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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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262
cccl_upstream/cudax/examples/simple_p2p.cu
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@@ -1,262 +0,0 @@
/* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* 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.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``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.
*/
/*
* This sample demonstrates a combination of Peer-to-Peer (P2P) and
* Unified Virtual Address Space (UVA) features.
*/
#include <cuda/algorithm>
#include <cuda/devices>
#include <cuda/memory_pool>
#include <cuda/memory_resource>
#include <cuda/experimental/container.cuh>
#include <cuda/experimental/launch.cuh>
#include <cuda/experimental/memory_resource.cuh>
#include <algorithm>
#include <cstdio>
#include <cstdlib>
namespace cudax = cuda::experimental;
struct simple_kernel
{
template <typename Configuration>
__device__ void operator()(Configuration config, ::cuda::std::span<const float> src, ::cuda::std::span<float> dst)
{
// Just a dummy kernel, doing enough for us to verify that everything worked
const auto idx = cuda::gpu_thread.rank(cuda::grid, config);
dst[idx] = src[idx] * 2.0f;
}
};
void print_peer_accessibility()
{
// Check possibility for peer access
printf("\nChecking GPU(s) for support of peer to peer memory access...\n");
for (auto& dev_i : cuda::devices)
{
for (auto& dev_j : cuda::devices)
{
if (dev_i != dev_j)
{
bool can_access_peer = dev_i.has_peer_access_to(dev_j);
const auto dev_i_name = dev_i.name();
const auto dev_j_name = dev_j.name();
printf("> Peer access from %.*s (GPU%d) -> %.*s (GPU%d) : %s\n",
static_cast<int>(dev_i_name.size()),
dev_i_name.data(),
dev_i.get(),
static_cast<int>(dev_j_name.size()),
dev_j_name.data(),
dev_j.get(),
can_access_peer ? "Yes" : "No");
}
}
}
}
template <typename BufferType>
void benchmark_cross_device_ping_pong_copy(
cudax::stream_ref dev0_stream, cudax::stream_ref dev1_stream, BufferType& dev0_buffer, BufferType& dev1_buffer)
{
// Use dev1 stream due to some surprising performance issue
constexpr int cpy_count = 100;
auto start_event = dev1_stream.record_timed_event();
for (int i = 0; i < cpy_count; i++)
{
// Ping-pong copy between GPUs
if (i % 2 == 0)
{
cuda::copy_bytes(dev1_stream, dev0_buffer, dev1_buffer);
}
else
{
cuda::copy_bytes(dev1_stream, dev1_buffer, dev0_buffer);
}
}
auto end_event = dev1_stream.record_timed_event();
dev1_stream.sync();
cuda::std::chrono::duration<double> duration(end_event - start_event);
printf("Peer copy between GPU%d and GPU%d: %.2fGB/s\n",
dev0_stream.device().get(),
dev1_stream.device().get(),
(static_cast<float>(cpy_count * dev0_buffer.size_bytes()) / static_cast<float>(1024 * 1024 * 1024)
/ duration.count()));
}
template <typename BufferType>
void test_cross_device_access_from_kernel(
cudax::stream_ref dev0_stream, cudax::stream_ref dev1_stream, BufferType& dev0_buffer, BufferType& dev1_buffer)
{
cuda::device_ref dev0 = dev0_stream.device();
cuda::device_ref dev1 = dev1_stream.device();
// Prepare host buffer and copy to GPU 0
printf("Preparing host buffer and copy to GPU%d...\n", dev0.get());
// This will be a pinned memory vector once available
cudax::uninitialized_buffer<float, cuda::mr::host_accessible> host_buffer(
cuda::mr::legacy_pinned_memory_resource(), dev0_buffer.size());
std::generate(host_buffer.begin(), host_buffer.end(), []() {
static int i = 0;
return static_cast<float>((i++) % 4096);
});
cuda::copy_bytes(dev0_stream, host_buffer, dev0_buffer);
dev1_stream.wait(dev0_stream);
// Kernel launch configuration
auto config = cuda::distribute<512>(dev0_buffer.size());
// Run kernel on GPU 1, reading input from the GPU 0 buffer, writing output to the GPU 1 buffer
printf("Run kernel on GPU%d, taking source data from GPU%d and writing to "
"GPU%d...\n",
dev1.get(),
dev0.get(),
dev1.get());
cudax::launch(dev1_stream, config, simple_kernel{}, dev0_buffer, dev1_buffer);
dev0_stream.wait(dev1_stream);
// Run kernel on GPU 0, reading input from the GPU 1 buffer, writing output to the GPU 0 buffer
printf("Run kernel on GPU%d, taking source data from GPU%d and writing to "
"GPU%d...\n",
dev0.get(),
dev1.get(),
dev0.get());
cudax::launch(dev0_stream, config, simple_kernel{}, dev1_buffer, dev0_buffer);
// Copy data back to host and verify
printf("Copy data back to host from GPU%d and verify results...\n", dev0.get());
cuda::copy_bytes(dev0_stream, dev0_buffer, host_buffer);
dev0_stream.sync();
int error_count = 0;
for (size_t i = 0; i < host_buffer.size(); i++)
{
cuda::std::span<float> host_span(host_buffer);
// Re-generate input data and apply 2x '* 2.0f' computation of both kernel runs
float expected = float(i % 4096) * 2.0f * 2.0f;
if (host_span[i] != expected)
{
printf("Verification error @ element %zu: val = %f, ref = %f\n", i, host_span[i], expected);
if (error_count++ > 10)
{
break;
}
}
}
if (error_count != 0)
{
printf("Test failed!\n");
exit(EXIT_FAILURE);
}
}
int main([[maybe_unused]] int argc, char** argv)
try
{
printf("[%s] - Starting...\n", argv[0]);
// Number of GPUs
printf("Checking for multiple GPUs...\n");
printf("CUDA-capable device count: %zu\n", cuda::devices.size());
if (cuda::devices.size() < 2)
{
printf("Two or more GPUs with Peer-to-Peer access capability are required for %s.\n", argv[0]);
printf("Waiving test.\n");
return 0;
}
// Print full peer access matrix
print_peer_accessibility();
// But use a shorthand to find all peers of a device
std::vector<cuda::device_ref> peers;
for (auto& dev : cuda::devices)
{
const auto dev_peers = dev.peers();
if (dev_peers.size() != 0)
{
peers.assign(dev_peers.begin(), dev_peers.end());
peers.insert(peers.begin(), dev);
break;
}
}
if (peers.size() == 0)
{
printf("Two or more GPUs with Peer-to-Peer access capability are required, waving the test.\n");
return 0;
}
cuda::stream dev0_stream(peers[0]);
cuda::stream dev1_stream(peers[1]);
printf("Enabling peer access between GPU%d and GPU%d...\n", peers[0].get(), peers[1].get());
cuda::device_memory_pool_ref dev0_resource = cuda::device_default_memory_pool(peers[0]);
dev0_resource.enable_access_from(peers[1]);
cuda::device_memory_pool_ref dev1_resource = cuda::device_default_memory_pool(peers[1]);
dev1_resource.enable_access_from(peers[0]);
// Allocate buffers
constexpr size_t buf_cnt = 1024 * 1024 * 16;
printf("Allocating buffers (%iMB on GPU%d, GPU%d and CPU Host)...\n",
int(buf_cnt / 1024 / 1024 * sizeof(float)),
peers[0].get(),
peers[1].get());
cudax::uninitialized_buffer<float, cuda::mr::device_accessible> dev0_buffer(dev0_resource, buf_cnt);
cudax::uninitialized_buffer<float, cuda::mr::device_accessible> dev1_buffer(dev1_resource, buf_cnt);
benchmark_cross_device_ping_pong_copy(dev0_stream, dev1_stream, dev0_buffer, dev1_buffer);
test_cross_device_access_from_kernel(dev0_stream, dev1_stream, dev0_buffer, dev1_buffer);
// Disable peer access
printf("Disabling peer access...\n");
dev0_resource.disable_access_from(peers[1]);
dev1_resource.disable_access_from(peers[0]);
// No cleanup needed
printf("Test passed\n");
return 0;
}
catch (const std::exception& e)
{
printf("caught an exception: \"%s\"\n", e.what());
}
catch (...)
{
printf("caught an unknown exception\n");
}