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project_6/cccl_upstream/cudax/test/group/mapping/binary_partition.cu
EngineX CI 56fd68e7dd [INFRA] Import NVIDIA/CCCL upstream as optimization reference library
CCCL (CUDA C++ Core Libraries) provides:
- CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk)
- Thrust: high-level parallel algorithms (transform_reduce, sort, scan)
- libcudacxx: CUDA C++ standard library (atomics, barriers, memory)
- cudax: experimental features (memory resources, allocators)
- Tuning policies: per-SM hardware-specific algorithm parameters

Competition optimization vectors mapped to CCCL:
- Output TPS (83% weight): warp_reduce, block_reduce, device_topk
- Input TPS (14% weight): device_scan, block_load, prefetch
- Cache TPS (3% weight): prefix caching strategy patterns
- Memory (0.9 util): pooled/cached/buddy allocators

Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only)
License: Apache-2.0
2026-07-30 09:35:51 +00:00

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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.
//
//===----------------------------------------------------------------------===//
#include <cuda/devices>
#include <cuda/hierarchy>
#include <cuda/launch>
#include <cuda/std/cstddef>
#include <cuda/std/type_traits>
#include <cuda/std/utility>
#include <cuda/stream>
#include <cuda/experimental/group.cuh>
#include "group_testing.cuh"
namespace
{
struct AlwaysTruePredFn
{
template <class MappingResult>
__device__ bool operator()(MappingResult mapping_result)
{
return true;
}
};
struct AlwaysFalsePredFn
{
template <class MappingResult>
__device__ bool operator()(MappingResult mapping_result) noexcept
{
return false;
}
};
struct IsEvenPredFn
{
template <class MappingResult>
__device__ bool operator()(MappingResult mapping_result)
{
return mapping_result.unit_rank() % 2 == 0;
}
};
template <class Config>
__device__ void test_binary_partition(Config config)
{
// Always true predicate.
{
using Pred = AlwaysTruePredFn;
using Mapping = cudax::binary_partition<Pred>;
// Test constructor from pred_fn.
{
static_assert(cuda::std::is_nothrow_constructible_v<Mapping, Pred>);
cudax::binary_partition mapping{Pred{}};
}
// Test map(...).
{
const cudax::this_warp parent_group{config};
const ThreadsInWarpMappingResult prev_mapping_result;
static_assert(cudax::__group_mapping_result<decltype(cuda::std::declval<const Mapping>().map(
cuda::gpu_thread, parent_group, prev_mapping_result))>);
static_assert(
!noexcept(cuda::std::declval<const Mapping>().map(cuda::gpu_thread, parent_group, prev_mapping_result)));
const Mapping mapping{Pred{}};
auto result = mapping.map(cuda::gpu_thread, parent_group, prev_mapping_result);
using Result = decltype(result);
static_assert(Result::static_group_count() == 2);
REQUIRE(result.group_count() == 2);
REQUIRE(result.group_rank() == 1);
static_assert(Result::static_unit_count() == cuda::std::dynamic_extent);
REQUIRE(result.unit_count() == cuda::gpu_thread.count(cuda::warp));
REQUIRE(result.unit_rank() == cuda::gpu_thread.rank(cuda::warp));
REQUIRE(result.lane_mask() == cuda::device::lane_mask::all());
REQUIRE(result.is_valid());
static_assert(Result::is_always_exhaustive());
static_assert(!Result::is_always_contiguous());
}
}
// Always false predicate.
{
using Pred = AlwaysFalsePredFn;
using Mapping = cudax::binary_partition<Pred>;
// Test constructor from pred_fn.
{
static_assert(cuda::std::is_nothrow_constructible_v<Mapping, Pred>);
cudax::binary_partition mapping{Pred{}};
}
// Test map(...).
{
const cudax::this_warp parent_group{config};
const ThreadsInWarpMappingResult prev_mapping_result;
static_assert(cudax::__group_mapping_result<decltype(cuda::std::declval<const Mapping>().map(
cuda::gpu_thread, parent_group, prev_mapping_result))>);
static_assert(
noexcept(cuda::std::declval<const Mapping>().map(cuda::gpu_thread, parent_group, prev_mapping_result)));
const Mapping mapping{Pred{}};
auto result = mapping.map(cuda::gpu_thread, parent_group, prev_mapping_result);
using Result = decltype(result);
static_assert(Result::static_group_count() == 2);
REQUIRE(result.group_count() == 2);
REQUIRE(result.group_rank() == 0);
static_assert(Result::static_unit_count() == cuda::std::dynamic_extent);
REQUIRE(result.unit_count() == cuda::gpu_thread.count(cuda::warp));
REQUIRE(result.unit_rank() == cuda::gpu_thread.rank(cuda::warp));
REQUIRE(result.lane_mask() == cuda::device::lane_mask::all());
REQUIRE(result.is_valid());
static_assert(Result::is_always_exhaustive());
static_assert(!Result::is_always_contiguous());
}
}
// True for even ranks predicate.
{
using Pred = IsEvenPredFn;
using Mapping = cudax::binary_partition<Pred>;
// Test constructor from pred_fn.
{
static_assert(cuda::std::is_nothrow_constructible_v<Mapping, Pred>);
cudax::binary_partition mapping{Pred{}};
}
// Test map(...).
{
const cudax::this_warp parent_group{config};
const ThreadsInWarpMappingResult prev_mapping_result;
static_assert(cudax::__group_mapping_result<decltype(cuda::std::declval<const Mapping>().map(
cuda::gpu_thread, parent_group, prev_mapping_result))>);
static_assert(
!noexcept(cuda::std::declval<const Mapping>().map(cuda::gpu_thread, parent_group, prev_mapping_result)));
const Mapping mapping{Pred{}};
auto result = mapping.map(cuda::gpu_thread, parent_group, prev_mapping_result);
using Result = decltype(result);
static_assert(Result::static_group_count() == 2);
REQUIRE(result.group_count() == 2);
REQUIRE(result.group_rank() == (cuda::gpu_thread.rank(cuda::warp) % 2 == 0));
static_assert(Result::static_unit_count() == cuda::std::dynamic_extent);
REQUIRE(result.unit_count() == cuda::gpu_thread.count(cuda::warp) / 2);
REQUIRE(result.unit_rank() == cuda::gpu_thread.rank(cuda::warp) / 2);
const auto lane_mask_ref =
(cuda::gpu_thread.rank(cuda::warp) % 2 == 0)
? cuda::device::lane_mask{0x5555'5555u}
: cuda::device::lane_mask(0xaaaa'aaaau);
REQUIRE(result.lane_mask() == lane_mask_ref);
REQUIRE(result.is_valid());
static_assert(Result::is_always_exhaustive());
static_assert(!Result::is_always_contiguous());
}
}
}
struct TestKernel
{
template <class Config>
__device__ void operator()(const Config& config)
{
test_binary_partition(config);
}
};
} // namespace
C2H_TEST("Binary partition mapping", "[group]")
{
const auto device = cuda::devices[0];
const cuda::stream stream{device};
{
const auto config = cuda::make_config(cuda::grid_dims<1>(), cuda::block_dims<8, 4>());
cuda::launch(stream, config, TestKernel{});
}
{
const auto config = cuda::make_config(cuda::grid_dims<1>(), cuda::block_dims(dim3{8, 4}));
cuda::launch(stream, config, TestKernel{});
}
stream.sync();
}