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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

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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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218
cccl_upstream/cudax/test/group/cooperative_algorithm.cu
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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 <cub/block/block_reduce.cuh>
#include <cub/thread/thread_reduce.cuh>
#include <cub/warp/warp_reduce.cuh>
#include <cuda/atomic>
#include <cuda/devices>
#include <cuda/hierarchy>
#include <cuda/launch>
#include <cuda/std/optional>
#include <cuda/std/type_traits>
#include <cuda/std/utility>
#include <cuda/stream>
#include <cuda/experimental/group.cuh>
#include <cooperative_groups.h>
#include "group_testing.cuh"
namespace
{
template <class Hierarchy, class T, cuda::std::size_t N>
__device__ cuda::std::optional<T> sum(cudax::this_thread<Hierarchy> group, T (&array)[N])
{
return {cub::ThreadReduce(array, cuda::std::plus<T>{})};
}
template <class Hierarchy, class T, cuda::std::size_t N>
__device__ cuda::std::optional<T> sum(cudax::this_warp<Hierarchy> group, T (&array)[N])
{
using WarpReduce = cub::WarpReduce<T>;
__shared__ typename WarpReduce::TempStorage scratch;
const auto partial = cub::ThreadReduce(array, cuda::std::plus<T>{});
const auto result = WarpReduce{scratch}.Sum(partial);
return (cuda::gpu_thread.is_root_rank(group)) ? cuda::std::optional{result} : cuda::std::nullopt;
}
template <class Hierarchy, class T, cuda::std::size_t N>
__device__ cuda::std::optional<T> sum(cudax::this_block<Hierarchy> group, T (&array)[N])
{
using BlockExts = decltype(cuda::gpu_thread.extents(cuda::block, group.hierarchy()));
static_assert(BlockExts::rank_dynamic() == 0, "This algorithm requires all static extents.");
using BlockReduce =
cub::BlockReduce<T,
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))>;
__shared__ typename BlockReduce::TempStorage scratch;
const auto result = BlockReduce{scratch}.Sum(array);
return (cuda::gpu_thread.is_root_rank(group)) ? cuda::std::optional{result} : cuda::std::nullopt;
}
template <class Hierarchy, class T, cuda::std::size_t N>
__device__ cuda::std::optional<T> sum(cudax::this_cluster<Hierarchy> group, T (&array)[N])
{
using BlockExts = decltype(cuda::gpu_thread.extents(cuda::block, group.hierarchy()));
static_assert(BlockExts::rank_dynamic() == 0, "This algorithm requires all static extents.");
using BlockReduce =
cub::BlockReduce<T,
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 SMem
{
typename BlockReduce::TempStorage block_scratch;
T cluster_scratch;
};
__shared__ SMem smem;
T result = BlockReduce{smem.block_scratch}.Sum(array);
NV_IF_TARGET(NV_PROVIDES_SM_90, ({
const auto dsmem = static_cast<T*>(__cluster_map_shared_rank(&smem.cluster_scratch, 0));
if (cuda::gpu_thread.is_root_rank(group))
{
smem.cluster_scratch = result;
}
group.sync_aligned();
cudax::this_block this_block{group.hierarchy()};
if (cuda::gpu_thread.is_root_rank(this_block) && !cuda::gpu_thread.is_root_rank(group))
{
[[maybe_unused]] unsigned old;
asm volatile("atom.relaxed.cluster.shared::cluster.add.s32 %0, [%1], %2;"
: "=r"(old)
: "l"(dsmem), "r"(result)
: "memory");
}
group.sync_aligned();
if (cuda::gpu_thread.is_root_rank(group))
{
result = smem.cluster_scratch;
}
}))
return (cuda::gpu_thread.is_root_rank(group)) ? cuda::std::optional{result} : cuda::std::nullopt;
}
// todo(dabayer): Add support for warp and cluster levels.
template <class Group, class T, cuda::std::size_t N>
__device__ cuda::std::optional<T> sum(Group group, T (&array)[N])
{
using Unit = typename Group::unit_type;
using MappingResult = typename Group::__mapping_result_type;
constexpr auto ngroups = MappingResult::static_group_count();
static_assert(ngroups != cuda::std::dynamic_extent, "group count must be statically known");
__shared__ T group_sums[ngroups];
if (!Unit{}.is_part_of(group))
{
return cuda::std::nullopt;
}
// todo(dabayer): Replace by group.rank(level) once this query is available.
const auto group_rank = group.__mapping_result().group_rank();
if (cuda::gpu_thread.is_root_rank(group))
{
group_sums[group_rank] = 0;
}
const auto unit_group = cudax::make_this_group(Unit{}, group.hierarchy());
const auto result_unit = sum(unit_group, array);
// Wait until group_sums are are filled with 0.
group.sync_aligned();
if (cuda::gpu_thread.is_root_rank(unit_group))
{
cuda::atomic_ref<T, cuda::thread_scope_block>{group_sums[group_rank]} += result_unit.value();
}
// Wait until all unit_group roots add the intermediate sum to the shared memory.
group.sync_aligned();
return (cuda::gpu_thread.is_root_rank(group)) ? cuda::std::optional{group_sums[group_rank]} : cuda::std::nullopt;
}
template <class Group>
__device__ void test_cooperative_algorithm(Group group)
{
using Level = typename Group::level_type;
unsigned array[]{1, 2, 3};
const auto result = sum(group, array);
const auto ref_sum = static_cast<unsigned>(6 * cuda::gpu_thread.count(group));
// Only the root rank should have the correct result.
if (cuda::gpu_thread.is_root_rank(group))
{
REQUIRE(result.has_value());
REQUIRE(result == ref_sum);
}
else
{
REQUIRE(!result.has_value());
}
}
struct TestKernel
{
template <class Config>
__device__ void operator()(const Config& config)
{
test_cooperative_algorithm(cudax::this_thread{config});
test_cooperative_algorithm(cudax::this_warp{config});
test_cooperative_algorithm(cudax::this_block{config});
test_cooperative_algorithm(cudax::this_cluster{config});
test_cooperative_algorithm(
cudax::group{cuda::gpu_thread, cudax::this_block{config}, cudax::group_by<2>{}, cudax::lane_synchronizer{}});
test_cooperative_algorithm(
cudax::group{cuda::gpu_thread, cudax::this_block{config}, cudax::group_by<16>{}, cudax::lane_synchronizer{}});
}
};
} // namespace
C2H_TEST("Collective algorithm", "[group]")
{
const auto device = cuda::devices[0];
const cuda::stream stream{device};
const auto config = cuda::make_config(cuda::grid_dims<2>(), cuda::block_dims<128>());
cuda::launch(stream, config, TestKernel{});
if (cuda::device_attributes::compute_capability(device) >= cuda::compute_capability{90})
{
const auto config_cluster =
cuda::make_config(cuda::grid_dims<2>(), cuda::cluster_dims<3>(), cuda::block_dims<128>());
cuda::launch(stream, config_cluster, TestKernel{});
}
stream.sync();
}