRankIn,
typename _ExtentTOut,
typename _StrideTOut,
typename _TpOut,
::cuda::std::size_t _MaxRankOut>
[[nodiscard]] _CCCL_HOST_API constexpr bool
__same_extents(const __raw_tensor<_ExtentTIn, _StrideTIn, _TpIn, _MaxRankIn>& __tensor_in,
const __raw_tensor<_ExtentTOut, _StrideTOut, _TpOut, _MaxRankOut>& __tensor_out) noexcept
{
if (__tensor_in.__rank != __tensor_out.__rank)
{
return false;
}
using __raw_tensor_t = __raw_tensor<_ExtentTIn, _StrideTIn, _TpIn, _MaxRankIn>;
using __rank_t = typename __raw_tensor_t::__rank_t;
for (__rank_t __i = 0; __i < __tensor_in.__rank; ++__i)
{
if (__tensor_in.__extents[__i] != __tensor_out.__extents[__i])
{
return false;
}
}
return true;
}
// lambdas are painful without --extended-lambda and when used with __host__ __device__ functions
template <typename _StrideT, ::cuda::std::size_t _MaxRank>
struct __stride_compare
{
const ::cuda::std::array<_StrideT, _MaxRank>& __strides;
template <typename _Idx>
[[nodiscard]] _CCCL_HOST_DEVICE_API bool operator()(const _Idx __lhs, const _Idx __rhs) const noexcept
{
return ::cuda::experimental::__abs_integer(__strides[__lhs])
< ::cuda::experimental::__abs_integer(__strides[__rhs]);
}
};
//! @brief Computes the mode permutation that orders a tensor by ascending absolute stride.
//!
//! @param[in] __tensor Raw tensor whose stride order is inspected
//! @return Mode permutation sorted by ascending absolute stride
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API constexpr ::cuda::std::array<::cuda::std::size_t, _MaxRank>
__stride_order(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor) noexcept
{
::cuda::std::array<::cuda::std::size_t, _MaxRank> __perm{};
for (::cuda::std::size_t __i = 0; __i < _MaxRank; ++__i)
{
__perm[__i] = __i;
}
::cuda::std::stable_sort(
__perm.begin(), __perm.begin() + __tensor.__rank, __stride_compare<_StrideT, _MaxRank>{__tensor.__strides});
return __perm;
}
//! @brief Reorders tensor modes by ascending absolute stride.
//!
//! After sorting, mode 0 has the smallest absolute stride (innermost) and mode rank-1 has the largest (outermost).
//!
//! @param[in] __tensor Raw tensor to sort
//! @return Raw tensor with modes reordered by ascending absolute stride
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
[[nodiscard]] _CCCL_HOST_API constexpr __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>
__sort_by_stride(const __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>& __tensor) noexcept
{
using __raw_tensor_t = __raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank>;
using __rank_t = typename __raw_tensor_t::__rank_t;
const auto __rank = __tensor.__rank;
const auto __perm = ::cuda::experimental::__stride_order(__tensor);
__raw_tensor<_ExtentT, _StrideT, _Tp, _MaxRank> __result{__tensor.__data, __rank};
for (__rank_t __i = 0; __i < __rank; ++__i)
{
__result.__extents[__i] = __tensor.__extents[__perm[__i]];
__result.__strides[__i] = __tensor.__strides[__perm[__i]];
}
return __result;
}
//! @brief Conservative check for interleaved stride order in tensor layouts.
//!
//! Sorts modes by ascending absolute stride, then verifies two conditions:
//! 1. No mode with extent > 1 has stride == 0 (broadcast)
//! 2. No mode's span (extent * |stride|) exceeds the next mode's |stride|
//!
//! Returns true when the layout fails this non-interleaving rule. This is stronger than a mathematical injectivity
//! check and may reject some layouts with distinct offsets.
//!
//! @param[in] __mdspan Mdspan view to inspect
//! @return true if the layout has interleaved strides
template <typename _Tp, typename _Extents, typename _LayoutPolicy, typename _AccessorPolicy>
[[nodiscard]] _CCCL_HOST_API constexpr bool __has_interleaved_stride_order(
const ::cuda::std::mdspan<_Tp, _Extents, _LayoutPolicy, _AccessorPolicy>& __mdspan) noexcept
{
if constexpr (_Extents::rank() > 0)
{
namespace cudax = ::cuda::experimental;
const auto __tensor = cudax::__to_raw_tensor(__mdspan);
const auto __sorted = cudax::__sort_by_stride(__tensor);
using __stride_t = decltype(__sorted.__strides[0]);
using __rank_t = typename _Extents::rank_type;
const auto& __extents = __sorted.__extents;
const auto& __strides = __sorted.__strides;
const auto __rank = __sorted.__rank;
for (__rank_t __i = 0; __i < __rank; ++__i)
{
if (__extents[__i] > 1 && __strides[__i] == 0)
{
return true;
}
}
for (__rank_t __i = 0; __i + 1 < __rank; ++__i)
{
const auto __extent = static_cast<__stride_t>(__extents[__i]);
if (__extent * cudax::__abs_integer(__strides[__i]) > cudax::__abs_integer(__strides[__i + 1]))
{
return true;
}
}
return false;
}
else
{
return false;
}
}
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // __CUDAX_COPY_TENSOR_QUERY_H

56
cccl_upstream/cudax/include/cuda/experimental/__copy_bytes/types.cuh
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@@ -1,56 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_COPY_TYPES_H
#define __CUDAX_COPY_TYPES_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if !_CCCL_COMPILER(NVRTC)
# include <cuda/std/__cstddef/types.h>
# include <cuda/std/array>
# include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
//! @brief Raw tensor descriptor with dynamic rank, extents, and strides.
template <typename _ExtentT, typename _StrideT, typename _Tp, ::cuda::std::size_t _MaxRank>
struct __raw_tensor
{
using __rank_t = ::cuda::std::size_t;
_Tp* __data;
__rank_t __rank;
::cuda::std::array<_ExtentT, _MaxRank> __extents;
::cuda::std::array<_StrideT, _MaxRank> __strides;
};
//! @brief Direction of an asynchronous memcpy operation.
enum class __copy_direction
{
__host_to_device,
__device_to_host,
};
} // namespace cuda::experimental
# include <cuda/std/__cccl/epilogue.h>
#endif // !_CCCL_COMPILER(NVRTC)
#endif // __CUDAX_COPY_TYPES_H

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_CAPACITY_CUH
#define _CUDAX___CUCO_CAPACITY_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__cmath/ceil_div.h>
#include <cuda/__numeric/mul_overflow.h>
#include <cuda/__utility/in_range.h>
#include <cuda/std/__algorithm/max.h>
#include <cuda/std/__cmath/rounding_functions.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__limits/numeric_limits.h>
#include <cuda/std/cstdint>
#include <cuda/experimental/__cuco/detail/prime.cuh>
#include <cuda/experimental/__cuco/probing_scheme.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief Rounds a requested capacity up to the smallest valid capacity for the given probing scheme
//! and bucket size.
//!
//! The probe stride is `_ProbingScheme::cg_size * _BucketSize`. For linear probing the result is a
//! multiple of the stride; for double hashing the probe cycle count `capacity / stride` is
//! additionally prime. The function is idempotent: applying it to an already valid capacity returns
//! the same value.
//!
//! @tparam _ProbingScheme Probing scheme type
//! @tparam _BucketSize Number of slots per bucket
//! @tparam _SizeType Size type
//!
//! @param __requested Requested capacity
//!
//! @return The smallest valid capacity that is greater than or equal to `__requested`
template <class _ProbingScheme, int _BucketSize, class _SizeType>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr _SizeType make_valid_capacity(_SizeType __requested)
{
constexpr auto __stride = _SizeType{_ProbingScheme::cg_size * _BucketSize};
const auto __cycles = ::cuda::ceil_div(::cuda::std::max(__requested, _SizeType{1}), __stride);
_SizeType __capacity{};
if constexpr (is_double_hashing_v<_ProbingScheme>)
{
const auto __prime = detail::__next_prime(static_cast<::cuda::std::uint64_t>(__cycles));
if (::cuda::mul_overflow(__capacity, __prime, __stride))
{
_CCCL_THROW(::std::logic_error, "Invalid input capacity");
}
}
else
{
const auto __num_buckets = __cycles + _SizeType{__requested == 0};
if (::cuda::mul_overflow(__capacity, __num_buckets, __stride))
{
_CCCL_THROW(::std::logic_error, "Invalid input capacity");
}
}
return __capacity;
}
//! @brief Rounds a requested capacity up to a valid capacity for a desired load factor.
//!
//! @tparam _ProbingScheme Probing scheme type
//! @tparam _BucketSize Number of slots per bucket
//! @tparam _SizeType Size type
//!
//! @param __requested Requested element count
//! @param __load_factor Desired load factor in (0, 1]
//!
//! @return The smallest valid capacity that fits `__requested` elements at `__load_factor`
template <class _ProbingScheme, int _BucketSize, class _SizeType>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr _SizeType make_valid_capacity(_SizeType __requested, double __load_factor)
{
if (__load_factor <= 0. || !::cuda::in_range(__load_factor, 0., 1.))
{
_CCCL_THROW(::std::logic_error, "Desired load factor must be in the range (0, 1]");
}
const auto __scaled = ::cuda::std::ceil(static_cast<double>(__requested) / __load_factor);
if (__scaled > static_cast<double>(::cuda::std::numeric_limits<_SizeType>::max()))
{
_CCCL_THROW(::std::logic_error,
"Invalid load factor: requested capacity divided by load factor exceeds the maximum representable "
"value");
}
return make_valid_capacity<_ProbingScheme, _BucketSize>(static_cast<_SizeType>(__scaled));
}
//! @brief Returns whether `__capacity` is already a valid capacity for the given probing scheme and
//! bucket size.
//!
//! @tparam _ProbingScheme Probing scheme type
//! @tparam _BucketSize Number of slots per bucket
//! @tparam _SizeType Size type
//!
//! @param __capacity Capacity to test
//!
//! @return `true` if `__capacity` needs no rounding
template <class _ProbingScheme, int _BucketSize, class _SizeType>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr bool is_valid_capacity(_SizeType __capacity)
{
return make_valid_capacity<_ProbingScheme, _BucketSize>(__capacity) == __capacity;
}
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_CAPACITY_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_BITWISE_COMPARE_CUH
#define _CUDAX___CUCO_DETAIL_BITWISE_COMPARE_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__type_traits/is_bitwise_comparable.h>
#include <cuda/std/__bit/bit_cast.h>
#include <cuda/std/__limits/numeric_limits.h>
#include <cuda/std/__type_traits/make_nbit_int.h>
#include <cuda/std/array>
#include <cuda/std/cstdint>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::detail
{
//! @brief Bitwise equality comparison.
//!
//! @tparam _Tp Value type
template <class _Tp>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr bool __bitwise_compare(const _Tp& __lhs, const _Tp& __rhs)
{
static_assert(::cuda::is_bitwise_comparable_v<_Tp>,
"Bitwise compared objects must have unique object representations or be explicitly declared safe.");
if constexpr (sizeof(_Tp) <= sizeof(::cuda::std::uint64_t)
|| (sizeof(_Tp) == 2 * sizeof(::cuda::std::uint64_t) && _CCCL_HAS_INT128()))
{
using _Up = ::cuda::std::__make_nbit_uint_t<sizeof(_Tp) * ::cuda::std::numeric_limits<unsigned char>::digits>;
return ::cuda::std::bit_cast<_Up>(__lhs) == ::cuda::std::bit_cast<_Up>(__rhs);
}
else
{
using _Array = ::cuda::std::array<::cuda::std::uint64_t, sizeof(_Tp) / sizeof(::cuda::std::uint64_t)>;
return ::cuda::std::bit_cast<_Array>(__lhs) == ::cuda::std::bit_cast<_Array>(__rhs);
}
}
} // namespace cuda::experimental::cuco::detail
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_BITWISE_COMPARE_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_EQUAL_WRAPPER_CUH
#define _CUDAX___CUCO_DETAIL_EQUAL_WRAPPER_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/cstdint>
#include <cuda/experimental/__cuco/detail/bitwise_compare.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::detail
{
//! @brief Enum of equality comparison results.
enum class __equal_result : ::cuda::std::int8_t
{
__unequal,
__equal,
__empty,
__available,
};
//! @brief Enum indicating whether the operation is an insert.
enum class __is_insert : ::cuda::std::int8_t
{
__yes,
__no
};
//! @brief Key equality wrapper.
//!
//! @tparam _Tp Right-hand side element type
//! @tparam _Equal Equality callable
//! @tparam _AllowsDuplicates Duplicate key flag
template <class _Tp, class _Equal, bool _AllowsDuplicates>
struct __equal_wrapper
{
_Tp __empty_sentinel;
_Tp __erased_sentinel;
_Equal __equal;
//! @brief Equality wrapper constructor.
//!
//! @param __empty Empty sentinel value
//! @param __erased Erased sentinel value
//! @param __eq Equality binary callable
_CCCL_HOST_DEVICE_API constexpr __equal_wrapper(_Tp __empty, _Tp __erased, const _Equal& __eq) noexcept
: __empty_sentinel{__empty}
, __erased_sentinel{__erased}
, __equal{__eq}
{}
#if _CCCL_CUDA_COMPILATION()
//! @brief Equality check with the given equality callable.
//!
//! @tparam _Lhs Left-hand side element type
//! @tparam _Rhs Right-hand side element type
//!
//! @param __lhs Left-hand side element to check equality
//! @param __rhs Right-hand side element to check equality
//!
//! @return `__equal` if `__lhs` and `__rhs` are equivalent, `__unequal` otherwise
template <class _Lhs, class _Rhs>
[[nodiscard]] _CCCL_DEVICE_API constexpr __equal_result __equal_to(const _Lhs& __lhs, const _Rhs& __rhs) const noexcept
{
return __equal(__lhs, __rhs) ? __equal_result::__equal : __equal_result::__unequal;
}
//! @brief Order-sensitive equality operator.
//!
//! @note This function always compares the right-hand side element against sentinel values first
//! then performs an equality check with the given `__equal` callable, i.e., `__equal(__lhs, __rhs)`.
//! @note Container (like set or map) slots MUST always be on the right-hand side.
//!
//! @tparam _IsInsert Flag indicating whether it's an insert equality check or not. Insert probing
//! stops when it's an empty or erased slot while query probing stops only when it's empty.
//! @tparam _Lhs Left-hand side element type
//! @tparam _Rhs Right-hand side element type
//!
//! @param __lhs Left-hand side element to check equality
//! @param __rhs Right-hand side element to check equality
//!
//! @return Three-way equality comparison result
template <__is_insert _IsInsert, class _Lhs, class _Rhs>
[[nodiscard]] _CCCL_DEVICE_API constexpr __equal_result operator()(const _Lhs& __lhs, const _Rhs& __rhs) const noexcept
{
if constexpr (_IsInsert == __is_insert::__yes)
{
if (detail::__bitwise_compare(__rhs, __empty_sentinel) || detail::__bitwise_compare(__rhs, __erased_sentinel))
{
return __equal_result::__available;
}
else if constexpr (_AllowsDuplicates)
{
return __equal_result::__unequal;
}
else
{
return __equal_to(__lhs, __rhs);
}
}
else
{
return detail::__bitwise_compare(__rhs, __empty_sentinel) ? __equal_result::__empty : __equal_to(__lhs, __rhs);
}
}
#endif // _CCCL_CUDA_COMPILATION()
};
} // namespace cuda::experimental::cuco::detail
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_EQUAL_WRAPPER_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
/* MurmurHash3_32 implementation from
* https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp
* -----------------------------------------------------------------------------
* MurmurHash3 was written by Austin Appleby, and is placed in the public domain. The author
* hereby disclaims copyright to this source code.
*
* Note - The x86 and x64 versions do _not_ produce the same results, as the algorithms are
* optimized for their respective platforms. You can still compile and run any of them on any
* platform, but your performance with the non-native version will be less than optimal.
*/
#ifndef _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_MURMURHASH3_CUH
#define _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_MURMURHASH3_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/static_for.h>
#include <cuda/std/__bit/bit_cast.h>
#include <cuda/std/__bit/rotate.h>
#include <cuda/std/array>
#include <cuda/std/cstddef>
#include <cuda/std/cstdint>
#include <cuda/std/span>
#include <cuda/experimental/__cuco/detail/hash_functions/utils.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
template <typename _Key>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint32_t
__fmix32(_Key __key, ::cuda::std::uint32_t __seed = 0) noexcept
{
static_assert(sizeof(_Key) == 4, "Key type must be 4 bytes in size.");
auto __h = ::cuda::std::bit_cast<::cuda::std::uint32_t>(__key) ^ __seed;
__h ^= __h >> 16;
__h *= 0x85ebca6b;
__h ^= __h >> 13;
__h *= 0xc2b2ae35;
__h ^= __h >> 16;
return __h;
}
#if _CCCL_HAS_INT128()
template <typename _Key>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint64_t
__fmix64(_Key __key, ::cuda::std::uint64_t __seed = 0) noexcept
{
static_assert(sizeof(_Key) == 8, "Key type must be 8 bytes in size.");
auto __h = ::cuda::std::bit_cast<::cuda::std::uint64_t>(__key) ^ __seed;
__h ^= __h >> 33;
__h *= 0xff51afd7ed558ccdULL;
__h ^= __h >> 33;
__h *= 0xc4ceb9fe1a85ec53ULL;
__h ^= __h >> 33;
return __h;
}
#endif // _CCCL_HAS_INT128()
//! @brief A `MurmurHash3_32` hash function to hash the given argument on host and device.
//!
//! @tparam _Key The type of the values to hash
template <typename _Key>
struct _MurmurHash3_32
{
static constexpr ::cuda::std::uint32_t __c1 = 0xcc9e2d51;
static constexpr ::cuda::std::uint32_t __c2 = 0x1b873593;
static constexpr ::cuda::std::uint32_t __block_size = 4;
static constexpr ::cuda::std::uint32_t __chunk_size = 4;
_CCCL_HOST_DEVICE_API constexpr _MurmurHash3_32(::cuda::std::uint32_t __seed = 0)
: __seed_{__seed}
{}
//! @brief Returns a hash value for its argument, as a value of type `::cuda::std::uint32_t`.
//! @param __key The input argument to hash
//! @return The resulting hash value
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint32_t operator()(const _Key& __key) const noexcept
{
using _Holder = _Byte_holder<sizeof(_Key), __chunk_size, __block_size, false, ::cuda::std::uint32_t>;
return __compute_hash(::cuda::std::bit_cast<_Holder>(__key));
}
//! @brief Returns a hash value for its argument, as a value of type `::cuda::std::uint32_t`.
//! @tparam _Extent The extent type
//! @param __keys span of keys to hash
//! @return The resulting hash value
template <size_t _Extent>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint64_t
operator()(::cuda::std::span<_Key, _Extent> __keys) const noexcept
{
return __compute_hash_span(__keys);
}
private:
template <class _Holder>
[[nodiscard]] _CCCL_HOST_DEVICE_API ::cuda::std::uint32_t __compute_hash(_Holder __holder) const noexcept
{
::cuda::std::uint32_t __h1 = __seed_;
//----------
// body
if constexpr (_Holder::__num_blocks > 0)
{
::cuda::static_for<_Holder::__num_blocks>([&](auto __i) {
::cuda::std::uint32_t __k1 = __holder.__blocks[__i];
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 15);
__k1 *= __c2;
__h1 ^= __k1;
__h1 = ::cuda::std::rotl(__h1, 13);
__h1 = __h1 * 5 + 0xe6546b64;
});
}
//----------
// tail
if constexpr (_Holder::__tail_size > 0)
{
::cuda::std::uint32_t __k1 = 0;
switch (__holder.__tail_size)
{
case 3:
__k1 ^= ::cuda::std::to_integer<::cuda::std::uint32_t>(__holder.__bytes[2]) << 16;
[[fallthrough]];
case 2:
__k1 ^= ::cuda::std::to_integer<::cuda::std::uint32_t>(__holder.__bytes[1]) << 8;
[[fallthrough]];
case 1:
__k1 ^= ::cuda::std::to_integer<::cuda::std::uint32_t>(__holder.__bytes[0]);
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 15);
__k1 *= __c2;
__h1 ^= __k1;
};
}
//----------
// finalization
__h1 ^= ::cuda::std::uint32_t{sizeof(_Holder)};
__h1 = ::cuda::experimental::cuco::__fmix32(__h1);
return __h1;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API ::cuda::std::uint32_t
__compute_hash_span(::cuda::std::span<const _Key> __keys) const noexcept
{
const auto __bytes = ::cuda::std::as_bytes(__keys).data();
const auto __size = __keys.size_bytes();
const auto __nblocks = __size / __block_size;
::cuda::std::uint32_t __h1 = __seed_;
//----------
// body
for (::cuda::std::remove_const_t<decltype(__nblocks)> __i = 0; __i < __nblocks; __i++)
{
::cuda::std::uint32_t __k1 = ::cuda::experimental::cuco::__load_chunk<::cuda::std::uint32_t>(__bytes, __i);
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 15);
__k1 *= __c2;
__h1 ^= __k1;
__h1 = ::cuda::std::rotl(__h1, 13);
__h1 = __h1 * 5 + 0xe6546b64;
}
//----------
// tail
::cuda::std::uint32_t __k1 = 0;
switch (__size % 4)
{
case 3:
__k1 ^= ::cuda::std::to_integer<::cuda::std::uint32_t>(__bytes[__nblocks * __block_size + 2]) << 16;
[[fallthrough]];
case 2:
__k1 ^= ::cuda::std::to_integer<::cuda::std::uint32_t>(__bytes[__nblocks * __block_size + 1]) << 8;
[[fallthrough]];
case 1:
__k1 ^= ::cuda::std::to_integer<::cuda::std::uint32_t>(__bytes[__nblocks * __block_size + 0]);
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 15);
__k1 *= __c2;
__h1 ^= __k1;
};
//----------
// finalization
__h1 ^= __size;
__h1 = ::cuda::experimental::cuco::__fmix32(__h1);
return __h1;
}
::cuda::std::uint32_t __seed_;
};
#if _CCCL_HAS_INT128()
template <typename _Key>
struct _MurmurHash3_x86_128
{
private:
static constexpr ::cuda::std::uint32_t __c1 = 0x239b961b;
static constexpr ::cuda::std::uint32_t __c2 = 0xab0e9789;
static constexpr ::cuda::std::uint32_t __c3 = 0x38b34ae5;
static constexpr ::cuda::std::uint32_t __c4 = 0xa1e38b93;
static constexpr ::cuda::std::uint32_t __block_size = 4;
static constexpr ::cuda::std::uint32_t __chunk_size = 16;
public:
_CCCL_HOST_DEVICE_API constexpr _MurmurHash3_x86_128(::cuda::std::uint32_t __seed = 0)
: __seed_{__seed}
{}
//! @brief Returns a hash value for its argument, as a value of type `__uint128_t`.
//! @param __key The input argument to hash
//! @return The resulting hash value
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __uint128_t operator()(const _Key& __key) const noexcept
{
using _Holder = _Byte_holder<sizeof(_Key), __chunk_size, __block_size, false, ::cuda::std::uint32_t>;
return __compute_hash(::cuda::std::bit_cast<_Holder>(__key));
}
//! @brief Returns a hash value for its argument, as a value of type `__uint128_t`.
//! @tparam _Extent The extent type
//! @param __keys span of keys to hash
//! @return The resulting hash value
template <size_t _Extent>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __uint128_t
operator()(::cuda::std::span<_Key, _Extent> __keys) const noexcept
{
return __compute_hash_span(__keys);
}
private:
template <class _Holder>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __uint128_t __compute_hash(_Holder __holder) const noexcept
{
::cuda::std::array<::cuda::std::uint32_t, 4> __h{__seed_, __seed_, __seed_, __seed_};
const auto __size = ::cuda::std::uint32_t{sizeof(_Holder)};
if constexpr (_Holder::__num_chunks > 0)
{
::cuda::static_for<_Holder::__num_chunks>([&](auto __i) {
::cuda::std::uint32_t __k1 = __holder.__blocks[4 * __i];
::cuda::std::uint32_t __k2 = __holder.__blocks[4 * __i + 1];
::cuda::std::uint32_t __k3 = __holder.__blocks[4 * __i + 2];
::cuda::std::uint32_t __k4 = __holder.__blocks[4 * __i + 3];
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 15);
__k1 *= __c2;
__h[0] ^= __k1;
__h[0] = ::cuda::std::rotl(__h[0], 19);
__h[0] += __h[1];
__h[0] = __h[0] * 5 + 0x561ccd1b;
__k2 *= __c2;
__k2 = ::cuda::std::rotl(__k2, 16);
__k2 *= __c3;
__h[1] ^= __k2;
__h[1] = ::cuda::std::rotl(__h[1], 17);
__h[1] += __h[2];
__h[1] = __h[1] * 5 + 0x0bcaa747;
__k3 *= __c3;
__k3 = ::cuda::std::rotl(__k3, 17);
__k3 *= __c4;
__h[2] ^= __k3;
__h[2] = ::cuda::std::rotl(__h[2], 15);
__h[2] += __h[3];
__h[2] = __h[2] * 5 + 0x96cd1c35;
__k4 *= __c4;
__k4 = ::cuda::std::rotl(__k4, 18);
__k4 *= __c1;
__h[3] ^= __k4;
__h[3] = ::cuda::std::rotl(__h[3], 13);
__h[3] += __h[0];
__h[3] = __h[3] * 5 + 0x32ac3b17;
});
}
// tail
if constexpr (_Holder::__tail_size > 0)
{
::cuda::std::uint32_t __k1 = 0;
::cuda::std::uint32_t __k2 = 0;
::cuda::std::uint32_t __k3 = 0;
::cuda::std::uint32_t __k4 = 0;
const auto __tail = __holder.__bytes;
switch (__size % __chunk_size)
{
case 15:
__k4 ^= static_cast<::cuda::std::uint32_t>(__tail[14]) << 16;
[[fallthrough]];
case 14:
__k4 ^= static_cast<::cuda::std::uint32_t>(__tail[13]) << 8;
[[fallthrough]];
case 13:
__k4 ^= static_cast<::cuda::std::uint32_t>(__tail[12]) << 0;
__k4 *= __c4;
__k4 = ::cuda::std::rotl(__k4, 18);
__k4 *= __c1;
__h[3] ^= __k4;
[[fallthrough]];
case 12:
__k3 ^= static_cast<::cuda::std::uint32_t>(__tail[11]) << 24;
[[fallthrough]];
case 11:
__k3 ^= static_cast<::cuda::std::uint32_t>(__tail[10]) << 16;
[[fallthrough]];
case 10:
__k3 ^= static_cast<::cuda::std::uint32_t>(__tail[9]) << 8;
[[fallthrough]];
case 9:
__k3 ^= static_cast<::cuda::std::uint32_t>(__tail[8]) << 0;
__k3 *= __c3;
__k3 = ::cuda::std::rotl(__k3, 17);
__k3 *= __c4;
__h[2] ^= __k3;
[[fallthrough]];
case 8:
__k2 ^= static_cast<::cuda::std::uint32_t>(__tail[7]) << 24;
[[fallthrough]];
case 7:
__k2 ^= static_cast<::cuda::std::uint32_t>(__tail[6]) << 16;
[[fallthrough]];
case 6:
__k2 ^= static_cast<::cuda::std::uint32_t>(__tail[5]) << 8;
[[fallthrough]];
case 5:
__k2 ^= static_cast<::cuda::std::uint32_t>(__tail[4]) << 0;
__k2 *= __c2;
__k2 = ::cuda::std::rotl(__k2, 16);
__k2 *= __c3;
__h[1] ^= __k2;
[[fallthrough]];
case 4:
__k1 ^= static_cast<::cuda::std::uint32_t>(__tail[3]) << 24;
[[fallthrough]];
case 3:
__k1 ^= static_cast<::cuda::std::uint32_t>(__tail[2]) << 16;
[[fallthrough]];
case 2:
__k1 ^= static_cast<::cuda::std::uint32_t>(__tail[1]) << 8;
[[fallthrough]];
case 1:
__k1 ^= static_cast<::cuda::std::uint32_t>(__tail[0]) << 0;
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 15);
__k1 *= __c2;
__h[0] ^= __k1;
};
}
// finalization
__h[0] ^= __size;
__h[1] ^= __size;
__h[2] ^= __size;
__h[3] ^= __size;
__h[0] += __h[1];
__h[0] += __h[2];
__h[0] += __h[3];
__h[1] += __h[0];
__h[2] += __h[0];
__h[3] += __h[0];
__h[0] = ::cuda::experimental::cuco::__fmix32(__h[0]);
__h[1] = ::cuda::experimental::cuco::__fmix32(__h[1]);
__h[2] = ::cuda::experimental::cuco::__fmix32(__h[2]);
__h[3] = ::cuda::experimental::cuco::__fmix32(__h[3]);
__h[0] += __h[1];
__h[0] += __h[2];
__h[0] += __h[3];
__h[1] += __h[0];
__h[2] += __h[0];
__h[3] += __h[0];
return ::cuda::std::bit_cast<__uint128_t>(__h);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __uint128_t
__compute_hash_span(::cuda::std::span<const _Key> __keys) const noexcept
{
const auto __bytes = ::cuda::std::as_bytes(__keys).data();
const auto __size = __keys.size_bytes();
const auto __nchunks = __size / __chunk_size;
::cuda::std::array<::cuda::std::uint32_t, 4> __h{__seed_, __seed_, __seed_, __seed_};
// body
for (::cuda::std::remove_const_t<decltype(__nchunks)> __i = 0; __size >= __chunk_size && __i < __nchunks; ++__i)
{
::cuda::std::uint32_t __k1 = ::cuda::experimental::cuco::__load_chunk<::cuda::std::uint32_t>(__bytes, 4 * __i);
::cuda::std::uint32_t __k2 =
::cuda::experimental::cuco::__load_chunk<::cuda::std::uint32_t>(__bytes, 4 * __i + 1);
::cuda::std::uint32_t __k3 =
::cuda::experimental::cuco::__load_chunk<::cuda::std::uint32_t>(__bytes, 4 * __i + 2);
::cuda::std::uint32_t __k4 =
::cuda::experimental::cuco::__load_chunk<::cuda::std::uint32_t>(__bytes, 4 * __i + 3);
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 15);
__k1 *= __c2;
__h[0] ^= __k1;
__h[0] = ::cuda::std::rotl(__h[0], 19);
__h[0] += __h[1];
__h[0] = __h[0] * 5 + 0x561ccd1b;
__k2 *= __c2;
__k2 = ::cuda::std::rotl(__k2, 16);
__k2 *= __c3;
__h[1] ^= __k2;
__h[1] = ::cuda::std::rotl(__h[1], 17);
__h[1] += __h[2];
__h[1] = __h[1] * 5 + 0x0bcaa747;
__k3 *= __c3;
__k3 = ::cuda::std::rotl(__k3, 17);
__k3 *= __c4;
__h[2] ^= __k3;
__h[2] = ::cuda::std::rotl(__h[2], 15);
__h[2] += __h[3];
__h[2] = __h[2] * 5 + 0x96cd1c35;
__k4 *= __c4;
__k4 = ::cuda::std::rotl(__k4, 18);
__k4 *= __c1;
__h[3] ^= __k4;
__h[3] = ::cuda::std::rotl(__h[3], 13);
__h[3] += __h[0];
__h[3] = __h[3] * 5 + 0x32ac3b17;
}
// tail
::cuda::std::uint32_t __k1 = 0;
::cuda::std::uint32_t __k2 = 0;
::cuda::std::uint32_t __k3 = 0;
::cuda::std::uint32_t __k4 = 0;
const auto __tail = __bytes + __nchunks * __chunk_size;
switch (__size % __chunk_size)
{
case 15:
__k4 ^= static_cast<::cuda::std::uint32_t>(__tail[14]) << 16;
[[fallthrough]];
case 14:
__k4 ^= static_cast<::cuda::std::uint32_t>(__tail[13]) << 8;
[[fallthrough]];
case 13:
__k4 ^= static_cast<::cuda::std::uint32_t>(__tail[12]) << 0;
__k4 *= __c4;
__k4 = ::cuda::std::rotl(__k4, 18);
__k4 *= __c1;
__h[3] ^= __k4;
[[fallthrough]];
case 12:
__k3 ^= static_cast<::cuda::std::uint32_t>(__tail[11]) << 24;
[[fallthrough]];
case 11:
__k3 ^= static_cast<::cuda::std::uint32_t>(__tail[10]) << 16;
[[fallthrough]];
case 10:
__k3 ^= static_cast<::cuda::std::uint32_t>(__tail[9]) << 8;
[[fallthrough]];
case 9:
__k3 ^= static_cast<::cuda::std::uint32_t>(__tail[8]) << 0;
__k3 *= __c3;
__k3 = ::cuda::std::rotl(__k3, 17);
__k3 *= __c4;
__h[2] ^= __k3;
[[fallthrough]];
case 8:
__k2 ^= static_cast<::cuda::std::uint32_t>(__tail[7]) << 24;
[[fallthrough]];
case 7:
__k2 ^= static_cast<::cuda::std::uint32_t>(__tail[6]) << 16;
[[fallthrough]];
case 6:
__k2 ^= static_cast<::cuda::std::uint32_t>(__tail[5]) << 8;
[[fallthrough]];
case 5:
__k2 ^= static_cast<::cuda::std::uint32_t>(__tail[4]) << 0;
__k2 *= __c2;
__k2 = ::cuda::std::rotl(__k2, 16);
__k2 *= __c3;
__h[1] ^= __k2;
[[fallthrough]];
case 4:
__k1 ^= static_cast<::cuda::std::uint32_t>(__tail[3]) << 24;
[[fallthrough]];
case 3:
__k1 ^= static_cast<::cuda::std::uint32_t>(__tail[2]) << 16;
[[fallthrough]];
case 2:
__k1 ^= static_cast<::cuda::std::uint32_t>(__tail[1]) << 8;
[[fallthrough]];
case 1:
__k1 ^= static_cast<::cuda::std::uint32_t>(__tail[0]) << 0;
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 15);
__k1 *= __c2;
__h[0] ^= __k1;
};
// finalization
__h[0] ^= __size;
__h[1] ^= __size;
__h[2] ^= __size;
__h[3] ^= __size;
__h[0] += __h[1];
__h[0] += __h[2];
__h[0] += __h[3];
__h[1] += __h[0];
__h[2] += __h[0];
__h[3] += __h[0];
__h[0] = ::cuda::experimental::cuco::__fmix32(__h[0]);
__h[1] = ::cuda::experimental::cuco::__fmix32(__h[1]);
__h[2] = ::cuda::experimental::cuco::__fmix32(__h[2]);
__h[3] = ::cuda::experimental::cuco::__fmix32(__h[3]);
__h[0] += __h[1];
__h[0] += __h[2];
__h[0] += __h[3];
__h[1] += __h[0];
__h[2] += __h[0];
__h[3] += __h[0];
return ::cuda::std::bit_cast<__uint128_t>(__h);
}
private:
::cuda::std::uint32_t __seed_;
};
template <typename _Key>
struct _MurmurHash3_x64_128
{
private:
static constexpr ::cuda::std::uint64_t __c1 = 0x87c37b91114253d5ull;
static constexpr ::cuda::std::uint64_t __c2 = 0x4cf5ad432745937full;
static constexpr ::cuda::std::uint32_t __block_size = 8;
static constexpr ::cuda::std::uint32_t __chunk_size = 16;
public:
_CCCL_HOST_DEVICE_API constexpr _MurmurHash3_x64_128(::cuda::std::uint64_t __seed = 0)
: __seed_{__seed}
{}
//! @brief Returns a hash value for its argument, as a value of type `__uint128_t`.
//! @param __key The input argument to hash
//! @return The resulting hash value
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __uint128_t operator()(const _Key& __key) const noexcept
{
using _Holder = _Byte_holder<sizeof(_Key), __chunk_size, __block_size, false, ::cuda::std::uint64_t>;
return __compute_hash(::cuda::std::bit_cast<_Holder>(__key));
}
//! @brief Returns a hash value for its argument, as a value of type `__uint128_t`.
//! @tparam _Extent The extent type
//! @param __keys span of keys to hash
//! @return The resulting hash value
template <size_t _Extent>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __uint128_t
operator()(::cuda::std::span<_Key, _Extent> __keys) const noexcept
{
return __compute_hash_span(__keys);
}
private:
template <class _Holder>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __uint128_t __compute_hash(_Holder __holder) const noexcept
{
::cuda::std::array<::cuda::std::uint64_t, 2> __h{__seed_, __seed_};
const auto __size = ::cuda::std::uint64_t{sizeof(_Holder)};
if constexpr (_Holder::__num_chunks > 0)
{
::cuda::static_for<_Holder::__num_chunks>([&](auto __i) {
::cuda::std::uint64_t __k1 = __holder.__blocks[2 * __i];
::cuda::std::uint64_t __k2 = __holder.__blocks[2 * __i + 1];
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 31);
__k1 *= __c2;
__h[0] ^= __k1;
__h[0] = ::cuda::std::rotl(__h[0], 27);
__h[0] += __h[1];
__h[0] = __h[0] * 5 + 0x52dce729;
__k2 *= __c2;
__k2 = ::cuda::std::rotl(__k2, 33);
__k2 *= __c1;
__h[1] ^= __k2;
__h[1] = ::cuda::std::rotl(__h[1], 31);
__h[1] += __h[0];
__h[1] = __h[1] * 5 + 0x38495ab5;
});
}
// tail
if constexpr (_Holder::__tail_size > 0)
{
::cuda::std::uint64_t __k1 = 0;
::cuda::std::uint64_t __k2 = 0;
const auto __tail = __holder.__bytes;
switch (__size % __chunk_size)
{
case 15:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[14]) << 48;
[[fallthrough]];
case 14:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[13]) << 40;
[[fallthrough]];
case 13:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[12]) << 32;
[[fallthrough]];
case 12:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[11]) << 24;
[[fallthrough]];
case 11:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[10]) << 16;
[[fallthrough]];
case 10:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[9]) << 8;
[[fallthrough]];
case 9:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[8]) << 0;
__k2 *= __c2;
__k2 = ::cuda::std::rotl(__k2, 33);
__k2 *= __c1;
__h[1] ^= __k2;
[[fallthrough]];
case 8:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[7]) << 56;
[[fallthrough]];
case 7:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[6]) << 48;
[[fallthrough]];
case 6:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[5]) << 40;
[[fallthrough]];
case 5:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[4]) << 32;
[[fallthrough]];
case 4:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[3]) << 24;
[[fallthrough]];
case 3:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[2]) << 16;
[[fallthrough]];
case 2:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[1]) << 8;
[[fallthrough]];
case 1:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[0]) << 0;
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 31);
__k1 *= __c2;
__h[0] ^= __k1;
}
}
// finalization
__h[0] ^= __size;
__h[1] ^= __size;
__h[0] += __h[1];
__h[1] += __h[0];
__h[0] = ::cuda::experimental::cuco::__fmix64(__h[0]);
__h[1] = ::cuda::experimental::cuco::__fmix64(__h[1]);
__h[0] += __h[1];
__h[1] += __h[0];
return ::cuda::std::bit_cast<__uint128_t>(__h);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __uint128_t
__compute_hash_span(::cuda::std::span<const _Key> __keys) const noexcept
{
const auto __bytes = ::cuda::std::as_bytes(__keys).data();
const auto __size = __keys.size_bytes();
const auto __nchunks = __size / __chunk_size;
::cuda::std::array<::cuda::std::uint64_t, 2> __h{__seed_, __seed_};
// body
for (::cuda::std::remove_const_t<decltype(__nchunks)> __i = 0; __size >= __chunk_size && __i < __nchunks; ++__i)
{
::cuda::std::uint64_t __k1 = ::cuda::experimental::cuco::__load_chunk<::cuda::std::uint64_t>(__bytes, 2 * __i);
::cuda::std::uint64_t __k2 =
::cuda::experimental::cuco::__load_chunk<::cuda::std::uint64_t>(__bytes, 2 * __i + 1);
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 31);
__k1 *= __c2;
__h[0] ^= __k1;
__h[0] = ::cuda::std::rotl(__h[0], 27);
__h[0] += __h[1];
__h[0] = __h[0] * 5 + 0x52dce729;
__k2 *= __c2;
__k2 = ::cuda::std::rotl(__k2, 33);
__k2 *= __c1;
__h[1] ^= __k2;
__h[1] = ::cuda::std::rotl(__h[1], 31);
__h[1] += __h[0];
__h[1] = __h[1] * 5 + 0x38495ab5;
}
// tail
::cuda::std::uint64_t __k1 = 0;
::cuda::std::uint64_t __k2 = 0;
const auto __tail = __bytes + __nchunks * __chunk_size;
switch (__size % __chunk_size)
{
case 15:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[14]) << 48;
[[fallthrough]];
case 14:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[13]) << 40;
[[fallthrough]];
case 13:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[12]) << 32;
[[fallthrough]];
case 12:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[11]) << 24;
[[fallthrough]];
case 11:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[10]) << 16;
[[fallthrough]];
case 10:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[9]) << 8;
[[fallthrough]];
case 9:
__k2 ^= static_cast<::cuda::std::uint64_t>(__tail[8]) << 0;
__k2 *= __c2;
__k2 = ::cuda::std::rotl(__k2, 33);
__k2 *= __c1;
__h[1] ^= __k2;
[[fallthrough]];
case 8:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[7]) << 56;
[[fallthrough]];
case 7:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[6]) << 48;
[[fallthrough]];
case 6:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[5]) << 40;
[[fallthrough]];
case 5:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[4]) << 32;
[[fallthrough]];
case 4:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[3]) << 24;
[[fallthrough]];
case 3:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[2]) << 16;
[[fallthrough]];
case 2:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[1]) << 8;
[[fallthrough]];
case 1:
__k1 ^= static_cast<::cuda::std::uint64_t>(__tail[0]) << 0;
__k1 *= __c1;
__k1 = ::cuda::std::rotl(__k1, 31);
__k1 *= __c2;
__h[0] ^= __k1;
};
// finalization
__h[0] ^= __size;
__h[1] ^= __size;
__h[0] += __h[1];
__h[1] += __h[0];
__h[0] = ::cuda::experimental::cuco::__fmix64(__h[0]);
__h[1] = ::cuda::experimental::cuco::__fmix64(__h[1]);
__h[0] += __h[1];
__h[1] += __h[0];
return ::cuda::std::bit_cast<__uint128_t>(__h);
}
private:
::cuda::std::uint64_t __seed_;
};
#endif // _CCCL_HAS_INT128()
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_MURMURHASH3_CUH

150
cccl_upstream/cudax/include/cuda/experimental/__cuco/detail/hash_functions/utils.cuh
View File

@@ -1,150 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_UTILS_CUH
#define _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_UTILS_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__memory/is_aligned.h>
#include <cuda/std/__cstring/memcpy.h>
#include <cuda/std/__memory/assume_aligned.h>
#include <cuda/std/cstddef>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief Loads a chunk of type _Tp from a byte pointer at a given index, handling alignment
//!
//! @tparam _Tp The type of the chunk to load (must be 4 or 8 bytes)
//! @tparam _Extent The index type
//! @param __bytes Pointer to the byte array
//! @param __index The index of the chunk to load
//! @return The loaded chunk of type _Tp
template <typename _Tp, typename _Extent>
[[nodiscard]] _CCCL_HOST_DEVICE_API _Tp __load_chunk(::cuda::std::byte const* const __bytes, _Extent __index) noexcept
{
static_assert(sizeof(_Tp) == 4 || sizeof(_Tp) == 8, "__load_chunk must be used with types of size 4 or 8 bytes");
const auto __ptr = __bytes + __index * sizeof(_Tp);
_Tp __chunk;
if constexpr (alignof(_Tp) == 8)
{
if (::cuda::is_aligned(__ptr, 8))
{
::cuda::std::memcpy(&__chunk, ::cuda::std::assume_aligned<8>(__ptr), sizeof(_Tp));
return __chunk;
}
}
if (::cuda::is_aligned(__ptr, 4))
{
::cuda::std::memcpy(&__chunk, ::cuda::std::assume_aligned<4>(__ptr), sizeof(_Tp));
}
else if (::cuda::is_aligned(__ptr, 2))
{
::cuda::std::memcpy(&__chunk, ::cuda::std::assume_aligned<2>(__ptr), sizeof(_Tp));
}
else
{
::cuda::std::memcpy(&__chunk, __ptr, sizeof(_Tp));
}
return __chunk;
}
//! @brief Type erased holder of all the bytes
//!
//! @tparam _KeySize The size of the key in bytes
//! @tparam _ChunkSize The size of a chunk in bytes
//! @tparam _BlockSize The size of a block in bytes (same as sizeof(_BlockT))
//! @tparam _UseTailBlock Whether to use a tail block for the last bytes
//! @tparam _BlockT The type of the block
//! @tparam _HasBlocksOrChunks Whether the key size is larger than the chunk size or block size
//! @tparam _HasTail Whether the key size is larger than the block size
//!
//! @note _UseTailBlock is true for xxhash and false for murmurhash, as xxhash consider's tail as blocks for the last
//! bytes, where as murmurhash considers the tail as a bytes
template <size_t _KeySize,
size_t _ChunkSize,
size_t _BlockSize,
bool _UseTailBlock,
typename _BlockT,
bool _HasBlocksOrChunks = _UseTailBlock ? (_KeySize >= _BlockSize) : (_KeySize >= _ChunkSize),
bool _HasTail = _UseTailBlock ? ((_KeySize % _BlockSize) != 0) : ((_KeySize % _ChunkSize) != 0)>
struct _Byte_holder
{
//! The number of trailing bytes that do not fit into a _BlockT
static constexpr size_t __tail_size = _UseTailBlock ? _KeySize % _BlockSize : _KeySize % _ChunkSize;
//! The number of `_ChunkSize` chunks
static constexpr size_t __num_chunks = _KeySize / _ChunkSize;
//! The number of `_BlockSize` blocks in a `_ChunkSize` chunk
static constexpr size_t __blocks_per_chunk = _ChunkSize / _BlockSize;
//! The number of `_BlockSize` blocks
static constexpr size_t __num_blocks = _UseTailBlock ? _KeySize / _BlockSize : __num_chunks * __blocks_per_chunk;
_BlockT __blocks[__num_blocks];
::cuda::std::byte __bytes[__tail_size];
};
//! @brief Type erased holder of small types < _BlockSize
template <size_t _KeySize, size_t _ChunkSize, size_t _BlockSize, bool _UseTailBlock, typename _BlockT>
struct _Byte_holder<_KeySize, _ChunkSize, _BlockSize, _UseTailBlock, _BlockT, false, true>
{
//! The number of trailing bytes that do not fit into a _BlockT
static constexpr size_t __tail_size = _UseTailBlock ? _KeySize % _BlockSize : _KeySize % _ChunkSize;
//! The number of `_ChunkSize` chunks
static constexpr size_t __num_chunks = _KeySize / _ChunkSize;
//! The number of `_BlockSize` blocks in a `_ChunkSize` chunk
static constexpr size_t __blocks_per_chunk = _ChunkSize / _BlockSize;
//! The number of `_BlockSize` blocks in a `_ChunkSize` chunk
static constexpr size_t __num_blocks = _UseTailBlock ? _KeySize / _BlockSize : __num_chunks * __blocks_per_chunk;
::cuda::std::byte __bytes[__tail_size];
};
//! @brief Type erased holder of types without trailing bytes
template <size_t _KeySize, size_t _ChunkSize, size_t _BlockSize, bool _UseTailBlock, typename _BlockT>
struct _Byte_holder<_KeySize, _ChunkSize, _BlockSize, _UseTailBlock, _BlockT, true, false>
{
//! The number of trailing bytes that do not fit into a _BlockT
static constexpr size_t __tail_size = _UseTailBlock ? _KeySize % _BlockSize : _KeySize % _ChunkSize;
//! The number of `_ChunkSize` chunks
static constexpr size_t __num_chunks = _KeySize / _ChunkSize;
//! The number of `_BlockSize` blocks in a `_ChunkSize` chunk
static constexpr size_t __blocks_per_chunk = _ChunkSize / _BlockSize;
//! The number of `_BlockSize` blocks
static constexpr size_t __num_blocks = _UseTailBlock ? _KeySize / _BlockSize : __num_chunks * __blocks_per_chunk;
_BlockT __blocks[__num_blocks];
};
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_UTILS_CUH

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@@ -1,426 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
/*
* `_XXHash_32` and `_XXHash_64` implementation from
* https://github.com/Cyan4973/xxHash
* -----------------------------------------------------------------------------
* xxHash - Extremely Fast Hash algorithm
* Header File
* Copyright (C) 2012-2021 Yann Collet
*
* BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php)
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are
* met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following disclaimer
* in the documentation and/or other materials provided with the
* distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
* OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_XXHASH_CUH
#define _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_XXHASH_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/static_for.h>
#include <cuda/std/__bit/bit_cast.h>
#include <cuda/std/__bit/rotate.h>
#include <cuda/std/array>
#include <cuda/std/cstddef>
#include <cuda/std/cstdint>
#include <cuda/std/span>
#include <cuda/experimental/__cuco/detail/hash_functions/utils.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief A `_XXHash_32` hash function to hash the given argument on host and device.
//!
//! @tparam Key The type of the values to hash
template <typename _Key>
struct _XXHash_32
{
private:
static constexpr ::cuda::std::uint32_t __prime1 = 0x9e3779b1u;
static constexpr ::cuda::std::uint32_t __prime2 = 0x85ebca77u;
static constexpr ::cuda::std::uint32_t __prime3 = 0xc2b2ae3du;
static constexpr ::cuda::std::uint32_t __prime4 = 0x27d4eb2fu;
static constexpr ::cuda::std::uint32_t __prime5 = 0x165667b1u;
static constexpr ::cuda::std::uint32_t __block_size = 4;
static constexpr ::cuda::std::uint32_t __chunk_size = 16;
public:
//! @brief Constructs a XXH32 hash function with the given `seed`.
//! @param seed A custom number to randomize the resulting hash value
_CCCL_HOST_DEVICE_API constexpr _XXHash_32(::cuda::std::uint32_t __seed = 0)
: __seed_{__seed}
{}
//! @brief Returns a hash value for its argument, as a value of type `::cuda::std::uint32_t`.
//! @param __key The input argument to hash
//! @return The resulting hash value for `__key`
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint32_t operator()(const _Key& __key) const noexcept
{
using _Holder = _Byte_holder<sizeof(_Key), __chunk_size, __block_size, true, ::cuda::std::uint32_t>;
// explicit copy to avoid emitting a bunch of LDG.8 instructions
const _Key __copy{__key};
return __compute_hash(::cuda::std::bit_cast<_Holder>(__copy));
}
//! @brief Returns a hash value for its argument, as a value of type `::cuda::std::uint32_t`.
//! @tparam _Extent The extent type
//! @param __keys span of keys to hash
//! @return The resulting hash value
template <size_t _Extent>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint32_t
operator()(::cuda::std::span<_Key, _Extent> __keys) const noexcept
{
return __compute_hash_span(__keys);
}
private:
//! @brief Returns a hash value for its argument, as a value of type `::cuda::std::uint32_t`.
//!
//! @tparam _Extent The extent type
//! @param __holder The input argument to hash in form of a byte holder
//! @return The resulting hash value
template <class _Holder>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint32_t __compute_hash(_Holder __holder) const noexcept
{
::cuda::std::uint32_t __offset = 0;
::cuda::std::uint32_t __h32 = {};
// process data in 16-byte chunks
if constexpr (_Holder::__num_chunks > 0)
{
::cuda::std::array<::cuda::std::uint32_t, 4> __v;
__v[0] = __seed_ + __prime1 + __prime2;
__v[1] = __seed_ + __prime2;
__v[2] = __seed_;
__v[3] = __seed_ - __prime1;
for (::cuda::std::uint32_t __i = 0; __i < _Holder::__num_chunks; ++__i)
{
::cuda::static_for<4>([&](auto i) {
__v[i] += __holder.__blocks[__offset++] * __prime2;
__v[i] = ::cuda::std::rotl(__v[i], 13);
__v[i] *= __prime1;
});
}
__h32 = ::cuda::std::rotl(__v[0], 1) + ::cuda::std::rotl(__v[1], 7) + ::cuda::std::rotl(__v[2], 12)
+ ::cuda::std::rotl(__v[3], 18);
}
else
{
__h32 = __seed_ + __prime5;
}
__h32 += ::cuda::std::uint32_t{sizeof(_Holder)};
// remaining data can be processed in 4-byte chunks
if constexpr (_Holder::__num_blocks % __chunk_size > 0)
{
for (; __offset < _Holder::__num_blocks; ++__offset)
{
__h32 += __holder.__blocks[__offset] * __prime3;
__h32 = ::cuda::std::rotl(__h32, 17) * __prime4;
}
}
// the following loop is only needed if the size of the key is not a multiple of the block size
if constexpr (_Holder::__tail_size > 0)
{
for (::cuda::std::uint32_t __i = 0; __i < _Holder::__tail_size; ++__i)
{
__h32 += (static_cast<::cuda::std::uint32_t>(__holder.__bytes[__i])) * __prime5;
__h32 = ::cuda::std::rotl(__h32, 11) * __prime1;
}
}
return __finalize(__h32);
}
//! @brief Returns a hash value for its argument, as a value of type `::cuda::std::uint32_t`.
//!
//! @tparam _Extent The extent type
//! @param __holder The input argument to hash in form of a span
//! @return The resulting hash value
[[nodiscard]] _CCCL_HOST_DEVICE_API ::cuda::std::uint32_t
__compute_hash_span(::cuda::std::span<_Key> __keys) const noexcept
{
auto __bytes = ::cuda::std::as_bytes(__keys).data();
const auto __size = __keys.size_bytes();
::cuda::std::uint32_t __offset = 0;
::cuda::std::uint32_t __h32 = {};
// data can be processed in 16-byte chunks
if (__size >= 16)
{
const auto __limit = __size - 16;
::cuda::std::array<::cuda::std::uint32_t, 4> __v;
__v[0] = __seed_ + __prime1 + __prime2;
__v[1] = __seed_ + __prime2;
__v[2] = __seed_;
__v[3] = __seed_ - __prime1;
for (; __offset <= __limit; __offset += 16)
{
// pipeline 4*4byte computations
const auto __pipeline_offset = __offset / 4;
::cuda::static_for<4>([&](auto i) {
__v[i] += ::cuda::experimental::cuco::__load_chunk<::cuda::std::uint32_t>(__bytes, __pipeline_offset + i)
* __prime2;
__v[i] = ::cuda::std::rotl(__v[i], 13);
__v[i] *= __prime1;
});
}
__h32 = ::cuda::std::rotl(__v[0], 1) + ::cuda::std::rotl(__v[1], 7) + ::cuda::std::rotl(__v[2], 12)
+ ::cuda::std::rotl(__v[3], 18);
}
else
{
__h32 = __seed_ + __prime5;
}
__h32 += __size;
// remaining data can be processed in 4-byte chunks
if ((__size % 16) >= 4)
{
_CCCL_PRAGMA_UNROLL(4)
for (; __offset <= __size - 4; __offset += 4)
{
__h32 += ::cuda::experimental::cuco::__load_chunk<::cuda::std::uint32_t>(__bytes, __offset / 4) * __prime3;
__h32 = ::cuda::std::rotl(__h32, 17) * __prime4;
}
}
// the following loop is only needed if the size of the key is not a multiple of the block size
if (__size % 4)
{
while (__offset < __size)
{
__h32 += (::cuda::std::to_integer<::cuda::std::uint32_t>(__bytes[__offset]) & 255) * __prime5;
__h32 = ::cuda::std::rotl(__h32, 11) * __prime1;
++__offset;
}
}
return __finalize(__h32);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint32_t
__finalize(::cuda::std::uint32_t __h) const noexcept
{
__h ^= __h >> 15;
__h *= __prime2;
__h ^= __h >> 13;
__h *= __prime3;
__h ^= __h >> 16;
return __h;
}
::cuda::std::uint32_t __seed_;
};
//! @brief A `XXHash_64` hash function to hash the given argument on host and device.
//!
//! @tparam _Key The type of the values to hash
template <typename _Key>
struct _XXHash_64
{
private:
static constexpr ::cuda::std::uint64_t __prime1 = 11400714785074694791ull;
static constexpr ::cuda::std::uint64_t __prime2 = 14029467366897019727ull;
static constexpr ::cuda::std::uint64_t __prime3 = 1609587929392839161ull;
static constexpr ::cuda::std::uint64_t __prime4 = 9650029242287828579ull;
static constexpr ::cuda::std::uint64_t __prime5 = 2870177450012600261ull;
public:
//! @brief Constructs a XXH64 hash function with the given `seed`.
//!
//! @param seed A custom number to randomize the resulting hash value
_CCCL_HOST_DEVICE_API constexpr _XXHash_64(::cuda::std::uint64_t __seed = 0)
: __seed_{__seed}
{}
//! @brief Returns a hash value for its argument, as a value of type `result_type`.
//!
//! @param _Key The input argument to hash
//! @return The resulting hash value for `key`
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint64_t operator()(const _Key& __key) const noexcept
{
if constexpr (sizeof(_Key) <= 16)
{
const _Key __copy{__key};
return __compute_hash_span(::cuda::std::span<const _Key, 1>{&__copy, 1});
}
else
{
return __compute_hash_span(::cuda::std::span<const _Key, 1>{&__key, 1});
}
}
//! @brief Returns a hash value for its argument, as a value of type `::cuda::std::uint64_t`.
//!
//! @tparam _Extent The extent type
//! @param __keys span of keys to hash
//! @return The resulting hash value
template <size_t _Extent>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint64_t
operator()(::cuda::std::span<_Key, _Extent> __keys) const noexcept
{
return __compute_hash_span(__keys);
}
private:
//! @brief Returns a hash value for its argument, as a value of type `::cuda::std::uint64_t`.
//!
//! @tparam _Extent The extent type
//! @param __keys span of keys to hash
//! @return The resulting hash value
[[nodiscard]] _CCCL_HOST_DEVICE_API ::cuda::std::uint64_t
__compute_hash_span(::cuda::std::span<const _Key> __keys) const noexcept
{
auto __bytes = ::cuda::std::as_bytes(__keys).data();
const auto __size = __keys.size_bytes();
size_t __offset = 0;
::cuda::std::uint64_t __h64 = {};
// process data in 32-byte chunks
if (__size >= 32)
{
const auto __limit = __size - 32;
::cuda::std::array<::cuda::std::uint64_t, 4> __v;
__v[0] = __seed_ + __prime1 + __prime2;
__v[1] = __seed_ + __prime2;
__v[2] = __seed_;
__v[3] = __seed_ - __prime1;
for (; __offset <= __limit; __offset += 32)
{
// pipeline 4*8byte computations
const auto __pipeline_offset = __offset / 8;
::cuda::static_for<4>([&](auto i) {
__v[i] += ::cuda::experimental::cuco::__load_chunk<::cuda::std::uint64_t>(__bytes, __pipeline_offset + i)
* __prime2;
__v[i] = ::cuda::std::rotl(__v[i], 31);
__v[i] *= __prime1;
});
}
__h64 = ::cuda::std::rotl(__v[0], 1) + ::cuda::std::rotl(__v[1], 7) + ::cuda::std::rotl(__v[2], 12)
+ ::cuda::std::rotl(__v[3], 18);
::cuda::static_for<4>([&](auto i) {
__v[i] *= __prime2;
__v[i] = ::cuda::std::rotl(__v[i], 31);
__v[i] *= __prime1;
__h64 ^= __v[i];
__h64 = __h64 * __prime1 + __prime4;
});
}
else
{
__h64 = __seed_ + __prime5;
}
__h64 += __size;
// remaining data can be processed in 8-byte chunks
if ((__size % 32) >= 8)
{
_CCCL_PRAGMA_UNROLL(4)
for (; __offset <= __size - 8; __offset += 8)
{
::cuda::std::uint64_t __k1 =
::cuda::experimental::cuco::__load_chunk<::cuda::std::uint64_t>(__bytes, __offset / 8) * __prime2;
__k1 = ::cuda::std::rotl(__k1, 31) * __prime1;
__h64 ^= __k1;
__h64 = ::cuda::std::rotl(__h64, 27) * __prime1 + __prime4;
}
}
// remaining data can be processed in 4-byte chunks
if ((__size % 8) >= 4)
{
for (; __offset <= __size - 4; __offset += 4)
{
__h64 ^= (::cuda::experimental::cuco::__load_chunk<::cuda::std::uint32_t>(__bytes, __offset / 4)) * __prime1;
__h64 = ::cuda::std::rotl(__h64, 23) * __prime2 + __prime3;
}
}
// the following loop is only needed if the size of the key is not a multiple of a previous
// block size
if (__size % 4)
{
while (__offset < __size)
{
__h64 ^= (::cuda::std::to_integer<::cuda::std::uint32_t>(__bytes[__offset])) * __prime5;
__h64 = ::cuda::std::rotl(__h64, 11) * __prime1;
++__offset;
}
}
return __finalize(__h64);
}
// avalanche helper
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint64_t __finalize(std::uint64_t __h) const noexcept
{
__h ^= __h >> 33;
__h *= __prime2;
__h ^= __h >> 29;
__h *= __prime3;
__h ^= __h >> 32;
return __h;
}
::cuda::std::uint64_t __seed_;
};
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_HASH_FUNCTIONS_XXHASH_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_HYPERLOGLOG_DEFAULT_POLICY_CUH
#define _CUDAX___CUCO_DETAIL_HYPERLOGLOG_DEFAULT_POLICY_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__bit/countl.h>
#include <cuda/std/__limits/numeric_limits.h>
#include <cuda/std/__type_traits/is_unsigned.h>
#include <cuda/std/__utility/declval.h>
#include <cuda/std/cstdint>
#include <cuda/experimental/__cuco/detail/hyperloglog/finalizer.cuh>
#include <cuda/experimental/__cuco/hash_functions.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief Default policy for `cuda::experimental::cuco::hyperloglog`.
//!
//! Bundles the three customization points of the HLL pipeline -- hash function, bit slicing,
//! and finalizer -- into a single policy. This default reproduces the behaviour shipped by
//! `cuCollections::hyperloglog`: MSB-indexed register selection, padded leading-zero count for
//! rho, and HyperLogLog++ bias correction. Custom policies (e.g. for binary interop with
//! third-party sketch libraries) can be supplied via the `_Policy` template parameter on
//! `hyperloglog` and `hyperloglog_ref`.
//!
//! @tparam _Key The item type the sketch counts.
//! @tparam _Algo The hash algorithm. Defaults to xxhash_64.
template <class _Key, hash_algorithm _Algo = hash_algorithm::xxhash_64>
struct default_hll_policy
{
using hasher = hash<_Key, _Algo>;
using hash_result_type = decltype(::cuda::std::declval<hasher>()(::cuda::std::declval<_Key>()));
using register_type = ::cuda::std::int32_t;
static_assert(::cuda::std::is_unsigned_v<hash_result_type>, "HyperLogLog requires an unsigned hash value type");
static_assert(::cuda::std::numeric_limits<hash_result_type>::digits == 32
|| ::cuda::std::numeric_limits<hash_result_type>::digits == 64,
"HyperLogLog requires a 32-bit or 64-bit hash value type");
hasher hasher_{};
//! @brief Returns the underlying hash functor.
//!
//! @return The hash functor.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr hasher hash_function() const noexcept
{
return hasher_;
}
//! @brief Hashes an item.
//!
//! @param[in] __k The item to hash.
//! @return The hash value of `__k`.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr hash_result_type hash(const _Key& __k) const noexcept
{
return hasher_(__k);
}
//! @brief Extracts the register index from the hash.
//!
//! @note Index is taken from the high `__precision` bits of the hash, matching Apache Spark's
//! HyperLogLog++ convention.
//!
//! @param[in] __h The hash value.
//! @param[in] __precision The HLL precision parameter.
//! @return The register index in `[0, 2^__precision)`.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint32_t
register_index(hash_result_type __h, ::cuda::std::int32_t __precision) const noexcept
{
constexpr auto __hash_bits = ::cuda::std::numeric_limits<hash_result_type>::digits;
return static_cast<::cuda::std::uint32_t>(__h >> (__hash_bits - __precision));
}
//! @brief Computes rho (1 + leading zeros of the rho source) from the hash.
//!
//! @note A one-bit padding bounds the leading-zero count at `hash_bits - __precision`,
//! preventing rho overflow when the low `hash_bits - __precision` bits of the hash are zero.
//!
//! @param[in] __h The hash value.
//! @param[in] __precision The HLL precision parameter.
//! @return rho, in `[1, hash_bits - __precision + 1]`.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint8_t
register_value(hash_result_type __h, ::cuda::std::int32_t __precision) const noexcept
{
const auto __w_padding = hash_result_type{1} << static_cast<hash_result_type>(__precision - 1);
return static_cast<::cuda::std::uint8_t>(::cuda::std::countl_zero((__h << __precision) | __w_padding) + 1);
}
//! @brief Finalizes the GPU reduction into a cardinality estimate using the HyperLogLog++
//! bias-corrected estimator.
//!
//! @param[in] __z Sum of `2^-register[i]` across all registers.
//! @param[in] __v Count of zero registers.
//! @param[in] __precision HLL precision parameter.
//! @return The bias-corrected cardinality estimate.
[[nodiscard]] static _CCCL_HOST_DEVICE_API constexpr double
finalize(double __z, ::cuda::std::int32_t __v, ::cuda::std::int32_t __precision) noexcept
{
return __hyperloglog_ns::hllpp_finalizer{__precision}(__z, __v);
}
};
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_HYPERLOGLOG_DEFAULT_POLICY_CUH

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@@ -1,189 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_HYPERLOGLOG_FINALIZER_CUH
#define _CUDAX___CUCO_DETAIL_HYPERLOGLOG_FINALIZER_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/in_range.h>
#include <cuda/std/__algorithm/max.h>
#include <cuda/std/__algorithm/min.h>
#include <cuda/std/__cmath/logarithms.h>
#include <cuda/std/__numeric/midpoint.h>
#include <cuda/std/cstdint>
#include <cuda/experimental/__cuco/detail/hyperloglog/tuning.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::__hyperloglog_ns
{
//! @brief Estimate correction algorithm based on HyperLogLog++.
//!
//! @note Variable names correspond to the definitions given in the HLL++ paper:
//! https://static.googleusercontent.com/media/research.google.com/de//pubs/archive/40671.pdf
//! @note Precision must be >= 4.
//!
class hllpp_finalizer
{
// Note: Most of the types in this implementation are explicit instead of relying on `auto` to
// avoid confusion with the reference implementation.
public:
//! @brief Constructs an HLL finalizer object.
//!
//! @param __precision_ HLL precision parameter
_CCCL_HOST_DEVICE_API constexpr hllpp_finalizer(::cuda::std::int32_t __precision_) noexcept
: __precision{__precision_}
, __m{static_cast<::cuda::std::int32_t>(1u << __precision_)}
{
_CCCL_ASSERT(::cuda::in_range(__precision_, 4, 18), "Precision must be between 4 and 18");
}
//! @brief Compute the bias-corrected cardinality estimate.
//!
//! @param __z Geometric mean of registers
//! @param __v Number of 0 registers
//!
//! @return Bias-corrected cardinality estimate
[[nodiscard]] _CCCL_HOST_DEVICE_API double operator()(double __z, ::cuda::std::int32_t __v) const noexcept
{
double __e = __alpha_mm() / __z;
if (__v > 0)
{
// Use linear counting for small cardinality estimates.
const double __h = __m * ::cuda::std::log(static_cast<double>(__m) / __v);
// The threshold `2.5 * m` is from the original HLL algorithm.
if (__e <= 2.5 * __m)
{
return __h;
}
if (__precision < 19)
{
__e = (__h <= __hyperloglog_ns::__threshold(__precision)) ? __h : __bias_corrected_estimate(__e);
}
}
else
{
// HLL++ is defined only when p < 19, otherwise we need to fallback to HLL.
if (__precision < 19)
{
__e = __bias_corrected_estimate(__e);
}
}
return __e;
}
private:
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr double __alpha_mm() const noexcept
{
const auto __m2 = static_cast<double>(__m) * __m;
switch (__m)
{
case 16:
return 0.673 * __m2;
case 32:
return 0.697 * __m2;
case 64:
return 0.709 * __m2;
default:
return (0.7213 / (1.0 + 1.079 / __m)) * __m2;
}
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr double __bias_corrected_estimate(double __e) const noexcept
{
return (__e < 5.0 * __m) ? __e - __bias(__e) : __e;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr double __bias(double __e) const noexcept
{
const auto __anchor_index = __interpolation_anchor_index(__e);
const auto __n = static_cast<::cuda::std::int32_t>(__hyperloglog_ns::__raw_estimate_data_size(__precision));
auto __low = ::cuda::std::max(__anchor_index - __k + 1, ::cuda::std::int32_t{0});
auto __high = ::cuda::std::min(__low + __k, __n);
// Keep moving bounds as long as the (exclusive) high bound is closer to the estimate than
// the lower (inclusive) bound.
while (__high < __n && __distance(__e, __high) < __distance(__e, __low))
{
__low += 1;
__high += 1;
}
const auto __biases = __hyperloglog_ns::__bias_data(__precision);
double __bias_sum = 0.0;
for (::cuda::std::int32_t __i = __low; __i < __high; ++__i)
{
__bias_sum += __biases[__i];
}
return __bias_sum / (__high - __low);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr double __distance(double __e, ::cuda::std::int32_t __i) const noexcept
{
const auto __diff = __e - __hyperloglog_ns::__raw_estimate_data(__precision)[__i];
return __diff * __diff;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::int32_t
__interpolation_anchor_index(double __e) const noexcept
{
const auto __estimates = __hyperloglog_ns::__raw_estimate_data(__precision);
const auto __n = static_cast<::cuda::std::int32_t>(__hyperloglog_ns::__raw_estimate_data_size(__precision));
::cuda::std::int32_t __left = 0;
::cuda::std::int32_t __right = __n - 1;
while (__left <= __right)
{
const ::cuda::std::int32_t __mid = ::cuda::std::midpoint(__left, __right);
if (__estimates[__mid] < __e)
{
__left = __mid + 1;
}
else if (__estimates[__mid] > __e)
{
__right = __mid - 1;
}
else
{
// Exact match found, no need to look further
return __mid;
}
}
// At this point, '__left' is the binary-search insertion point. Spark uses the insertion
// point as the anchor index when the exact estimate is not present in the table.
return __left;
}
static constexpr ::cuda::std::int32_t __k = 6; ///< Number of interpolation points to consider
::cuda::std::int32_t __precision; ///< HLL precision parameter
::cuda::std::int32_t __m; ///< Number of registers (2^precision)
};
} // namespace cuda::experimental::cuco::__hyperloglog_ns
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_HYPERLOGLOG_FINALIZER_CUH

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cccl_upstream/cudax/include/cuda/experimental/__cuco/detail/hyperloglog/hyperloglog_impl.cuh
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@@ -1,618 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_HYPERLOGLOG_IMPL_CUH
#define _CUDAX___CUCO_DETAIL_HYPERLOGLOG_IMPL_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__container/buffer.h>
#include <cuda/__memory/is_aligned.h>
#include <cuda/__memory_resource/legacy_pinned_memory_resource.h>
#include <cuda/__runtime/api_wrapper.h>
#include <cuda/__stream/stream_ref.h>
#include <cuda/__utility/in_range.h>
#include <cuda/atomic>
#include <cuda/std/__algorithm/max.h>
#include <cuda/std/__bit/countr.h>
#include <cuda/std/__bit/integral.h>
#include <cuda/std/__cmath/rounding_functions.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__host_stdlib/stdexcept>
#include <cuda/std/__iterator/concepts.h>
#include <cuda/std/__memory/pointer_traits.h>
#include <cuda/std/span>
#include <cuda/experimental/__cuco/detail/hyperloglog/finalizer.cuh>
#include <cuda/experimental/__cuco/detail/hyperloglog/kernels.cuh>
#include <cuda/experimental/__cuco/detail/utility/strong_type.cuh>
#include <cuda/experimental/__cuco/hash_functions.cuh>
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
CUDAX_CUCO_DEFINE_STRONG_TYPE(__sketch_size_kb_t, double);
CUDAX_CUCO_DEFINE_STRONG_TYPE(__standard_deviation_t, double);
CUDAX_CUCO_DEFINE_STRONG_TYPE(__precision_t, ::cuda::std::int32_t);
//! @brief A GPU-accelerated utility for approximating the number of distinct items in a multiset.
//!
//! @note This class implements the HyperLogLog/HyperLogLog++ algorithm:
//! https://static.googleusercontent.com/media/research.google.com/de//pubs/archive/40671.pdf.
//!
//! @tparam _Tp Type of items to count
//! @tparam _Scope The scope in which operations will be performed by individual threads
//! @tparam _Policy Policy bundling hash function, bit-slicing rule, and finalizer
template <class _Tp, ::cuda::thread_scope _Scope, class _Policy>
class __hyperloglog_impl
{
using __fp_type = double; ///< Floating point type used for reduction
public:
using __value_type = _Tp; ///< Type of items to count
using __policy_type = _Policy; ///< Policy type
using __hasher = typename _Policy::hasher; ///< Hash function type
using __register_type = typename _Policy::register_type; ///< HLL register type
private:
_Policy __policy; ///< Policy used to hash items, slice the hash, and finalize the estimate
::cuda::std::int32_t __precision; ///< HLL precision parameter
::cuda::std::span<__register_type> __sketch; ///< HLL sketch storage
template <class _Tp_, ::cuda::thread_scope _Scope_, class _Policy_>
friend struct __hyperloglog_impl;
public:
static constexpr auto __thread_scope = _Scope; ///< CUDA thread scope
template <::cuda::thread_scope _NewScope>
using __rebind_scope = __hyperloglog_impl<_Tp, _NewScope, _Policy>; ///< Ref type with different thread scope
//! @brief Constructs a non-owning `__hyperloglog_impl` object.
//!
//! @throw If sketch size < 0.0625KB or 64B or standard deviation > 0.2765. Throws if called from
//! host; __trap() if called from device.
//! @throw If sketch size implies precision outside [4, 18]. Throws if called from host; __trap() if
//! called from device.
//! @throw If sketch storage has insufficient alignment. Throws if called from host; __trap() if called from device.
//!
//! @param __sketch_span Reference to sketch storage
//! @param __policy The policy used to hash items and finalize the estimate
_CCCL_HOST_DEVICE_API constexpr __hyperloglog_impl(::cuda::std::span<::cuda::std::byte> __sketch_span,
const _Policy& __policy)
: __policy{__policy}
, __precision{::cuda::std::countr_zero(
__sketch_bytes(static_cast<::cuda::experimental::cuco::__sketch_size_kb_t>(__sketch_span.size() / 1024.0))
/ sizeof(__register_type))}
, __sketch{reinterpret_cast<int*>(__sketch_span.data()), __sketch_bytes() / sizeof(__register_type)}
// MSVC fails with __register_type*, use int* instead
{
constexpr ::cuda::std::size_t __minimum_sketch_bytes = sizeof(__register_type) * (1ull << 4);
if (__sketch_span.size() < __minimum_sketch_bytes)
{
_CCCL_THROW(::std::invalid_argument, "Minimum required sketch size is 0.0625KB or 64B");
}
if (!::cuda::is_aligned(__sketch_span.data(), __sketch_alignment()))
{
_CCCL_THROW(::std::invalid_argument, "Sketch storage has insufficient alignment");
}
if (!::cuda::in_range(__precision, 4, 18))
{
_CCCL_THROW(::std::invalid_argument, "Minimum required sketch size is 0.0625KB or 64B");
}
}
//! @brief Resets the estimator, i.e., clears the current count estimate.
//!
//! @tparam _CG CUDA Cooperative Group type
//!
//! @param __group CUDA Cooperative group this operation is executed in
template <class _CG>
_CCCL_DEVICE_API constexpr void __clear(_CG __group) noexcept
{
for (int __i = __group.thread_rank(); __i < __sketch.size(); __i += __group.size())
{
__sketch[__i] = 0;
}
}
//! @brief Resets the estimator, i.e., clears the current count estimate.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `__clear_async`.
//!
//! @param __stream CUDA stream this operation is executed in
_CCCL_HOST_API constexpr void __clear(::cuda::stream_ref __stream)
{
__clear_async(__stream);
__stream.sync();
}
//! @brief Asynchronously resets the estimator, i.e., clears the current count estimate.
//!
//! @param __stream CUDA stream this operation is executed in
_CCCL_HOST_API constexpr void __clear_async(::cuda::stream_ref __stream)
{
constexpr auto __block_size = 1024;
::cuda::experimental::cuco::__hyperloglog_ns::__clear<<<1, __block_size, 0, __stream.get()>>>(*this);
}
//! @brief Adds an item to the estimator.
//!
//! @note Hash, register index, and rho are determined by the active policy.
//!
//! @param __item The item to be counted
_CCCL_DEVICE_API constexpr void __add(const _Tp& __item) noexcept
{
const auto __h = __policy.hash(__item);
__update_max(__policy.register_index(__h, __precision), __policy.register_value(__h, __precision));
}
//! @brief Asynchronously adds to be counted items to the estimator.
//!
//! @tparam _InputIt Device accessible random access input iterator where
//! <tt>std::is_convertible<std::iterator_traits<_InputIt>::value_type,
//! _Tp></tt> is `true`
//!
//! @param __first Beginning of the sequence of items
//! @param __last End of the sequence of items
//! @param __stream CUDA stream this operation is executed in
template <class _InputIt>
_CCCL_HOST_API constexpr void __add_async(_InputIt __first, _InputIt __last, ::cuda::stream_ref __stream)
{
const auto __num_items = ::cuda::std::distance(__first, __last);
if (__num_items == 0)
{
return;
}
int __grid_size = 0;
int __block_size = 0;
const int __shmem_bytes = __sketch_bytes();
const void* __kernel = nullptr;
// In case the input iterator represents a contiguous memory segment we can employ efficient
// vectorized loads
if constexpr (::cuda::std::contiguous_iterator<_InputIt>)
{
const auto __ptr = ::cuda::std::to_address(__first);
constexpr auto __max_vector_bytes = 32;
const auto __alignment =
1u << ::cuda::std::countr_zero(reinterpret_cast<::cuda::std::uintptr_t>(__ptr) | __max_vector_bytes);
const auto __vector_size = __alignment / sizeof(__value_type);
switch (__vector_size)
{
using ::cuda::experimental::cuco::__hyperloglog_ns::__add_shmem_vectorized;
case 2:
__kernel = reinterpret_cast<const void*>(__add_shmem_vectorized<2, __hyperloglog_impl>);
break;
case 4:
__kernel = reinterpret_cast<const void*>(__add_shmem_vectorized<4, __hyperloglog_impl>);
break;
case 8:
__kernel = reinterpret_cast<const void*>(__add_shmem_vectorized<8, __hyperloglog_impl>);
break;
case 16:
__kernel = reinterpret_cast<const void*>(__add_shmem_vectorized<16, __hyperloglog_impl>);
break;
};
}
if (__kernel != nullptr && __try_reserve_shmem(__kernel, __shmem_bytes))
{
if constexpr (::cuda::std::contiguous_iterator<_InputIt>)
{
// We make use of the occupancy calculator to get the minimum number of blocks which still
// saturates the GPU. This reduces the shmem initialization overhead and atomic contention
// on the final register array during the merge phase.
_CCCL_TRY_CUDA_API(
::cudaOccupancyMaxPotentialBlockSize,
"cudaOccupancyMaxPotentialBlockSize failed",
&__grid_size,
&__block_size,
__kernel,
__shmem_bytes);
const auto __ptr = ::cuda::std::to_address(__first);
void* __kernel_args[] = {const_cast<void*>(reinterpret_cast<const void*>(&__ptr)),
const_cast<void*>(reinterpret_cast<const void*>(&__num_items)),
reinterpret_cast<void*>(this)};
_CCCL_TRY_CUDA_API(
::cudaLaunchKernel,
"cudaLaunchKernel failed",
__kernel,
__grid_size,
__block_size,
__kernel_args,
__shmem_bytes,
__stream.get());
}
}
else
{
__kernel = reinterpret_cast<const void*>(
::cuda::experimental::cuco::__hyperloglog_ns::__add_shmem<_InputIt, __hyperloglog_impl>);
void* __kernel_args[] = {const_cast<void*>(reinterpret_cast<const void*>(&__first)),
const_cast<void*>(reinterpret_cast<const void*>(&__num_items)),
reinterpret_cast<void*>(this)};
if (__try_reserve_shmem(__kernel, __shmem_bytes))
{
_CCCL_TRY_CUDA_API(
::cudaOccupancyMaxPotentialBlockSize,
"cudaOccupancyMaxPotentialBlockSize failed",
&__grid_size,
&__block_size,
__kernel,
__shmem_bytes);
_CCCL_TRY_CUDA_API(
::cudaLaunchKernel,
"cudaLaunchKernel failed",
__kernel,
__grid_size,
__block_size,
__kernel_args,
__shmem_bytes,
__stream.get());
}
else
{
// Computes sketch directly in global memory. (Fallback path in case there is not enough
// shared memory available)
__kernel = reinterpret_cast<const void*>(
::cuda::experimental::cuco::__hyperloglog_ns::__add_gmem<_InputIt, __hyperloglog_impl>);
_CCCL_TRY_CUDA_API(
::cudaOccupancyMaxPotentialBlockSize,
"cudaOccupancyMaxPotentialBlockSize failed",
&__grid_size,
&__block_size,
__kernel,
0);
_CCCL_TRY_CUDA_API(
::cudaLaunchKernel,
"cudaLaunchKernel failed",
__kernel,
__grid_size,
__block_size,
__kernel_args,
0,
__stream.get());
}
}
}
//! @brief Adds to be counted items to the estimator.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `__add_async`.
//!
//! @tparam _InputIt Device accessible random access input iterator where
//! <tt>std::is_convertible<std::iterator_traits<_InputIt>::value_type,
//! _Tp></tt> is `true`
//!
//! @param __first Beginning of the sequence of items
//! @param __last End of the sequence of items
//! @param __stream CUDA stream this operation is executed in
template <class _InputIt>
_CCCL_HOST_API constexpr void __add(_InputIt __first, _InputIt __last, ::cuda::stream_ref __stream)
{
__add_async(__first, __last, __stream);
__stream.sync();
}
//! @brief Merges the result of `other` estimator reference into `*this` estimator reference.
//!
//! @throw If __sketch_bytes() != other.__sketch_bytes(), then terminates execution with a device __trap()
//!
//! @tparam _CG CUDA Cooperative Group type
//! @tparam _OtherScope Thread scope of `other` estimator
//!
//! @param __group CUDA Cooperative group this operation is executed in
//! @param __other Other estimator reference to be merged into `*this`
template <class _CG, ::cuda::thread_scope _OtherScope>
_CCCL_DEVICE_API constexpr void __merge(_CG __group, const __hyperloglog_impl<_Tp, _OtherScope, _Policy>& __other)
{
if (__other.__precision != __precision)
{
_CCCL_THROW(::std::invalid_argument, "Cannot merge estimators with different sketch sizes");
}
for (int __i = __group.thread_rank(); __i < __sketch.size(); __i += __group.size())
{
__update_max(__i, __other.__sketch[__i]);
}
}
//! @brief Asynchronously merges the result of `other` estimator reference into `*this`
//! estimator.
//!
//! @throw If __sketch_bytes() != __other.__sketch_bytes(), then terminates execution with a device __trap()
//!
//! @tparam _OtherScope Thread scope of `other` estimator
//!
//! @param __other Other estimator reference to be merged into `*this`
//! @param __stream CUDA stream this operation is executed in
template <::cuda::thread_scope _OtherScope>
_CCCL_HOST_API constexpr void
__merge_async(const __hyperloglog_impl<_Tp, _OtherScope, _Policy>& __other, ::cuda::stream_ref __stream)
{
if (__other.__precision != __precision)
{
_CCCL_THROW(::std::invalid_argument, "Cannot merge estimators with different sketch sizes");
}
constexpr auto __block_size = 1024;
::cuda::experimental::cuco::__hyperloglog_ns::__merge<<<1, __block_size, 0, __stream.get()>>>(__other, *this);
}
//! @brief Merges the result of `other` estimator reference into `*this` estimator.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `__merge_async`.
//!
//! @throw If __sketch_bytes() != __other.__sketch_bytes(), then terminates execution with a device __trap()
//!
//! @tparam _OtherScope Thread scope of `other` estimator
//!
//! @param __other Other estimator reference to be merged into `*this`
//! @param __stream CUDA stream this operation is executed in
template <::cuda::thread_scope _OtherScope>
_CCCL_HOST_API constexpr void
__merge(const __hyperloglog_impl<_Tp, _OtherScope, _Policy>& __other, ::cuda::stream_ref __stream)
{
__merge_async(__other, __stream);
__stream.sync();
}
//! @brief Compute the estimated distinct items count.
//!
//! @param __group CUDA thread block group this operation is executed in
//!
//! @return Approximate distinct items count
[[nodiscard]] _CCCL_DEVICE_API double __estimate(const ::cooperative_groups::thread_block& __group) const noexcept
{
__shared__ ::cuda::atomic<__fp_type, ::cuda::std::thread_scope_block> __block_sum;
__shared__ ::cuda::atomic<::cuda::std::int32_t, ::cuda::std::thread_scope_block> __block_zeroes;
__shared__ __fp_type __estimate;
if (__group.thread_rank() == 0)
{
__block_sum.store(0);
__block_zeroes.store(0);
}
__group.sync();
__fp_type __thread_sum = 0;
::cuda::std::int32_t __thread_zeroes = 0;
for (int __i = __group.thread_rank(); __i < __sketch.size(); __i += __group.size())
{
const auto __reg = __sketch[__i];
__thread_sum += __fp_type{1} / static_cast<__fp_type>(1ull << __reg);
__thread_zeroes += __reg == 0;
}
// warp reduce Z and V
const auto __warp = ::cooperative_groups::tiled_partition<32, ::cooperative_groups::thread_block>(__group);
::cooperative_groups::reduce_update_async(
__warp, __block_sum, __thread_sum, ::cooperative_groups::plus<__fp_type>());
::cooperative_groups::reduce_update_async(
__warp, __block_zeroes, __thread_zeroes, ::cooperative_groups::plus<::cuda::std::int32_t>());
__group.sync();
if (__group.thread_rank() == 0)
{
const auto __z = __block_sum.load(::cuda::std::memory_order_relaxed);
const auto __v = __block_zeroes.load(::cuda::std::memory_order_relaxed);
__estimate = _Policy::finalize(__z, __v, __precision);
}
__group.sync();
return __estimate;
}
//! @brief Compute the estimated distinct items count.
//!
//! @note This function synchronizes the given stream.
//!
//! @tparam _HostMemoryResource Host memory resource used for allocating the host buffer required to
//! compute the final estimate by copying the sketch from device to host
//!
//! @param __host_mr Host memory resource used for copying the sketch
//! @param __stream CUDA stream this operation is executed in
//!
//! @return Approximate distinct items count
template <typename _HostMemoryResource>
[[nodiscard]] _CCCL_HOST_API double __estimate(_HostMemoryResource __host_mr, ::cuda::stream_ref __stream) const
{
const auto __num_regs = __sketch.size();
::cuda::host_buffer<__register_type> __host_sketch_buf{__stream, __host_mr, __sketch.size(), ::cuda::no_init};
::cuda::__driver::__memcpyAsync(
__host_sketch_buf.data(), __sketch.data(), sizeof(__register_type) * __num_regs, __stream.get());
__stream.sync();
__fp_type __sum = 0;
::cuda::std::int32_t __zeroes = 0;
// geometric mean computation + count registers with 0s
for (const auto __reg : __host_sketch_buf)
{
__sum += __fp_type{1} / static_cast<__fp_type>(1ull << __reg);
__zeroes += __reg == 0;
}
// dispatch to the policy's finalizer for bias correction, etc.
return _Policy::finalize(__sum, __zeroes, __precision);
}
// #endif
//! @brief Gets the hash function.
//!
//! @return The hash function, as exposed by the policy via `hash_function()`.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto __hash_function() const noexcept
{
return __policy.hash_function();
}
//! @brief Gets the policy.
//!
//! @return The policy
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr const _Policy& __policy_() const noexcept
{
return __policy;
}
//! @brief Gets the span of the sketch.
//!
//! @return The ::cuda::std::span of the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::span<::cuda::std::byte> __sketch_span() const noexcept
{
return ::cuda::std::span<::cuda::std::byte>(reinterpret_cast<::cuda::std::byte*>(__sketch.data()), __sketch_bytes());
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::size_t __sketch_bytes() const noexcept
{
return (1ull << __precision) * sizeof(__register_type);
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param sketch_size_kb Upper bound sketch size in KB
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t
__sketch_bytes(::cuda::experimental::cuco::__sketch_size_kb_t __sketch_size_kb) noexcept
{
// minimum precision is 4 or 64 bytes
return ::cuda::std::max(static_cast<::cuda::std::size_t>(sizeof(__register_type) * (1ull << 4)),
::cuda::std::bit_floor(static_cast<::cuda::std::size_t>(__sketch_size_kb * 1024)));
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param __standard_deviation Upper bound standard deviation for approximation error
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t
sketch_bytes(::cuda::experimental::cuco::__standard_deviation_t __standard_deviation) noexcept
{
// implementation taken from
// https://github.com/apache/spark/blob/6a27789ad7d59cd133653a49be0bb49729542abe/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/HyperLogLogPlusPlusHelper.scala#L43
const auto __precision_from_sd =
static_cast<::cuda::std::int32_t>(::cuda::std::ceil(2.0 * ::cuda::std::log2(1.106 / __standard_deviation)));
// minimum precision is 4 or 64 bytes
const auto __precision_ = ::cuda::std::max(::cuda::std::int32_t{4}, __precision_from_sd);
// inverse of this function (omitting the minimum precision constraint) is
// standard_deviation = 1.106 / exp((__precision_ * log(2.0)) / 2.0)
return sizeof(__register_type) * (1ull << __precision_);
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param __precision HyperLogLog precision parameter
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t
sketch_bytes(::cuda::experimental::cuco::__precision_t __precision) noexcept
{
const auto __precision_value = static_cast<::cuda::std::int32_t>(__precision);
return sizeof(__register_type) * (1ull << __precision_value);
}
//! @brief Gets the alignment required for the sketch storage.
//!
//! @return The required alignment
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t __sketch_alignment() noexcept
{
return alignof(__register_type);
}
private:
//! @brief Atomically updates the register at position `i` with `max(reg[i], value)`.
//!
//! @param __i Register index
//! @param __value New value
_CCCL_DEVICE_API constexpr void __update_max(int __i, __register_type __value) noexcept
{
::cuda::atomic_ref<__register_type, _Scope> __register_ref(__sketch[__i]);
__register_ref.fetch_max(__value, ::cuda::memory_order_relaxed);
}
//! @brief Try expanding the shmem partition for a given kernel beyond 48KB if necessary.
//!
//! @tparam _Kernel Type of kernel function
//!
//! @param __kernel The kernel function
//! @param __shmem_bytes Number of requested dynamic shared memory bytes
//!
//! @returns True iff kernel configuration is successful
template <typename _Kernel>
[[nodiscard]] _CCCL_HOST_API constexpr bool __try_reserve_shmem(_Kernel __kernel, int __shmem_bytes) const
{
int __device = -1;
_CCCL_TRY_CUDA_API(::cudaGetDevice, "cudaGetDevice failed", &__device);
int __max_shmem_bytes = 0;
_CCCL_TRY_CUDA_API(
::cudaDeviceGetAttribute,
"cudaDeviceGetAttribute failed",
&__max_shmem_bytes,
::cudaDevAttrMaxSharedMemoryPerBlockOptin,
__device);
if (__shmem_bytes <= __max_shmem_bytes)
{
_CCCL_TRY_CUDA_API(
::cudaFuncSetAttribute,
"cudaFuncSetAttribute failed",
reinterpret_cast<const void*>(__kernel),
cudaFuncAttributeMaxDynamicSharedMemorySize,
__shmem_bytes);
return true;
}
else
{
return false;
}
}
};
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_HYPERLOGLOG_IMPL_CUH

182
cccl_upstream/cudax/include/cuda/experimental/__cuco/detail/hyperloglog/kernels.cuh
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@@ -1,182 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_HYPERLOGLOG_KERNELS_CUH
#define _CUDAX___CUCO_DETAIL_HYPERLOGLOG_KERNELS_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__memory/assume_aligned.h>
#include <cuda/std/array>
#include <cuda/std/cstdint>
#include <cuda/std/span>
#include <cooperative_groups.h>
#include <cuda/std/__cccl/prologue.h>
#if _CCCL_CUDA_COMPILATION()
_CCCL_DIAG_PUSH
_CCCL_DIAG_SUPPRESS_GCC("-Wattributes")
namespace cuda::experimental::cuco::__hyperloglog_ns
{
//! @brief Returns the global thread ID in a 1D grid
//!
//! @return The global thread ID
[[nodiscard]] _CCCL_DEVICE_API inline ::cuda::std::int64_t __global_thread_id() noexcept
{
return static_cast<::cuda::std::int64_t>(blockDim.x) * blockIdx.x + threadIdx.x;
}
//! @brief Returns the grid stride of a 1D grid
//!
//! @return The grid stride
[[nodiscard]] _CCCL_DEVICE_API inline ::cuda::std::int64_t __grid_stride() noexcept
{
return static_cast<::cuda::std::int64_t>(gridDim.x) * blockDim.x;
}
template <class _RefType>
_CCCL_KERNEL_ATTRIBUTES void __clear(_RefType __ref)
{
const auto __block = ::cooperative_groups::this_thread_block();
if (__block.group_index().x == 0)
{
__ref.__clear(__block);
}
}
template <int _VectorSize, class _RefType>
_CCCL_KERNEL_ATTRIBUTES void
__add_shmem_vectorized(const typename _RefType::__value_type* __first, ::cuda::std::int64_t __n, _RefType __ref)
{
using __value_type = typename _RefType::__value_type;
// TODO: replace with ::cuda::__vector_type
using __vector_type = ::cuda::std::array<__value_type, _VectorSize>;
using __local_ref_type = typename _RefType::template __rebind_scope<::cuda::std::thread_scope_block>;
// Base address of dynamic shared memory is guaranteed to be aligned to at least 16 bytes which is
// sufficient for this purpose
extern __shared__ ::cuda::std::byte __local_sketch[];
const auto __loop_stride = __grid_stride();
auto __idx = __global_thread_id();
const auto __grid = ::cooperative_groups::this_grid();
const auto __block = ::cooperative_groups::this_thread_block();
__local_ref_type __local_ref(::cuda::std::span{__local_sketch, __ref.__sketch_bytes()}, {});
__local_ref.__clear(__block);
__block.sync();
// each thread processes VectorSize-many items per iteration
__vector_type __vec;
while (__idx < __n / _VectorSize)
{
__vec = *reinterpret_cast<const __vector_type*>(
::cuda::std::assume_aligned<sizeof(__vector_type)>(__first + __idx * _VectorSize));
for (int i = 0; i < _VectorSize; ++i)
{
__local_ref.__add(__vec[i]);
}
__idx += __loop_stride;
}
// a single thread processes the remaining items
# if _CCCL_CTK_AT_LEAST(12, 1)
::cooperative_groups::invoke_one(__grid, [&]() {
const auto __remainder = __n % _VectorSize;
for (int __i = 0; __i < __remainder; ++__i)
{
__local_ref.__add(*(__first + __n - __i - 1));
}
});
# else // ^^^ _CCCL_CTK_AT_LEAST(12, 1) ^^^ / vvv _CCCL_CTK_BELOW(12, 1) vvv
if (__grid.thread_rank() == 0)
{
const auto __remainder = __n % _VectorSize;
for (int __i = 0; __i < __remainder; ++__i)
{
__local_ref.__add(*(__first + __n - __i - 1));
}
}
# endif // ^^^ _CCCL_CTK_BELOW(12, 1) ^^^
__block.sync();
__ref.__merge(__block, __local_ref);
}
template <class _InputIt, class _RefType>
_CCCL_KERNEL_ATTRIBUTES void __add_shmem(_InputIt __first, ::cuda::std::int64_t __n, _RefType __ref)
{
using __local_ref_type = typename _RefType::template __rebind_scope<::cuda::std::thread_scope_block>;
// TODO assert alignment
extern __shared__ ::cuda::std::byte __local_sketch[];
const auto __loop_stride = __grid_stride();
auto __idx = __global_thread_id();
const auto __block = ::cooperative_groups::this_thread_block();
__local_ref_type __local_ref(::cuda::std::span{__local_sketch, __ref.__sketch_bytes()}, {});
__local_ref.__clear(__block);
__block.sync();
while (__idx < __n)
{
__local_ref.__add(*(__first + __idx));
__idx += __loop_stride;
}
__block.sync();
__ref.__merge(__block, __local_ref);
}
template <class _InputIt, class _RefType>
_CCCL_KERNEL_ATTRIBUTES void __add_gmem(_InputIt __first, ::cuda::std::int64_t __n, _RefType __ref)
{
const auto __loop_stride = __grid_stride();
auto __idx = __global_thread_id();
while (__idx < __n)
{
__ref.__add(*(__first + __idx));
__idx += __loop_stride;
}
}
template <class _OtherRefType, class _RefType>
_CCCL_KERNEL_ATTRIBUTES void __merge(_OtherRefType __other_ref, _RefType __ref)
{
const auto __block = ::cooperative_groups::this_thread_block();
if (__block.group_index().x == 0)
{
__ref.__merge(__block, __other_ref);
}
}
} // namespace cuda::experimental::cuco::__hyperloglog_ns
_CCCL_DIAG_POP
#endif // _CCCL_CUDA_COMPILATION()
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_HYPERLOGLOG_KERNELS_CUH

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@@ -1,166 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_HYPERLOGLOG_TUNING_CUH
#define _CUDAX___CUCO_DETAIL_HYPERLOGLOG_TUNING_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/cstdint>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::__hyperloglog_ns
{
#ifndef _CUDAX_CUCO_HLL_TUNING_ARR_DECL
# if _CCCL_OS(WINDOWS)
# define _CUDAX_CUCO_HLL_TUNING_ARR_DECL _CCCL_GLOBAL_CONSTANT double
# else
# define _CUDAX_CUCO_HLL_TUNING_ARR_DECL _CCCL_DEVICE inline constexpr double
# endif
#endif
// clang-format off
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __threshold_data[] = {10.0, 20.0, 40.0, 80.0, 220.0, 400.0, 900.0, 1800.0, 3100.0, 6500.0, 15500.0, 20000.0, 50000.0, 120000.0, 350000.0};
//! @brief Get threshold value for a given precision
//!
//! @param __precision The precision value (4-18)
//! @return The threshold value for the given precision
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto __threshold(::cuda::std::int32_t __precision) noexcept {
return __threshold_data[__precision - 4];
}
// HLL++ uses an interpolation method over the raw estimated cardinality to select the optimal bias.
// Parameters/interpolation points taken from
// https://docs.google.com/document/d/1gyjfMHy43U9OWBXxfaeG-3MjGzejW1dlpyMwEYAAWEI/mobilebasic
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p4[] = {11.0, 11.717, 12.207, 12.7896, 13.2882, 13.8204, 14.3772, 14.9342, 15.5202, 16.161, 16.7722, 17.4636, 18.0396, 18.6766, 19.3566, 20.0454, 20.7936, 21.4856, 22.2666, 22.9946, 23.766, 24.4692, 25.3638, 26.0764, 26.7864, 27.7602, 28.4814, 29.433, 30.2926, 31.0664, 31.9996, 32.7956, 33.5366, 34.5894, 35.5738, 36.2698, 37.3682, 38.0544, 39.2342, 40.0108, 40.7966, 41.9298, 42.8704, 43.6358, 44.5194, 45.773, 46.6772, 47.6174, 48.4888, 49.3304, 50.2506, 51.4996, 52.3824, 53.3078, 54.3984, 55.5838, 56.6618, 57.2174, 58.3514, 59.0802, 60.1482, 61.0376, 62.3598, 62.8078, 63.9744, 64.914, 65.781, 67.1806, 68.0594, 68.8446, 69.7928, 70.8248, 71.8324, 72.8598, 73.6246, 74.7014, 75.393, 76.6708, 77.2394};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p5[] = {23.0, 23.1194, 23.8208, 24.2318, 24.77, 25.2436, 25.7774, 26.2848, 26.8224, 27.3742, 27.9336, 28.503, 29.0494, 29.6292, 30.2124, 30.798, 31.367, 31.9728, 32.5944, 33.217, 33.8438, 34.3696, 35.0956, 35.7044, 36.324, 37.0668, 37.6698, 38.3644, 39.049, 39.6918, 40.4146, 41.082, 41.687, 42.5398, 43.2462, 43.857, 44.6606, 45.4168, 46.1248, 46.9222, 47.6804, 48.447, 49.3454, 49.9594, 50.7636, 51.5776, 52.331, 53.19, 53.9676, 54.7564, 55.5314, 56.4442, 57.3708, 57.9774, 58.9624, 59.8796, 60.755, 61.472, 62.2076, 63.1024, 63.8908, 64.7338, 65.7728, 66.629, 67.413, 68.3266, 69.1524, 70.2642, 71.1806, 72.0566, 72.9192, 73.7598, 74.3516, 75.5802, 76.4386, 77.4916, 78.1524, 79.1892, 79.8414, 80.8798, 81.8376, 82.4698, 83.7656, 84.331, 85.5914, 86.6012, 87.7016, 88.5582, 89.3394, 90.3544, 91.4912, 92.308, 93.3552, 93.9746, 95.2052, 95.727, 97.1322, 98.3944, 98.7588, 100.242, 101.1914, 102.2538, 102.8776, 103.6292, 105.1932, 105.9152, 107.0868, 107.6728, 108.7144, 110.3114, 110.8716, 111.245, 112.7908, 113.7064, 114.636, 115.7464, 116.1788, 117.7464, 118.4896, 119.6166, 120.5082, 121.7798, 122.9028, 123.4426, 124.8854, 125.705, 126.4652, 128.3464, 128.3462, 130.0398, 131.0342, 131.0042, 132.4766, 133.511, 134.7252, 135.425, 136.5172, 138.0572, 138.6694, 139.3712, 140.8598, 141.4594, 142.554, 143.4006, 144.7374, 146.1634, 146.8994, 147.605, 147.9304, 149.1636, 150.2468, 151.5876, 152.2096, 153.7032, 154.7146, 155.807, 156.9228, 157.0372, 158.5852};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p6[] = {46.0, 46.1902, 47.271, 47.8358, 48.8142, 49.2854, 50.317, 51.354, 51.8924, 52.9436, 53.4596, 54.5262, 55.6248, 56.1574, 57.2822, 57.837, 58.9636, 60.074, 60.7042, 61.7976, 62.4772, 63.6564, 64.7942, 65.5004, 66.686, 67.291, 68.5672, 69.8556, 70.4982, 71.8204, 72.4252, 73.7744, 75.0786, 75.8344, 77.0294, 77.8098, 79.0794, 80.5732, 81.1878, 82.5648, 83.2902, 84.6784, 85.3352, 86.8946, 88.3712, 89.0852, 90.499, 91.2686, 92.6844, 94.2234, 94.9732, 96.3356, 97.2286, 98.7262, 100.3284, 101.1048, 102.5962, 103.3562, 105.1272, 106.4184, 107.4974, 109.0822, 109.856, 111.48, 113.2834, 114.0208, 115.637, 116.5174, 118.0576, 119.7476, 120.427, 122.1326, 123.2372, 125.2788, 126.6776, 127.7926, 129.1952, 129.9564, 131.6454, 133.87, 134.5428, 136.2, 137.0294, 138.6278, 139.6782, 141.792, 143.3516, 144.2832, 146.0394, 147.0748, 148.4912, 150.849, 151.696, 153.5404, 154.073, 156.3714, 157.7216, 158.7328, 160.4208, 161.4184, 163.9424, 165.2772, 166.411, 168.1308, 168.769, 170.9258, 172.6828, 173.7502, 175.706, 176.3886, 179.0186, 180.4518, 181.927, 183.4172, 184.4114, 186.033, 188.5124, 189.5564, 191.6008, 192.4172, 193.8044, 194.997, 197.4548, 198.8948, 200.2346, 202.3086, 203.1548, 204.8842, 206.6508, 206.6772, 209.7254, 210.4752, 212.7228, 214.6614, 215.1676, 217.793, 218.0006, 219.9052, 221.66, 223.5588, 225.1636, 225.6882, 227.7126, 229.4502, 231.1978, 232.9756, 233.1654, 236.727, 238.1974, 237.7474, 241.1346, 242.3048, 244.1948, 245.3134, 246.879, 249.1204, 249.853, 252.6792, 253.857, 254.4486, 257.2362, 257.9534, 260.0286, 260.5632, 262.663, 264.723, 265.7566, 267.2566, 267.1624, 270.62, 272.8216, 273.2166, 275.2056, 276.2202, 278.3726, 280.3344, 281.9284, 283.9728, 284.1924, 286.4872, 287.587, 289.807, 291.1206, 292.769, 294.8708, 296.665, 297.1182, 299.4012, 300.6352, 302.1354, 304.1756, 306.1606, 307.3462, 308.5214, 309.4134, 310.8352, 313.9684, 315.837, 316.7796, 318.9858};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p7[] = {92.0, 93.4934, 94.9758, 96.4574, 97.9718, 99.4954, 101.5302, 103.0756, 104.6374, 106.1782, 107.7888, 109.9522, 111.592, 113.2532, 114.9086, 116.5938, 118.9474, 120.6796, 122.4394, 124.2176, 125.9768, 128.4214, 130.2528, 132.0102, 133.8658, 135.7278, 138.3044, 140.1316, 142.093, 144.0032, 145.9092, 148.6306, 150.5294, 152.5756, 154.6508, 156.662, 159.552, 161.3724, 163.617, 165.5754, 167.7872, 169.8444, 172.7988, 174.8606, 177.2118, 179.3566, 181.4476, 184.5882, 186.6816, 189.0824, 191.0258, 193.6048, 196.4436, 198.7274, 200.957, 203.147, 205.4364, 208.7592, 211.3386, 213.781, 215.8028, 218.656, 221.6544, 223.996, 226.4718, 229.1544, 231.6098, 234.5956, 237.0616, 239.5758, 242.4878, 244.5244, 248.2146, 250.724, 252.8722, 255.5198, 258.0414, 261.941, 264.9048, 266.87, 269.4304, 272.028, 274.4708, 278.37, 281.0624, 283.4668, 286.5532, 289.4352, 293.2564, 295.2744, 298.2118, 300.7472, 304.1456, 307.2928, 309.7504, 312.5528, 315.979, 318.2102, 322.1834, 324.3494, 327.325, 330.6614, 332.903, 337.2544, 339.9042, 343.215, 345.2864, 348.0814, 352.6764, 355.301, 357.139, 360.658, 363.1732, 366.5902, 369.9538, 373.0828, 375.922, 378.9902, 382.7328, 386.4538, 388.1136, 391.2234, 394.0878, 396.708, 401.1556, 404.1852, 406.6372, 409.6822, 412.7796, 416.6078, 418.4916, 422.131, 424.5376, 428.1988, 432.211, 434.4502, 438.5282, 440.912, 444.0448, 447.7432, 450.8524, 453.7988, 456.7858, 458.8868, 463.9886, 466.5064, 468.9124, 472.6616, 475.4682, 478.582, 481.304, 485.2738, 488.6894, 490.329, 496.106, 497.6908, 501.1374, 504.5322, 506.8848, 510.3324, 513.4512, 516.179, 520.4412, 522.6066, 526.167, 528.7794, 533.379, 536.067, 538.46, 542.9116, 545.692, 547.9546, 552.493, 555.2722, 557.335, 562.449, 564.2014, 569.0738, 571.0974, 574.8564, 578.2996, 581.409, 583.9704, 585.8098, 589.6528, 594.5998, 595.958, 600.068, 603.3278, 608.2016, 609.9632, 612.864, 615.43, 620.7794, 621.272, 625.8644, 629.206, 633.219, 634.5154, 638.6102};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p8[] = {184.2152, 187.2454, 190.2096, 193.6652, 196.6312, 199.6822, 203.249, 206.3296, 210.0038, 213.2074, 216.4612, 220.27, 223.5178, 227.4412, 230.8032, 234.1634, 238.1688, 241.6074, 245.6946, 249.2664, 252.8228, 257.0432, 260.6824, 264.9464, 268.6268, 272.2626, 276.8376, 280.4034, 284.8956, 288.8522, 292.7638, 297.3552, 301.3556, 305.7526, 309.9292, 313.8954, 318.8198, 322.7668, 327.298, 331.6688, 335.9466, 340.9746, 345.1672, 349.3474, 354.3028, 358.8912, 364.114, 368.4646, 372.9744, 378.4092, 382.6022, 387.843, 392.5684, 397.1652, 402.5426, 407.4152, 412.5388, 417.3592, 422.1366, 427.486, 432.3918, 437.5076, 442.509, 447.3834, 453.3498, 458.0668, 463.7346, 469.1228, 473.4528, 479.7, 484.644, 491.0518, 495.5774, 500.9068, 506.432, 512.1666, 517.434, 522.6644, 527.4894, 533.6312, 538.3804, 544.292, 550.5496, 556.0234, 562.8206, 566.6146, 572.4188, 579.117, 583.6762, 590.6576, 595.7864, 601.509, 607.5334, 612.9204, 619.772, 624.2924, 630.8654, 636.1836, 642.745, 649.1316, 655.0386, 660.0136, 666.6342, 671.6196, 678.1866, 684.4282, 689.3324, 695.4794, 702.5038, 708.129, 713.528, 720.3204, 726.463, 732.7928, 739.123, 744.7418, 751.2192, 756.5102, 762.6066, 769.0184, 775.2224, 781.4014, 787.7618, 794.1436, 798.6506, 805.6378, 811.766, 819.7514, 824.5776, 828.7322, 837.8048, 843.6302, 849.9336, 854.4798, 861.3388, 867.9894, 873.8196, 880.3136, 886.2308, 892.4588, 899.0816, 905.4076, 912.0064, 917.3878, 923.619, 929.998, 937.3482, 943.9506, 947.991, 955.1144, 962.203, 968.8222, 975.7324, 981.7826, 988.7666, 994.2648, 1000.3128, 1007.4082, 1013.7536, 1020.3376, 1026.7156, 1031.7478, 1037.4292, 1045.393, 1051.2278, 1058.3434, 1062.8726, 1071.884, 1076.806, 1082.9176, 1089.1678, 1095.5032, 1102.525, 1107.2264, 1115.315, 1120.93, 1127.252, 1134.1496, 1139.0408, 1147.5448, 1153.3296, 1158.1974, 1166.5262, 1174.3328, 1175.657, 1184.4222, 1190.9172, 1197.1292, 1204.4606, 1210.4578, 1218.8728, 1225.3336, 1226.6592, 1236.5768, 1241.363, 1249.4074, 1254.6566, 1260.8014, 1266.5454, 1274.5192};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p9[] = {369.0, 374.8294, 381.2452, 387.6698, 394.1464, 400.2024, 406.8782, 413.6598, 420.462, 427.2826, 433.7102, 440.7416, 447.9366, 455.1046, 462.285, 469.0668, 476.306, 483.8448, 491.301, 498.9886, 506.2422, 513.8138, 521.7074, 529.7428, 537.8402, 545.1664, 553.3534, 561.594, 569.6886, 577.7876, 585.65, 594.228, 602.8036, 611.1666, 620.0818, 628.0824, 637.2574, 646.302, 655.1644, 664.0056, 672.3802, 681.7192, 690.5234, 700.2084, 708.831, 718.485, 728.1112, 737.4764, 746.76, 756.3368, 766.5538, 775.5058, 785.2646, 795.5902, 804.3818, 814.8998, 824.9532, 835.2062, 845.2798, 854.4728, 864.9582, 875.3292, 886.171, 896.781, 906.5716, 916.7048, 927.5322, 937.875, 949.3972, 958.3464, 969.7274, 980.2834, 992.1444, 1003.4264, 1013.0166, 1024.018, 1035.0438, 1046.34, 1057.6856, 1068.9836, 1079.0312, 1091.677, 1102.3188, 1113.4846, 1124.4424, 1135.739, 1147.1488, 1158.9202, 1169.406, 1181.5342, 1193.2834, 1203.8954, 1216.3286, 1226.2146, 1239.6684, 1251.9946, 1262.123, 1275.4338, 1285.7378, 1296.076, 1308.9692, 1320.4964, 1333.0998, 1343.9864, 1357.7754, 1368.3208, 1380.4838, 1392.7388, 1406.0758, 1416.9098, 1428.9728, 1440.9228, 1453.9292, 1462.617, 1476.05, 1490.2996, 1500.6128, 1513.7392, 1524.5174, 1536.6322, 1548.2584, 1562.3766, 1572.423, 1587.1232, 1596.5164, 1610.5938, 1622.5972, 1633.1222, 1647.7674, 1658.5044, 1671.57, 1683.7044, 1695.4142, 1708.7102, 1720.6094, 1732.6522, 1747.841, 1756.4072, 1769.9786, 1782.3276, 1797.5216, 1808.3186, 1819.0694, 1834.354, 1844.575, 1856.2808, 1871.1288, 1880.7852, 1893.9622, 1906.3418, 1920.6548, 1932.9302, 1945.8584, 1955.473, 1968.8248, 1980.6446, 1995.9598, 2008.349, 2019.8556, 2033.0334, 2044.0206, 2059.3956, 2069.9174, 2082.6084, 2093.7036, 2106.6108, 2118.9124, 2132.301, 2144.7628, 2159.8422, 2171.0212, 2183.101, 2193.5112, 2208.052, 2221.3194, 2233.3282, 2247.295, 2257.7222, 2273.342, 2286.5638, 2299.6786, 2310.8114, 2322.3312, 2335.516, 2349.874, 2363.5968, 2373.865, 2387.1918, 2401.8328, 2414.8496, 2424.544, 2436.7592, 2447.1682, 2464.1958, 2474.3438, 2489.0006, 2497.4526, 2513.6586, 2527.19, 2540.7028, 2553.768};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p10[] = {738.1256, 750.4234, 763.1064, 775.4732, 788.4636, 801.0644, 814.488, 827.9654, 841.0832, 854.7864, 868.1992, 882.2176, 896.5228, 910.1716, 924.7752, 938.899, 953.6126, 968.6492, 982.9474, 998.5214, 1013.1064, 1028.6364, 1044.2468, 1059.4588, 1075.3832, 1091.0584, 1106.8606, 1123.3868, 1139.5062, 1156.1862, 1172.463, 1189.339, 1206.1936, 1223.1292, 1240.1854, 1257.2908, 1275.3324, 1292.8518, 1310.5204, 1328.4854, 1345.9318, 1364.552, 1381.4658, 1400.4256, 1419.849, 1438.152, 1456.8956, 1474.8792, 1494.118, 1513.62, 1532.5132, 1551.9322, 1570.7726, 1590.6086, 1610.5332, 1630.5918, 1650.4294, 1669.7662, 1690.4106, 1710.7338, 1730.9012, 1750.4486, 1770.1556, 1791.6338, 1812.7312, 1833.6264, 1853.9526, 1874.8742, 1896.8326, 1918.1966, 1939.5594, 1961.07, 1983.037, 2003.1804, 2026.071, 2047.4884, 2070.0848, 2091.2944, 2114.333, 2135.9626, 2158.2902, 2181.0814, 2202.0334, 2224.4832, 2246.39, 2269.7202, 2292.1714, 2314.2358, 2338.9346, 2360.891, 2384.0264, 2408.3834, 2430.1544, 2454.8684, 2476.9896, 2501.4368, 2522.8702, 2548.0408, 2570.6738, 2593.5208, 2617.0158, 2640.2302, 2664.0962, 2687.4986, 2714.2588, 2735.3914, 2759.6244, 2781.8378, 2808.0072, 2830.6516, 2856.2454, 2877.2136, 2903.4546, 2926.785, 2951.2294, 2976.468, 3000.867, 3023.6508, 3049.91, 3073.5984, 3098.162, 3121.5564, 3146.2328, 3170.9484, 3195.5902, 3221.3346, 3242.7032, 3271.6112, 3296.5546, 3317.7376, 3345.072, 3369.9518, 3394.326, 3418.1818, 3444.6926, 3469.086, 3494.2754, 3517.8698, 3544.248, 3565.3768, 3588.7234, 3616.979, 3643.7504, 3668.6812, 3695.72, 3719.7392, 3742.6224, 3770.4456, 3795.6602, 3819.9058, 3844.002, 3869.517, 3895.6824, 3920.8622, 3947.1364, 3973.985, 3995.4772, 4021.62, 4046.628, 4074.65, 4096.2256, 4121.831, 4146.6406, 4173.276, 4195.0744, 4223.9696, 4251.3708, 4272.9966, 4300.8046, 4326.302, 4353.1248, 4374.312, 4403.0322, 4426.819, 4450.0598, 4478.5206, 4504.8116, 4528.8928, 4553.9584, 4578.8712, 4603.8384, 4632.3872, 4655.5128, 4675.821, 4704.6222, 4731.9862, 4755.4174, 4781.2628, 4804.332, 4832.3048, 4862.8752, 4883.4148, 4906.9544, 4935.3516, 4954.3532, 4984.0248, 5011.217, 5035.3258, 5057.3672, 5084.1828};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p11[] = {1477.0, 1501.6014, 1526.5802, 1551.7942, 1577.3042, 1603.2062, 1629.8402, 1656.2292, 1682.9462, 1709.9926, 1737.3026, 1765.4252, 1793.0578, 1821.6092, 1849.626, 1878.5568, 1908.527, 1937.5154, 1967.1874, 1997.3878, 2027.37, 2058.1972, 2089.5728, 2120.1012, 2151.9668, 2183.292, 2216.0772, 2247.8578, 2280.6562, 2313.041, 2345.714, 2380.3112, 2414.1806, 2447.9854, 2481.656, 2516.346, 2551.5154, 2586.8378, 2621.7448, 2656.6722, 2693.5722, 2729.1462, 2765.4124, 2802.8728, 2838.898, 2876.408, 2913.4926, 2951.4938, 2989.6776, 3026.282, 3065.7704, 3104.1012, 3143.7388, 3181.6876, 3221.1872, 3261.5048, 3300.0214, 3339.806, 3381.409, 3421.4144, 3461.4294, 3502.2286, 3544.651, 3586.6156, 3627.337, 3670.083, 3711.1538, 3753.5094, 3797.01, 3838.6686, 3882.1678, 3922.8116, 3967.9978, 4009.9204, 4054.3286, 4097.5706, 4140.6014, 4185.544, 4229.5976, 4274.583, 4316.9438, 4361.672, 4406.2786, 4451.8628, 4496.1834, 4543.505, 4589.1816, 4632.5188, 4678.2294, 4724.8908, 4769.0194, 4817.052, 4861.4588, 4910.1596, 4956.4344, 5002.5238, 5048.13, 5093.6374, 5142.8162, 5187.7894, 5237.3984, 5285.6078, 5331.0858, 5379.1036, 5428.6258, 5474.6018, 5522.7618, 5571.5822, 5618.59, 5667.9992, 5714.88, 5763.454, 5808.6982, 5860.3644, 5910.2914, 5953.571, 6005.9232, 6055.1914, 6104.5882, 6154.5702, 6199.7036, 6251.1764, 6298.7596, 6350.0302, 6398.061, 6448.4694, 6495.933, 6548.0474, 6597.7166, 6646.9416, 6695.9208, 6742.6328, 6793.5276, 6842.1934, 6894.2372, 6945.3864, 6996.9228, 7044.2372, 7094.1374, 7142.2272, 7192.2942, 7238.8338, 7288.9006, 7344.0908, 7394.8544, 7443.5176, 7490.4148, 7542.9314, 7595.6738, 7641.9878, 7694.3688, 7743.0448, 7797.522, 7845.53, 7899.594, 7950.3132, 7996.455, 8050.9442, 8092.9114, 8153.1374, 8197.4472, 8252.8278, 8301.8728, 8348.6776, 8401.4698, 8453.551, 8504.6598, 8553.8944, 8604.1276, 8657.6514, 8710.3062, 8758.908, 8807.8706, 8862.1702, 8910.4668, 8960.77, 9007.2766, 9063.164, 9121.0534, 9164.1354, 9218.1594, 9267.767, 9319.0594, 9372.155, 9419.7126, 9474.3722, 9520.1338, 9572.368, 9622.7702, 9675.8448, 9726.5396, 9778.7378, 9827.6554, 9878.1922, 9928.7782, 9978.3984, 10026.578, 10076.5626, 10137.1618, 10177.5244, 10229.9176};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p12[] = {2954.0, 3003.4782, 3053.3568, 3104.3666, 3155.324, 3206.9598, 3259.648, 3312.539, 3366.1474, 3420.2576, 3474.8376, 3530.6076, 3586.451, 3643.38, 3700.4104, 3757.5638, 3815.9676, 3875.193, 3934.838, 3994.8548, 4055.018, 4117.1742, 4178.4482, 4241.1294, 4304.4776, 4367.4044, 4431.8724, 4496.3732, 4561.4304, 4627.5326, 4693.949, 4761.5532, 4828.7256, 4897.6182, 4965.5186, 5034.4528, 5104.865, 5174.7164, 5244.6828, 5316.6708, 5387.8312, 5459.9036, 5532.476, 5604.8652, 5679.6718, 5753.757, 5830.2072, 5905.2828, 5980.0434, 6056.6264, 6134.3192, 6211.5746, 6290.0816, 6367.1176, 6447.9796, 6526.5576, 6606.1858, 6686.9144, 6766.1142, 6847.0818, 6927.9664, 7010.9096, 7091.0816, 7175.3962, 7260.3454, 7344.018, 7426.4214, 7511.3106, 7596.0686, 7679.8094, 7765.818, 7852.4248, 7936.834, 8022.363, 8109.5066, 8200.4554, 8288.5832, 8373.366, 8463.4808, 8549.7682, 8642.0522, 8728.3288, 8820.9528, 8907.727, 9001.0794, 9091.2522, 9179.988, 9269.852, 9362.6394, 9453.642, 9546.9024, 9640.6616, 9732.6622, 9824.3254, 9917.7484, 10007.9392, 10106.7508, 10196.2152, 10289.8114, 10383.5494, 10482.3064, 10576.8734, 10668.7872, 10764.7156, 10862.0196, 10952.793, 11049.9748, 11146.0702, 11241.4492, 11339.2772, 11434.2336, 11530.741, 11627.6136, 11726.311, 11821.5964, 11918.837, 12015.3724, 12113.0162, 12213.0424, 12306.9804, 12408.4518, 12504.8968, 12604.586, 12700.9332, 12798.705, 12898.5142, 12997.0488, 13094.788, 13198.475, 13292.7764, 13392.9698, 13486.8574, 13590.1616, 13686.5838, 13783.6264, 13887.2638, 13992.0978, 14081.0844, 14189.9956, 14280.0912, 14382.4956, 14486.4384, 14588.1082, 14686.2392, 14782.276, 14888.0284, 14985.1864, 15088.8596, 15187.0998, 15285.027, 15383.6694, 15495.8266, 15591.3736, 15694.2008, 15790.3246, 15898.4116, 15997.4522, 16095.5014, 16198.8514, 16291.7492, 16402.6424, 16499.1266, 16606.2436, 16697.7186, 16796.3946, 16902.3376, 17005.7672, 17100.814, 17206.8282, 17305.8262, 17416.0744, 17508.4092, 17617.0178, 17715.4554, 17816.758, 17920.1748, 18012.9236, 18119.7984, 18223.2248, 18324.2482, 18426.6276, 18525.0932, 18629.8976, 18733.2588, 18831.0466, 18940.1366, 19032.2696, 19131.729, 19243.4864, 19349.6932, 19442.866, 19547.9448, 19653.2798, 19754.4034, 19854.0692, 19965.1224, 20065.1774, 20158.2212, 20253.353, 20366.3264, 20463.22};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p13[] = {5908.5052, 6007.2672, 6107.347, 6208.5794, 6311.2622, 6414.5514, 6519.3376, 6625.6952, 6732.5988, 6841.3552, 6950.5972, 7061.3082, 7173.5646, 7287.109, 7401.8216, 7516.4344, 7633.3802, 7751.2962, 7870.3784, 7990.292, 8110.79, 8233.4574, 8356.6036, 8482.2712, 8607.7708, 8735.099, 8863.1858, 8993.4746, 9123.8496, 9255.6794, 9388.5448, 9522.7516, 9657.3106, 9792.6094, 9930.5642, 10068.794, 10206.7256, 10347.81, 10490.3196, 10632.0778, 10775.9916, 10920.4662, 11066.124, 11213.073, 11358.0362, 11508.1006, 11659.1716, 11808.7514, 11959.4884, 12112.1314, 12265.037, 12420.3756, 12578.933, 12734.311, 12890.0006, 13047.2144, 13207.3096, 13368.5144, 13528.024, 13689.847, 13852.7528, 14018.3168, 14180.5372, 14346.9668, 14513.5074, 14677.867, 14846.2186, 15017.4186, 15184.9716, 15356.339, 15529.2972, 15697.3578, 15871.8686, 16042.187, 16216.4094, 16389.4188, 16565.9126, 16742.3272, 16919.0042, 17094.7592, 17273.965, 17451.8342, 17634.4254, 17810.5984, 17988.9242, 18171.051, 18354.7938, 18539.466, 18721.0408, 18904.9972, 19081.867, 19271.9118, 19451.8694, 19637.9816, 19821.2922, 20013.1292, 20199.3858, 20387.8726, 20572.9514, 20770.7764, 20955.1714, 21144.751, 21329.9952, 21520.709, 21712.7016, 21906.3868, 22096.2626, 22286.0524, 22475.051, 22665.5098, 22862.8492, 23055.5294, 23249.6138, 23437.848, 23636.273, 23826.093, 24020.3296, 24213.3896, 24411.7392, 24602.9614, 24805.7952, 24998.1552, 25193.9588, 25389.0166, 25585.8392, 25780.6976, 25981.2728, 26175.977, 26376.5252, 26570.1964, 26773.387, 26962.9812, 27163.0586, 27368.164, 27565.0534, 27758.7428, 27961.1276, 28163.2324, 28362.3816, 28565.7668, 28758.644, 28956.9768, 29163.4722, 29354.7026, 29561.1186, 29767.9948, 29959.9986, 30164.0492, 30366.9818, 30562.5338, 30762.9928, 30976.1592, 31166.274, 31376.722, 31570.3734, 31770.809, 31974.8934, 32179.5286, 32387.5442, 32582.3504, 32794.076, 32989.9528, 33191.842, 33392.4684, 33595.659, 33801.8672, 34000.3414, 34200.0922, 34402.6792, 34610.0638, 34804.0084, 35011.13, 35218.669, 35418.6634, 35619.0792, 35830.6534, 36028.4966, 36229.7902, 36438.6422, 36630.7764, 36833.3102, 37048.6728, 37247.3916, 37453.5904, 37669.3614, 37854.5526, 38059.305, 38268.0936, 38470.2516, 38674.7064, 38876.167, 39068.3794, 39281.9144, 39492.8566, 39684.8628, 39898.4108, 40093.1836, 40297.6858, 40489.7086, 40717.2424};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p14[] = {11817.475, 12015.0046, 12215.3792, 12417.7504, 12623.1814, 12830.0086, 13040.0072, 13252.503, 13466.178, 13683.2738, 13902.0344, 14123.9798, 14347.394, 14573.7784, 14802.6894, 15033.6824, 15266.9134, 15502.8624, 15741.4944, 15980.7956, 16223.8916, 16468.6316, 16715.733, 16965.5726, 17217.204, 17470.666, 17727.8516, 17986.7886, 18247.6902, 18510.9632, 18775.304, 19044.7486, 19314.4408, 19587.202, 19862.2576, 20135.924, 20417.0324, 20697.9788, 20979.6112, 21265.0274, 21550.723, 21841.6906, 22132.162, 22428.1406, 22722.127, 23020.5606, 23319.7394, 23620.4014, 23925.2728, 24226.9224, 24535.581, 24845.505, 25155.9618, 25470.3828, 25785.9702, 26103.7764, 26420.4132, 26742.0186, 27062.8852, 27388.415, 27714.6024, 28042.296, 28365.4494, 28701.1526, 29031.8008, 29364.2156, 29704.497, 30037.1458, 30380.111, 30723.8168, 31059.5114, 31404.9498, 31751.6752, 32095.2686, 32444.7792, 32794.767, 33145.204, 33498.4226, 33847.6502, 34209.006, 34560.849, 34919.4838, 35274.9778, 35635.1322, 35996.3266, 36359.1394, 36722.8266, 37082.8516, 37447.7354, 37815.9606, 38191.0692, 38559.4106, 38924.8112, 39294.6726, 39663.973, 40042.261, 40416.2036, 40779.2036, 41161.6436, 41540.9014, 41921.1998, 42294.7698, 42678.5264, 43061.3464, 43432.375, 43818.432, 44198.6598, 44583.0138, 44970.4794, 45353.924, 45729.858, 46118.2224, 46511.5724, 46900.7386, 47280.6964, 47668.1472, 48055.6796, 48446.9436, 48838.7146, 49217.7296, 49613.7796, 50010.7508, 50410.0208, 50793.7886, 51190.2456, 51583.1882, 51971.0796, 52376.5338, 52763.319, 53165.5534, 53556.5594, 53948.2702, 54346.352, 54748.7914, 55138.577, 55543.4824, 55941.1748, 56333.7746, 56745.1552, 57142.7944, 57545.2236, 57935.9956, 58348.5268, 58737.5474, 59158.5962, 59542.6896, 59958.8004, 60349.3788, 60755.0212, 61147.6144, 61548.194, 61946.0696, 62348.6042, 62763.603, 63162.781, 63560.635, 63974.3482, 64366.4908, 64771.5876, 65176.7346, 65597.3916, 65995.915, 66394.0384, 66822.9396, 67203.6336, 67612.2032, 68019.0078, 68420.0388, 68821.22, 69235.8388, 69640.0724, 70055.155, 70466.357, 70863.4266, 71276.2482, 71677.0306, 72080.2006, 72493.0214, 72893.5952, 73314.5856, 73714.9852, 74125.3022, 74521.2122, 74933.6814, 75341.5904, 75743.0244, 76166.0278, 76572.1322, 76973.1028, 77381.6284, 77800.6092, 78189.328, 78607.0962, 79012.2508, 79407.8358, 79825.725, 80238.701, 80646.891, 81035.6436, 81460.0448, 81876.3884};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p15[] = {23635.0036, 24030.8034, 24431.4744, 24837.1524, 25246.7928, 25661.326, 26081.3532, 26505.2806, 26933.9892, 27367.7098, 27805.318, 28248.799, 28696.4382, 29148.8244, 29605.5138, 30066.8668, 30534.2344, 31006.32, 31480.778, 31962.2418, 32447.3324, 32938.0232, 33432.731, 33930.728, 34433.9896, 34944.1402, 35457.5588, 35974.5958, 36497.3296, 37021.9096, 37554.326, 38088.0826, 38628.8816, 39171.3192, 39723.2326, 40274.5554, 40832.3142, 41390.613, 41959.5908, 42532.5466, 43102.0344, 43683.5072, 44266.694, 44851.2822, 45440.7862, 46038.0586, 46640.3164, 47241.064, 47846.155, 48454.7396, 49076.9168, 49692.542, 50317.4778, 50939.65, 51572.5596, 52210.2906, 52843.7396, 53481.3996, 54127.236, 54770.406, 55422.6598, 56078.7958, 56736.7174, 57397.6784, 58064.5784, 58730.308, 59404.9784, 60077.0864, 60751.9158, 61444.1386, 62115.817, 62808.7742, 63501.4774, 64187.5454, 64883.6622, 65582.7468, 66274.5318, 66976.9276, 67688.7764, 68402.138, 69109.6274, 69822.9706, 70543.6108, 71265.5202, 71983.3848, 72708.4656, 73433.384, 74158.4664, 74896.4868, 75620.9564, 76362.1434, 77098.3204, 77835.7662, 78582.6114, 79323.9902, 80067.8658, 80814.9246, 81567.0136, 82310.8536, 83061.9952, 83821.4096, 84580.8608, 85335.547, 86092.5802, 86851.6506, 87612.311, 88381.2016, 89146.3296, 89907.8974, 90676.846, 91451.4152, 92224.5518, 92995.8686, 93763.5066, 94551.2796, 95315.1944, 96096.1806, 96881.0918, 97665.679, 98442.68, 99229.3002, 100011.0994, 100790.6386, 101580.1564, 102377.7484, 103152.1392, 103944.2712, 104730.216, 105528.6336, 106324.9398, 107117.6706, 107890.3988, 108695.2266, 109485.238, 110294.7876, 111075.0958, 111878.0496, 112695.2864, 113464.5486, 114270.0474, 115068.608, 115884.3626, 116673.2588, 117483.3716, 118275.097, 119085.4092, 119879.2808, 120687.5868, 121499.9944, 122284.916, 123095.9254, 123912.5038, 124709.0454, 125503.7182, 126323.259, 127138.9412, 127943.8294, 128755.646, 129556.5354, 130375.3298, 131161.4734, 131971.1962, 132787.5458, 133588.1056, 134431.351, 135220.2906, 136023.398, 136846.6558, 137667.0004, 138463.663, 139283.7154, 140074.6146, 140901.3072, 141721.8548, 142543.2322, 143356.1096, 144173.7412, 144973.0948, 145794.3162, 146609.5714, 147420.003, 148237.9784, 149050.5696, 149854.761, 150663.1966, 151494.0754, 152313.1416, 153112.6902, 153935.7206, 154746.9262, 155559.547, 156401.9746, 157228.7036, 158008.7254, 158820.75, 159646.9184, 160470.4458, 161279.5348, 162093.3114, 162918.542, 163729.2842};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p16[] = {47271.0, 48062.3584, 48862.7074, 49673.152, 50492.8416, 51322.9514, 52161.03, 53009.407, 53867.6348, 54734.206, 55610.5144, 56496.2096, 57390.795, 58297.268, 59210.6448, 60134.665, 61068.0248, 62010.4472, 62962.5204, 63923.5742, 64895.0194, 65876.4182, 66862.6136, 67862.6968, 68868.8908, 69882.8544, 70911.271, 71944.0924, 72990.0326, 74040.692, 75100.6336, 76174.7826, 77252.5998, 78340.2974, 79438.2572, 80545.4976, 81657.2796, 82784.6336, 83915.515, 85059.7362, 86205.9368, 87364.4424, 88530.3358, 89707.3744, 90885.9638, 92080.197, 93275.5738, 94479.391, 95695.918, 96919.2236, 98148.4602, 99382.3474, 100625.6974, 101878.0284, 103141.6278, 104409.4588, 105686.2882, 106967.5402, 108261.6032, 109548.1578, 110852.0728, 112162.231, 113479.0072, 114806.2626, 116137.9072, 117469.5048, 118813.5186, 120165.4876, 121516.2556, 122875.766, 124250.5444, 125621.2222, 127003.2352, 128387.848, 129775.2644, 131181.7776, 132577.3086, 133979.9458, 135394.1132, 136800.9078, 138233.217, 139668.5308, 141085.212, 142535.2122, 143969.0684, 145420.2872, 146878.1542, 148332.7572, 149800.3202, 151269.66, 152743.6104, 154213.0948, 155690.288, 157169.4246, 158672.1756, 160160.059, 161650.6854, 163145.7772, 164645.6726, 166159.1952, 167682.1578, 169177.3328, 170700.0118, 172228.8964, 173732.6664, 175265.5556, 176787.799, 178317.111, 179856.6914, 181400.865, 182943.4612, 184486.742, 186033.4698, 187583.7886, 189148.1868, 190688.4526, 192250.1926, 193810.9042, 195354.2972, 196938.7682, 198493.5898, 200079.2824, 201618.912, 203205.5492, 204765.5798, 206356.1124, 207929.3064, 209498.7196, 211086.229, 212675.1324, 214256.7892, 215826.2392, 217412.8474, 218995.6724, 220618.6038, 222207.1166, 223781.0364, 225387.4332, 227005.7928, 228590.4336, 230217.8738, 231805.1054, 233408.9, 234995.3432, 236601.4956, 238190.7904, 239817.2548, 241411.2832, 243002.4066, 244640.1884, 246255.3128, 247849.3508, 249479.9734, 251106.8822, 252705.027, 254332.9242, 255935.129, 257526.9014, 259154.772, 260777.625, 262390.253, 264004.4906, 265643.59, 267255.4076, 268873.426, 270470.7252, 272106.4804, 273722.4456, 275337.794, 276945.7038, 278592.9154, 280204.3726, 281841.1606, 283489.171, 285130.1716, 286735.3362, 288364.7164, 289961.1814, 291595.5524, 293285.683, 294899.6668, 296499.3434, 298128.0462, 299761.8946, 301394.2424, 302997.6748, 304615.1478, 306269.7724, 307886.114, 309543.1028, 311153.2862, 312782.8546, 314421.2008, 316033.2438, 317692.9636, 319305.2648, 320948.7406, 322566.3364, 324228.4224, 325847.1542};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p17[] = {94542.0, 96125.811, 97728.019, 99348.558, 100987.9705, 102646.7565, 104324.5125, 106021.7435, 107736.7865, 109469.272, 111223.9465, 112995.219, 114787.432, 116593.152, 118422.71, 120267.2345, 122134.6765, 124020.937, 125927.2705, 127851.255, 129788.9485, 131751.016, 133726.8225, 135722.592, 137736.789, 139770.568, 141821.518, 143891.343, 145982.1415, 148095.387, 150207.526, 152355.649, 154515.6415, 156696.05, 158887.7575, 161098.159, 163329.852, 165569.053, 167837.4005, 170121.6165, 172420.4595, 174732.6265, 177062.77, 179412.502, 181774.035, 184151.939, 186551.6895, 188965.691, 191402.8095, 193857.949, 196305.0775, 198774.6715, 201271.2585, 203764.78, 206299.3695, 208818.1365, 211373.115, 213946.7465, 216532.076, 219105.541, 221714.5375, 224337.5135, 226977.5125, 229613.0655, 232270.2685, 234952.2065, 237645.3555, 240331.1925, 243034.517, 245756.0725, 248517.6865, 251232.737, 254011.3955, 256785.995, 259556.44, 262368.335, 265156.911, 267965.266, 270785.583, 273616.0495, 276487.4835, 279346.639, 282202.509, 285074.3885, 287942.2855, 290856.018, 293774.0345, 296678.5145, 299603.6355, 302552.6575, 305492.9785, 308466.8605, 311392.581, 314347.538, 317319.4295, 320285.9785, 323301.7325, 326298.3235, 329301.3105, 332301.987, 335309.791, 338370.762, 341382.923, 344431.1265, 347464.1545, 350507.28, 353619.2345, 356631.2005, 359685.203, 362776.7845, 365886.488, 368958.2255, 372060.6825, 375165.4335, 378237.935, 381328.311, 384430.5225, 387576.425, 390683.242, 393839.648, 396977.8425, 400101.9805, 403271.296, 406409.8425, 409529.5485, 412678.7, 415847.423, 419020.8035, 422157.081, 425337.749, 428479.6165, 431700.902, 434893.1915, 438049.582, 441210.5415, 444379.2545, 447577.356, 450741.931, 453959.548, 457137.0935, 460329.846, 463537.4815, 466732.3345, 469960.5615, 473164.681, 476347.6345, 479496.173, 482813.1645, 486025.6995, 489249.4885, 492460.1945, 495675.8805, 498908.0075, 502131.802, 505374.3855, 508550.9915, 511806.7305, 515026.776, 518217.0005, 521523.9855, 524705.9855, 527950.997, 531210.0265, 534472.497, 537750.7315, 540926.922, 544207.094, 547429.4345, 550666.3745, 553975.3475, 557150.7185, 560399.6165, 563662.697, 566916.7395, 570146.1215, 573447.425, 576689.6245, 579874.5745, 583202.337, 586503.0255, 589715.635, 592910.161, 596214.3885, 599488.035, 602740.92, 605983.0685, 609248.67, 612491.3605, 615787.912, 619107.5245, 622307.9555, 625577.333, 628840.4385, 632085.2155, 635317.6135, 638691.7195, 641887.467, 645139.9405, 648441.546, 651666.252, 654941.845};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __raw_estimate_data_p18[] = {189084.0, 192250.913, 195456.774, 198696.946, 201977.762, 205294.444, 208651.754, 212042.099, 215472.269, 218941.91, 222443.912, 225996.845, 229568.199, 233193.568, 236844.457, 240543.233, 244279.475, 248044.27, 251854.588, 255693.2, 259583.619, 263494.621, 267445.385, 271454.061, 275468.769, 279549.456, 283646.446, 287788.198, 291966.099, 296181.164, 300431.469, 304718.618, 309024.004, 313393.508, 317760.803, 322209.731, 326675.061, 331160.627, 335654.47, 340241.442, 344841.833, 349467.132, 354130.629, 358819.432, 363574.626, 368296.587, 373118.482, 377914.93, 382782.301, 387680.669, 392601.981, 397544.323, 402529.115, 407546.018, 412593.658, 417638.657, 422762.865, 427886.169, 433017.167, 438213.273, 443441.254, 448692.421, 453937.533, 459239.049, 464529.569, 469910.083, 475274.03, 480684.473, 486070.26, 491515.237, 496995.651, 502476.617, 507973.609, 513497.19, 519083.233, 524726.509, 530305.505, 535945.728, 541584.404, 547274.055, 552967.236, 558667.862, 564360.216, 570128.148, 575965.08, 581701.952, 587532.523, 593361.144, 599246.128, 605033.418, 610958.779, 616837.117, 622772.818, 628672.04, 634675.369, 640574.831, 646585.739, 652574.547, 658611.217, 664642.684, 670713.914, 676737.681, 682797.313, 688837.897, 694917.874, 701009.882, 707173.648, 713257.254, 719415.392, 725636.761, 731710.697, 737906.209, 744103.074, 750313.39, 756504.185, 762712.579, 768876.985, 775167.859, 781359.0, 787615.959, 793863.597, 800245.477, 806464.582, 812785.294, 819005.925, 825403.057, 831676.197, 837936.284, 844266.968, 850642.711, 856959.756, 863322.774, 869699.931, 876102.478, 882355.787, 888694.463, 895159.952, 901536.143, 907872.631, 914293.672, 920615.14, 927130.974, 933409.404, 939922.178, 946331.47, 952745.93, 959209.264, 965590.224, 972077.284, 978501.961, 984953.19, 991413.271, 997817.479, 1004222.658, 1010725.676, 1017177.138, 1023612.529, 1030098.236, 1036493.719, 1043112.207, 1049537.036, 1056008.096, 1062476.184, 1068942.337, 1075524.95, 1081932.864, 1088426.025, 1094776.005, 1101327.448, 1107901.673, 1114423.639, 1120884.602, 1127324.923, 1133794.24, 1140328.886, 1146849.376, 1153346.682, 1159836.502, 1166478.703, 1172953.304, 1179391.502, 1185950.982, 1192544.052, 1198913.41, 1205430.994, 1212015.525, 1218674.042, 1225121.683, 1231551.101, 1238126.379, 1244673.795, 1251260.649, 1257697.86, 1264320.983, 1270736.319, 1277274.694, 1283804.95, 1290211.514, 1296858.568, 1303455.691};
//! @brief Get raw estimate data array for a given precision
//!
//! @param __precision The precision value (4-18)
//! @return Pointer to the raw estimate data array for the given precision
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr const double* __raw_estimate_data(::cuda::std::int32_t __precision) noexcept {
switch (__precision) {
case 4: return __raw_estimate_data_p4;
case 5: return __raw_estimate_data_p5;
case 6: return __raw_estimate_data_p6;
case 7: return __raw_estimate_data_p7;
case 8: return __raw_estimate_data_p8;
case 9: return __raw_estimate_data_p9;
case 10: return __raw_estimate_data_p10;
case 11: return __raw_estimate_data_p11;
case 12: return __raw_estimate_data_p12;
case 13: return __raw_estimate_data_p13;
case 14: return __raw_estimate_data_p14;
case 15: return __raw_estimate_data_p15;
case 16: return __raw_estimate_data_p16;
case 17: return __raw_estimate_data_p17;
case 18: return __raw_estimate_data_p18;
default: return nullptr;
}
}
//! @brief Get size of raw estimate data array for a given precision
//!
//! @param __precision The precision value (4-18)
//! @return Size of the raw estimate data array for the given precision
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::size_t __raw_estimate_data_size(::cuda::std::int32_t __precision) noexcept {
constexpr auto __size_of_double = sizeof(double);
switch (__precision) {
case 4: return sizeof(__raw_estimate_data_p4) / __size_of_double;
case 5: return sizeof(__raw_estimate_data_p5) / __size_of_double;
case 6: return sizeof(__raw_estimate_data_p6) / __size_of_double;
case 7: return sizeof(__raw_estimate_data_p7) / __size_of_double;
case 8: return sizeof(__raw_estimate_data_p8) / __size_of_double;
case 9: return sizeof(__raw_estimate_data_p9) / __size_of_double;
case 10: return sizeof(__raw_estimate_data_p10) / __size_of_double;
case 11: return sizeof(__raw_estimate_data_p11) / __size_of_double;
case 12: return sizeof(__raw_estimate_data_p12) / __size_of_double;
case 13: return sizeof(__raw_estimate_data_p13) / __size_of_double;
case 14: return sizeof(__raw_estimate_data_p14) / __size_of_double;
case 15: return sizeof(__raw_estimate_data_p15) / __size_of_double;
case 16: return sizeof(__raw_estimate_data_p16) / __size_of_double;
case 17: return sizeof(__raw_estimate_data_p17) / __size_of_double;
case 18: return sizeof(__raw_estimate_data_p18) / __size_of_double;
default: return 0;
}
}
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p4[] = {10.0, 9.717, 9.207, 8.7896, 8.2882, 7.8204, 7.3772, 6.9342, 6.5202, 6.161, 5.7722, 5.4636, 5.0396, 4.6766, 4.3566, 4.0454, 3.7936, 3.4856, 3.2666, 2.9946, 2.766, 2.4692, 2.3638, 2.0764, 1.7864, 1.7602, 1.4814, 1.433, 1.2926, 1.0664, 0.999600000000001, 0.7956, 0.5366, 0.589399999999998, 0.573799999999999, 0.269799999999996, 0.368200000000002, 0.0544000000000011, 0.234200000000001, 0.0108000000000033, -0.203400000000002, -0.0701999999999998, -0.129600000000003, -0.364199999999997, -0.480600000000003, -0.226999999999997, -0.322800000000001, -0.382599999999996, -0.511200000000002, -0.669600000000003, -0.749400000000001, -0.500399999999999, -0.617600000000003, -0.6922, -0.601599999999998, -0.416200000000003, -0.338200000000001, -0.782600000000002, -0.648600000000002, -0.919800000000002, -0.851799999999997, -0.962400000000002, -0.6402, -1.1922, -1.0256, -1.086, -1.21899999999999, -0.819400000000002, -0.940600000000003, -1.1554, -1.2072, -1.1752, -1.16759999999999, -1.14019999999999, -1.3754, -1.29859999999999, -1.607, -1.3292, -1.7606};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p5[] = {22.0, 21.1194, 20.8208, 20.2318, 19.77, 19.2436, 18.7774, 18.2848, 17.8224, 17.3742, 16.9336, 16.503, 16.0494, 15.6292, 15.2124, 14.798, 14.367, 13.9728, 13.5944, 13.217, 12.8438, 12.3696, 12.0956, 11.7044, 11.324, 11.0668, 10.6698, 10.3644, 10.049, 9.6918, 9.4146, 9.082, 8.687, 8.5398, 8.2462, 7.857, 7.6606, 7.4168, 7.1248, 6.9222, 6.6804, 6.447, 6.3454, 5.9594, 5.7636, 5.5776, 5.331, 5.19, 4.9676, 4.7564, 4.5314, 4.4442, 4.3708, 3.9774, 3.9624, 3.8796, 3.755, 3.472, 3.2076, 3.1024, 2.8908, 2.7338, 2.7728, 2.629, 2.413, 2.3266, 2.1524, 2.2642, 2.1806, 2.0566, 1.9192, 1.7598, 1.3516, 1.5802, 1.43859999999999, 1.49160000000001, 1.1524, 1.1892, 0.841399999999993, 0.879800000000003, 0.837599999999995, 0.469800000000006, 0.765600000000006, 0.331000000000003, 0.591399999999993, 0.601200000000006, 0.701599999999999, 0.558199999999999, 0.339399999999998, 0.354399999999998, 0.491200000000006, 0.308000000000007, 0.355199999999996, -0.0254000000000048, 0.205200000000005, -0.272999999999996, 0.132199999999997, 0.394400000000005, -0.241200000000006, 0.242000000000004, 0.191400000000002, 0.253799999999998, -0.122399999999999, -0.370800000000003, 0.193200000000004, -0.0848000000000013, 0.0867999999999967, -0.327200000000005, -0.285600000000002, 0.311400000000006, -0.128399999999999, -0.754999999999995, -0.209199999999996, -0.293599999999998, -0.364000000000004, -0.253600000000006, -0.821200000000005, -0.253600000000006, -0.510400000000004, -0.383399999999995, -0.491799999999998, -0.220200000000006, -0.0972000000000008, -0.557400000000001, -0.114599999999996, -0.295000000000002, -0.534800000000004, 0.346399999999988, -0.65379999999999, 0.0398000000000138, 0.0341999999999985, -0.995800000000003, -0.523400000000009, -0.489000000000004, -0.274799999999999, -0.574999999999989, -0.482799999999997, 0.0571999999999946, -0.330600000000004, -0.628800000000012, -0.140199999999993, -0.540600000000012, -0.445999999999998, -0.599400000000003, -0.262599999999992, 0.163399999999996, -0.100599999999986, -0.39500000000001, -1.06960000000001, -0.836399999999998, -0.753199999999993, -0.412399999999991, -0.790400000000005, -0.29679999999999, -0.28540000000001, -0.193000000000012, -0.0772000000000048, -0.962799999999987, -0.414800000000014};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p6[] = {45.0, 44.1902, 43.271, 42.8358, 41.8142, 41.2854, 40.317, 39.354, 38.8924, 37.9436, 37.4596, 36.5262, 35.6248, 35.1574, 34.2822, 33.837, 32.9636, 32.074, 31.7042, 30.7976, 30.4772, 29.6564, 28.7942, 28.5004, 27.686, 27.291, 26.5672, 25.8556, 25.4982, 24.8204, 24.4252, 23.7744, 23.0786, 22.8344, 22.0294, 21.8098, 21.0794, 20.5732, 20.1878, 19.5648, 19.2902, 18.6784, 18.3352, 17.8946, 17.3712, 17.0852, 16.499, 16.2686, 15.6844, 15.2234, 14.9732, 14.3356, 14.2286, 13.7262, 13.3284, 13.1048, 12.5962, 12.3562, 12.1272, 11.4184, 11.4974, 11.0822, 10.856, 10.48, 10.2834, 10.0208, 9.637, 9.51739999999999, 9.05759999999999, 8.74760000000001, 8.42700000000001, 8.1326, 8.2372, 8.2788, 7.6776, 7.79259999999999, 7.1952, 6.9564, 6.6454, 6.87, 6.5428, 6.19999999999999, 6.02940000000001, 5.62780000000001, 5.6782, 5.792, 5.35159999999999, 5.28319999999999, 5.0394, 5.07480000000001, 4.49119999999999, 4.84899999999999, 4.696, 4.54040000000001, 4.07300000000001, 4.37139999999999, 3.7216, 3.7328, 3.42080000000001, 3.41839999999999, 3.94239999999999, 3.27719999999999, 3.411, 3.13079999999999, 2.76900000000001, 2.92580000000001, 2.68279999999999, 2.75020000000001, 2.70599999999999, 2.3886, 3.01859999999999, 2.45179999999999, 2.92699999999999, 2.41720000000001, 2.41139999999999, 2.03299999999999, 2.51240000000001, 2.5564, 2.60079999999999, 2.41720000000001, 1.80439999999999, 1.99700000000001, 2.45480000000001, 1.8948, 2.2346, 2.30860000000001, 2.15479999999999, 1.88419999999999, 1.6508, 0.677199999999999, 1.72540000000001, 1.4752, 1.72280000000001, 1.66139999999999, 1.16759999999999, 1.79300000000001, 1.00059999999999, 0.905200000000008, 0.659999999999997, 1.55879999999999, 1.1636, 0.688199999999995, 0.712600000000009, 0.450199999999995, 1.1978, 0.975599999999986, 0.165400000000005, 1.727, 1.19739999999999, -0.252600000000001, 1.13460000000001, 1.3048, 1.19479999999999, 0.313400000000001, 0.878999999999991, 1.12039999999999, 0.853000000000009, 1.67920000000001, 0.856999999999999, 0.448599999999999, 1.2362, 0.953399999999988, 1.02859999999998, 0.563199999999995, 0.663000000000011, 0.723000000000013, 0.756599999999992, 0.256599999999992, -0.837600000000009, 0.620000000000005, 0.821599999999989, 0.216600000000028, 0.205600000000004, 0.220199999999977, 0.372599999999977, 0.334400000000016, 0.928400000000011, 0.972800000000007, 0.192400000000021, 0.487199999999973, -0.413000000000011, 0.807000000000016, 0.120600000000024, 0.769000000000005, 0.870799999999974, 0.66500000000002, 0.118200000000002, 0.401200000000017, 0.635199999999998, 0.135400000000004, 0.175599999999974, 1.16059999999999, 0.34620000000001, 0.521400000000028, -0.586599999999976, -1.16480000000001, 0.968399999999974, 0.836999999999989, 0.779600000000016, 0.985799999999983};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p7[] = {91.0, 89.4934, 87.9758, 86.4574, 84.9718, 83.4954, 81.5302, 80.0756, 78.6374, 77.1782, 75.7888, 73.9522, 72.592, 71.2532, 69.9086, 68.5938, 66.9474, 65.6796, 64.4394, 63.2176, 61.9768, 60.4214, 59.2528, 58.0102, 56.8658, 55.7278, 54.3044, 53.1316, 52.093, 51.0032, 49.9092, 48.6306, 47.5294, 46.5756, 45.6508, 44.662, 43.552, 42.3724, 41.617, 40.5754, 39.7872, 38.8444, 37.7988, 36.8606, 36.2118, 35.3566, 34.4476, 33.5882, 32.6816, 32.0824, 31.0258, 30.6048, 29.4436, 28.7274, 27.957, 27.147, 26.4364, 25.7592, 25.3386, 24.781, 23.8028, 23.656, 22.6544, 21.996, 21.4718, 21.1544, 20.6098, 19.5956, 19.0616, 18.5758, 18.4878, 17.5244, 17.2146, 16.724, 15.8722, 15.5198, 15.0414, 14.941, 14.9048, 13.87, 13.4304, 13.028, 12.4708, 12.37, 12.0624, 11.4668, 11.5532, 11.4352, 11.2564, 10.2744, 10.2118, 9.74720000000002, 10.1456, 9.2928, 8.75040000000001, 8.55279999999999, 8.97899999999998, 8.21019999999999, 8.18340000000001, 7.3494, 7.32499999999999, 7.66140000000001, 6.90300000000002, 7.25439999999998, 6.9042, 7.21499999999997, 6.28640000000001, 6.08139999999997, 6.6764, 6.30099999999999, 5.13900000000001, 5.65800000000002, 5.17320000000001, 4.59019999999998, 4.9538, 5.08280000000002, 4.92200000000003, 4.99020000000002, 4.7328, 5.4538, 4.11360000000002, 4.22340000000003, 4.08780000000002, 3.70800000000003, 4.15559999999999, 4.18520000000001, 3.63720000000001, 3.68220000000002, 3.77960000000002, 3.6078, 2.49160000000001, 3.13099999999997, 2.5376, 3.19880000000001, 3.21100000000001, 2.4502, 3.52820000000003, 2.91199999999998, 3.04480000000001, 2.7432, 2.85239999999999, 2.79880000000003, 2.78579999999999, 1.88679999999999, 2.98860000000002, 2.50639999999999, 1.91239999999999, 2.66160000000002, 2.46820000000002, 1.58199999999999, 1.30399999999997, 2.27379999999999, 2.68939999999998, 1.32900000000001, 3.10599999999999, 1.69080000000002, 2.13740000000001, 2.53219999999999, 1.88479999999998, 1.33240000000001, 1.45119999999997, 1.17899999999997, 2.44119999999998, 1.60659999999996, 2.16700000000003, 0.77940000000001, 2.37900000000002, 2.06700000000001, 1.46000000000004, 2.91160000000002, 1.69200000000001, 0.954600000000028, 2.49300000000005, 2.2722, 1.33500000000004, 2.44899999999996, 1.20140000000004, 3.07380000000001, 2.09739999999999, 2.85640000000001, 2.29960000000005, 2.40899999999999, 1.97040000000004, 0.809799999999996, 1.65279999999996, 2.59979999999996, 0.95799999999997, 2.06799999999998, 2.32780000000002, 4.20159999999998, 1.96320000000003, 1.86400000000003, 1.42999999999995, 3.77940000000001, 1.27200000000005, 1.86440000000005, 2.20600000000002, 3.21900000000005, 1.5154, 2.61019999999996};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p8[] = {183.2152, 180.2454, 177.2096, 173.6652, 170.6312, 167.6822, 164.249, 161.3296, 158.0038, 155.2074, 152.4612, 149.27, 146.5178, 143.4412, 140.8032, 138.1634, 135.1688, 132.6074, 129.6946, 127.2664, 124.8228, 122.0432, 119.6824, 116.9464, 114.6268, 112.2626, 109.8376, 107.4034, 104.8956, 102.8522, 100.7638, 98.3552, 96.3556, 93.7526, 91.9292, 89.8954, 87.8198, 85.7668, 83.298, 81.6688, 79.9466, 77.9746, 76.1672, 74.3474, 72.3028, 70.8912, 69.114, 67.4646, 65.9744, 64.4092, 62.6022, 60.843, 59.5684, 58.1652, 56.5426, 55.4152, 53.5388, 52.3592, 51.1366, 49.486, 48.3918, 46.5076, 45.509, 44.3834, 43.3498, 42.0668, 40.7346, 40.1228, 38.4528, 37.7, 36.644, 36.0518, 34.5774, 33.9068, 32.432, 32.1666, 30.434, 29.6644, 28.4894, 27.6312, 26.3804, 26.292, 25.5496000000001, 25.0234, 24.8206, 22.6146, 22.4188, 22.117, 20.6762, 20.6576, 19.7864, 19.509, 18.5334, 17.9204, 17.772, 16.2924, 16.8654, 15.1836, 15.745, 15.1316, 15.0386, 14.0136, 13.6342, 12.6196, 12.1866, 12.4281999999999, 11.3324, 10.4794000000001, 11.5038, 10.129, 9.52800000000002, 10.3203999999999, 9.46299999999997, 9.79280000000006, 9.12300000000005, 8.74180000000001, 9.2192, 7.51020000000005, 7.60659999999996, 7.01840000000004, 7.22239999999999, 7.40139999999997, 6.76179999999999, 7.14359999999999, 5.65060000000005, 5.63779999999997, 5.76599999999996, 6.75139999999999, 5.57759999999996, 3.73220000000003, 5.8048, 5.63019999999995, 4.93359999999996, 3.47979999999995, 4.33879999999999, 3.98940000000005, 3.81960000000004, 3.31359999999995, 3.23080000000004, 3.4588, 3.08159999999998, 3.4076, 3.00639999999999, 2.38779999999997, 2.61900000000003, 1.99800000000005, 3.34820000000002, 2.95060000000001, 0.990999999999985, 2.11440000000005, 2.20299999999997, 2.82219999999995, 2.73239999999998, 2.7826, 3.76660000000004, 2.26480000000004, 2.31280000000004, 2.40819999999997, 2.75360000000001, 3.33759999999995, 2.71559999999999, 1.7478000000001, 1.42920000000004, 2.39300000000003, 2.22779999999989, 2.34339999999997, 0.87259999999992, 3.88400000000001, 1.80600000000004, 1.91759999999999, 1.16779999999994, 1.50320000000011, 2.52500000000009, 0.226400000000012, 2.31500000000005, 0.930000000000064, 1.25199999999995, 2.14959999999996, 0.0407999999999902, 2.5447999999999, 1.32960000000003, 0.197400000000016, 2.52620000000002, 3.33279999999991, -1.34300000000007, 0.422199999999975, 0.917200000000093, 1.12920000000008, 1.46060000000011, 1.45779999999991, 2.8728000000001, 3.33359999999993, -1.34079999999994, 1.57680000000005, 0.363000000000056, 1.40740000000005, 0.656600000000026, 0.801400000000058, -0.454600000000028, 1.51919999999996};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p9[] = {368.0, 361.8294, 355.2452, 348.6698, 342.1464, 336.2024, 329.8782, 323.6598, 317.462, 311.2826, 305.7102, 299.7416, 293.9366, 288.1046, 282.285, 277.0668, 271.306, 265.8448, 260.301, 254.9886, 250.2422, 244.8138, 239.7074, 234.7428, 229.8402, 225.1664, 220.3534, 215.594, 210.6886, 205.7876, 201.65, 197.228, 192.8036, 188.1666, 184.0818, 180.0824, 176.2574, 172.302, 168.1644, 164.0056, 160.3802, 156.7192, 152.5234, 149.2084, 145.831, 142.485, 139.1112, 135.4764, 131.76, 129.3368, 126.5538, 122.5058, 119.2646, 116.5902, 113.3818, 110.8998, 107.9532, 105.2062, 102.2798, 99.4728, 96.9582, 94.3292, 92.171, 89.7809999999999, 87.5716, 84.7048, 82.5322, 79.875, 78.3972, 75.3464, 73.7274, 71.2834, 70.1444, 68.4263999999999, 66.0166, 64.018, 62.0437999999999, 60.3399999999999, 58.6856, 57.9836, 55.0311999999999, 54.6769999999999, 52.3188, 51.4846, 49.4423999999999, 47.739, 46.1487999999999, 44.9202, 43.4059999999999, 42.5342000000001, 41.2834, 38.8954000000001, 38.3286000000001, 36.2146, 36.6684, 35.9946, 33.123, 33.4338, 31.7378000000001, 29.076, 28.9692, 27.4964, 27.0998, 25.9864, 26.7754, 24.3208, 23.4838, 22.7388000000001, 24.0758000000001, 21.9097999999999, 20.9728, 19.9228000000001, 19.9292, 16.617, 17.05, 18.2996000000001, 15.6128000000001, 15.7392, 14.5174, 13.6322, 12.2583999999999, 13.3766000000001, 11.423, 13.1232, 9.51639999999998, 10.5938000000001, 9.59719999999993, 8.12220000000002, 9.76739999999995, 7.50440000000003, 7.56999999999994, 6.70440000000008, 6.41419999999994, 6.71019999999999, 5.60940000000005, 4.65219999999999, 6.84099999999989, 3.4072000000001, 3.97859999999991, 3.32760000000007, 5.52160000000003, 3.31860000000006, 2.06940000000009, 4.35400000000004, 1.57500000000005, 0.280799999999999, 2.12879999999996, -0.214799999999968, -0.0378000000000611, -0.658200000000079, 0.654800000000023, -0.0697999999999865, 0.858400000000074, -2.52700000000004, -2.1751999999999, -3.35539999999992, -1.04019999999991, -0.651000000000067, -2.14439999999991, -1.96659999999997, -3.97939999999994, -0.604400000000169, -3.08260000000018, -3.39159999999993, -5.29640000000018, -5.38920000000007, -5.08759999999984, -4.69900000000007, -5.23720000000003, -3.15779999999995, -4.97879999999986, -4.89899999999989, -7.48880000000008, -5.94799999999987, -5.68060000000014, -6.67180000000008, -4.70499999999993, -7.27779999999984, -4.6579999999999, -4.4362000000001, -4.32139999999981, -5.18859999999995, -6.66879999999992, -6.48399999999992, -5.1260000000002, -4.4032000000002, -6.13500000000022, -5.80819999999994, -4.16719999999987, -4.15039999999999, -7.45600000000013, -7.24080000000004, -9.83179999999993, -5.80420000000004, -8.6561999999999, -6.99940000000015, -10.5473999999999, -7.34139999999979, -6.80999999999995, -6.29719999999998, -6.23199999999997};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p10[] = {737.1256, 724.4234, 711.1064, 698.4732, 685.4636, 673.0644, 660.488, 647.9654, 636.0832, 623.7864, 612.1992, 600.2176, 588.5228, 577.1716, 565.7752, 554.899, 543.6126, 532.6492, 521.9474, 511.5214, 501.1064, 490.6364, 480.2468, 470.4588, 460.3832, 451.0584, 440.8606, 431.3868, 422.5062, 413.1862, 404.463, 395.339, 386.1936, 378.1292, 369.1854, 361.2908, 353.3324, 344.8518, 337.5204, 329.4854, 321.9318, 314.552, 306.4658, 299.4256, 292.849, 286.152, 278.8956, 271.8792, 265.118, 258.62, 252.5132, 245.9322, 239.7726, 233.6086, 227.5332, 222.5918, 216.4294, 210.7662, 205.4106, 199.7338, 194.9012, 188.4486, 183.1556, 178.6338, 173.7312, 169.6264, 163.9526, 159.8742, 155.8326, 151.1966, 147.5594, 143.07, 140.037, 134.1804, 131.071, 127.4884, 124.0848, 120.2944, 117.333, 112.9626, 110.2902, 107.0814, 103.0334, 99.4832000000001, 96.3899999999999, 93.7202000000002, 90.1714000000002, 87.2357999999999, 85.9346, 82.8910000000001, 80.0264000000002, 78.3834000000002, 75.1543999999999, 73.8683999999998, 70.9895999999999, 69.4367999999999, 64.8701999999998, 65.0408000000002, 61.6738, 59.5207999999998, 57.0158000000001, 54.2302, 53.0962, 50.4985999999999, 52.2588000000001, 47.3914, 45.6244000000002, 42.8377999999998, 43.0072, 40.6516000000001, 40.2453999999998, 35.2136, 36.4546, 33.7849999999999, 33.2294000000002, 32.4679999999998, 30.8670000000002, 28.6507999999999, 28.9099999999999, 27.5983999999999, 26.1619999999998, 24.5563999999999, 23.2328000000002, 21.9484000000002, 21.5902000000001, 21.3346000000001, 17.7031999999999, 20.6111999999998, 19.5545999999999, 15.7375999999999, 17.0720000000001, 16.9517999999998, 15.326, 13.1817999999998, 14.6925999999999, 13.0859999999998, 13.2754, 10.8697999999999, 11.248, 7.3768, 4.72339999999986, 7.97899999999981, 8.7503999999999, 7.68119999999999, 9.7199999999998, 7.73919999999998, 5.6224000000002, 7.44560000000001, 6.6601999999998, 5.9058, 4.00199999999995, 4.51699999999983, 4.68240000000014, 3.86220000000003, 5.13639999999987, 5.98500000000013, 2.47719999999981, 2.61999999999989, 1.62800000000016, 4.65000000000009, 0.225599999999758, 0.831000000000131, -0.359400000000278, 1.27599999999984, -2.92559999999958, -0.0303999999996449, 2.37079999999969, -2.0033999999996, 0.804600000000391, 0.30199999999968, 1.1247999999996, -2.6880000000001, 0.0321999999996478, -1.18099999999959, -3.9402, -1.47940000000017, -0.188400000000001, -2.10720000000038, -2.04159999999956, -3.12880000000041, -4.16160000000036, -0.612799999999879, -3.48719999999958, -8.17900000000009, -5.37780000000021, -4.01379999999972, -5.58259999999973, -5.73719999999958, -7.66799999999967, -5.69520000000011, -1.1247999999996, -5.58520000000044, -8.04560000000038, -4.64840000000004, -11.6468000000004, -7.97519999999986, -5.78300000000036, -7.67420000000038, -10.6328000000003, -9.81720000000041};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p11[] = {1476.0, 1449.6014, 1423.5802, 1397.7942, 1372.3042, 1347.2062, 1321.8402, 1297.2292, 1272.9462, 1248.9926, 1225.3026, 1201.4252, 1178.0578, 1155.6092, 1132.626, 1110.5568, 1088.527, 1066.5154, 1045.1874, 1024.3878, 1003.37, 982.1972, 962.5728, 942.1012, 922.9668, 903.292, 884.0772, 864.8578, 846.6562, 828.041, 809.714, 792.3112, 775.1806, 757.9854, 740.656, 724.346, 707.5154, 691.8378, 675.7448, 659.6722, 645.5722, 630.1462, 614.4124, 600.8728, 585.898, 572.408, 558.4926, 544.4938, 531.6776, 517.282, 505.7704, 493.1012, 480.7388, 467.6876, 456.1872, 445.5048, 433.0214, 420.806, 411.409, 400.4144, 389.4294, 379.2286, 369.651, 360.6156, 350.337, 342.083, 332.1538, 322.5094, 315.01, 305.6686, 298.1678, 287.8116, 280.9978, 271.9204, 265.3286, 257.5706, 249.6014, 242.544, 235.5976, 229.583, 220.9438, 214.672, 208.2786, 201.8628, 195.1834, 191.505, 186.1816, 178.5188, 172.2294, 167.8908, 161.0194, 158.052, 151.4588, 148.1596, 143.4344, 138.5238, 133.13, 127.6374, 124.8162, 118.7894, 117.3984, 114.6078, 109.0858, 105.1036, 103.6258, 98.6018000000004, 95.7618000000002, 93.5821999999998, 88.5900000000001, 86.9992000000002, 82.8800000000001, 80.4539999999997, 74.6981999999998, 74.3644000000004, 73.2914000000001, 65.5709999999999, 66.9232000000002, 65.1913999999997, 62.5882000000001, 61.5702000000001, 55.7035999999998, 56.1764000000003, 52.7596000000003, 53.0302000000001, 49.0609999999997, 48.4694, 44.933, 46.0474000000004, 44.7165999999997, 41.9416000000001, 39.9207999999999, 35.6328000000003, 35.5276000000003, 33.1934000000001, 33.2371999999996, 33.3864000000003, 33.9228000000003, 30.2371999999996, 29.1373999999996, 25.2272000000003, 24.2942000000003, 19.8338000000003, 18.9005999999999, 23.0907999999999, 21.8544000000002, 19.5176000000001, 15.4147999999996, 16.9314000000004, 18.6737999999996, 12.9877999999999, 14.3688000000002, 12.0447999999997, 15.5219999999999, 12.5299999999997, 14.5940000000001, 14.3131999999996, 9.45499999999993, 12.9441999999999, 3.91139999999996, 13.1373999999996, 5.44720000000052, 9.82779999999912, 7.87279999999919, 3.67760000000089, 5.46980000000076, 5.55099999999948, 5.65979999999945, 3.89439999999922, 3.1275999999998, 5.65140000000065, 6.3062000000009, 3.90799999999945, 1.87060000000019, 5.17020000000048, 2.46680000000015, 0.770000000000437, -3.72340000000077, 1.16400000000067, 8.05340000000069, 0.135399999999208, 2.15940000000046, 0.766999999999825, 1.0594000000001, 3.15500000000065, -0.287399999999252, 2.37219999999979, -2.86620000000039, -1.63199999999961, -2.22979999999916, -0.15519999999924, -1.46039999999994, -0.262199999999211, -2.34460000000036, -2.8078000000005, -3.22179999999935, -5.60159999999996, -8.42200000000048, -9.43740000000071, 0.161799999999857, -10.4755999999998, -10.0823999999993};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p12[] = {2953.0, 2900.4782, 2848.3568, 2796.3666, 2745.324, 2694.9598, 2644.648, 2595.539, 2546.1474, 2498.2576, 2450.8376, 2403.6076, 2357.451, 2311.38, 2266.4104, 2221.5638, 2176.9676, 2134.193, 2090.838, 2048.8548, 2007.018, 1966.1742, 1925.4482, 1885.1294, 1846.4776, 1807.4044, 1768.8724, 1731.3732, 1693.4304, 1657.5326, 1621.949, 1586.5532, 1551.7256, 1517.6182, 1483.5186, 1450.4528, 1417.865, 1385.7164, 1352.6828, 1322.6708, 1291.8312, 1260.9036, 1231.476, 1201.8652, 1173.6718, 1145.757, 1119.2072, 1092.2828, 1065.0434, 1038.6264, 1014.3192, 988.5746, 965.0816, 940.1176, 917.9796, 894.5576, 871.1858, 849.9144, 827.1142, 805.0818, 783.9664, 763.9096, 742.0816, 724.3962, 706.3454, 688.018, 667.4214, 650.3106, 633.0686, 613.8094, 597.818, 581.4248, 563.834, 547.363, 531.5066, 520.455400000001, 505.583199999999, 488.366, 476.480799999999, 459.7682, 450.0522, 434.328799999999, 423.952799999999, 408.727000000001, 399.079400000001, 387.252200000001, 373.987999999999, 360.852000000001, 351.6394, 339.642, 330.902400000001, 322.661599999999, 311.662200000001, 301.3254, 291.7484, 279.939200000001, 276.7508, 263.215200000001, 254.811400000001, 245.5494, 242.306399999999, 234.8734, 223.787200000001, 217.7156, 212.0196, 200.793, 195.9748, 189.0702, 182.449199999999, 177.2772, 170.2336, 164.741, 158.613600000001, 155.311, 147.5964, 142.837, 137.3724, 132.0162, 130.0424, 121.9804, 120.451800000001, 114.8968, 111.585999999999, 105.933199999999, 101.705, 98.5141999999996, 95.0488000000005, 89.7880000000005, 91.4750000000004, 83.7764000000006, 80.9698000000008, 72.8574000000008, 73.1615999999995, 67.5838000000003, 62.6263999999992, 63.2638000000006, 66.0977999999996, 52.0843999999997, 58.9956000000002, 47.0912000000008, 46.4956000000002, 48.4383999999991, 47.1082000000006, 43.2392, 37.2759999999998, 40.0283999999992, 35.1864000000005, 35.8595999999998, 32.0998, 28.027, 23.6694000000007, 33.8266000000003, 26.3736000000008, 27.2008000000005, 21.3245999999999, 26.4115999999995, 23.4521999999997, 19.5013999999992, 19.8513999999996, 10.7492000000002, 18.6424000000006, 13.1265999999996, 18.2436000000016, 6.71860000000015, 3.39459999999963, 6.33759999999893, 7.76719999999841, 0.813999999998487, 3.82819999999992, 0.826199999999517, 8.07440000000133, -1.59080000000176, 5.01780000000144, 0.455399999998917, -0.24199999999837, 0.174800000000687, -9.07640000000174, -4.20160000000033, -3.77520000000004, -4.75179999999818, -5.3724000000002, -8.90680000000066, -6.10239999999976, -5.74120000000039, -9.95339999999851, -3.86339999999836, -13.7304000000004, -16.2710000000006, -7.51359999999841, -3.30679999999847, -13.1339999999982, -10.0551999999989, -6.72019999999975, -8.59660000000076, -10.9307999999983, -1.8775999999998, -4.82259999999951, -13.7788, -21.6470000000008, -10.6735999999983, -15.7799999999988};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p13[] = {5907.5052, 5802.2672, 5697.347, 5593.5794, 5491.2622, 5390.5514, 5290.3376, 5191.6952, 5093.5988, 4997.3552, 4902.5972, 4808.3082, 4715.5646, 4624.109, 4533.8216, 4444.4344, 4356.3802, 4269.2962, 4183.3784, 4098.292, 4014.79, 3932.4574, 3850.6036, 3771.2712, 3691.7708, 3615.099, 3538.1858, 3463.4746, 3388.8496, 3315.6794, 3244.5448, 3173.7516, 3103.3106, 3033.6094, 2966.5642, 2900.794, 2833.7256, 2769.81, 2707.3196, 2644.0778, 2583.9916, 2523.4662, 2464.124, 2406.073, 2347.0362, 2292.1006, 2238.1716, 2182.7514, 2128.4884, 2077.1314, 2025.037, 1975.3756, 1928.933, 1879.311, 1831.0006, 1783.2144, 1738.3096, 1694.5144, 1649.024, 1606.847, 1564.7528, 1525.3168, 1482.5372, 1443.9668, 1406.5074, 1365.867, 1329.2186, 1295.4186, 1257.9716, 1225.339, 1193.2972, 1156.3578, 1125.8686, 1091.187, 1061.4094, 1029.4188, 1000.9126, 972.3272, 944.004199999999, 915.7592, 889.965, 862.834200000001, 840.4254, 812.598399999999, 785.924200000001, 763.050999999999, 741.793799999999, 721.466, 699.040799999999, 677.997200000002, 649.866999999998, 634.911800000002, 609.8694, 591.981599999999, 570.2922, 557.129199999999, 538.3858, 521.872599999999, 502.951400000002, 495.776399999999, 475.171399999999, 459.751, 439.995200000001, 426.708999999999, 413.7016, 402.3868, 387.262599999998, 372.0524, 357.050999999999, 342.5098, 334.849200000001, 322.529399999999, 311.613799999999, 295.848000000002, 289.273000000001, 274.093000000001, 263.329600000001, 251.389599999999, 245.7392, 231.9614, 229.7952, 217.155200000001, 208.9588, 199.016599999999, 190.839199999999, 180.6976, 176.272799999999, 166.976999999999, 162.5252, 151.196400000001, 149.386999999999, 133.981199999998, 130.0586, 130.164000000001, 122.053400000001, 110.7428, 108.1276, 106.232400000001, 100.381600000001, 98.7668000000012, 86.6440000000002, 79.9768000000004, 82.4722000000002, 68.7026000000005, 70.1186000000016, 71.9948000000004, 58.998599999999, 59.0492000000013, 56.9818000000014, 47.5338000000011, 42.9928, 51.1591999999982, 37.2740000000013, 42.7220000000016, 31.3734000000004, 26.8090000000011, 25.8934000000008, 26.5286000000015, 29.5442000000003, 19.3503999999994, 26.0760000000009, 17.9527999999991, 14.8419999999969, 10.4683999999979, 8.65899999999965, 9.86720000000059, 4.34139999999752, -0.907800000000861, -3.32080000000133, -0.936199999996461, -11.9916000000012, -8.87000000000262, -6.33099999999831, -11.3366000000024, -15.9207999999999, -9.34659999999712, -15.5034000000014, -19.2097999999969, -15.357799999998, -28.2235999999975, -30.6898000000001, -19.3271999999997, -25.6083999999973, -24.409599999999, -13.6385999999984, -33.4473999999973, -32.6949999999997, -28.9063999999998, -31.7483999999968, -32.2935999999972, -35.8329999999987, -47.620600000002, -39.0855999999985, -33.1434000000008, -46.1371999999974, -37.5892000000022, -46.8164000000033, -47.3142000000007, -60.2914000000019, -37.7575999999972};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p14[] = {11816.475, 11605.0046, 11395.3792, 11188.7504, 10984.1814, 10782.0086, 10582.0072, 10384.503, 10189.178, 9996.2738, 9806.0344, 9617.9798, 9431.394, 9248.7784, 9067.6894, 8889.6824, 8712.9134, 8538.8624, 8368.4944, 8197.7956, 8031.8916, 7866.6316, 7703.733, 7544.5726, 7386.204, 7230.666, 7077.8516, 6926.7886, 6778.6902, 6631.9632, 6487.304, 6346.7486, 6206.4408, 6070.202, 5935.2576, 5799.924, 5671.0324, 5541.9788, 5414.6112, 5290.0274, 5166.723, 5047.6906, 4929.162, 4815.1406, 4699.127, 4588.5606, 4477.7394, 4369.4014, 4264.2728, 4155.9224, 4055.581, 3955.505, 3856.9618, 3761.3828, 3666.9702, 3575.7764, 3482.4132, 3395.0186, 3305.8852, 3221.415, 3138.6024, 3056.296, 2970.4494, 2896.1526, 2816.8008, 2740.2156, 2670.497, 2594.1458, 2527.111, 2460.8168, 2387.5114, 2322.9498, 2260.6752, 2194.2686, 2133.7792, 2074.767, 2015.204, 1959.4226, 1898.6502, 1850.006, 1792.849, 1741.4838, 1687.9778, 1638.1322, 1589.3266, 1543.1394, 1496.8266, 1447.8516, 1402.7354, 1361.9606, 1327.0692, 1285.4106, 1241.8112, 1201.6726, 1161.973, 1130.261, 1094.2036, 1048.2036, 1020.6436, 990.901400000002, 961.199800000002, 924.769800000002, 899.526400000002, 872.346400000002, 834.375, 810.432000000001, 780.659800000001, 756.013800000001, 733.479399999997, 707.923999999999, 673.858, 652.222399999999, 636.572399999997, 615.738599999997, 586.696400000001, 564.147199999999, 541.679600000003, 523.943599999999, 505.714599999999, 475.729599999999, 461.779600000002, 449.750800000002, 439.020799999998, 412.7886, 400.245600000002, 383.188199999997, 362.079599999997, 357.533799999997, 334.319000000003, 327.553399999997, 308.559399999998, 291.270199999999, 279.351999999999, 271.791400000002, 252.576999999997, 247.482400000001, 236.174800000001, 218.774599999997, 220.155200000001, 208.794399999999, 201.223599999998, 182.995600000002, 185.5268, 164.547400000003, 176.5962, 150.689599999998, 157.8004, 138.378799999999, 134.021200000003, 117.614399999999, 108.194000000003, 97.0696000000025, 89.6042000000016, 95.6030000000028, 84.7810000000027, 72.635000000002, 77.3482000000004, 59.4907999999996, 55.5875999999989, 50.7346000000034, 61.3916000000027, 50.9149999999936, 39.0384000000049, 58.9395999999979, 29.633600000001, 28.2032000000036, 26.0078000000067, 17.0387999999948, 9.22000000000116, 13.8387999999977, 8.07240000000456, 14.1549999999988, 15.3570000000036, 3.42660000000615, 6.24820000000182, -2.96940000000177, -8.79940000000352, -5.97860000000219, -14.4048000000039, -3.4143999999942, -13.0148000000045, -11.6977999999945, -25.7878000000055, -22.3185999999987, -24.409599999999, -31.9756000000052, -18.9722000000038, -22.8678000000073, -30.8972000000067, -32.3715999999986, -22.3907999999938, -43.6720000000059, -35.9038, -39.7492000000057, -54.1641999999993, -45.2749999999942, -42.2989999999991, -44.1089999999967, -64.3564000000042, -49.9551999999967, -42.6116000000038};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p15[] = {23634.0036, 23210.8034, 22792.4744, 22379.1524, 21969.7928, 21565.326, 21165.3532, 20770.2806, 20379.9892, 19994.7098, 19613.318, 19236.799, 18865.4382, 18498.8244, 18136.5138, 17778.8668, 17426.2344, 17079.32, 16734.778, 16397.2418, 16063.3324, 15734.0232, 15409.731, 15088.728, 14772.9896, 14464.1402, 14157.5588, 13855.5958, 13559.3296, 13264.9096, 12978.326, 12692.0826, 12413.8816, 12137.3192, 11870.2326, 11602.5554, 11340.3142, 11079.613, 10829.5908, 10583.5466, 10334.0344, 10095.5072, 9859.694, 9625.2822, 9395.7862, 9174.0586, 8957.3164, 8738.064, 8524.155, 8313.7396, 8116.9168, 7913.542, 7718.4778, 7521.65, 7335.5596, 7154.2906, 6968.7396, 6786.3996, 6613.236, 6437.406, 6270.6598, 6107.7958, 5945.7174, 5787.6784, 5635.5784, 5482.308, 5337.9784, 5190.0864, 5045.9158, 4919.1386, 4771.817, 4645.7742, 4518.4774, 4385.5454, 4262.6622, 4142.74679999999, 4015.5318, 3897.9276, 3790.7764, 3685.13800000001, 3573.6274, 3467.9706, 3368.61079999999, 3271.5202, 3170.3848, 3076.4656, 2982.38400000001, 2888.4664, 2806.4868, 2711.9564, 2634.1434, 2551.3204, 2469.7662, 2396.61139999999, 2318.9902, 2243.8658, 2171.9246, 2105.01360000001, 2028.8536, 1960.9952, 1901.4096, 1841.86079999999, 1777.54700000001, 1714.5802, 1654.65059999999, 1596.311, 1546.2016, 1492.3296, 1433.8974, 1383.84600000001, 1339.4152, 1293.5518, 1245.8686, 1193.50659999999, 1162.27959999999, 1107.19439999999, 1069.18060000001, 1035.09179999999, 999.679000000004, 957.679999999993, 925.300199999998, 888.099400000006, 848.638600000006, 818.156400000007, 796.748399999997, 752.139200000005, 725.271200000003, 692.216, 671.633600000001, 647.939799999993, 621.670599999998, 575.398799999995, 561.226599999995, 532.237999999998, 521.787599999996, 483.095799999996, 467.049599999998, 465.286399999997, 415.548599999995, 401.047399999996, 380.607999999993, 377.362599999993, 347.258799999996, 338.371599999999, 310.096999999994, 301.409199999995, 276.280799999993, 265.586800000005, 258.994399999996, 223.915999999997, 215.925399999993, 213.503800000006, 191.045400000003, 166.718200000003, 166.259000000005, 162.941200000001, 148.829400000002, 141.645999999993, 123.535399999993, 122.329800000007, 89.473399999988, 80.1962000000058, 77.5457999999926, 59.1056000000099, 83.3509999999951, 52.2906000000075, 36.3979999999865, 40.6558000000077, 42.0003999999899, 19.6630000000005, 19.7153999999864, -8.38539999999921, -0.692799999989802, 0.854800000000978, 3.23219999999856, -3.89040000000386, -5.25880000001052, -24.9052000000083, -22.6837999999989, -26.4286000000138, -34.997000000003, -37.0216000000073, -43.430400000012, -58.2390000000014, -68.8034000000043, -56.9245999999985, -57.8583999999973, -77.3097999999882, -73.2793999999994, -81.0738000000129, -87.4530000000086, -65.0254000000132, -57.296399999992, -96.2746000000043, -103.25, -96.081600000005, -91.5542000000132, -102.465200000006, -107.688599999994, -101.458000000013, -109.715800000005};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p16[] = {47270.0, 46423.3584, 45585.7074, 44757.152, 43938.8416, 43130.9514, 42330.03, 41540.407, 40759.6348, 39988.206, 39226.5144, 38473.2096, 37729.795, 36997.268, 36272.6448, 35558.665, 34853.0248, 34157.4472, 33470.5204, 32793.5742, 32127.0194, 31469.4182, 30817.6136, 30178.6968, 29546.8908, 28922.8544, 28312.271, 27707.0924, 27114.0326, 26526.692, 25948.6336, 25383.7826, 24823.5998, 24272.2974, 23732.2572, 23201.4976, 22674.2796, 22163.6336, 21656.515, 21161.7362, 20669.9368, 20189.4424, 19717.3358, 19256.3744, 18795.9638, 18352.197, 17908.5738, 17474.391, 17052.918, 16637.2236, 16228.4602, 15823.3474, 15428.6974, 15043.0284, 14667.6278, 14297.4588, 13935.2882, 13578.5402, 13234.6032, 12882.1578, 12548.0728, 12219.231, 11898.0072, 11587.2626, 11279.9072, 10973.5048, 10678.5186, 10392.4876, 10105.2556, 9825.766, 9562.5444, 9294.2222, 9038.2352, 8784.848, 8533.2644, 8301.7776, 8058.30859999999, 7822.94579999999, 7599.11319999999, 7366.90779999999, 7161.217, 6957.53080000001, 6736.212, 6548.21220000001, 6343.06839999999, 6156.28719999999, 5975.15419999999, 5791.75719999999, 5621.32019999999, 5451.66, 5287.61040000001, 5118.09479999999, 4957.288, 4798.4246, 4662.17559999999, 4512.05900000001, 4364.68539999999, 4220.77720000001, 4082.67259999999, 3957.19519999999, 3842.15779999999, 3699.3328, 3583.01180000001, 3473.8964, 3338.66639999999, 3233.55559999999, 3117.799, 3008.111, 2909.69140000001, 2814.86499999999, 2719.46119999999, 2624.742, 2532.46979999999, 2444.7886, 2370.1868, 2272.45259999999, 2196.19260000001, 2117.90419999999, 2023.2972, 1969.76819999999, 1885.58979999999, 1833.2824, 1733.91200000001, 1682.54920000001, 1604.57980000001, 1556.11240000001, 1491.3064, 1421.71960000001, 1371.22899999999, 1322.1324, 1264.7892, 1196.23920000001, 1143.8474, 1088.67240000001, 1073.60380000001, 1023.11660000001, 959.036400000012, 927.433199999999, 906.792799999996, 853.433599999989, 841.873800000001, 791.1054, 756.899999999994, 704.343200000003, 672.495599999995, 622.790399999998, 611.254799999995, 567.283200000005, 519.406599999988, 519.188400000014, 495.312800000014, 451.350799999986, 443.973399999988, 431.882199999993, 392.027000000002, 380.924200000009, 345.128999999986, 298.901400000002, 287.771999999997, 272.625, 247.253000000026, 222.490600000019, 223.590000000026, 196.407599999977, 176.425999999978, 134.725199999986, 132.4804, 110.445599999977, 86.7939999999944, 56.7038000000175, 64.915399999998, 38.3726000000024, 37.1606000000029, 46.170999999973, 49.1716000000015, 15.3362000000197, 6.71639999997569, -34.8185999999987, -39.4476000000141, 12.6830000000191, -12.3331999999937, -50.6565999999875, -59.9538000000175, -65.1054000000004, -70.7576000000117, -106.325200000021, -126.852200000023, -110.227599999984, -132.885999999999, -113.897200000007, -142.713800000027, -151.145399999979, -150.799200000009, -177.756200000003, -156.036399999983, -182.735199999996, -177.259399999981, -198.663600000029, -174.577600000019, -193.84580000001};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p17[] = {94541.0, 92848.811, 91174.019, 89517.558, 87879.9705, 86262.7565, 84663.5125, 83083.7435, 81521.7865, 79977.272, 78455.9465, 76950.219, 75465.432, 73994.152, 72546.71, 71115.2345, 69705.6765, 68314.937, 66944.2705, 65591.255, 64252.9485, 62938.016, 61636.8225, 60355.592, 59092.789, 57850.568, 56624.518, 55417.343, 54231.1415, 53067.387, 51903.526, 50774.649, 49657.6415, 48561.05, 47475.7575, 46410.159, 45364.852, 44327.053, 43318.4005, 42325.6165, 41348.4595, 40383.6265, 39436.77, 38509.502, 37594.035, 36695.939, 35818.6895, 34955.691, 34115.8095, 33293.949, 32465.0775, 31657.6715, 30877.2585, 30093.78, 29351.3695, 28594.1365, 27872.115, 27168.7465, 26477.076, 25774.541, 25106.5375, 24452.5135, 23815.5125, 23174.0655, 22555.2685, 21960.2065, 21376.3555, 20785.1925, 20211.517, 19657.0725, 19141.6865, 18579.737, 18081.3955, 17578.995, 17073.44, 16608.335, 16119.911, 15651.266, 15194.583, 14749.0495, 14343.4835, 13925.639, 13504.509, 13099.3885, 12691.2855, 12328.018, 11969.0345, 11596.5145, 11245.6355, 10917.6575, 10580.9785, 10277.8605, 9926.58100000001, 9605.538, 9300.42950000003, 8989.97850000003, 8728.73249999998, 8448.3235, 8175.31050000002, 7898.98700000002, 7629.79100000003, 7413.76199999999, 7149.92300000001, 6921.12650000001, 6677.1545, 6443.28000000003, 6278.23450000002, 6014.20049999998, 5791.20299999998, 5605.78450000001, 5438.48800000001, 5234.2255, 5059.6825, 4887.43349999998, 4682.935, 4496.31099999999, 4322.52250000002, 4191.42499999999, 4021.24200000003, 3900.64799999999, 3762.84250000003, 3609.98050000001, 3502.29599999997, 3363.84250000003, 3206.54849999998, 3079.70000000001, 2971.42300000001, 2867.80349999998, 2727.08100000001, 2630.74900000001, 2496.6165, 2440.902, 2356.19150000002, 2235.58199999999, 2120.54149999999, 2012.25449999998, 1933.35600000003, 1820.93099999998, 1761.54800000001, 1663.09350000002, 1578.84600000002, 1509.48149999999, 1427.3345, 1379.56150000001, 1306.68099999998, 1212.63449999999, 1084.17300000001, 1124.16450000001, 1060.69949999999, 1007.48849999998, 941.194499999983, 879.880500000028, 836.007500000007, 782.802000000025, 748.385499999975, 647.991500000004, 626.730500000005, 570.776000000013, 484.000500000024, 513.98550000001, 418.985499999952, 386.996999999974, 370.026500000036, 355.496999999974, 356.731499999994, 255.92200000002, 259.094000000041, 205.434499999974, 165.374500000034, 197.347500000033, 95.718499999959, 67.6165000000037, 54.6970000000438, 31.7395000000251, -15.8784999999916, 8.42500000004657, -26.3754999999655, -118.425500000012, -66.6629999999423, -42.9745000000112, -107.364999999991, -189.839000000036, -162.611499999999, -164.964999999967, -189.079999999958, -223.931499999948, -235.329999999958, -269.639500000048, -249.087999999989, -206.475499999942, -283.04449999996, -290.667000000016, -304.561499999953, -336.784499999951, -380.386500000022, -283.280499999993, -364.533000000054, -389.059499999974, -364.454000000027, -415.748000000021, -417.155000000028};
_CUDAX_CUCO_HLL_TUNING_ARR_DECL __bias_data_p18[] = {189083.0, 185696.913, 182348.774, 179035.946, 175762.762, 172526.444, 169329.754, 166166.099, 163043.269, 159958.91, 156907.912, 153906.845, 150924.199, 147996.568, 145093.457, 142239.233, 139421.475, 136632.27, 133889.588, 131174.2, 128511.619, 125868.621, 123265.385, 120721.061, 118181.769, 115709.456, 113252.446, 110840.198, 108465.099, 106126.164, 103823.469, 101556.618, 99308.004, 97124.508, 94937.803, 92833.731, 90745.061, 88677.627, 86617.47, 84650.442, 82697.833, 80769.132, 78879.629, 77014.432, 75215.626, 73384.587, 71652.482, 69895.93, 68209.301, 66553.669, 64921.981, 63310.323, 61742.115, 60205.018, 58698.658, 57190.657, 55760.865, 54331.169, 52908.167, 51550.273, 50225.254, 48922.421, 47614.533, 46362.049, 45098.569, 43926.083, 42736.03, 41593.473, 40425.26, 39316.237, 38243.651, 37170.617, 36114.609, 35084.19, 34117.233, 33206.509, 32231.505, 31318.728, 30403.404, 29540.0550000001, 28679.236, 27825.862, 26965.216, 26179.148, 25462.08, 24645.952, 23922.523, 23198.144, 22529.128, 21762.4179999999, 21134.779, 20459.117, 19840.818, 19187.04, 18636.3689999999, 17982.831, 17439.7389999999, 16874.547, 16358.2169999999, 15835.684, 15352.914, 14823.681, 14329.313, 13816.897, 13342.874, 12880.882, 12491.648, 12021.254, 11625.392, 11293.7610000001, 10813.697, 10456.209, 10099.074, 9755.39000000001, 9393.18500000006, 9047.57900000003, 8657.98499999999, 8395.85900000005, 8033.0, 7736.95900000003, 7430.59699999995, 7258.47699999996, 6924.58200000005, 6691.29399999999, 6357.92500000005, 6202.05700000003, 5921.19700000004, 5628.28399999999, 5404.96799999999, 5226.71100000001, 4990.75600000005, 4799.77399999998, 4622.93099999998, 4472.478, 4171.78700000001, 3957.46299999999, 3868.95200000005, 3691.14300000004, 3474.63100000005, 3341.67200000002, 3109.14000000001, 3071.97400000005, 2796.40399999998, 2756.17799999996, 2611.46999999997, 2471.93000000005, 2382.26399999997, 2209.22400000005, 2142.28399999999, 2013.96100000001, 1911.18999999994, 1818.27099999995, 1668.47900000005, 1519.65800000005, 1469.67599999998, 1367.13800000004, 1248.52899999998, 1181.23600000003, 1022.71900000004, 1088.20700000005, 959.03600000008, 876.095999999903, 791.183999999892, 703.337000000058, 731.949999999953, 586.86400000006, 526.024999999907, 323.004999999888, 320.448000000091, 340.672999999952, 309.638999999966, 216.601999999955, 102.922999999952, 19.2399999999907, -0.114000000059605, -32.6240000000689, -89.3179999999702, -153.497999999905, -64.2970000000205, -143.695999999996, -259.497999999905, -253.017999999924, -213.948000000091, -397.590000000084, -434.006000000052, -403.475000000093, -297.958000000101, -404.317000000039, -528.898999999976, -506.621000000043, -513.205000000075, -479.351000000024, -596.139999999898, -527.016999999993, -664.681000000099, -680.306000000099, -704.050000000047, -850.486000000034, -757.43200000003, -713.308999999892};
//! @brief Get bias data array for a given precision
//!
//! @param __precision The precision value (4-18)
//! @return Pointer to the bias data array for the given precision
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr const double* __bias_data(::cuda::std::int32_t __precision) noexcept {
switch (__precision) {
case 4: return __bias_data_p4;
case 5: return __bias_data_p5;
case 6: return __bias_data_p6;
case 7: return __bias_data_p7;
case 8: return __bias_data_p8;
case 9: return __bias_data_p9;
case 10: return __bias_data_p10;
case 11: return __bias_data_p11;
case 12: return __bias_data_p12;
case 13: return __bias_data_p13;
case 14: return __bias_data_p14;
case 15: return __bias_data_p15;
case 16: return __bias_data_p16;
case 17: return __bias_data_p17;
case 18: return __bias_data_p18;
default: return nullptr;
}
}
// clang-format on
} // namespace cuda::experimental::cuco::__hyperloglog_ns
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_HYPERLOGLOG_TUNING_CUH

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@@ -1,294 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_KERNELS_CUH
#define _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_KERNELS_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cub/block/block_reduce.cuh>
#include <cuda/__atomic/atomic.h>
#include <cuda/std/__iterator/iterator_traits.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/std/__type_traits/void_t.h>
#include <cuda/experimental/__cuco/detail/utility/cuda.cuh>
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
#include <cuda/std/__cccl/prologue.h>
#if _CCCL_CUDA_COMPILATION()
_CCCL_DIAG_PUSH
_CCCL_DIAG_SUPPRESS_GCC("-Wattributes")
namespace cuda::experimental::cuco::__open_addressing
{
//! @brief Scalar (cooperative-group size 1) functor inserting `first[i]` when `pred(stencil[i])` holds.
template <class _InputIt, class _StencilIt, class _Predicate, class _Ref>
struct __insert_if_fn
{
_InputIt __first;
_StencilIt __stencil;
_Predicate __pred;
_Ref __ref;
_CCCL_DEVICE_API void operator()(detail::__index_type __idx)
{
if (__pred(*(__stencil + __idx)))
{
__ref.insert(*(__first + __idx));
}
}
};
template <class _InputIt, class _StencilIt, class _Predicate, class _Ref>
__insert_if_fn(_InputIt, _StencilIt, _Predicate, _Ref) -> __insert_if_fn<_InputIt, _StencilIt, _Predicate, _Ref>;
//! @brief Scalar (cooperative-group size 1) functor writing `pred(stencil[i]) ? contains(first[i]) : false`.
template <class _InputIt, class _StencilIt, class _Predicate, class _OutputIt, class _Ref>
struct __contains_if_fn
{
_InputIt __first;
_StencilIt __stencil;
_Predicate __pred;
_OutputIt __output_begin;
_Ref __ref;
_CCCL_DEVICE_API void operator()(detail::__index_type __idx) const
{
*(__output_begin + __idx) = __pred(*(__stencil + __idx)) ? __ref.contains(*(__first + __idx)) : false;
}
};
template <class _InputIt, class _StencilIt, class _Predicate, class _OutputIt, class _Ref>
__contains_if_fn(_InputIt, _StencilIt, _Predicate, _OutputIt, _Ref)
-> __contains_if_fn<_InputIt, _StencilIt, _Predicate, _OutputIt, _Ref>;
//! @brief Inserts all elements in the range `[first, first + n)` and returns the number of
//! successful insertions if `pred` of the corresponding stencil returns true.
template <int _CgSize, int _BlockSize, class _InputIt, class _StencilIt, class _Predicate, class _Ref>
_CCCL_KERNEL_ATTRIBUTES _CCCL_LAUNCH_BOUNDS(_BlockSize) void __insert_if_n(
_InputIt __first,
detail::__index_type __n,
_StencilIt __stencil,
_Predicate __pred,
typename _Ref::size_type* __num_successes,
_Ref __ref)
{
using __block_reduce = CUB_NS_QUALIFIER::BlockReduce<typename _Ref::size_type, _BlockSize>;
__shared__ typename __block_reduce::TempStorage __temp_storage;
typename _Ref::size_type __thread_num_successes = 0;
const auto __loop_stride = detail::__grid_stride() / _CgSize;
auto __idx = detail::__global_thread_id() / _CgSize;
while (__idx < __n)
{
if (__pred(*(__stencil + __idx)))
{
using __value_t = typename ::cuda::std::iterator_traits<_InputIt>::value_type;
const __value_t __insert_element{*(__first + __idx)};
if constexpr (_CgSize == 1)
{
if (__ref.insert(__insert_element))
{
__thread_num_successes++;
}
}
else
{
const auto __tile = ::cooperative_groups::tiled_partition<_CgSize, ::cooperative_groups::thread_block>(
::cooperative_groups::this_thread_block());
if (__ref.insert(__tile, __insert_element) && __tile.thread_rank() == 0)
{
__thread_num_successes++;
}
}
}
__idx += __loop_stride;
}
const auto __block_num_successes = __block_reduce(__temp_storage).Sum(__thread_num_successes);
if (threadIdx.x == 0)
{
::cuda::atomic_ref<typename _Ref::size_type, _Ref::thread_scope>{*__num_successes}.fetch_add(
__block_num_successes, ::cuda::std::memory_order_relaxed);
}
}
//! @brief Inserts all elements in the range `[first, first + n)` if `pred` of the corresponding
//! stencil returns true.
template <int _CgSize, int _BlockSize, class _InputIt, class _StencilIt, class _Predicate, class _Ref>
_CCCL_KERNEL_ATTRIBUTES _CCCL_LAUNCH_BOUNDS(_BlockSize) void
__insert_if_n(_InputIt __first, detail::__index_type __n, _StencilIt __stencil, _Predicate __pred, _Ref __ref)
{
const auto __loop_stride = detail::__grid_stride() / _CgSize;
auto __idx = detail::__global_thread_id() / _CgSize;
while (__idx < __n)
{
if (__pred(*(__stencil + __idx)))
{
using __value_t = typename ::cuda::std::iterator_traits<_InputIt>::value_type;
const __value_t __insert_element{*(__first + __idx)};
const auto __tile = ::cooperative_groups::tiled_partition<_CgSize, ::cooperative_groups::thread_block>(
::cooperative_groups::this_thread_block());
__ref.insert(__tile, __insert_element);
}
__idx += __loop_stride;
}
}
//! @brief Contains test with predicate.
template <int _CgSize, int _BlockSize, class _InputIt, class _StencilIt, class _Predicate, class _OutputIt, class _Ref>
_CCCL_KERNEL_ATTRIBUTES _CCCL_LAUNCH_BOUNDS(_BlockSize) void __contains_if_n(
_InputIt __first,
detail::__index_type __n,
_StencilIt __stencil,
_Predicate __pred,
_OutputIt __output_begin,
_Ref __ref)
{
const auto __block = ::cooperative_groups::this_thread_block();
const auto __loop_stride = detail::__grid_stride() / _CgSize;
auto __idx = detail::__global_thread_id() / _CgSize;
while (__idx < __n)
{
const auto __tile = ::cooperative_groups::tiled_partition<_CgSize, ::cooperative_groups::thread_block>(__block);
using __value_t = typename ::cuda::std::iterator_traits<_InputIt>::value_type;
const __value_t __key = *(__first + __idx);
const auto __found = __pred(*(__stencil + __idx)) ? __ref.contains(__tile, __key) : false;
if (__tile.thread_rank() == 0)
{
*(__output_begin + __idx) = __found;
}
__idx += __loop_stride;
}
}
//! @brief Helper to determine the buffer type for the find kernel.
template <class _Container, class = void>
struct __find_buffer
{
using type = typename _Container::key_type;
};
//! @brief Helper to determine the buffer type for the find kernel when `mapped_type` exists.
template <class _Container>
struct __find_buffer<_Container, ::cuda::std::void_t<typename _Container::mapped_type>>
{
using type = typename _Container::mapped_type;
};
//! @brief Converts a find result to the output value or the appropriate empty sentinel.
template <class _Ref, class _Iterator>
[[nodiscard]] _CCCL_DEVICE_API typename __find_buffer<_Ref>::type __find_output(_Ref const& __ref, _Iterator __found)
{
constexpr bool __has_payload = !::cuda::std::is_same_v<typename _Ref::key_type, typename _Ref::value_type>;
if constexpr (__has_payload)
{
return __found == __ref.end() ? __ref.empty_value_sentinel() : __found->second;
}
else
{
return __found == __ref.end() ? __ref.empty_key_sentinel() : *__found;
}
}
//! @brief Find with predicate.
template <int _CgSize, int _BlockSize, class _InputIt, class _StencilIt, class _Predicate, class _OutputIt, class _Ref>
_CCCL_KERNEL_ATTRIBUTES _CCCL_LAUNCH_BOUNDS(_BlockSize) void __find_if_n(
_InputIt __first,
detail::__index_type __n,
_StencilIt __stencil,
_Predicate __pred,
_OutputIt __output_begin,
_Ref __ref)
{
const auto __block = ::cooperative_groups::this_thread_block();
const auto __thread_idx = __block.thread_rank();
const auto __loop_stride = detail::__grid_stride() / _CgSize;
auto __idx = detail::__global_thread_id() / _CgSize;
using __output_type = typename __find_buffer<_Ref>::type;
__shared__ __output_type __output_buffer[_BlockSize / _CgSize];
while ((__idx - __thread_idx / _CgSize) < __n)
{
if constexpr (_CgSize == 1)
{
if (__idx < __n)
{
using __value_t = typename ::cuda::std::iterator_traits<_InputIt>::value_type;
const __value_t __key = *(__first + __idx);
const auto __selected = __pred(*(__stencil + __idx));
const auto __found = __selected ? __ref.find(__key) : __ref.end();
/*
* The ld.relaxed.gpu instruction causes L1 to flush more frequently, causing increased
* sector stores from L2 to global memory. By writing results to shared memory and then
* synchronizing before writing back to global, we no longer rely on L1, preventing the
* increase in sector stores from L2 to global and improving performance.
*/
__output_buffer[__thread_idx] = __find_output(__ref, __found);
}
__block.sync();
if (__idx < __n)
{
*(__output_begin + __idx) = __output_buffer[__thread_idx];
}
}
else
{
const auto __tile = ::cooperative_groups::tiled_partition<_CgSize, ::cooperative_groups::thread_block>(__block);
if (__idx < __n)
{
using __value_t = typename ::cuda::std::iterator_traits<_InputIt>::value_type;
const __value_t __key = *(__first + __idx);
bool __selected = false;
if (__tile.thread_rank() == 0)
{
__selected = __pred(*(__stencil + __idx));
}
__selected = __tile.shfl(__selected, 0);
const auto __found = __selected ? __ref.find(__tile, __key) : __ref.end();
if (__tile.thread_rank() == 0)
{
*(__output_begin + __idx) = __find_output(__ref, __found);
}
}
}
__idx += __loop_stride;
}
}
} // namespace cuda::experimental::cuco::__open_addressing
_CCCL_DIAG_POP
#endif // _CCCL_CUDA_COMPILATION()
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_KERNELS_CUH

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cccl_upstream/cudax/include/cuda/experimental/__cuco/detail/open_addressing/open_addressing_impl.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_IMPL_CUH
#define _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_IMPL_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cub/device/device_for.cuh>
#include <cub/device/device_transform.cuh>
#include <cuda/__container/buffer.h>
#include <cuda/__driver/driver_api.h>
#include <cuda/__iterator/constant_iterator.h>
#include <cuda/__runtime/api_wrapper.h>
#include <cuda/__type_traits/is_bitwise_comparable.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__functional/identity.h>
#include <cuda/std/__type_traits/is_base_of.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/experimental/__cuco/capacity.cuh>
#include <cuda/experimental/__cuco/detail/open_addressing/kernels.cuh>
#include <cuda/experimental/__cuco/detail/open_addressing/slot_storage_ref.cuh>
#include <cuda/experimental/__cuco/detail/utility/cuda.cuh>
#include <cuda/experimental/__cuco/probing_scheme.cuh>
#include <cuda/std/__cccl/prologue.h>
#if !_CCCL_COMPILER(NVRTC)
namespace cuda::experimental::cuco::__open_addressing
{
//! @brief Open addressing implementation class.
//!
//! @note This class should NOT be used directly.
//!
//! @throw If the size of the given key type is larger than 8 bytes
//! @throw If the size of the given slot type is larger than 16 bytes
//! @throw If the given key type doesn't have unique object representations, i.e.,
//! `cuda::is_bitwise_comparable_v<_Key> == false`
//! @throw If the probing scheme type is not inherited from
//! `cuda::experimental::cuco::detail::__probing_scheme_base`
//!
//! @tparam _Key Type used for keys. Requires `cuda::is_bitwise_comparable_v<_Key>`
//! @tparam _Value Type used for storage values
//! @tparam _Scope The scope in which operations will be performed by individual threads
//! @tparam _KeyEqual Binary callable type used to compare two keys for equality
//! @tparam _ProbingScheme Probing scheme type
//! @tparam _BucketSize Number of slots per bucket
//! @tparam _MemoryResource Type of memory resource used for device storage
template <class _Key,
class _Value,
::cuda::thread_scope _Scope,
class _KeyEqual,
class _ProbingScheme,
int _BucketSize,
class _MemoryResource>
class __open_addressing_impl
{
public:
using __key_type = _Key;
using __value_type = _Value;
using __probing_scheme_type = _ProbingScheme;
using __hasher = typename __probing_scheme_type::hasher;
using __size_type = ::cuda::std::size_t;
using __key_equal = _KeyEqual;
using __storage_ref_type = __slot_storage_ref<__value_type, _BucketSize>;
static constexpr auto __has_payload = !::cuda::std::is_same_v<_Key, _Value>;
static constexpr auto __cg_size = _ProbingScheme::cg_size;
static constexpr auto __bucket_size = _BucketSize;
static constexpr auto __thread_scope = _Scope;
static_assert(sizeof(_Key) <= 8, "Container does not support key types larger than 8 bytes.");
static_assert(sizeof(_Value) <= 16, "Container does not support slot types larger than 16 bytes.");
static_assert(::cuda::is_bitwise_comparable_v<_Key>,
"Key type must have unique object representations or have been explicitly declared as safe for "
"bitwise comparison via specialization of cuda::is_bitwise_comparable_v<Key>.");
static_assert(::cuda::std::is_base_of_v<detail::__probing_scheme_base<_ProbingScheme::cg_size>, _ProbingScheme>,
"ProbingScheme must inherit from cuda::experimental::cuco::detail::__probing_scheme_base");
private:
__value_type __empty_slot_sentinel;
__key_type __erased_key_sentinel;
__key_equal __predicate;
__probing_scheme_type __probing_scheme;
mutable _MemoryResource __memory_resource;
::cuda::device_buffer<__value_type> __slots;
//! @brief Computes the number of buckets for a requested capacity.
[[nodiscard]] _CCCL_HOST_API static __size_type __compute_num_buckets(__size_type __requested_capacity)
{
return make_valid_capacity<_ProbingScheme, _BucketSize>(__requested_capacity) / _BucketSize;
}
//! @brief Computes the number of buckets for a given number of keys and load factor.
[[nodiscard]] _CCCL_HOST_API static __size_type __compute_num_buckets(__size_type __n, double __load_factor)
{
return make_valid_capacity<_ProbingScheme, _BucketSize>(__n, __load_factor) / _BucketSize;
}
//! @brief Extracts the key from a slot.
[[nodiscard]] _CCCL_HOST_API constexpr const __key_type& __extract_key(const __value_type& __slot) const noexcept
{
if constexpr (__has_payload)
{
return __slot.first;
}
else
{
return __slot;
}
}
//! @brief Allocates and zero-initializes an RAII device counter.
[[nodiscard]] _CCCL_HOST_API ::cuda::device_buffer<__size_type> __make_counter(::cuda::stream_ref __stream) const
{
return ::cuda::device_buffer<__size_type>{__stream, __memory_resource, {__size_type{0}}};
}
//! @brief Reads a device counter to host.
[[nodiscard]] _CCCL_HOST_API __size_type
__read_counter(const ::cuda::device_buffer<__size_type>& __counter, ::cuda::stream_ref __stream) const
{
__size_type __result;
::cuda::__driver::__memcpyAsync(&__result, __counter.data(), sizeof(__size_type), __stream.get());
__stream.sync();
return __result;
}
public:
//! @brief Constructs an open addressing implementation with the given capacity.
_CCCL_HOST_API __open_addressing_impl(
::cuda::stream_ref __stream,
_MemoryResource __mr,
__size_type __capacity,
__value_type __empty_slot_sentinel,
const _KeyEqual& __pred,
const _ProbingScheme& __probing_scheme)
: __empty_slot_sentinel{__empty_slot_sentinel}
, __erased_key_sentinel{__extract_key(__empty_slot_sentinel)}
, __predicate{__pred}
, __probing_scheme{__probing_scheme}
, __memory_resource{__mr}
, __slots{__stream, __mr, __compute_num_buckets(__capacity) * _BucketSize, ::cuda::no_init}
{
clear_async(__stream);
}
//! @brief Constructs an open addressing implementation with capacity derived from desired load
//! factor.
_CCCL_HOST_API __open_addressing_impl(
::cuda::stream_ref __stream,
_MemoryResource __mr,
__size_type __n,
double __desired_load_factor,
__value_type __empty_slot_sentinel,
const _KeyEqual& __pred,
const _ProbingScheme& __probing_scheme)
: __empty_slot_sentinel{__empty_slot_sentinel}
, __erased_key_sentinel{__extract_key(__empty_slot_sentinel)}
, __predicate{__pred}
, __probing_scheme{__probing_scheme}
, __memory_resource{__mr}
, __slots{__stream, __mr, __compute_num_buckets(__n, __desired_load_factor) * _BucketSize, ::cuda::no_init}
{
clear_async(__stream);
}
//! @brief Constructs an open addressing implementation with erasure support.
_CCCL_HOST_API __open_addressing_impl(
::cuda::stream_ref __stream,
_MemoryResource __mr,
__size_type __capacity,
__value_type __empty_slot_sentinel,
__key_type __erased_key_sentinel,
const _KeyEqual& __pred,
const _ProbingScheme& __probing_scheme)
: __empty_slot_sentinel{__empty_slot_sentinel}
, __erased_key_sentinel{__erased_key_sentinel}
, __predicate{__pred}
, __probing_scheme{__probing_scheme}
, __memory_resource{__mr}
, __slots{__stream, __mr, __compute_num_buckets(__capacity) * _BucketSize, ::cuda::no_init}
{
if (empty_key_sentinel() == erased_key_sentinel())
{
_CCCL_THROW(::std::invalid_argument, "The empty key sentinel and erased key sentinel cannot be the same value.");
}
clear_async(__stream);
}
//! @brief Fills all slots with the empty sentinel.
_CCCL_HOST_API void clear(::cuda::stream_ref __stream)
{
clear_async(__stream);
__stream.sync();
}
//! @brief Asynchronously fills all slots with the empty sentinel.
//!
//! @throws cuda_error if the clear operation fails to launch
_CCCL_HOST_API void clear_async(::cuda::stream_ref __stream)
{
const auto __n = capacity();
if (__n == 0)
{
return;
}
_CCCL_TRY_CUDA_API(
CUB_NS_QUALIFIER::DeviceTransform::Fill,
"cuco: failed to clear slot storage",
__slots.data(),
static_cast<detail::__index_type>(__n),
__empty_slot_sentinel,
__stream);
}
//! @brief Inserts keys in `[first, last)` and returns the number of successful insertions.
template <class _InputIt, class _Ref>
_CCCL_HOST_API __size_type insert(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last, _Ref __container_ref)
{
const auto __num_keys = detail::__distance(__first, __last);
if (__num_keys == 0)
{
return 0;
}
auto __counter = __make_counter(__stream);
const auto __grid_size = detail::__grid_size(__num_keys, __cg_size);
__open_addressing::__insert_if_n<__cg_size, detail::__default_block_size>
<<<static_cast<unsigned>(__grid_size), detail::__default_block_size, 0, __stream.get()>>>(
__first,
__num_keys,
::cuda::constant_iterator<bool>{true},
::cuda::std::identity{},
__counter.data(),
__container_ref);
return __read_counter(__counter, __stream);
}
//! @brief Asynchronously inserts keys in `[first, last)`.
//!
//! @throws cuda_error if the insert operation fails to launch
template <class _InputIt, class _Ref>
_CCCL_HOST_API void insert_async(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last, _Ref __container_ref)
{
const auto __num_keys = detail::__distance(__first, __last);
if (__num_keys == 0)
{
return;
}
if constexpr (__cg_size == 1)
{
__open_addressing::__insert_if_fn __op{
__first, ::cuda::constant_iterator<bool>{true}, ::cuda::std::identity{}, __container_ref};
_CCCL_TRY_CUDA_API(CUB_NS_QUALIFIER::DeviceFor::Bulk, "cuco: failed to insert keys", __num_keys, __op, __stream);
}
else
{
const auto __grid_size = detail::__grid_size(__num_keys, __cg_size);
__open_addressing::__insert_if_n<__cg_size, detail::__default_block_size>
<<<static_cast<unsigned>(__grid_size), detail::__default_block_size, 0, __stream.get()>>>(
__first, __num_keys, ::cuda::constant_iterator<bool>{true}, ::cuda::std::identity{}, __container_ref);
}
}
//! @brief Asynchronously checks if keys in `[first, last)` exist in the container.
//!
//! @throws cuda_error if the query operation fails to launch
template <class _InputIt, class _OutputIt, class _Ref>
_CCCL_HOST_API void contains_async(
::cuda::stream_ref __stream, _InputIt __first, _InputIt __last, _OutputIt __output_begin, _Ref __container_ref) const
{
const auto __num_keys = detail::__distance(__first, __last);
if (__num_keys == 0)
{
return;
}
if constexpr (__cg_size == 1)
{
__open_addressing::__contains_if_fn __op{
__first, ::cuda::constant_iterator<bool>{true}, ::cuda::std::identity{}, __output_begin, __container_ref};
_CCCL_TRY_CUDA_API(CUB_NS_QUALIFIER::DeviceFor::Bulk, "cuco: failed to query keys", __num_keys, __op, __stream);
}
else
{
const auto __grid_size = detail::__grid_size(__num_keys, __cg_size);
__open_addressing::__contains_if_n<__cg_size, detail::__default_block_size>
<<<static_cast<unsigned>(__grid_size), detail::__default_block_size, 0, __stream.get()>>>(
__first,
__num_keys,
::cuda::constant_iterator<bool>{true},
::cuda::std::identity{},
__output_begin,
__container_ref);
}
}
//! @brief Asynchronously finds payloads for keys in `[first, last)` whose stencil satisfies `pred`.
//!
//! For each key `first[i]` with `pred(stencil[i]) == true` that is present, the associated payload is
//! written to the corresponding output position; otherwise the empty value sentinel is written.
//!
//! @throws cuda_error if the query operation fails to launch
template <class _InputIt, class _StencilIt, class _Predicate, class _OutputIt, class _Ref>
_CCCL_HOST_API void find_if_async(
::cuda::stream_ref __stream,
_InputIt __first,
_InputIt __last,
_StencilIt __stencil,
_Predicate __pred,
_OutputIt __output_begin,
_Ref __container_ref) const
{
const auto __num_keys = detail::__distance(__first, __last);
if (__num_keys == 0)
{
return;
}
const auto __grid_size = detail::__grid_size(__num_keys, __cg_size);
__open_addressing::__find_if_n<__cg_size, detail::__default_block_size>
<<<static_cast<unsigned>(__grid_size), detail::__default_block_size, 0, __stream.get()>>>(
__first, __num_keys, __stencil, __pred, __output_begin, __container_ref);
}
//! @brief Asynchronously finds the payloads for keys in `[first, last)`.
//!
//! For each key that is present, the associated payload is written to the corresponding output
//! position; for each key that is absent, the empty value sentinel is written instead.
//!
//! @throws cuda_error if the query operation fails to launch
template <class _InputIt, class _OutputIt, class _Ref>
_CCCL_HOST_API void find_async(
::cuda::stream_ref __stream, _InputIt __first, _InputIt __last, _OutputIt __output_begin, _Ref __container_ref) const
{
this->find_if_async(
__stream,
__first,
__last,
::cuda::constant_iterator<bool>{true},
::cuda::std::identity{},
__output_begin,
__container_ref);
}
//! @brief Returns the total number of slots.
[[nodiscard]] _CCCL_HOST_API constexpr __size_type capacity() const noexcept
{
return static_cast<__size_type>(__slots.size());
}
//! @brief Returns a pointer to the underlying slot array.
[[nodiscard]] _CCCL_HOST_API __value_type* data() const noexcept
{
return const_cast<__value_type*>(__slots.data());
}
//! @brief Returns the empty key sentinel.
[[nodiscard]] _CCCL_HOST_API constexpr __key_type empty_key_sentinel() const noexcept
{
return __extract_key(__empty_slot_sentinel);
}
//! @brief Returns the erased key sentinel.
[[nodiscard]] _CCCL_HOST_API constexpr __key_type erased_key_sentinel() const noexcept
{
return __erased_key_sentinel;
}
//! @brief Returns the key comparison function.
[[nodiscard]] _CCCL_HOST_API constexpr __key_equal key_eq() const noexcept
{
return __predicate;
}
//! @brief Returns the probing scheme.
[[nodiscard]] _CCCL_HOST_API constexpr __probing_scheme_type probing_scheme() const noexcept
{
return __probing_scheme;
}
//! @brief Returns the hash function.
[[nodiscard]] _CCCL_HOST_API constexpr __hasher hash_function() const noexcept
{
return probing_scheme().hash_function();
}
//! @brief Returns a non-owning reference to the stored slots.
[[nodiscard]] _CCCL_HOST_API __storage_ref_type storage_ref() const noexcept
{
return __storage_ref_type{const_cast<__value_type*>(__slots.data()), capacity()};
}
};
} // namespace cuda::experimental::cuco::__open_addressing
#endif // !_CCCL_COMPILER(NVRTC)
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_IMPL_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_SLOT_STORAGE_REF_CUH
#define _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_SLOT_STORAGE_REF_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__memory/is_aligned.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__mdspan/extents.h>
#include <cuda/std/span>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::__open_addressing
{
//! @brief Lightweight non-owning reference to a contiguous slot array with bucket abstraction.
//!
//! Provides indexing into the slot array organized as buckets; within each bucket there are
//! `_BucketSize` value-typed slots. The total slot count is carried as a `cuda::std::extents`, so a
//! static `_Capacity` folds the probing reduction to a constant while a dynamic `_Capacity` stores
//! the slot count. The probing layer works in slot offsets bounded by `capacity()`.
//!
//! @tparam _Value The slot value type (e.g. `::cuda::std::pair<Key, T>`)
//! @tparam _BucketSize Number of slots per bucket (compile-time constant)
//! @tparam _Capacity Valid total slot count, or `cuda::std::dynamic_extent` for runtime sizing
template <class _Value, int _BucketSize, ::cuda::std::size_t _Capacity = ::cuda::std::dynamic_extent>
struct __slot_storage_ref
{
using __size_type = ::cuda::std::size_t;
using __value_type = _Value;
using __capacity_extent_type = ::cuda::std::extents<__size_type, _Capacity>;
using __iterator = _Value*;
using __const_iterator = const _Value*;
static constexpr int __bucket_size = _BucketSize;
using __bucket_type = ::cuda::std::span<_Value, _BucketSize>;
static_assert(_BucketSize > 0, "bucket size must be greater than zero");
static_assert(_Capacity == ::cuda::std::dynamic_extent || _Capacity % _BucketSize == 0,
"static capacity must be divisible by the bucket size");
_Value* __data_;
_CCCL_NO_UNIQUE_ADDRESS __capacity_extent_type __capacity_;
//! @brief Constructs a slot storage ref.
//!
//! @param __data Pointer to the first slot
//! @param __capacity Total slot count (must equal the static `_Capacity` when it is static)
_CCCL_HOST_DEVICE_API constexpr __slot_storage_ref(_Value* __data, __size_type __capacity) noexcept
: __data_{__data}
, __capacity_{__capacity}
{}
//! @brief Returns the bucket at position `__i`.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __bucket_type operator[](__size_type __i) const noexcept
{
return __bucket_type{__data_ + __i, typename __bucket_type::size_type{_BucketSize}};
}
//! @brief Returns the total number of slots.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __size_type capacity() const noexcept
{
return __capacity_.extent(0);
}
//! @brief Returns the number of buckets.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __size_type num_buckets() const noexcept
{
return capacity() / __size_type{_BucketSize};
}
//! @brief Returns the total slot count as a `cuda::std::extents` (the probing reduction bound).
//!
//! Returning the extent rather than a plain size keeps the static slot count in the type, so the
//! probing iterator's modular reduction folds to a constant for static `_Capacity`.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __capacity_extent_type capacity_extent() const noexcept
{
return __capacity_;
}
//! @brief Returns a pointer to the underlying slot array.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr _Value* data() const noexcept
{
return __data_;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API bool __is_packed_cas_aligned() const noexcept
{
return ::cuda::is_aligned(__data_, sizeof(_Value));
}
//! @brief Returns an iterator to the first slot.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __iterator begin() const noexcept
{
return __data_;
}
//! @brief Returns an iterator to one past the last slot.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __iterator end() const noexcept
{
return __data_ + capacity();
}
};
} // namespace cuda::experimental::cuco::__open_addressing
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_OPEN_ADDRESSING_SLOT_STORAGE_REF_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_PRIME_CUH
#define _CUDAX___CUCO_DETAIL_PRIME_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__numeric/add_overflow.h>
#include <cuda/std/cstdint>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::detail
{
//! @brief Modular multiplication: `(__n1 * __n2) % __m` without overflow.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint64_t
__mod_mul(::cuda::std::uint64_t __n1, ::cuda::std::uint64_t __n2, ::cuda::std::uint64_t __m) noexcept
{
#if _CCCL_HAS_INT128()
auto __r = static_cast<__uint128_t>(__n1) * __n2;
return static_cast<::cuda::std::uint64_t>(__r % __m);
#else
// Fallback: Russian-peasant multiplication in modular arithmetic.
::cuda::std::uint64_t __r = 0;
__n1 %= __m;
__n2 %= __m;
while (__n2 > 0)
{
const ::cuda::std::uint64_t __mod_diff = __m - __n1;
if (__n2 & 1)
{
__r = (__r >= __mod_diff) ? __r - __mod_diff : __r + __n1;
}
__n1 = (__n1 >= __mod_diff) ? __n1 - __mod_diff : __n1 + __n1;
__n2 >>= 1;
}
return __r;
#endif // _CCCL_HAS_INT128()
}
//! @brief Modular exponentiation: `(__b ^ __e) % __m` via binary exponentiation.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint64_t
__mod_pow(::cuda::std::uint64_t __b, ::cuda::std::uint64_t __e, ::cuda::std::uint64_t __m) noexcept
{
::cuda::std::uint64_t __r = 1;
__b %= __m;
for (; __e > 0; __e >>= 1)
{
if (__e & 1)
{
__r = detail::__mod_mul(__r, __b, __m);
}
__b = detail::__mod_mul(__b, __b, __m);
}
return __r;
}
//! @brief Single Miller-Rabin witness test.
//!
//! Given `n - 1 == 2^s * d`, checks whether `a^d == 1 (mod n)` or
//! `a^(2^r * d) == n - 1 (mod n)` for some `0 <= r < s`.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr bool __miller_rabin_test(
::cuda::std::uint64_t __n, ::cuda::std::uint64_t __a, ::cuda::std::uint64_t __d, ::cuda::std::uint32_t __s) noexcept
{
::cuda::std::uint64_t __x = detail::__mod_pow(__a % __n, __d, __n);
const auto __neg_one = __n - 1;
if (__x == 1 || __x == __neg_one)
{
return true;
}
for (::cuda::std::uint32_t __i = 1; __i < __s; ++__i)
{
__x = detail::__mod_mul(__x, __x, __n);
if (__x == __neg_one)
{
return true;
}
}
return false;
}
//! @brief Deterministic primality test for all 64-bit integers.
//!
//! Uses trial division by small primes followed by Miller-Rabin with a fixed
//! set of bases that make the test deterministic for every `uint64_t`.
//! Bases from https://cp-algorithms.com/algebra/primality_tests.html.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr bool __is_prime(::cuda::std::uint64_t __n) noexcept
{
if (__n < 2)
{
return false;
}
// Trial division by small primes.
constexpr ::cuda::std::uint64_t __small_primes[]{
2ull, 3ull, 5ull, 7ull, 11ull, 13ull, 17ull, 19ull, 23ull, 29ull, 31ull, 37ull};
for (::cuda::std::uint64_t __p : __small_primes)
{
if (__n % __p == 0)
{
return __n == __p;
}
}
// Decompose `__n - 1 == 2^__s * __d`.
::cuda::std::uint64_t __d = __n - 1;
::cuda::std::uint32_t __s = 0;
while ((__d & 1) == 0)
{
__d >>= 1;
++__s;
}
// Deterministic witness bases for all `uint64_t` values.
constexpr ::cuda::std::uint64_t __witnesses[]{2ull, 325ull, 9375ull, 28178ull, 450775ull, 9780504ull, 1795265022ull};
for (::cuda::std::uint64_t __a : __witnesses)
{
if (!detail::__miller_rabin_test(__n, __a, __d, __s))
{
return false;
}
}
return true;
}
//! @brief Returns the smallest prime `>= __n`.
//!
//! For `__n <= 2`, returns 2. Otherwise searches odd numbers starting from
//! `__n` (or `__n + 1` if `__n` is even).
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::uint64_t __next_prime(::cuda::std::uint64_t __n) noexcept
{
if (__n <= 2)
{
return 2;
}
__n |= 1; // make odd
while (!detail::__is_prime(__n))
{
const auto __next = ::cuda::add_overflow(__n, ::cuda::std::uint64_t{2});
if (__next.overflow)
{
return __n;
}
__n = __next.value;
}
return __n;
}
} // namespace cuda::experimental::cuco::detail
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_PRIME_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_PROBING_SCHEME_BASE_CUH
#define _CUDAX___CUCO_DETAIL_PROBING_SCHEME_BASE_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::detail
{
//! @brief Base class of public probing schemes.
//!
//! @tparam _CgSize Cooperative group size
template <int _CgSize>
class __probing_scheme_base
{
public:
static constexpr int __cg_size = _CgSize;
};
//! @brief Probing iterator class.
//!
//! Yields slot offsets and wraps modulo the total capacity (in slots). The capacity is held as a
//! `cuda::std::extents` so a static slot count folds the reduction to a constant.
//!
//! @tparam _CapacityExtent Capacity extent type (total slots), a `cuda::std::extents`
//! @tparam _StepExtent Probe-step extent type, a `cuda::std::extents` (static for linear probing)
template <class _CapacityExtent, class _StepExtent>
class __probing_iterator
{
public:
using __capacity_extent_type = _CapacityExtent;
using __step_extent_type = _StepExtent;
using __size_type = typename _CapacityExtent::index_type;
_CCCL_HOST_DEVICE_API constexpr __probing_iterator(
__size_type __start, _StepExtent __step, _CapacityExtent __capacity) noexcept
: __curr_index{__start}
, __step_{__step}
, __capacity_{__capacity}
{}
#if _CCCL_CUDA_COMPILATION()
_CCCL_DEVICE_API constexpr auto operator*() const noexcept
{
return __curr_index;
}
_CCCL_DEVICE_API constexpr auto operator++() noexcept
{
__curr_index = (__curr_index + __step_.extent(0)) % __capacity_.extent(0);
return *this;
}
_CCCL_DEVICE_API constexpr auto operator++(int) noexcept
{
auto __temp = *this;
++(*this);
return __temp;
}
#endif // _CCCL_CUDA_COMPILATION()
private:
__size_type __curr_index;
_CCCL_NO_UNIQUE_ADDRESS _StepExtent __step_;
_CCCL_NO_UNIQUE_ADDRESS _CapacityExtent __capacity_;
};
} // namespace cuda::experimental::cuco::detail
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_PROBING_SCHEME_BASE_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_UTILITY_CUDA_CUH
#define _CUDAX___CUCO_DETAIL_UTILITY_CUDA_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__cmath/ceil_div.h>
#include <cuda/__hierarchy/hierarchy_levels.h>
#include <cuda/std/__iterator/concepts.h>
#include <cuda/std/__iterator/distance.h>
#include <cuda/std/cstdint>
#include <cooperative_groups.h>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::detail
{
using __index_type = ::cuda::std::int64_t;
#if _CCCL_CUDA_COMPILATION()
[[nodiscard]] _CCCL_DEVICE_API inline __index_type __global_thread_id() noexcept
{
return ::cuda::gpu_thread.rank_as<__index_type>(::cuda::grid);
}
[[nodiscard]] _CCCL_DEVICE_API inline __index_type __grid_stride() noexcept
{
return ::cuda::gpu_thread.count_as<__index_type>(::cuda::grid);
}
#endif // _CCCL_CUDA_COMPILATION()
inline constexpr int __default_block_size = 128;
inline constexpr int __default_stride = 1;
inline constexpr int __warp_size = 32;
template <class _Tile>
struct __tile_size;
template <::cuda::std::uint32_t _Size, class _ParentCG>
struct __tile_size<::cooperative_groups::thread_block_tile<_Size, _ParentCG>>
{
static constexpr int __value = _Size;
};
template <class _Tile>
inline constexpr int __tile_size_v = __tile_size<_Tile>::__value;
constexpr _CCCL_HOST_DEVICE_API __index_type __grid_size(
__index_type __num,
int __cg_size = 1,
int __stride = __default_stride,
int __block_size = __default_block_size) noexcept
{
return ::cuda::ceil_div(__cg_size * __num, __stride * __block_size);
}
//! @brief Distance helper requiring random access iterators.
template <class _Iterator>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr __index_type __distance(_Iterator __begin, _Iterator __end)
{
static_assert(::cuda::std::random_access_iterator<_Iterator>, "Input iterator should be a random access iterator.");
return __index_type{::cuda::std::distance(__begin, __end)};
}
} // namespace cuda::experimental::cuco::detail
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_UTILITY_CUDA_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_UTILITY_STRONG_TYPE_CUH
#define _CUDAX___CUCO_DETAIL_UTILITY_STRONG_TYPE_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief A strong type wrapper.
//!
//! @tparam _Tp Type of the underlying value
template <class _Tp>
struct __strong_type
{
//! @brief Constructs a strong type.
//!
//! @param __v Value to be wrapped as a strong type
_CCCL_HOST_DEVICE_API explicit constexpr __strong_type(_Tp __v)
: __value{__v}
{}
//! @brief Implicit conversion operator to the underlying value.
//!
//! @return The underlying value
_CCCL_HOST_DEVICE_API constexpr operator _Tp() const noexcept
{
return __value;
}
_Tp __value; //!< Underlying data value
};
} // namespace cuda::experimental::cuco
//! Convenience wrapper for defining a strong type
#define CUDAX_CUCO_DEFINE_STRONG_TYPE(Name, Type) \
struct Name : __strong_type<Type> \
{ \
_CCCL_HOST_DEVICE_API explicit constexpr Name(Type __value) \
: __strong_type<Type>(__value) \
{} \
};
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_UTILITY_STRONG_TYPE_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_DETAIL_UTILITY_TRAITS_CUH
#define _CUDAX___CUCO_DETAIL_UTILITY_TRAITS_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <thrust/device_reference.h>
#include <cuda/std/__tuple_dir/tuple_like.h>
#include <cuda/std/__type_traits/remove_reference.h>
#include <cuda/std/__utility/declval.h>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco::detail
{
//! @brief Trait value indicating whether `_Tp`, after unwrapping any thrust reference, is a pair-like
//! type (tuple-like with exactly two elements).
//!
//! @tparam _Tp Type to inspect
template <class _Tp>
inline constexpr bool __is_pair_like_v = ::cuda::std::__pair_like<
::cuda::std::remove_reference_t<decltype(::thrust::raw_reference_cast(::cuda::std::declval<_Tp>()))>>;
} // namespace cuda::experimental::cuco::detail
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_DETAIL_UTILITY_TRAITS_CUH

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@@ -1,538 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_FIXED_CAPACITY_MAP_CUH
#define _CUDAX___CUCO_FIXED_CAPACITY_MAP_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__memory_pool/device_memory_pool.h>
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__fwd/extents.h>
#include <cuda/std/__memory/unique_ptr.h>
#include <cuda/std/__utility/pair.h>
#include <cuda/experimental/__cuco/capacity.cuh>
#include <cuda/experimental/__cuco/detail/bitwise_compare.cuh>
#include <cuda/experimental/__cuco/detail/open_addressing/open_addressing_impl.cuh>
#include <cuda/experimental/__cuco/fixed_capacity_map_ref.cuh>
#include <cuda/experimental/__cuco/hash_functions.cuh>
#include <cuda/experimental/__cuco/probing_scheme.cuh>
#include <cuda/experimental/__cuco/types.cuh>
#include <cuda/std/__cccl/prologue.h>
#if !_CCCL_COMPILER(NVRTC)
namespace cuda::experimental::cuco
{
//! @brief A GPU-accelerated, unordered, associative container of key-value pairs with unique keys.
//!
//! Allows constant-time inserts and lookups from device code. Many threads may perform
//! the same kind of operation concurrently (e.g. concurrent inserts, or concurrent lookups).
//! Storage is bulk-allocated ahead of time and requires the user to provide sentinel values
//! for empty and, optionally, erased keys.
//!
//! @note Concurrent modification (insert) and lookup (contains) on the same map are not
//! supported: lookups perform non-atomic loads, so a lookup that overlaps a concurrent insert
//! is a data race and results in undefined behavior. Concurrent inserts (with other inserts)
//! and concurrent lookups (with other lookups) are supported; the two kinds must not be mixed.
//! @note `_Capacity` is a span-style `size_t` non-type parameter holding the *valid* (post-rounding)
//! slot count, or `cuda::std::dynamic_extent` (the default) for runtime-sized maps. Obtain a valid
//! value with `cuco::make_valid_capacity`.
//!
//! @tparam _Key Key type. Requires `cuda::is_bitwise_comparable_v<_Key>`
//! @tparam _Tp Mapped value type
//! @tparam _Capacity Requested slot count, or `cuda::std::dynamic_extent` for runtime sizing
//! @tparam _Scope Thread scope for atomic operations
//! @tparam _KeyEqual Key equality comparator
//! @tparam _ProbingScheme Probing scheme type
//! @tparam _BucketSize Slots per bucket
//! @tparam _MemoryResource Memory resource for device storage
template <class _Key,
class _Tp,
::cuda::std::size_t _Capacity = ::cuda::std::dynamic_extent,
::cuda::thread_scope _Scope = ::cuda::thread_scope_device,
class _KeyEqual = ::cuda::std::equal_to<_Key>,
class _ProbingScheme = linear_probing<4, hash<_Key>>,
int _BucketSize = 1,
class _MemoryResource = ::cuda::device_memory_pool_ref>
class fixed_capacity_map
{
public:
using key_type = _Key; ///< Key type
using mapped_type = _Tp; ///< Payload (mapped value) type
using value_type = ::cuda::std::pair<_Key, _Tp>; ///< Key-payload pair type
using size_type = ::cuda::std::size_t; ///< Size type
using key_equal = _KeyEqual; ///< Key equality comparator type
using probing_scheme_type = _ProbingScheme; ///< Probing scheme type
using hasher = typename probing_scheme_type::hasher; ///< Hash function type
static constexpr auto cg_size = _ProbingScheme::cg_size; ///< Cooperative-group size used for probing
static constexpr auto bucket_size = _BucketSize; ///< Number of slots per bucket
static constexpr auto thread_scope = _Scope; ///< CUDA thread scope for atomic operations
static_assert(_Capacity == ::cuda::std::dynamic_extent || is_valid_capacity<_ProbingScheme, _BucketSize>(_Capacity),
"Capacity must be a valid open-addressing capacity; obtain it via cuco::make_valid_capacity");
//! @brief Valid (post-rounding) slot count; `cuda::std::dynamic_extent` for dynamic maps.
static constexpr size_type capacity_v = _Capacity;
using ref_type =
fixed_capacity_map_ref<_Key, _Tp, _Scope, _KeyEqual, _ProbingScheme, _BucketSize, _Capacity>; ///< Device
///< non-owning
///< ref type
private:
using __impl_type = __open_addressing::
__open_addressing_impl<_Key, value_type, _Scope, _KeyEqual, _ProbingScheme, _BucketSize, _MemoryResource>;
::cuda::std::unique_ptr<__impl_type> __impl;
mapped_type __empty_value_sentinel;
//! @brief Synchronizes the CUDA stream.
static void __sync(::cuda::stream_ref __stream)
{
__stream.sync();
}
public:
//! @brief Constructs a map with static capacity (encoded in `_Capacity`) and no erasure.
//!
//! @param __stream Stream used for allocation and initialization
//! @param __mr Memory resource for device storage
//! @param __empty_key_sentinel Sentinel indicating an empty key slot
//! @param __empty_value_sentinel Sentinel indicating an empty payload
//! @param __pred Key equality binary callable
//! @param __probing_scheme Probing scheme
_CCCL_TEMPLATE(::cuda::std::size_t _C = _Capacity)
_CCCL_REQUIRES((_C != ::cuda::std::dynamic_extent))
_CCCL_HOST_API fixed_capacity_map(
::cuda::stream_ref __stream,
_MemoryResource __mr,
empty_key<_Key> __empty_key_sentinel,
empty_value<_Tp> __empty_value_sentinel,
const _KeyEqual& __pred = {},
const _ProbingScheme& __probing_scheme = {})
: __impl{::cuda::std::make_unique<__impl_type>(
__stream,
__mr,
_Capacity,
value_type{key_type(__empty_key_sentinel), mapped_type(__empty_value_sentinel)},
__pred,
__probing_scheme)}
, __empty_value_sentinel{mapped_type(__empty_value_sentinel)}
{}
//! @brief Constructs a map with dynamic capacity and no erasure.
//!
//! @param __stream Stream used for allocation and initialization
//! @param __mr Memory resource for device storage
//! @param __capacity Requested slot count (prime/stride-adjusted internally)
//! @param __empty_key_sentinel Sentinel indicating an empty key slot
//! @param __empty_value_sentinel Sentinel indicating an empty payload
//! @param __pred Key equality binary callable
//! @param __probing_scheme Probing scheme
_CCCL_TEMPLATE(::cuda::std::size_t _C = _Capacity)
_CCCL_REQUIRES((_C == ::cuda::std::dynamic_extent))
_CCCL_HOST_API fixed_capacity_map(
::cuda::stream_ref __stream,
_MemoryResource __mr,
size_type __capacity,
empty_key<_Key> __empty_key_sentinel,
empty_value<_Tp> __empty_value_sentinel,
const _KeyEqual& __pred = {},
const _ProbingScheme& __probing_scheme = {})
: __impl{::cuda::std::make_unique<__impl_type>(
__stream,
__mr,
__capacity,
value_type{key_type(__empty_key_sentinel), mapped_type(__empty_value_sentinel)},
__pred,
__probing_scheme)}
, __empty_value_sentinel{mapped_type(__empty_value_sentinel)}
{}
//! @brief Constructs a map sized by a target load factor (dynamic capacity only).
//!
//! @param __stream Stream used for allocation and initialization
//! @param __mr Memory resource for device storage
//! @param __n Expected number of keys
//! @param __desired_load_factor Target load factor in (0, 1]
//! @param __empty_key_sentinel Sentinel indicating an empty key slot
//! @param __empty_value_sentinel Sentinel indicating an empty payload
//! @param __pred Key equality binary callable
//! @param __probing_scheme Probing scheme
_CCCL_TEMPLATE(::cuda::std::size_t _C = _Capacity)
_CCCL_REQUIRES((_C == ::cuda::std::dynamic_extent))
_CCCL_HOST_API fixed_capacity_map(
::cuda::stream_ref __stream,
_MemoryResource __mr,
size_type __n,
double __desired_load_factor,
empty_key<_Key> __empty_key_sentinel,
empty_value<_Tp> __empty_value_sentinel,
const _KeyEqual& __pred = {},
const _ProbingScheme& __probing_scheme = {})
: __impl{::cuda::std::make_unique<__impl_type>(
__stream,
__mr,
__n,
__desired_load_factor,
value_type{key_type(__empty_key_sentinel), mapped_type(__empty_value_sentinel)},
__pred,
__probing_scheme)}
, __empty_value_sentinel{mapped_type(__empty_value_sentinel)}
{}
//! @brief Constructs a map with static capacity and erasure support.
//!
//! @param __stream Stream used for allocation and initialization
//! @param __mr Memory resource for device storage
//! @param __empty_key_sentinel Sentinel indicating an empty key slot
//! @param __empty_value_sentinel Sentinel indicating an empty payload
//! @param __erased_key_sentinel Sentinel indicating an erased key slot
//! @param __pred Key equality binary callable
//! @param __probing_scheme Probing scheme
_CCCL_TEMPLATE(::cuda::std::size_t _C = _Capacity)
_CCCL_REQUIRES((_C != ::cuda::std::dynamic_extent))
_CCCL_HOST_API fixed_capacity_map(
::cuda::stream_ref __stream,
_MemoryResource __mr,
empty_key<_Key> __empty_key_sentinel,
empty_value<_Tp> __empty_value_sentinel,
erased_key<_Key> __erased_key_sentinel,
const _KeyEqual& __pred = {},
const _ProbingScheme& __probing_scheme = {})
: __impl{::cuda::std::make_unique<__impl_type>(
__stream,
__mr,
_Capacity,
value_type{key_type(__empty_key_sentinel), mapped_type(__empty_value_sentinel)},
key_type(__erased_key_sentinel),
__pred,
__probing_scheme)}
, __empty_value_sentinel{mapped_type(__empty_value_sentinel)}
{}
//! @brief Constructs a map with dynamic capacity and erasure support.
//!
//! @param __stream Stream used for allocation and initialization
//! @param __mr Memory resource for device storage
//! @param __capacity Requested slot count (prime/stride-adjusted internally)
//! @param __empty_key_sentinel Sentinel indicating an empty key slot
//! @param __empty_value_sentinel Sentinel indicating an empty payload
//! @param __erased_key_sentinel Sentinel indicating an erased key slot
//! @param __pred Key equality binary callable
//! @param __probing_scheme Probing scheme
_CCCL_TEMPLATE(::cuda::std::size_t _C = _Capacity)
_CCCL_REQUIRES((_C == ::cuda::std::dynamic_extent))
_CCCL_HOST_API fixed_capacity_map(
::cuda::stream_ref __stream,
_MemoryResource __mr,
size_type __capacity,
empty_key<_Key> __empty_key_sentinel,
empty_value<_Tp> __empty_value_sentinel,
erased_key<_Key> __erased_key_sentinel,
const _KeyEqual& __pred = {},
const _ProbingScheme& __probing_scheme = {})
: __impl{::cuda::std::make_unique<__impl_type>(
__stream,
__mr,
__capacity,
value_type{key_type(__empty_key_sentinel), mapped_type(__empty_value_sentinel)},
key_type(__erased_key_sentinel),
__pred,
__probing_scheme)}
, __empty_value_sentinel{mapped_type(__empty_value_sentinel)}
{}
// ===== Clear =====
//! @brief Erases all elements from the container. After this call, `size()` returns zero.
//!
//! @param __stream CUDA stream this operation is executed in
void clear(::cuda::stream_ref __stream)
{
__impl->clear(__stream);
}
//! @brief Asynchronously erases all elements from the container. After this call, `size()`
//! returns zero.
//!
//! @param __stream CUDA stream this operation is executed in
void clear_async(::cuda::stream_ref __stream) noexcept
{
__impl->clear_async(__stream);
}
// ===== Insert =====
//! @brief Inserts all keys in the range `[__first, __last)` and returns the number of successful
//! insertions.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `insert_async`.
//!
//! @tparam _InputIt Device accessible random access input iterator whose `value_type` is
//! convertible to the map's `value_type`
//!
//! @param __stream CUDA stream used for insert
//! @param __first Beginning of the sequence of keys
//! @param __last End of the sequence of keys
//!
//! @return Number of successful insertions
template <class _InputIt>
size_type insert(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last)
{
return __impl->insert(__stream, __first, __last, ref());
}
//! @brief Asynchronously inserts all keys in the range `[__first, __last)`.
//!
//! @tparam _InputIt Device accessible random access input iterator whose `value_type` is
//! convertible to the map's `value_type`
//!
//! @param __stream CUDA stream used for insert
//! @param __first Beginning of the sequence of keys
//! @param __last End of the sequence of keys
template <class _InputIt>
void insert_async(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last) noexcept
{
__impl->insert_async(__stream, __first, __last, ref());
}
// ===== Contains =====
//! @brief Indicates whether each key in `[__first, __last)` is contained in the map.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `contains_async`.
//!
//! @tparam _InputIt Device accessible input iterator
//! @tparam _OutputIt Device accessible output iterator assignable from `bool`
//!
//! @param __stream CUDA stream used for executing the kernels
//! @param __first Beginning of the sequence of keys
//! @param __last End of the sequence of keys
//! @param __output_begin Beginning of the output sequence of booleans
template <class _InputIt, class _OutputIt>
void contains(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last, _OutputIt __output_begin) const
{
contains_async(__stream, __first, __last, __output_begin);
__sync(__stream);
}
//! @brief Asynchronously indicates whether each key in `[__first, __last)` is contained in the map.
//!
//! @tparam _InputIt Device accessible input iterator
//! @tparam _OutputIt Device accessible output iterator assignable from `bool`
//!
//! @param __stream CUDA stream used for executing the kernels
//! @param __first Beginning of the sequence of keys
//! @param __last End of the sequence of keys
//! @param __output_begin Beginning of the output sequence of booleans
template <class _InputIt, class _OutputIt>
void contains_async(
::cuda::stream_ref __stream, _InputIt __first, _InputIt __last, _OutputIt __output_begin) const noexcept
{
__impl->contains_async(__stream, __first, __last, __output_begin, ref());
}
// ===== Find =====
//! @brief For each key in `[__first, __last)` writes the associated payload, or `empty_value_sentinel()`
//! if the key is not present.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use `find_async`.
//!
//! @tparam _InputIt Device accessible input iterator
//! @tparam _OutputIt Device accessible output iterator assignable from `mapped_type`
//!
//! @param __stream CUDA stream used for executing the kernels
//! @param __first Beginning of the sequence of keys
//! @param __last End of the sequence of keys
//! @param __output_begin Beginning of the output sequence of payloads
template <class _InputIt, class _OutputIt>
void find(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last, _OutputIt __output_begin) const
{
find_async(__stream, __first, __last, __output_begin);
__sync(__stream);
}
//! @brief Asynchronously, for each key in `[__first, __last)` writes the associated payload, or
//! `empty_value_sentinel()` if the key is not present.
//!
//! @tparam _InputIt Device accessible input iterator
//! @tparam _OutputIt Device accessible output iterator assignable from `mapped_type`
//!
//! @param __stream CUDA stream used for executing the kernels
//! @param __first Beginning of the sequence of keys
//! @param __last End of the sequence of keys
//! @param __output_begin Beginning of the output sequence of payloads
template <class _InputIt, class _OutputIt>
void
find_async(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last, _OutputIt __output_begin) const noexcept
{
__impl->find_async(__stream, __first, __last, __output_begin, ref());
}
//! @brief For each key `__first[i]` with `__pred(__stencil[i]) == true` writes the associated payload,
//! or `empty_value_sentinel()` if the key is not present; writes `empty_value_sentinel()` for the rest.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use `find_if_async`.
//!
//! @tparam _InputIt Device accessible input iterator
//! @tparam _StencilIt Device accessible random access iterator whose value type is convertible to
//! `_Predicate`'s argument type
//! @tparam _Predicate Unary callable returning `bool`
//! @tparam _OutputIt Device accessible output iterator assignable from `mapped_type`
//!
//! @param __stream CUDA stream used for executing the kernels
//! @param __first Beginning of the sequence of keys
//! @param __last End of the sequence of keys
//! @param __stencil Beginning of the stencil sequence
//! @param __pred Predicate applied to the stencil to determine which keys to query
//! @param __output_begin Beginning of the output sequence of payloads
template <class _InputIt, class _StencilIt, class _Predicate, class _OutputIt>
void find_if(::cuda::stream_ref __stream,
_InputIt __first,
_InputIt __last,
_StencilIt __stencil,
_Predicate __pred,
_OutputIt __output_begin) const
{
find_if_async(__stream, __first, __last, __stencil, __pred, __output_begin);
__sync(__stream);
}
//! @brief Asynchronous version of `find_if`.
//!
//! @tparam _InputIt Device accessible input iterator
//! @tparam _StencilIt Device accessible random access iterator whose value type is convertible to
//! `_Predicate`'s argument type
//! @tparam _Predicate Unary callable returning `bool`
//! @tparam _OutputIt Device accessible output iterator assignable from `mapped_type`
//!
//! @param __stream CUDA stream used for executing the kernels
//! @param __first Beginning of the sequence of keys
//! @param __last End of the sequence of keys
//! @param __stencil Beginning of the stencil sequence
//! @param __pred Predicate applied to the stencil to determine which keys to query
//! @param __output_begin Beginning of the output sequence of payloads
template <class _InputIt, class _StencilIt, class _Predicate, class _OutputIt>
void find_if_async(
::cuda::stream_ref __stream,
_InputIt __first,
_InputIt __last,
_StencilIt __stencil,
_Predicate __pred,
_OutputIt __output_begin) const noexcept
{
__impl->find_if_async(__stream, __first, __last, __stencil, __pred, __output_begin, ref());
}
// ===== Accessors =====
//! @brief Returns the total number of slots the map can hold (the prime/stride-adjusted capacity).
//!
//! @return Total slot count
[[nodiscard]] constexpr size_type capacity() const noexcept
{
return __impl->capacity();
}
//! @brief Gets a device pointer to the underlying slot storage.
//!
//! @return Pointer to the underlying slot storage
[[nodiscard]] _CCCL_HOST_API value_type* data() const
{
return __impl->data();
}
//! @brief Gets the sentinel value used to represent an empty key slot.
//!
//! @return The sentinel value used to represent an empty key slot
[[nodiscard]] constexpr key_type empty_key_sentinel() const noexcept
{
return __impl->empty_key_sentinel();
}
//! @brief Gets the sentinel value used to represent an empty payload slot.
//!
//! @return The sentinel value used to represent an empty payload slot
[[nodiscard]] constexpr mapped_type empty_value_sentinel() const noexcept
{
return __empty_value_sentinel;
}
//! @brief Gets the sentinel value used to represent an erased key slot.
//!
//! @return The sentinel value used to represent an erased key slot
[[nodiscard]] constexpr key_type erased_key_sentinel() const noexcept
{
return __impl->erased_key_sentinel();
}
//! @brief Gets the function used to compare keys for equality.
//!
//! @return The function used to compare keys for equality
[[nodiscard]] constexpr key_equal key_eq() const noexcept
{
return __impl->key_eq();
}
//! @brief Gets the function(s) used to hash keys.
//!
//! @return The function(s) used to hash keys
[[nodiscard]] constexpr hasher hash_function() const noexcept
{
return __impl->hash_function();
}
//! @brief Gets a device-usable non-owning reference to this map.
//!
//! The returned ref borrows the map's slot storage and sentinel values and is trivially copyable
//! — safe to pass by value to kernels. The ref's lifetime must not exceed the map's lifetime.
//!
//! @return A `ref_type` referring to this map
[[nodiscard]] auto ref() const noexcept -> ref_type
{
auto __slots = typename ref_type::storage_span_type{__impl->storage_ref().data(), __impl->capacity()};
return detail::__bitwise_compare(empty_key_sentinel(), erased_key_sentinel())
? ref_type{empty_key{empty_key_sentinel()},
empty_value{empty_value_sentinel()},
__impl->key_eq(),
__impl->probing_scheme(),
__slots}
: ref_type{empty_key{empty_key_sentinel()},
empty_value{empty_value_sentinel()},
erased_key{erased_key_sentinel()},
__impl->key_eq(),
__impl->probing_scheme(),
__slots};
}
};
} // namespace cuda::experimental::cuco
#endif // !_CCCL_COMPILER(NVRTC)
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_FIXED_CAPACITY_MAP_CUH

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@@ -1,354 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_FIXED_CAPACITY_MAP_REF_CUH
#define _CUDAX___CUCO_FIXED_CAPACITY_MAP_REF_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__atomic/atomic.h>
#include <cuda/__cmath/pow2.h>
#include <cuda/__type_traits/is_bitwise_comparable.h>
#include <cuda/std/__mdspan/extents.h>
#include <cuda/std/__utility/pair.h>
#include <cuda/std/span>
#include <cuda/experimental/__cuco/capacity.cuh>
#include <cuda/experimental/__cuco/detail/open_addressing/open_addressing_ref_impl.cuh>
#include <cuda/experimental/__cuco/detail/open_addressing/slot_storage_ref.cuh>
#include <cuda/experimental/__cuco/probing_scheme.cuh>
#include <cuda/experimental/__cuco/types.cuh>
#include <cooperative_groups.h>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief Device non-owning reference type for `fixed_capacity_map`.
//!
//! This lightweight, trivially-copyable reference is intended to be passed by value to device code
//! for performing insert and lookup operations on the hash map.
//!
//! @note Concurrent modify and lookup on the same map are not supported: lookups perform non-atomic
//! loads, so a lookup must not run concurrently with an insert (doing so is a data race).
//! @note cuCollections data structures always place the slot keys on the right-hand side when
//! invoking the key comparison predicate, i.e., `__pred(__query_key, __slot_key)`.
//! @note `_ProbingScheme::cg_size` indicates how many threads are used to handle one independent
//! device operation. `cg_size == 1` uses the scalar (or non-CG) code paths.
//! @note `_Capacity` is a span-style `size_t` non-type parameter encoding the *requested* slot
//! count. Pass `cuda::std::dynamic_extent` (the default) for runtime-sized maps; any concrete
//! value encodes the requested slot count at compile time. The actual slot count is the
//! prime/stride-adjusted value exposed as `capacity_v` and matches the owning map's
//! `fixed_capacity_map::capacity_v` for the same parameters.
//!
//! @tparam _Key Type used for keys
//! @tparam _Tp Type used for mapped values
//! @tparam _Scope The scope in which operations will be performed by individual threads
//! @tparam _KeyEqual Binary callable type used to compare two keys for equality
//! @tparam _ProbingScheme Probing scheme type
//! @tparam _BucketSize Number of slots per bucket
//! @tparam _Capacity Requested slot count, or `cuda::std::dynamic_extent` for runtime sizing
template <class _Key,
class _Tp,
::cuda::thread_scope _Scope,
class _KeyEqual,
class _ProbingScheme,
int _BucketSize,
::cuda::std::size_t _Capacity = ::cuda::std::dynamic_extent>
class fixed_capacity_map_ref
{
static_assert(sizeof(_Key) <= 8, "Container does not support key types larger than 8 bytes.");
static_assert(::cuda::is_power_of_two(sizeof(_Key)), "key_type size must be a power of two");
static_assert(sizeof(_Tp) <= 8, "sizeof(mapped_type) must be no larger than 8 bytes.");
static_assert(::cuda::is_power_of_two(sizeof(::cuda::std::pair<_Key, _Tp>)),
"value_type size must be a power of two");
static_assert(::cuda::is_bitwise_comparable_v<_Key>,
"Key type must have unique object representations or have been explicitly declared as safe for "
"bitwise comparison via specialization of cuda::is_bitwise_comparable_v<Key>.");
static constexpr bool __allows_duplicates = false;
static_assert(_Capacity == ::cuda::std::dynamic_extent || is_valid_capacity<_ProbingScheme, _BucketSize>(_Capacity),
"Capacity must be a valid open-addressing capacity; obtain it via cuco::make_valid_capacity");
public:
using key_type = _Key; ///< Key type
using mapped_type = _Tp; ///< Payload (mapped value) type
using value_type = ::cuda::std::pair<_Key, _Tp>; ///< Key-payload pair type
using probing_scheme_type = _ProbingScheme; ///< Probing scheme type
using hasher = typename probing_scheme_type::hasher; ///< Hash function type
using size_type = ::cuda::std::size_t; ///< Size type
using key_equal = _KeyEqual; ///< Key equality comparator type
using iterator = value_type*; ///< Slot iterator
using const_iterator = const value_type*; ///< Const slot iterator
static constexpr auto cg_size = probing_scheme_type::cg_size; ///< Cooperative-group size for probing
static constexpr auto bucket_size = _BucketSize; ///< Number of slots per bucket
static constexpr auto thread_scope = _Scope; ///< CUDA thread scope for atomic operations
//! @brief Compile-time adjusted slot count; `cuda::std::dynamic_extent` when `_Capacity` is dynamic.
static constexpr size_type capacity_v = _Capacity;
//! @brief Slot-storage span type. For static `_Capacity`, the span carries the adjusted
//! `capacity_v` extent at compile time; for dynamic `_Capacity`, the extent is dynamic.
using storage_span_type = ::cuda::std::span<value_type, capacity_v>;
private:
// Internal adapter to the open-addressing impl. The storage's `_Capacity` template arg receives
// the (already valid) `capacity_v`, so when `_Capacity` is static the slot count travels through
// the storage's extent at compile time and the probing iterator's modular reduction folds to a
// constant.
using __storage_ref_type = __open_addressing::__slot_storage_ref<value_type, _BucketSize, capacity_v>;
//! @brief Returns the slot count of the given span, validating it for the dynamic case.
//!
//! @param __slots Span over the slot storage
//!
//! @return The total slot count
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr size_type __checked_capacity(storage_span_type __slots) noexcept
{
if constexpr (_Capacity == ::cuda::std::dynamic_extent)
{
_CCCL_ASSERT((is_valid_capacity<_ProbingScheme, _BucketSize>(__slots.size())),
"storage size is not a valid capacity");
}
return __slots.size();
}
using __impl_type = __open_addressing::
__open_addressing_ref_impl<_Key, _Scope, _KeyEqual, _ProbingScheme, __storage_ref_type, __allows_duplicates>;
__impl_type __impl;
public:
//! @brief Constructs a ref without erasure support.
//!
//! @param __empty_key_sentinel Sentinel indicating an empty key slot
//! @param __empty_value_sentinel Sentinel indicating an empty payload
//! @param __predicate Key equality binary callable
//! @param __probing_scheme Probing scheme
//! @param __slots Span over the slot storage; must contain `capacity()` slots
_CCCL_HOST_DEVICE_API explicit constexpr fixed_capacity_map_ref(
empty_key<_Key> __empty_key_sentinel,
empty_value<_Tp> __empty_value_sentinel,
const _KeyEqual& __predicate,
const _ProbingScheme& __probing_scheme,
storage_span_type __slots) noexcept
: __impl{value_type{key_type(__empty_key_sentinel), mapped_type(__empty_value_sentinel)},
__predicate,
__probing_scheme,
__storage_ref_type{__slots.data(), __checked_capacity(__slots)}}
{}
//! @brief Constructs a ref with erasure support.
//!
//! @param __empty_key_sentinel Sentinel indicating an empty key slot
//! @param __empty_value_sentinel Sentinel indicating an empty payload
//! @param __erased_key_sentinel Sentinel indicating an erased key slot
//! @param __predicate Key equality binary callable
//! @param __probing_scheme Probing scheme
//! @param __slots Span over the slot storage; must contain `capacity()` slots
_CCCL_HOST_DEVICE_API explicit constexpr fixed_capacity_map_ref(
empty_key<_Key> __empty_key_sentinel,
empty_value<_Tp> __empty_value_sentinel,
erased_key<_Key> __erased_key_sentinel,
const _KeyEqual& __predicate,
const _ProbingScheme& __probing_scheme,
storage_span_type __slots) noexcept
: __impl{value_type{key_type(__empty_key_sentinel), mapped_type(__empty_value_sentinel)},
key_type(__erased_key_sentinel),
__predicate,
__probing_scheme,
__storage_ref_type{__slots.data(), __checked_capacity(__slots)}}
{}
// ===== Accessors =====
//! @brief Returns the total number of slots.
//!
//! @return Total slot count (equal to the owning map's `capacity()`)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr size_type capacity() const noexcept
{
return __impl.capacity();
}
//! @brief Returns the sentinel value used to represent an empty key slot.
//!
//! @return The sentinel value used to represent an empty key slot
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr key_type empty_key_sentinel() const noexcept
{
return __impl.empty_key_sentinel();
}
//! @brief Returns the sentinel value used to represent an empty payload slot.
//!
//! @return The sentinel value used to represent an empty payload slot
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr mapped_type empty_value_sentinel() const noexcept
{
return __impl.empty_value_sentinel();
}
//! @brief Returns the sentinel value used to represent an erased key slot.
//!
//! @return The sentinel value used to represent an erased key slot
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr key_type erased_key_sentinel() const noexcept
{
return __impl.erased_key_sentinel();
}
//! @brief Returns the function used to compare keys for equality.
//!
//! @return The key equality comparator
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr key_equal key_eq() const noexcept
{
return __impl.key_eq();
}
//! @brief Returns the function(s) used to hash keys.
//!
//! @return The hasher used by this ref's probing scheme
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr hasher hash_function() const noexcept
{
return __impl.hash_function();
}
//! @brief Returns the probing scheme used to resolve hash collisions.
//!
//! @return The probing scheme object
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr probing_scheme_type probing_scheme() const noexcept
{
return __impl.probing_scheme();
}
//! @brief Returns a const iterator to one past the last slot (the end sentinel).
//!
//! @return Past-the-end const iterator
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr const_iterator end() const noexcept
{
return __impl.end();
}
//! @brief Returns an iterator to one past the last slot (the end sentinel).
//!
//! @return Past-the-end iterator
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr iterator end() noexcept
{
return __impl.end();
}
//! @brief Returns a span over the slot storage backing this ref.
//!
//! @return Span of `capacity()` slots
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr storage_span_type storage_span() const noexcept
{
return storage_span_type{__impl.storage_ref().data(), __impl.capacity()};
}
#if _CCCL_CUDA_COMPILATION()
// ===== Insert operations =====
//! @brief Inserts a key-value pair.
//!
//! @param __value The key-value pair to insert
//!
//! @return `true` if the pair was inserted, `false` if the key already exists
_CCCL_DEVICE_API bool insert(value_type __value) noexcept
{
return __impl.insert(__value);
}
//! @brief Inserts a key-value pair using a cooperative group.
//!
//! @tparam _ParentCG Parent cooperative group type
//!
//! @param __group The cooperative group used for this operation
//! @param __value The key-value pair to insert
//!
//! @return `true` if the pair was inserted, `false` if the key already exists
template <class _ParentCG>
_CCCL_DEVICE_API bool
insert(::cooperative_groups::thread_block_tile<cg_size, _ParentCG> __group, value_type __value) noexcept
{
return __impl.insert(__group, __value);
}
// ===== Lookup operations =====
//! @brief Checks if a key exists in the map.
//!
//! @param __key The key to search for
//!
//! @return `true` if the key is found
template <class _ProbeKey = key_type>
[[nodiscard]] _CCCL_DEVICE_API bool contains(_ProbeKey __key) const noexcept
{
return __impl.contains(__key);
}
//! @brief Cooperative-group variant of `contains`.
//!
//! @tparam _ParentCG Parent cooperative group type
//! @tparam _ProbeKey Probe key type (defaults to `key_type`)
//!
//! @param __group Cooperative group of size `cg_size` performing this lookup
//! @param __key The key to search for
//!
//! @return `true` if the key is found
template <class _ParentCG, class _ProbeKey = key_type>
[[nodiscard]] _CCCL_DEVICE_API bool
contains(::cooperative_groups::thread_block_tile<cg_size, _ParentCG> __group, _ProbeKey __key) const noexcept
{
return __impl.contains(__group, __key);
}
//! @brief Finds the slot associated with a key.
//!
//! @tparam _ProbeKey Probe key type (defaults to `key_type`)
//!
//! @param __key The key to search for
//!
//! @return An iterator to the slot holding `__key`, or `end()` if the key is not found
template <class _ProbeKey = key_type>
[[nodiscard]] _CCCL_DEVICE_API iterator find(_ProbeKey __key) const noexcept
{
return __impl.find(__key);
}
//! @brief Cooperative-group variant of `find`.
//!
//! @tparam _ParentCG Parent cooperative group type
//! @tparam _ProbeKey Probe key type (defaults to `key_type`)
//!
//! @param __group Cooperative group of size `cg_size` performing this lookup
//! @param __key The key to search for
//!
//! @return An iterator to the slot holding `__key`, or `end()` if the key is not found
template <class _ParentCG, class _ProbeKey = key_type>
[[nodiscard]] _CCCL_DEVICE_API iterator
find(::cooperative_groups::thread_block_tile<cg_size, _ParentCG> __group, _ProbeKey __key) const noexcept
{
return __impl.find(__group, __key);
}
#endif // _CCCL_CUDA_COMPILATION()
};
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_FIXED_CAPACITY_MAP_REF_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_HASH_FUNCTIONS_CUH
#define _CUDAX___CUCO_HASH_FUNCTIONS_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/experimental/__cuco/detail/hash_functions/murmurhash3.cuh>
#include <cuda/experimental/__cuco/detail/hash_functions/xxhash.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
enum class hash_algorithm
{
xxhash_32,
xxhash_64,
murmurhash3_32
#if _CCCL_HAS_INT128()
,
murmurhash3_x86_128,
murmurhash3_x64_128
#endif // _CCCL_HAS_INT128()
};
//! @brief A hash function class specialized for different hash algorithms.
//!
//! @tparam _Key The type of the values to hash
//! @tparam _S The hash strategy to use, defaults to `hash_algorithm::xxhash_32`
template <typename _Key, hash_algorithm _S = hash_algorithm::xxhash_32>
class hash;
template <typename _Key>
class hash<_Key, hash_algorithm::xxhash_32> : private ::cuda::experimental::cuco::_XXHash_32<_Key>
{
public:
using ::cuda::experimental::cuco::_XXHash_32<_Key>::_XXHash_32;
using ::cuda::experimental::cuco::_XXHash_32<_Key>::operator();
};
template <typename _Key>
class hash<_Key, hash_algorithm::xxhash_64> : private ::cuda::experimental::cuco::_XXHash_64<_Key>
{
public:
using ::cuda::experimental::cuco::_XXHash_64<_Key>::_XXHash_64;
using ::cuda::experimental::cuco::_XXHash_64<_Key>::operator();
};
template <typename _Key>
class hash<_Key, hash_algorithm::murmurhash3_32> : private ::cuda::experimental::cuco::_MurmurHash3_32<_Key>
{
public:
using ::cuda::experimental::cuco::_MurmurHash3_32<_Key>::_MurmurHash3_32;
using ::cuda::experimental::cuco::_MurmurHash3_32<_Key>::operator();
};
#if _CCCL_HAS_INT128()
template <typename _Key>
class hash<_Key, hash_algorithm::murmurhash3_x86_128> : private ::cuda::experimental::cuco::_MurmurHash3_x86_128<_Key>
{
public:
using ::cuda::experimental::cuco::_MurmurHash3_x86_128<_Key>::_MurmurHash3_x86_128;
using ::cuda::experimental::cuco::_MurmurHash3_x86_128<_Key>::operator();
};
template <typename _Key>
class hash<_Key, hash_algorithm::murmurhash3_x64_128> : private ::cuda::experimental::cuco::_MurmurHash3_x64_128<_Key>
{
public:
using ::cuda::experimental::cuco::_MurmurHash3_x64_128<_Key>::_MurmurHash3_x64_128;
using ::cuda::experimental::cuco::_MurmurHash3_x64_128<_Key>::operator();
};
#endif // _CCCL_HAS_INT128()
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_HASH_FUNCTIONS_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_HLL_POLICIES_CUH
#define _CUDAX___CUCO_HLL_POLICIES_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/experimental/__cuco/detail/hyperloglog/default_policy.cuh>
#endif // _CUDAX___CUCO_HLL_POLICIES_CUH

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@@ -1,437 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_HYPERLOGLOG_CUH
#define _CUDAX___CUCO_HYPERLOGLOG_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__container/buffer.h>
#include <cuda/__memory_pool/device_memory_pool.h>
#include <cuda/__memory_resource/legacy_pinned_memory_resource.h>
#include <cuda/__stream/stream_ref.h>
#include <cuda/__utility/in_range.h>
#include <cuda/__utility/no_init.h>
#include <cuda/std/__bit/countr.h>
#include <cuda/std/__cccl/assert.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__host_stdlib/stdexcept>
#include <cuda/std/__utility/forward.h>
#include <cuda/std/span>
#include <cuda/experimental/__cuco/hll_policies.cuh>
#include <cuda/experimental/__cuco/hyperloglog_ref.cuh>
#include <cuda/std/__cccl/prologue.h>
#if !_CCCL_COMPILER(NVRTC)
namespace cuda::experimental::cuco
{
//! @brief A GPU-accelerated utility for approximating the number of distinct items in a multiset.
//!
//! @note This implementation is based on the HyperLogLog++ algorithm:
//! https://static.googleusercontent.com/media/research.google.com/de//pubs/archive/40671.pdf.
//!
//! @tparam _Tp Type of items to count
//! @tparam _MemoryResource Type of memory resource used for device storage
//! @tparam _Scope The scope in which operations will be performed by individual threads
//! @tparam _Policy Policy bundling hash function, bit-slicing rule, and finalizer
template <class _Tp,
class _MemoryResource = ::cuda::device_memory_pool_ref,
::cuda::thread_scope _Scope = ::cuda::thread_scope_device,
class _Policy = ::cuda::experimental::cuco::default_hll_policy<_Tp>>
class hyperloglog
{
public:
static constexpr auto thread_scope = _Scope; ///< CUDA thread scope
template <::cuda::thread_scope _NewScope = thread_scope>
using ref_type = hyperloglog_ref<_Tp, _NewScope, _Policy>; ///< Non-owning reference type
using value_type = typename ref_type<>::value_type; ///< Type of items to count
using policy_type = typename ref_type<>::policy_type; ///< Policy type
using hasher = typename ref_type<>::hasher; ///< Hash function type
using register_type = typename ref_type<>::register_type; ///< HLL register type
//! A strong type wrapper `sketch_size_kb` of `double`, for specifying the upper-bound
//! sketch size of `cuda::experimental::cuco::hyperloglog(_ref)` in KB.
//!
//! @note Valid sketch sizes are in [0.0625 KB, 1024 KB], which correspond to precision [4, 18].
using sketch_size_kb = ::cuda::experimental::cuco::__sketch_size_kb_t;
//! A strong type wrapper `standard_deviation` of `double`, for specifying the desired
//! standard deviation for the cardinality estimate of `cuda::experimental::cuco::hyperloglog(_ref)`.
//!
//! @note Valid standard deviations are approximately in [0.00216, 0.2765], which correspond to
//! precision [4, 18].
using standard_deviation = ::cuda::experimental::cuco::__standard_deviation_t;
//! A strong type wrapper `precision` of `int`, for specifying the HyperLogLog precision
//! parameter of `cuda::experimental::cuco::hyperloglog(_ref)`.
//!
//! @note Valid precision values are in [4, 18], which correspond to sketch sizes in
//! [0.0625 KB, 1024 KB] and standard deviations approximately in [0.00216, 0.2765].
using precision = ::cuda::experimental::cuco::__precision_t;
private:
::cuda::device_buffer<register_type> __sketch_buffer; ///< Storage for sketch
ref_type<> __ref; ///< Device ref of the current `hyperloglog` object
// Needs to be friends with other instantiations of this class template to have access to their
// storage
template <class _Tp_, class _MemoryResource_, ::cuda::thread_scope _Scope_, class _Policy_>
friend class hyperloglog;
public:
// TODO enable CTAD
//! @brief Constructs a `hyperloglog` host object.
//!
//! @note Construction is stream-ordered: the initial clear is enqueued on `__stream` without
//! synchronizing it.
//!
//! @param __stream CUDA stream used to initialize the object
//! @param __memory_resource A memory resource used for allocating device storage
//! @param __sketch_size_kb Maximum sketch size in KB
//! @param __policy The policy used to hash items and finalize the estimate
//!
//! @throw If sketch size implies precision outside [4, 18].
template <typename _MemoryResource_ = _MemoryResource>
_CCCL_HOST_API constexpr hyperloglog(
::cuda::stream_ref __stream,
_MemoryResource_&& __memory_resource,
sketch_size_kb __sketch_size_kb = sketch_size_kb{32.0},
const _Policy& __policy = {})
: hyperloglog{__stream,
::cuda::std::forward<_MemoryResource_>(__memory_resource),
__to_precision(__sketch_size_kb),
__policy}
{}
//! @brief Constructs a `hyperloglog` host object.
//!
//! @note Construction is stream-ordered: the initial clear is enqueued on `__stream` without
//! synchronizing it.
//!
//! @param __stream CUDA stream used to initialize the object
//! @param __memory_resource A memory resource used for allocating device storage
//! @param __sd Desired standard deviation for the approximation error
//! @param __policy The policy used to hash items and finalize the estimate
//!
//! @throw If standard deviation implies precision outside [4, 18].
template <typename _MemoryResource_ = _MemoryResource>
_CCCL_HOST_API constexpr hyperloglog(
::cuda::stream_ref __stream,
_MemoryResource_&& __memory_resource,
standard_deviation __sd,
const _Policy& __policy = {})
: hyperloglog{__stream, ::cuda::std::forward<_MemoryResource_>(__memory_resource), __to_precision(__sd), __policy}
{}
//! @brief Constructs a `hyperloglog` host object.
//!
//! @note Construction is stream-ordered: the initial clear is enqueued on `__stream` without
//! synchronizing it.
//!
//! @param __stream CUDA stream used to initialize the object
//! @param __memory_resource A memory resource used for allocating device storage
//! @param __precision HyperLogLog precision parameter (determines number of registers as 2^precision)
//! @param __policy The policy used to hash items and finalize the estimate
//!
//! @throw If precision is outside [4, 18].
template <typename _MemoryResource_ = _MemoryResource>
_CCCL_HOST_API constexpr hyperloglog(
::cuda::stream_ref __stream,
_MemoryResource_&& __memory_resource,
precision __precision,
const _Policy& __policy = {})
: __sketch_buffer{__stream,
::cuda::std::forward<_MemoryResource_>(__memory_resource),
ref_type<>::sketch_bytes(
__precision_in_bounds(__precision, "HyperLogLog precision must be in [4, 18]"))
/ sizeof(register_type),
::cuda::no_init}
, __ref{::cuda::std::as_writable_bytes(::cuda::std::span{__sketch_buffer.data(), __sketch_buffer.size()}),
__policy}
{
clear_async(__stream);
}
_CCCL_HIDE_FROM_ABI ~hyperloglog() = default;
hyperloglog(const hyperloglog&) = delete;
//! @brief Copy-assignment operator.
//!
//! @return Copy of `*this`
hyperloglog& operator=(const hyperloglog&) = delete;
_CCCL_HIDE_FROM_ABI hyperloglog(hyperloglog&&) = default; ///< Move constructor
_CCCL_HIDE_FROM_ABI hyperloglog& operator=(hyperloglog&&) = default;
//! @brief Asynchronously resets the estimator, i.e., clears the current count estimate.
//!
//! @param __stream CUDA stream this operation is executed in
_CCCL_HOST_API constexpr void clear_async(::cuda::stream_ref __stream) noexcept
{
__ref.clear_async(__stream);
}
//! @brief Resets the estimator, i.e., clears the current count estimate.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `clear_async`.
//!
//! @param __stream CUDA stream this operation is executed in
_CCCL_HOST_API constexpr void clear(::cuda::stream_ref __stream)
{
__ref.clear(__stream);
}
//! @brief Asynchronously adds to be counted items to the estimator.
//!
//! @tparam _InputIt Device accessible random access input iterator where
//! <tt>std::is_convertible<std::iterator_traits<_InputIt>::value_type,
//! _Tp></tt> is `true`
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __first Beginning of the sequence of items
//! @param __last End of the sequence of items
template <class _InputIt>
_CCCL_HOST_API constexpr void add_async(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last)
{
__ref.add_async(__stream, __first, __last);
}
//! @brief Adds to be counted items to the estimator.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `add_async`.
//!
//! @tparam _InputIt Device accessible random access input iterator where
//! <tt>std::is_convertible<std::iterator_traits<_InputIt>::value_type,
//! _Tp></tt> is `true`
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __first Beginning of the sequence of items
//! @param __last End of the sequence of items
template <class _InputIt>
_CCCL_HOST_API constexpr void add(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last)
{
__ref.add(__stream, __first, __last);
}
//! @brief Asynchronously merges the result of `other` estimator into `*this` estimator.
//!
//! @throw If sketch_bytes() != __other.sketch_bytes()
//!
//! @tparam _OtherScope Thread scope of `other` estimator
//! @tparam _OtherMemoryResource Memory resource type of `other` estimator
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __other Other estimator to be merged into `*this`
template <::cuda::thread_scope _OtherScope, class _OtherMemoryResource>
_CCCL_HOST_API constexpr void
merge_async(::cuda::stream_ref __stream, const hyperloglog<_Tp, _OtherMemoryResource, _OtherScope, _Policy>& __other)
{
__ref.merge_async(__stream, __other.__ref);
}
//! @brief Merges the result of `other` estimator into `*this` estimator.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `merge_async`.
//!
//! @throw If sketch_bytes() != __other.sketch_bytes()
//!
//! @tparam _OtherScope Thread scope of `other` estimator
//! @tparam _OtherMemoryResource Memory resource type of `other` estimator
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __other Other estimator to be merged into `*this`
template <::cuda::thread_scope _OtherScope, class _OtherMemoryResource>
_CCCL_HOST_API constexpr void
merge(::cuda::stream_ref __stream, const hyperloglog<_Tp, _OtherMemoryResource, _OtherScope, _Policy>& __other)
{
__ref.merge(__stream, __other.__ref);
}
//! @brief Asynchronously merges the result of `other` estimator reference into `*this` estimator.
//!
//! @throw If sketch_bytes() != __other.sketch_bytes()
//!
//! @tparam _OtherScope Thread scope of `other` estimator
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __other_ref Other estimator reference to be merged into `*this`
template <::cuda::thread_scope _OtherScope>
_CCCL_HOST_API constexpr void merge_async(::cuda::stream_ref __stream, const ref_type<_OtherScope>& __other_ref)
{
__ref.merge_async(__stream, __other_ref);
}
//! @brief Merges the result of `other` estimator reference into `*this` estimator.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `merge_async`.
//!
//! @throw If sketch_bytes() != __other.sketch_bytes()
//!
//! @tparam _OtherScope Thread scope of `other` estimator
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __other_ref Other estimator reference to be merged into `*this`
template <::cuda::thread_scope _OtherScope>
_CCCL_HOST_API constexpr void merge(::cuda::stream_ref __stream, const ref_type<_OtherScope>& __other_ref)
{
__ref.merge(__stream, __other_ref);
}
//! @brief Compute the estimated distinct items count.
//!
//! @note This function synchronizes the given stream.
//!
//! @tparam _MemoryResource Host memory resource used for allocating the host buffer required to
//! compute the final estimate by copying the sketch from device to host
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __host_mr Host memory resource used for copying the sketch
//!
//! @return Approximate distinct items count
template <typename _HostMemoryResource = ::cuda::mr::legacy_pinned_memory_resource>
[[nodiscard]] _CCCL_HOST_API constexpr double
estimate(::cuda::stream_ref __stream, _HostMemoryResource __host_mr = {}) const
{
return __ref.estimate(__stream, __host_mr);
}
//! @brief Get device ref.
//!
//! @return Device ref object of the current `hyperloglog` host object
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ref_type<> ref() const noexcept
{
return {sketch(), policy()};
}
//! @brief Get hash function.
//!
//! @return The hash function
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto hash_function() const noexcept
{
return __ref.hash_function();
}
//! @brief Get the policy.
//!
//! @return The policy
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr const _Policy& policy() const noexcept
{
return __ref.policy();
}
//! @brief Gets the span of the sketch.
//!
//! @return The ::cuda::std::span of the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::span<::cuda::std::byte> sketch() const noexcept
{
return __ref.sketch();
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::size_t sketch_bytes() const noexcept
{
return __ref.sketch_bytes();
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param __sketch_size_kb Upper bound sketch size in KB
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t
sketch_bytes(sketch_size_kb __sketch_size_kb) noexcept
{
return ref_type<>::sketch_bytes(__sketch_size_kb);
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param __standard_deviation Upper bound standard deviation for approximation error
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t
sketch_bytes(standard_deviation __standard_deviation) noexcept
{
return ref_type<>::sketch_bytes(__standard_deviation);
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param __precision HyperLogLog precision parameter
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t sketch_bytes(precision __precision) noexcept
{
return ref_type<>::sketch_bytes(__precision);
}
//! @brief Gets the alignment required for the sketch storage.
//!
//! @return The required alignment
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t sketch_alignment() noexcept
{
return ref_type<>::sketch_alignment();
}
private:
[[nodiscard]] _CCCL_HOST_API static constexpr precision
__precision_in_bounds(precision __precision, const char* __message)
{
const auto __value = static_cast<::cuda::std::int32_t>(__precision);
const auto __in_range = ::cuda::in_range(__value, 4, 18);
if (!__in_range)
{
_CCCL_THROW(::std::invalid_argument, __message);
}
return __precision;
}
[[nodiscard]] _CCCL_HOST_API static constexpr precision __to_precision(sketch_size_kb __sketch_size_kb)
{
const auto __bytes = ref_type<>::sketch_bytes(__sketch_size_kb) / sizeof(register_type);
const auto __precision = static_cast<int>(::cuda::std::countr_zero(static_cast<::cuda::std::size_t>(__bytes)));
return __precision_in_bounds(
precision{__precision}, "HyperLogLog sketch size must be in range [0.0625 KB, 1024 KB]");
}
[[nodiscard]] _CCCL_HOST_API static constexpr precision __to_precision(standard_deviation __standard_deviation)
{
const auto __bytes = ref_type<>::sketch_bytes(__standard_deviation) / sizeof(register_type);
const auto __precision = static_cast<int>(::cuda::std::countr_zero(static_cast<::cuda::std::size_t>(__bytes)));
return __precision_in_bounds(
precision{__precision}, "HyperLogLog standard deviation must be in range [0.00216, 0.2765]");
}
};
} // namespace cuda::experimental::cuco
#endif // !_CCCL_COMPILER(NVRTC)
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_HYPERLOGLOG_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_HYPERLOGLOG_REF_CUH
#define _CUDAX___CUCO_HYPERLOGLOG_REF_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__stream/stream_ref.h>
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__type_traits/is_convertible.h>
#include <cuda/std/span>
#include <cuda/experimental/__cuco/detail/hyperloglog/hyperloglog_impl.cuh>
#include <cuda/experimental/__cuco/hll_policies.cuh>
#include <cooperative_groups.h>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief A non-owning reference to a HyperLogLog sketch for approximating the number of distinct
//! items in a multiset.
//!
//! @note This implementation is based on the HyperLogLog++ algorithm:
//! https://static.googleusercontent.com/media/research.google.com/de//pubs/archive/40671.pdf.
//!
//! @tparam _Tp Type of items to count
//! @tparam _Scope The scope in which operations will be performed by individual threads
//! @tparam _Policy Policy bundling hash function, bit-slicing rule, and finalizer
template <class _Tp,
::cuda::thread_scope _Scope = ::cuda::thread_scope_device,
class _Policy = ::cuda::experimental::cuco::default_hll_policy<_Tp>>
class hyperloglog_ref
{
using __impl_type = ::cuda::experimental::cuco::__hyperloglog_impl<_Tp, _Scope, _Policy>;
__impl_type __impl; ///< Implementation object
template <class _Tp_, ::cuda::thread_scope _Scope_, class _Policy_>
friend class hyperloglog_ref;
public:
static constexpr auto thread_scope = __impl_type::__thread_scope; ///< CUDA thread scope
using value_type = typename __impl_type::__value_type; ///< Type of items to count
using policy_type = typename __impl_type::__policy_type; ///< Policy type
using hasher = typename __impl_type::__hasher; ///< Type of hash function
using register_type = typename __impl_type::__register_type; ///< HLL register type
//! A strong type wrapper `sketch_size_kb` of `double`, for specifying the upper-bound
//! sketch size of `cuda::experimental::cuco::hyperloglog(_ref)` in KB.
//!
//! @note Valid sketch sizes are in [0.0625 KB, 1024 KB], which correspond to precision [4, 18].
using sketch_size_kb = ::cuda::experimental::cuco::__sketch_size_kb_t;
//! A strong type wrapper `standard_deviation` of `double`, for specifying the desired
//! standard deviation for the cardinality estimate of `cuda::experimental::cuco::hyperloglog(_ref)`.
//!
//! @note Valid standard deviations are approximately in [0.00216, 0.2765], which correspond to
//! precision [4, 18].
using standard_deviation = ::cuda::experimental::cuco::__standard_deviation_t;
//! A strong type wrapper `precision` of `int`, for specifying the HyperLogLog precision
//! parameter of `cuda::experimental::cuco::hyperloglog(_ref)`.
//!
//! @note Valid precision values are in [4, 18], which correspond to sketch sizes in
//! [0.0625 KB, 1024 KB] and standard deviations approximately in [0.00216, 0.2765].
using precision = ::cuda::experimental::cuco::__precision_t;
template <::cuda::thread_scope _NewScope>
using rebind_scope = hyperloglog_ref<_Tp, _NewScope, _Policy>; ///< Ref type with different thread scope
//! @brief Constructs a non-owning `hyperloglog_ref` object.
//!
//! @throw If sketch size < 0.0625KB or 64B or standard deviation > 0.2765. Throws if called from
//! host; __trap() if called from device.
//! @throw If sketch size or standard deviation imply precision outside [4, 18].
//! @throw If sketch storage has insufficient alignment. Throws if called from host; __trap() if called
//! from device.
//!
//! @param __sketch_span Reference to sketch storage
//! @param __policy The policy used to hash items and finalize the estimate
_CCCL_HOST_DEVICE_API constexpr hyperloglog_ref(::cuda::std::span<::cuda::std::byte> __sketch_span,
const _Policy& __policy = {})
: __impl{__sketch_span, __policy}
{}
//! @brief Resets the estimator, i.e., clears the current count estimate.
//!
//! @tparam _CG CUDA Cooperative Group type
//!
//! @param __group CUDA Cooperative group this operation is executed in
_CCCL_TEMPLATE(class _CG)
_CCCL_REQUIRES((!::cuda::std::is_convertible_v<_CG, ::cuda::stream_ref>) )
_CCCL_DEVICE_API constexpr void clear(_CG __group) noexcept
{
// The constraint above is to work around an incompatibility between host and device
// overload preference for clang and NVCC. See
// https://llvm.org/docs/CompileCudaWithLLVM.html#overloading-based-on-host-and-device-attributes
// for further reading, but the bottom line is when:
//
// 1. Compiling in device mode (and clang compiles CUDA in a "hybrid" host-device mode,
// also explained by the link above).
// 2. And the current function is __host__ __device__.
// 3. And the function whose overload needs to be resolved has both a __host__ __device__,
// and __device__ (and/or __host__) overload.
//
// Then clang will prefer these overloads (assuming they have equal priority under C++
// rules) in the following order:
//
// 1. __host__ __device__
// 2. __device__
// 3. __host__
//
// In this particular case, `clear(_CG)` conflicts with `clear(::cuda::stream_ref)` when called
// from `hyperloglog::clear(::cuda::stream_ref)`. `hyperloglog::clear(::cuda::stream_ref)`
// is constexpr, and therefore implicitly __host__ __device__. Since
// `clear(::cuda::stream_ref)` on this class is only __host__, it will take lower priority
// that `clear(_CG)`, and we get:
//
// cudax/include/cuda/experimental/__cuco/detail/hyperloglog/hyperloglog_impl.cuh:131:28: error: no member named
// 'thread_rank' in 'cuda::stream_ref' [clang-diagnostic-error]
//
// 131 | for (int __i = __group.thread_rank(); __i < __sketch.size(); __i += __group.size())
// | ~~~~~~~ ^
__impl.__clear(__group);
}
//! @brief Asynchronously resets the estimator, i.e., clears the current count estimate.
//!
//! @param __stream CUDA stream this operation is executed in
_CCCL_HOST_API constexpr void clear_async(::cuda::stream_ref __stream) noexcept
{
__impl.__clear_async(__stream);
}
//! @brief Resets the estimator, i.e., clears the current count estimate.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `clear_async`.
//!
//! @param __stream CUDA stream this operation is executed in
_CCCL_HOST_API constexpr void clear(::cuda::stream_ref __stream)
{
__impl.__clear(__stream);
}
//! @brief Adds an item to the estimator.
//!
//! @param __item The item to be counted
_CCCL_DEVICE_API constexpr void add(const _Tp& __item) noexcept
{
__impl.__add(__item);
}
//! @brief Asynchronously adds to be counted items to the estimator.
//!
//! @tparam _InputIt Device accessible random access input iterator where
//! <tt>std::is_convertible<std::iterator_traits<_InputIt>::value_type,
//! _Tp></tt> is `true`
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __first Beginning of the sequence of items
//! @param __last End of the sequence of items
template <class _InputIt>
_CCCL_HOST_API constexpr void add_async(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last)
{
__impl.__add_async(__first, __last, __stream);
}
//! @brief Adds to be counted items to the estimator.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `add_async`.
//!
//! @tparam _InputIt Device accessible random access input iterator where
//! <tt>std::is_convertible<std::iterator_traits<_InputIt>::value_type,
//! _Tp></tt> is `true`
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __first Beginning of the sequence of items
//! @param __last End of the sequence of items
template <class _InputIt>
_CCCL_HOST_API constexpr void add(::cuda::stream_ref __stream, _InputIt __first, _InputIt __last)
{
__impl.__add(__first, __last, __stream);
}
//! @brief Merges the result of `other` estimator reference into `*this` estimator reference.
//!
//! @throw If sketch_bytes() != __other.sketch_bytes(), then terminates execution with a device __trap()
//!
//! @tparam _CG CUDA Cooperative Group type
//! @tparam _OtherScope Thread scope of `other` estimator
//!
//! @param __group CUDA Cooperative group this operation is executed in
//! @param __other Other estimator reference to be merged into `*this`
_CCCL_TEMPLATE(class _CG, ::cuda::thread_scope _OtherScope)
_CCCL_REQUIRES((!::cuda::std::is_convertible_v<_CG, ::cuda::stream_ref>) )
_CCCL_DEVICE_API constexpr void merge(_CG __group, const hyperloglog_ref<_Tp, _OtherScope, _Policy>& __other)
{
// The constraint above works around the same host/device overload preference issue as
// documented in `clear(_CG)`: `merge(_CG, ...)` would otherwise conflict with
// `merge(::cuda::stream_ref, ...)` when called from `hyperloglog::merge(::cuda::stream_ref, ...)`.
__impl.__merge(__group, __other.__impl);
}
//! @brief Asynchronously merges the result of `other` estimator reference into `*this`
//! estimator.
//!
//! @throw If sketch_bytes() != __other.sketch_bytes()
//!
//! @tparam _OtherScope Thread scope of `other` estimator
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __other Other estimator reference to be merged into `*this`
template <::cuda::thread_scope _OtherScope>
_CCCL_HOST_API constexpr void
merge_async(::cuda::stream_ref __stream, const hyperloglog_ref<_Tp, _OtherScope, _Policy>& __other)
{
__impl.__merge_async(__other.__impl, __stream);
}
//! @brief Merges the result of `other` estimator reference into `*this` estimator.
//!
//! @note This function synchronizes the given stream. For asynchronous execution use
//! `merge_async`.
//!
//! @throw If sketch_bytes() != __other.sketch_bytes()
//!
//! @tparam _OtherScope Thread scope of `other` estimator
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __other Other estimator reference to be merged into `*this`
template <::cuda::thread_scope _OtherScope>
_CCCL_HOST_API constexpr void
merge(::cuda::stream_ref __stream, const hyperloglog_ref<_Tp, _OtherScope, _Policy>& __other)
{
__impl.__merge(__other.__impl, __stream);
}
//! @brief Compute the estimated distinct items count.
//!
//! @param __group CUDA thread block group this operation is executed in
//!
//! @return Approximate distinct items count
[[nodiscard]] _CCCL_DEVICE_API double estimate(const ::cooperative_groups::thread_block& __group) const noexcept
{
return __impl.__estimate(__group);
}
//! @brief Compute the estimated distinct items count.
//!
//! @note This function synchronizes the given stream.
//!
//! @tparam _HostMemoryResource Host memory resource used for allocating the host buffer required to
//! compute the final estimate by copying the sketch from device to host
//!
//! @param __stream CUDA stream this operation is executed in
//! @param __host_mr Host memory resource used for copying the sketch
//!
//! @return Approximate distinct items count
template <typename _HostMemoryResource = ::cuda::mr::legacy_pinned_memory_resource>
[[nodiscard]] _CCCL_HOST_API constexpr double
estimate(::cuda::stream_ref __stream, _HostMemoryResource __host_mr = {}) const
{
return __impl.__estimate(__host_mr, __stream);
}
//! @brief Gets the hash function.
//!
//! @return The hash function
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto hash_function() const noexcept
{
return __impl.__hash_function();
}
//! @brief Gets the policy.
//!
//! @return The policy
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr const _Policy& policy() const noexcept
{
return __impl.__policy_();
}
//! @brief Gets the span of the sketch.
//!
//! @return The ::cuda::std::span of the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::span<::cuda::std::byte> sketch() const noexcept
{
return __impl.__sketch_span();
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr ::cuda::std::size_t sketch_bytes() const noexcept
{
return __impl.__sketch_bytes();
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param __sketch_size_kb Upper bound sketch size in KB
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t
sketch_bytes(sketch_size_kb __sketch_size_kb) noexcept
{
return __impl_type::__sketch_bytes(__sketch_size_kb);
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param __standard_deviation Upper bound standard deviation for approximation error
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t
sketch_bytes(standard_deviation __standard_deviation) noexcept
{
return __impl_type::sketch_bytes(__standard_deviation);
}
//! @brief Gets the number of bytes required for the sketch storage.
//!
//! @param __precision HyperLogLog precision parameter
//!
//! @return The number of bytes required for the sketch
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t sketch_bytes(precision __precision) noexcept
{
return __impl_type::sketch_bytes(__precision);
}
//! @brief Gets the alignment required for the sketch storage.
//!
//! @return The required alignment
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr ::cuda::std::size_t sketch_alignment() noexcept
{
return __impl_type::__sketch_alignment();
}
};
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_HYPERLOGLOG_REF_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_PROBING_SCHEME_CUH
#define _CUDAX___CUCO_PROBING_SCHEME_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__mdspan/extents.h>
#include <cuda/std/__tuple_dir/get.h>
#include <cuda/std/__tuple_dir/tuple.h>
#include <cuda/std/__tuple_dir/tuple_like.h>
#include <cuda/std/__tuple_dir/tuple_size.h>
#include <cuda/std/__type_traits/decay.h>
#include <cuda/experimental/__cuco/detail/probing_scheme_base.cuh>
#include <cooperative_groups.h>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief Public linear probing scheme class.
//!
//! @note Linear probing is efficient when few collisions are present, e.g., low occupancy or low
//! multiplicity.
//!
//! @note `_Hash` should be a callable object type.
//!
//! @tparam _CgSize Cooperative group size
//! @tparam _Hash Hash functor type
template <int _CgSize, class _Hash>
class linear_probing : detail::__probing_scheme_base<_CgSize>
{
using __base_type = detail::__probing_scheme_base<_CgSize>;
public:
static constexpr int cg_size = __base_type::__cg_size;
using hasher = _Hash;
//! @brief Constructs a linear probing scheme with the given hasher callable.
//!
//! @param __hash Hasher
_CCCL_HOST_DEVICE_API constexpr linear_probing(const _Hash& __hash = {})
: __hash{__hash}
{}
//! @brief Makes a copy of the current probing scheme with the given hasher.
//!
//! @tparam _NewHash New hasher type
//!
//! @param __hash Hasher
//!
//! @return Copy of the current probing scheme
template <class _NewHash>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto rebind_hash_function(const _NewHash& __hash) const noexcept
{
return linear_probing<cg_size, _NewHash>{__hash};
}
//! @brief Returns a probing iterator.
//!
//! @tparam _BucketSize Size of the bucket
//! @tparam _ProbeKey Type of probing key
//! @tparam _Capacity Capacity extent type (total slots)
//!
//! @param __probe_key The probing key
//! @param __cap Capacity extent (total slots) bounding the iteration
//!
//! @return An iterator whose value_type is convertible to the slot index type
template <int _BucketSize, class _ProbeKey, class _Capacity>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto make_iterator(_ProbeKey __probe_key, _Capacity __cap) const noexcept
{
using __size_type = typename _Capacity::index_type;
using __step_extent = ::cuda::std::extents<__size_type, _BucketSize>;
const __size_type __init = __hash(__probe_key) % (__cap.extent(0) / _BucketSize) * _BucketSize;
return detail::__probing_iterator<_Capacity, __step_extent>{__init, __step_extent{}, __cap};
}
//! @brief Returns a cooperative group based probing iterator.
//!
//! @tparam _BucketSize Size of the bucket
//! @tparam _ProbeKey Type of probing key
//! @tparam _Capacity Capacity extent type (total slots)
//! @tparam _ParentCG Type of parent cooperative group
//!
//! @param __group The cooperative group used to generate the probing iterator
//! @param __probe_key The probing key
//! @param __cap Capacity extent (total slots) bounding the iteration
//!
//! @return An iterator whose value_type is convertible to the slot index type
template <int _BucketSize, class _ProbeKey, class _Capacity, class _ParentCG>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
make_iterator(::cooperative_groups::thread_block_tile<cg_size, _ParentCG> __group,
_ProbeKey __probe_key,
_Capacity __cap) const noexcept
{
using __size_type = typename _Capacity::index_type;
constexpr __size_type __stride = cg_size * _BucketSize;
using __step_extent = ::cuda::std::extents<__size_type, __stride>;
const __size_type __init =
__hash(__probe_key) % (__cap.extent(0) / __stride) * __stride + __size_type{__group.thread_rank() * _BucketSize};
return detail::__probing_iterator<_Capacity, __step_extent>{__init, __step_extent{}, __cap};
}
//! @brief Gets the function used to hash keys.
//!
//! @return The function used to hash keys
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr hasher hash_function() const noexcept
{
return __hash;
}
private:
_Hash __hash;
};
//! @brief Public double hashing scheme class.
//!
//! @note Default probing scheme for cuco data structures. It shows superior performance over linear
//! probing especially when dealing with high multiplicity and/or high occupancy use cases.
//!
//! @note `_Hash1` and `_Hash2` should be callable object types.
//!
//! @note `_Hash2` needs to be able to construct from an integer value to avoid secondary clustering.
//!
//! @tparam _CgSize Cooperative group size
//! @tparam _Hash1 First hash functor
//! @tparam _Hash2 Second hash functor
template <int _CgSize, class _Hash1, class _Hash2 = _Hash1>
class double_hashing : detail::__probing_scheme_base<_CgSize>
{
using __base_type = detail::__probing_scheme_base<_CgSize>;
public:
static constexpr int cg_size = __base_type::__cg_size;
using hasher = ::cuda::std::tuple<_Hash1, _Hash2>;
//! @brief Constructs a double hashing probing scheme with the two hasher callables.
//!
//! @param __hash1 First hasher
//! @param __hash2 Second hasher
_CCCL_HOST_DEVICE_API constexpr double_hashing(const _Hash1& __hash1 = {}, const _Hash2& __hash2 = {1})
: __hash1{__hash1}
, __hash2{__hash2}
{}
//! @brief Constructs a double hashing probing scheme with the given hasher tuple.
//!
//! @param __hash Hasher tuple
_CCCL_HOST_DEVICE_API constexpr double_hashing(const ::cuda::std::tuple<_Hash1, _Hash2>& __hash)
: __hash1{::cuda::std::get<0>(__hash)}
, __hash2{::cuda::std::get<1>(__hash)}
{}
//! @brief Makes a copy of the current probing scheme with the given hasher.
//!
//! @tparam _NewHash Tuple-like new hasher type
//!
//! @param __hash Hasher
//!
//! @return Copy of the current probing scheme
_CCCL_TEMPLATE(class _NewHash)
_CCCL_REQUIRES(::cuda::std::__tuple_like<_NewHash>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto rebind_hash_function(const _NewHash& __hash) const
{
static_assert(::cuda::std::__tuple_like<_NewHash> && ::cuda::std::tuple_size<_NewHash>::value == 2,
"The given hasher must be a tuple-like object with exactly two elements");
const auto& [__hash1, __hash2] = __hash;
using __hash1_type = ::cuda::std::decay_t<decltype(__hash1)>;
using __hash2_type = ::cuda::std::decay_t<decltype(__hash2)>;
return double_hashing<cg_size, __hash1_type, __hash2_type>{__hash1, __hash2};
}
//! @brief Returns a probing iterator.
//!
//! @tparam _BucketSize Size of the bucket
//! @tparam _ProbeKey Type of probing key
//! @tparam _Capacity Capacity extent type (total slots)
//!
//! @param __probe_key The probing key
//! @param __cap Capacity extent (total slots) bounding the iteration
//!
//! @return An iterator whose value_type is convertible to the slot index type
template <int _BucketSize, class _ProbeKey, class _Capacity>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto make_iterator(_ProbeKey __probe_key, _Capacity __cap) const noexcept
{
using __size_type = typename _Capacity::index_type;
using __step_extent = ::cuda::std::extents<__size_type, ::cuda::std::dynamic_extent>;
return detail::__probing_iterator<_Capacity, __step_extent>{
__size_type{__hash1(__probe_key)} % (__cap.extent(0) / _BucketSize) * _BucketSize,
__step_extent{__size_type{(__hash2(__probe_key) % (__cap.extent(0) / _BucketSize - 1) + 1) * _BucketSize}},
__cap};
}
//! @brief Returns a cooperative group based probing iterator.
//!
//! @tparam _BucketSize Size of the bucket
//! @tparam _ProbeKey Type of probing key
//! @tparam _Capacity Capacity extent type (total slots)
//! @tparam _ParentCG Type of parent cooperative group
//!
//! @param __group The cooperative group used to generate the probing iterator
//! @param __probe_key The probing key
//! @param __cap Capacity extent (total slots) bounding the iteration
//!
//! @return An iterator whose value_type is convertible to the slot index type
template <int _BucketSize, class _ProbeKey, class _Capacity, class _ParentCG>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
make_iterator(::cooperative_groups::thread_block_tile<cg_size, _ParentCG> __group,
_ProbeKey __probe_key,
_Capacity __cap) const noexcept
{
using __size_type = typename _Capacity::index_type;
constexpr __size_type __stride = cg_size * _BucketSize;
using __step_extent = ::cuda::std::extents<__size_type, ::cuda::std::dynamic_extent>;
return detail::__probing_iterator<_Capacity, __step_extent>{
__size_type{__hash1(__probe_key)} % (__cap.extent(0) / __stride) * __stride
+ __size_type{__group.thread_rank() * _BucketSize},
__step_extent{__size_type{(__hash2(__probe_key) % (__cap.extent(0) / __stride - 1) + 1) * __stride}},
__cap};
}
//! @brief Gets the functions used to hash keys.
//!
//! @return The functions used to hash keys
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr hasher hash_function() const noexcept
{
return {__hash1, __hash2};
}
private:
_Hash1 __hash1;
_Hash2 __hash2;
};
//! @brief Trait value indicating whether a probing scheme is double hashing.
//!
//! @tparam _Tp Input probing scheme type
template <class _Tp>
inline constexpr bool is_double_hashing_v = false;
//! @brief Specialization indicating that `double_hashing` is a double hashing scheme.
//!
//! @tparam _CgSize Cooperative group size
//! @tparam _Hash1 First hash functor
//! @tparam _Hash2 Second hash functor
template <int _CgSize, class _Hash1, class _Hash2>
inline constexpr bool is_double_hashing_v<double_hashing<_CgSize, _Hash1, _Hash2>> = true;
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_PROBING_SCHEME_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX___CUCO_TYPES_CUH
#define _CUDAX___CUCO_TYPES_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/experimental/__cuco/detail/utility/strong_type.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental::cuco
{
//! @brief Strong type wrapper for an empty key sentinel.
//!
//! @tparam _Key The key type
template <class _Key>
struct empty_key : __strong_type<_Key>
{
_CCCL_HOST_DEVICE_API explicit constexpr empty_key(_Key __value) noexcept
: __strong_type<_Key>(__value)
{}
};
//! @brief Strong type wrapper for an empty value sentinel.
//!
//! @tparam _Tp The mapped value type
template <class _Tp>
struct empty_value : __strong_type<_Tp>
{
_CCCL_HOST_DEVICE_API explicit constexpr empty_value(_Tp __value) noexcept
: __strong_type<_Tp>(__value)
{}
};
//! @brief Strong type wrapper for an erased key sentinel.
//!
//! @tparam _Key The key type
template <class _Key>
struct erased_key : __strong_type<_Key>
{
_CCCL_HOST_DEVICE_API explicit constexpr erased_key(_Key __value) noexcept
: __strong_type<_Key>(__value)
{}
};
} // namespace cuda::experimental::cuco
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX___CUCO_TYPES_CUH

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cccl_upstream/cudax/include/cuda/experimental/__cufile/cufile.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#pragma once
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__host_stdlib/stdexcept>
#include <cuda/std/__utility/exchange.h>
#include <cuda/std/string_view>
#include <cuda/experimental/__cufile/cufile_ref.cuh>
#include <cuda/experimental/__cufile/driver.cuh>
#include <cuda/experimental/__cufile/exception.cuh>
#include <cuda/experimental/__cufile/open_mode.cuh>
#include <string>
#include <cufile.h>
#include <errno.h>
#include <fcntl.h>
#include <unistd.h>
namespace cuda::experimental
{
//! @brief An owning wrapper of \c CUfileHandle_t and the OS specific native file handle.
class cufile : public cufile_ref
{
public:
using native_handle_type = __cufile_os_native_type; //!< The underlying OS native handle type.
private:
using __oflags_type = int;
static constexpr native_handle_type __invalid_native_handle = -1;
native_handle_type __native_handle_{__invalid_native_handle}; //< The native handle.
//! @brief Constructs the object from native handle and cuFile file handle.
_CCCL_HIDE_FROM_ABI cufile(cufile_ref __cufile_handle, native_handle_type __native_handle) noexcept
: cufile_ref{__cufile_handle}
, __native_handle_{__native_handle}
{}
//! @brief Make open flags from the \c cuda::cufile_open_mode.
//!
//! @param __om The cuFile open mode.
//!
//! @return The flags mask to be passed to open function.
[[nodiscard]] static _CCCL_HOST_API constexpr __oflags_type __make_oflags(cufile_open_mode __om) noexcept
{
__oflags_type __ret{};
if ((__om & (cufile_open_mode::in | cufile_open_mode::out)) == (cufile_open_mode::in | cufile_open_mode::out))
{
__ret |= O_RDWR | O_CREAT;
}
else if ((__om & cufile_open_mode::in) == cufile_open_mode::in)
{
__ret |= O_RDONLY;
}
else if ((__om & cufile_open_mode::out) == cufile_open_mode::out)
{
__ret |= O_WRONLY | O_CREAT;
}
__ret |= ((__om & cufile_open_mode::trunc) == cufile_open_mode::trunc) ? O_TRUNC : 0;
__ret |= ((__om & cufile_open_mode::noreplace) == cufile_open_mode::noreplace) ? O_EXCL : 0;
__ret |= ((__om & cufile_open_mode::direct) == cufile_open_mode::direct) ? O_DIRECT : 0;
return __ret;
}
//! @brief Wrapper for opening the native handle.
[[nodiscard]] static _CCCL_HOST_API native_handle_type __open_file(const char* __filename, __oflags_type __oflags)
{
// if O_CREAT flag is specified, use the same mode as if opened by fopend
::mode_t __ocreat_mode{};
if (__oflags & O_CREAT)
{
__ocreat_mode = S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH;
}
int __fd = ::open(__filename, __oflags, __ocreat_mode);
if (__fd == -1)
{
errno = 0; // clear errno
_CCCL_THROW(::std::runtime_error, "Failed to open file.");
}
return __fd;
}
//! @brief Wrapper for retrieving the open mode.
[[nodiscard]] static _CCCL_HOST_API cufile_open_mode __open_mode(native_handle_type __native_handle)
{
int __oflags = ::fcntl(__native_handle, F_GETFL);
if (__oflags == -1)
{
errno = 0; // clear errno
_CCCL_THROW(::std::runtime_error, "Failed to retrieve open flags.");
}
cufile_open_mode __om{};
if (__oflags & O_RDWR)
{
__om |= cufile_open_mode::in | cufile_open_mode::out;
}
else if (__oflags & O_RDONLY)
{
__om |= cufile_open_mode::in;
}
else if (__oflags & O_WRONLY)
{
__om |= cufile_open_mode::out;
}
__om |= (__oflags & O_TRUNC) ? cufile_open_mode::trunc : cufile_open_mode{};
__om |= (__oflags & O_EXCL) ? cufile_open_mode::noreplace : cufile_open_mode{};
__om |= (__oflags & O_DIRECT) ? cufile_open_mode::direct : cufile_open_mode{};
return __om;
}
//! @brief Wrapper for closing the native handle.
[[nodiscard]] static _CCCL_HOST_API bool __close_file_no_throw(native_handle_type __native_handle) noexcept
{
return ::close(__native_handle) == 0;
}
//! @brief Wrapper for closing the native handle. Throws \c cuda::std::runtime_error if an error occurs.
static _CCCL_HOST_API void __close_file(native_handle_type __native_handle)
{
if (!__close_file_no_throw(__native_handle))
{
errno = 0; // clear errno
_CCCL_THROW(::std::runtime_error, "Failed to close file.");
}
}
public:
//! @brief Make a cufile object from already existing native handle.
//!
// The ownership of the handle is transferred to the object and the handle is registered by the cuFile driver.
//!
//! @param __native_handle The native handle.
//!
//! @return The created cufile object.
[[nodiscard]] static _CCCL_HOST_API cufile from_native_handle(native_handle_type __native_handle)
{
return cufile{cufile_driver.register_native_handle(__native_handle), __native_handle};
}
_CCCL_HIDE_FROM_ABI cufile() noexcept = default;
//! @brief Constructs the object by opening file @c __filename in mode @c __open_mode.
//!
//! @param __filename Path to the file. Must be a zero terminated string.
//! @param __open_mode Open mode to open the file with.
//!
//! @throws cuda::std::runtime_error if the file cannot be opened.
//! @throws cuda::cuda_error if a CUDA driver error occurs.
//! @throws cuda::cufile_error if a cuFile driver error occurs.
_CCCL_HOST_API cufile(const char* __filename, cufile_open_mode __open_mode)
{
__native_handle_ = __open_file(__filename, __make_oflags(__open_mode));
try
{
__cufile_handle_ = cufile_driver.register_native_handle(__native_handle_).get();
}
catch (...)
{
__close_file(__native_handle_);
throw;
}
}
cufile(const cufile&) = delete;
//! @brief Move-construct a new @c cufile.
//!
//! @param __other The other @c cufile.
//!
//! @post `__other` is in moved-from state.
_CCCL_HOST_API cufile(cufile&& __other) noexcept
: cufile_ref{::cuda::std::exchange(__other.__cufile_handle_, nullptr)}
, __native_handle_{::cuda::std::exchange(__other.__native_handle_, __invalid_native_handle)}
{}
cufile& operator=(const cufile&) = delete;
//! @brief Move-assign from a @c cufile object.
//!
//! @param __other The other @c cufile.
//!
//! @post `__other` is in moved-from state.
//!
//! @throws cuda::std::runtime_error if the currently opened file fails to close.
_CCCL_HOST_API cufile& operator=(cufile&& __other)
{
if (this != ::cuda::std::addressof(__other))
{
close();
__native_handle_ = ::cuda::std::exchange(__other.__native_handle_, __invalid_native_handle);
__cufile_handle_ = ::cuda::std::exchange(__other.__cufile_handle_, nullptr);
}
return *this;
}
//! @brief Destructor. Deregisters the cuFile file handle and closes the native handle.
_CCCL_HOST_API ~cufile()
{
if (is_open())
{
cufile_driver.deregister_native_handle(__cufile_handle_);
[[maybe_unused]] const auto __ignore_close_retval = __close_file_no_throw(__native_handle_);
}
}
//! @brief Queries whether the file is opened.
//!
//! @return True, if opened, false otherwise.
[[nodiscard]] _CCCL_HOST_API bool is_open() const noexcept
{
return __native_handle_ != __invalid_native_handle;
}
//! @brief Queries the open mode the object was opened with.
//!
//! @return The \c cuda::cufile_open_mode value if opened, empty value otherwise.
[[nodiscard]] _CCCL_HOST_API cufile_open_mode open_mode() const
{
return is_open() ? __open_mode(__native_handle_) : cufile_open_mode{};
}
//! @brief Opens file @c __filename in mode @c __open_mode.
//!
//! @param __filename Path to the file.
//! @param __open_mode Open mode to open the file with.
//!
//! @throws cuda::std::runtime_error if the file cannot be opened or if a file is already opened.
//! @throws cuda::cuda_error if a CUDA driver error occurs.
//! @throws cuda::cufile_error if a cuFile driver error occurs.
_CCCL_HOST_API void open(const char* __filename, cufile_open_mode __open_mode)
{
if (is_open())
{
_CCCL_THROW(::std::runtime_error, "File is already opened.");
}
__native_handle_ = __open_file(__filename, __make_oflags(__open_mode));
try
{
__cufile_handle_ = cufile_driver.register_native_handle(__native_handle_).get();
}
catch (...)
{
__close_file(::cuda::std::exchange(__native_handle_, __invalid_native_handle));
throw;
}
}
//! @brief Closes the currently opened file. If there is no opened file, no action is taken.
//!
//! @throws cuda::std::runtime_error if the file fails to close.
//! @throws cuda::cuda_error if a CUDA driver error occurs.
//! @throws cuda::cufile_error if a cuFile driver error occurs.
_CCCL_HOST_API void close()
{
if (!is_open())
{
return;
}
cufile_driver.deregister_native_handle(::cuda::std::exchange(__cufile_handle_, nullptr));
__close_file(::cuda::std::exchange(__native_handle_, __invalid_native_handle));
}
//! @brief Gets the OS native handle.
//!
//! @return The native handle.
[[nodiscard]] _CCCL_HOST_API native_handle_type native_handle() const noexcept
{
return __native_handle_;
}
//! @brief Deregisters the cuFile file handle and releases the native handle. The ownership of the native handle is
//! transferred to the caller.
//!
//! @returns The native handle.
[[nodiscard]] _CCCL_HOST_API native_handle_type release() noexcept
{
cufile_driver.deregister_native_handle(::cuda::std::exchange(__cufile_handle_, nullptr));
return ::cuda::std::exchange(__native_handle_, __invalid_native_handle);
}
};
} // namespace cuda::experimental

61
cccl_upstream/cudax/include/cuda/experimental/__cufile/cufile_ref.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#pragma once
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cstddef/types.h>
#include <cufile.h>
namespace cuda::experimental
{
using __cufile_os_native_type = int;
//! @brief A non-owning wrapper of \c CUfileHandle_t.
class cufile_ref
{
protected:
::CUfileHandle_t __cufile_handle_{}; //!< The cuFile file handle.
_CCCL_HIDE_FROM_ABI cufile_ref() noexcept = default;
public:
using off_type = ::off_t;
//! @brief Constructs the object from a \c CUfileHandle_t handle.
_CCCL_HOST_API cufile_ref(::CUfileHandle_t __cufile_handle) noexcept
: __cufile_handle_{__cufile_handle}
{}
//! @brief Disallow construction from nullptr.
cufile_ref(::cuda::std::nullptr_t) = delete;
_CCCL_HIDE_FROM_ABI cufile_ref(const cufile_ref&) noexcept = default;
_CCCL_HIDE_FROM_ABI cufile_ref& operator=(const cufile_ref&) noexcept = default;
//! @brief Retrieve the \c CUfileHandle_t handle.
//!
//! @returns The handle being held by the object.
[[nodiscard]] _CCCL_HOST_API ::CUfileHandle_t get() const noexcept
{
return __cufile_handle_;
}
};
} // namespace cuda::experimental

314
cccl_upstream/cudax/include/cuda/experimental/__cufile/driver.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#pragma once
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__host_stdlib/stdexcept>
#include <cuda/std/__type_traits/always_false.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/experimental/__cufile/cufile_ref.cuh>
#include <cuda/experimental/__cufile/driver_attributes.cuh>
#include <cuda/experimental/__cufile/exception.cuh>
#include <cufile.h>
namespace cuda::experimental
{
#if _CCCL_CTK_AT_LEAST(13, 0)
//! @brief Structure representing the range of valid values for a cuFile driver attribute.
//!
//! @tparam _Attr The attribute type. Must be one of the types defined in cufile_driver_attributes that has a queryable
//! range.
template <class _Attr>
struct cufile_driver_attribute_range
{
static_assert(_Attr::__has_queryable_range, "Attribute does not have a queryable range");
typename _Attr::type min; //!< Minimum value of the attribute.
typename _Attr::type max; //!< Maximum value of the attribute.
};
#endif // _CCCL_CTK_AT_LEAST(13, 0)
//! @brief Implementation defined type that implements the cuFILE driver interface.
class cufile_driver_t
{
_CCCL_HIDE_FROM_ABI constexpr cufile_driver_t() noexcept = default;
public:
[[nodiscard]] static _CCCL_HOST_API constexpr cufile_driver_t __make_instance() noexcept
{
return cufile_driver_t{};
}
cufile_driver_t(const cufile_driver_t&) = delete;
cufile_driver_t& operator=(const cufile_driver_t&) = delete;
cufile_driver_t(cufile_driver_t&&) = delete;
cufile_driver_t& operator=(cufile_driver_t&&) = delete;
//! @brief Check if the driver is open.
//!
//! @return true if the driver is open, false otherwise.
[[nodiscard]] _CCCL_HOST_API bool is_open() const noexcept
{
return ::cuFileUseCount() > 0;
}
//! @brief Open the cuFile driver if it is not already open.
//!
//! @throws cufile_error if cuFileDriverOpen fails.
//! @throws cuda_error if a CUDA driver error occurs.
//!
//! @note Some driver attributes cannot be modified after the driver is opened.
//! Attempting to modify these attributes after the driver is opened will result in a runtime error.
_CCCL_HOST_API void open() const
{
if (!is_open())
{
_CCCL_TRY_CUFILE_API(::cuFileDriverOpen, "Failed to open cuFile driver");
}
}
//! @brief Close the cuFile driver if it is open.
//!
//! @throws cufile_error if cuFileDriverClose fails.
//! @throws cuda_error if a CUDA driver error occurs.
_CCCL_HOST_API void close() const
{
if (is_open())
{
_CCCL_TRY_CUFILE_API(::cuFileDriverClose, "Failed to close cuFile driver");
}
}
//! @brief Get the value of a cuFile driver attribute.
//!
//! @tparam _Attr The attribute type to query. Must be one of the types defined in cufile_driver_attributes.
//!
//! @param __attr The attribute to query.
//!
//! @return The value of the attribute.
//!
//! @throws cufile_error if the underlying cuFile API call fails.
//! @throws cuda_error if a CUDA driver error occurs.
//! @throws std::runtime_error if the driver is not open when querying certain attributes.
//!
//! @note Some attributes can only be queried when the driver is open. Attempting to query these attributes
//! when the driver is not open will result in a runtime error. See attribute documentation for details.
template <class _Attr>
[[nodiscard]] _CCCL_HOST_API typename _Attr::type attribute([[maybe_unused]] const _Attr& __attr) const
{
using _AttrEnum = typename _Attr::__enum_type;
typename _Attr::type __ret{};
if constexpr (::cuda::std::is_same_v<_AttrEnum, ::CUFileSizeTConfigParameter_t>)
{
_CCCL_TRY_CUFILE_API(::cuFileGetParameterSizeT, "Failed to get cuFile parameter", _Attr::__enum_value, &__ret);
}
else if constexpr (::cuda::std::is_same_v<_AttrEnum, ::CUFileBoolConfigParameter_t>)
{
_CCCL_TRY_CUFILE_API(::cuFileGetParameterBool, "Failed to get cuFile parameter", _Attr::__enum_value, &__ret);
}
else if constexpr (::cuda::std::is_same_v<_AttrEnum, ::CUfileDriverStatusFlags_t>
|| ::cuda::std::is_same_v<_AttrEnum, ::CUfileFeatureFlags_t>)
{
if (!is_open())
{
_CCCL_THROW(::std::runtime_error, "cuFile driver must be opened to query this attribute.");
}
::CUfileDrvProps_t __props{};
_CCCL_TRY_CUFILE_API(::cuFileDriverGetProperties, "Failed to get cuFile driver properties", &__props);
if constexpr (::cuda::std::is_same_v<_AttrEnum, ::CUfileDriverStatusFlags_t>)
{
__ret = __props.nvfs.dstatusflags & _Attr::__enum_value;
}
else
{
__ret = __props.fflags & _Attr::__enum_value;
}
}
else
{
static_assert(::cuda::std::__always_false_v<_AttrEnum>, "Unsupported parameter type");
}
return __ret;
}
#if _CCCL_CTK_AT_LEAST(13, 0)
//! @brief Get the valid range of values for a cuFile driver attribute.
//!
//! @tparam _Attr The attribute type to query. Must be one of the types defined in cufile_driver_attributes that has
//! a queryable range.
//!
//! @param __attr The attribute to query.
//!
//! @return The valid range of values for the attribute.
//!
//! @throws cufile_error if the underlying cuFile API call fails.
//! @throws cuda_error if a CUDA driver error occurs.
template <class _Attr>
[[nodiscard]] _CCCL_HOST_API cufile_driver_attribute_range<_Attr>
attribute_range([[maybe_unused]] const _Attr& __attr) const
{
static_assert(_Attr::__has_queryable_range, "Attribute does not have a queryable range");
using _AttrEnum = typename _Attr::__enum_type;
if constexpr (::cuda::std::is_same_v<_Attr, cufile_driver_attributes::max_device_cache_size_kb_t>
|| ::cuda::std::is_same_v<_Attr, cufile_driver_attributes::max_device_pinned_mem_size_kb_t>)
{
if (!is_open())
{
_CCCL_THROW(::std::runtime_error,
"This cuFile driver attribute range must be queried after the driver is opened.");
}
}
cufile_driver_attribute_range<_Attr> __ret{};
if constexpr (::cuda::std::is_same_v<_AttrEnum, ::CUFileSizeTConfigParameter_t>)
{
_CCCL_TRY_CUFILE_API(
::cuFileGetParameterMinMaxValue,
"Failed to get cuFile parameter range",
_Attr::__enum_value,
&__ret.min,
&__ret.max);
}
else
{
static_assert(::cuda::std::__always_false_v<_AttrEnum>, "Unsupported parameter type");
}
return __ret;
}
#endif // _CCCL_CTK_AT_LEAST(13, 0)
//! @brief Set the value of a cuFile driver attribute.
//!
//! @tparam _Attr The attribute type to set. Must be one of the types defined in cufile_driver_attributes that is not
//! read-only.
//!
//! @param __attr The attribute to set.
//! @param __value The value to set the attribute to.
//!
//! @throws cufile_error if the underlying cuFile API call fails.
//! @throws cuda_error if a CUDA driver error occurs.
//! @throws std::runtime_error if the attribute cannot be modified after the driver is opened.
//!
//! @note Some attributes cannot be modified after the driver is opened. Attempting to modify these attributes
//! after the driver is opened will result in a runtime error. See attribute documentation for details.
template <class _Attr>
_CCCL_HOST_API void set_attribute([[maybe_unused]] const _Attr& __attr, typename _Attr::type __value) const
{
static_assert(_Attr::__can_be_set_when_closed || _Attr::__can_be_set_when_opened,
"Cannot modify read-only attribute");
using _AttrEnum = typename _Attr::__enum_type;
if (is_open())
{
if constexpr (::cuda::std::is_same_v<_Attr, cufile_driver_attributes::use_poll_mode_t>)
{
const auto __pollthreshold_size = attribute(cufile_driver_attributes::pollthreshold_size_kb);
_CCCL_TRY_CUFILE_API(
::cuFileDriverSetPollMode, "Failed to set cuFile driver poll mode", __value, __pollthreshold_size);
}
else if constexpr (::cuda::std::is_same_v<_Attr, cufile_driver_attributes::pollthreshold_size_kb_t>)
{
const auto __use_poll_mode = attribute(cufile_driver_attributes::use_poll_mode);
_CCCL_TRY_CUFILE_API(
::cuFileDriverSetPollMode, "Failed to set cuFile driver poll mode", __use_poll_mode, __value);
}
else if constexpr (::cuda::std::is_same_v<_Attr, cufile_driver_attributes::max_direct_io_size_kb_t>)
{
_CCCL_TRY_CUFILE_API(
::cuFileDriverSetMaxDirectIOSize, "Failed to set cuFile driver max direct IO size", __value);
}
else if constexpr (::cuda::std::is_same_v<_Attr, cufile_driver_attributes::max_device_cache_size_kb_t>)
{
_CCCL_TRY_CUFILE_API(::cuFileDriverSetMaxCacheSize, "Failed to set cuFile driver max cache size", __value);
}
else if constexpr (::cuda::std::is_same_v<_Attr, cufile_driver_attributes::max_device_pinned_mem_size_kb_t>)
{
_CCCL_TRY_CUFILE_API(
::cuFileDriverSetMaxPinnedMemSize, "Failed to set cuFile driver max pinned mem size", __value);
}
else
{
_CCCL_THROW(::std::runtime_error,
"This cuFile driver attribute cannot be modified after the driver is opened.");
}
}
else
{
if constexpr (::cuda::std::is_same_v<_AttrEnum, ::CUFileSizeTConfigParameter_t>)
{
_CCCL_TRY_CUFILE_API(
::cuFileSetParameterSizeT, "Failed to set cuFile parameter size", _Attr::__enum_value, __value);
}
else if constexpr (::cuda::std::is_same_v<_AttrEnum, ::CUFileBoolConfigParameter_t>)
{
_CCCL_TRY_CUFILE_API(
::cuFileSetParameterBool, "Failed to set cuFile parameter bool", _Attr::__enum_value, __value);
}
else
{
static_assert(::cuda::std::__always_false_v<_AttrEnum>, "Unsupported parameter type");
}
}
}
//! @brief Registers an OS native file handle type in the cuFile driver. The registered cuFile handle can be used with
//! other cuFile APIs. The handle must be deregistered calling the \c
//! cuda::cufile_driver.deregister_native_handle(...) method with the obtained cuFile handle. For each OS
//! native file handle can be called once before deregistered.
//!
//! @param __native_handle The OS native file handle.
//!
//! @return \c cuda::cufile_ref handle.
//!
//! @throws cuda::cuda_error if a CUDA driver error occurs.
//! @throws cuda::cufile_error if a cuFile driver error occurs.
[[nodiscard]] _CCCL_HOST_API cufile_ref register_native_handle(__cufile_os_native_type __native_handle) const
{
::CUfileDescr_t __desc{};
__desc.type = ::CU_FILE_HANDLE_TYPE_OPAQUE_FD;
__desc.handle.fd = __native_handle;
::CUfileHandle_t __handle{};
_CCCL_TRY_CUFILE_API(::cuFileHandleRegister, "Failed to register cuFile handle", &__handle, &__desc);
return __handle;
}
//! @brief Deregisters the previously registered cuFile handle in the driver.
//!
//! @param __file The cuFile handle.
//!
//! @note The \c cuda::cufile implementation relies on this function being \c noexcept.
_CCCL_HOST_API void deregister_native_handle(cufile_ref __file) const noexcept
{
::cuFileHandleDeregister(__file.get());
}
};
//! @brief Global instance of the cuFile driver interface.
inline constexpr cufile_driver_t cufile_driver = cufile_driver_t::__make_instance();
} // namespace cuda::experimental

173
cccl_upstream/cudax/include/cuda/experimental/__cufile/driver_attributes.cuh
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@@ -1,173 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#pragma once
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cstddef/types.h>
#include <cuda/std/__type_traits/always_false.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cufile.h>
namespace cuda::experimental::cufile_driver_attributes
{
template <class _ParamEnum>
[[nodiscard]] _CCCL_CONSTEVAL auto __attr_from_param_type() noexcept
{
if constexpr (::cuda::std::is_same_v<_ParamEnum, ::CUFileSizeTConfigParameter_t>)
{
return ::cuda::std::size_t{};
}
else if constexpr (::cuda::std::is_same_v<_ParamEnum, ::CUFileBoolConfigParameter_t>)
{
return bool{};
}
else
{
static_assert(::cuda::std::__always_false_v<_ParamEnum>, "Unsupported parameter type");
}
}
template <auto _Param, bool _CanBeSetWhenOpened = false>
struct __attr_from_param
{
using __enum_type = decltype(_Param);
static constexpr auto __enum_value = _Param;
static constexpr auto __can_be_get_when_closed = true;
static constexpr auto __can_be_set_when_closed = true;
static constexpr auto __can_be_set_when_opened = _CanBeSetWhenOpened;
static constexpr auto __has_queryable_range = ::cuda::std::is_same_v<__enum_type, ::CUFileSizeTConfigParameter_t>;
using type = decltype(__attr_from_param_type<__enum_type>());
};
template <::CUfileDriverStatusFlags_t _Status>
struct __attr_from_status
{
using __enum_type = ::CUfileDriverStatusFlags_t;
static constexpr auto __enum_value = _Status;
static constexpr auto __can_be_get_when_closed = false;
static constexpr auto __can_be_set_when_closed = false;
static constexpr auto __can_be_set_when_opened = false;
static constexpr auto __has_queryable_range = false;
using type = bool;
};
template <::CUfileFeatureFlags_t _Feature>
struct __attr_from_feature
{
using __enum_type = ::CUfileFeatureFlags_t;
static constexpr auto __enum_value = _Feature;
static constexpr auto __can_be_get_when_closed = false;
static constexpr auto __can_be_set_when_closed = false;
static constexpr auto __can_be_set_when_opened = false;
static constexpr auto __has_queryable_range = false;
using type = bool;
};
using max_io_queue_depth_t = __attr_from_param<::CUFILE_PARAM_EXECUTION_MAX_IO_QUEUE_DEPTH>;
using max_io_threads_t = __attr_from_param<::CUFILE_PARAM_EXECUTION_MAX_IO_THREADS>;
using min_io_threshold_size_kb_t = __attr_from_param<::CUFILE_PARAM_EXECUTION_MIN_IO_THRESHOLD_SIZE_KB>;
using max_request_parallelism_t = __attr_from_param<::CUFILE_PARAM_EXECUTION_MAX_REQUEST_PARALLELISM>;
using max_direct_io_size_kb_t = __attr_from_param<::CUFILE_PARAM_PROPERTIES_MAX_DIRECT_IO_SIZE_KB, true>;
using max_device_cache_size_kb_t = __attr_from_param<::CUFILE_PARAM_PROPERTIES_MAX_DEVICE_CACHE_SIZE_KB, true>;
using per_buffer_cache_size_kb_t = __attr_from_param<::CUFILE_PARAM_PROPERTIES_PER_BUFFER_CACHE_SIZE_KB>;
using max_device_pinned_mem_size_kb_t =
__attr_from_param<::CUFILE_PARAM_PROPERTIES_MAX_DEVICE_PINNED_MEM_SIZE_KB, true>;
using io_batchsize_t = __attr_from_param<::CUFILE_PARAM_PROPERTIES_IO_BATCHSIZE>;
using pollthreshold_size_kb_t = __attr_from_param<::CUFILE_PARAM_POLLTHRESHOLD_SIZE_KB, true>;
using batch_io_timeout_ms_t = __attr_from_param<::CUFILE_PARAM_PROPERTIES_BATCH_IO_TIMEOUT_MS>;
using use_poll_mode_t = __attr_from_param<::CUFILE_PARAM_PROPERTIES_USE_POLL_MODE, true>;
using allow_compat_mode_t = __attr_from_param<::CUFILE_PARAM_PROPERTIES_ALLOW_COMPAT_MODE>;
using force_compat_mode_t = __attr_from_param<::CUFILE_PARAM_FORCE_COMPAT_MODE>;
using fs_misc_api_check_aggressive_t = __attr_from_param<::CUFILE_PARAM_FS_MISC_API_CHECK_AGGRESSIVE>;
using parallel_io_t = __attr_from_param<::CUFILE_PARAM_EXECUTION_PARALLEL_IO>;
using profile_nvtx_t = __attr_from_param<::CUFILE_PARAM_PROFILE_NVTX>;
using allow_system_memory_t = __attr_from_param<::CUFILE_PARAM_PROPERTIES_ALLOW_SYSTEM_MEMORY>;
using use_pcip2pdma_t = __attr_from_param<::CUFILE_PARAM_USE_PCIP2PDMA>;
using prefer_io_uring_t = __attr_from_param<::CUFILE_PARAM_PREFER_IO_URING>;
using force_odirect_mode_t = __attr_from_param<::CUFILE_PARAM_FORCE_ODIRECT_MODE>;
using skip_topology_detection_t = __attr_from_param<::CUFILE_PARAM_SKIP_TOPOLOGY_DETECTION>;
using stream_memops_bypass_t = __attr_from_param<::CUFILE_PARAM_STREAM_MEMOPS_BYPASS>;
using has_luster_support_t = __attr_from_status<::CU_FILE_LUSTRE_SUPPORTED>;
using has_wekafs_support_t = __attr_from_status<::CU_FILE_WEKAFS_SUPPORTED>;
using has_nfs_support_t = __attr_from_status<::CU_FILE_NFS_SUPPORTED>;
using has_gpfs_support_t = __attr_from_status<::CU_FILE_GPFS_SUPPORTED>;
using has_nvme_support_t = __attr_from_status<::CU_FILE_NVME_SUPPORTED>;
using has_nvmeof_support_t = __attr_from_status<::CU_FILE_NVMEOF_SUPPORTED>;
using has_scsi_support_t = __attr_from_status<::CU_FILE_SCSI_SUPPORTED>;
using has_scaleflux_csd_support_t = __attr_from_status<::CU_FILE_SCALEFLUX_CSD_SUPPORTED>;
using has_nvmesh_support_t = __attr_from_status<::CU_FILE_NVMESH_SUPPORTED>;
using has_beegfs_support_t = __attr_from_status<::CU_FILE_BEEGFS_SUPPORTED>;
using has_nvme_p2p_support_t = __attr_from_status<::CU_FILE_NVME_P2P_SUPPORTED>;
using has_scatefs_support_t = __attr_from_status<::CU_FILE_SCATEFS_SUPPORTED>;
using has_dynamic_routing_support_t = __attr_from_feature<::CU_FILE_DYN_ROUTING_SUPPORTED>;
using has_batch_io_support_t = __attr_from_feature<::CU_FILE_BATCH_IO_SUPPORTED>;
using has_streams_support_t = __attr_from_feature<::CU_FILE_STREAMS_SUPPORTED>;
using has_parallel_io_support_t = __attr_from_feature<::CU_FILE_PARALLEL_IO_SUPPORTED>;
// todo: add documentation of each attribute
// 1. type
// 2. whether it is read-only or can be set
// 3. if it can be set/read when driver is open/closed
// 4. default value, constraints
inline constexpr max_io_queue_depth_t max_io_queue_depth{};
inline constexpr max_io_threads_t max_io_threads{};
inline constexpr min_io_threshold_size_kb_t min_io_threshold_size_kb{};
inline constexpr max_request_parallelism_t max_request_parallelism{};
inline constexpr max_direct_io_size_kb_t max_direct_io_size_kb{};
inline constexpr max_device_cache_size_kb_t max_device_cache_size_kb{};
inline constexpr per_buffer_cache_size_kb_t per_buffer_cache_size_kb{};
inline constexpr max_device_pinned_mem_size_kb_t max_device_pinned_mem_size_kb{};
inline constexpr io_batchsize_t io_batchsize{};
inline constexpr pollthreshold_size_kb_t pollthreshold_size_kb{};
inline constexpr batch_io_timeout_ms_t batch_io_timeout_ms{};
inline constexpr use_poll_mode_t use_poll_mode{};
inline constexpr allow_compat_mode_t allow_compat_mode{};
inline constexpr force_compat_mode_t force_compat_mode{};
inline constexpr fs_misc_api_check_aggressive_t fs_misc_api_check_aggressive{};
inline constexpr parallel_io_t parallel_io{};
inline constexpr profile_nvtx_t profile_nvtx{};
inline constexpr allow_system_memory_t allow_system_memory{};
inline constexpr use_pcip2pdma_t use_pcip2pdma{};
inline constexpr prefer_io_uring_t prefer_io_uring{};
inline constexpr force_odirect_mode_t force_odirect_mode{};
inline constexpr skip_topology_detection_t skip_topology_detection{};
inline constexpr stream_memops_bypass_t stream_memops_bypass{};
inline constexpr has_luster_support_t has_luster_support{};
inline constexpr has_wekafs_support_t has_wekafs_support{};
inline constexpr has_nfs_support_t has_nfs_support{};
inline constexpr has_gpfs_support_t has_gpfs_support{};
inline constexpr has_nvme_support_t has_nvme_support{};
inline constexpr has_nvmeof_support_t has_nvmeof_support{};
inline constexpr has_scsi_support_t has_scsi_support{};
inline constexpr has_scaleflux_csd_support_t has_scaleflux_csd_support{};
inline constexpr has_nvmesh_support_t has_nvmesh_support{};
inline constexpr has_beegfs_support_t has_beegfs_support{};
inline constexpr has_nvme_p2p_support_t has_nvme_p2p_support{};
inline constexpr has_scatefs_support_t has_scatefs_support{};
inline constexpr has_dynamic_routing_support_t has_dynamic_routing_support{};
inline constexpr has_batch_io_support_t has_batch_io_support{};
inline constexpr has_streams_support_t has_streams_support{};
inline constexpr has_parallel_io_support_t has_parallel_io_support{};
} // namespace cuda::experimental::cufile_driver_attributes

107
cccl_upstream/cudax/include/cuda/experimental/__cufile/exception.cuh
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@@ -1,107 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#pragma once
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__exception/cuda_error.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__exception/terminate.h>
#include <cuda/std/__host_stdlib/stdexcept>
#include <cuda/std/source_location>
#include <cstdio>
#include <cufile.h>
namespace cuda::experimental
{
#if _CCCL_HAS_CTK()
using __cufile_error_t = ::CUfileOpError;
#else // ^^^ _CCCL_HAS_CTK() ^^^ // vvv !_CCCL_HAS_CTK() vvv
using __cufile_error_t = int;
#endif // ^^^ !_CCCL_HAS_CTK() ^^^
struct __cufile_msg_storage
{
char __buffer[512]{};
};
static char* __format_cufile_error_message(
__cufile_msg_storage& __msg_buffer,
const __cufile_error_t __status,
const char* __msg,
const char* __api = nullptr,
::cuda::std::source_location __loc = ::cuda::std::source_location::current()) noexcept
{
::snprintf(
__msg_buffer.__buffer,
512,
"%s:%d %s%s%s(%d): %s",
__loc.file_name(),
__loc.line(),
__api ? __api : "",
__api ? " " : "",
#if _CCCL_HAS_CTK()
::cufileop_status_error(::CUfileOpError{__status}),
#else // ^^^ _CCCL_HAS_CTK() ^^^ / vvv !_CCCL_HAS_CTK() vvv
"cuFile error",
#endif // ^^^ !_CCCL_HAS_CTK() ^^^
__status,
__msg);
return __msg_buffer.__buffer;
}
//! @brief Exception class for errors from cuFile APIs.
class cufile_error : public ::std::runtime_error
{
__cufile_error_t __status_; //!< The cuFile error code.
public:
_CCCL_HOST_API cufile_error(
__cufile_error_t __status,
const char* __msg,
const char* __api,
::cuda::std::source_location loc = ::cuda::std::source_location::current(),
__cufile_msg_storage __msg_buffer = {})
: ::std::runtime_error{__format_cufile_error_message(__msg_buffer, __status, __msg, __api, loc)}
, __status_{__status}
{}
[[nodiscard]] _CCCL_HOST_API __cufile_error_t status() const noexcept
{
return __status_;
}
};
//! @brief Macro to call a cuFile API and throw a cufile_error or cuda_error if it fails.
#define _CCCL_TRY_CUFILE_API(_NAME, _MSG, ...) \
do \
{ \
const ::CUfileError_t __cufile_error_status = _NAME(__VA_ARGS__); \
switch (__cufile_error_status.err) \
{ \
case ::CU_FILE_SUCCESS: \
break; \
case ::CU_FILE_CUDA_DRIVER_ERROR: \
_CCCL_THROW(::cuda::cuda_error, static_cast<::cudaError_t>(__cufile_error_status.cu_err), _MSG, #_NAME); \
default: \
_CCCL_THROW(::cuda::experimental::cufile_error, __cufile_error_status.err, _MSG, #_NAME); \
} \
} while (0)
} // namespace cuda::experimental

73
cccl_upstream/cudax/include/cuda/experimental/__cufile/open_mode.cuh
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@@ -1,73 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#pragma once
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__utility/to_underlying.h>
namespace cuda::experimental
{
//! @brief Open mode for cufile.
enum class cufile_open_mode : unsigned
{
in = (1u << 0),
out = (1u << 1),
trunc = (1u << 2),
noreplace = (1u << 3),
direct = (1u << 4),
};
[[nodiscard]] _CCCL_HOST_API constexpr cufile_open_mode
operator|(cufile_open_mode __lhs, cufile_open_mode __rhs) noexcept
{
return static_cast<cufile_open_mode>(::cuda::std::to_underlying(__lhs) | ::cuda::std::to_underlying(__rhs));
}
_CCCL_HOST_API constexpr cufile_open_mode& operator|=(cufile_open_mode& __lhs, cufile_open_mode __rhs) noexcept
{
return __lhs = __lhs | __rhs;
}
[[nodiscard]] _CCCL_HOST_API constexpr cufile_open_mode
operator&(cufile_open_mode __lhs, cufile_open_mode __rhs) noexcept
{
return static_cast<cufile_open_mode>(::cuda::std::to_underlying(__lhs) & ::cuda::std::to_underlying(__rhs));
}
_CCCL_HOST_API constexpr cufile_open_mode& operator&=(cufile_open_mode& __lhs, cufile_open_mode __rhs) noexcept
{
return __lhs = __lhs & __rhs;
}
[[nodiscard]] _CCCL_HOST_API constexpr cufile_open_mode
operator^(cufile_open_mode __lhs, cufile_open_mode __rhs) noexcept
{
return static_cast<cufile_open_mode>(::cuda::std::to_underlying(__lhs) ^ ::cuda::std::to_underlying(__rhs));
}
_CCCL_HOST_API constexpr cufile_open_mode& operator^=(cufile_open_mode& __lhs, cufile_open_mode __rhs) noexcept
{
return __lhs = __lhs ^ __rhs;
}
[[nodiscard]] _CCCL_HOST_API constexpr cufile_open_mode operator~(cufile_open_mode __b) noexcept
{
return static_cast<cufile_open_mode>(~::cuda::std::to_underlying(__b));
}
} // namespace cuda::experimental

119
cccl_upstream/cudax/include/cuda/experimental/__detail/type_traits.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_DETAIL_TYPE_TRAITS_CUH
#define __CUDAX_DETAIL_TYPE_TRAITS_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__type_traits/decay.h>
#include <cuda/std/__type_traits/enable_if.h>
#include <cuda/std/__type_traits/integral_constant.h>
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/std/__type_traits/is_constructible.h>
#include <cuda/std/__type_traits/is_copy_constructible.h>
#include <cuda/std/__type_traits/is_move_constructible.h>
#include <cuda/std/__type_traits/is_nothrow_constructible.h>
#include <cuda/std/__type_traits/is_nothrow_copy_constructible.h>
#include <cuda/std/__type_traits/is_nothrow_move_constructible.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/std/__type_traits/is_valid_expansion.h>
#include <cuda/std/__utility/declval.h>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
using ::cuda::std::__declfn_t;
using ::cuda::std::decay_t;
template <class _Ty, bool _Nothrow = true>
[[noreturn]] _CCCL_HOST_DEVICE_API auto __declfn() noexcept(_Nothrow) -> _Ty
{
_CCCL_ASSERT(false, "__declfn should never be called at runtime.");
_CCCL_UNREACHABLE();
}
template <class _Ty, class _Uy>
_CCCL_CONCEPT __same_as = ::cuda::std::_IsSame<_Ty, _Uy>::value;
template <class _Ty, class _Uy>
_CCCL_CONCEPT __not_same_as = !::cuda::std::_IsSame<_Ty, _Uy>::value;
template <class _Ty, class... _Us>
_CCCL_CONCEPT __one_of = (__same_as<_Ty, _Us> || ...);
template <class _Ty, class... _Us>
_CCCL_CONCEPT __none_of = (__not_same_as<_Ty, _Us> && ...);
#if _CCCL_HAS_CONCEPTS()
template <template <class...> class _Fn, class... _Ts>
_CCCL_CONCEPT __is_instantiable_with = requires { typename _Fn<_Ts...>; };
template <class _Fn, class... _As>
_CCCL_CONCEPT __callable = requires(__declfn_t<_Fn> __fn, __declfn_t<_As>... __as) { __fn()(__as()...); };
#else // ^^^ _CCCL_HAS_CONCEPTS() ^^^ / vvv !_CCCL_HAS_CONCEPTS() vvv
template <template <class...> class _Fn, class... _Ts>
_CCCL_CONCEPT __is_instantiable_with = ::cuda::std::_IsValidExpansion<_Fn, _Ts...>::value;
template <class _Fn, class... _As>
_CCCL_CONCEPT __callable = ::cuda::std::__is_callable_v<_Fn, _As...>;
#endif // !_CCCL_HAS_CONCEPTS()
template <class _Fn, class... _As>
_CCCL_CONCEPT __constructible = ::cuda::std::is_constructible_v<_Fn, _As...>;
template <class... _As>
_CCCL_CONCEPT __decay_copyable = (::cuda::std::is_constructible_v<decay_t<_As>, _As> && ...);
template <class... _As>
_CCCL_CONCEPT __movable = (::cuda::std::is_move_constructible_v<_As> && ...);
template <class... _As>
_CCCL_CONCEPT __copyable = (::cuda::std::is_copy_constructible_v<_As> && ...);
template <class _Fn, class... _As>
_CCCL_CONCEPT __nothrow_callable = ::cuda::std::__is_nothrow_callable_v<_Fn, _As...>;
template <class _Ty, class... _As>
_CCCL_CONCEPT __nothrow_constructible = ::cuda::std::is_nothrow_constructible_v<_Ty, _As...>;
template <class... _As>
_CCCL_CONCEPT __nothrow_decay_copyable = (::cuda::std::is_nothrow_constructible_v<decay_t<_As>, _As> && ...);
template <class... _As>
_CCCL_CONCEPT __nothrow_movable = (::cuda::std::is_nothrow_move_constructible_v<_As> && ...);
template <class... _As>
_CCCL_CONCEPT __nothrow_copyable = (::cuda::std::is_nothrow_copy_constructible_v<_As> && ...);
template <class... _As>
using __nothrow_decay_copyable_t _CCCL_NODEBUG_ALIAS = ::cuda::std::bool_constant<__nothrow_decay_copyable<_As...>>;
using ::cuda::std::__call_result_t;
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif // __CUDAX_DETAIL_TYPE_TRAITS_CUH

54
cccl_upstream/cudax/include/cuda/experimental/__detail/utility.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_DETAIL_UTILITY_H
#define __CUDAX_DETAIL_UTILITY_H
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/std/__type_traits/type_list.h>
#include <cuda/std/__utility/declval.h>
#include <cuda/std/__utility/move.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
// NOLINTBEGIN(misc-unused-using-decls)
using ::cuda::std::declval;
// NOLINTEND(misc-unused-using-decls)
struct _CCCL_TYPE_VISIBILITY_DEFAULT no_init_t
{
_CCCL_HIDE_FROM_ABI explicit no_init_t() = default;
};
_CCCL_GLOBAL_CONSTANT no_init_t no_init{};
using uninit_t CCCL_DEPRECATED_BECAUSE("Use cuda::experimental::no_init_t instead") = no_init_t;
// TODO: CCCL_DEPRECATED_BECAUSE("Use cuda::experimental::no_init instead")
_CCCL_GLOBAL_CONSTANT no_init_t uninit{};
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif // __CUDAX_DETAIL_UTILITY_H

140
cccl_upstream/cudax/include/cuda/experimental/__device/logical_device.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__DEVICE_LOGICAL_DEVICE_CUH
#define _CUDAX__DEVICE_LOGICAL_DEVICE_CUH
#include <cuda/__cccl_config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__device/all_devices.h>
#include <cuda/__device/physical_device.h>
#include <cuda/experimental/__green_context/green_ctx.cuh>
#include <cuda/std/__cccl/prologue.h>
namespace cuda::experimental
{
struct __logical_device_access;
//! @brief A non-owning representation of a CUDA device or a green context
class logical_device
{
public:
//! @brief Enum to indicate the kind of logical device stored
enum class kinds
{
// Indicates logical device is a full device
device,
// Indicated logical device is a green context
green_context
};
// We might want to make this private depending on how this type ends up looking like long term,
// not documenting it for now
[[nodiscard]] constexpr CUcontext context() const noexcept
{
return __ctx;
}
//! @brief Retrieve the device on which this logical device resides
[[nodiscard]] constexpr device_ref underlying_device() const noexcept
{
return __dev_id;
}
//! @brief Retrieve the kind of logical device stored in this object
//! The kind indicates if this logical_device holds a device or green_context
[[nodiscard]] constexpr kinds kind() const noexcept
{
return __kind;
}
//! @brief Construct logical_device from a device ordinal
//!
//! Constructing a logical_device for a given device ordinal has a side effect of initializing that device
explicit logical_device(int __id)
: __dev_id(__id)
, __kind(kinds::device)
, __ctx(::cuda::__physical_devices()[__id].__primary_context())
{}
//! @brief Construct logical_device from a device_ref
//!
//! Constructing a logical_device for a given device_ref has a side effect of initializing that device
explicit logical_device(device_ref __dev)
: logical_device(__dev.get())
{}
#if _CCCL_CTK_AT_LEAST(12, 5)
//! @brief Construct logical_device from a green_context
logical_device(const green_context& __gctx)
: __dev_id(__gctx.__dev_id)
, __kind(kinds::green_context)
, __ctx(__gctx.__transformed)
{}
#endif // _CCCL_CTK_AT_LEAST(12, 5)
//! @brief Compares two logical_devices for equality
//!
//! @param __lhs The first `logical_device` to compare
//! @param __rhs The second `logical_device` to compare
//! @return true if `lhs` and `rhs` refer to the same logical device
[[nodiscard]] friend bool operator==(logical_device __lhs, logical_device __rhs) noexcept
{
return __lhs.__ctx == __rhs.__ctx;
}
#if _CCCL_STD_VER <= 2017
//! @brief Compares two logical_devices for inequality
//!
//! @param __lhs The first `logical_device` to compare
//! @param __rhs The second `logical_device` to compare
//! @return true if `lhs` and `rhs` refer to the different logical device
[[nodiscard]] friend bool operator!=(logical_device __lhs, logical_device __rhs) noexcept
{
return __lhs.__ctx != __rhs.__ctx;
}
#endif // _CCCL_STD_VER <= 2017
private:
friend __logical_device_access;
// This might be a CUdevice as well
int __dev_id = 0;
kinds __kind;
CUcontext __ctx = nullptr;
logical_device(int __id, CUcontext __context, kinds __k)
: __dev_id(__id)
, __kind(__k)
, __ctx(__context)
{}
};
struct __logical_device_access
{
static logical_device make_logical_device(int __id, CUcontext __context, logical_device::kinds __k)
{
return logical_device(__id, __context, __k);
}
};
} // namespace cuda::experimental
#include <cuda/std/__cccl/epilogue.h>
#endif // _CUDAX__DEVICE_LOGICAL_DEVICE_CUH

332
cccl_upstream/cudax/include/cuda/experimental/__driver/driver_api.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__DRIVER_DRIVER_API_CUH
#define _CUDAX__DRIVER_DRIVER_API_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#if _CCCL_HAS_CTK() && !_CCCL_COMPILER(NVRTC)
# include <cuda/__driver/driver_api.h>
# include <cuda/std/cstddef>
# include <cuda.h>
# include <cudaTypedefs.h>
# include <cuda/std/__cccl/prologue.h>
// Get a driver function pointer, casting to the PFN typedef for type safety.
// Uses PFN_ typedefs from cudaTypedefs.h to avoid ABI mismatches caused by
// #define'd version aliases in cuda.h (e.g. #define cuFoo cuFoo_v2).
// The ## operator suppresses macro expansion of the function name, so this is
// safe even for names that are #define'd to versioned variants.
# define _CUDAX_GET_DRIVER_FUNCTION(pfn_name, major, minor) \
reinterpret_cast<::PFN_##pfn_name##_v##major##0##minor##0>( \
::cuda::__driver::__get_driver_entry_point(#pfn_name, major, minor))
namespace cuda::experimental::__driver
{
// ── Graph: polymorphic add node ─────────────────────────────────────────────
# if _CCCL_CTK_AT_LEAST(12, 2)
[[nodiscard]] _CCCL_HOST_API inline ::CUgraphNode __graphAddNode(
::CUgraph __graph, const ::CUgraphNode* __deps, ::cuda::std::size_t __ndeps, ::CUgraphNodeParams* __params)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphAddNode, 12, 2);
::CUgraphNode __node{};
::cuda::__driver::__call_driver_fn(
__driver_fn, "Failed to add a node to graph", &__node, __graph, __deps, __ndeps, __params);
return __node;
}
# endif // _CCCL_CTK_AT_LEAST(12, 2)
// ── Graph: memory allocation node ───────────────────────────────────────────
struct __graphAddMemAllocNodeResult
{
::CUgraphNode __node;
::CUdeviceptr __dptr;
};
[[nodiscard]] _CCCL_HOST_API inline __graphAddMemAllocNodeResult __graphAddMemAllocNode(
::CUgraph __graph,
const ::CUgraphNode* __deps,
::cuda::std::size_t __ndeps,
::cuda::std::size_t __bytesize,
int __device_id)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphAddMemAllocNode, 11, 4);
::CUgraphNode __node{};
::CUDA_MEM_ALLOC_NODE_PARAMS __params{};
__params.poolProps.allocType = ::CU_MEM_ALLOCATION_TYPE_PINNED;
__params.poolProps.handleTypes = ::CU_MEM_HANDLE_TYPE_NONE;
__params.poolProps.location = {::CU_MEM_LOCATION_TYPE_DEVICE, __device_id};
__params.bytesize = __bytesize;
::CUmemAccessDesc __access_desc{};
__access_desc.location = {::CU_MEM_LOCATION_TYPE_DEVICE, __device_id};
__access_desc.flags = ::CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
__params.accessDescs = &__access_desc;
__params.accessDescCount = 1;
::cuda::__driver::__call_driver_fn(
__driver_fn, "Failed to add a memory allocation node to graph", &__node, __graph, __deps, __ndeps, &__params);
return {__node, __params.dptr};
}
// ── Graph: memory free node ─────────────────────────────────────────────────
// ── Graph: memory free node (no-throw, for use in noexcept deallocate) ──────
struct __graphAddMemFreeNodeResult
{
::CUgraphNode __node;
::cudaError_t __status;
};
[[nodiscard]] _CCCL_HOST_API inline __graphAddMemFreeNodeResult __graphAddMemFreeNodeNoThrow(
::CUgraph __graph, const ::CUgraphNode* __deps, ::cuda::std::size_t __ndeps, ::CUdeviceptr __dptr) noexcept
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphAddMemFreeNode, 11, 4);
::CUgraphNode __node{};
auto __status = static_cast<::cudaError_t>(__driver_fn(&__node, __graph, __deps, __ndeps, __dptr));
return {__node, __status};
}
// ── Graph: user object (ref-counted data lifetime tied to graph) ─────────────
_CCCL_HOST_API inline void __graphRetainUserObject(::CUgraph __graph, void* __ptr, ::CUhostFn __destroy)
{
static auto __create_fn = _CUDAX_GET_DRIVER_FUNCTION(cuUserObjectCreate, 11, 3);
static auto __retain_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphRetainUserObject, 11, 3);
::CUuserObject __obj{};
::cuda::__driver::__call_driver_fn(
__create_fn, "Failed to create user object", &__obj, __ptr, __destroy, 1u, ::CU_USER_OBJECT_NO_DESTRUCTOR_SYNC);
// CU_GRAPH_USER_OBJECT_MOVE transfers our reference to the graph without incrementing.
// After this call, the graph owns the sole reference — do not release.
::cuda::__driver::__call_driver_fn(
__retain_fn, "Failed to retain user object on graph", __graph, __obj, 1u, ::CU_GRAPH_USER_OBJECT_MOVE);
}
// ── Graph: conditional handle ───────────────────────────────────────────────
# if _CCCL_CTK_AT_LEAST(12, 4)
[[nodiscard]] _CCCL_HOST_API inline ::CUgraphConditionalHandle
__graphConditionalHandleCreate(::CUgraph __graph, ::CUcontext __ctx, unsigned int __default_val, unsigned int __flags)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphConditionalHandleCreate, 12, 3);
::CUgraphConditionalHandle __handle{};
::cuda::__driver::__call_driver_fn(
__driver_fn, "Failed to create a conditional handle", &__handle, __graph, __ctx, __default_val, __flags);
return __handle;
}
# endif // _CCCL_CTK_AT_LEAST(12, 4)
// ── Graph: create ───────────────────────────────────────────────────────────
[[nodiscard]] _CCCL_HOST_API inline ::CUgraph __graphCreate()
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphCreate, 10, 0);
::CUgraph __graph{};
::cuda::__driver::__call_driver_fn(__driver_fn, "Failed to create graph", &__graph, 0u);
return __graph;
}
// ── Graph: destroy (no-throw, for use in destructors) ───────────────────────
[[nodiscard]] _CCCL_HOST_API inline ::cudaError_t __graphDestroyNoThrow(::CUgraph __graph) noexcept
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphDestroy, 10, 0);
return static_cast<::cudaError_t>(__driver_fn(__graph));
}
// ── Graph: clone ────────────────────────────────────────────────────────────
[[nodiscard]] _CCCL_HOST_API inline ::CUgraph __graphClone(::CUgraph __original)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphClone, 10, 0);
::CUgraph __clone{};
::cuda::__driver::__call_driver_fn(__driver_fn, "Failed to clone graph", &__clone, __original);
return __clone;
}
// ── Graph: get node count ───────────────────────────────────────────────────
[[nodiscard]] _CCCL_HOST_API inline ::cuda::std::size_t __graphGetNodeCount(::CUgraph __graph)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphGetNodes, 10, 0);
::cuda::std::size_t __count = 0;
::cuda::__driver::__call_driver_fn(__driver_fn, "Failed to get graph node count", __graph, nullptr, &__count);
return __count;
}
// ── Graph: instantiate ──────────────────────────────────────────────────────
[[nodiscard]] _CCCL_HOST_API inline ::CUgraphExec __graphInstantiate(::CUgraph __graph, unsigned long long __flags = 0)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphInstantiateWithFlags, 11, 4);
::CUgraphExec __exec{};
::cuda::__driver::__call_driver_fn(__driver_fn, "Failed to instantiate graph", &__exec, __graph, __flags);
return __exec;
}
// ── Graph: launch ───────────────────────────────────────────────────────────
_CCCL_HOST_API inline void __graphLaunch(::CUgraphExec __exec, ::CUstream __stream)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphLaunch, 10, 0);
::cuda::__driver::__call_driver_fn(__driver_fn, "Failed to launch graph", __exec, __stream);
}
// ── Graph exec: destroy (no-throw, for use in destructors) ──────────────────
[[nodiscard]] _CCCL_HOST_API inline ::cudaError_t __graphExecDestroyNoThrow(::CUgraphExec __exec) noexcept
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphExecDestroy, 10, 0);
return static_cast<::cudaError_t>(__driver_fn(__exec));
}
// ── Graph: add empty node ───────────────────────────────────────────────────
[[nodiscard]] _CCCL_HOST_API inline ::CUgraphNode
__graphAddEmptyNode(::CUgraph __graph, const ::CUgraphNode* __deps, ::cuda::std::size_t __ndeps)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphAddEmptyNode, 10, 0);
::CUgraphNode __node{};
::cuda::__driver::__call_driver_fn(
__driver_fn, "Failed to add an empty node to graph", &__node, __graph, __deps, __ndeps);
return __node;
}
// ── Graph: add dependencies ─────────────────────────────────────────────────
_CCCL_HOST_API inline void __graphAddDependencies(
::CUgraph __graph, const ::CUgraphNode* __from, const ::CUgraphNode* __to, ::cuda::std::size_t __ndeps)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphAddDependencies, 10, 0);
::cuda::__driver::__call_driver_fn(__driver_fn, "Failed to add graph dependencies", __graph, __from, __to, __ndeps);
}
# if _CCCL_CTK_AT_LEAST(12, 3)
_CCCL_HOST_API inline void __graphAddDependencies(
::CUgraph __graph,
const ::CUgraphNode* __from,
const ::CUgraphNode* __to,
::cuda::std::size_t __ndeps,
const ::CUgraphEdgeData* __edge_data)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphAddDependencies, 12, 3);
::cuda::__driver::__call_driver_fn(
__driver_fn, "Failed to add graph dependencies", __graph, __from, __to, __edge_data, __ndeps);
}
# endif // _CCCL_CTK_AT_LEAST(12, 3)
// ── Graph node: get type ────────────────────────────────────────────────────
[[nodiscard]] _CCCL_HOST_API inline ::CUgraphNodeType __graphNodeGetType(::CUgraphNode __node)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuGraphNodeGetType, 10, 0);
::CUgraphNodeType __type{};
::cuda::__driver::__call_driver_fn(__driver_fn, "Failed to get graph node type", __node, &__type);
return __type;
}
// ── Stream capture: begin capture to graph ──────────────────────────────────
# if _CCCL_CTK_AT_LEAST(12, 3)
_CCCL_HOST_API inline void __streamBeginCaptureToGraph(
::CUstream __stream,
::CUgraph __graph,
const ::CUgraphNode* __deps,
::cuda::std::size_t __ndeps,
::CUstreamCaptureMode __mode)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuStreamBeginCaptureToGraph, 12, 3);
::cuda::__driver::__call_driver_fn(
__driver_fn, "Failed to begin stream capture to graph", __stream, __graph, __deps, nullptr, __ndeps, __mode);
}
// ── Stream capture: get capture info ────────────────────────────────────────
struct __stream_capture_info
{
::CUstreamCaptureStatus __status;
const ::CUgraphNode* __deps;
const ::CUgraphEdgeData* __edge_data;
::cuda::std::size_t __ndeps;
};
[[nodiscard]] _CCCL_HOST_API inline __stream_capture_info
__streamGetCaptureInfo(::CUstream __stream, const ::CUgraphEdgeData** __edge_data_out = nullptr)
{
__stream_capture_info __info{};
# if _CCCL_CTK_AT_LEAST(12, 4)
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuStreamGetCaptureInfo, 12, 3);
::cuda::__driver::__call_driver_fn(
__driver_fn,
"Failed to get stream capture info",
__stream,
&__info.__status,
nullptr, // id_out
nullptr, // graph_out
&__info.__deps,
&__info.__edge_data,
&__info.__ndeps);
# else
_CCCL_ASSERT(__edge_data_out == nullptr, "Edge data requires CUDA Toolkit 12.4 or later");
__info.__edge_data = nullptr;
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuStreamGetCaptureInfo, 11, 3);
::cuda::__driver::__call_driver_fn(
__driver_fn,
"Failed to get stream capture info",
__stream,
&__info.__status,
nullptr, // id_out
nullptr, // graph_out
&__info.__deps,
&__info.__ndeps);
# endif
return __info;
}
// ── Stream capture: end capture ─────────────────────────────────────────────
_CCCL_HOST_API inline void __streamEndCapture(::CUstream __stream, ::CUgraph* __graph_out)
{
static auto __driver_fn = _CUDAX_GET_DRIVER_FUNCTION(cuStreamEndCapture, 10, 0);
::cuda::__driver::__call_driver_fn(__driver_fn, "Failed to end stream capture", __stream, __graph_out);
}
# endif // _CCCL_CTK_AT_LEAST(12, 3)
} // namespace cuda::experimental::__driver
# undef _CUDAX_GET_DRIVER_FUNCTION
# include <cuda/std/__cccl/epilogue.h>
#endif // _CCCL_HAS_CTK() && !_CCCL_COMPILER(NVRTC)
#endif // _CUDAX__DRIVER_DRIVER_API_CUH

145
cccl_upstream/cudax/include/cuda/experimental/__execution/any_allocator.cuh
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@@ -1,145 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_ANY_ALLOCATOR
#define __CUDAX_EXECUTION_ANY_ALLOCATOR
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__type_traits/is_specialization_of.h>
#include <cuda/__utility/basic_any.h>
#include <cuda/std/__fwd/optional.h>
#include <cuda/std/__memory/allocator.h>
#include <cuda/std/__memory/allocator_traits.h>
#include <cuda/std/__type_traits/decay.h>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
template <class _Value>
struct any_allocator;
namespace __detail
{
template <class _Allocator, class _Value = typename _Allocator::value_type>
_CCCL_PUBLIC_API auto __any_allocator_allocate(_Allocator& __alloc, size_t __count) -> _Value*
{
return ::cuda::std::allocator_traits<_Allocator>::allocate(__alloc, __count);
}
template <class _Allocator, class _Value = typename _Allocator::value_type>
_CCCL_PUBLIC_API void __any_allocator_deallocate(_Allocator& __alloc, _Value* __ptr, size_t __count) noexcept
{
::cuda::std::allocator_traits<_Allocator>::deallocate(__alloc, static_cast<_Value*>(__ptr), __count);
}
template <class...>
struct __iallocator : __basic_interface<__iallocator, ::cuda::__extends<::cuda::__icopyable<>>>
{
using value_type = ::cuda::std::byte;
template <class _Other>
struct rebind
{
static_assert(__same_as<_Other, value_type>);
using other = __iallocator;
};
_CCCL_HOST_DEVICE_API auto allocate(size_t __bytes) -> value_type*
{
constexpr auto __allocate_vfn = &__any_allocator_allocate<__iallocator<>>;
return ::cuda::__virtcall<__allocate_vfn>(this, __bytes);
}
_CCCL_HOST_DEVICE_API void deallocate(value_type* __ptr, size_t __bytes) noexcept
{
constexpr auto __deallocate_vfn = &__any_allocator_deallocate<__iallocator<>>;
::cuda::__virtcall<__deallocate_vfn>(this, __ptr, __bytes);
}
template <class _Allocator>
using overrides =
__overrides_for<_Allocator, &__any_allocator_allocate<_Allocator>, &__any_allocator_deallocate<_Allocator>>;
};
using __any_allocator = ::cuda::__basic_any<__iallocator<>>;
template <class _Allocator>
_CCCL_CONCEPT __is_any_allocator = __is_specialization_of_v<_Allocator, execution::any_allocator>;
} // namespace __detail
template <class _Value>
struct any_allocator : private __detail::__any_allocator
{
using value_type = _Value;
template <class _Other>
struct rebind
{
using other = any_allocator<_Other>;
};
_CCCL_HOST_DEVICE_API any_allocator(::cuda::std::allocator<void>) noexcept
: __detail::__any_allocator{::cuda::std::allocator<::cuda::std::byte>{}}
{}
_CCCL_TEMPLATE(class _Allocator)
_CCCL_REQUIRES((!__detail::__is_any_allocator<_Allocator>) //
_CCCL_AND(!::cuda::std::__is_cuda_std_optional_v<_Allocator>)
_CCCL_AND ::cuda::__satisfies<_Allocator, __detail::__iallocator<>>)
_CCCL_HOST_DEVICE_API any_allocator(_Allocator __alloc)
: __detail::__any_allocator{__byte_allocator_t<_Allocator>(static_cast<_Allocator&&>(__alloc))}
{}
_CCCL_TEMPLATE(class _OtherValue)
_CCCL_REQUIRES(__not_same_as<_OtherValue, _Value>)
_CCCL_HOST_DEVICE_API any_allocator(any_allocator<_OtherValue> __other) noexcept
: __detail::__any_allocator{static_cast<__detail::__any_allocator&&>(__other)}
{}
_CCCL_HOST_DEVICE_API auto allocate(size_t __count) -> _Value*
{
return reinterpret_cast<_Value*>(this->__basic_any::allocate(__count * sizeof(_Value)));
}
_CCCL_HOST_DEVICE_API void deallocate(_Value* __ptr, size_t __count) noexcept
{
this->__basic_any::deallocate(reinterpret_cast<::cuda::std::byte*>(__ptr), __count * sizeof(_Value));
}
private:
template <class>
friend struct any_allocator;
template <class _Allocator>
using __byte_allocator_t = ::cuda::std::__rebind_alloc<::cuda::std::allocator_traits<_Allocator>, ::cuda::std::byte>;
};
template <class _Allocator>
_CCCL_DEDUCTION_GUIDE_ATTRIBUTES any_allocator(_Allocator) -> any_allocator<typename _Allocator::value_type>;
_CCCL_DEDUCTION_GUIDE_ATTRIBUTES any_allocator(::cuda::std::allocator<void>) -> any_allocator<::cuda::std::byte>;
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_ANY_ALLOCATOR

84
cccl_upstream/cudax/include/cuda/experimental/__execution/apply_sender.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_APPLY_SENDER
#define __CUDAX_EXECUTION_APPLY_SENDER
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__type_traits/conditional.h>
#include <cuda/std/__type_traits/is_valid_expansion.h>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/domain.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
//! A callable object that implements the `std::execution::apply_sender` functionality.
//! This is used to apply a sender to a domain, tag, and arguments, as specified in the
//! C++ standard draft. The implementation ensures compatibility with CUDA C++ Core
//! Libraries.
//! @see https://eel.is/c++draft/exec.snd.apply
struct _CCCL_TYPE_VISIBILITY_DEFAULT apply_sender_t
{
private:
//! A type alias that determines the domain to apply the sender to. If the expansion of
//! `__apply_sender_result_t` is valid for the given domain and arguments, the domain is
//! used; otherwise, the `default_domain` is used.
//! @tparam _Domain The domain to check.
//! @tparam _Args The arguments to validate against the domain.
template <class _Domain, class... _Args>
using __apply_domain_t _CCCL_NODEBUG_ALIAS = ::cuda::std::
_If<::cuda::std::_IsValidExpansion<__apply_sender_result_t, _Domain, _Args...>::value, _Domain, default_domain>;
public:
//! Applies a sender to a domain, tag, and arguments.
//! @tparam _Domain The domain used to select the algorithm implementation.
//! @tparam _Tag The tag associated with the algorithm.
//! @tparam _Sndr The sender to be applied.
//! @tparam _Args The arguments to pass to the algorithm.
//! @param __sndr The sender object.
//! @param __args The arguments to pass to the algorithm.
//! @return `DOM{}.apply_sender(_Tag{}, __sndr, __args...)`, where `DOM` is the first of
//! [`_Domain`, `default_domain`] to make the expression well-formed.
//! @note This function is `constexpr` and `noexcept` if the underlying domain's
//! `apply_sender` is `noexcept`.
//! @throws Any exception thrown by the underlying domain's `apply_sender`.
_CCCL_EXEC_CHECK_DISABLE
template <class _Domain, class _Tag, class _Sndr, class... _Args>
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Domain, _Tag, _Sndr&& __sndr, _Args&&... __args) const
noexcept(noexcept(__apply_domain_t<_Domain, _Tag, _Sndr, _Args...>{}.apply_sender(
_Tag{}, static_cast<_Sndr&&>(__sndr), static_cast<_Args&&>(__args)...)))
-> __apply_sender_result_t<__apply_domain_t<_Domain, _Tag, _Sndr, _Args...>, _Tag, _Sndr, _Args...>
{
using __dom_t _CCCL_NODEBUG_ALIAS = __apply_domain_t<_Domain, _Tag, _Sndr, _Args...>;
//! Calls the algorithm specified by _Tag using the determined domain.
return __dom_t{}.apply_sender(_Tag{}, static_cast<_Sndr&&>(__sndr), static_cast<_Args&&>(__args)...);
}
};
//! A global constant instance of `apply_sender_t`.
//! This can be used directly to invoke the `apply_sender` functionality.
_CCCL_GLOBAL_CONSTANT apply_sender_t apply_sender{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_APPLY_SENDER

83
cccl_upstream/cudax/include/cuda/experimental/__execution/atomic_intrusive_queue.cuh
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@@ -1,83 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
// Copyright (c) 2023 Maikel Nadolski
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_ATOMIC_INTRUSIVE_QUEUE
#define __CUDAX_EXECUTION_ATOMIC_INTRUSIVE_QUEUE
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/atomic>
#include <cuda/experimental/__execution/intrusive_queue.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
// An atomic queue that supports multiple producers and a single consumer.
template <auto _NextPtr>
class _CCCL_TYPE_VISIBILITY_DEFAULT __atomic_intrusive_queue;
template <class _Tp, _Tp* _Tp::* _NextPtr>
class alignas(64) __atomic_intrusive_queue<_NextPtr>
{
public:
_CCCL_HOST_DEVICE_API auto push(_Tp* __node) noexcept -> bool
{
_CCCL_ASSERT(__node != nullptr, "Cannot push a null pointer to the queue");
_Tp* __old_head = __head_.load(::cuda::std::memory_order_relaxed);
do
{
__node->*_NextPtr = __old_head;
} while (!__head_.compare_exchange_weak(__old_head, __node, ::cuda::std::memory_order_acq_rel));
// If the queue was empty before, we notify the consumer thread that there is now an
// item available. If the queue was not empty, we do not notify, because the consumer
// thread has already been notified.
if (__old_head != nullptr)
{
return false;
}
// There can be only one consumer thread, so we can use notify_one here instead of
// notify_all:
__head_.notify_one();
return true;
}
_CCCL_HOST_DEVICE_API void wait_for_item() noexcept
{
// Wait until the queue has an item in it:
__head_.wait(nullptr);
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API auto pop_all() noexcept -> __intrusive_queue<_NextPtr>
{
auto* const __list = __head_.exchange(nullptr, ::cuda::std::memory_order_acquire);
return __intrusive_queue<_NextPtr>::make_reversed(__list);
}
private:
::cuda::std::atomic<_Tp*> __head_{nullptr};
};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_ATOMIC_INTRUSIVE_QUEUE

472
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_BULK
#define __CUDAX_EXECUTION_BULK
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__cmath/ceil_div.h>
#include <cuda/__launch/configuration.h>
#include <cuda/__utility/immovable.h>
#include <cuda/std/__concepts/arithmetic.h>
#include <cuda/std/__concepts/same_as.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__tuple_dir/ignore.h>
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/std/__type_traits/is_void.h>
#include <cuda/std/__utility/forward_like.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/concepts.cuh>
#include <cuda/experimental/__execution/domain.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/get_completion_signatures.cuh>
#include <cuda/experimental/__execution/policy.cuh>
#include <cuda/experimental/__execution/queries.cuh>
#include <cuda/experimental/__execution/rcvr_ref.cuh>
#include <cuda/experimental/__execution/transform_completion_signatures.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
_CCCL_DIAG_PUSH
_CCCL_DIAG_SUPPRESS_MSVC(4702) // warning: unreachable code
namespace cuda::experimental::execution
{
namespace __bulk
{
template <class _Shape, class _Fn, class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __state_t
{
_Rcvr __rcvr_;
_Shape __shape_;
_Fn __fn_;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
// attributes for bulk senders
template <class _Sndr, class _Shape>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __attrs_t
{
[[nodiscard]] _CCCL_HOST_API static constexpr auto __get_launch_config(_Shape __shape) noexcept
{
constexpr int __threads_per_block = 256;
const int __grid_blocks = ::cuda::ceil_div(static_cast<int>(__shape), __threads_per_block);
auto __dims = ::cuda::make_hierarchy(block_dims<__threads_per_block>(), grid_dims(__grid_blocks));
return make_config(__dims, cooperative_launch());
}
using __launch_config_t = decltype(__get_launch_config(_Shape()));
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_launch_config_t) const noexcept -> __launch_config_t
{
NV_IF_ELSE_TARGET(NV_IS_HOST, (return __get_launch_config(__shape_);), ({
_CCCL_ASSERT(false, "cannot get a launch configuration from device");
::cuda::std::terminate();
}))
}
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Query, class... _Args)
_CCCL_REQUIRES(__forwarding_query<_Query> _CCCL_AND __queryable_with<env_of_t<_Sndr>, _Query, _Args...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Query, _Args&&... __args) const
noexcept(__nothrow_queryable_with<env_of_t<_Sndr>, _Query, _Args...>)
-> __query_result_t<env_of_t<_Sndr>, _Query, _Args...>
{
return execution::get_env(__sndr_).query(_Query{}, static_cast<_Args&&>(__args)...);
}
_Shape __shape_;
const _Sndr& __sndr_;
};
} // namespace __bulk
////////////////////////////////////////////////////////////////////////////////////////////////////
// generic bulk utilities
template <class _BulkTag>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __bulk_t
{
// This is a function object that is used to transform the value completion signatures
// of a bulk sender's child operation. It does type checking and "throws" if the bulk
// function is not callable with the value datums of the predecessor.
template <class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __transform_value_completion_fn
{
template <class... _Ts>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto operator()() const
{
// The function objects passed to the "chunked" and "unchunked" flavors of bulk have
// different signatures, so we need to type-check them separately.
if constexpr (_BulkTag::__is_chunked())
{
if constexpr (__callable<_Fn&, _Shape, _Shape, _Ts&...>)
{
return completion_signatures<set_value_t(_Ts...)>{}
+ __eptr_completion_if<!__nothrow_callable<_Fn&, _Shape, _Shape, _Ts&...>>();
}
else
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, _BulkTag),
_WHAT(_FUNCTION_IS_NOT_CALLABLE),
_WITH_FUNCTION(_Fn&),
_WITH_ARGUMENTS(_Shape, _Shape, _Ts & ...)>();
}
}
else if constexpr (__callable<_Fn&, _Shape, _Ts&...>)
{
return completion_signatures<set_value_t(_Ts...)>{}
+ __eptr_completion_if<!__nothrow_callable<_Fn&, _Shape, _Ts&...>>();
}
else
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, _BulkTag),
_WHAT(_FUNCTION_IS_NOT_CALLABLE),
_WITH_FUNCTION(_Fn&),
_WITH_ARGUMENTS(_Shape, _Ts & ...)>();
}
}
};
template <class _Shape, class _Fn, class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_base_t
{
using receiver_concept = receiver_t;
template <class _Error>
_CCCL_HOST_DEVICE_API constexpr void set_error(_Error&& __err) noexcept
{
execution::set_error(static_cast<_Rcvr&&>(__state_->__rcvr_), static_cast<_Error&&>(__err));
}
_CCCL_HOST_DEVICE_API constexpr void set_stopped() noexcept
{
execution::set_stopped(static_cast<_Rcvr&&>(__state_->__rcvr_));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __fwd_env_t<env_of_t<_Rcvr>>
{
return __fwd_env(execution::get_env(__state_->__rcvr_));
}
__bulk::__state_t<_Shape, _Fn, _Rcvr>* __state_;
};
// This is the operation state for bulk senders. It connects the child sender with
// a receiver defined by _BulkTag.
template <class _CvSndr, class _Shape, class _Fn, class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t
{
using operation_state_concept = operation_state_t;
using __rcvr_t = typename _BulkTag::template __rcvr_t<_Shape, _Fn, _Rcvr>;
_CCCL_HOST_DEVICE_API constexpr explicit __opstate_t(_CvSndr&& __sndr, _Rcvr __rcvr, _Shape __shape, _Fn __fn)
: __state_{static_cast<_Rcvr&&>(__rcvr), __shape, static_cast<_Fn&&>(__fn)}
, __opstate_{execution::connect(static_cast<_CvSndr&&>(__sndr), __rcvr_t{{&__state_}})}
{}
_CCCL_IMMOVABLE(__opstate_t);
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
execution::start(__opstate_);
}
__bulk::__state_t<_Shape, _Fn, _Rcvr> __state_;
connect_result_t<_CvSndr, __rcvr_t> __opstate_;
};
template <class _Policy, class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_base_t
{
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr&& __sndr) &&
{
static_assert(__is_sender<_Sndr>);
if constexpr (!dependent_sender<_Sndr>)
{
using __sndr_t = typename _BulkTag::template __sndr_t<_Sndr, _Policy, _Shape, _Fn>;
__assert_valid_completion_signatures(execution::get_completion_signatures<__sndr_t>());
}
return typename _BulkTag::template __sndr_t<_Sndr, _Policy, _Shape, _Fn>{
{{}, static_cast<__closure_base_t&&>(*this), static_cast<_Sndr&&>(__sndr)}};
}
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr&& __sndr) const&
{
return __closure_base_t(*this)(static_cast<_Sndr&&>(__sndr));
}
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API friend constexpr auto operator|(_Sndr&& __sndr, __closure_base_t __self)
{
return static_cast<__closure_base_t&&>(__self)(static_cast<_Sndr&&>(__sndr));
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ _Policy __policy_;
_Shape __shape_;
_Fn __fn_;
};
// This is the sender type for the three bulk algorithms.
template <class _Sndr, class _Policy, class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_base_t
{
using sender_concept = sender_t;
template <class _Self, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
_CUDAX_LET_COMPLETIONS(
auto(__child_completions) = execution::get_child_completion_signatures<_Self, _Sndr, _Env...>())
{
return transform_completion_signatures(__child_completions, __transform_value_completion_fn<_Shape, _Fn>{});
}
}
// The bulk algorithm lowers to a bulk_chunked sender. The bulk sender itself should
// not have `connect` functions, since they should never be called. Hence, we
// constrain these functions with !same_as<_BulkTag, bulk_t>.
_CCCL_TEMPLATE(class _Rcvr)
_CCCL_REQUIRES((!::cuda::std::same_as<_BulkTag, bulk_t>) )
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) && -> __opstate_t<_Sndr, _Shape, _Fn, _Rcvr>
{
return __opstate_t<_Sndr, _Shape, _Fn, _Rcvr>{
static_cast<_Sndr&&>(__sndr_),
static_cast<_Rcvr&&>(__rcvr),
__state_.__shape_,
static_cast<_Fn&&>(__state_.__fn_)};
}
_CCCL_TEMPLATE(class _Rcvr)
_CCCL_REQUIRES((!::cuda::std::same_as<_BulkTag, bulk_t>) )
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
connect(_Rcvr __rcvr) const& -> __opstate_t<const _Sndr&, _Shape, _Fn, _Rcvr>
{
return __opstate_t<const _Sndr&, _Shape, _Fn, _Rcvr>{
__sndr_, static_cast<_Rcvr&&>(__rcvr), __state_.__shape_, __state_.__fn_};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __bulk::__attrs_t<_Sndr, _Shape>
{
return {__state_.__shape_, __sndr_};
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ _BulkTag __tag_;
__closure_base_t<_Policy, _Shape, _Fn> __state_;
_Sndr __sndr_;
};
// This function call operator is the entry point for the bulk algorithms. It takes a
// predecessor sender, a policy, a shape, and a function, and returns a sender that can
// be connected to a receiver.
template <class _Sndr, class _Policy, class _Shape, class _Fn>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(_Sndr&& __sndr, _Policy __policy, _Shape __shape, _Fn __fn) const
{
return (static_cast<_Sndr&&>(__sndr) | (*this)(__policy, __shape, static_cast<_Fn&&>(__fn)));
}
// This function call operator creates a sender adaptor closure object that can appear
// on the right-hand side of a pipe operator, like: sndr | bulk(par, shape, fn).
template <class _Policy, class _Shape, class _Fn>
[[nodiscard]] _CCCL_HOST_DEVICE_API auto operator()(_Policy __policy, _Shape __shape, _Fn __fn) const
{
static_assert(::cuda::std::integral<_Shape>);
static_assert(::cuda::std::is_execution_policy_v<_Policy>);
using __closure_t = typename _BulkTag::template __closure_t<_Policy, _Shape, _Fn>;
return __closure_t{{__policy, __shape, static_cast<_Fn&&>(__fn)}};
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
// bulk_chunked
struct _CCCL_TYPE_VISIBILITY_DEFAULT bulk_chunked_t : __bulk_t<bulk_chunked_t>
{
template <class _Sndr, class _Policy, class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t : __bulk_t::__sndr_base_t<_Sndr, _Policy, _Shape, _Fn>
{};
template <class _Policy, class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_t : __bulk_t::__closure_base_t<_Policy, _Shape, _Fn>
{};
// This is the receiver for the bulk_chunked sender. It provides the implementation for
// `set_value` that calls the function with the begin and end shapes, and the value
// results of the predecessor.
template <class _Shape, class _Fn, class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_t : __bulk_t::__rcvr_base_t<_Shape, _Fn, _Rcvr>
{
_CCCL_EXEC_CHECK_DISABLE
template <class... _Values>
_CCCL_HOST_DEVICE_API void set_value(_Values&&... __values) noexcept
{
_CCCL_TRY //
{
this->__state_->__fn_(_Shape(0), _Shape(this->__state_->__shape_), __values...);
execution::set_value(static_cast<_Rcvr&&>(this->__state_->__rcvr_), static_cast<_Values&&>(__values)...);
}
_CCCL_CATCH_ALL //
{
if constexpr (!__nothrow_callable<_Fn&, _Shape, _Shape, _Values&...>)
{
execution::set_error(static_cast<_Rcvr&&>(this->__state_->__rcvr_), execution::current_exception());
}
}
}
};
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr bool __is_chunked() noexcept
{
return true;
}
};
_CCCL_GLOBAL_CONSTANT auto bulk_chunked = bulk_chunked_t{};
////////////////////////////////////////////////////////////////////////////////////////////////////
// bulk_unchunked
struct _CCCL_TYPE_VISIBILITY_DEFAULT bulk_unchunked_t : __bulk_t<bulk_unchunked_t>
{
// This is the receiver for the bulk_unchunked sender. It provides the implementation
// for `set_value` that calls the function repeatedly with an index and the value
// results of the predecessor. The index is monotonically increasing from 0 to the shape
// minus one.
template <class _Shape, class _Fn, class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_t : __bulk_t::__rcvr_base_t<_Shape, _Fn, _Rcvr>
{
_CCCL_EXEC_CHECK_DISABLE
template <class... _Values>
_CCCL_HOST_DEVICE_API void set_value(_Values&&... __values) noexcept
{
_CCCL_TRY //
{
for (_Shape __index{}; __index != this->__state_->__shape_; ++__index)
{
this->__state_->__fn_(_Shape(__index), __values...);
}
execution::set_value(static_cast<_Rcvr&&>(this->__state_->__rcvr_), static_cast<_Values&&>(__values)...);
}
_CCCL_CATCH_ALL //
{
if constexpr (!__nothrow_callable<_Fn&, _Shape, _Values&...>)
{
execution::set_error(static_cast<_Rcvr&&>(this->__state_->__rcvr_), execution::current_exception());
}
}
}
};
template <class _Sndr, class _Policy, class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t : __bulk_t::__sndr_base_t<_Sndr, _Policy, _Shape, _Fn>
{};
template <class _Policy, class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_t : __bulk_t::__closure_base_t<_Policy, _Shape, _Fn>
{};
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr bool __is_chunked() noexcept
{
return false;
}
};
_CCCL_GLOBAL_CONSTANT auto bulk_unchunked = bulk_unchunked_t{};
////////////////////////////////////////////////////////////////////////////////////////////////////
// bulk
struct _CCCL_TYPE_VISIBILITY_DEFAULT bulk_t : __bulk_t<bulk_t>
{
template <class _Sndr, class _Policy, class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t : __bulk_t::__sndr_base_t<_Sndr, _Policy, _Shape, _Fn>
{};
template <class _Policy, class _Shape, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_t : __bulk_t::__closure_base_t<_Policy, _Shape, _Fn>
{};
// This is a function adaptor that transforms a `bulk` function that takes a single
// shape to a `bulk_chunked` function that takes a begin and end shape.
template <class _Shape, class _Fn>
struct __bulk_chunked_fn
{
_CCCL_EXEC_CHECK_DISABLE
template <class... _Ts>
_CCCL_HOST_DEVICE_API void operator()(_Shape __begin, _Shape __end, _Ts&&... __values) noexcept(
__nothrow_callable<_Fn&, _Shape, decltype(__values)&...>)
{
for (; __begin != __end; ++__begin)
{
// Pass a copy of `__begin` to the function so it can't do anything funny with it.
__fn_(_Shape(__begin), __values...);
}
}
_Fn __fn_;
};
// This function is called when `connect` is called on a `bulk` sender. It transforms
// the `bulk` sender into a `bulk_chunked` sender.
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API static auto transform_sender(set_value_t, _Sndr&& __sndr, ::cuda::std::__ignore_t)
{
static_assert(__same_as<tag_of_t<_Sndr>, bulk_t>);
auto& [__tag, __data, __child] = __sndr;
auto& [__policy, __shape, __fn] = __data;
using __chunked_fn_t = __bulk_chunked_fn<decltype(__shape), decltype(__fn)>;
// Lower `bulk` to `bulk_chunked`. If `bulk_chunked` has a late customization, we will
// see the customization.
return bulk_chunked(::cuda::std::forward_like<_Sndr>(__child),
__policy,
__shape,
__chunked_fn_t{::cuda::std::forward_like<_Sndr>(__fn)});
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr bool __is_chunked() noexcept
{
return false;
}
};
_CCCL_GLOBAL_CONSTANT auto bulk = bulk_t{};
template <class _Sndr, class _Policy, class _Shape, class _Fn>
inline constexpr int structured_binding_size<bulk_t::__sndr_t<_Sndr, _Policy, _Shape, _Fn>> = 3;
template <class _Sndr, class _Policy, class _Shape, class _Fn>
inline constexpr int structured_binding_size<bulk_chunked_t::__sndr_t<_Sndr, _Policy, _Shape, _Fn>> = 3;
template <class _Sndr, class _Policy, class _Shape, class _Fn>
inline constexpr int structured_binding_size<bulk_unchunked_t::__sndr_t<_Sndr, _Policy, _Shape, _Fn>> = 3;
} // namespace cuda::experimental::execution
_CCCL_DIAG_POP
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_BULK

193
cccl_upstream/cudax/include/cuda/experimental/__execution/completion_behavior.cuh
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@@ -1,193 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_COMPLETION_BEHAVIOR
#define __CUDAX_EXECUTION_COMPLETION_BEHAVIOR
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__tuple_dir/ignore.h>
#include <cuda/std/__type_traits/integral_constant.h>
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/std/__type_traits/is_convertible.h>
#include <cuda/std/__utility/rel_ops.h>
#include <cuda/std/initializer_list>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
//////////////////////////////////////////////////////////////////////////////////////////
// get_completion_behavior
namespace __completion_behavior
{
enum class _CCCL_TYPE_VISIBILITY_DEFAULT completion_behavior : int
{
unknown, ///< The completion behavior is unknown.
asynchronous, ///< The operation's completion will not happen on the calling thread before `start()`
///< returns.
synchronous, ///< The operation's completion happens-before the return of `start()`.
inline_completion ///< The operation completes synchronously within `start()` on the same thread that called
///< `start()`.
};
template <completion_behavior _CB>
using __constant_t = ::cuda::std::integral_constant<completion_behavior, _CB>;
using __unknown_t = __constant_t<completion_behavior::unknown>;
using __asynchronous_t = __constant_t<completion_behavior::asynchronous>;
using __synchronous_t = __constant_t<completion_behavior::synchronous>;
using __inline_completion_t = __constant_t<completion_behavior::inline_completion>;
} // namespace __completion_behavior
struct _CCCL_TYPE_VISIBILITY_DEFAULT min_t;
struct completion_behavior
{
private:
template <__completion_behavior::completion_behavior _CB>
using __constant_t = ::cuda::std::integral_constant<__completion_behavior::completion_behavior, _CB>;
friend struct min_t;
public:
struct _CCCL_TYPE_VISIBILITY_DEFAULT unknown_t : __completion_behavior::__unknown_t
{};
struct _CCCL_TYPE_VISIBILITY_DEFAULT asynchronous_t : __completion_behavior::__asynchronous_t
{};
struct _CCCL_TYPE_VISIBILITY_DEFAULT synchronous_t : __completion_behavior::__synchronous_t
{};
struct _CCCL_TYPE_VISIBILITY_DEFAULT inline_completion_t : __completion_behavior::__inline_completion_t
{};
static constexpr unknown_t unknown{};
static constexpr asynchronous_t asynchronous{};
static constexpr synchronous_t synchronous{};
static constexpr inline_completion_t inline_completion{};
};
//////////////////////////////////////////////////////////////////////////////////////////
// get_completion_behavior: A sender can define this attribute to describe the sender's
// completion behavior
struct get_completion_behavior_t
{
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(::cuda::std::__ignore_t, ::cuda::std::__ignore_t = {}) const noexcept
{
return completion_behavior::unknown;
}
_CCCL_TEMPLATE(class _Attrs)
_CCCL_REQUIRES(__queryable_with<_Attrs, get_completion_behavior_t>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(const _Attrs& __attrs, ::cuda::std::__ignore_t = {}) const noexcept
{
static_assert(__nothrow_queryable_with<_Attrs, get_completion_behavior_t>,
"The get_completion_behavior query must be noexcept.");
static_assert(::cuda::std::is_convertible_v<__query_result_t<_Attrs, get_completion_behavior_t>,
__completion_behavior::completion_behavior>,
"The get_completion_behavior query must return one of the static member variables in "
"execution::completion_behavior.");
return __attrs.query(*this);
}
_CCCL_TEMPLATE(class _Attrs, class _Env)
_CCCL_REQUIRES(__queryable_with<_Attrs, get_completion_behavior_t, const _Env&>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Attrs& __attrs, const _Env& __env) const noexcept
{
static_assert(__nothrow_queryable_with<_Attrs, get_completion_behavior_t, const _Env&>,
"The get_completion_behavior query must be noexcept.");
static_assert(::cuda::std::is_convertible_v<__query_result_t<_Attrs, get_completion_behavior_t, const _Env&>,
__completion_behavior::completion_behavior>,
"The get_completion_behavior query must return one of the static member variables in "
"execution::completion_behavior.");
return __attrs.query(*this, __env);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return true;
}
};
struct _CCCL_TYPE_VISIBILITY_DEFAULT min_t
{
using __completion_behavior_t = __completion_behavior::completion_behavior;
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto
__minimum(::cuda::std::initializer_list<__completion_behavior_t> __cbs) noexcept -> __completion_behavior_t
{
auto __result = __completion_behavior::completion_behavior::inline_completion;
for (auto __cb : __cbs)
{
if (__cb < __result)
{
__result = __cb;
}
}
return __result;
}
template <__completion_behavior_t... _CBs>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(completion_behavior::__constant_t<_CBs>...) const noexcept
{
constexpr auto __behavior = __minimum({_CBs...});
if constexpr (__behavior == completion_behavior::unknown)
{
return completion_behavior::unknown;
}
else if constexpr (__behavior == completion_behavior::asynchronous)
{
return completion_behavior::asynchronous;
}
else if constexpr (__behavior == completion_behavior::synchronous)
{
return completion_behavior::synchronous;
}
else if constexpr (__behavior == completion_behavior::inline_completion)
{
return completion_behavior::inline_completion;
}
_CCCL_UNREACHABLE();
}
};
_CCCL_GLOBAL_CONSTANT min_t min{};
template <class _Sndr, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_completion_behavior() noexcept
{
using __behavior_t = __call_result_t<get_completion_behavior_t, env_of_t<_Sndr>, const _Env&...>;
return __behavior_t{};
}
template <class _Attrs, class... _Env>
_CCCL_CONCEPT __completes_inline =
(__call_result_t<get_completion_behavior_t, const _Attrs&, const _Env&...>{}
== completion_behavior::inline_completion);
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_COMPLETION_BEHAVIOR

663
cccl_upstream/cudax/include/cuda/experimental/__execution/completion_signatures.cuh
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@@ -1,663 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_COMPLETION_SIGNATURES
#define __CUDAX_EXECUTION_COMPLETION_SIGNATURES
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__type_traits/is_specialization_of.h>
#include <cuda/std/__tuple_dir/ignore.h>
#include <cuda/std/__type_traits/conditional.h>
#include <cuda/std/__type_traits/integral_constant.h>
#include <cuda/std/__type_traits/is_empty.h>
#include <cuda/std/__type_traits/is_trivially_constructible.h>
#include <cuda/std/__type_traits/remove_const.h>
#include <cuda/std/__type_traits/type_list.h>
#include <cuda/std/__type_traits/type_set.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
// include this last:
#include <cuda/experimental/__execution/prologue.cuh>
_CCCL_DIAG_PUSH
_CCCL_DIAG_SUPPRESS_GCC("-Wunused-but-set-parameter")
namespace cuda::experimental::execution
{
using ::cuda::std::__type_list;
// __partitioned_completions is a cache of completion signatures for fast
// access. The completion_signatures<Sigs...>::__partitioned nested struct
// inherits from __partitioned_completions. If the cache is never accessed,
// it is never instantiated.
template <class _ValueTuplesList = __type_list<>, class _ErrorsList = __type_list<>, class _StoppedList = __type_list<>>
struct __partitioned_completions;
template <class... _ValueTuples, class... _Errors, class... _Stopped>
struct __partitioned_completions<__type_list<_ValueTuples...>, __type_list<_Errors...>, __type_list<_Stopped...>>
{
template <template <class...> class _Tuple, template <class...> class _Variant>
using __value_types _CCCL_NODEBUG_ALIAS =
_Variant<::cuda::std::__type_call1<_ValueTuples, ::cuda::std::__type_quote<_Tuple>>...>;
template <template <class...> class _Variant, template <class...> class _Transform = ::cuda::std::__type_self_t>
using __error_types _CCCL_NODEBUG_ALIAS = _Variant<_Transform<_Errors>...>;
template <template <class...> class _Variant, class _Type = set_stopped_t()>
using __stopped_types _CCCL_NODEBUG_ALIAS = _Variant<__type_second<_Stopped, _Type>...>;
using __count_values = ::cuda::std::integral_constant<size_t, sizeof...(_ValueTuples)>;
using __count_errors = ::cuda::std::integral_constant<size_t, sizeof...(_Errors)>;
using __count_stopped = ::cuda::std::integral_constant<size_t, sizeof...(_Stopped)>;
struct __nothrow_decay_copyable
{
// These aliases are placed in a separate struct to avoid computing them
// if they are not needed.
using __fn = ::cuda::std::__type_quote<__nothrow_decay_copyable_t>;
using __values = ::cuda::std::_And<::cuda::std::__type_call1<_ValueTuples, __fn>...>;
using __errors = __nothrow_decay_copyable_t<_Errors...>;
using __all = ::cuda::std::_And<__values, __errors>;
};
};
template <class _Tag>
struct __partitioned_fold_fn;
template <>
struct __partitioned_fold_fn<set_value_t>
{
template <class... _ValueTuples, class _Errors, class _Stopped, class _Values>
_CCCL_HOST_DEVICE_API auto operator()(__partitioned_completions<__type_list<_ValueTuples...>, _Errors, _Stopped>&,
::cuda::std::__undefined<_Values>&) const
-> ::cuda::std::__undefined<__partitioned_completions<__type_list<_ValueTuples..., _Values>, _Errors, _Stopped>>&;
};
template <>
struct __partitioned_fold_fn<set_error_t>
{
template <class _Values, class... _Errors, class _Stopped, class _Error>
_CCCL_HOST_DEVICE_API auto operator()(__partitioned_completions<_Values, __type_list<_Errors...>, _Stopped>&,
::cuda::std::__undefined<__type_list<_Error>>&) const
-> ::cuda::std::__undefined<__partitioned_completions<_Values, __type_list<_Errors..., _Error>, _Stopped>>&;
};
template <>
struct __partitioned_fold_fn<set_stopped_t>
{
template <class _Values, class _Errors, class _Stopped>
_CCCL_HOST_DEVICE_API auto
operator()(__partitioned_completions<_Values, _Errors, _Stopped>&, ::cuda::std::__ignore_t) const
-> ::cuda::std::__undefined<__partitioned_completions<_Values, _Errors, __type_list<set_stopped_t()>>>&;
};
// The following overload of binary operator* is used to build up the cache of completion
// signatures. We fold over operator*, accumulating the completion signatures in the
// cache. `__undefined` is used here to prevent the instantiation of the intermediate
// types.
template <class _Partitioned, class _Tag, class... _Args>
_CCCL_HOST_DEVICE_API auto operator*(::cuda::std::__undefined<_Partitioned>&, _Tag (*)(_Args...)) -> ::cuda::std::
__call_result_t<__partitioned_fold_fn<_Tag>, _Partitioned&, ::cuda::std::__undefined<__type_list<_Args...>>&>;
// This function declaration is used to extract the cache from the `__undefined` type.
template <class _Partitioned>
_CCCL_HOST_DEVICE_API auto __unpack_partitioned_completions(::cuda::std::__undefined<_Partitioned>&) -> _Partitioned;
template <class... _Sigs>
using __partition_completion_signatures_t _CCCL_NODEBUG_ALIAS = //
decltype(execution::__unpack_partitioned_completions(
(declval<::cuda::std::__undefined<__partitioned_completions<>>&>() * ... * static_cast<_Sigs*>(nullptr))));
template <class _Completions>
using __partitioned_completions_of_t _CCCL_NODEBUG_ALIAS = typename _Completions::__partitioned::type;
////////////////////////////////////////////////////////////////////////////////////////////////////
// completion signatures type traits
template <class _Sigs, template <class...> class _Tuple, template <class...> class _Variant>
using __value_types _CCCL_NODEBUG_ALIAS =
typename __partitioned_completions_of_t<_Sigs>::template __value_types<_Tuple, _Variant>;
template <class _Sndr, class _Env, template <class...> class _Tuple, template <class...> class _Variant>
using value_types_of_t _CCCL_NODEBUG_ALIAS =
__value_types<completion_signatures_of_t<_Sndr, _Env>,
::cuda::std::__type_indirect_quote<_Tuple>::template __call,
::cuda::std::__type_indirect_quote<_Variant>::template __call>;
template <class _Sigs,
template <class...> class _Variant,
template <class...> class _Transform = ::cuda::std::__type_self_t>
using __error_types _CCCL_NODEBUG_ALIAS =
typename __partitioned_completions_of_t<_Sigs>::template __error_types<_Variant, _Transform>;
template <class _Sndr, class _Env, template <class...> class _Variant>
using error_types_of_t _CCCL_NODEBUG_ALIAS =
__error_types<completion_signatures_of_t<_Sndr, _Env>, ::cuda::std::__type_indirect_quote<_Variant>::template __call>;
template <class _Sigs, template <class...> class _Variant, class _Type = set_stopped_t()>
using __stopped_types _CCCL_NODEBUG_ALIAS =
typename __partitioned_completions_of_t<_Sigs>::template __stopped_types<_Variant, _Type>;
template <class _Sigs>
inline constexpr bool __sends_stopped = __partitioned_completions_of_t<_Sigs>::__count_stopped::value != 0;
template <class _Sndr, class... _Env>
inline constexpr bool sends_stopped = __sends_stopped<completion_signatures_of_t<_Sndr, _Env...>>;
////////////////////////////////////////////////////////////////////////////////////////////////////
// __valid_completion_signatures
template <class _Ty>
_CCCL_CONCEPT __valid_completion_signatures =
::cuda::__is_specialization_of_v<::cuda::std::remove_const_t<_Ty>, completion_signatures>;
template <class... _Sigs>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL void __assert_valid_completion_signatures(const completion_signatures<_Sigs...>&)
{}
////////////////////////////////////////////////////////////////////////////////////////////////////
// make_completion_signatures
template <class _Tag, class... _As>
_CCCL_HOST_DEVICE_API auto __normalize_impl(_As&&...) -> _Tag (*)(_As...);
template <class _Tag, class... _As>
_CCCL_HOST_DEVICE_API auto __normalize(_Tag (*)(_As...))
-> decltype(execution::__normalize_impl<_Tag>(declval<_As>()...));
template <class... _Sigs>
_CCCL_HOST_DEVICE_API auto __make_unique(_Sigs*...)
-> ::cuda::std::__type_apply<::cuda::std::__type_quote<completion_signatures>, ::cuda::std::__make_type_set<_Sigs...>>;
template <class... _Sigs>
using __make_completion_signatures_t _CCCL_NODEBUG_ALIAS =
decltype(execution::__make_unique(execution::__normalize(static_cast<_Sigs*>(nullptr))...));
template <class... _ExplicitSigs, class... _DeducedSigs>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto make_completion_signatures(_DeducedSigs*...) noexcept
-> __make_completion_signatures_t<_ExplicitSigs..., _DeducedSigs...>
{
return {};
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// concat_completion_signatures
struct __concat_completion_signatures_impl;
template <class... _Sigs>
using __concat_completion_signatures_t _CCCL_NODEBUG_ALIAS =
__call_result_t<__call_result_t<__concat_completion_signatures_impl, const _Sigs&...>>;
struct __concat_completion_signatures_fn
{
template <class... _Sigs>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto operator()(const _Sigs&...) const noexcept
-> __concat_completion_signatures_t<_Sigs...>
{
return {};
}
};
extern const completion_signatures<>& __empty_completion_signatures;
struct __concat_completion_signatures_impl
{
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto operator()() const noexcept -> completion_signatures<> (*)()
{
return nullptr;
}
template <class... _Sigs>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto operator()(const completion_signatures<_Sigs...>&) const noexcept
-> __make_completion_signatures_t<_Sigs...> (*)()
{
return nullptr;
}
template <class _Self = __concat_completion_signatures_impl,
class... _As,
class... _Bs,
class... _Cs,
class... _Ds,
class... _Rest>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto operator()(
const completion_signatures<_As...>&,
const completion_signatures<_Bs...>&,
const completion_signatures<_Cs...>& = __empty_completion_signatures,
const completion_signatures<_Ds...>& = __empty_completion_signatures,
const _Rest&...) const noexcept
{
using _Tmp = completion_signatures<_As..., _Bs..., _Cs..., _Ds...>;
using _SigsFnPtr _CCCL_NODEBUG_ALIAS = __call_result_t<_Self, const _Tmp&, const _Rest&...>;
return static_cast<_SigsFnPtr>(nullptr);
}
template <class _Ap,
class _Bp = ::cuda::std::__ignore_t,
class _Cp = ::cuda::std::__ignore_t,
class _Dp = ::cuda::std::__ignore_t,
class... _Rest>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto
operator()(const _Ap&, const _Bp& = {}, const _Cp& = {}, const _Dp& = {}, const _Rest&...) const noexcept
{
if constexpr (!__valid_completion_signatures<_Ap>)
{
return static_cast<_Ap (*)()>(nullptr);
}
else if constexpr (!__valid_completion_signatures<_Bp>)
{
return static_cast<_Bp (*)()>(nullptr);
}
else if constexpr (!__valid_completion_signatures<_Cp>)
{
return static_cast<_Cp (*)()>(nullptr);
}
else
{
static_assert(!__valid_completion_signatures<_Dp>);
return static_cast<_Dp (*)()>(nullptr);
}
}
};
_CCCL_GLOBAL_CONSTANT __concat_completion_signatures_fn concat_completion_signatures{};
////////////////////////////////////////////////////////////////////////////////////////////////////
// implementation details of the completion_signatures class template
struct _IN_COMPLETION_SIGNATURES_APPLY;
struct _IN_COMPLETION_SIGNATURES_TRANSFORM_REDUCE;
struct _FUNCTION_IS_NOT_CALLABLE_WITH_THESE_SIGNATURES;
template <class... _Sigs>
struct __remove_sigs
{
template <class _Sig>
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Sig*) const noexcept -> bool
{
return !::cuda::std::__type_set_contains_v<::cuda::std::__type_set<_Sigs...>, _Sig>;
}
};
template <class _Fn, class _Sig>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto __filer_one() noexcept
-> ::cuda::std::_If<_Fn{}(static_cast<_Sig*>(nullptr)), completion_signatures<_Sig>, completion_signatures<>>
{
return {};
}
// working around compiler bugs in gcc and msvc
template <class... _Sigs>
using __completion_signatures = completion_signatures<_Sigs...>;
template <class... _Values>
using __set_value_sig_t = set_value_t(_Values...);
template <class _Error>
using __set_error_sig_t = set_error_t(_Error);
//! @brief Represents a set of completion signatures for senders in the CUDA C++ execution
//! model.
//!
//! The `completion_signatures` class template is used to describe the possible ways a
//! sender may complete. Each signature is a function type of the form
//! `set_value_t(Ts...)`, `set_error_t(E)`, or `set_stopped_t()`. This type provides
//! compile-time utilities for querying, combining, and transforming sets of completion
//! signatures.
//!
//! @tparam _Sigs... The completion signature types to include in this set.
//!
//! Example usage:
//! @code
//! constexpr auto sigs = completion_signatures<set_value_t(int), set_error_t(float), set_stopped_t()>{};
//! static_assert(sigs.size() == 3);
//! static_assert(sigs.contains<set_value_t(int)>());
//! @endcode
template <class... _Sigs>
struct _CCCL_TYPE_VISIBILITY_DEFAULT completion_signatures
{
//! @brief Partitioned view of the completion signatures for efficient querying.
struct __partitioned
{
// This is defined in a nested struct to avoid computing these types if they are not
// needed.
using type _CCCL_NODEBUG_ALIAS = __partition_completion_signatures_t<_Sigs...>;
};
//! @brief Type set view of the completion signatures for set operations.
struct __type_set
{
// This is defined in a nested struct to avoid computing this type if it is not
// needed.
using type _CCCL_NODEBUG_ALIAS = ::cuda::std::__make_type_set<_Sigs...>;
};
//! @brief Applies a metafunction to each signature and collects the results.
//! @tparam _Fn The metafunction to apply.
//! @tparam _Continuation The template to collect results into.
template <template <class...> class _Fn, template <class...> class _Continuation = __completion_signatures>
using __transform_q _CCCL_NODEBUG_ALIAS = _Continuation<::cuda::std::__type_apply_q<_Fn, _Sigs>...>;
//! @brief Applies a callable metafunction to each signature and collects the results.
//! @tparam _Fn The callable metafunction to apply.
//! @tparam _Continuation The template to collect results into.
template <class _Fn, class _Continuation = ::cuda::std::__type_quote<__completion_signatures>>
using __transform _CCCL_NODEBUG_ALIAS =
::cuda::std::__type_call<_Continuation, ::cuda::std::__type_apply<_Fn, _Sigs>...>;
//! @brief Calls a metafunction with the signatures as arguments.
//! @tparam _Fn The metafunction to call.
//! @tparam _More Additional arguments to pass.
template <class _Fn, class... _More>
using __call _CCCL_NODEBUG_ALIAS = ::cuda::std::__type_call<_Fn, _More..., _Sigs...>;
//! @brief Default constructor.
_CCCL_HIDE_FROM_ABI constexpr completion_signatures() = default;
//! @brief Returns the number of completion signatures in the set.
//! @return The number of signatures.
[[nodiscard]]
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto size() noexcept -> size_t
{
return sizeof...(_Sigs);
}
//! @brief Counts the number of signatures with the given tag.
//! @tparam _Tag The tag to count (e.g., set_value, set_error, set_stopped).
//! @return The number of signatures with the given tag.
template <class _Tag>
[[nodiscard]]
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto count(_Tag) noexcept -> size_t
{
if constexpr (_Tag{} == set_value)
{
return __partitioned::type::__count_values::value;
}
else if constexpr (_Tag{} == set_error)
{
return __partitioned::type::__count_errors::value;
}
else
{
return __partitioned::type::__count_stopped::value;
}
}
//! @brief Checks if the set contains the given signature.
//! @tparam _Sig The signature type to check.
//! @return true if the signature is present, false otherwise.
template <class _Sig>
[[nodiscard]]
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto contains(_Sig* = nullptr) noexcept -> bool
{
return ::cuda::std::__type_set_contains_v<typename __type_set::type, _Sig>;
}
//! @brief Applies a callable to all signatures in the set.
//! @tparam _Fn The callable to apply.
//! @param __fn The callable instance.
//! @return The result of calling __fn with all signatures as arguments.
_CCCL_EXEC_CHECK_DISABLE
template <class _Fn>
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto apply(_Fn __fn) -> __call_result_t<_Fn, _Sigs*...>
{
return __fn(static_cast<_Sigs*>(nullptr)...);
}
//! @brief Filters the set using a predicate, returning a new set with only matching
//! signatures.
//! @tparam _Fn The predicate type (must be empty and trivially constructible).
//! @param The predicate instance.
//! @return A new completion_signatures set with only the signatures for which the
//! predicate returns true.
_CCCL_EXEC_CHECK_DISABLE
template <class _Fn>
[[nodiscard]]
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto filter(_Fn)
{
static_assert(::cuda::std::is_empty_v<_Fn> && ::cuda::std::is_trivially_constructible_v<_Fn>,
"The filter function must be empty and trivially constructible.");
return concat_completion_signatures(execution::__filer_one<_Fn, _Sigs>()...);
}
//! @brief Selects all signatures with the given tag.
//! @tparam _Tag The tag to select (e.g., set_value, set_error, set_stopped).
//! @return A new completion_signatures set containing only signatures with the given
//! tag.
template <class _Tag>
[[nodiscard]]
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto select(_Tag) noexcept
{
if constexpr (_Tag{} == set_value)
{
return __value_types<completion_signatures, __set_value_sig_t, __completion_signatures>{};
}
else if constexpr (_Tag{} == set_error)
{
return __error_types<completion_signatures, __completion_signatures, __set_error_sig_t>{};
}
else
{
static_assert(_Tag{} == set_stopped, "The tag must be set_value, set_error, or set_stopped.");
return __stopped_types<completion_signatures, __completion_signatures>{};
}
}
//! @brief Applies a transform and then reduces the results.
//! @tparam _Transform The transform callable.
//! @tparam _Reduce The reduce callable.
//! @param __transform The transform instance.
//! @param __reduce The reduce instance.
//! @return The result of reducing the transformed signatures.
_CCCL_EXEC_CHECK_DISABLE
template <class _Transform, class _Reduce>
[[nodiscard]]
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto transform_reduce(_Transform __transform, _Reduce __reduce)
-> __call_result_t<_Reduce, __call_result_t<_Transform, _Sigs*>...>
{
return __reduce(__transform(static_cast<_Sigs*>(nullptr))...);
}
};
_CCCL_DEDUCTION_GUIDE_ATTRIBUTES completion_signatures() -> completion_signatures<>;
// work-around for https://gcc.gnu.org/bugzilla/show_bug.cgi?id=95629
#if _CCCL_COMPILER(GCC, ==, 11)
# define _CCCL_CONSTEVAL_OPERATOR constexpr
#else // ^^^ GCC 11 ^^^ / vvv other compilers vvv
# define _CCCL_CONSTEVAL_OPERATOR _CCCL_CONSTEVAL
#endif // ^^^ other compilers ^^^
//! @brief Returns the union of two sets of completion signatures.
//! @tparam _SelfSigs The first set of signature types.
//! @tparam _OtherSigs The other set of signature types.
//! @param __self The first `completion_signatures` object.
//! @param __other The other `completion_signatures` object.
//! @return The union of the two sets.
template <class... _SelfSigs, class... _OtherSigs>
[[nodiscard]]
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL_OPERATOR auto
operator+([[maybe_unused]] completion_signatures<_SelfSigs...> __self,
[[maybe_unused]] completion_signatures<_OtherSigs...> __other) noexcept
{
if constexpr (sizeof...(_SelfSigs) == 0) // short-circuit some common cases
{
return __other;
}
else if constexpr (sizeof...(_OtherSigs) == 0)
{
return __self;
}
else
{
return concat_completion_signatures(__self, __other);
}
}
//! @brief Returns the set difference between two sets of completion signatures.
//! @tparam _SelfSigs The first set of signature types.
//! @tparam _OtherSigs The second set of signature types.
//! @return A new set with all signatures from the other set removed.
template <class... _SelfSigs, class... _OtherSigs>
[[nodiscard]]
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL_OPERATOR auto
operator-(completion_signatures<_SelfSigs...> __self, completion_signatures<_OtherSigs...>) noexcept
{
if constexpr (sizeof...(_OtherSigs) == 0 || sizeof...(_SelfSigs) == 0) // short-circuit some common cases
{
return __self;
}
else
{
return __self.filter(__remove_sigs<_OtherSigs...>{});
}
}
//! @brief Checks if two completion_signatures sets are equal.
//! @tparam _SelfSigs The first set of signature types.
//! @tparam _OtherSigs The second set of signature types.
//! @return `true` if the sets are equal, `false` otherwise.
template <class... _SelfSigs, class... _OtherSigs>
[[nodiscard]]
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL_OPERATOR auto
operator==(completion_signatures<_SelfSigs...>, completion_signatures<_OtherSigs...>) noexcept -> bool
{
if constexpr (sizeof...(_OtherSigs) != sizeof...(_SelfSigs))
{
return false;
}
else
{
using __signatures_set_t = typename completion_signatures<_SelfSigs...>::__type_set::type;
return ::cuda::std::__type_set_contains_v<__signatures_set_t, _OtherSigs...>;
}
}
//! @brief Checks if two completion_signatures sets are not equal.
//! @tparam _SelfSigs The first set of signature types.
//! @tparam _OtherSigs The second set of signature types.
//! @param __self The other `completion_signatures` object.
//! @param __other The other `completion_signatures` object.
//! @return `true` if the sets are not equal, `false` otherwise.
template <class... _SelfSigs, class... _OtherSigs>
[[nodiscard]]
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL_OPERATOR auto
operator!=(completion_signatures<_SelfSigs...> __self, completion_signatures<_OtherSigs...> __other) noexcept -> bool
{
return !(__self == __other);
}
#undef _CCCL_CONSTEVAL_OPERATOR
////////////////////////////////////////////////////////////////////////////////////////////////////
// __gather_completion_signatures
template <class _WantedTag>
struct __gather_sigs_fn;
template <>
struct __gather_sigs_fn<set_value_t>
{
template <class _Sigs, template <class...> class _Tuple, template <class...> class _Variant>
using __call _CCCL_NODEBUG_ALIAS = __value_types<_Sigs, _Tuple, _Variant>;
};
template <>
struct __gather_sigs_fn<set_error_t>
{
template <class _Sigs, template <class...> class _Tuple, template <class...> class _Variant>
using __call _CCCL_NODEBUG_ALIAS = __error_types<_Sigs, _Variant, _Tuple>;
};
template <>
struct __gather_sigs_fn<set_stopped_t>
{
template <class _Sigs, template <class...> class _Tuple, template <class...> class _Variant>
using __call _CCCL_NODEBUG_ALIAS = __stopped_types<_Sigs, _Variant, _Tuple<>>;
};
template <class _Sigs, class _WantedTag, template <class...> class _Tuple, template <class...> class _Variant>
using __gather_completion_signatures _CCCL_NODEBUG_ALIAS =
typename __gather_sigs_fn<_WantedTag>::template __call<_Sigs, _Tuple, _Variant>;
////////////////////////////////////////////////////////////////////////////////////////////////////
// __eptr_completion and __eptr_completion_if
#if _CCCL_HAS_EXCEPTIONS()
[[nodiscard]] _CCCL_HOST_DEVICE_API inline _CCCL_CONSTEVAL auto __eptr_completion() noexcept
{
return completion_signatures<set_error_t(exception_ptr)>{};
}
#else // ^^^ _CCCL_HAS_EXCEPTIONS() ^^^ / vvv !_CCCL_HAS_EXCEPTIONS() vvv
[[nodiscard]] _CCCL_HOST_DEVICE_API inline _CCCL_CONSTEVAL auto __eptr_completion() noexcept
{
return completion_signatures{};
}
#endif // !_CCCL_HAS_EXCEPTIONS()
template <bool _PotentiallyThrowing>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto __eptr_completion_if() noexcept
{
if constexpr (_PotentiallyThrowing)
{
return __eptr_completion();
}
else
{
return completion_signatures{};
}
}
using __eptr_completion_t _CCCL_NODEBUG_ALIAS = decltype(execution::__eptr_completion());
template <bool _PotentiallyThrowing>
using __eptr_completion_if_t _CCCL_NODEBUG_ALIAS = decltype(execution::__eptr_completion_if<_PotentiallyThrowing>());
////////////////////////////////////////////////////////////////////////////////////////////////////
// invalid_completion_signature
#if _CCCL_HAS_CONSTEXPR_EXCEPTIONS()
template <class... _What, class... _Values>
[[noreturn, nodiscard]] _CCCL_HOST_DEVICE_API consteval auto invalid_completion_signature(_Values... __values)
-> completion_signatures<>
{
if constexpr (sizeof...(_Values) == 1)
{
throw __sender_type_check_failure<_Values..., _What...>(__values...);
}
else
{
throw __sender_type_check_failure<::cuda::std::__tuple<_Values...>, _What...>(::cuda::std::__tuple{__values...});
}
}
#else // ^^^ _CCCL_HAS_CONSTEXPR_EXCEPTIONS() ^^^ / vvv !_CCCL_HAS_CONSTEXPR_EXCEPTIONS() vvv
template <class... _What, class... _Values>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto invalid_completion_signature(_Values...)
{
return _ERROR<_What...>{};
}
#endif // ^^^ !_CCCL_HAS_CONSTEXPR_EXCEPTIONS() ^^^
} // namespace cuda::experimental::execution
_CCCL_DIAG_POP
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // _CUDAX_EXECUTION_COMPLETION_SIGNATURES_H

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_CONCEPTS
#define __CUDAX_EXECUTION_CONCEPTS
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__concepts/constructible.h>
#include <cuda/std/__concepts/copyable.h>
#include <cuda/std/__concepts/equality_comparable.h>
#include <cuda/std/__type_traits/decay.h>
#include <cuda/std/__type_traits/integral_constant.h>
#include <cuda/std/__type_traits/is_nothrow_move_constructible.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/get_completion_signatures.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
// Receiver concepts:
template <class _Rcvr>
_CCCL_CONCEPT receiver = //
_CCCL_REQUIRES_EXPR((_Rcvr)) //
( //
requires(__is_receiver<decay_t<_Rcvr>>), //
requires(::cuda::std::move_constructible<decay_t<_Rcvr>>), //
requires(::cuda::std::constructible_from<decay_t<_Rcvr>, _Rcvr>), //
requires(__nothrow_movable<decay_t<_Rcvr>>) //
);
template <class _Rcvr, class _Sig>
inline constexpr bool __valid_completion_for = false;
template <class _Rcvr, class _Tag, class... _As>
inline constexpr bool __valid_completion_for<_Rcvr, _Tag(_As...)> = __callable<_Tag, _Rcvr, _As...>;
template <class _Rcvr, class _Completions>
inline constexpr bool __has_completions = false;
template <class _Rcvr, class... _Sigs>
inline constexpr bool __has_completions<_Rcvr, completion_signatures<_Sigs...>> =
(__valid_completion_for<_Rcvr, _Sigs> && ...);
template <class _Rcvr, class _Completions>
_CCCL_CONCEPT receiver_of = //
_CCCL_REQUIRES_EXPR((_Rcvr, _Completions)) //
( //
requires(receiver<_Rcvr>), //
requires(__has_completions<decay_t<_Rcvr>, _Completions>) //
);
// Queryable traits:
template <class _Ty>
_CCCL_CONCEPT __queryable = ::cuda::std::destructible<_Ty>;
// Awaitable traits:
template <class>
_CCCL_CONCEPT __is_awaitable = false; // TODO: Implement this concept.
// Sender traits:
template <class _Sndr>
_CCCL_HOST_DEVICE_API constexpr auto __enable_sender() -> bool
{
if constexpr (__is_sender<_Sndr>)
{
return true;
}
else
{
return __is_awaitable<_Sndr>;
}
_CCCL_UNREACHABLE();
}
template <class _Sndr>
inline constexpr bool enable_sender = __enable_sender<_Sndr>();
// Sender concepts:
template <class... _Env>
struct __completions_tester
{
template <class _Sndr, bool _EnableIfConstexpr = ((void) execution::get_completion_signatures<_Sndr, _Env...>(), true)>
_CCCL_HOST_DEVICE_API static constexpr auto __is_valid(int) -> bool
{
return __valid_completion_signatures<completion_signatures_of_t<_Sndr, _Env...>>;
}
template <class _Sndr>
_CCCL_HOST_DEVICE_API static constexpr auto __is_valid(long) -> bool
{
return false;
}
};
template <class _Sndr, class... _Env>
_CCCL_CONCEPT __has_valid_completion_signatures = __completions_tester<_Env...>::template __is_valid<_Sndr>(0);
template <class _Sndr>
_CCCL_CONCEPT sender = //
_CCCL_REQUIRES_EXPR((_Sndr)) //
( //
requires(enable_sender<decay_t<_Sndr>>), //
requires(::cuda::std::move_constructible<decay_t<_Sndr>>), //
requires(::cuda::std::constructible_from<decay_t<_Sndr>, _Sndr>) //
);
template <class _Sndr, class... _Env>
_CCCL_CONCEPT sender_in = //
_CCCL_REQUIRES_EXPR((_Sndr, variadic _Env)) //
( //
requires(sender<_Sndr>), //
requires(sizeof...(_Env) <= 1), //
requires((__queryable<_Env> && ... && true)), //
requires(__has_valid_completion_signatures<_Sndr, _Env...>) //
);
template <class _Sndr>
_CCCL_CONCEPT dependent_sender = //
_CCCL_REQUIRES_EXPR((_Sndr)) //
( //
requires(sender<_Sndr>), //
requires(__is_dependent_sender<_Sndr>()) //
);
// Scheduler concepts:
template <class _Sch>
_CCCL_CONCEPT scheduler = //
_CCCL_REQUIRES_EXPR((_Sch), __declfn_t<_Sch> __sch) //
( //
requires(__is_scheduler<_Sch>), //
schedule(__sch()), //
requires(::cuda::std::equality_comparable<::cuda::std::remove_cvref_t<_Sch>>), //
requires(::cuda::std::copyable<::cuda::std::remove_cvref_t<_Sch>>) //
);
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_CONCEPTS

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_CONDITIONAL
#define __CUDAX_EXECUTION_CONDITIONAL
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/std/__type_traits/is_convertible.h>
#include <cuda/std/__type_traits/type_list.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/concepts.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/just_from.cuh>
#include <cuda/experimental/__execution/meta.cuh>
#include <cuda/experimental/__execution/rcvr_ref.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/variant.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
//! @file conditional.cuh
//! This file defines the @c conditional sender. @c conditional is a sender that
//! selects between two continuations based on the result of a predecessor. It
//! accepts a predecessor, a predicate, and two continuations. It passes the
//! result of the predecessor to the predicate. If the predicate returns @c true,
//! the result is passed to the first continuation; otherwise, it is passed to
//! the second continuation.
//!
//! By "continuation", we mean a so-called sender adaptor closure: a unary function
//! that takes a sender and returns a new sender. The expression `then(f)` is an
//! example of a continuation.
namespace cuda::experimental::execution
{
struct _FUNCTION_MUST_RETURN_A_BOOLEAN_TESTABLE_VALUE;
struct _CCCL_TYPE_VISIBILITY_DEFAULT conditional_t
{
_CUDAX_SEMI_PRIVATE :
template <class _Pred, class _Then, class _Else>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_base_t;
template <class... _As>
_CCCL_HOST_DEVICE _CCCL_FORCEINLINE static auto __mk_complete_fn(_As&&... __as) noexcept
{
return [&](auto __sink) noexcept {
return __sink(static_cast<_As&&>(__as)...);
};
}
template <class... _As>
using __just_from_t _CCCL_NODEBUG_ALIAS = decltype(just_from(conditional_t::__mk_complete_fn(declval<_As>()...)));
template <class _Pred, class _Then, class _Else, class... _Env>
struct __either_sig_fn
{
template <class... _As>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto operator()() const
{
if constexpr (!__callable<_Pred, _As&...>)
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, conditional_t),
_WHAT(_FUNCTION_IS_NOT_CALLABLE),
_WITH_FUNCTION(_Pred),
_WITH_ARGUMENTS(_As & ...)>();
}
else if constexpr (!::cuda::std::is_convertible_v<__call_result_t<_Pred, _As&...>, bool>)
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, conditional_t),
_WHAT(_FUNCTION_MUST_RETURN_A_BOOLEAN_TESTABLE_VALUE),
_WITH_FUNCTION(_Pred),
_WITH_ARGUMENTS(_As & ...)>();
}
else
{
return concat_completion_signatures(
get_completion_signatures<__call_result_t<_Then, __just_from_t<_As...>>, _Env...>(),
get_completion_signatures<__call_result_t<_Else, __just_from_t<_As...>>, _Env...>());
}
}
};
template <class _Rcvr, class _Pred, class _Then, class _Else, class _Completions>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __state_t
{
using __params_t = __closure_base_t<_Pred, _Then, _Else>;
template <class... _As>
using __opstate_list_t =
::cuda::std::__type_list<connect_result_t<__call_result_t<_Then, __just_from_t<_As...>>, __rcvr_ref_t<_Rcvr>>,
connect_result_t<__call_result_t<_Else, __just_from_t<_As...>>, __rcvr_ref_t<_Rcvr>>>;
using __next_ops_variant_t _CCCL_NODEBUG_ALIAS =
__value_types<_Completions, __opstate_list_t, __type_concat_into_quote<__variant>::__call>;
_Rcvr __rcvr_;
__params_t __params_;
__next_ops_variant_t __ops_{};
};
template <class _Rcvr, class _Pred, class _Then, class _Else, class _Completions>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_t
{
using receiver_concept = receiver_t;
_CCCL_EXEC_CHECK_DISABLE
template <class... _As>
_CCCL_HOST_DEVICE_API void set_value(_As&&... __as) noexcept
{
auto __just = just_from(conditional_t::__mk_complete_fn(static_cast<_As&&>(__as)...));
_CCCL_TRY
{
if (static_cast<_Pred&&>(__state_->__params_.pred)(__as...))
{
auto& __op = __state_->__ops_.__emplace_from(
connect, static_cast<_Then&&>(__state_->__params_.on_true)(__just), __ref_rcvr(__state_->__rcvr_));
execution::start(__op);
}
else
{
auto& __op = __state_->__ops_.__emplace_from(
connect, static_cast<_Else&&>(__state_->__params_.on_false)(__just), __ref_rcvr(__state_->__rcvr_));
execution::start(__op);
}
}
_CCCL_CATCH_ALL
{
execution::set_error(static_cast<_Rcvr&&>(__state_->__rcvr_), execution::current_exception());
}
}
template <class _Error>
_CCCL_HOST_DEVICE_API constexpr void set_error(_Error&& __error) noexcept
{
execution::set_error(static_cast<_Rcvr&&>(__state_->__rcvr_), static_cast<_Error&&>(__error));
}
_CCCL_HOST_DEVICE_API constexpr void set_stopped() noexcept
{
execution::set_stopped(static_cast<_Rcvr&&>(__state_->__rcvr_));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __fwd_env_t<env_of_t<_Rcvr>>
{
return __fwd_env(execution::get_env(__state_->__rcvr_));
}
__state_t<_Rcvr, _Pred, _Then, _Else, _Completions>* __state_;
};
template <class _CvSndr, class _Rcvr, class _Pred, class _Then, class _Else>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t
{
using operation_state_concept = operation_state_t;
using __completions_t = completion_signatures_of_t<_CvSndr, __fwd_env_t<env_of_t<_Rcvr>>>;
using __params_t = __closure_base_t<_Pred, _Then, _Else>;
using __rcvr_t = conditional_t::__rcvr_t<_Rcvr, _Pred, _Then, _Else, __completions_t>;
_CCCL_HOST_DEVICE_API __opstate_t(_CvSndr&& __sndr, _Rcvr&& __rcvr, __params_t&& __params)
: __state_{static_cast<_Rcvr&&>(__rcvr), static_cast<__params_t&&>(__params)}
, __op_{execution::connect(static_cast<_CvSndr&&>(__sndr), __rcvr_t{&__state_})}
{}
_CCCL_IMMOVABLE(__opstate_t);
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
execution::start(__op_);
}
__state_t<_Rcvr, _Pred, _Then, _Else, __completions_t> __state_;
connect_result_t<_CvSndr, __rcvr_t> __op_;
};
public:
template <class _Pred, class _Then, class _Else>
using params _CCCL_NODEBUG_ALIAS = __closure_base_t<_Pred, _Then, _Else>;
template <class _Params, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
template <class _Sndr, class _Pred, class _Then, class _Else>
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr __sndr, _Pred __pred, _Then __then, _Else __else) const;
template <class _Pred, class _Then, class _Else>
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Pred __pred, _Then __then, _Else __else) const;
};
template <class _Pred, class _Then, class _Else, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT conditional_t::__sndr_t<conditional_t::__closure_base_t<_Pred, _Then, _Else>, _Sndr>
{
using __params_t _CCCL_NODEBUG_ALIAS = conditional_t::__closure_base_t<_Pred, _Then, _Else>;
/*_CCCL_NO_UNIQUE_ADDRESS*/ conditional_t __tag_;
__params_t __params_;
_Sndr __sndr_;
template <class _Self, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
_CUDAX_LET_COMPLETIONS(auto(__child_completions) = get_child_completion_signatures<_Self, _Sndr, _Env...>())
{
return concat_completion_signatures(
transform_completion_signatures(__child_completions, __either_sig_fn<_Pred, _Then, _Else, _Env...>{}),
__eptr_completion());
}
_CCCL_UNREACHABLE();
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
connect(_Rcvr __rcvr) && -> __opstate_t<_Sndr, _Rcvr, _Pred, _Then, _Else>
{
return {static_cast<_Sndr&&>(__sndr_), static_cast<_Rcvr&&>(__rcvr), static_cast<__params_t&&>(__params_)};
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
connect(_Rcvr __rcvr) const& -> __opstate_t<_Sndr const&, _Rcvr, _Pred, _Then, _Else>
{
return {__sndr_, static_cast<_Rcvr&&>(__rcvr), static_cast<__params_t&&>(__params_)};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __fwd_env_t<env_of_t<_Sndr>>
{
return __fwd_env(execution::get_env(__sndr_));
}
};
template <class _Pred, class _Then, class _Else>
struct _CCCL_TYPE_VISIBILITY_DEFAULT conditional_t::__closure_base_t
{
template <class _Sndr>
_CCCL_HOST_DEVICE_API auto operator()(_Sndr __sndr) &&
{
using __sndr_t = conditional_t::__sndr_t<__closure_base_t, _Sndr>;
// If the incoming sender is non-dependent, we can check the completion signatures of
// the composed sender immediately.
if constexpr (!dependent_sender<_Sndr>)
{
__assert_valid_completion_signatures(execution::get_completion_signatures<__sndr_t>());
}
return __sndr_t{{}, static_cast<__closure_base_t&&>(*this), static_cast<_Sndr&&>(__sndr)};
}
template <class _Sndr>
_CCCL_HOST_DEVICE_API auto operator()(_Sndr __sndr) const&
{
return __closure_base_t(*this)(static_cast<_Sndr&&>(__sndr));
}
template <class _Sndr>
_CCCL_HOST_DEVICE_API friend auto operator|(_Sndr __sndr, __closure_base_t __self)
{
return static_cast<__closure_base_t&&>(__self)(static_cast<_Sndr&&>(__sndr));
}
_Pred pred;
_Then on_true;
_Else on_false;
};
template <class _Sndr, class _Pred, class _Then, class _Else>
_CCCL_HOST_DEVICE_API constexpr auto
conditional_t::operator()(_Sndr __sndr, _Pred __pred, _Then __then, _Else __else) const
{
using __params_t _CCCL_NODEBUG_ALIAS = __closure_base_t<_Pred, _Then, _Else>;
__params_t __params{static_cast<_Pred&&>(__pred), static_cast<_Then&&>(__then), static_cast<_Else&&>(__else)};
return static_cast<__params_t&&>(__params)(static_cast<_Sndr&&>(__sndr));
}
template <class _Pred, class _Then, class _Else>
_CCCL_HOST_DEVICE_API constexpr auto conditional_t::operator()(_Pred __pred, _Then __then, _Else __else) const
{
return __closure_base_t<_Pred, _Then, _Else>{
static_cast<_Pred&&>(__pred), static_cast<_Then&&>(__then), static_cast<_Else&&>(__else)};
}
template <class _Params, class _Sndr>
inline constexpr int structured_binding_size<conditional_t::__sndr_t<_Params, _Sndr>> = 3;
_CCCL_GLOBAL_CONSTANT conditional_t conditional{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_CONDITIONAL

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cccl_upstream/cudax/include/cuda/experimental/__execution/continues_on.cuh
View File

@@ -1,481 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_CONTINUES_ON
#define __CUDAX_EXECUTION_CONTINUES_ON
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__type_traits/conditional.h>
#include <cuda/std/__utility/pod_tuple.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/concepts.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/get_completion_signatures.cuh>
#include <cuda/experimental/__execution/meta.cuh>
#include <cuda/experimental/__execution/queries.cuh>
#include <cuda/experimental/__execution/rcvr_ref.cuh>
#include <cuda/experimental/__execution/schedule_from.cuh>
#include <cuda/experimental/__execution/transform_completion_signatures.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/variant.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
namespace __detail
{
template <class _Tag>
struct __decay_args
{
template <class... _Ts>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto operator()() const noexcept
{
if constexpr (!__decay_copyable<_Ts...>)
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, continues_on_t),
_WHAT(_ARGUMENTS_ARE_NOT_DECAY_COPYABLE),
_WITH_ARGUMENTS(_Ts...)>();
}
else if constexpr (!__nothrow_decay_copyable<_Ts...>)
{
return completion_signatures<_Tag(decay_t<_Ts>...), set_error_t(exception_ptr)>{};
}
else
{
return completion_signatures<_Tag(decay_t<_Ts>...)>{};
}
}
};
} // namespace __detail
struct _CCCL_TYPE_VISIBILITY_DEFAULT continues_on_t
{
_CUDAX_SEMI_PRIVATE :
struct __send_result_fn
{
template <class _Rcvr, class _Tag, class... _As>
_CCCL_HOST_DEVICE_API constexpr void operator()(_Rcvr& __rcvr, _Tag, _As&... __args) const noexcept
{
// moves from lvalues here is intentional:
_Tag{}(static_cast<_Rcvr&&>(__rcvr), static_cast<_As&&>(__args)...);
}
};
struct __send_result_visitor
{
template <class _Rcvr, class _Tuple>
_CCCL_HOST_DEVICE_API constexpr void operator()(_Rcvr& __rcvr, _Tuple& __tuple) const noexcept
{
::cuda::std::__apply(__send_result_fn{}, __tuple, __rcvr);
}
};
template <class _Rcvr, class _Results>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __state_base_t
{
_Rcvr __rcvr_;
_Results __result_;
};
// This receiver is connected to the scheduler. It forwards the results of the child sender,
// which are stored in a variant, to the parent receiver.
template <class _Rcvr, class _Results>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_t
{
using receiver_concept = receiver_t;
_CCCL_HOST_DEVICE_API constexpr void set_value() noexcept
{
__visit(__send_result_visitor{}, __state_->__result_, __state_->__rcvr_);
}
template <class _Error>
_CCCL_HOST_DEVICE_API constexpr void set_error(_Error&& __error) noexcept
{
execution::set_error(static_cast<_Rcvr&&>(__state_->__rcvr_), static_cast<_Error&&>(__error));
}
_CCCL_HOST_DEVICE_API constexpr void set_stopped() noexcept
{
execution::set_stopped(static_cast<_Rcvr&&>(__state_->__rcvr_));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __fwd_env_t<env_of_t<_Rcvr>>
{
return __fwd_env(execution::get_env(__state_->__rcvr_));
}
__state_base_t<_Rcvr, _Results>* __state_;
};
template <class _Sch, class _Rcvr, class _Results>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __state_t : __state_base_t<_Rcvr, _Results>
{
connect_result_t<schedule_result_t<_Sch>, __rcvr_t<_Rcvr, _Results>> __opstate2_;
};
// This receiver is connected to the child sender. It stashes the sender's results into
// a variant.
template <class _Sch, class _Rcvr, class _Results>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __stash_rcvr_t
{
using receiver_concept = receiver_t;
template <class _Tag, class... _As>
_CCCL_HOST_DEVICE_API void __set_result(_Tag, _As&&... __as) noexcept
{
using __tupl_t _CCCL_NODEBUG_ALIAS = ::cuda::std::__tuple<_Tag, decay_t<_As>...>;
_CCCL_TRY
{
__state_->__result_.template __emplace<__tupl_t>(_Tag{}, static_cast<_As&&>(__as)...);
}
_CCCL_CATCH_ALL
{
// Avoid ODR-using this completion operation if this code path is not taken.
if constexpr (!__nothrow_decay_copyable<_As...>)
{
execution::set_error(static_cast<_Rcvr&&>(__state_->__rcvr_), execution::current_exception());
}
}
}
template <class... _As>
_CCCL_HOST_DEVICE_API void set_value(_As&&... __as) noexcept
{
__set_result(set_value_t{}, static_cast<_As&&>(__as)...);
execution::start(__state_->__opstate2_);
}
template <class _Error>
_CCCL_HOST_DEVICE_API void set_error(_Error&& __error) noexcept
{
__set_result(set_error_t{}, static_cast<_Error&&>(__error));
execution::start(__state_->__opstate2_);
}
_CCCL_HOST_DEVICE_API void set_stopped() noexcept
{
__set_result(set_stopped_t{});
execution::start(__state_->__opstate2_);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __fwd_env_t<env_of_t<_Rcvr>>
{
return __fwd_env(execution::get_env(__state_->__rcvr_));
}
__state_t<_Sch, _Rcvr, _Results>* __state_;
};
template <class _Sch, class _CvSndr, class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t
{
using operation_state_concept = operation_state_t;
using __completions_t _CCCL_NODEBUG_ALIAS = completion_signatures_of_t<_CvSndr, __fwd_env_t<env_of_t<_Rcvr>>>;
using __results_t _CCCL_NODEBUG_ALIAS =
typename __completions_t::template __transform_q<::cuda::std::__decayed_tuple, __variant>;
using __rcvr_t = continues_on_t::__rcvr_t<_Rcvr, __results_t>;
using __stash_rcvr_t = continues_on_t::__stash_rcvr_t<_Sch, _Rcvr, __results_t>;
_CCCL_EXEC_CHECK_DISABLE
_CCCL_HOST_DEVICE_API constexpr explicit __opstate_t(_CvSndr&& __sndr, _Sch __sch, _Rcvr __rcvr)
: __state_{{static_cast<_Rcvr&&>(__rcvr), {}}, execution::connect(schedule(__sch), __rcvr_t{&__state_})}
, __opstate1_{execution::connect(static_cast<_CvSndr&&>(__sndr), __stash_rcvr_t{&__state_})}
{}
_CCCL_IMMOVABLE(__opstate_t);
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
execution::start(__opstate1_);
}
__state_t<_Sch, _Rcvr, __results_t> __state_;
connect_result_t<_CvSndr, __stash_rcvr_t> __opstate1_;
};
public:
template <class _Sch, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __attrs_t;
template <class _Sch, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
template <class _Sch>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_t
{
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr>
[[nodiscard]]
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr __sndr) const
-> __sndr_t<_Sch, __call_result_t<schedule_from_t, _Sndr>>
{
static_assert(__is_sender<_Sndr>);
using __child_t = __call_result_t<schedule_from_t, _Sndr>;
return __sndr_t<_Sch, __child_t>{{}, __sch_, schedule_from(static_cast<_Sndr&&>(__sndr))};
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr>
[[nodiscard]]
_CCCL_HOST_DEVICE_API constexpr friend auto operator|(_Sndr __sndr, __closure_t __clsur)
-> __sndr_t<_Sch, __call_result_t<schedule_from_t, _Sndr>>
{
static_assert(__is_sender<_Sndr>);
using __child_t = __call_result_t<schedule_from_t, _Sndr>;
return __sndr_t<_Sch, __child_t>{{}, __clsur.__sch_, schedule_from(static_cast<_Sndr&&>(__sndr))};
}
_Sch __sch_;
};
_CCCL_EXEC_CHECK_DISABLE
template <class _Sch>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sch __sch) const -> __closure_t<_Sch>
{
static_assert(__is_scheduler<_Sch>);
return __closure_t<_Sch>{__sch};
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Sch, class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr __sndr, _Sch __sch) const
-> __sndr_t<_Sch, __call_result_t<schedule_from_t, _Sndr>>
{
static_assert(__is_sender<_Sndr>);
static_assert(__is_scheduler<_Sch>);
using __child_t = __call_result_t<schedule_from_t, _Sndr>;
return __sndr_t<_Sch, __child_t>{{}, __sch, schedule_from(static_cast<_Sndr&&>(__sndr))};
}
};
//! @brief The @c continues_on sender's attributes.
template <class _Sch, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT continues_on_t::__attrs_t
{
private:
//! @brief Returns `true` when:
//! - _SetTag is set_error_t, and
//! - _Sndr has value completions, and
//! - at least one of the value completions is not nothrow decay-copyable.
//! In that case, error completions can come from the sender's value completions.
template <class _SetTag, class... _Env>
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL bool __has_decay_copy_errors() noexcept
{
if constexpr (__same_as<_SetTag, set_error_t>)
{
if constexpr (__has_completions_for<_Sndr, set_value_t, __fwd_env_t<_Env>...>)
{
using __completion_parts_t =
__partitioned_completions_of_t<completion_signatures_of_t<_Sndr, __fwd_env_t<_Env>...>>;
return !__completion_parts_t::__nothrow_decay_copyable::__values::value;
}
}
return false;
}
const __sndr_t<_Sch, _Sndr>& __self_;
public:
_CCCL_HOST_DEVICE_API constexpr explicit __attrs_t(const __sndr_t<_Sch, _Sndr>& __self) noexcept
: __self_(__self)
{}
//! @brief Queries the completion scheduler for a given @c _SetTag.
//! @tparam _SetTag The completion tag to query for.
//! @tparam _Env The environment to consider when querying for the completion
//! scheduler.
//!
//! @note If @c _SetTag is @c set_value_t, then we are in the happy path: everything
//! succeeded and execution continues on @c _Sch.
//!
//! Otherwise, if @c _Sndr never completes with @c _SetTag, and either @c _SetTag is
//! @c set_stopped_t or decay-copying @c _Sndr's value results cannot throw, then a
//! @c _SetTag completion can only come from the scheduler's sender. In this case, return
//! the scheduler's completion scheduler if it has one.
//!
//! Otherwise, if the scheduler's sender never completes with @c _SetTag, then a
//! @c _SetTag completion can only come from the original sender, so return the
//! original sender's completion scheduler.
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _SetTag, class... _Env)
_CCCL_REQUIRES((__same_as<_SetTag, set_value_t> || __never_completes_with<_Sndr, _SetTag, __fwd_env_t<_Env>...>)
_CCCL_AND(!__has_decay_copy_errors<_SetTag, _Env...>()))
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_scheduler_t<_SetTag>, const _Env&... __env) const noexcept
-> __call_result_t<get_completion_scheduler_t<_SetTag>, _Sch, __fwd_env_t<_Env>...>
{
return get_completion_scheduler<_SetTag>(__self_.__sch_, __fwd_env(__env)...);
}
//! @overload
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _SetTag, class... _Env)
_CCCL_REQUIRES(__never_completes_with<schedule_result_t<_Sch>, _SetTag, __fwd_env_t<_Env>...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_scheduler_t<_SetTag>, const _Env&... __env) const noexcept
-> __call_result_t<get_completion_scheduler_t<_SetTag>, env_of_t<_Sndr>, __fwd_env_t<_Env>...>
{
return get_completion_scheduler<_SetTag>(get_env(__self_.__sndr_), __fwd_env(__env)...);
}
//! @brief Queries the completion domain for a given @c _SetTag.
//! @tparam _SetTag The completion tag to query for.
//! @tparam _Env The environment to consider when querying for the completion domain.
//!
//! @note If @c _SetTag is @c set_value_t, then we are in the happy path: everything
//! succeeded and execution continues on @c _Sch.
//!
//! Otherwise, if @c _SetTag is @c set_stopped_t or if decay-copying @c _Sndr's value
//! results cannot throw, then a @c _SetTag completion can happen on the sender's
//! completion domain (if it has one) or the scheduler's completion domain (if it has
//! one).
//!
//! @note Otherwise, @c _SetTag is @c set_error_t and decay-copying @c _Sndr's value
//! results can throw, so error completions can also come from the sender's value
//! completions.
_CCCL_TEMPLATE(class _SetTag, class... _Env)
_CCCL_REQUIRES(__same_as<_SetTag, set_value_t>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_domain_t<_SetTag>, const _Env&...) const noexcept
-> __unless_one_of_t<__compl_domain_t<_SetTag, schedule_result_t<_Sch>, __fwd_env_t<_Env>...>, __not_a_domain>
{
return {};
}
//! @overload
_CCCL_TEMPLATE(class _SetTag, class... _Env)
_CCCL_REQUIRES((!__same_as<_SetTag, set_value_t>) _CCCL_AND(!__has_decay_copy_errors<_SetTag, _Env...>()))
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_domain_t<_SetTag>, const _Env&...) const noexcept
-> __unless_one_of_t<__common_domain_t<__compl_domain_t<_SetTag, _Sndr, __fwd_env_t<_Env>...>,
__compl_domain_t<_SetTag, schedule_result_t<_Sch>, __fwd_env_t<_Env>...>>,
__not_a_domain>
{
return {};
}
//! @overload
_CCCL_TEMPLATE(class _SetTag, class... _Env)
_CCCL_REQUIRES((__has_decay_copy_errors<_SetTag, _Env...>()))
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_domain_t<_SetTag>, const _Env&...) const noexcept
-> __unless_one_of_t<__common_domain_t<__compl_domain_t<_SetTag, _Sndr, __fwd_env_t<_Env>...>,
__compl_domain_t<_SetTag, schedule_result_t<_Sch>, __fwd_env_t<_Env>...>,
__compl_domain_t<set_value_t, _Sndr, __fwd_env_t<_Env>...>>,
__not_a_domain>
{
return {};
}
//! @brief Queries the completion behavior of the combined sender.
//! @tparam _Env The environment to consider when querying for the completion behavior.
//! @note The completion behavior is the minimum between the scheduler's sender and
//! the original sender.
template <class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_behavior_t, const _Env&...) const noexcept
{
return (execution::min) (execution::get_completion_behavior<schedule_result_t<_Sch>, __fwd_env_t<_Env>...>(),
execution::get_completion_behavior<_Sndr, _Env...>());
}
//! @brief Forwards other queries to the underlying sender's environment.
//! @pre @c _Tag is a forwarding query but not a completion query.
_CCCL_TEMPLATE(class _Tag, class... _Args)
_CCCL_REQUIRES(__forwarding_query<_Tag> _CCCL_AND(!__is_completion_query<_Tag>)
_CCCL_AND __queryable_with<env_of_t<_Sndr>, _Tag, _Args...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Tag, _Args&&... __args) const
noexcept(__nothrow_queryable_with<env_of_t<_Sndr>, _Tag, _Args...>)
-> __query_result_t<env_of_t<_Sndr>, _Tag, _Args...>
{
return get_env(__self_.__sndr_).query(_Tag{}, static_cast<_Args&&>(__args)...);
}
};
//////////////////////////////////////////////////////////////////////////////////////////
// continues_on sender
template <class _Sch, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT continues_on_t::__sndr_t
{
using sender_concept = sender_t;
template <class _Self, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
_CUDAX_LET_COMPLETIONS(auto(__child_completions) = get_child_completion_signatures<_Self, _Sndr, _Env...>())
{
_CUDAX_LET_COMPLETIONS(
auto(__sch_completions) = execution::get_completion_signatures<schedule_result_t<_Sch>, __fwd_env_t<_Env>...>())
{
// The scheduler contributes error and stopped completions.
return concat_completion_signatures(
transform_completion_signatures(__sch_completions, __swallow_transform{}),
transform_completion_signatures(
__child_completions, __detail::__decay_args<set_value_t>{}, __detail::__decay_args<set_error_t>{}));
}
}
_CCCL_UNREACHABLE();
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) && -> __opstate_t<_Sch, _Sndr, _Rcvr>
{
return __opstate_t<_Sch, _Sndr, _Rcvr>{static_cast<_Sndr&&>(__sndr_), __sch_, static_cast<_Rcvr&&>(__rcvr)};
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
connect(_Rcvr __rcvr) const& -> __opstate_t<_Sch, const _Sndr&, _Rcvr>
{
return __opstate_t<_Sch, const _Sndr&, _Rcvr>{__sndr_, __sch_, static_cast<_Rcvr&&>(__rcvr)};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __attrs_t<_Sch, _Sndr>
{
return __attrs_t<_Sch, _Sndr>(*this);
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ continues_on_t __tag_;
_Sch __sch_;
_Sndr __sndr_;
};
template <class _Sch, class _Sndr>
inline constexpr int structured_binding_size<continues_on_t::__sndr_t<_Sch, _Sndr>> = 3;
_CCCL_GLOBAL_CONSTANT continues_on_t continues_on{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_CONTINUES_ON

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_CPOS
#define __CUDAX_EXECUTION_CPOS
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__type_traits/is_same.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
// make the completion tags equality comparable
template <__disposition _Disposition>
struct __completion_tag
{
template <__disposition _OtherDisposition>
_CCCL_TRIVIAL_API constexpr auto operator==(__completion_tag<_OtherDisposition>) const noexcept -> bool
{
return _Disposition == _OtherDisposition;
}
template <__disposition _OtherDisposition>
_CCCL_TRIVIAL_API constexpr auto operator!=(__completion_tag<_OtherDisposition>) const noexcept -> bool
{
return _Disposition != _OtherDisposition;
}
static constexpr __disposition __disposition = _Disposition;
};
template <class _Rcvr, class... _Ts>
_CCCL_CONCEPT __has_set_value_mbr = //
_CCCL_REQUIRES_EXPR((_Rcvr, variadic _Ts), _Rcvr& __rcvr) //
( //
static_cast<_Rcvr&&>(__rcvr).set_value(::cuda::std::declval<_Ts>()...) //
);
struct set_value_t : __completion_tag<__disposition::__value>
{
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Rcvr, class... _Ts)
_CCCL_REQUIRES(__has_set_value_mbr<_Rcvr, _Ts...>)
_CCCL_TRIVIAL_API constexpr void operator()(_Rcvr&& __rcvr, _Ts&&... __ts) const noexcept
{
static_assert(__same_as<decltype(static_cast<_Rcvr&&>(__rcvr).set_value(static_cast<_Ts&&>(__ts)...)), void>);
static_assert(noexcept(static_cast<_Rcvr&&>(__rcvr).set_value(static_cast<_Ts&&>(__ts)...)));
static_cast<_Rcvr&&>(__rcvr).set_value(static_cast<_Ts&&>(__ts)...);
}
};
template <class _Rcvr, class _Ey>
_CCCL_CONCEPT __has_set_error_mbr = //
_CCCL_REQUIRES_EXPR((_Rcvr, _Ey), _Rcvr& __rcvr, _Ey&& __e) //
( //
static_cast<_Rcvr&&>(__rcvr).set_error(static_cast<_Ey&&>(__e)) //
);
struct set_error_t : __completion_tag<__disposition::__error>
{
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Rcvr, class _Ey)
_CCCL_REQUIRES(__has_set_error_mbr<_Rcvr, _Ey>)
_CCCL_TRIVIAL_API constexpr void operator()(_Rcvr&& __rcvr, _Ey&& __e) const noexcept
{
static_assert(__same_as<decltype(static_cast<_Rcvr&&>(__rcvr).set_error(static_cast<_Ey&&>(__e))), void>);
static_assert(noexcept(static_cast<_Rcvr&&>(__rcvr).set_error(static_cast<_Ey&&>(__e))));
static_cast<_Rcvr&&>(__rcvr).set_error(static_cast<_Ey&&>(__e));
}
};
template <class _Rcvr>
_CCCL_CONCEPT __has_set_stopped_mbr = //
_CCCL_REQUIRES_EXPR((_Rcvr), _Rcvr& __rcvr) //
( //
static_cast<_Rcvr&&>(__rcvr).set_stopped() //
);
struct set_stopped_t : __completion_tag<__disposition::__stopped>
{
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Rcvr)
_CCCL_REQUIRES(__has_set_stopped_mbr<_Rcvr>)
_CCCL_TRIVIAL_API constexpr void operator()(_Rcvr&& __rcvr) const noexcept
{
static_assert(__same_as<decltype(static_cast<_Rcvr&&>(__rcvr).set_stopped()), void>);
static_assert(noexcept(static_cast<_Rcvr&&>(__rcvr).set_stopped()));
static_cast<_Rcvr&&>(__rcvr).set_stopped();
}
};
template <class _OpState>
_CCCL_CONCEPT __has_start_mbr = //
_CCCL_REQUIRES_EXPR((_OpState), _OpState& __opstate) //
( //
__opstate.start() //
);
struct start_t
{
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _OpState)
_CCCL_REQUIRES(__has_start_mbr<_OpState>)
_CCCL_TRIVIAL_API constexpr void operator()(_OpState& __opstate) const noexcept
{
static_assert(__same_as<decltype(__opstate.start()), void>);
static_assert(noexcept(__opstate.start()));
__opstate.start();
}
};
template <class _Sndr, class _Rcvr>
_CCCL_CONCEPT __has_connect_mbr = //
_CCCL_REQUIRES_EXPR((_Sndr, _Rcvr), _Sndr& __sndr, _Rcvr& __rcvr) //
( //
static_cast<_Sndr&&>(__sndr).connect(static_cast<_Rcvr&&>(__rcvr)) //
);
// connect
struct connect_t
{
private:
template <class _Sndr, class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto __get_declfn() noexcept
{
using __new_sender_t = transform_sender_result_t<_Sndr, env_of_t<_Rcvr>>;
if constexpr (__has_connect_mbr<__new_sender_t, _Rcvr>)
{
constexpr auto __sndr = __declfn<_Sndr>;
constexpr auto __rcvr = __declfn<_Rcvr>;
using __result_t = decltype(transform_sender(__sndr(), get_env(__rcvr())).connect(__rcvr()));
constexpr bool __is_nothrow = noexcept(transform_sender(__sndr(), get_env(__rcvr())).connect(__rcvr()));
return __declfn<__result_t, __is_nothrow>;
}
}
public:
template <class _Sndr, class _Rcvr, auto _DeclFn = __get_declfn<_Sndr, _Rcvr>()>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr&& __sndr, _Rcvr __rcvr) const
noexcept(noexcept(_DeclFn())) -> decltype(_DeclFn())
{
auto&& __env = get_env(__rcvr);
return transform_sender(static_cast<_Sndr&&>(__sndr), static_cast<decltype(__env)>(__env))
.connect(static_cast<_Rcvr&&>(__rcvr));
}
};
struct schedule_t
{
_CCCL_EXEC_CHECK_DISABLE
template <class _Sch>
_CCCL_TRIVIAL_API constexpr auto operator()(_Sch&& __sch) const noexcept
{
static_assert(noexcept(static_cast<_Sch&&>(__sch).schedule()));
return static_cast<_Sch&&>(__sch).schedule();
}
};
_CCCL_GLOBAL_CONSTANT set_value_t set_value{};
_CCCL_GLOBAL_CONSTANT set_error_t set_error{};
_CCCL_GLOBAL_CONSTANT set_stopped_t set_stopped{};
_CCCL_GLOBAL_CONSTANT start_t start{};
_CCCL_GLOBAL_CONSTANT connect_t connect{};
_CCCL_GLOBAL_CONSTANT schedule_t schedule{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_CPOS

173
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_DIAGNOSTICS
#define __CUDAX_EXECUTION_DIAGNOSTICS
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
// The following must be left undefined
template <class...>
struct _DIAGNOSTIC;
struct _UNKNOWN;
struct _WHERE;
struct _WHAT;
struct _TO_FIX_THIS_ERROR;
struct _IN_ALGORITHM;
struct _WITH_FUNCTION;
struct _WITH_SENDER;
struct _WITH_ARGUMENTS;
struct _WITH_QUERY;
struct _WITH_ENVIRONMENT;
struct _WITH_SIGNATURES;
template <class>
struct _WITH_COMPLETION_SIGNATURE;
struct _FUNCTION_IS_NOT_CALLABLE;
struct _FUNCTION_MUST_RETURN_A_SENDER;
struct _FUNCTION_MUST_RETURN_SENDERS_THAT_ALL_COMPLETE_IN_A_COMMON_DOMAIN;
struct _WITH_RETURN_TYPE;
struct _SENDER_HAS_TOO_MANY_SUCCESS_COMPLETIONS;
struct _ARGUMENTS_ARE_NOT_DECAY_COPYABLE;
struct _THE_ENVIRONMENT_OF_THE_RECEIVER_DOES_NOT_HAVE_A_SCHEDULER_FOR_ON_TO_RETURN_TO;
struct __merror_base
{
// _CCCL_HIDE_FROM_ABI virtual ~__merror_base() = default;
_CCCL_HOST_DEVICE friend constexpr auto __ustdex_unhandled_error(void*) noexcept -> bool
{
return true;
}
};
template <class... _What>
struct _ERROR : __merror_base
{
// The following aliases are to simplify error propagation
// in the completion signatures meta-programming.
template <class...>
using __call _CCCL_NODEBUG_ALIAS = _ERROR;
using __partitioned _CCCL_NODEBUG_ALIAS = _ERROR;
template <template <class...> class, template <class...> class>
using __value_types _CCCL_NODEBUG_ALIAS = _ERROR;
template <template <class...> class>
using __error_types _CCCL_NODEBUG_ALIAS = _ERROR;
using __sends_stopped _CCCL_NODEBUG_ALIAS = _ERROR;
// The following operator overloads also simplify error propagation.
_CCCL_HOST_DEVICE auto operator+() -> _ERROR;
template <class _Ty>
_CCCL_HOST_DEVICE auto operator,(_Ty&) -> _ERROR&;
template <class... _With>
_CCCL_HOST_DEVICE auto with(_ERROR<_With...>&) -> _ERROR<_What..., _With...>&;
};
_CCCL_HOST_DEVICE constexpr auto __ustdex_unhandled_error(...) noexcept -> bool
{
return false;
}
template <class _Ty>
inline constexpr bool __type_is_error = false;
template <class... _What>
inline constexpr bool __type_is_error<_ERROR<_What...>> = true;
template <class... _What>
inline constexpr bool __type_is_error<_ERROR<_What...>&> = true;
// True if any of the types in _Ts... are errors; false otherwise.
template <class... _Ts>
inline constexpr bool __type_contains_error =
#if _CCCL_COMPILER(MSVC)
(__type_is_error<_Ts> || ...);
#else
__ustdex_unhandled_error(static_cast<::cuda::std::__type_list<_Ts...>*>(nullptr));
#endif
template <class... _Ts>
using __type_find_error _CCCL_NODEBUG_ALIAS = decltype(+(declval<_Ts&>(), ..., declval<_ERROR<_UNKNOWN>&>()));
template <class... _What>
struct __not_a_sender
{
using sender_concept = sender_t;
template <class...>
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
return execution::invalid_completion_signature<_What...>();
}
};
template <class... _What>
struct __not_a_scheduler
{
using scheduler_concept = scheduler_t;
_CCCL_HOST_DEVICE_API auto schedule() noexcept
{
return __not_a_sender<_What...>{};
}
_CCCL_HOST_DEVICE_API constexpr bool operator==(__not_a_scheduler) const noexcept
{
return true;
}
_CCCL_HOST_DEVICE_API constexpr bool operator!=(__not_a_scheduler) const noexcept
{
return false;
}
};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_DIAGNOSTICS

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_DOMAIN
#define __CUDAX_EXECUTION_DOMAIN
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__concepts/same_as.h>
#include <cuda/std/__execution/env.h>
#include <cuda/std/__tuple_dir/ignore.h>
#include <cuda/std/__type_traits/common_type.h>
#include <cuda/std/__type_traits/conditional.h>
#include <cuda/std/__type_traits/decay.h>
#include <cuda/std/__type_traits/is_empty.h>
#include <cuda/std/__type_traits/is_nothrow_copy_constructible.h>
#include <cuda/std/__type_traits/is_nothrow_default_constructible.h>
#include <cuda/std/__utility/undefined.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/completion_behavior.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
template <class _DomainOrTag, class... _Args>
using __apply_sender_result_t _CCCL_NODEBUG_ALIAS = decltype(_DomainOrTag{}.apply_sender(declval<_Args>()...));
// _DomainOrTag: eg, default_domain or then_t
// _OpTag: either start_t or set_value_t
template <class _DomainOrTag, class _OpTag, class _Sndr, class... _Env>
using __transform_sender_result_t =
decltype(declval<_DomainOrTag>().transform_sender(declval<_OpTag>(), declval<_Sndr>(), declval<const _Env&>()...));
template <class _DomainOrTag, class _OpTag, class _Sndr, class... _Env>
_CCCL_CONCEPT __has_transform_sender =
__is_instantiable_with<__transform_sender_result_t, _DomainOrTag, _OpTag, _Sndr, _Env...>;
template <class _DomainOrTag, class _OpTag, class _Sndr, class... _Env>
_CCCL_CONCEPT __nothrow_transform_sender =
_CCCL_REQUIRES_EXPR((_DomainOrTag, _OpTag, _Sndr, variadic _Env), __declfn_t<_Sndr> __sndr, const _Env&... __env) //
( //
noexcept(_DomainOrTag{}.transform_sender(_OpTag{}, __sndr(), __env...)) //
);
template <class _Domain>
_CCCL_CONCEPT __domain_like =
::cuda::std::is_empty_v<_Domain> && //
::cuda::std::is_nothrow_default_constructible_v<_Domain> && //
::cuda::std::is_nothrow_copy_constructible_v<_Domain>;
//! @brief A structure that selects the default set of algorithm implementations for
//! senders.
//!
//! This structure defines static member functions to handle operations on senders, such
//! as applying and transforming them. It is designed to work with CUDA's experimental
//! execution framework.
//! @see https://eel.is/c++draft/exec.domain.default
struct _CCCL_TYPE_VISIBILITY_DEFAULT default_domain
{
//! @brief Applies a sender operation using the specified tag and arguments.
//!
//! @tparam _Tag The tag type that defines the operation to be applied.
//! @tparam _Sndr The type of the sender.
//! @tparam _Args Variadic template for additional arguments.
//! @param _Tag The tag instance specifying the operation.
//! @param __sndr The sender to which the operation is applied.
//! @param __args Additional arguments for the operation.
//! @return The result of applying the sender operation.
_CCCL_EXEC_CHECK_DISABLE
template <class _Tag, class _Sndr, class... _Args>
_CCCL_HOST_DEVICE_API static constexpr auto apply_sender(_Tag, _Sndr&& __sndr, _Args&&... __args) noexcept(
noexcept(_Tag{}.apply_sender(declval<_Sndr>(), declval<_Args>()...))) //
-> __apply_sender_result_t<_Tag, _Sndr, _Args...>
{
return _Tag{}.apply_sender(static_cast<_Sndr&&>(__sndr), static_cast<_Args&&>(__args)...);
}
//! @brief Transforms a sender with an environment.
//!
//! @tparam _OpTag Either start_t or set_value_t.
//! @tparam _Sndr The type of the sender.
//! @tparam _Env The type of the environment.
//! @param __sndr The sender to be transformed.
//! @param __env The environment used for the transformation.
//! @return The result of transforming the sender with the given environment.
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _OpTag, class _Sndr, class _Env)
_CCCL_REQUIRES(__has_transform_sender<tag_of_t<_Sndr>, _OpTag, _Sndr, _Env>)
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto
transform_sender(_OpTag, _Sndr&& __sndr, const _Env& __env) //
noexcept(__nothrow_transform_sender<tag_of_t<_Sndr>, _OpTag, _Sndr, _Env>)
-> __transform_sender_result_t<tag_of_t<_Sndr>, _OpTag, _Sndr, _Env>
{
return tag_of_t<_Sndr>{}.transform_sender(_OpTag{}, static_cast<_Sndr&&>(__sndr), __env);
}
//! @overload
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto
transform_sender(::cuda::std::__ignore_t, _Sndr&& __sndr, ::cuda::std::__ignore_t) //
noexcept(__nothrow_movable<_Sndr>) -> _Sndr
{
return static_cast<_Sndr&&>(__sndr);
}
};
//! @brief Concept that checks whether a domain's sender transform behaves like that of
//! @c default_domain when passed the same arguments. The concept is modeled when either
//! of the following is
template <class _Domain, class _OpTag, class _Sndr, class _Env>
_CCCL_CONCEPT __default_domain_like =
__same_as<decay_t<__transform_sender_result_t<default_domain, _OpTag, _Sndr, _Env>>,
decay_t<::cuda::std::__type_call<
::cuda::std::__type_try_catch<
::cuda::std::__type_quote<__transform_sender_result_t>,
::cuda::std::__type_always<__transform_sender_result_t<default_domain, _OpTag, _Sndr, _Env>>>,
_Domain,
_OpTag,
_Sndr,
_Env>>>;
/**
* @brief Tag type representing an indeterminate (unspecified) execution domain.
*
* This domain tag is used when a sender can complete with a given disposition
* from multiple execution domains.
*
* @tparam _Domains...: the (possibly empty) set of domains that a sender's
* completion may originate from.
*/
template <class... _Domains>
struct _CCCL_TYPE_VISIBILITY_DEFAULT indeterminate_domain
{
_CCCL_HIDE_FROM_ABI indeterminate_domain() = default;
_CCCL_HOST_DEVICE_API constexpr indeterminate_domain(::cuda::std::__ignore_t) noexcept {}
//! @brief Transforms a sender with an optional environment.
//!
//! @tparam _OpTag Either start_t or set_value_t.
//! @tparam _Sndr The type of the sender.
//! @tparam _Env The type of the environment.
//! @param __sndr The sender to be transformed.
//! @param __env The environment used for the transformation.
//! @return `default_domain{}.transform_sender(_OpTag{}, std::forward<_Sndr>(__sndr), __env)`
//! @pre Every type in @c _Domains... must behave like @c default_domain when passed the
//! same arguments. If this check fails, the @c static_assert triggers with: "ERROR:
//! indeterminate domains: cannot pick an algorithm customization"
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _OpTag, class _Sndr, class _Env)
_CCCL_REQUIRES(__has_transform_sender<tag_of_t<_Sndr>, _OpTag, _Sndr, _Env>)
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto
transform_sender(_OpTag, _Sndr&& __sndr, const _Env& __env) //
noexcept(__nothrow_transform_sender<tag_of_t<_Sndr>, _OpTag, _Sndr, _Env>)
-> __transform_sender_result_t<tag_of_t<_Sndr>, _OpTag, _Sndr, _Env>
{
static_assert((__default_domain_like<_Domains, _OpTag, _Sndr, _Env> && ...),
"ERROR: indeterminate domains: cannot pick an algorithm customization");
return tag_of_t<_Sndr>{}.transform_sender(_OpTag{}, static_cast<_Sndr&&>(__sndr), __env);
}
};
//! @brief A wrapper around an environment that hides a set of queries.
template <class _Env, class... _Queries>
struct __hide_query
{
static_assert(__nothrow_movable<_Env>);
_CCCL_HOST_DEVICE_API explicit constexpr __hide_query(_Env&& __env, _Queries...) noexcept
: __env_{static_cast<_Env&&>(__env)}
{}
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Query, class... _As)
_CCCL_REQUIRES(__none_of<_Query, _Queries...> _CCCL_AND __queryable_with<_Env, _Query, _As...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Query __query, const _As&... __as) const
noexcept(__nothrow_queryable_with<_Env, _Query, _As...>) -> __query_result_t<_Env, _Query, _As...>
{
return __env_.query(__query, __as...);
}
private:
_Env __env_;
};
template <class _Env>
struct __hide_scheduler : __hide_query<_Env, get_scheduler_t, get_domain_t>
{
_CCCL_HOST_DEVICE_API explicit constexpr __hide_scheduler(_Env&& __env) noexcept
: __hide_query<_Env, get_scheduler_t, get_domain_t>{static_cast<_Env&&>(__env), {}, {}}
{}
};
template <class _Env>
_CCCL_DEDUCTION_GUIDE_ATTRIBUTES __hide_scheduler(_Env&&) -> __hide_scheduler<_Env>;
template <class _Sch, class... _Env>
using __scheduler_domain_t _CCCL_NODEBUG_ALIAS = __call_result_t<get_completion_domain_t<set_value_t>, _Sch, _Env...>;
//////////////////////////////////////////////////////////////////////////////////////////
//! @brief A query type for asking a receiver's environment for its domain, which is an
//! empty class type that is used in tag dispatching to find a custom implementation of a
//! sender algorithm. The result of this query is the "current" domain; that is, the domain
//! where `start` will be called on the operation state that results from connecting the
//! receiver to a sender.
struct get_domain_t
{
//! @brief If there is a @c get_domain_t query in @c __env, return it.
_CCCL_TEMPLATE(class _Env)
_CCCL_REQUIRES(__queryable_with<_Env, get_domain_t>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Env&) const noexcept
-> decay_t<__query_result_t<_Env, get_domain_t>>
{
using __domain_t = decay_t<__query_result_t<_Env, get_domain_t>>;
static_assert(__domain_like<__domain_t>, "Domain types are required to be empty class types");
return __domain_t{};
}
//! @brief If there is not a @c get_domain_t query in @c __env, but there is a
//! scheduler, return the domain of the scheduler if it has one, and @c default_domain
//! otherwise.
_CCCL_TEMPLATE(class _Env)
_CCCL_REQUIRES((!__queryable_with<_Env, get_domain_t>) _CCCL_AND __callable<get_scheduler_t, const _Env&>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Env&) const noexcept
{
using __sch_t = __scheduler_of_t<const _Env&>;
using __env_t = __hide_scheduler<const _Env&>; // to prevent recursion
using __cmpl_sch_t = __call_result_or_t<get_completion_scheduler_t<set_value_t>, __sch_t, __sch_t, __env_t>;
using __domain_t = __scheduler_domain_t<__cmpl_sch_t, __env_t>;
static_assert(__domain_like<__domain_t>, "Domain types are required to be empty class types");
return __domain_t{};
}
//! @brief Fall back to the default domain if no other domain is found.
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(::cuda::std::__ignore_t) const noexcept
-> default_domain
{
return {};
}
_CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept
{
return true;
}
};
_CCCL_GLOBAL_CONSTANT get_domain_t get_domain{};
//////////////////////////////////////////////////////////////////////////////////////////
//! @brief A query type for asking a sender's attributes for the domain on which that
//! sender will complete. As with @c get_domain, it is used in tag dispatching to find a
//! custom implementation of a sender algorithm.
//!
//! @tparam _Tag one of set_value_t, set_error_t, or set_stopped_t
template <class _Tag>
struct get_completion_domain_t
{
// This function object reads the completion domain from an attribute object or a
// scheduler, accounting for the fact that the query member function may or may not
// accept an environment.
struct __read_query_t
{
_CCCL_TEMPLATE(class _Attrs)
_CCCL_REQUIRES(__queryable_with<_Attrs, get_completion_domain_t>)
_CCCL_HOST_DEVICE_API constexpr auto operator()(const _Attrs&, cuda::std::__ignore_t = {}) const noexcept
{
return decay_t<__query_result_t<_Attrs, get_completion_domain_t>>{};
}
_CCCL_TEMPLATE(class _Attrs, class _Env)
_CCCL_REQUIRES(__queryable_with<_Attrs, get_completion_domain_t, const _Env&>)
_CCCL_HOST_DEVICE_API constexpr auto operator()(const _Attrs&, const _Env&) const noexcept
{
return decay_t<__query_result_t<_Attrs, get_completion_domain_t, const _Env&>>{};
}
};
private:
template <class _Sch, class Domain, class... _Env>
_CCCL_HOST_DEVICE_API static constexpr void __check_scheduler_domain() noexcept
{
static_assert(__same_as<Domain, __scheduler_domain_t<_Sch, const _Env&...>>,
"the sender's completion scheduler's domain does not match the domain returned by the scheduler");
}
template <class _Attrs, class... _Env, class _Domain>
[[nodiscard]] _CCCL_TRIVIAL_API static _CCCL_CONSTEVAL auto __check_domain(_Domain) noexcept
{
// Sanity check: if a completion scheduler can be determined, then its domain must match
// the domain returned by the attributes.
if constexpr (__callable<get_completion_scheduler_t<_Tag>, const _Attrs&, const _Env&...>)
{
using __sch_t = decay_t<__call_result_t<get_completion_scheduler_t<_Tag>, const _Attrs&, const _Env&...>>;
if constexpr (!__same_as<__sch_t, _Attrs>) // prevent infinite recursion
{
get_completion_domain_t::__check_scheduler_domain<__sch_t, _Domain, _Env...>();
}
}
return __declfn<_Domain>;
}
template <class _Attrs, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto __get_declfn() noexcept
{
// If __attrs has a completion domain, then return it:
if constexpr (__callable<__read_query_t, const _Attrs&, const _Env&...>)
{
using __domain_t = __call_result_t<__read_query_t, const _Attrs&, const _Env&...>;
static_assert(__domain_like<__domain_t>, "Domain types are required to be empty class types");
return __check_domain<_Attrs, _Env...>(__domain_t{});
}
// Otherwise, if __attrs has a completion scheduler, we can ask that scheduler for its
// completion domain.
else if constexpr (__callable<get_completion_scheduler_t<_Tag>, const _Attrs&, const _Env&...>)
{
using __sch_t = __call_result_t<get_completion_scheduler_t<_Tag>, const _Attrs&, const _Env&...>;
using __read_query_t = typename get_completion_domain_t<set_value_t>::__read_query_t;
if constexpr (__callable<__read_query_t, __sch_t, const _Env&...>)
{
using __domain_t = __call_result_t<__read_query_t, __sch_t, const _Env&...>;
static_assert(__domain_like<__domain_t>, "Domain types are required to be empty class types");
return __declfn<__domain_t>;
}
// Otherwise, if the scheduler's sender indicates that it completes inline, we can ask
// the environment for its domain.
else if constexpr (__completes_inline<env_of_t<schedule_result_t<__sch_t>>, _Env...>
&& __callable<get_domain_t, const _Env&...>)
{
using __domain_t = __call_result_t<get_domain_t, const _Env&...>;
return __declfn<__domain_t>;
}
// Otherwise, if we are asking "late" (with an environment), return the default_domain
else if constexpr (sizeof...(_Env) != 0)
{
return __declfn<default_domain>;
}
}
// Otherwise, if the attributes indicates that the sender completes inline, we can ask
// the environment for its domain.
else if constexpr (__completes_inline<_Attrs, _Env...> && __callable<get_domain_t, const _Env&...>)
{
using __domain_t = __call_result_t<get_domain_t, const _Env&...>;
return __declfn<__domain_t>;
}
// Otherwise, if we are asking "late" (with an environment), return the default_domain
else if constexpr (sizeof...(_Env) != 0)
{
return __declfn<default_domain>;
}
// Otherwise, no completion domain can be determined. Return void.
}
public:
template <class _Attrs, class... _Env, auto _DeclFn = __get_declfn<_Attrs, _Env...>()>
[[nodiscard]] _CCCL_TRIVIAL_API constexpr auto operator()(const _Attrs&, const _Env&...) const noexcept
-> decltype(_DeclFn())
{
return {};
}
[[nodiscard]] _CCCL_TRIVIAL_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return true;
}
};
template <class _Tag>
extern ::cuda::std::__undefined<_Tag> get_completion_domain;
// Explicitly instantiate these because of variable template weirdness in device code
template <>
_CCCL_GLOBAL_CONSTANT get_completion_domain_t<set_value_t> get_completion_domain<set_value_t>{};
template <>
_CCCL_GLOBAL_CONSTANT get_completion_domain_t<set_error_t> get_completion_domain<set_error_t>{};
template <>
_CCCL_GLOBAL_CONSTANT get_completion_domain_t<set_stopped_t> get_completion_domain<set_stopped_t>{};
struct __not_a_domain
{
_CCCL_HIDE_FROM_ABI __not_a_domain() = default;
template <class _Domain>
_CCCL_HOST_DEVICE_API constexpr __not_a_domain(_Domain&&) noexcept
{}
};
template <class... _Domains>
using __indeterminate_domain_t =
::cuda::std::_If<sizeof...(_Domains) == 1, decltype((_Domains(), ...)), indeterminate_domain<_Domains...>>;
template <class _DomainSet>
using __domain_from_set_t =
::cuda::std::__type_apply<::cuda::std::_If<::cuda::std::__type_set_contains_v<_DomainSet, __not_a_domain>,
::cuda::std::__type_always<__not_a_domain>,
::cuda::std::__type_quote<__indeterminate_domain_t>>,
_DomainSet>;
template <class... _Domains>
using __make_domain_t = __domain_from_set_t<::cuda::std::__make_type_set<_Domains...>>;
// Common domain for a set of domains
template <class... _Domains>
struct __common_domain
{
using type =
::cuda::std::__type_call<::cuda::std::__type_try_catch<::cuda::std::__type_quote<::cuda::std::common_type_t>,
::cuda::std::__type_quote<__make_domain_t>>,
_Domains...>;
};
template <class... _Domains>
using __common_domain_t = typename __common_domain<_Domains...>::type;
namespace __detail
{
template <class _Tag, class _Sndr, class... _Env>
extern __call_result_or_t<get_completion_domain_t<_Tag>, indeterminate_domain<>, env_of_t<_Sndr>, _Env...>
__compl_domain_v;
template <class _Tag, class _Sndr>
extern __call_result_or_t<get_completion_domain_t<_Tag>,
// If we ask for the completion domain early (without an env)
// and it cannot be determined, then:
// - if the sender knows it can never complete with _Tag, return
// indeterminate_domain<>
// - otherwise, return __not_a_domain (indicating that the
// completion domain may only be knowable later, when an env
// is available)
::cuda::std::_If<__never_completes_with<_Sndr, _Tag>, indeterminate_domain<>, __not_a_domain>,
env_of_t<_Sndr>>
__compl_domain_v<_Tag, _Sndr>;
} // namespace __detail
template <class _Tag, class _Sndr, class... _Env>
using __compl_domain_t = decltype(__detail::__compl_domain_v<_Tag, _Sndr, _Env...>);
} // namespace cuda::experimental::execution
// Specializations of cuda::std::common_type for execution::indeterminate_domain
_CCCL_BEGIN_NAMESPACE_CUDA_STD
template <class... _Ds, class _Domain>
struct common_type<::cuda::experimental::execution::indeterminate_domain<_Ds...>, _Domain>
{
using type = ::cuda::experimental::execution::__make_domain_t<_Ds..., _Domain>;
};
template <class _Domain, class... _Ds>
struct common_type<_Domain, ::cuda::experimental::execution::indeterminate_domain<_Ds...>>
{
using type = ::cuda::experimental::execution::__make_domain_t<_Ds..., _Domain>;
};
template <class... _As, class... _Bs>
struct common_type<::cuda::experimental::execution::indeterminate_domain<_As...>,
::cuda::experimental::execution::indeterminate_domain<_Bs...>>
{
using type = ::cuda::experimental::execution::__make_domain_t<_As..., _Bs...>;
};
_CCCL_END_NAMESPACE_CUDA_STD
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_DOMAIN

352
cccl_upstream/cudax/include/cuda/experimental/__execution/env.cuh
View File

@@ -1,352 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX___EXECUTION_ENV_CUH
#define __CUDAX___EXECUTION_ENV_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__memory_resource/any_resource.h>
#include <cuda/__memory_resource/get_memory_resource.h>
#include <cuda/__memory_resource/properties.h>
#include <cuda/__stream/get_stream.h>
#include <cuda/__type_traits/is_specialization_of.h>
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__execution/env.h>
#include <cuda/std/__type_traits/remove_cvref.h>
#include <cuda/std/__utility/move.h>
#include <cuda/std/cstdint>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/policy.cuh>
#include <cuda/experimental/__execution/queries.cuh>
#include <cuda/experimental/__stream/stream_ref.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental
{
namespace execution
{
template <class _Env>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __fwd_env_;
//////////////////////////////////////////////////////////////////////////////////////////
// __env_ref
//! @brief __env_ref_ is a utility that builds a queryable object from a reference
//! to another queryable object.
template <class _Env>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __env_ref_
{
_CCCL_TEMPLATE(class _Query, class... _Args)
_CCCL_REQUIRES(__queryable_with<_Env, _Query, _Args...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Query, _Args&&... __args) const
noexcept(__nothrow_queryable_with<_Env, _Query, _Args...>) -> __query_result_t<_Env, _Query, _Args...>
{
return __env_.query(_Query{}, static_cast<_Args&&>(__args)...);
}
_Env const& __env_;
};
namespace __detail
{
struct _CCCL_TYPE_VISIBILITY_DEFAULT __env_ref_fn
{
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(env<>) const noexcept -> env<>
{
return {};
}
_CCCL_TEMPLATE(class _Env, class = _Env*) // not considered if _Env is a reference type
_CCCL_REQUIRES((!::cuda::__is_specialization_of_v<_Env, __fwd_env_>) )
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Env&& __env) const noexcept -> _Env
{
return static_cast<_Env&&>(__env);
}
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Env& __env) const noexcept -> __env_ref_<_Env>
{
return __env_ref_<_Env>{__env};
}
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(__env_ref_<_Env> __env) const noexcept
-> __env_ref_<_Env>
{
return __env;
}
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const __fwd_env_<_Env>& __env) const noexcept
-> __fwd_env_<_Env const&>
{
return __fwd_env_<_Env const&>{__env.__env_};
}
};
} // namespace __detail
template <class _Env>
using __env_ref_t _CCCL_NODEBUG_ALIAS = __call_result_t<__detail::__env_ref_fn, _Env>;
_CCCL_GLOBAL_CONSTANT __detail::__env_ref_fn __env_ref{};
//////////////////////////////////////////////////////////////////////////////////////////
// __fwd_env
//! @brief __fwd_env_ is a utility that forwards queries to a given queryable object
//! provided those queries that satisfy the __forwarding_query concept.
template <class _Env>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __fwd_env_
{
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Query, class... _Args)
_CCCL_REQUIRES(__forwarding_query<_Query> _CCCL_AND __queryable_with<_Env, _Query, _Args...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Query, _Args&&... __args) const
noexcept(__nothrow_queryable_with<_Env, _Query, _Args...>) -> __query_result_t<_Env, _Query, _Args...>
{
return __env_.query(_Query{}, static_cast<_Args&&>(__args)...);
}
_Env __env_;
};
namespace __detail
{
struct _CCCL_TYPE_VISIBILITY_DEFAULT __fwd_env_fn
{
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(env<>) const noexcept -> env<>
{
return {};
}
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(__env_ref_<_Env> __env) const noexcept
-> __fwd_env_<_Env const&>
{
return __fwd_env_<_Env const&>{__env.__env_};
}
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Env&& __env) const noexcept
{
static_assert(__nothrow_movable<_Env>);
// If the environment is already a forwarding environment, we can just return it.
if constexpr (__is_specialization_of_v<::cuda::std::remove_cvref_t<_Env>, __fwd_env_>)
{
return static_cast<_Env&&>(__env);
}
else
{
return __fwd_env_<_Env>{static_cast<_Env&&>(__env)};
}
}
};
} // namespace __detail
template <class _Env>
using __fwd_env_t _CCCL_NODEBUG_ALIAS = __call_result_t<__detail::__fwd_env_fn, _Env>;
_CCCL_GLOBAL_CONSTANT __detail::__fwd_env_fn __fwd_env{};
//////////////////////////////////////////////////////////////////////////////////////////
// __sch_env
//! @brief __sch_env_t is a utility that builds an environment from a scheduler. It
//! defines the `get_scheduler` query and provides a default for the `get_domain` query.
template <class _Sch>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sch_env_t
{
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_scheduler_t) const noexcept -> _Sch
{
return __sch_;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_domain_t) const noexcept
{
return __query_result_or_t<_Sch, get_completion_domain_t<set_value_t>, default_domain>{};
}
_Sch __sch_;
};
template <class _Sch>
_CCCL_DEDUCTION_GUIDE_ATTRIBUTES __sch_env_t(_Sch) -> __sch_env_t<_Sch>;
struct __mk_sch_env_t
{
template <class _Sch, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sch __sch, const _Env&... __env) const noexcept
{
return __sch_env_t{__call_or(get_completion_scheduler<set_value_t>, __sch, __sch, __env...)};
}
};
_CCCL_GLOBAL_CONSTANT __mk_sch_env_t __mk_sch_env{};
//////////////////////////////////////////////////////////////////////////////////////////
// __sch_attrs
//! @brief __sch_attrs_t is a utility that builds attributes for a sender from a
//! scheduler. It defines the `get_completion_scheduler<set_value_t>` query and provides a default for the
//! `get_completion_domain_t<set_value_t>` query.
template <class _Sch>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sch_attrs_t
{
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_scheduler_t<set_value_t>) const noexcept
-> const _Sch&
{
return __sch_;
}
_CCCL_TEMPLATE(class... _Env)
_CCCL_REQUIRES(__callable<get_completion_domain_t<set_value_t>, _Sch, _Env...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_domain_t<set_value_t>, const _Env&...) const noexcept
{
return __call_result_t<get_completion_domain_t<set_value_t>, _Sch, _Env...>{};
}
_Sch __sch_;
};
template <class _Sch>
_CCCL_DEDUCTION_GUIDE_ATTRIBUTES __sch_attrs_t(_Sch) -> __sch_attrs_t<_Sch>;
//////////////////////////////////////////////////////////////////////////////////////////
// __inln_attrs
//! @brief __inln_attrs_t is a utility that builds an attributes queryable for a sender
//! that completes inline. It implements get_completion_behavior to return
//! completion_behavior::inline_completion, and relies on the logic of
//! get_completion_scheduler and get_completion_domain to provide the current scheduler
//! and domain based on the environment.
struct _CCCL_TYPE_VISIBILITY_DEFAULT __inln_attrs_t
{
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_behavior_t) const noexcept
{
return completion_behavior::inline_completion;
}
};
//////////////////////////////////////////////////////////////////////////////////////////
// __join_env
namespace __detail
{
struct __join_env_fn
{
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Env&& __env, env<> = {}) const noexcept -> _Env
{
static_assert(__nothrow_movable<_Env>);
return static_cast<_Env&&>(__env);
}
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(env<>, _Env&& __env) const noexcept -> __fwd_env_t<_Env>
{
return __fwd_env(static_cast<_Env&&>(__env));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(env<>, env<>) const noexcept -> env<>
{
return {};
}
template <class _First, class _Second>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_First&& __first, _Second&& __second) const noexcept
-> env<_First, __fwd_env_t<_Second>>
{
static_assert(__nothrow_movable<_First>);
return {static_cast<_First&&>(__first), __fwd_env(static_cast<_Second&&>(__second))};
}
};
} // namespace __detail
_CCCL_GLOBAL_CONSTANT __detail::__join_env_fn __join_env{};
template <class... _Envs>
using __join_env_t _CCCL_NODEBUG_ALIAS = __call_result_t<__detail::__join_env_fn, _Envs...>;
} // namespace execution
template <class... _Properties>
class env_t
{
private:
using __resource = ::cuda::mr::any_resource<_Properties...>;
using __stream_ref = stream_ref;
__resource __mr_;
__stream_ref __stream_ = ::cuda::__invalid_stream();
execution::any_execution_policy __policy_ = {};
public:
//! @brief Construct an env_t from an any_resource, a stream and a policy
//! @param __mr The any_resource passed in
//! @param __stream The stream_ref passed in
//! @param __policy The execution_policy passed in
_CCCL_HIDE_FROM_ABI env_t(::cuda::mr::any_resource<_Properties...> __mr,
__stream_ref __stream = ::cuda::__invalid_stream(),
execution::any_execution_policy __policy = {}) noexcept
: __mr_(::cuda::std::move(__mr))
, __stream_(__stream)
, __policy_(__policy)
{}
//! @brief Checks whether another env is compatible with this one. That requires it to have queries for the three
//! properties we need
template <class _Env>
static constexpr bool __is_compatible_env =
(::cuda::std::execution::__queryable_with<_Env, ::cuda::mr::get_memory_resource_t>) //
&&(::cuda::std::execution::__queryable_with<_Env, ::cuda::get_stream_t>)
&& (::cuda::std::execution::__queryable_with<_Env, execution::get_execution_policy_t>);
//! @brief Construct from an environment that has the right queries
//! @param __env The environment we are querying for the required information
_CCCL_TEMPLATE(class _Env)
_CCCL_REQUIRES((!__same_as<_Env, env_t>) _CCCL_AND __is_compatible_env<_Env>)
_CCCL_HIDE_FROM_ABI env_t(const _Env& __env) noexcept
: __mr_(__env.query(::cuda::mr::get_memory_resource))
, __stream_(__env.query(::cuda::get_stream))
, __policy_(__env.query(execution::get_execution_policy))
{}
[[nodiscard]] _CCCL_HIDE_FROM_ABI const __resource& query(::cuda::mr::get_memory_resource_t) const noexcept
{
return __mr_;
}
[[nodiscard]] _CCCL_HIDE_FROM_ABI __stream_ref query(::cuda::get_stream_t) const noexcept
{
return __stream_;
}
[[nodiscard]] _CCCL_HIDE_FROM_ABI execution::any_execution_policy
query(execution::get_execution_policy_t) const noexcept
{
return __policy_;
}
};
} // namespace cuda::experimental
#include <cuda/experimental/__execution/epilogue.cuh>
#endif //__CUDAX___EXECUTION_ENV_CUH

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
// IMPORTANT: This file intentionally lacks a header guard.
#if !defined(_CUDAX_ASYNC_PROLOGUE_INCLUDED)
# error epilogue.cuh included without a prior inclusion of prologue.cuh
#endif
#undef _CUDAX_ASYNC_PROLOGUE_INCLUDED
#if _CCCL_CUDA_COMPILER(NVHPC)
_CCCL_END_NV_DIAG_SUPPRESS()
#endif // _CCCL_CUDA_COMPILER(NVHPC)
_CCCL_DIAG_POP
#include <cuda/std/__cccl/epilogue.h>

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_EXCEPTION
#define __CUDAX_EXECUTION_EXCEPTION
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__exception/cuda_error.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__exception/terminate.h>
#include <cuda/std/__utility/move.h>
#if _CCCL_HOSTED()
# include <exception> // IWYU pragma: keep
#endif // _CCCL_HOSTED()
namespace cuda::experimental::execution
{
// Since there are no exceptions in device code, we provide a stub implementation of
// std::exception_ptr and related functions.
#if _CCCL_FREESTANDING() || !_CCCL_HOST_COMPILATION()
struct exception_ptr
{
private:
struct __nullptr_t
{};
//! In libstdc++ and libc++, std::exception_ptr is the size of a pointer, but in MSVC it
//! is the size of two pointers. We must match that size here to avoid breaking the ABI
//! of any types that contain an exception_ptr.
void* __ptrs[1 + _CCCL_COMPILER(MSVC)] = {};
public:
_CCCL_HIDE_FROM_ABI exception_ptr() noexcept = default;
//! For conversion from nullptr so that code like:
//!
//! @code
//! std::exception_ptr eptr = nullptr;
//! @endcode
//!
//! and
//!
//! @code
//! eptr == nullptr
//! @endcode
//!
//! works as expected.
_CCCL_HOST_DEVICE_API constexpr exception_ptr(const __nullptr_t* __ptr) noexcept
: exception_ptr()
{
_CCCL_ASSERT(__ptr == nullptr, "Can only construct exception_ptr from nullptr");
}
[[nodiscard]] _CCCL_HOST_DEVICE_API explicit constexpr operator bool() const noexcept
{
return false;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr bool operator!() const noexcept
{
return true;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API friend constexpr bool
operator==(const exception_ptr&, const exception_ptr&) noexcept
{
return true;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API friend constexpr bool
operator!=(const exception_ptr&, const exception_ptr&) noexcept
{
return false;
}
};
[[nodiscard]] _CCCL_HOST_DEVICE_API inline exception_ptr current_exception() noexcept
{
return exception_ptr{};
}
[[noreturn]] _CCCL_HOST_DEVICE_API inline void rethrow_exception(const exception_ptr&)
{
_CCCL_THROW(::cuda::cuda_error, cudaErrorUnknown, "unknown exception");
}
// ^^^ _CCCL_FREESTANDING() || !_CCCL_HOST_COMPILATION() ^^^
#else
// vvv _CCCL_HOSTED() && _CCCL_HOST_COMPILATION() vvv
using ::std::current_exception;
using ::std::exception_ptr;
using ::std::rethrow_exception;
#endif // _CCCL_HOSTED() && _CCCL_HOST_COMPILATION()
} // namespace cuda::experimental::execution
#endif // __CUDAX_EXECUTION_EXCEPTION

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_FWD
#define __CUDAX_EXECUTION_FWD
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__concepts/same_as.h>
#include <cuda/std/__exception/terminate.h>
#include <cuda/std/__execution/env.h>
#include <cuda/std/__tuple_dir/ignore.h>
#include <cuda/std/__type_traits/decay.h>
#include <cuda/std/__type_traits/remove_reference.h>
#include <cuda/std/__type_traits/type_list.h>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
_CCCL_BEGIN_NV_DIAG_SUPPRESS(2642) // call through incomplete class "cuda::experimental::execution::schedule_t"
// will always produce an error when instantiated.
namespace cuda::experimental
{
// so we can refer to the cuda::experimental::__detail namespace below
namespace __detail
{
}
namespace execution
{
namespace __detail
{
using namespace cuda::experimental::__detail; // NOLINT(misc-unused-using-decls)
} // namespace __detail
// NOLINTBEGIN(misc-unused-using-decls)
using ::cuda::std::execution::__forwarding_query;
using ::cuda::std::execution::__unwrap_reference_t;
using ::cuda::std::execution::env;
using ::cuda::std::execution::env_of_t;
using ::cuda::std::execution::forwarding_query;
using ::cuda::std::execution::forwarding_query_t;
using ::cuda::std::execution::get_env;
using ::cuda::std::execution::get_env_t;
using ::cuda::std::execution::prop;
using ::cuda::std::execution::__nothrow_queryable_with;
using ::cuda::std::execution::__query_result_t;
using ::cuda::std::execution::__queryable_with;
using ::cuda::std::execution::__query_or;
using ::cuda::std::execution::__query_result_or_t;
// NOLINTEND(misc-unused-using-decls)
struct _CCCL_TYPE_VISIBILITY_DEFAULT never_stop_token;
class _CCCL_TYPE_VISIBILITY_DEFAULT inplace_stop_source;
class _CCCL_TYPE_VISIBILITY_DEFAULT inplace_stop_token;
template <class _Callback>
class _CCCL_TYPE_VISIBILITY_DEFAULT inplace_stop_callback;
template <class _Token, class _Callback>
using stop_callback_for_t _CCCL_NODEBUG_ALIAS = typename _Token::template callback_type<_Callback>;
template <class _Env, class _Query, bool _Default>
_CCCL_CONCEPT __nothrow_queryable_with_or =
bool(__queryable_with<_Env, _Query> ? __nothrow_queryable_with<_Env, _Query> : _Default);
struct _CCCL_TYPE_VISIBILITY_DEFAULT receiver_t
{};
struct _CCCL_TYPE_VISIBILITY_DEFAULT operation_state_t
{};
struct _CCCL_TYPE_VISIBILITY_DEFAULT sender_t
{};
struct _CCCL_TYPE_VISIBILITY_DEFAULT scheduler_t
{};
template <class _Ty>
using __sender_concept_t _CCCL_NODEBUG_ALIAS = typename ::cuda::std::remove_reference_t<_Ty>::sender_concept;
template <class _Ty>
using __receiver_concept_t _CCCL_NODEBUG_ALIAS = typename ::cuda::std::remove_reference_t<_Ty>::receiver_concept;
template <class _Ty>
using __scheduler_concept_t _CCCL_NODEBUG_ALIAS = typename ::cuda::std::remove_reference_t<_Ty>::scheduler_concept;
template <class _Ty>
using __operation_state_concept_t _CCCL_NODEBUG_ALIAS =
typename ::cuda::std::remove_reference_t<_Ty>::operation_state_concept;
template <class _Ty>
inline constexpr bool __is_sender = __is_instantiable_with<__sender_concept_t, _Ty>;
template <class _Ty>
inline constexpr bool __is_receiver = __is_instantiable_with<__receiver_concept_t, _Ty>;
template <class _Ty>
inline constexpr bool __is_scheduler = __is_instantiable_with<__scheduler_concept_t, _Ty>;
template <class _Ty>
inline constexpr bool __is_operation_state = __is_instantiable_with<__operation_state_concept_t, _Ty>;
struct _CCCL_TYPE_VISIBILITY_DEFAULT dependent_sender_error;
struct _CCCL_TYPE_VISIBILITY_DEFAULT default_domain;
template <class... _Sigs>
struct _CCCL_TYPE_VISIBILITY_DEFAULT completion_signatures;
template <class _Sndr, class... _Env>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto get_completion_signatures();
template <class _Sndr, class... _Env>
using completion_signatures_of_t _CCCL_NODEBUG_ALIAS = decltype(execution::get_completion_signatures<_Sndr, _Env...>());
#if _CCCL_HAS_CONSTEXPR_EXCEPTIONS()
template <class... _What, class... _Values>
_CCCL_HOST_DEVICE_API consteval auto invalid_completion_signature(_Values... __values) -> completion_signatures<>;
#else // ^^^ _CCCL_HAS_CONSTEXPR_EXCEPTIONS() ^^^ / vvv !_CCCL_HAS_CONSTEXPR_EXCEPTIONS() vvv
template <class... _What, class... _Values>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto invalid_completion_signature(_Values...);
#endif // ^^^ !_CCCL_HAS_CONSTEXPR_EXCEPTIONS() ^^^
// handy enumerations for keeping type names readable
enum class __disposition : int8_t
{
__invalid = -1,
__value,
__error,
__stopped
};
// customization point objects:
struct _CCCL_TYPE_VISIBILITY_DEFAULT set_value_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT set_error_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT set_stopped_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT start_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT connect_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT schedule_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT transform_sender_t;
template <class _Sch>
using schedule_result_t _CCCL_NODEBUG_ALIAS = decltype(declval<schedule_t>()(declval<_Sch>()));
template <class _Sndr, class _Rcvr>
using connect_result_t _CCCL_NODEBUG_ALIAS = decltype(declval<connect_t>()(declval<_Sndr>(), declval<_Rcvr>()));
template <class _Sndr, class _Env>
using transform_sender_result_t _CCCL_NODEBUG_ALIAS =
decltype(declval<transform_sender_t>()(declval<_Sndr>(), declval<_Env>()));
template <class _Sndr, class _Rcvr>
inline constexpr bool __nothrow_connectable = noexcept(declval<connect_t>()(declval<_Sndr>(), declval<_Rcvr>()));
// sender factory algorithms:
struct _CCCL_TYPE_VISIBILITY_DEFAULT read_env_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_error_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_stopped_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_from_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_error_from_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_stopped_from_t;
// sender adaptor algorithms:
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_value_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_error_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_stopped_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT then_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT upon_error_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT upon_stopped_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT when_all_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT conditional_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT sequence_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT write_env_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT starts_on_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT continues_on_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT on_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT schedule_from_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT bulk_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT bulk_chunked_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT bulk_unchunked_t;
// sender consumer algorithms:
struct _CCCL_TYPE_VISIBILITY_DEFAULT sync_wait_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT start_detached_t;
// queries:
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_allocator_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_stop_token_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_scheduler_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_delegation_scheduler_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_forward_progress_guarantee_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_available_parallelism_t;
template <class _Tag>
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_completion_scheduler_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_domain_t;
template <class _Tag>
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_completion_domain_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT get_completion_behavior_t;
template <class _Ty>
using stop_token_of_t _CCCL_NODEBUG_ALIAS = decay_t<__call_result_t<get_stop_token_t, _Ty>>;
template <class _Env>
using __scheduler_of_t _CCCL_NODEBUG_ALIAS = decay_t<__call_result_t<get_scheduler_t, _Env>>;
template <class _Env>
using __domain_of_t _CCCL_NODEBUG_ALIAS = __call_result_t<get_domain_t, _Env>;
template <class _Tag, class _Sndr, class... _Env>
using __completion_domain_of_t _CCCL_NODEBUG_ALIAS =
__call_result_t<get_completion_domain_t<_Tag>, env_of_t<_Sndr>, _Env...>;
// get_forward_progress_guarantee:
enum class forward_progress_guarantee
{
concurrent,
parallel,
weakly_parallel
};
namespace __detail
{
struct __get_tag
{
template <class _Tag, class... _Child>
_CCCL_HOST_DEVICE_API constexpr auto operator()(int, _Tag, ::cuda::std::__ignore_t, _Child&&...) const -> _Tag
{
return _Tag{};
}
};
template <class _Sndr, class _Tag = __visit_result_t<__get_tag&, _Sndr, int&>>
extern __fn_ptr_t<_Tag> __tag_of_v;
} // namespace __detail
_CCCL_TEMPLATE(class _Sndr)
_CCCL_REQUIRES(__is_sender<_Sndr>)
using tag_of_t _CCCL_NODEBUG_ALIAS = decltype(__detail::__tag_of_v<_Sndr>());
template <class _Sndr, class... _Tag>
inline constexpr bool __sender_for_v = _CCCL_REQUIRES_EXPR((_Sndr, variadic _Tag))(tag_of_t<_Sndr>{});
template <class _Sndr, class _Tag>
inline constexpr bool __sender_for_v<_Sndr, _Tag> =
_CCCL_REQUIRES_EXPR((_Sndr, _Tag))(_Same_as(_Tag) tag_of_t<_Sndr>{});
template <class _Sndr, class... _Tag>
_CCCL_CONCEPT sender_for = __sender_for_v<_Sndr, _Tag...>;
namespace __detail
{
template <class _Sig>
inline constexpr __disposition __signature_disposition = __disposition::__invalid;
template <class... _Ts>
inline constexpr __disposition __signature_disposition<set_value_t(_Ts...)> = __disposition::__value;
template <class _Ty>
inline constexpr __disposition __signature_disposition<set_error_t(_Ty)> = __disposition::__error;
template <>
inline constexpr __disposition __signature_disposition<set_stopped_t()> = __disposition::__stopped;
} // namespace __detail
struct inline_scheduler;
class task_scheduler;
struct stream_domain;
struct stream_context;
struct stream_scheduler;
////////////////////////////////////////////////////////////////////////////////////////////////////
// __has_completions_for and __never_completes_with
template <class _SetTag, class _Sndr, class... _Env>
_CCCL_CONCEPT __has_completions_for = _CCCL_REQUIRES_EXPR((_SetTag, _Sndr, variadic _Env)) //
( //
typename(completion_signatures_of_t<_Sndr, _Env...>),
requires(completion_signatures_of_t<_Sndr, _Env...>::count(_SetTag{}) != 0) //
);
template <class _Sndr, class _SetTag, class... _Env>
_CCCL_CONCEPT __never_completes_with = _CCCL_REQUIRES_EXPR((_SetTag, _Sndr, variadic _Env)) //
( //
typename(completion_signatures_of_t<_Sndr, _Env...>),
requires(completion_signatures_of_t<_Sndr, _Env...>::count(_SetTag{}) == 0) //
);
////////////////////////////////////////////////////////////////////////////////////////////////////
// __receiver_archetype
template <class _Env>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __receiver_archetype
{
using receiver_concept = receiver_t;
template <class... _As>
_CCCL_HOST_DEVICE_API constexpr void set_value(_As&&...) noexcept;
template <class _Error>
_CCCL_HOST_DEVICE_API constexpr void set_error(_Error&&) noexcept;
_CCCL_HOST_DEVICE_API constexpr void set_stopped() noexcept;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> _Env;
};
} // namespace execution
} // namespace cuda::experimental
_CCCL_END_NV_DIAG_SUPPRESS()
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_FWD

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_GET_COMPLETION_SIGNATURES
#define __CUDAX_EXECUTION_GET_COMPLETION_SIGNATURES
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__type_traits/copy_cvref.h>
#include <cuda/std/__type_traits/is_base_of.h>
#include <cuda/std/__type_traits/remove_reference.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh> // IWYU pragma: export
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
// include this last:
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
#if __cpp_lib_constexpr_exceptions >= 202502L // constexpr exception types, https://wg21.link/p3378
using __exception = ::std::exception;
#elif __cpp_constexpr >= 202411L // constexpr virtual functions
struct _CCCL_TYPE_VISIBILITY_DEFAULT __exception
{
_CCCL_HIDE_FROM_ABI constexpr __exception() noexcept = default;
_CCCL_HIDE_FROM_ABI virtual constexpr ~__exception() = default;
[[nodiscard]] _CCCL_HOST_DEVICE_API virtual constexpr auto what() const noexcept -> const char*
{
return "<exception>";
}
};
#else // no constexpr virtual functions:
struct _CCCL_TYPE_VISIBILITY_DEFAULT __exception
{
_CCCL_HIDE_FROM_ABI constexpr __exception() noexcept = default;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto what() const noexcept -> const char*
{
return "<exception>";
}
};
#endif // __cpp_lib_constexpr_exceptions >= 202502L
template <class _Derived>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __compile_time_error : __exception
{
_CCCL_HIDE_FROM_ABI __compile_time_error() = default;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto what() const noexcept -> const char*
{
return static_cast<_Derived const*>(this)->__what();
}
};
template <class _Data, class... _What>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sender_type_check_failure //
: __compile_time_error<__sender_type_check_failure<_Data, _What...>>
{
static_assert(__nothrow_movable<_Data>,
"The data member of __sender_type_check_failure must be nothrow move constructible.");
_CCCL_HIDE_FROM_ABI constexpr __sender_type_check_failure() noexcept = default;
_CCCL_HOST_DEVICE_API constexpr explicit __sender_type_check_failure(_Data __data)
: __data_(static_cast<_Data&&>(__data))
{}
private:
friend struct __compile_time_error<__sender_type_check_failure>;
_CCCL_HOST_DEVICE_API constexpr auto __what() const noexcept -> const char*
{
return "This sender is not well-formed. It does not meet the requirements of a sender type.";
}
_Data __data_{};
};
struct _CCCL_TYPE_VISIBILITY_DEFAULT dependent_sender_error : __compile_time_error<dependent_sender_error>
{
_CCCL_HOST_DEVICE_API constexpr explicit dependent_sender_error(char const* __what) noexcept
: __what_(__what)
{}
private:
friend struct __compile_time_error<dependent_sender_error>;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto __what() const noexcept -> char const*
{
return __what_;
}
char const* __what_;
};
template <class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __dependent_sender_error : dependent_sender_error
{
_CCCL_HOST_DEVICE_API constexpr __dependent_sender_error() noexcept
: dependent_sender_error{"This sender needs to know its execution " //
"environment before it can know how it will complete."}
{}
_CCCL_HOST_DEVICE auto operator+() -> __dependent_sender_error;
template <class _Ty>
_CCCL_HOST_DEVICE auto operator,(_Ty&) -> __dependent_sender_error&;
template <class... _What>
_CCCL_HOST_DEVICE auto operator,(_ERROR<_What...>&) -> _ERROR<_What...>&;
};
// Below is the definition of the _CUDAX_LET_COMPLETIONS portability macro. It
// is used to check that an expression's type is a valid completion_signature
// specialization.
//
// USAGE:
//
// _CUDAX_LET_COMPLETIONS(auto(__cs) = <expression>)
// {
// // __cs is guaranteed to be a specialization of completion_signatures.
// }
//
// When constexpr exceptions are available (C++26), the macro simply expands to
// the moral equivalent of:
//
// // With constexpr exceptions:
// auto __cs = <expression>; // throws if __cs is not a completion_signatures
//
// When constexpr exceptions are not available, the macro expands to:
//
// // Without constexpr exceptions:
// if constexpr (auto __cs = <expression>; !__valid_completion_signatures<decltype(__cs)>)
// {
// return __cs;
// }
// else
#if _CCCL_HAS_CONSTEXPR_EXCEPTIONS()
# define _CUDAX_LET_COMPLETIONS(...) \
if constexpr ([[maybe_unused]] __VA_ARGS__; false) \
{ \
} \
else
template <class... _Sndr>
[[noreturn, nodiscard]] _CCCL_HOST_DEVICE_API consteval auto __dependent_sender() -> completion_signatures<>
{
throw __dependent_sender_error<_Sndr...>{};
}
#else // ^^^ _CCCL_HAS_CONSTEXPR_EXCEPTIONS() ^^^ / vvv !_CCCL_HAS_CONSTEXPR_EXCEPTIONS() vvv
# define _CUDAX_PP_EAT_AUTO_auto(_ID) _ID _CCCL_PP_EAT _CCCL_PP_LPAREN
# define _CUDAX_PP_EXPAND_AUTO_auto(_ID) auto _ID
# define _CUDAX_LET_COMPLETIONS_ID(...) _CCCL_PP_EXPAND(_CCCL_PP_CAT(_CUDAX_PP_EAT_AUTO_, __VA_ARGS__) _CCCL_PP_RPAREN)
# define _CUDAX_LET_COMPLETIONS(...) \
if constexpr (_CCCL_PP_CAT(_CUDAX_PP_EXPAND_AUTO_, __VA_ARGS__); \
!::cuda::experimental::execution::__valid_completion_signatures<decltype(_CUDAX_LET_COMPLETIONS_ID( \
__VA_ARGS__))>) \
{ \
return _CUDAX_LET_COMPLETIONS_ID(__VA_ARGS__); \
} \
else
template <class... _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto __dependent_sender() -> __dependent_sender_error<_Sndr...>
{
return __dependent_sender_error<_Sndr...>{};
}
#endif // ^^^ !_CCCL_HAS_CONSTEXPR_EXCEPTIONS() ^^^
////////////////////////////////////////////////////////////////////////////////////////////////////
// get_completion_signatures
_CCCL_DIAG_PUSH
// warning C4913: user defined binary operator ',' exists but no overload could convert all operands,
// default built-in binary operator ',' used
_CCCL_DIAG_SUPPRESS_MSVC(4913)
#define _CUDAX_GET_COMPLSIGS(...) \
::cuda::std::remove_reference_t<_CCCL_PP_FIRST(__VA_ARGS__)>::template get_completion_signatures<__VA_ARGS__>()
#define _CUDAX_CHECKED_COMPLSIGS(...) \
(static_cast<void>(__VA_ARGS__), void(), execution::__checked_complsigs<decltype(__VA_ARGS__)>())
struct _A_GET_COMPLETION_SIGNATURES_CUSTOMIZATION_RETURNED_A_TYPE_THAT_IS_NOT_A_COMPLETION_SIGNATURES_SPECIALIZATION
{};
template <class _Completions>
_CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto __checked_complsigs()
{
_CUDAX_LET_COMPLETIONS(auto(__cs) = _Completions())
{
if constexpr (__valid_completion_signatures<_Completions>)
{
return __cs;
}
else
{
return invalid_completion_signature<
_A_GET_COMPLETION_SIGNATURES_CUSTOMIZATION_RETURNED_A_TYPE_THAT_IS_NOT_A_COMPLETION_SIGNATURES_SPECIALIZATION,
_WITH_SIGNATURES(_Completions)>();
}
}
}
template <class _Sndr, class... _Env>
using __get_complsigs_t = decltype(_CUDAX_GET_COMPLSIGS(_Sndr, _Env...));
template <class _Sndr, class... _Env>
inline constexpr bool __has_get_completion_signatures = false;
// clang-format off
template <class _Sndr>
inline constexpr bool __has_get_completion_signatures<_Sndr> =
_CCCL_REQUIRES_EXPR((_Sndr))
(
typename(__get_complsigs_t<_Sndr>)
);
template <class _Sndr, class _Env>
inline constexpr bool __has_get_completion_signatures<_Sndr, _Env> =
_CCCL_REQUIRES_EXPR((_Sndr, _Env))
(
typename(__get_complsigs_t<_Sndr, _Env>)
);
// clang-format on
struct _COULD_NOT_DETERMINE_COMPLETION_SIGNATURES_FOR_THIS_SENDER
{};
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto __get_completion_signatures_helper()
{
if constexpr (__has_get_completion_signatures<_Sndr, _Env...>)
{
return _CUDAX_CHECKED_COMPLSIGS(_CUDAX_GET_COMPLSIGS(_Sndr, _Env...));
}
else if constexpr (__has_get_completion_signatures<_Sndr>)
{
return _CUDAX_CHECKED_COMPLSIGS(_CUDAX_GET_COMPLSIGS(_Sndr));
}
// else if constexpr (__is_awaitable<_Sndr, __env_promise<_Env>...>)
// {
// using Result _CCCL_NODEBUG_ALIAS = __await_result_t<_Sndr, __env_promise<_Env>...>;
// return completion_signatures{__set_value_v<Result>, __set_error_v<>, __set_stopped_v};
// }
else if constexpr (sizeof...(_Env) == 0)
{
return __dependent_sender<_Sndr>();
}
else
{
return invalid_completion_signature<_COULD_NOT_DETERMINE_COMPLETION_SIGNATURES_FOR_THIS_SENDER,
_WITH_SENDER(_Sndr),
_WITH_ENVIRONMENT(_Env...)>();
}
}
template <class _Sndr, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto get_completion_signatures()
{
static_assert(sizeof...(_Env) <= 1, "At most one environment is allowed.");
if constexpr (0 == sizeof...(_Env))
{
return execution::__get_completion_signatures_helper<_Sndr>();
}
else
{
// Apply a lazy sender transform if one exists before computing the completion signatures:
using __new_sndr_t = __call_result_t<transform_sender_t, _Sndr, _Env...>;
return execution::__get_completion_signatures_helper<__new_sndr_t, _Env...>();
}
}
template <class _Parent, class _Child, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto get_child_completion_signatures()
{
return get_completion_signatures<::cuda::std::__copy_cvref_t<_Parent, _Child>, __fwd_env_t<_Env>...>();
}
#undef _CUDAX_GET_COMPLSIGS
#undef _CUDAX_CHECKED_COMPLSIGS
_CCCL_DIAG_POP
#if _CCCL_HAS_CONSTEXPR_EXCEPTIONS()
// When asked for its completions without an envitonment, a dependent sender
// will throw an exception of a type derived from `dependent_sender_error`.
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API consteval bool __is_dependent_sender() noexcept
try
{
(void) get_completion_signatures<_Sndr>();
return false; // didn't throw, not a dependent sender
}
catch (dependent_sender_error&)
{
return true;
}
catch (...)
{
return false; // different kind of exception was thrown; not a dependent sender
}
#else // ^^^ _CCCL_HAS_CONSTEXPR_EXCEPTIONS() ^^^ / vvv !_CCCL_HAS_CONSTEXPR_EXCEPTIONS() vvv
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto __is_dependent_sender() noexcept -> bool
{
using _Completions _CCCL_NODEBUG_ALIAS = decltype(get_completion_signatures<_Sndr>());
return ::cuda::std::is_base_of_v<dependent_sender_error, _Completions>;
}
#endif // ^^^ !_CCCL_HAS_CONSTEXPR_EXCEPTIONS() ^^^
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_GET_COMPLETION_SIGNATURES

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@@ -1,102 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_INLINE_SCHEDULER
#define __CUDAX_EXECUTION_INLINE_SCHEDULER
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/experimental/__execution/completion_behavior.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/domain.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
//! Scheduler that returns a sender that always completes inline (successfully).
struct _CCCL_TYPE_VISIBILITY_DEFAULT inline_scheduler : __inln_attrs_t
{
private:
struct _CCCL_TYPE_VISIBILITY_DEFAULT __attrs_t : __inln_attrs_t
{};
template <class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t : __immovable
{
using operation_state_concept = operation_state_t;
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
set_value(static_cast<_Rcvr&&>(__rcvr));
}
_Rcvr __rcvr;
};
public:
using scheduler_concept = scheduler_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t
{
using sender_concept = sender_t;
template <class Self>
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto get_completion_signatures() noexcept
{
return completion_signatures<set_value_t()>{};
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) const noexcept -> __opstate_t<_Rcvr>
{
return {{}, static_cast<_Rcvr&&>(__rcvr)};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto get_env() noexcept -> __attrs_t
{
return {};
}
};
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto schedule() const noexcept -> __sndr_t
{
return {};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API friend constexpr bool operator==(inline_scheduler, inline_scheduler) noexcept
{
return true;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API friend constexpr bool operator!=(inline_scheduler, inline_scheduler) noexcept
{
return false;
}
};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_INLINE_SCHEDULER

295
cccl_upstream/cudax/include/cuda/experimental/__execution/intrusive_queue.cuh
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@@ -1,295 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
// Copyright (c) 2021-2022 Facebook, Inc & AFFILIATES.
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_INTRUSIVE_QUEUE
#define __CUDAX_EXECUTION_INTRUSIVE_QUEUE
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__iterator/iterator_traits.h>
#include <cuda/std/__utility/exchange.h>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
template <auto _Next>
class _CCCL_TYPE_VISIBILITY_DEFAULT __intrusive_queue;
template <class _Item, _Item* _Item::* _Next>
class _CCCL_TYPE_VISIBILITY_DEFAULT __intrusive_queue<_Next>
{
public:
_CCCL_HIDE_FROM_ABI __intrusive_queue() noexcept = default;
_CCCL_HOST_DEVICE_API __intrusive_queue(__intrusive_queue&& __other) noexcept
: __head_(::cuda::std::exchange(__other.__head_, nullptr))
, __tail_(::cuda::std::exchange(__other.__tail_, nullptr))
{}
_CCCL_HOST_DEVICE_API auto operator=(__intrusive_queue&& __other) noexcept -> __intrusive_queue&
{
__head_ = ::cuda::std::exchange(__other.__head_, nullptr);
__tail_ = ::cuda::std::exchange(__other.__tail_, nullptr);
return *this;
}
_CCCL_HOST_DEVICE_API ~__intrusive_queue()
{
_CCCL_ASSERT(empty(), "");
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API static auto make_reversed(_Item* __list) noexcept -> __intrusive_queue
{
_Item* __new_head = nullptr;
_Item* __new_tail = __list;
while (__list != nullptr)
{
_Item* __next = __list->*_Next;
__list->*_Next = __new_head;
__new_head = __list;
__list = __next;
}
__intrusive_queue __result;
__result.__head_ = __new_head;
__result.__tail_ = __new_tail;
return __result;
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API static auto make(_Item* __list) noexcept -> __intrusive_queue
{
__intrusive_queue __result{};
__result.__head_ = __list;
__result.__tail_ = __list;
if (__list == nullptr)
{
return __result;
}
while (__result.__tail_->*_Next != nullptr)
{
__result.__tail_ = __result.__tail_->*_Next;
}
return __result;
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API auto empty() const noexcept -> bool
{
return __head_ == nullptr;
}
_CCCL_HOST_DEVICE_API void clear() noexcept
{
__head_ = nullptr;
__tail_ = nullptr;
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API auto pop_front() noexcept -> _Item*
{
_CCCL_ASSERT(!empty(), "");
_Item* __item = ::cuda::std::exchange(__head_, __head_->*_Next);
// This should test if __head_ == nullptr, but due to a bug in
// nvc++'s optimization, `__head_` isn't assigned until later.
// Filed as NVBug#3952534.
if (__item->*_Next == nullptr)
{
__tail_ = nullptr;
}
return __item;
}
_CCCL_HOST_DEVICE_API void push_front(_Item* __item) noexcept
{
_CCCL_ASSERT(__item != nullptr, "");
__item->*_Next = __head_;
__head_ = __item;
if (__tail_ == nullptr)
{
__tail_ = __item;
}
}
_CCCL_HOST_DEVICE_API void push_back(_Item* __item) noexcept
{
_CCCL_ASSERT(__item != nullptr, "");
__item->*_Next = nullptr;
(empty() ? __head_ : __tail_->*_Next) = __item;
__tail_ = __item;
}
_CCCL_HOST_DEVICE_API void append(__intrusive_queue __other) noexcept
{
if (!__other.empty())
{
(empty() ? __head_ : __tail_->*_Next) = ::cuda::std::exchange(__other.__head_, nullptr);
__tail_ = ::cuda::std::exchange(__other.__tail_, nullptr);
}
}
_CCCL_HOST_DEVICE_API void prepend(__intrusive_queue __other) noexcept
{
if (!__other.empty())
{
__other.__tail_->*_Next = __head_;
__head_ = __other.__head_;
if (__tail_ == nullptr)
{
__tail_ = __other.__tail_;
}
__other.clear();
}
}
struct _CCCL_TYPE_VISIBILITY_DEFAULT iterator
{
using value_type _CCCL_NODEBUG_ALIAS = _Item*;
using difference_type _CCCL_NODEBUG_ALIAS = ::cuda::std::ptrdiff_t;
using pointer _CCCL_NODEBUG_ALIAS = _Item* const*;
using reference _CCCL_NODEBUG_ALIAS = _Item* const&;
using iterator_category _CCCL_NODEBUG_ALIAS = ::cuda::std::forward_iterator_tag;
_CCCL_HIDE_FROM_ABI iterator() noexcept = default;
_CCCL_HOST_DEVICE_API explicit iterator(_Item* __pred, _Item* __item) noexcept
: __predecessor_(__pred)
, __item_(__item)
{}
[[nodiscard]]
_CCCL_HOST_DEVICE_API auto operator*() const noexcept -> _Item* const&
{
_CCCL_ASSERT(__item_ != nullptr, "");
return __item_;
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API auto operator->() const noexcept -> _Item* const*
{
_CCCL_ASSERT(__item_ != nullptr, "");
return &__item_;
}
_CCCL_HOST_DEVICE_API auto operator++() noexcept -> iterator&
{
_CCCL_ASSERT(__item_ != nullptr, "");
__predecessor_ = ::cuda::std::exchange(__item_, __item_->*_Next);
return *this;
}
_CCCL_HOST_DEVICE_API auto operator++(int) noexcept -> iterator
{
iterator __result = *this;
++*this;
return __result;
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API friend auto operator==(const iterator& __lhs, const iterator& __rhs) noexcept -> bool
{
return __lhs.__item_ == __rhs.__item_;
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API friend auto operator!=(const iterator& __lhs, const iterator& __rhs) noexcept -> bool
{
return __lhs.__item_ != __rhs.__item_;
}
_Item* __predecessor_ = nullptr;
_Item* __item_ = nullptr;
};
[[nodiscard]]
_CCCL_HOST_DEVICE_API auto begin() const noexcept -> iterator
{
return iterator(nullptr, __head_);
}
[[nodiscard]]
_CCCL_HOST_DEVICE_API auto end() const noexcept -> iterator
{
return iterator(__tail_, nullptr);
}
_CCCL_HOST_DEVICE_API void splice(iterator pos, __intrusive_queue& other, iterator first, iterator last) noexcept
{
if (first == last)
{
return;
}
_CCCL_ASSERT(first.__item_ != nullptr, "");
_CCCL_ASSERT(last.__predecessor_ != nullptr, "");
if (other.__head_ == first.__item_)
{
other.__head_ = last.__item_;
if (other.__head_ == nullptr)
{
other.__tail_ = nullptr;
}
}
else
{
_CCCL_ASSERT(first.__predecessor_ != nullptr, "");
first.__predecessor_->*_Next = last.__item_;
last.__predecessor_->*_Next = pos.__item_;
}
if (empty())
{
__head_ = first.__item_;
__tail_ = last.__predecessor_;
}
else
{
pos.__predecessor_->*_Next = first.__item_;
if (pos.__item_ == nullptr)
{
__tail_ = last.__predecessor_;
}
}
}
_CCCL_HOST_DEVICE_API auto front() const noexcept -> _Item* const&
{
return __head_;
}
_CCCL_HOST_DEVICE_API auto back() const noexcept -> _Item* const&
{
return __tail_;
}
private:
_CCCL_HOST_DEVICE_API explicit __intrusive_queue(_Item* __head, _Item* __tail) noexcept
: __head_(__head)
, __tail_(__tail)
{}
_Item* __head_ = nullptr;
_Item* __tail_ = nullptr;
};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_INTRUSIVE_QUEUE

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_JUST
#define __CUDAX_EXECUTION_JUST
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/std/__utility/pod_tuple.h>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
template <class _JustTag, class _SetTag>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __just_t
{
private:
friend struct just_t;
friend struct just_error_t;
friend struct just_stopped_t;
using __just_tag_t = _JustTag;
using __set_tag_t = _SetTag;
template <class _Rcvr, class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t
{
using operation_state_concept = operation_state_t;
using __tuple_t = ::cuda::std::__tuple<_Ts...>;
_CCCL_HOST_DEVICE_API constexpr explicit __opstate_t(_Rcvr&& __rcvr, __tuple_t __values)
: __rcvr_{static_cast<_Rcvr&&>(__rcvr)}
, __values_{static_cast<__tuple_t&&>(__values)}
{}
#if !_CCCL_COMPILER(GCC)
// Because of gcc#98995, making this operation state immovable will cause errors in
// functions that return composite operation states by value. Fortunately, the `just`
// operation state doesn't strictly need to be immovable, since its address never
// escapes. So for gcc, we let this operation state be movable.
// https://gcc.gnu.org/bugzilla/show_bug.cgi?id=98995
_CCCL_IMMOVABLE(__opstate_t);
#endif // !_CCCL_COMPILER(GCC)
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
::cuda::std::__apply(
_SetTag{}, static_cast<::cuda::std::__tuple<_Ts...>&&>(__values_), static_cast<_Rcvr&&>(__rcvr_));
}
_Rcvr __rcvr_;
__tuple_t __values_;
};
template <class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_base_t;
public:
_CCCL_EXEC_CHECK_DISABLE
template <class... _Ts>
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Ts... __ts) const;
};
struct just_t : __just_t<just_t, set_value_t>
{
template <class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
};
struct just_error_t : __just_t<just_error_t, set_error_t>
{
template <class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
};
struct just_stopped_t : __just_t<just_stopped_t, set_stopped_t>
{
template <class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
};
template <class _JustTag, class _SetTag>
template <class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __just_t<_JustTag, _SetTag>::__sndr_base_t
{
using sender_concept = sender_t;
template <class>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures() noexcept
{
return completion_signatures<__set_tag_t(_Ts...)>{};
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
connect(_Rcvr __rcvr) && noexcept(__nothrow_decay_copyable<_Rcvr, _Ts...>) -> __opstate_t<_Rcvr, _Ts...>
{
return __opstate_t<_Rcvr, _Ts...>{
static_cast<_Rcvr&&>(__rcvr), static_cast<::cuda::std::__tuple<_Ts...>&&>(__values_)};
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
connect(_Rcvr __rcvr) const& noexcept(__nothrow_decay_copyable<_Rcvr, _Ts const&...>) -> __opstate_t<_Rcvr, _Ts...>
{
return __opstate_t<_Rcvr, _Ts...>{static_cast<_Rcvr&&>(__rcvr), __values_};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto get_env() noexcept
{
return __inln_attrs_t{};
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ __just_tag_t __tag_;
::cuda::std::__tuple<_Ts...> __values_;
};
template <class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_t::__sndr_t : __just_t<just_t, set_value_t>::__sndr_base_t<_Ts...>
{};
template <class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_error_t::__sndr_t : __just_t<just_error_t, set_error_t>::__sndr_base_t<_Ts...>
{
static_assert(sizeof...(_Ts) == 1, "just_error_t must be called with exactly one error type.");
};
template <class... _Ts>
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_stopped_t::__sndr_t
: __just_t<just_stopped_t, set_stopped_t>::__sndr_base_t<_Ts...>
{
static_assert(sizeof...(_Ts) == 0, "just_stopped_t must not be called with any types.");
};
_CCCL_EXEC_CHECK_DISABLE
template <class _JustTag, class _SetTag>
template <class... _Ts>
_CCCL_HOST_DEVICE_API constexpr auto __just_t<_JustTag, _SetTag>::operator()(_Ts... __ts) const
{
using __sndr_t = typename _JustTag::template __sndr_t<_Ts...>;
return __sndr_t{{{}, {static_cast<_Ts&&>(__ts)...}}};
}
template <class... _Ts>
inline constexpr int structured_binding_size<just_t::__sndr_t<_Ts...>> = 2;
template <class... _Ts>
inline constexpr int structured_binding_size<just_error_t::__sndr_t<_Ts...>> = 2;
template <class... _Ts>
inline constexpr int structured_binding_size<just_stopped_t::__sndr_t<_Ts...>> = 2;
_CCCL_GLOBAL_CONSTANT auto just = just_t{};
_CCCL_GLOBAL_CONSTANT auto just_error = just_error_t{};
_CCCL_GLOBAL_CONSTANT auto just_stopped = just_stopped_t{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_JUST

198
cccl_upstream/cudax/include/cuda/experimental/__execution/just_from.cuh
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@@ -1,198 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_JUST_FROM
#define __CUDAX_EXECUTION_JUST_FROM
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__type_traits/conditional.h>
#include <cuda/std/__utility/pod_tuple.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/transform_completion_signatures.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
struct _AN_ERROR_COMPLETION_MUST_HAVE_EXACTLY_ONE_ERROR_ARGUMENT;
struct _A_STOPPED_COMPLETION_MUST_HAVE_NO_ARGUMENTS;
template <class _JustFromTag, class _SetTag>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __just_from_t
{
_CUDAX_SEMI_PRIVATE:
friend struct just_from_t;
friend struct just_error_from_t;
friend struct just_stopped_from_t;
using __just_from_tag_t = _JustFromTag;
using __diag_t _CCCL_NODEBUG_ALIAS =
::cuda::std::conditional_t<_SetTag{} == set_error,
_AN_ERROR_COMPLETION_MUST_HAVE_EXACTLY_ONE_ERROR_ARGUMENT,
_A_STOPPED_COMPLETION_MUST_HAVE_NO_ARGUMENTS>;
template <class... _Ts>
using __error_t _CCCL_NODEBUG_ALIAS =
_ERROR<_WHERE(_IN_ALGORITHM, _JustFromTag), _WHAT(__diag_t), _WITH_COMPLETION_SIGNATURE<_SetTag(_Ts...)>>;
struct _CCCL_TYPE_VISIBILITY_DEFAULT __probe_fn
{
template <class... _Ts>
_CCCL_HOST_DEVICE_API auto operator()(_Ts&&... __ts) const noexcept
-> ::cuda::std::_If<__detail::__signature_disposition<_SetTag(_Ts...)> != __disposition::__invalid,
completion_signatures<_SetTag(_Ts...)>,
__error_t<_Ts...>>;
};
template <class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __complete_fn
{
template <class... _Ts>
_CCCL_HOST_DEVICE_API void operator()(_Ts&&... __ts) const noexcept
{
_SetTag{}(static_cast<_Rcvr&&>(__rcvr_), static_cast<_Ts&&>(__ts)...);
}
_Rcvr& __rcvr_;
};
template <class _Rcvr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t
{
using operation_state_concept = operation_state_t;
_CCCL_EXEC_CHECK_DISABLE
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
static_cast<_Fn&&>(__fn_)(__complete_fn<_Rcvr>{__rcvr_});
}
_Rcvr __rcvr_;
_Fn __fn_;
};
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_base_t;
public:
template <class _Fn>
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Fn __fn) const noexcept;
};
struct just_from_t : __just_from_t<just_from_t, set_value_t>
{
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
};
struct just_error_from_t : __just_from_t<just_error_from_t, set_error_t>
{
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
};
struct just_stopped_from_t : __just_from_t<just_stopped_from_t, set_stopped_t>
{
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
};
template <class _JustFromTag, class _SetTag>
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __just_from_t<_JustFromTag, _SetTag>::__sndr_base_t
{
using sender_concept = sender_t;
template <class _Self, class...>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures() noexcept
{
return __call_result_t<_Fn, __probe_fn>{};
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) && //
noexcept(__nothrow_decay_copyable<_Rcvr, _Fn>) -> __opstate_t<_Rcvr, _Fn>
{
return __opstate_t<_Rcvr, _Fn>{static_cast<_Rcvr&&>(__rcvr), static_cast<_Fn&&>(__fn_)};
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) const& //
noexcept(__nothrow_decay_copyable<_Rcvr, _Fn const&>) -> __opstate_t<_Rcvr, _Fn>
{
return __opstate_t<_Rcvr, _Fn>{static_cast<_Rcvr&&>(__rcvr), __fn_};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept
{
return __inln_attrs_t{};
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ __just_from_tag_t __tag_;
_Fn __fn_;
};
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_from_t::__sndr_t : __just_from_t<just_t, set_value_t>::__sndr_base_t<_Fn>
{};
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_error_from_t::__sndr_t
: __just_from_t<just_error_t, set_error_t>::__sndr_base_t<_Fn>
{};
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT just_stopped_from_t::__sndr_t
: __just_from_t<just_stopped_t, set_stopped_t>::__sndr_base_t<_Fn>
{};
template <class _JustFromTag, class _SetTag>
template <class _Fn>
_CCCL_HOST_DEVICE_API constexpr auto __just_from_t<_JustFromTag, _SetTag>::operator()(_Fn __fn) const noexcept
{
using __sndr_t = typename _JustFromTag::template __sndr_t<_Fn>;
using __completions _CCCL_NODEBUG_ALIAS = __call_result_t<_Fn, __probe_fn>;
static_assert(__valid_completion_signatures<__completions>,
"The function passed to just_from must return an instance of a specialization of "
"completion_signatures<>.");
return __sndr_t{{{}, static_cast<_Fn&&>(__fn)}};
}
template <class _Fn>
inline constexpr int structured_binding_size<just_from_t::__sndr_t<_Fn>> = 2;
template <class _Fn>
inline constexpr int structured_binding_size<just_error_from_t::__sndr_t<_Fn>> = 2;
template <class _Fn>
inline constexpr int structured_binding_size<just_stopped_from_t::__sndr_t<_Fn>> = 2;
_CCCL_GLOBAL_CONSTANT auto just_from = just_from_t{};
_CCCL_GLOBAL_CONSTANT auto just_error_from = just_error_from_t{};
_CCCL_GLOBAL_CONSTANT auto just_stopped_from = just_stopped_from_t{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_JUST_FROM

135
cccl_upstream/cudax/include/cuda/experimental/__execution/lazy.cuh
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@@ -1,135 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_LAZY
#define __CUDAX_EXECUTION_LAZY
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cstddef/byte.h>
#include <cuda/std/__memory/addressof.h>
#include <cuda/std/__memory/construct_at.h>
#include <cuda/std/__new/device_new.h>
#include <cuda/std/__new/launder.h>
#include <cuda/std/__type_traits/copy_cvref.h>
#include <cuda/std/__utility/integer_sequence.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/meta.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
#include <cuda/experimental/__utility/manual_lifetime.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
/// @brief A lazy type that can be used to delay the construction of a type.
template <class _Ty>
using __lazy = ::cuda::experimental::__manual_lifetime<_Ty>;
namespace __detail
{
template <size_t _Idx, size_t _Size, size_t _Align>
struct __lazy_box_
{
static_assert(_Size != 0);
alignas(_Align)::cuda::std::byte __data_[_Size];
};
template <size_t _Idx, class _Ty>
using __lazy_box _CCCL_NODEBUG_ALIAS = __lazy_box_<_Idx, sizeof(_Ty), alignof(_Ty)>;
} // namespace __detail
template <class _Idx, class... _Ts>
struct __lazy_tupl;
template <>
struct __lazy_tupl<::cuda::std::index_sequence<>>
{
template <class _Fn, class _Self, class... _Us>
_CCCL_HOST_DEVICE_API static auto __apply(_Fn&& __fn, _Self&&, _Us&&... __us) //
noexcept(__nothrow_callable<_Fn, _Us...>) -> __call_result_t<_Fn, _Us...>
{
return static_cast<_Fn&&>(__fn)(static_cast<_Us&&>(__us)...);
}
};
template <size_t... _Idx, class... _Ts>
struct __lazy_tupl<::cuda::std::index_sequence<_Idx...>, _Ts...> : __detail::__lazy_box<_Idx, _Ts>...
{
template <size_t _Ny>
using __at _CCCL_NODEBUG_ALIAS = ::cuda::std::__type_index_c<_Ny, _Ts...>;
_CCCL_HOST_DEVICE_API __lazy_tupl() noexcept {}
_CCCL_HOST_DEVICE_API ~__lazy_tupl()
{
((__engaged_[_Idx] ? ::cuda::std::destroy_at(__get<_Idx, _Ts>()) : void(0)), ...);
}
template <size_t _Ny, class _Ty>
_CCCL_HOST_DEVICE_API _Ty* __get() noexcept
{
return reinterpret_cast<_Ty*>(this->__detail::__lazy_box<_Ny, _Ty>::__data_);
}
template <size_t _Ny, class... _Us>
_CCCL_HOST_DEVICE_API __at<_Ny>& __emplace(_Us&&... __us) //
noexcept(__nothrow_constructible<__at<_Ny>, _Us...>)
{
using _Ty _CCCL_NODEBUG_ALIAS = __at<_Ny>;
_Ty* __value_ = ::new (static_cast<void*>(__get<_Ny, _Ty>())) _Ty{static_cast<_Us&&>(__us)...};
__engaged_[_Ny] = true;
return *::cuda::std::launder(__value_);
}
template <class _Fn, class _Self, class... _Us>
_CCCL_HOST_DEVICE_API static auto __apply(_Fn&& __fn, _Self&& __self, _Us&&... __us) //
noexcept(__nothrow_callable<_Fn, _Us..., ::cuda::std::__copy_cvref_t<_Self, _Ts>...>)
-> __call_result_t<_Fn, _Us..., ::cuda::std::__copy_cvref_t<_Self, _Ts>...>
{
return static_cast<_Fn&&>(
__fn)(static_cast<_Us&&>(__us)...,
static_cast<::cuda::std::__copy_cvref_t<_Self, _Ts>&&>(*__self.template __get<_Idx, _Ts>())...);
}
bool __engaged_[sizeof...(_Ts)] = {};
};
#if _CCCL_COMPILER(MSVC)
template <class... _Ts>
struct __mk_lazy_tuple_
{
using __indices_t _CCCL_NODEBUG_ALIAS = ::cuda::std::make_index_sequence<sizeof...(_Ts)>;
using type _CCCL_NODEBUG_ALIAS = __lazy_tupl<__indices_t, _Ts...>;
};
template <class... _Ts>
using __lazy_tuple _CCCL_NODEBUG_ALIAS = typename __mk_lazy_tuple_<_Ts...>::type;
#else // ^^^^ _CCCL_COMPILER(MSVC) ^^^ / vvv !_CCCL_COMPILER(MSVC) vvv
template <class... _Ts>
using __lazy_tuple _CCCL_NODEBUG_ALIAS = __lazy_tupl<::cuda::std::make_index_sequence<sizeof...(_Ts)>, _Ts...>;
#endif // !_CCCL_COMPILER(MSVC)
template <class... _Ts>
using __decayed_lazy_tuple _CCCL_NODEBUG_ALIAS = __lazy_tuple<decay_t<_Ts>...>;
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_LAZY

655
cccl_upstream/cudax/include/cuda/experimental/__execution/let_value.cuh
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@@ -1,655 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_LET_VALUE
#define __CUDAX_EXECUTION_LET_VALUE
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__type_traits/common_type.h>
#include <cuda/std/__type_traits/decay.h>
#include <cuda/std/__type_traits/fold.h>
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/std/__utility/auto_cast.h>
#include <cuda/std/__utility/pod_tuple.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/concepts.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/rcvr_ref.cuh>
#include <cuda/experimental/__execution/rcvr_with_env.cuh>
#include <cuda/experimental/__execution/transform_completion_signatures.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/variant.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
struct _CCCL_TYPE_VISIBILITY_DEFAULT __let_t
{
template <class _LetTag>
static ::cuda::std::__undefined<_LetTag> __set_tag;
template <class _LetTag>
using __set_tag_for_t = decltype(_LIBCUDACXX_AUTO_CAST(__set_tag<_LetTag>));
//! @brief Computes the type of a variant of tuples to hold the results of the
//! predecessor sender.
template <class _SetTag, class _Completions, class _Env>
using __sndr1_results_t _CCCL_NODEBUG_ALIAS =
__gather_completion_signatures<_Completions, _SetTag, ::cuda::std::__decayed_tuple, __variant>;
// This environment is part of the receiver used to connect the secondary sender.
template <class _SetTag, class _Attrs, class... _Env>
_CCCL_HOST_DEVICE_API static constexpr auto __mk_env2(const _Attrs& __attrs, const _Env&... __env) noexcept
{
if constexpr (__callable<get_completion_scheduler_t<_SetTag>, const _Attrs&, __fwd_env_t<const _Env&>...>)
{
return __mk_sch_env(get_completion_scheduler<_SetTag>(__attrs, __fwd_env(__env)...), __fwd_env(__env)...);
}
else if constexpr (__callable<get_completion_domain_t<_SetTag>, const _Attrs&, __fwd_env_t<const _Env&>...>)
{
using __domain_t = __call_result_t<get_completion_domain_t<_SetTag>, const _Attrs&, __fwd_env_t<const _Env&>...>;
return prop{get_domain, __domain_t{}};
}
else
{
return env{};
}
}
template <class _SetTag, class _Attrs, class... _Env>
using __env2_t _CCCL_NODEBUG_ALIAS =
decltype(__let_t::__mk_env2<_SetTag>(::cuda::std::declval<_Attrs>(), ::cuda::std::declval<_Env>()...));
template <class _SetTag, class _Attrs, class _Env>
using __join_env2_t _CCCL_NODEBUG_ALIAS = __join_env_t<__env2_t<_SetTag, _Attrs, _Env>, _Env>;
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr2_fn
{
template <class... _As>
using __call _CCCL_NODEBUG_ALIAS = __call_result_t<_Fn, decay_t<_As>&...>;
};
template <class _Rcvr, class _Env2>
struct __sndr2_rcvr_t : __rcvr_ref_t<__rcvr_with_env_t<_Rcvr, _Env2>>
{
using __base_t = __rcvr_ref_t<__rcvr_with_env_t<_Rcvr, _Env2>>;
_CCCL_HOST_DEVICE_API explicit constexpr __sndr2_rcvr_t(__rcvr_with_env_t<_Rcvr, _Env2>& __rcvr) noexcept
: __base_t(__ref_rcvr(__rcvr))
{}
};
template <class _Fn, class _Rcvr, class _Env2>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __state_base_t
{
//! @brief For a given set of result datums, compute the type of the secondary
//! sender's operation state.
template <class... _As>
using __sndr2_opstate_fn _CCCL_NODEBUG_ALIAS =
connect_result_t<::cuda::std::__type_call<__sndr2_fn<_Fn>, _As...>, __sndr2_rcvr_t<_Rcvr, _Env2>>;
__rcvr_with_env_t<_Rcvr, _Env2> __rcvr_;
_Fn __fn_;
};
template <class _SetTag, class _Fn, class _Rcvr, class _Env2, class _Completions>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __state_t : __state_base_t<_Fn, _Rcvr, _Env2>
{
using __sndr2_opstate_t _CCCL_NODEBUG_ALIAS =
__gather_completion_signatures<_Completions,
_SetTag,
__state_t::__state_base_t::template __sndr2_opstate_fn,
__variant>;
__sndr1_results_t<_SetTag, _Completions, __fwd_env_t<env_of_t<_Rcvr>>> __result_{};
__sndr2_opstate_t __opstate2_{};
};
//! @brief This is the receiver that gets connected to the predecessor sender. It caches
//! the results of the predecessor and then calls the user-provided function with them
//! to produce the secondary sender, which it then connects and starts.
template <class _SetTag, class _Fn, class _Rcvr, class _Env2, class _Completions>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr1_rcvr_t
{
using receiver_concept = receiver_t;
template <class... _As>
_CCCL_HOST_DEVICE_API void __complete(_SetTag, _As&&... __as) noexcept
{
_CCCL_TRY
{
// Store the results so the lvalue refs we pass to the function will be valid for
// the duration of the async op.
auto& __tupl =
__state_->__result_.template __emplace<::cuda::std::__decayed_tuple<_As...>>(static_cast<_As&&>(__as)...);
// Call the function with the results and connect the resulting sender, storing
// the operation state in __state_->__opstate2_.
auto& __next_op = __state_->__opstate2_.__emplace_from(
execution::connect,
::cuda::std::__apply(static_cast<_Fn&&>(__state_->__fn_), __tupl),
__sndr2_rcvr_t(__state_->__rcvr_));
execution::start(__next_op);
}
_CCCL_CATCH_ALL
{
execution::set_error(static_cast<_Rcvr&&>(__state_->__rcvr_.__base()), execution::current_exception());
}
}
template <class _Tag, class... _As>
_CCCL_HOST_DEVICE_API void __complete(_Tag, _As&&... __as) noexcept
{
// Forward the completion to the receiver unchanged.
_Tag{}(static_cast<_Rcvr&&>(__state_->__rcvr_.__base()), static_cast<_As&&>(__as)...);
}
template <class... _As>
_CCCL_HOST_DEVICE_API void set_value(_As&&... __as) noexcept
{
__complete(execution::set_value, static_cast<_As&&>(__as)...);
}
template <class _Error>
_CCCL_HOST_DEVICE_API void set_error(_Error&& __error) noexcept
{
__complete(execution::set_error, static_cast<_Error&&>(__error));
}
_CCCL_HOST_DEVICE_API void set_stopped() noexcept
{
__complete(execution::set_stopped);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __fwd_env_t<env_of_t<_Rcvr>>
{
return __fwd_env(execution::get_env(__state_->__rcvr_.__base()));
}
__state_t<_SetTag, _Fn, _Rcvr, _Env2, _Completions>* __state_;
};
//! @brief The `let_(value|error|stopped)` operation state.
//! @tparam _CvSndr The cvref-qualified predecessor sender type.
//! @tparam _Fn The user-provided function to be called with the result datums of the
//! predecessor sender.
//! @tparam _Rcvr The receiver connected to the `let_(value|error|stopped)`
//! sender.
template <class _SetTag, class _CvSndr, class _Fn, class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t
{
using operation_state_concept = operation_state_t;
using __completions_t = completion_signatures_of_t<_CvSndr, __fwd_env_t<env_of_t<_Rcvr>>>;
using __env2_t = __let_t::__env2_t<_SetTag, env_of_t<_CvSndr>, env_of_t<_Rcvr>>;
using __sndr1_rcvr_t = __sndr1_rcvr_t<_SetTag, _Fn, _Rcvr, __env2_t, __completions_t>;
_CCCL_HOST_DEVICE_API constexpr explicit __opstate_t(
_CvSndr& __sndr,
_Fn& __fn,
_Rcvr& __rcvr,
__env2_t&& __env2) noexcept(__nothrow_decay_copyable<_Fn, _Rcvr, __env2_t>
&& __nothrow_connectable<_CvSndr, __sndr1_rcvr_t>)
: __state_{{{static_cast<_Rcvr&&>(__rcvr), static_cast<__env2_t&&>(__env2)}, static_cast<_Fn&&>(__fn)}}
, __opstate1_(execution::connect(static_cast<_CvSndr&&>(__sndr), __sndr1_rcvr_t{&__state_}))
{}
_CCCL_HOST_DEVICE_API constexpr explicit __opstate_t(_CvSndr&& __sndr, _Fn __fn, _Rcvr __rcvr) noexcept(
__nothrow_decay_copyable<_Fn, _Rcvr, __env2_t> && __nothrow_connectable<_CvSndr, __sndr1_rcvr_t>)
: __opstate_t(
__sndr, __fn, __rcvr, __let_t::__mk_env2<_SetTag>(execution::get_env(__sndr), execution::get_env(__rcvr)))
{}
_CCCL_IMMOVABLE(__opstate_t);
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
execution::start(__opstate1_);
}
__state_t<_SetTag, _Fn, _Rcvr, __env2_t, __completions_t> __state_;
connect_result_t<_CvSndr, __sndr1_rcvr_t> __opstate1_;
};
template <class _SetTag, class _Fn, class _Attrs, class... _Env>
struct __domain_transform_fn
{
template <class... _As>
using __call _CCCL_NODEBUG_ALIAS =
__compl_domain_t<_SetTag,
::cuda::std::__type_call<__sndr2_fn<_Fn>, _As...>,
__join_env2_t<_SetTag, _Attrs, _Env>...>;
};
//! @tparam _SetTag The completion signal of the predecessor sender that triggers the
//! function call. For let_value, this is `set_value`.
//! @tparam _SetTag2 The completion signal of the let_ sender itself that is being
//! queried. For example, you may be querying a let_value sender for its set_error
//! completion domain.
template <class _SetTag, class _SetTag2, class _Sndr, class _Fn, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto __get_completion_domain() noexcept
{
if constexpr (sender_in<_Sndr, _Env...>)
{
using __domain_transform_fn = __let_t::__domain_transform_fn<_SetTag2, _Fn, env_of_t<_Sndr>, _Env...>;
return __gather_completion_signatures<completion_signatures_of_t<_Sndr, _Env...>,
_SetTag,
__domain_transform_fn::template __call,
__common_domain_t>();
}
else
{
return __not_a_domain{};
}
}
template <class _SetTag, class _SetTag2, class _Sndr, class _Fn, class... _Env>
using __let_completion_domain_t _CCCL_NODEBUG_ALIAS =
__unless_one_of_t<decltype(__let_t::__get_completion_domain<_SetTag, _SetTag2, _Sndr, _Fn, _Env...>()),
__not_a_domain>;
template <class _LetTag, class _Fn, class... _JoinEnv2>
struct __transform_args_fn
{
template <class... _Ts>
[[nodiscard]] _CCCL_HOST_DEVICE_API _CCCL_CONSTEVAL auto operator()() const
{
if constexpr (!__decay_copyable<_Ts...>)
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, _LetTag),
_WHAT(_ARGUMENTS_ARE_NOT_DECAY_COPYABLE),
_WITH_ARGUMENTS(_Ts...)>();
}
else if constexpr (!::cuda::std::__type_callable<__sndr2_fn<_Fn>, _Ts...>::value)
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, _LetTag),
_WHAT(_FUNCTION_IS_NOT_CALLABLE),
_WITH_FUNCTION(_Fn),
_WITH_ARGUMENTS(decay_t<_Ts> & ...)>();
}
else
{
using __sndr2_t = ::cuda::std::__type_call<__sndr2_fn<_Fn>, _Ts...>;
if constexpr (!sender<__sndr2_t>)
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, _LetTag),
_WHAT(_FUNCTION_MUST_RETURN_A_SENDER),
_WITH_FUNCTION(_Fn),
_WITH_ARGUMENTS(decay_t<_Ts> & ...),
_WITH_RETURN_TYPE(__sndr2_t)>();
}
else
{
return get_completion_signatures<__sndr2_t, _JoinEnv2...>();
}
}
}
};
template <class _Fn, class... _Env>
struct __completion_behavior_transform_fn
{
template <class _Tag, class... _Ts>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Tag (*)(_Ts...)) const noexcept
{
if constexpr (::cuda::std::__type_callable<__sndr2_fn<_Fn>, _Ts...>::value)
{
using __sndr2_t = ::cuda::std::__type_call<__sndr2_fn<_Fn>, _Ts...>;
return execution::get_completion_behavior<__sndr2_t, _Env...>();
}
else
{
return completion_behavior::unknown;
}
}
};
// A metafunction to check whether the predecessor's completion results are nothrow
// decay-copyable and whether connecting the secondary sender is nothrow.
template <class _SetTag, class _Sndr, class _Fn, class _Env>
struct __has_nothrow_completions_fn
{
using __env2_t = __let_t::__join_env2_t<_SetTag, env_of_t<_Sndr>, _Env>;
template <class... _Ts>
using __call _CCCL_NODEBUG_ALIAS = ::cuda::std::bool_constant<
(noexcept(_LIBCUDACXX_AUTO_CAST(declval<_Ts>())) && ...)
&& noexcept(execution::connect(declval<_Fn>()(declval<decay_t<_Ts>&>()...), __receiver_archetype<__env2_t>()))>;
};
template <class _SetTag, class _Sndr, class _Fn, class _Env>
using __has_nothrow_completions _CCCL_NODEBUG_ALIAS =
__gather_completion_signatures<completion_signatures_of_t<_Sndr, _Env>,
_SetTag,
__has_nothrow_completions_fn<_SetTag, _Sndr, _Fn, _Env>::template __call,
::cuda::std::__type_strict_and::__call>;
template <class _LetTag, class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __attrs_t;
template <class _LetTag, class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
template <class _LetTag, class _Fn>
struct _CCCL_TYPE_VISIBILITY_HIDDEN __closure_t // hidden visibility because member __fn_ is hidden if it is an
// extended (host/device) lambda
{
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API auto operator()(_Sndr __sndr) &&
{
return _LetTag()(static_cast<_Sndr&&>(__sndr), static_cast<_Fn&&>(__fn_));
}
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API auto operator()(_Sndr __sndr) const&
{
return _LetTag()(static_cast<_Sndr&&>(__sndr), __fn_);
}
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API friend auto operator|(_Sndr __sndr, __closure_t __self)
{
return _LetTag()(static_cast<_Sndr&&>(__sndr), static_cast<_Fn&&>(__self.__fn_));
}
_Fn __fn_;
};
};
template <class _LetTag>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __let_base_t : __let_t
{
//! @brief The `let_(value|error|stopped)` sender.
//! @tparam _Sndr The predecessor sender.
//! @tparam _Fn The function to be called when the predecessor sender
//! completes.
template <class _Sndr, class _Fn>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr __sndr, _Fn __fn) const;
template <class _Fn>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Fn __fn) const noexcept;
};
struct let_value_t : __let_base_t<let_value_t>
{
template <class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_t;
};
struct let_error_t : __let_base_t<let_error_t>
{
template <class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_t;
};
struct let_stopped_t : __let_base_t<let_stopped_t>
{
template <class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_t;
};
template <class _LetTag, class _Sndr, class _Fn>
struct __let_t::__attrs_t
{
using __set_tag_t = __set_tag_for_t<_LetTag>;
_CCCL_HOST_DEVICE_API constexpr explicit __attrs_t(const __sndr_t<_LetTag, _Sndr, _Fn>& __self) noexcept
: __self_(__self)
{}
template <class _Tag>
_CCCL_HOST_DEVICE_API constexpr auto query(get_completion_scheduler_t<_Tag>) const = delete;
template <class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_domain_t<__set_tag_t>, const _Env&...) const noexcept
-> __let_completion_domain_t<__set_tag_t, __set_tag_t, _Sndr, _Fn, _Env...>
{
return {};
}
_CCCL_TEMPLATE(class _Tag, class... _Env)
_CCCL_REQUIRES(::cuda::std::__is_included_in_v<_Tag, set_error_t, set_stopped_t> _CCCL_AND(
__has_nothrow_completions<__set_tag_t, _Sndr, _Fn, _Env>::value&&...))
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_domain_t<_Tag>, const _Env&...) const noexcept
-> __common_domain_t<__compl_domain_t<_Tag, _Sndr, __fwd_env_t<_Env>...>,
__let_completion_domain_t<__set_tag_t, _Tag, _Sndr, _Fn, _Env...>>
{
return {};
}
_CCCL_TEMPLATE(class _Env)
_CCCL_REQUIRES((!__has_nothrow_completions<__set_tag_t, _Sndr, _Fn, _Env>::value))
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_domain_t<set_error_t>, const _Env&) const noexcept
-> __common_domain_t<__compl_domain_t<__set_tag_t, _Sndr, __fwd_env_t<_Env>>,
__compl_domain_t<set_error_t, _Sndr, __fwd_env_t<_Env>>,
__let_completion_domain_t<__set_tag_t, set_error_t, _Sndr, _Fn, _Env>>
{
return {};
}
template <class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_behavior_t, const _Env&...) const noexcept
{
#if _CCCL_CUDACC_BELOW(12, 9)
if constexpr (sender_in<_Sndr> || (sender_in<_Sndr, __fwd_env_t<_Env>> && ...))
#else
if constexpr (sender_in<_Sndr, __fwd_env_t<_Env>...>)
#endif
{
// The completion behavior of let_value(sndr, fn) is the weakest completion
// behavior of sndr and all the senders that fn can potentially produce. (MSVC
// needs the constexpr computation broken up, hence the local variables.)
constexpr auto __completions =
execution::get_completion_signatures<_Sndr, __fwd_env_t<_Env>...>().select(__set_tag_t{});
constexpr auto __transform_fn =
__completion_behavior_transform_fn<_Fn, __join_env2_t<__set_tag_t, env_of_t<_Sndr>, _Env>...>{};
constexpr auto __behavior = __completions.transform_reduce(__transform_fn, execution::min);
return (execution::min) (execution::get_completion_behavior<_Sndr, __fwd_env_t<_Env>...>(), __behavior);
}
else
{
return completion_behavior::unknown;
}
}
private:
const __sndr_t<_LetTag, _Sndr, _Fn>& __self_;
};
template <class _LetTag, class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __let_t::__sndr_t
{
using sender_concept = sender_t;
using __set_tag_t = __set_tag_for_t<_LetTag>;
template <class _Self, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto __get_completion_signatures()
{
_CUDAX_LET_COMPLETIONS(auto(__child_completions) = get_child_completion_signatures<_Self, _Sndr, _Env...>())
{
constexpr auto __transform_fn =
__transform_args_fn<_LetTag, _Fn, __join_env2_t<__set_tag_t, env_of_t<_Sndr>, _Env>...>{};
if constexpr (__set_tag_t{} == execution::set_value)
{
return transform_completion_signatures(__child_completions, __transform_fn);
}
else if constexpr (__set_tag_t{} == execution::set_error)
{
return transform_completion_signatures(__child_completions, {}, __transform_fn);
}
else
{
return transform_completion_signatures(__child_completions, {}, {}, __transform_fn);
}
}
_CCCL_UNREACHABLE();
}
template <class _Self, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
_CUDAX_LET_COMPLETIONS(auto(__completions) = __get_completion_signatures<_Self, _Env...>())
{
// If we do not have an environment yet, assume that connecting the secondary sender
// might throw.
constexpr bool __nothrow_completions = (__has_nothrow_completions<__set_tag_t, _Sndr, _Fn, _Env>::value || ...);
constexpr auto __eptr_completion = execution::__eptr_completion_if<!__nothrow_completions>();
return __completions + __eptr_completion;
}
_CCCL_UNREACHABLE();
}
template <class _Rcvr>
_CCCL_HOST_DEVICE_API auto connect(_Rcvr __rcvr) && noexcept(
__nothrow_constructible<__opstate_t<__set_tag_t, _Sndr, _Fn, _Rcvr>, _Sndr, _Fn, _Rcvr>)
-> __opstate_t<__set_tag_t, _Sndr, _Fn, _Rcvr>
{
return __opstate_t<__set_tag_t, _Sndr, _Fn, _Rcvr>(
static_cast<_Sndr&&>(__sndr_), static_cast<_Fn&&>(__fn_), static_cast<_Rcvr&&>(__rcvr));
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) const& noexcept(
__nothrow_constructible<__opstate_t<__set_tag_t, const _Sndr&, _Fn, _Rcvr>, const _Sndr&, const _Fn&, _Rcvr>)
-> __opstate_t<__set_tag_t, const _Sndr&, _Fn, _Rcvr>
{
return __opstate_t<__set_tag_t, const _Sndr&, _Fn, _Rcvr>(__sndr_, __fn_, static_cast<_Rcvr&&>(__rcvr));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept
{
return __attrs_t<_LetTag, _Sndr, _Fn>(*this);
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ _LetTag __tag_;
_Fn __fn_;
_Sndr __sndr_;
};
template <class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_value_t::__sndr_t : __let_t::__sndr_t<let_value_t, _Sndr, _Fn>
{};
template <class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_error_t::__sndr_t : __let_t::__sndr_t<let_error_t, _Sndr, _Fn>
{};
template <class _Sndr, class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_stopped_t::__sndr_t : __let_t::__sndr_t<let_stopped_t, _Sndr, _Fn>
{};
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_value_t::__closure_t : __let_t::__closure_t<let_value_t, _Fn>
{};
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_error_t::__closure_t : __let_t::__closure_t<let_error_t, _Fn>
{};
template <class _Fn>
struct _CCCL_TYPE_VISIBILITY_DEFAULT let_stopped_t::__closure_t : __let_t::__closure_t<let_stopped_t, _Fn>
{};
template <class... _Sndr>
using __all_non_dependent_t = ::cuda::std::__fold_and<(!dependent_sender<_Sndr>) ...>;
template <class _LetTag>
template <class _Sndr, class _Fn>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto __let_base_t<_LetTag>::operator()(_Sndr __sndr, _Fn __fn) const
{
using __sndr_t = typename _LetTag::template __sndr_t<_Sndr, _Fn>;
// If the incoming sender is non-dependent, we can check the completion signatures of
// the composed sender immediately.
if constexpr (!dependent_sender<_Sndr>)
{
// Although the input sender is not dependent, the sender(s) returned from the
// function might be. Only do eager type-checking if all the possible senders returned
// by the function are non-dependent. If any of them is dependent, we will defer the
// type-checking to the point where the sender is connected.
using __completions_t = completion_signatures_of_t<_Sndr>;
constexpr bool __all_non_dependent =
__gather_completion_signatures<__completions_t,
__set_tag_for_t<_LetTag>,
__sndr2_fn<_Fn>::template __call,
__all_non_dependent_t>::value;
if constexpr (__all_non_dependent)
{
execution::__assert_valid_completion_signatures(get_completion_signatures<__sndr_t>());
}
}
return __sndr_t{{{}, static_cast<_Fn&&>(__fn), static_cast<_Sndr&&>(__sndr)}};
}
template <class _LetTag>
template <class _Fn>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto __let_base_t<_LetTag>::operator()(_Fn __fn) const noexcept
{
using __closure_t = typename _LetTag::template __closure_t<_Fn>;
return __closure_t{{static_cast<_Fn&&>(__fn)}};
}
template <>
constexpr set_value_t __let_t::__set_tag<let_value_t>{};
template <>
constexpr set_error_t __let_t::__set_tag<let_error_t>{};
template <>
constexpr set_stopped_t __let_t::__set_tag<let_stopped_t>{};
template <class _Sndr, class _Fn>
inline constexpr int structured_binding_size<let_value_t::__sndr_t<_Sndr, _Fn>> = 3;
template <class _Sndr, class _Fn>
inline constexpr int structured_binding_size<let_error_t::__sndr_t<_Sndr, _Fn>> = 3;
template <class _Sndr, class _Fn>
inline constexpr int structured_binding_size<let_stopped_t::__sndr_t<_Sndr, _Fn>> = 3;
_CCCL_GLOBAL_CONSTANT auto let_value = let_value_t{};
_CCCL_GLOBAL_CONSTANT auto let_error = let_error_t{};
_CCCL_GLOBAL_CONSTANT auto let_stopped = let_stopped_t{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_LET_VALUE

170
cccl_upstream/cudax/include/cuda/experimental/__execution/meta.cuh
View File

@@ -1,170 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_META
#define __CUDAX_EXECUTION_META
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__type_traits/conditional.h>
#include <cuda/std/__type_traits/integral_constant.h>
#include <cuda/std/__type_traits/is_valid_expansion.h>
#include <cuda/std/__type_traits/type_list.h>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/diagnostics.cuh>
#if __cpp_lib_three_way_comparison >= 201907L
# include <compare> // IWYU pragma: keep
#endif // __cpp_lib_three_way_comparison >= 201907L
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
template <bool>
struct __type_try__;
template <>
struct __type_try__<false>
{
template <template <class...> class _Fn, class... _Ts>
using __call_q _CCCL_NODEBUG_ALIAS = _Fn<_Ts...>;
template <class _Fn, class... _Ts>
using __call _CCCL_NODEBUG_ALIAS = typename _Fn::template __call<_Ts...>;
};
template <>
struct __type_try__<true>
{
template <template <class...> class _Fn, class... _Ts>
using __call_q _CCCL_NODEBUG_ALIAS = __type_find_error<_Ts...>;
template <class _Fn, class... _Ts>
using __call _CCCL_NODEBUG_ALIAS = __type_find_error<_Fn, _Ts...>;
};
template <class _Fn, class... _Ts>
using __type_try_call _CCCL_NODEBUG_ALIAS =
typename __type_try__<__type_contains_error<_Fn, _Ts...>>::template __call<_Fn, _Ts...>;
template <template <class...> class _Fn, class... _Ts>
using __type_try_call_quote _CCCL_NODEBUG_ALIAS =
typename __type_try__<__type_contains_error<_Ts...>>::template __call_q<_Fn, _Ts...>;
// wraps a meta-callable such that if any of the arguments are errors, the
// result is an error.
template <class _Fn>
struct __type_try
{
template <class... _Ts>
using __call _CCCL_NODEBUG_ALIAS = __type_try_call<_Fn, _Ts...>;
};
template <template <class...> class _Fn, class... _Default>
struct __type_try_quote;
// equivalent to __type_try<__type_quote<_Fn>>
template <template <class...> class _Fn>
struct __type_try_quote<_Fn>
{
template <class... _Ts>
using __call _CCCL_NODEBUG_ALIAS =
typename __type_try__<__type_contains_error<_Ts...>>::template __call_q<_Fn, _Ts...>;
};
// equivalent to __type_try<__type_quote<_Fn, _Default>>
template <template <class...> class _Fn, class _Default>
struct __type_try_quote<_Fn, _Default>
{
template <class... _Ts>
using __call _CCCL_NODEBUG_ALIAS =
typename ::cuda::std::_If<__is_instantiable_with<_Fn, _Ts...>, //
__type_try_quote<_Fn>,
::cuda::std::__type_always<_Default>>::template __call<_Ts...>;
};
template <class _Return>
struct __type_function
{
template <class... _Args>
using __call _CCCL_NODEBUG_ALIAS = _Return(_Args...);
};
template <class _Return>
struct __type_function1
{
template <class _Arg>
using __call _CCCL_NODEBUG_ALIAS = _Return(_Arg);
};
template <class _First, class _Second>
using __type_first = _First;
template <class _First, class _Second>
using __type_second = _Second;
template <template <class...> class _Second, template <class...> class _First>
struct __type_compose_quote
{
template <class... _Ts>
using __call _CCCL_NODEBUG_ALIAS = _Second<_First<_Ts...>>;
};
struct __type_count
{
template <class... _Ts>
using __call _CCCL_NODEBUG_ALIAS = ::cuda::std::integral_constant<size_t, sizeof...(_Ts)>;
};
template <class _Continuation>
struct __type_concat_into
{
template <class... _Args>
using __call _CCCL_NODEBUG_ALIAS =
::cuda::std::__type_call1<::cuda::std::__type_concat<::cuda::std::__as_type_list<_Args>...>, _Continuation>;
};
template <template <class...> class _Continuation>
struct __type_concat_into_quote : __type_concat_into<::cuda::std::__type_quote<_Continuation>>
{};
template <class _Ty>
struct __type_self_or
{
template <class _Uy = _Ty>
using __call _CCCL_NODEBUG_ALIAS = _Uy;
};
template <template <class...> class _Fn, class _Default, class... _Ts>
using __type_call_or_quote =
typename ::cuda::std::_If<__is_instantiable_with<_Fn, _Ts...>,
::cuda::std::__type_quote<_Fn>,
::cuda::std::__type_always<_Default>>::template __call<_Ts...>;
template <class _Fn, class _Default, class... _Ts>
using __type_call_or =
typename ::cuda::std::_If<__is_instantiable_with<_Fn::template __call, _Ts...>,
_Fn,
::cuda::std::__type_always<_Default>>::template __call<_Ts...>;
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_META

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_ON
#define __CUDAX_EXECUTION_ON
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/concepts.cuh>
#include <cuda/experimental/__execution/continues_on.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/domain.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/meta.cuh>
#include <cuda/experimental/__execution/queries.cuh>
#include <cuda/experimental/__execution/sndr_ref.cuh>
#include <cuda/experimental/__execution/starts_on.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/write_env.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
//! @brief Sender adaptor that transfers execution to a specified scheduler and back.
//!
//! The `on` algorithm provides execution context control by moving computation to
//! different execution resources. It has two primary forms:
//!
//! ## Form 1: `on(scheduler, sender)`
//!
//! Starts a sender on an execution agent belonging to the specified scheduler's execution
//! resource, and upon completion, transfers execution back to the original execution
//! resource where the `on` sender was started.
//!
//! @code
//! auto sndr = on(gpu_scheduler, some_computation);
//! auto [result] = sync_wait(std::move(sndr)).value();
//! @endcode
//!
//! ## Form 2: `on(sender, scheduler, closure)` or `sender | on(scheduler, closure)`
//!
//! Upon completion of the input sender, transfers execution to the specified scheduler's
//! execution resource, executes the closure with the sender's results, and then transfers
//! execution back to where the original sender completed.
//!
//! @code
//! auto sndr = some_computation | on(gpu_scheduler, then([](auto value) { /*...*/ }));
//! auto [result] = sync_wait(std::move(sndr)).value();
//! @endcode
//!
//! ## Behavior
//!
//! - **Form 1**: Execution flow: current → target scheduler → back to current
//! - **Form 2**: Execution flow: current → (sender completes) → target scheduler → back to sender's completion context
//!
//! The algorithm remembers the original scheduler context and ensures execution returns
//! to it after the target scheduler's work is complete. If no scheduler is available in
//! the current execution context, the operation is ill-formed and results in a
//! compilation error.
//!
//! ## Error Handling
//!
//! If any scheduling operation fails, an error completion is executed on an unspecified
//! execution agent.
//!
//! @note This is CUDA's experimental implementation of the C++26 `std::execution::on` algorithm
//! as specified in [exec.on].
//!
//! @see @c starts_on and @c continues_on for related scheduling primitives
struct on_t
{
template <class _Sch, class _Sndr, class... _Closure>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
_CUDAX_SEMI_PRIVATE :
template <class _Sndr, class _NewSch, class _OldSch, class... _Closure>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __lowered_sndr_t;
struct __lower_sndr_fn
{
// This is the the lowering for the `on(sch, sndr)` case
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr, class _NewSch, class _OldSch>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(_Sndr __sndr, _NewSch __new_sch, _OldSch __old_sch) const
{
return continues_on(starts_on(static_cast<_NewSch&&>(__new_sch), static_cast<_Sndr&&>(__sndr)), __old_sch);
}
// This is the the lowering for the `sndr | on(sch, clsr)` case
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr, class _NewSch, class _OldSch, class _Closure>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(_Sndr __sndr, _NewSch __new_sch, _OldSch __old_sch, _Closure&& __closure) const
{
return continues_on(static_cast<_Closure&&>(__closure)(continues_on(static_cast<_Sndr&&>(__sndr), __new_sch)),
__old_sch);
}
};
template <class _Sch, class _Closure>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __closure_t
{
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr __sndr) &&
{
return on_t{}(static_cast<_Sndr&&>(__sndr), __sch_, static_cast<_Closure&&>(__closure_));
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr __sndr) const&
{
return on_t{}(static_cast<_Sndr&&>(__sndr), __sch_, __closure_);
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API friend constexpr auto operator|(_Sndr __sndr, __closure_t __self)
{
return on_t{}(static_cast<_Sndr&&>(__sndr), __self.__sch_, static_cast<_Closure&&>(__self.__closure_));
}
_Sch __sch_;
_Closure __closure_;
};
template <class _Sch, class _Sndr, class... _Closure>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __attrs_t
{
template <class _Env>
using __new_sndr_t =
__call_result_t<__lower_sndr_fn, __sndr_ref<const _Sndr&>, _Sch, __scheduler_of_t<_Env>, const _Closure&...>;
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Tag, class _Env)
_CCCL_REQUIRES(__queryable_with<env_of_t<__new_sndr_t<_Env>>, _Tag, _Env>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Tag, const _Env& __env) const
noexcept(__nothrow_queryable_with<env_of_t<__new_sndr_t<_Env>>, _Tag, _Env>) -> decltype(auto)
{
if constexpr (sizeof...(_Closure) == 0)
{
auto __tmp_sndr = __lower_sndr_fn()(__sndr_ref(__self_->__sndr_), __self_->__sch_, get_scheduler(__env));
return execution::get_env(__tmp_sndr).query(_Tag(), __env);
}
else
{
auto& [__sch, __closure] = __self_->__sch_closure_;
auto __tmp_sndr = __lower_sndr_fn()(__sndr_ref(__self_->__sndr_), __sch, get_scheduler(__env), __closure);
return execution::get_env(__tmp_sndr).query(_Tag(), __env);
}
}
const __sndr_t<_Sch, _Sndr, _Closure...>* __self_;
};
public:
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Sch, class _Sndr)
_CCCL_REQUIRES(__is_scheduler<_Sch> _CCCL_AND __is_sender<_Sndr>)
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Sch __sch, _Sndr __sndr) const
{
static_assert(__is_scheduler<_Sch>);
return __sndr_t<_Sch, _Sndr>{{}, __sch, static_cast<_Sndr&&>(__sndr)};
}
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Sch, class _Closure)
_CCCL_REQUIRES(__is_scheduler<_Sch> _CCCL_AND(!__is_sender<_Closure>))
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Sch __sch, _Closure __closure) const
{
static_assert(__is_scheduler<_Sch>);
return __closure_t<_Sch, _Closure>{__sch, static_cast<_Closure&&>(__closure)};
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr, class _Sch, class _Closure>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr __sndr, _Sch __sch, _Closure __closure) const
{
static_assert(__is_scheduler<_Sch>);
static_assert(__is_sender<_Sndr>);
using __sndr_t = on_t::__sndr_t<_Sch, _Sndr, _Closure>;
return __sndr_t{{}, {__sch, static_cast<_Closure&&>(__closure)}, static_cast<_Sndr&&>(__sndr)};
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr, class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto
transform_sender(set_value_t, _Sndr&& __sndr, const _Env& __env)
{
using __not_a_scheduler =
execution::__not_a_scheduler<_WHAT(_THE_ENVIRONMENT_OF_THE_RECEIVER_DOES_NOT_HAVE_A_SCHEDULER_FOR_ON_TO_RETURN_TO),
_WHERE(_IN_ALGORITHM, on_t),
_WITH_ENVIRONMENT(_Env)>;
auto&& [__ign, __data, __child] = __sndr;
if constexpr (__is_scheduler<decltype(__data)>)
{
// The on(sch, sndr) case:
auto __old_sch = __call_or(get_scheduler, __not_a_scheduler{}, __env);
using __sndr_t = __lowered_sndr_t<decltype(__child), decltype(__data), decltype(__old_sch)>;
static_assert(sender_for<__sndr_t, continues_on_t>);
return __sndr_t{::cuda::std::forward_like<_Sndr>(__child), __data, __old_sch};
}
else
{
// The on(sndr, sch, closure) case:
auto& [__new_sch, __closure] = __data;
auto __old_sch =
__call_or(get_completion_scheduler<set_value_t>, __not_a_scheduler{}, execution::get_env(__child), __env);
using __sndr_t =
__lowered_sndr_t<decltype(__child), decltype(__new_sch), decltype(__old_sch), decltype(__closure)>;
return __sndr_t{
::cuda::std::forward_like<_Sndr>(__child), __new_sch, __old_sch, ::cuda::std::forward_like<_Sndr>(__closure)};
}
}
};
template <class _Sndr, class _NewSch, class _OldSch, class... _Closure>
struct _CCCL_TYPE_VISIBILITY_DEFAULT on_t::__lowered_sndr_t
: __call_result_t<on_t::__lower_sndr_fn, _Sndr, _NewSch, _OldSch, _Closure...>
{
using __base_t = __call_result_t<on_t::__lower_sndr_fn, _Sndr, _NewSch, _OldSch, _Closure...>;
_CCCL_EXEC_CHECK_DISABLE
template <class _CvrefSndr>
_CCCL_HOST_DEVICE_API constexpr __lowered_sndr_t(
_CvrefSndr&& __sndr, _NewSch __new_sch, _OldSch __old_sch, _Closure... __closure)
: __base_t{on_t::__lower_sndr_fn{}(
static_cast<_CvrefSndr&&>(__sndr), __new_sch, __old_sch, static_cast<_Closure&&>(__closure)...)}
{}
};
// This is the sender used for `on(sch, sndr)`
template <class _Sch, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT on_t::__sndr_t<_Sch, _Sndr>
{
using sender_concept = sender_t;
_CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __attrs_t<_Sch, _Sndr>
{
return __attrs_t<_Sch, _Sndr>{this};
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ on_t __tag_;
_Sch __sch_;
_Sndr __sndr_;
};
// This is the sender used for `on(sndr, sch, closure)` and `sndr | on(sch, closure)`.
template <class _Sch, class _Sndr, class _Closure>
struct _CCCL_TYPE_VISIBILITY_DEFAULT on_t::__sndr_t<_Sch, _Sndr, _Closure>
{
using sender_concept = sender_t;
_CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __attrs_t<_Sch, _Sndr, _Closure>
{
return __attrs_t<_Sch, _Sndr, _Closure>{this};
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ on_t __tag_;
__closure_t<_Sch, _Closure> __sch_closure_;
_Sndr __sndr_;
};
_CCCL_GLOBAL_CONSTANT on_t on{};
template <class _Sch, class _Sndr>
inline constexpr int structured_binding_size<on_t::__sndr_t<_Sch, _Sndr>> = 3;
template <class _Sch, class _Sndr, class _Closure>
inline constexpr int structured_binding_size<on_t::__sndr_t<_Sch, _Sndr, _Closure>> = 3;
template <class _Sndr, class _NewSch, class _OldSch, class... _Closure>
inline constexpr int structured_binding_size<on_t::__lowered_sndr_t<_Sndr, _NewSch, _OldSch, _Closure...>> = 3;
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_ON

213
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@@ -1,213 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_PARALLEL_SCHEDULER
#define __CUDAX_EXECUTION_PARALLEL_SCHEDULER
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__type_traits/is_specialization_of.h>
#include <cuda/__utility/immovable.h>
#include <cuda/std/__memory/allocator.h>
#include <cuda/std/__memory/allocator_traits.h>
#include <cuda/std/__type_traits/type_list.h>
#include <cuda/std/__utility/typeid.h>
#include <cuda/std/cstddef>
#include <cuda/std/optional>
#include <cuda/std/span>
#include <cuda/experimental/__execution/any_allocator.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/stop_token.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
_CCCL_DIAG_PUSH
_CCCL_DIAG_SUPPRESS_MSVC(4702) // warning C4702: unreachable code
namespace cuda::experimental::execution
{
namespace __detail
{
struct __env_proxy : __immovable
{
_CCCL_HOST_DEVICE_API virtual auto query(const get_stop_token_t&) const noexcept -> inplace_stop_token = 0;
_CCCL_HOST_DEVICE_API virtual auto query(const get_allocator_t&) const noexcept
-> any_allocator<::cuda::std::byte> = 0;
_CCCL_HOST_DEVICE_API virtual auto query(const get_scheduler_t&) const noexcept -> task_scheduler = 0;
};
} // namespace __detail
class receiver_proxy : __detail::__env_proxy
{
public:
_CCCL_HOST_DEVICE_API virtual ~receiver_proxy() = 0;
_CCCL_HOST_DEVICE_API virtual void set_value() noexcept = 0;
_CCCL_HOST_DEVICE_API virtual void set_error(exception_ptr&&) noexcept = 0;
_CCCL_HOST_DEVICE_API virtual void set_stopped() noexcept = 0;
[[nodiscard]]
_CCCL_HOST_DEVICE_API auto get_env() const noexcept -> const __detail::__env_proxy&
{
return *this;
}
// _CCCL_EXEC_CHECK_DISABLE
// _CCCL_TEMPLATE(class _Value, class Query)
// _CCCL_REQUIRES(__callable<const __detail::__try_queryable&, Query, ::cuda::std::optional<_Value>&>)
// [[nodiscard]] _CCCL_HOST_DEVICE_API auto try_query(const Query& __query) const noexcept ->
// ::cuda::std::optional<_Value>
// {
// const __detail::__try_queryable& __queryable = *this;
// ::cuda::std::optional<_Value> __value;
// __queryable(__query, __value);
// return __value;
// }
};
inline receiver_proxy::~receiver_proxy() = default;
struct bulk_item_receiver_proxy : receiver_proxy
{
_CCCL_HOST_DEVICE_API virtual void execute(size_t, size_t) noexcept = 0;
};
struct parallel_scheduler_backend
{
_CCCL_HOST_DEVICE_API virtual ~parallel_scheduler_backend() = 0;
_CCCL_HOST_DEVICE_API virtual void schedule(receiver_proxy&, ::cuda::std::span<::cuda::std::byte>) noexcept = 0;
_CCCL_HOST_DEVICE_API virtual void
schedule_bulk_chunked(size_t, bulk_item_receiver_proxy&, ::cuda::std::span<::cuda::std::byte>) noexcept = 0;
_CCCL_HOST_DEVICE_API virtual void
schedule_bulk_unchunked(size_t, bulk_item_receiver_proxy&, ::cuda::std::span<::cuda::std::byte>) noexcept = 0;
};
inline parallel_scheduler_backend::~parallel_scheduler_backend() = default;
namespace __detail
{
// Partially implements the _RcvrProxy interface (either receiver_proxy or
// bulk_item_receiver_proxy) in terms of a concrete receiver type _Rcvr.
template <class _Rcvr, class _RcvrProxy>
struct __receiver_proxy_base : _RcvrProxy
{
public:
using receiver_concept = receiver_t;
_CCCL_HOST_DEVICE_API explicit __receiver_proxy_base(_Rcvr rcvr) noexcept
: __rcvr_(static_cast<_Rcvr&&>(rcvr))
{}
_CCCL_HOST_DEVICE_API void set_error(exception_ptr&& eptr) noexcept final override
{
execution::set_error(_CCCL_MOVE(__rcvr_), _CCCL_MOVE(eptr));
}
_CCCL_HOST_DEVICE_API void set_stopped() noexcept final override
{
execution::set_stopped(_CCCL_MOVE(__rcvr_));
}
protected:
_CCCL_HOST_DEVICE_API auto query(const get_stop_token_t&) const noexcept -> inplace_stop_token final override
{
if constexpr (__callable<const get_stop_token_t&, env_of_t<_Rcvr>>)
{
if constexpr (__same_as<stop_token_of_t<env_of_t<_Rcvr>>, inplace_stop_token>)
{
return get_stop_token(get_env(__rcvr_));
}
}
return inplace_stop_token{}; // MSVC thinks this is unreachable. :-?
}
_CCCL_HOST_DEVICE_API auto query(const get_allocator_t&) const noexcept
-> any_allocator<::cuda::std::byte> final override
{
return any_allocator{get_allocator(get_env(__rcvr_))};
}
// defined in task_scheduler.cuh:
_CCCL_HOST_DEVICE_API auto query(const get_scheduler_t& __query) const noexcept -> task_scheduler final override;
_Rcvr __rcvr_;
};
template <class _Rcvr>
struct __receiver_proxy : __receiver_proxy_base<_Rcvr, receiver_proxy>
{
using __receiver_proxy_base<_Rcvr, receiver_proxy>::__receiver_proxy_base;
_CCCL_HOST_DEVICE_API void set_value() noexcept final override
{
execution::set_value(_CCCL_MOVE(this->__rcvr_));
}
};
// A receiver type that forwards its completion operations to a _RcvrProxy member held by
// reference (where _RcvrProxy is one of receiver_proxy or bulk_item_receiver_proxy). It
// is also responsible to destroying and, if necessary, deallocating the operation state.
template <class _RcvrProxy>
struct __proxy_receiver
{
using receiver_concept = receiver_t;
using __delete_fn_t = void(void*) noexcept;
_CCCL_HOST_DEVICE_API void set_value() noexcept
{
auto& __proxy = __rcvr_proxy_;
__delete_fn_(__opstate_storage_);
__proxy.set_value();
}
_CCCL_HOST_DEVICE_API void set_error(exception_ptr eptr) noexcept
{
auto& __proxy = __rcvr_proxy_;
__delete_fn_(__opstate_storage_);
__proxy.set_error(_CCCL_MOVE(eptr));
}
_CCCL_HOST_DEVICE_API void set_stopped() noexcept
{
auto& __proxy = __rcvr_proxy_;
__delete_fn_(__opstate_storage_);
__proxy.set_stopped();
}
[[nodiscard]] _CCCL_HOST_DEVICE_API auto get_env() const noexcept -> env_of_t<_RcvrProxy>
{
return execution::get_env(__rcvr_proxy_);
}
_RcvrProxy& __rcvr_proxy_;
void* __opstate_storage_;
__delete_fn_t* __delete_fn_;
};
} // namespace __detail
} // namespace cuda::experimental::execution
_CCCL_DIAG_POP
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_PARALLEL_SCHEDULER

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@@ -1,137 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX___EXECUTION_POLICY_CUH
#define __CUDAX___EXECUTION_POLICY_CUH
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__execution/env.h>
#include <cuda/std/__execution/policy.h>
#include <cuda/std/__type_traits/is_convertible.h>
#include <cuda/std/__type_traits/is_execution_policy.h>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
using ::cuda::std::execution::__execution_policy;
using ::cuda::std::execution::par;
using ::cuda::std::execution::par_unseq;
using ::cuda::std::execution::seq;
using ::cuda::std::execution::unseq;
struct any_execution_policy
{
using type = any_execution_policy;
using value_type = __execution_policy;
_CCCL_HIDE_FROM_ABI any_execution_policy() = default;
template <uint32_t _Policy>
_CCCL_HOST_API constexpr any_execution_policy(::cuda::std::execution::__execution_policy_base<_Policy>) noexcept
: value(value_type{_Policy})
{}
_CCCL_HOST_API constexpr operator __execution_policy() const noexcept
{
return value;
}
_CCCL_HOST_API constexpr auto operator()() const noexcept -> value_type
{
return value;
}
template <uint32_t _Policy>
[[nodiscard]] _CCCL_HOST_API friend constexpr bool
operator==(const any_execution_policy& pol, const ::cuda::std::execution::__execution_policy_base<_Policy>&) noexcept
{
return pol.value == value_type{_Policy};
}
#if _CCCL_STD_VER <= 2017
template <uint32_t _Policy>
[[nodiscard]] _CCCL_HOST_API friend constexpr bool
operator==(const ::cuda::std::execution::__execution_policy_base<_Policy>&, const any_execution_policy& pol) noexcept
{
return pol.value == value_type{_Policy};
}
template <uint32_t _Policy>
[[nodiscard]] _CCCL_HOST_API friend constexpr bool
operator!=(const any_execution_policy& pol, const ::cuda::std::execution::__execution_policy_base<_Policy>&) noexcept
{
return pol.value != value_type{_Policy};
}
template <uint32_t _Policy>
[[nodiscard]] _CCCL_HOST_API friend constexpr bool
operator!=(const ::cuda::std::execution::__execution_policy_base<_Policy>&, const any_execution_policy& pol)
{
return pol.value != value_type{_Policy};
}
#endif // _CCCL_STD_VER <= 2017
__execution_policy value = __execution_policy::__invalid_execution_policy;
};
struct get_execution_policy_t;
template <class _Tp>
_CCCL_CONCEPT __has_member_get_execution_policy = _CCCL_REQUIRES_EXPR((_Tp), const _Tp& __t)(
requires(::cuda::std::is_convertible_v<decltype(__t.get_execution_policy()), __execution_policy>));
template <class _Env>
_CCCL_CONCEPT __has_query_get_execution_policy = _CCCL_REQUIRES_EXPR((_Env))(
requires(!__has_member_get_execution_policy<_Env>),
requires(::cuda::std::is_convertible_v<::cuda::std::execution::__query_result_t<const _Env&, get_execution_policy_t>,
__execution_policy>));
struct get_execution_policy_t
{
_CCCL_TEMPLATE(class _Tp)
_CCCL_REQUIRES(__has_member_get_execution_policy<_Tp>)
[[nodiscard]] _CCCL_HIDE_FROM_ABI auto operator()(const _Tp& __t) const noexcept
{
return __t.get_execution_policy();
}
_CCCL_TEMPLATE(class _Env)
_CCCL_REQUIRES(__has_query_get_execution_policy<_Env>)
[[nodiscard]] _CCCL_HIDE_FROM_ABI auto operator()(const _Env& __env) const noexcept
{
static_assert(noexcept(__env.query(*this)));
return __env.query(*this);
}
};
_CCCL_GLOBAL_CONSTANT get_execution_policy_t get_execution_policy{};
} // namespace cuda::experimental::execution
_CCCL_BEGIN_NAMESPACE_CUDA_STD
template <>
inline constexpr bool is_execution_policy_v<::cuda::experimental::execution::any_execution_policy> = true;
_CCCL_END_NAMESPACE_CUDA_STD
#include <cuda/experimental/__execution/epilogue.cuh>
#endif //__CUDAX___EXECUTION_POLICY_CUH

43
cccl_upstream/cudax/include/cuda/experimental/__execution/prologue.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
// IMPORTANT: This file intionally lacks a header guard.
#include <cuda/std/detail/__config>
#if defined(_CUDAX_ASYNC_PROLOGUE_INCLUDED)
# error multiple inclusion of prologue.cuh
#endif
#define _CUDAX_ASYNC_PROLOGUE_INCLUDED
#include <cuda/std/__cccl/prologue.h>
_CCCL_DIAG_PUSH
_CCCL_DIAG_SUPPRESS_GCC("-Wsubobject-linkage")
_CCCL_DIAG_SUPPRESS_CLANG("-Wunused-value")
_CCCL_DIAG_SUPPRESS_MSVC(4714) // function 'foo' marked as __forceinline not inlined
_CCCL_DIAG_SUPPRESS_GCC("-Wmissing-braces")
_CCCL_DIAG_SUPPRESS_CLANG("-Wmissing-braces")
_CCCL_DIAG_SUPPRESS_MSVC(5246) // missing braces around initializer
#if _CCCL_CUDA_COMPILER(NVHPC)
_CCCL_BEGIN_NV_DIAG_SUPPRESS(cuda_compile)
#endif // _CCCL_CUDA_COMPILER(NVHPC)
// private and protected nested class types cannot be used as tparams to __global__
// functions. _CUDAX_SEMI_PRIVATE expands to public when _CCCL_CUDA_COMPILATION() is true,
// and private otherwise.
#if _CCCL_CUDA_COMPILATION()
# define _CUDAX_SEMI_PRIVATE public
#else // ^^^ _CCCL_CUDA_COMPILATION() ^^^ / vvv !_CCCL_CUDA_COMPILATION() vvv
# define _CUDAX_SEMI_PRIVATE private
#endif

409
cccl_upstream/cudax/include/cuda/experimental/__execution/queries.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_QUERIES
#define __CUDAX_EXECUTION_QUERIES
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
_CCCL_SUPPRESS_DEPRECATED_PUSH
_CCCL_SUPPRESS_DEPRECATED_NVRTC_DIAG
#include <cuda/std/__memory/allocator.h>
_CCCL_SUPPRESS_DEPRECATED_POP
#include <cuda/__launch/configuration.h>
#include <cuda/hierarchy>
#include <cuda/std/__concepts/concept_macros.h>
#include <cuda/std/__concepts/convertible_to.h>
#include <cuda/std/__execution/env.h>
#include <cuda/std/__type_traits/enable_if.h>
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/std/__type_traits/remove_cvref.h>
#include <cuda/std/__utility/exchange.h>
#include <cuda/std/__utility/unreachable.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__execution/completion_behavior.cuh>
#include <cuda/experimental/__execution/domain.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/meta.cuh>
#include <cuda/experimental/__execution/stop_token.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
namespace __detail
{
template <class _Env, class _Query>
using __statically_queryable_with_t = decltype(::cuda::std::remove_cvref_t<_Env>::query(declval<_Query>()));
} // namespace __detail
template <class _Env, class _Query>
_CCCL_CONCEPT __statically_queryable_with =
__is_instantiable_with<__detail::__statically_queryable_with_t, _Env, _Query>;
//////////////////////////////////////////////////////////////////////////////////////////
// get_allocator
_CCCL_GLOBAL_CONSTANT struct get_allocator_t
{
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Env& __env) const noexcept
-> __query_result_or_t<_Env, get_allocator_t, ::cuda::std::allocator<::cuda::std::byte>>
{
static_assert(__nothrow_queryable_with_or<_Env, get_allocator_t, true>,
"The get_allocator query must be noexcept.");
// NOT TO SPEC: return a default allocator if the query is not supported.
return __query_or(__env, *this, ::cuda::std::allocator<::cuda::std::byte>{});
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return true;
}
} get_allocator{};
//////////////////////////////////////////////////////////////////////////////////////////
// get_stop_token
_CCCL_GLOBAL_CONSTANT struct get_stop_token_t
{
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Env& __env) const noexcept
-> __query_result_or_t<_Env, get_stop_token_t, never_stop_token>
{
static_assert(__nothrow_queryable_with_or<_Env, get_stop_token_t, true>,
"The get_stop_token query must be noexcept.");
return __query_or(__env, *this, never_stop_token{});
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return true;
}
} get_stop_token{};
//////////////////////////////////////////////////////////////////////////////////////////
// get_scheduler
_CCCL_GLOBAL_CONSTANT struct get_scheduler_t
{
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Tag = set_value_t, class _Env)
_CCCL_REQUIRES(__queryable_with<_Env, get_scheduler_t>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Env& __env) const noexcept
-> __call_result_t<get_completion_scheduler_t<_Tag>,
__query_result_t<_Env, get_scheduler_t>,
__hide_scheduler<const _Env&>>
{
static_assert(noexcept(__env.query(*this)));
static_assert(__is_scheduler<__query_result_t<_Env, get_scheduler_t>>);
return get_completion_scheduler_t<_Tag>()(__env.query(*this), __hide_scheduler{__env});
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return true;
}
} get_scheduler{};
//////////////////////////////////////////////////////////////////////////////////////////
// get_delegation_scheduler
_CCCL_GLOBAL_CONSTANT struct get_delegation_scheduler_t
{
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Env)
_CCCL_REQUIRES(__queryable_with<_Env, get_delegation_scheduler_t>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Env& __env) const noexcept
-> __query_result_t<_Env, get_delegation_scheduler_t>
{
static_assert(noexcept(__env.query(*this)));
static_assert(__is_scheduler<decltype(__env.query(*this))>);
return __env.query(*this);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return true;
}
} get_delegation_scheduler{};
//////////////////////////////////////////////////////////////////////////////////////////
// get_completion_scheduler
//! @brief A query type for asking a sender's attributes for the scheduler on which that
//! sender will complete.
//!
//! @tparam _Tag one of set_value_t, set_error_t, or set_stopped_t
template <class _Tag>
struct get_completion_scheduler_t
{
// This function object reads the completion scheduler from an attribute object or a
// scheduler, accounting for the fact that the query member function may or may not
// accept an environment.
struct __read_query_t
{
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Attrs, class _GetComplSch = get_completion_scheduler_t)
_CCCL_REQUIRES(__queryable_with<_Attrs, _GetComplSch>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(const _Attrs& __attrs, cuda::std::__ignore_t = {}) const noexcept
-> decay_t<__query_result_t<_Attrs, _GetComplSch>>
{
static_assert(noexcept(__attrs.query(_GetComplSch{})));
static_assert(__is_scheduler<decltype(__attrs.query(_GetComplSch{}))>,
"The get_completion_scheduler query must return a scheduler type.");
return __attrs.query(_GetComplSch{});
}
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Attrs, class _Env, class _GetComplSch = get_completion_scheduler_t)
_CCCL_REQUIRES(__queryable_with<_Attrs, _GetComplSch, const _Env&>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(const _Attrs& __attrs, const _Env& __env) const noexcept
-> decay_t<__query_result_t<_Attrs, _GetComplSch, const _Env&>>
{
static_assert(noexcept(__attrs.query(_GetComplSch{}, __env)));
static_assert(__is_scheduler<decltype(__attrs.query(_GetComplSch{}, __env))>,
"The get_completion_scheduler query must return a scheduler type.");
return __attrs.query(_GetComplSch{}, __env);
}
};
private:
// A scheduler might have a completion scheduler different from itself; for example, an
// inline_scheduler completes wherever the scheduler's sender is started. So we
// recursively ask the scheduler for its completion scheduler until we find one whose
// completion scheduler is equal to itself (or it doesn't have one).
struct __recurse_query_t
{
_CCCL_EXEC_CHECK_DISABLE
template <class _Self = __recurse_query_t, class _Sch, class... _Env>
[[nodiscard]]
_CCCL_HOST_DEVICE_API constexpr auto operator()([[maybe_unused]] _Sch __sch, const _Env&... __env) const noexcept
{
// When determining where the scheduler's operations will complete, we query
// for the completion scheduler of the value channel:
using __read_query_t = typename get_completion_scheduler_t<set_value_t>::__read_query_t;
if constexpr (__callable<__read_query_t, _Sch, const _Env&...>)
{
using __sch2_t = decay_t<__call_result_t<__read_query_t, _Sch, const _Env&...>>;
if constexpr (__same_as<_Sch, __sch2_t>)
{
_Sch __prev = __sch;
do
{
__prev = cuda::std::exchange(__sch, __read_query_t{}(__sch, __env...));
} while (__prev != __sch);
return __sch;
}
else
{
// New scheduler has different type. Recurse!
return _Self{}(__read_query_t{}(__sch, __env...), __env...);
}
}
else
{
if constexpr (__callable<__read_query_t, env_of_t<schedule_result_t<_Sch>>, const _Env&...>)
{
_CCCL_ASSERT(__sch == __read_query_t{}(get_env(__sch.schedule()), __env...),
"the scheduler's sender must have a completion scheduler attribute equal to the scheduler that "
"provided it.");
}
return __sch;
}
}
};
template <class _Attrs, class... _Env, class _Sch>
[[nodiscard]] _CCCL_TRIVIAL_API constexpr static auto __check_domain(_Sch __sch) noexcept -> _Sch
{
// Sanity check: if a completion domain can be determined, then it must match the
// domain of the completion scheduler.
if constexpr (__callable<get_completion_domain_t<_Tag>, const _Attrs&, const _Env&...>)
{
using __domain_t = __call_result_t<get_completion_domain_t<_Tag>, const _Attrs&, const _Env&...>;
static_assert(__same_as<__domain_t, __scheduler_domain_t<_Sch, const _Env&...>>,
"the sender claims to complete on a domain that is not the domain of its completion scheduler");
}
return __sch;
}
template <class _Attrs, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto __get_declfn() noexcept
{
// If __attrs has a completion scheduler, then return it (after checking the scheduler
// for _its_ completion scheduler):
if constexpr (__callable<__read_query_t, const _Attrs&, const _Env&...>)
{
using __result_t =
decltype(__recurse_query_t{}(__read_query_t{}(declval<_Attrs>(), declval<_Env>()...), declval<_Env>()...));
return __declfn<__result_t>;
}
// Otherwise, if __attrs indicates that its sender completes inline, then we can ask
// the environment for the current scheduler and return that (after checking the
// scheduler for _its_ completion scheduler).
else if constexpr (__completes_inline<_Attrs, _Env...> && __callable<get_scheduler_t, const _Env&...>)
{
using __result_t =
decltype(__recurse_query_t{}(get_scheduler(declval<_Env>()...), __hide_scheduler{declval<_Env>()}...));
return __declfn<__result_t>;
}
else if constexpr (__is_scheduler<_Attrs> && sizeof...(_Env) != 0)
{
return __declfn<decay_t<_Attrs>>;
}
// Otherwise, no completion scheduler can be determined. Return void.
}
public:
template <class _Attrs, class... _Env, auto _DeclFn = __get_declfn<const _Attrs&, const _Env&...>()>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
operator()(const _Attrs& __attrs, const _Env&... __env) const noexcept -> __unless_one_of_t<decltype(_DeclFn()), void>
{
// If __attrs has a completion scheduler, then return it (after checking the scheduler
// for _its_ completion scheduler):
if constexpr (__callable<__read_query_t, const _Attrs&, const _Env&...>)
{
return __check_domain<_Attrs, _Env...>(__recurse_query_t{}(__read_query_t{}(__attrs, __env...), __env...));
}
// Otherwise, if __attrs indicates that its sender completes inline, then we can ask
// the environment for the current scheduler and return that (after checking the
// scheduler for _its_ completion scheduler).
else if constexpr (__completes_inline<_Attrs, _Env...> && __callable<get_scheduler_t, const _Env&...>)
{
return __check_domain<_Attrs, _Env...>(__recurse_query_t{}(get_scheduler(__env...), __hide_scheduler{__env}...));
}
else
{
return __attrs;
}
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return true;
}
};
template <class _Tag>
extern ::cuda::std::__undefined<_Tag> get_completion_scheduler;
// Explicitly instantiate these because of variable template weirdness in device code
template <>
_CCCL_GLOBAL_CONSTANT get_completion_scheduler_t<set_value_t> get_completion_scheduler<set_value_t>{};
template <>
_CCCL_GLOBAL_CONSTANT get_completion_scheduler_t<set_error_t> get_completion_scheduler<set_error_t>{};
template <>
_CCCL_GLOBAL_CONSTANT get_completion_scheduler_t<set_stopped_t> get_completion_scheduler<set_stopped_t>{};
//////////////////////////////////////////////////////////////////////////////////////////
// __is_completion_query
template <class _Query>
inline constexpr bool __is_completion_query = false;
template <class _Tag>
inline constexpr bool __is_completion_query<get_completion_domain_t<_Tag>> = true;
template <class _Tag>
inline constexpr bool __is_completion_query<get_completion_scheduler_t<_Tag>> = true;
template <>
inline constexpr bool __is_completion_query<get_completion_behavior_t> = true;
//////////////////////////////////////////////////////////////////////////////////////////
// get_forward_progress_guarantee
// This query is not a forwarding query.
_CCCL_GLOBAL_CONSTANT struct get_forward_progress_guarantee_t
{
template <class _Sch>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Sch& __sch) const noexcept
-> forward_progress_guarantee
{
static_assert(__nothrow_queryable_with_or<_Sch, get_forward_progress_guarantee_t, true>,
"The get_forward_progress_guarantee query must be noexcept.");
return __query_or(__sch, *this, forward_progress_guarantee::weakly_parallel);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return false;
}
} get_forward_progress_guarantee{};
//////////////////////////////////////////////////////////////////////////////////////////
// get_available_parallelism
// This query is not a forwarding query.
_CCCL_GLOBAL_CONSTANT struct get_available_parallelism_t
{
template <class _Sch>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Sch& __sch) const noexcept
{
static_assert(__nothrow_queryable_with_or<const _Sch&, get_available_parallelism_t, true>,
"The get_available_parallelism query must be noexcept.");
static_assert(
cuda::std::convertible_to<__query_result_or_t<const _Sch&, get_available_parallelism_t, size_t>, size_t>,
"The get_available_parallelism query must return a type convertible to size_t.");
return __query_or(__sch, *this, size_t(1));
}
[[nodiscard]] _CCCL_NODEBUG_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return false;
}
} get_available_parallelism{};
// By default, CUDA kernels are launched with a single thread and a single block.
using __single_threaded_config_base_t = decltype(make_config(grid_dims<1>(), block_dims<1>()));
// We hide the complicated type of the default launch configuration so diagnositics are
// easier to read.
struct __single_threaded_config_t : __single_threaded_config_base_t
{
_CCCL_HOST_API constexpr __single_threaded_config_t() noexcept
: __single_threaded_config_base_t{make_config(grid_dims<1>(), block_dims<1>())}
{}
};
_CCCL_GLOBAL_CONSTANT __single_threaded_config_t __single_threaded_config{};
//////////////////////////////////////////////////////////////////////////////////////////
// get_launch_config: A sender can define this attribute to control the launch configuration
// of the kernel it will launch when executed on a CUDA stream scheduler.
_CCCL_GLOBAL_CONSTANT struct get_launch_config_t
{
template <class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(const _Env& __env) const noexcept
-> __query_result_or_t<_Env, get_launch_config_t, __single_threaded_config_t>
{
static_assert(__nothrow_queryable_with_or<_Env, get_launch_config_t, true>,
"The get_launch_config query must be noexcept.");
return __query_or(__env, *this, __single_threaded_config);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto query(forwarding_query_t) noexcept -> bool
{
return true;
}
} get_launch_config{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_QUERIES

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_RCVR_REF
#define __CUDAX_EXECUTION_RCVR_REF
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__type_traits/is_specialization_of.h>
#include <cuda/std/__memory/addressof.h>
#include <cuda/std/__type_traits/is_copy_constructible.h>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/type_traits.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
_CCCL_BEGIN_NV_DIAG_SUPPRESS(114) // function "foo" was referenced but not defined
namespace cuda::experimental::execution
{
template <class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_ref
{
using receiver_concept = receiver_t;
_CCCL_HOST_DEVICE_API explicit constexpr __rcvr_ref(_Rcvr& __rcvr) noexcept
: __rcvr_{::cuda::std::addressof(__rcvr)}
{}
template <class... _As>
_CCCL_HOST_DEVICE_API constexpr void set_value(_As&&... __as) noexcept
{
execution::set_value(static_cast<_Rcvr&&>(*__rcvr_), static_cast<_As&&>(__as)...);
}
template <class _Error>
_CCCL_HOST_DEVICE_API constexpr void set_error(_Error&& __err) noexcept
{
execution::set_error(static_cast<_Rcvr&&>(*__rcvr_), static_cast<_Error&&>(__err));
}
_CCCL_HOST_DEVICE_API constexpr void set_stopped() noexcept
{
execution::set_stopped(static_cast<_Rcvr&&>(*__rcvr_));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> env_of_t<_Rcvr>
{
return execution::get_env(*__rcvr_);
}
private:
_Rcvr* __rcvr_;
};
// The __ref_rcvr function and its helpers are used to avoid wrapping a receiver in a
// __rcvr_ref when that is possible. The logic goes as follows:
//
// 1. If the receiver is an instance of __rcvr_ref, return it.
// 2. If the receiver is nothrow copy constructible, return it.
// 3. Otherwise, return a __rcvr_ref wrapping the receiver.
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto __ref_rcvr(_Rcvr& __rcvr) noexcept
{
if constexpr (__is_specialization_of_v<_Rcvr, __rcvr_ref>)
{
return __rcvr;
}
else if constexpr (__nothrow_constructible<_Rcvr, const _Rcvr&>)
{
return const_cast<const _Rcvr&>(__rcvr);
}
else
{
return __rcvr_ref{__rcvr};
}
_CCCL_UNREACHABLE();
}
template <class _Rcvr>
using __rcvr_ref_t _CCCL_NODEBUG_ALIAS = decltype(execution::__ref_rcvr(::cuda::std::declval<_Rcvr&>()));
} // namespace cuda::experimental::execution
_CCCL_END_NV_DIAG_SUPPRESS() // function "foo" was references but not defined
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_RCVR_REF

103
cccl_upstream/cudax/include/cuda/experimental/__execution/rcvr_with_env.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the _Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: _Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_RCVR_WITH_ENV
#define __CUDAX_EXECUTION_RCVR_WITH_ENV
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
template <class _Rcvr, class _Env>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_with_env_t;
// If _Env has a value for the `get_scheduler` query, then we must ensure that we report
// the domain correctly. Under no circumstances should we forward the `get_domain` query
// to the receiver's environment. That environment may have a domain that does not
// conform to the scheduler in _Env.
template <class _Env, class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __env_with_rcvr_t
{
// Prefer to query _Env
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Query, class... _Args)
_CCCL_REQUIRES(__queryable_with<_Env, _Query, _Args...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Query, _Args&&... __args) const
noexcept(__nothrow_queryable_with<_Env, _Query, _Args...>) -> __query_result_t<_Env, _Query, _Args...>
{
return __rcvr_->__env_.query(_Query{}, static_cast<_Args&&>(__args)...);
}
// Fallback to querying the inner receiver's environment, but only for forwarding
// queries.
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Query, class... _Args)
_CCCL_REQUIRES((!__queryable_with<_Env, _Query, _Args...>)
_CCCL_AND __forwarding_query<_Query> _CCCL_AND __queryable_with<env_of_t<_Rcvr>, _Query, _Args...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Query, _Args&&... __args) const
noexcept(__nothrow_queryable_with<env_of_t<_Rcvr>, _Query, _Args...>)
-> __query_result_t<env_of_t<_Rcvr>, _Query, _Args...>
{
// If _Env has a value for the `get_scheduler` query, then we should not be
// forwarding a get_domain query to the parent receiver's environment.
static_assert(!__same_as<_Query, get_domain_t> || !__queryable_with<_Env, get_scheduler_t>,
"_Env specifies a scheduler but not a domain.");
return execution::get_env(__rcvr_->__base()).query(_Query{}, static_cast<_Args&&>(__args)...);
}
__rcvr_with_env_t<_Rcvr, _Env> const* __rcvr_;
};
template <class _Rcvr, class _Env>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_with_env_t : _Rcvr
{
[[nodiscard]] _CCCL_HOST_DEVICE_API auto __base() && noexcept -> _Rcvr&&
{
return static_cast<_Rcvr&&>(*this);
}
[[nodiscard]] _CCCL_HOST_DEVICE_API auto __base() & noexcept -> _Rcvr&
{
return *this;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API auto __base() const& noexcept -> _Rcvr const&
{
return *this;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __env_with_rcvr_t<_Env, _Rcvr>
{
return __env_with_rcvr_t<_Env, _Rcvr>{this};
}
_Env __env_;
};
template <class _Rcvr, class _Env>
_CCCL_DEDUCTION_GUIDE_ATTRIBUTES __rcvr_with_env_t(_Rcvr, _Env) -> __rcvr_with_env_t<_Rcvr, _Env>;
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_RCVR_WITH_ENV

169
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@@ -1,169 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_READ_ENV
#define __CUDAX_EXECUTION_READ_ENV
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__exception/exception_macros.h>
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/std/__type_traits/is_void.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/get_completion_signatures.cuh>
#include <cuda/experimental/__execution/queries.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
struct _THE_CURRENT_ENVIRONMENT_LACKS_THIS_QUERY;
struct _THE_CURRENT_ENVIRONMENT_RETURNED_VOID_FOR_THIS_QUERY;
struct _CCCL_TYPE_VISIBILITY_DEFAULT read_env_t
{
private:
template <class _Rcvr, class _Query>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t
{
using operation_state_concept = operation_state_t;
_Rcvr __rcvr_;
_CCCL_HOST_DEVICE_API constexpr explicit __opstate_t(_Rcvr __rcvr) noexcept
: __rcvr_(static_cast<_Rcvr&&>(__rcvr))
{}
_CCCL_IMMOVABLE(__opstate_t);
_CCCL_EXEC_CHECK_DISABLE
_CCCL_HOST_DEVICE_API void start() noexcept
{
// If the query invocation is noexcept, call it directly. Otherwise,
// wrap it in a try-catch block and forward the exception to the
// receiver.
if constexpr (__nothrow_callable<_Query, env_of_t<_Rcvr>>)
{
// This looks like a use after move, but `set_value` takes its
// arguments by forwarding reference, so it's safe.
execution::set_value(static_cast<_Rcvr&&>(__rcvr_), _Query{}(execution::get_env(__rcvr_)));
}
else
{
_CCCL_TRY
{
execution::set_value(static_cast<_Rcvr&&>(__rcvr_), _Query{}(execution::get_env(__rcvr_)));
}
_CCCL_CATCH_ALL
{
execution::set_error(static_cast<_Rcvr&&>(__rcvr_), execution::current_exception());
}
}
}
};
struct __attrs_t
{
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_behavior_t) const noexcept
{
return completion_behavior::inline_completion;
}
};
public:
template <class _Query>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
/// @brief Returns a sender that, when connected to a receiver and started,
/// invokes the query with the receiver's environment and forwards the result
/// to the receiver's `set_value` member.
template <class _Query>
_CCCL_HOST_DEVICE_API constexpr __sndr_t<_Query> operator()(_Query) const noexcept;
};
template <class _Query>
struct _CCCL_TYPE_VISIBILITY_DEFAULT read_env_t::__sndr_t
{
using sender_concept = sender_t;
template <class _Self, class _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
if constexpr (!__callable<_Query, _Env>)
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, read_env_t),
_WHAT(_THE_CURRENT_ENVIRONMENT_LACKS_THIS_QUERY),
_WITH_QUERY(_Query),
_WITH_ENVIRONMENT(_Env)>();
}
else if constexpr (::cuda::std::is_void_v<__call_result_t<_Query, _Env>>)
{
return invalid_completion_signature<_WHERE(_IN_ALGORITHM, read_env_t),
_WHAT(_THE_CURRENT_ENVIRONMENT_RETURNED_VOID_FOR_THIS_QUERY),
_WITH_QUERY(_Query),
_WITH_ENVIRONMENT(_Env)>();
}
else
{
return completion_signatures<set_value_t(__call_result_t<_Query, _Env>)>{}
+ __eptr_completion_if<!__nothrow_callable<_Query, _Env>>();
}
_CCCL_UNREACHABLE();
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) const noexcept -> __opstate_t<_Rcvr, _Query>
{
return __opstate_t<_Rcvr, _Query>{static_cast<_Rcvr&&>(__rcvr)};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto get_env() noexcept
{
return __attrs_t{};
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ read_env_t __tag;
/*_CCCL_NO_UNIQUE_ADDRESS*/ _Query __query;
};
template <class _Query>
_CCCL_HOST_DEVICE_API constexpr read_env_t::__sndr_t<_Query> read_env_t::operator()(_Query __query) const noexcept
{
return __sndr_t<_Query>{{}, __query};
}
template <class _Query>
inline constexpr int structured_binding_size<read_env_t::__sndr_t<_Query>> = 2;
_CCCL_GLOBAL_CONSTANT read_env_t read_env{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_READ_ENV

313
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@@ -1,313 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_RUN_LOOP
#define __CUDAX_EXECUTION_RUN_LOOP
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/atomic_intrusive_queue.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/fwd.cuh>
#include <cuda/experimental/__execution/queries.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
class _CCCL_TYPE_VISIBILITY_DEFAULT __run_loop_base : __immovable
{
public:
_CCCL_HIDE_FROM_ABI __run_loop_base() = default;
_CCCL_HOST_DEVICE_API void run() noexcept
{
// execute work items until the __finishing_ flag is set:
while (!__finishing_.load(::cuda::std::memory_order_acquire))
{
__queue_.wait_for_item();
__execute_all();
}
// drain the queue, taking care to execute any tasks that get added while
// executing the remaining tasks:
while (__execute_all())
;
}
_CCCL_HOST_DEVICE_API void finish() noexcept
{
if (!__finishing_.exchange(true, ::cuda::std::memory_order_acq_rel))
{
// push an empty work item to the queue to wake up the consuming thread
// and let it finish:
__queue_.push(&__noop_task);
}
}
struct _CCCL_TYPE_VISIBILITY_DEFAULT __task : __immovable
{
using __execute_fn_t _CCCL_NODEBUG_ALIAS = void(__task*) noexcept;
_CCCL_HIDE_FROM_ABI __task() = default;
_CCCL_HOST_DEVICE_API explicit __task(__execute_fn_t* __execute_fn) noexcept
: __execute_fn_(__execute_fn)
{}
_CCCL_HOST_DEVICE_API void __execute() noexcept
{
(*__execute_fn_)(this);
}
__execute_fn_t* __execute_fn_ = nullptr;
__task* __next_ = nullptr;
};
template <class _Rcvr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t : __task
{
__atomic_intrusive_queue<&__task::__next_>* __queue_;
_Rcvr __rcvr_;
_CCCL_HOST_DEVICE_API static void __execute_impl(__task* __p) noexcept
{
static_assert(noexcept(get_stop_token(declval<env_of_t<_Rcvr>>()).stop_requested()));
auto& __rcvr = static_cast<__opstate_t*>(__p)->__rcvr_;
if (get_stop_token(get_env(__rcvr)).stop_requested())
{
set_stopped(static_cast<_Rcvr&&>(__rcvr));
}
else
{
set_value(static_cast<_Rcvr&&>(__rcvr));
}
}
_CCCL_HOST_DEVICE_API constexpr explicit __opstate_t(
__atomic_intrusive_queue<&__task::__next_>* __queue, _Rcvr __rcvr)
: __task{&__execute_impl}
, __queue_{__queue}
, __rcvr_{static_cast<_Rcvr&&>(__rcvr)}
{}
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
__queue_->push(this);
}
};
// Returns true if any tasks were executed.
_CCCL_HOST_DEVICE_API bool __execute_all() noexcept
{
// Dequeue all tasks at once. This returns an __intrusive_queue.
auto __queue = __queue_.pop_all();
// Execute all the tasks in the queue.
auto __it = __queue.begin();
if (__it == __queue.end())
{
return false; // No tasks to execute.
}
do
{
// Take care to increment the iterator before executing the task,
// because __execute() may invalidate the current node.
auto __prev = __it++;
(*__prev)->__execute();
} while (__it != __queue.end());
__queue.clear();
return true;
}
_CCCL_HOST_DEVICE_API static void __noop_(__task*) noexcept {}
::cuda::std::atomic<bool> __finishing_{false};
__atomic_intrusive_queue<&__task::__next_> __queue_{};
__task __noop_task{&__noop_};
};
template <class _Env>
struct _CCCL_TYPE_VISIBILITY_DEFAULT basic_run_loop : __run_loop_base
{
private:
struct _CCCL_TYPE_VISIBILITY_DEFAULT __attrs_t
{
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_scheduler_t<set_value_t>) const noexcept;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_scheduler_t<set_stopped_t>) const noexcept;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_domain_t<set_value_t>) const noexcept;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_domain_t<set_stopped_t>) const noexcept;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_behavior_t) const noexcept
{
return completion_behavior::asynchronous;
}
basic_run_loop* __loop_;
};
public:
_CCCL_HOST_DEVICE_API constexpr explicit basic_run_loop(_Env __env) noexcept
: __env_{static_cast<_Env&&>(__env)}
{}
class _CCCL_TYPE_VISIBILITY_DEFAULT scheduler : __attrs_t
{
private:
friend basic_run_loop;
_CCCL_HOST_DEVICE_API constexpr explicit scheduler(basic_run_loop* __loop) noexcept
: __attrs_t{__loop}
{}
public:
using scheduler_concept = scheduler_t;
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t
{
using sender_concept = sender_t;
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) const noexcept -> __opstate_t<_Rcvr>
{
return __opstate_t<_Rcvr>{&__loop_->__queue_, static_cast<_Rcvr&&>(__rcvr)};
}
template <class _Self>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures() noexcept
{
return completion_signatures<set_value_t(), set_stopped_t()>{};
}
_CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __attrs_t
{
return __attrs_t{__loop_};
}
private:
friend scheduler;
_CCCL_HOST_DEVICE_API constexpr explicit __sndr_t(basic_run_loop* __loop) noexcept
: __loop_(__loop)
{}
basic_run_loop* __loop_;
};
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto schedule() const noexcept -> __sndr_t
{
return __sndr_t{this->__loop_};
}
using __attrs_t::query;
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_forward_progress_guarantee_t) const noexcept
-> forward_progress_guarantee
{
return forward_progress_guarantee::parallel;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API friend constexpr bool
operator==(const scheduler& __a, const scheduler& __b) noexcept
{
return __a.__loop_ == __b.__loop_;
}
[[nodiscard]] _CCCL_HOST_DEVICE_API friend constexpr bool
operator!=(const scheduler& __a, const scheduler& __b) noexcept
{
return __a.__loop_ != __b.__loop_;
}
};
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_scheduler() noexcept -> scheduler
{
return scheduler{this};
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> const _Env&
{
return __env_;
}
private:
/*_CCCL_NO_UNIQUE_ADDRESS*/ _Env __env_;
};
// A run_loop with an empty environment. This is a struct instead of a type alias to give
// it a simpler type name that is easier to read in diagnostics.
struct _CCCL_TYPE_VISIBILITY_DEFAULT run_loop : basic_run_loop<env<>>
{
_CCCL_HIDE_FROM_ABI constexpr run_loop() noexcept
: basic_run_loop<env<>>{env{}}
{}
};
template <class _Env>
_CCCL_HOST_DEVICE_API constexpr auto
basic_run_loop<_Env>::__attrs_t::query(get_completion_scheduler_t<set_value_t>) const noexcept
{
if constexpr (__callable<get_scheduler_t, _Env&>)
{
return execution::get_scheduler(__loop_->__env_);
}
else
{
return scheduler{__loop_};
}
}
template <class _Env>
_CCCL_HOST_DEVICE_API constexpr auto
basic_run_loop<_Env>::__attrs_t::query(get_completion_scheduler_t<set_stopped_t>) const noexcept
{
return query(get_completion_scheduler<set_value_t>);
}
template <class _Env>
_CCCL_HOST_DEVICE_API constexpr auto
basic_run_loop<_Env>::__attrs_t::query(get_completion_domain_t<set_value_t>) const noexcept
{
if constexpr (__callable<get_domain_t, _Env&>)
{
return __call_result_t<get_domain_t, _Env&>();
}
else
{
return default_domain{};
}
}
template <class _Env>
_CCCL_HOST_DEVICE_API constexpr auto
basic_run_loop<_Env>::__attrs_t::query(get_completion_domain_t<set_stopped_t>) const noexcept
{
return query(get_completion_domain<set_value_t>);
}
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_RUN_LOOP

104
cccl_upstream/cudax/include/cuda/experimental/__execution/schedule_from.cuh
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@@ -1,104 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_SCHEDULE_FROM
#define __CUDAX_EXECUTION_SCHEDULE_FROM
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no sys
#include <cuda/__utility/immovable.h>
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__type_traits/conditional.h>
#include <cuda/std/__utility/pod_tuple.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/concepts.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/get_completion_signatures.cuh>
#include <cuda/experimental/__execution/meta.cuh>
#include <cuda/experimental/__execution/queries.cuh>
#include <cuda/experimental/__execution/rcvr_ref.cuh>
#include <cuda/experimental/__execution/transform_completion_signatures.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/variant.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
struct schedule_from_t
{
template <class _Sndr>
struct __sndr_t;
template <class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr __sndr) const noexcept
{
return __sndr_t<_Sndr>{{}, {}, _CCCL_MOVE(__sndr)};
}
};
template <class _Sndr>
struct schedule_from_t::__sndr_t
{
using sender_concept = sender_t;
template <class _Self, class... _Env>
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
return get_child_completion_signatures<_Self, _Sndr, _Env...>();
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) && -> connect_result_t<_Sndr, _Rcvr>
{
return execution::connect(_CCCL_MOVE(__sndr_), _CCCL_MOVE(__rcvr));
}
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
connect(_Rcvr __rcvr) const& -> connect_result_t<const _Sndr&, _Rcvr>
{
return execution::connect(__sndr_, _CCCL_MOVE(__rcvr));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __fwd_env_t<env_of_t<_Sndr>>
{
return __fwd_env(execution::get_env(__sndr_));
}
schedule_from_t __tag{};
::cuda::std::__ignore_t __ignore_;
_Sndr __sndr_;
};
template <class _Sndr>
inline constexpr int structured_binding_size<schedule_from_t::__sndr_t<_Sndr>> = 3;
_CCCL_GLOBAL_CONSTANT schedule_from_t schedule_from{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_SCHEDULE_FROM

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_SEQUENCE
#define __CUDAX_EXECUTION_SEQUENCE
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__tuple_dir/ignore.h>
#include <cuda/std/__type_traits/conditional.h>
#include <cuda/std/__type_traits/is_callable.h>
#include <cuda/experimental/__detail/type_traits.cuh>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/completion_signatures.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/exception.cuh>
#include <cuda/experimental/__execution/get_completion_signatures.cuh>
#include <cuda/experimental/__execution/rcvr_ref.cuh>
#include <cuda/experimental/__execution/rcvr_with_env.cuh>
#include <cuda/experimental/__execution/transform_completion_signatures.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/variant.cuh>
#include <cuda/experimental/__execution/visit.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
namespace __detail
{
_CCCL_EXEC_CHECK_DISABLE
template <class _Attrs, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
__mk_seq_env_next(const _Attrs& __attrs, const _Env&... __env) noexcept
{
if constexpr (__callable<get_completion_scheduler_t<set_value_t>, const _Attrs&, const _Env&...>)
{
return __mk_sch_env(get_completion_scheduler<set_value_t>(__attrs, __env...), __env...);
}
else if constexpr (__callable<get_completion_domain_t<set_value_t>, const _Attrs&, const _Env&...>)
{
using __domain_t = __call_result_t<get_completion_domain_t<set_value_t>, const _Attrs&, const _Env&...>;
return prop{get_domain, __domain_t{}};
}
else
{
return env{};
}
}
template <class _Attrs, class... _Env>
using __seq_env_next_t = decltype(__detail::__mk_seq_env_next(declval<_Attrs>(), declval<_Env>()...));
//! @brief Given a completion tag type, an environment, and a pack of attributes objects
//! obtained from a sequence of senders, return the scheduler on which the final sender
//! would complete assuming each sender was started where the previous sender completed.
// template <class _Tag, class _Env, class _Attrs>
// [[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto __seq_compl_sch_for(const _Env& __env, const _Attrs& __attrs)
// noexcept
// {
// return __call_or(get_completion_scheduler<_Tag>, __nil{}, __attrs, __env);
// }
// template <class _Tag, class _Env, class _Attrs0, class _Attrs1, class... _Attrs>
// [[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto __seq_compl_sch_for(
// const _Env& __env, const _Attrs0& __attrs0, const _Attrs1& __attrs1, const _Attrs&... __attrs) noexcept
// {
// return __seq_compl_sch_for<_Tag>(__detail::__mk_seq_env_next(__attrs0, __env), __attrs1, __attrs...);
// if constexpr (__callable<get_completion_scheduler_t<set_value_t>, const _Attrs0&, const _Env&>)
// {
// return;
// }
// auto __env_next = __detail::__mk_seq_env_next(__attrs0, __env);
// return;
// }
} // namespace __detail
struct _CCCL_TYPE_VISIBILITY_DEFAULT sequence_t
{
_CUDAX_SEMI_PRIVATE :
template <class _Attrs, class... _Env>
using __env2_t = __join_env_t<__detail::__seq_env_next_t<_Attrs, __fwd_env_t<_Env>...>, __fwd_env_t<_Env>...>;
_CCCL_EXEC_CHECK_DISABLE
template <class _Attrs, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto
__mk_env2(const _Attrs& __attrs, const _Env&... __env) noexcept -> __env2_t<_Attrs, _Env...>
{
return __join_env(__detail::__mk_seq_env_next(__attrs, __fwd_env(__env)...), __fwd_env(__env)...);
}
template <class _Rcvr, class _Env2, class _Sndr2>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __state_t
{
_CCCL_HOST_DEVICE_API constexpr explicit __state_t(_Rcvr&& __rcvr, _Env2 __env, _Sndr2&& __sndr2)
: __rcvr2_{static_cast<_Rcvr&&>(__rcvr), __env}
, __opstate2_(execution::connect(static_cast<_Sndr2&&>(__sndr2), __ref_rcvr(__rcvr2_)))
{}
__rcvr_with_env_t<_Rcvr, _Env2> __rcvr2_;
connect_result_t<_Sndr2, __rcvr_ref_t<__rcvr_with_env_t<_Rcvr, _Env2>>> __opstate2_;
};
template <class _Rcvr, class _Env2, class _Sndr2>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_t
{
using receiver_concept = receiver_t;
template <class... _Values>
_CCCL_HOST_DEVICE_API constexpr void set_value(_Values&&...) noexcept
{
execution::start(__state_->__opstate2_);
}
template <class _Error>
_CCCL_HOST_DEVICE_API constexpr void set_error(_Error&& __error) noexcept
{
execution::set_error(static_cast<_Rcvr&&>(__state_->__rcvr2_.__base()), static_cast<_Error&&>(__error));
}
_CCCL_HOST_DEVICE_API constexpr void set_stopped() noexcept
{
execution::set_stopped(static_cast<_Rcvr&&>(__state_->__rcvr2_.__base()));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __fwd_env_t<env_of_t<_Rcvr>>
{
return __fwd_env(execution::get_env(__state_->__rcvr2_.__base()));
}
__state_t<_Rcvr, _Env2, _Sndr2>* __state_;
};
template <class _Rcvr, class _Sndr1, class _Sndr2>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t
{
using operation_state_concept = operation_state_t;
using __env2_t _CCCL_NODEBUG_ALIAS = __detail::__seq_env_next_t<env_of_t<_Sndr1>, env_of_t<_Rcvr>>;
// The moves from lvalues here is intentional:
_CCCL_EXEC_CHECK_DISABLE
_CCCL_HOST_DEVICE_API constexpr __opstate_t(_Sndr1& __sndr1, _Sndr2& __sndr2, _Rcvr& __rcvr, __env2_t __env2)
: __state_(static_cast<_Rcvr&&>(__rcvr), static_cast<__env2_t&&>(__env2), static_cast<_Sndr2&&>(__sndr2))
, __opstate1_(execution::connect(static_cast<_Sndr1&&>(__sndr1), __rcvr_t<_Rcvr, __env2_t, _Sndr2>{&__state_}))
{}
_CCCL_EXEC_CHECK_DISABLE
_CCCL_HOST_DEVICE_API constexpr __opstate_t(_Sndr1&& __sndr1, _Sndr2&& __sndr2, _Rcvr&& __rcvr)
: __opstate_t(__sndr1, __sndr2, __rcvr, __detail::__mk_seq_env_next(get_env(__sndr1), get_env(__rcvr)))
{}
_CCCL_IMMOVABLE(__opstate_t);
_CCCL_HOST_DEVICE_API ~__opstate_t() {}
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
execution::start(__opstate1_);
}
private:
__state_t<_Rcvr, __env2_t, _Sndr2> __state_;
connect_result_t<_Sndr1, __rcvr_t<_Rcvr, __env2_t, _Sndr2>> __opstate1_;
};
public:
template <class _Sndr1, class _Sndr2>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr1, class _Sndr2>
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Sndr1 __sndr1, _Sndr2 __sndr2) const;
};
template <class _Sndr1, class _Sndr2>
struct _CCCL_TYPE_VISIBILITY_DEFAULT sequence_t::__sndr_t
{
using sender_concept = sender_t;
template <class... _Env>
using __env2_t _CCCL_NODEBUG_ALIAS = sequence_t::__env2_t<env_of_t<_Sndr1>, _Env...>;
template <class _Self, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
_CUDAX_LET_COMPLETIONS(auto(__completions1) = get_child_completion_signatures<_Self, _Sndr1, _Env...>())
{
_CUDAX_LET_COMPLETIONS(auto(__completions2) = get_child_completion_signatures<_Self, _Sndr2, __env2_t<_Env...>>())
{
// __swallow_transform to ignore the first sender's value completions
return __completions2 + transform_completion_signatures(__completions1, __swallow_transform{});
}
}
_CCCL_UNREACHABLE();
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) && //
-> sequence_t::__opstate_t<_Rcvr, _Sndr1, _Sndr2>
{
using __opstate_t = sequence_t::__opstate_t<_Rcvr, _Sndr1, _Sndr2>;
return __opstate_t{static_cast<_Sndr1&&>(__sndr1_), static_cast<_Sndr2>(__sndr2_), static_cast<_Rcvr&&>(__rcvr)};
}
_CCCL_EXEC_CHECK_DISABLE
template <class _Rcvr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) const& //
-> sequence_t::__opstate_t<_Rcvr, const _Sndr1&, const _Sndr2&>
{
using __opstate_t = sequence_t::__opstate_t<_Rcvr, const _Sndr1&, const _Sndr2&>;
return __opstate_t{__sndr1_, __sndr2_, static_cast<_Rcvr&&>(__rcvr)};
}
struct __attrs_t
{
// If _Sndr2 has _SetTag completions but does not know its _SetTag completion scheduler,
// then we cannot know it either. Delete the function to prevent its use.
_CCCL_TEMPLATE(class _SetTag, class... _Env)
_CCCL_REQUIRES(__has_completions_for<_Sndr2, _SetTag, __env2_t<_Env...>> _CCCL_AND(
!__callable<get_completion_scheduler_t<_SetTag>, env_of_t<_Sndr2>, __env2_t<_Env...>>))
_CCCL_HOST_DEVICE_API auto query(get_completion_scheduler_t<_SetTag>, const _Env&...) const = delete;
// If _Sndr2 has _SetTag completions but does not know its _SetTag completion domain,
// then we cannot know it either. Delete the function to prevent its use.
_CCCL_TEMPLATE(class _SetTag, class... _Env)
_CCCL_REQUIRES(__has_completions_for<_Sndr2, _SetTag, __env2_t<_Env...>> _CCCL_AND(
!__callable<get_completion_domain_t<_SetTag>, env_of_t<_Sndr2>, __env2_t<_Env...>>))
_CCCL_HOST_DEVICE_API auto query(get_completion_domain_t<_SetTag>, const _Env&...) const = delete;
template <class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_behavior_t, const _Env&...) const noexcept
{
return (execution::min) (execution::get_completion_behavior<_Sndr1, __fwd_env_t<_Env>...>(),
execution::get_completion_behavior<_Sndr2, __env2_t<_Env...>>());
}
using __child_attrs_t = __join_env_t<env_of_t<_Sndr2>, env_of_t<_Sndr1>>;
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Query, class... _Args)
_CCCL_REQUIRES(__forwarding_query<_Query> _CCCL_AND __queryable_with<__child_attrs_t, _Query, _Args...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Query, _Args&&... __args) const
noexcept(__nothrow_queryable_with<__child_attrs_t, _Query, _Args...>)
-> __query_result_t<__child_attrs_t, _Query, _Args...>
{
auto&& __env = __join_env(execution::get_env(__self_->__sndr2_), execution::get_env(__self_->__sndr1_));
return __env.query(_Query{}, static_cast<_Args&&>(__args)...);
}
__sndr_t const* __self_;
};
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __attrs_t
{
return {this};
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ sequence_t __tag_;
/*_CCCL_NO_UNIQUE_ADDRESS*/ ::cuda::std::__ignore_t __ign_;
_Sndr1 __sndr1_;
_Sndr2 __sndr2_;
};
_CCCL_EXEC_CHECK_DISABLE
template <class _Sndr1, class _Sndr2>
_CCCL_HOST_DEVICE_API constexpr auto sequence_t::operator()(_Sndr1 __sndr1, _Sndr2 __sndr2) const
{
using __sndr_t _CCCL_NODEBUG_ALIAS = sequence_t::__sndr_t<_Sndr1, _Sndr2>;
return __sndr_t{{}, {}, static_cast<_Sndr1&&>(__sndr1), static_cast<_Sndr2&&>(__sndr2)};
}
template <class _Sndr1, class _Sndr2>
inline constexpr int structured_binding_size<sequence_t::__sndr_t<_Sndr1, _Sndr2>> = 4;
_CCCL_GLOBAL_CONSTANT sequence_t sequence{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_SEQUENCE

68
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_SNDR_REF
#define __CUDAX_EXECUTION_SNDR_REF
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/get_completion_signatures.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
template <class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_ref
{
using sender_concept = receiver_t;
_CCCL_HOST_DEVICE_API explicit constexpr __sndr_ref(_Sndr&& __sndr) noexcept
: __sndr_(static_cast<_Sndr&&>(__sndr))
{}
template <class _Self, class... _Env>
_CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
return execution::get_completion_signatures<_Sndr, _Env...>();
}
template <class _Rcvr>
_CCCL_HOST_DEVICE_API constexpr auto connect(_Rcvr __rcvr) const
{
return execution::connect(static_cast<_Sndr&&>(__sndr_), static_cast<_Rcvr&&>(__rcvr));
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> env_of_t<_Sndr>
{
return execution::get_env(__sndr_);
}
private:
_Sndr&& __sndr_;
};
template <class _Sndr>
_CCCL_DEDUCTION_GUIDE_ATTRIBUTES __sndr_ref(_Sndr&& __sndr) -> __sndr_ref<_Sndr>;
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_SNDR_REF

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_START_DETACHED
#define __CUDAX_EXECUTION_START_DETACHED
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/__utility/immovable.h>
#include <cuda/std/__exception/terminate.h>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__execution/apply_sender.cuh>
#include <cuda/experimental/__execution/cpos.cuh>
#include <cuda/experimental/__execution/env.cuh>
#include <cuda/experimental/__execution/utility.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
struct start_detached_t
{
private:
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_base_t
{};
struct _CCCL_TYPE_VISIBILITY_DEFAULT __rcvr_t
{
using receiver_concept = receiver_t;
__opstate_base_t* __opstate_;
void (*__destroy)(__opstate_base_t*) noexcept;
template <class... _As>
constexpr void set_value(_As&&...) noexcept
{
__destroy(__opstate_);
}
template <class _Error>
constexpr void set_error(_Error&&) noexcept
{
::cuda::std::terminate();
}
constexpr void set_stopped() noexcept
{
__destroy(__opstate_);
}
};
template <class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __opstate_t : __opstate_base_t
{
using operation_state_concept = operation_state_t;
connect_result_t<_Sndr, __rcvr_t> __opstate_;
static void __destroy(__opstate_base_t* __ptr) noexcept
{
delete static_cast<__opstate_t*>(__ptr);
}
_CCCL_HOST_DEVICE_API constexpr explicit __opstate_t(_Sndr&& __sndr)
: __opstate_(execution::connect(static_cast<_Sndr&&>(__sndr), __rcvr_t{this, &__destroy}))
{}
_CCCL_IMMOVABLE(__opstate_t);
_CCCL_HOST_DEVICE_API constexpr void start() noexcept
{
execution::start(__opstate_);
}
};
public:
template <class _Sndr>
_CCCL_HOST_DEVICE_API static auto apply_sender(_Sndr __sndr)
{
execution::start(*new __opstate_t<_Sndr>{static_cast<_Sndr&&>(__sndr)});
}
/// run detached.
template <class _Sndr>
_CCCL_HOST_DEVICE_API void operator()(_Sndr __sndr) const
{
using __domain_t _CCCL_NODEBUG_ALIAS = __completion_domain_of_t<set_value_t, _Sndr, env<>>;
execution::apply_sender(__domain_t{}, *this, static_cast<_Sndr&&>(__sndr));
}
};
_CCCL_GLOBAL_CONSTANT start_detached_t start_detached{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_START_DETACHED

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cccl_upstream/cudax/include/cuda/experimental/__execution/starts_on.cuh
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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef __CUDAX_EXECUTION_STARTS_ON
#define __CUDAX_EXECUTION_STARTS_ON
#include <cuda/std/detail/__config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cuda/std/__cccl/unreachable.h>
#include <cuda/std/__tuple_dir/ignore.h>
#include <cuda/std/__utility/forward_like.h>
#include <cuda/experimental/__execution/continues_on.cuh>
#include <cuda/experimental/__execution/get_completion_signatures.cuh>
#include <cuda/experimental/__execution/just.cuh>
#include <cuda/experimental/__execution/sequence.cuh>
#include <cuda/experimental/__execution/transform_sender.cuh>
#include <cuda/experimental/__execution/prologue.cuh>
namespace cuda::experimental::execution
{
template <class _Query>
_CCCL_CONCEPT __forwarding_starts_on_query = __forwarding_query<_Query> && !__is_completion_query<_Query>;
//! @brief Execution algorithm that starts a given sender on a specified scheduler.
//!
//! The `starts_on` algorithm takes a scheduler and a sender, and returns a new sender
//! that, when connected and started, will first schedule work on the provided scheduler,
//! and then start the original sender on that scheduler's execution context.
//!
//! This algorithm is particularly useful for ensuring that a chain of work begins
//! execution on a specific execution context, such as a particular GPU stream or thread
//! pool.
//!
//! @details The operation proceeds in two phases:
//! 1. **Scheduling Phase**: The algorithm first calls `schedule()` on the provided
//! scheduler to obtain a sender that represents scheduling work on that scheduler's
//! execution context.
//! 2. **Execution Phase**: Once the scheduling operation completes successfully, the
//! original sender is started on the scheduler's execution context.
//!
//! The resulting sender's completion signatures are derived from both the scheduler's
//! `schedule()` sender and the original sender. Error and stopped signals from either
//! operation are propagated to the final receiver.
//!
//! @tparam _Sch A scheduler type that satisfies the `scheduler` concept
//! @tparam _Sndr A sender type that satisfies the `sender` concept
//!
//! @param __sch The scheduler on which the sender should start execution
//! @param __sndr The sender to be started on the scheduler's execution context
//!
//! @return A sender that, when started, will first schedule on `__sch` and then execute
//! `__sndr`
//!
//! @note The returned sender's environment includes the provided scheduler as the current
//! scheduler, allowing nested senders to query and use the same execution context.
//!
//! @note This implementation follows the C++26 standard specification for
//! `std::execution::starts_on` as defined in [exec.starts.on].
//!
//! Example usage:
//! @code
//! auto work = cuda::experimental::execution::just(42)
//! | cuda::experimental::execution::then([](int x) { return x * 2; });
//!
//! auto scheduled_work = cuda::experimental::execution::starts_on(some_scheduler, work);
//! @endcode
//!
//! @see schedule
//! @see scheduler
//! @see sender
//! @see receiver
struct starts_on_t
{
template <class _Sch, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __sndr_t;
private:
template <class _Sch, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static constexpr auto __mk_env2(_Sch __sch, _Env&&... __env)
{
return __join_env(__mk_sch_env(__sch, __env...), __fwd_env(static_cast<_Env&&>(__env))...);
}
template <class _Sch, class... _Env>
using __env2_t = decltype(__mk_env2(declval<_Sch>(), declval<_Env>()...));
template <class _Sch, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT __attrs_t
{
// If the sender has a _SetTag completion, then the completion scheduler for _SetTag
// is the sender's.
_CCCL_EXEC_CHECK_DISABLE
template <class _SetTag, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_scheduler_t<_SetTag>, _Env&&... __env) const noexcept
-> __call_result_t<get_completion_scheduler_t<_SetTag>, env_of_t<_Sndr>, __env2_t<_Sch, _Env>...>
{
return get_completion_scheduler<_SetTag>(
execution::get_env(__self_->__sndr_), __mk_env2(__self_->__sch_, static_cast<_Env&&>(__env))...);
}
// If the sender has a _SetTag completion, then the completion scheduler for _SetTag
// is the sender's.
template <class _SetTag, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto
query(get_completion_domain_t<_SetTag>, _Env&&... __env) const noexcept
-> __call_result_t<get_completion_domain_t<_SetTag>, env_of_t<_Sndr>, __env2_t<_Sch, _Env>...>
{
return {};
}
template <class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(get_completion_behavior_t, _Env&&...) const noexcept
{
return (execution::min) (execution::get_completion_behavior<schedule_result_t<_Sch>, __fwd_env_t<_Env>...>(),
execution::get_completion_behavior<_Sndr, __env2_t<_Sch, _Env>...>());
}
_CCCL_EXEC_CHECK_DISABLE
_CCCL_TEMPLATE(class _Query, class... _Args)
_CCCL_REQUIRES(__forwarding_starts_on_query<_Query> _CCCL_AND __queryable_with<env_of_t<_Sndr>, _Query, _Args...>)
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto query(_Query, _Args&&... __args) const
noexcept(__nothrow_queryable_with<env_of_t<_Sndr>, _Query, _Args...>)
-> __query_result_t<env_of_t<_Sndr>, _Query, _Args...>
{
return execution::get_env(__self_->__sndr_).query(_Query{}, static_cast<_Args&&>(__args)...);
}
const __sndr_t<_Sch, _Sndr>* __self_;
};
public:
template <class _Sndr>
[[nodiscard]] static _CCCL_HOST_DEVICE_API constexpr auto
transform_sender(start_t, _Sndr&& __sndr, ::cuda::std::__ignore_t)
{
auto&& [__ign, __sch, __child] = __sndr;
return sequence(continues_on(just(), __sch), ::cuda::std::forward_like<_Sndr>(__child));
}
template <class _Sch, class _Sndr>
_CCCL_HOST_DEVICE_API constexpr auto operator()(_Sch __sch, _Sndr __sndr) const;
};
template <class _Sch, class _Sndr>
struct _CCCL_TYPE_VISIBILITY_DEFAULT starts_on_t::__sndr_t
{
using sender_concept = sender_t;
template <class _Self, class... _Env>
[[nodiscard]] _CCCL_HOST_DEVICE_API static _CCCL_CONSTEVAL auto get_completion_signatures()
{
_CUDAX_LET_COMPLETIONS(
auto(__child_completions) = execution::get_child_completion_signatures<_Self, _Sndr, __env2_t<_Sch, _Env>...>())
{
_CUDAX_LET_COMPLETIONS(
auto(__sch_completions) = execution::get_completion_signatures<schedule_result_t<_Sch>, __fwd_env_t<_Env>...>())
{
// The scheduler contributes error and stopped completions.
auto __sch_err_stop_completions = transform_completion_signatures(__sch_completions, __swallow_transform{});
return __child_completions + __sch_err_stop_completions;
}
}
_CCCL_UNREACHABLE();
}
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto get_env() const noexcept -> __attrs_t<_Sch, _Sndr>
{
return __attrs_t<_Sch, _Sndr>{this};
}
/*_CCCL_NO_UNIQUE_ADDRESS*/ starts_on_t __tag_;
_Sch __sch_;
_Sndr __sndr_;
};
_CCCL_EXEC_CHECK_DISABLE
template <class _Sch, class _Sndr>
[[nodiscard]] _CCCL_HOST_DEVICE_API constexpr auto starts_on_t::operator()(_Sch __sch, _Sndr __sndr) const
{
static_assert(__is_scheduler<_Sch>, "starts_on requires a scheduler as the first argument");
static_assert(__is_sender<_Sndr>, "starts_on requires a sender as the second argument");
return __sndr_t<_Sch, _Sndr>{{}, static_cast<_Sch&&>(__sch), static_cast<_Sndr&&>(__sndr)};
}
template <class _Sch, class _Sndr>
inline constexpr int structured_binding_size<starts_on_t::__sndr_t<_Sch, _Sndr>> = 3;
_CCCL_GLOBAL_CONSTANT starts_on_t starts_on{};
} // namespace cuda::experimental::execution
#include <cuda/experimental/__execution/epilogue.cuh>
#endif // __CUDAX_EXECUTION_STARTS_ON

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