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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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74
cccl_upstream/cudax/test/stf/reclaiming/graph.cu
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@@ -1,74 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDASTF 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) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <iostream>
#if !_CCCL_COMPILER(MSVC)
using namespace cuda::experimental::stf;
__global__ void kernel()
{
// No-op
}
#endif // !_CCCL_COMPILER(MSVC)
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
// TODO fix setenv
#if !_CCCL_COMPILER(MSVC)
int nblocks = 4;
size_t block_size = 1024 * 1024;
if (argc > 1)
{
nblocks = atoi(argv[1]);
}
if (argc > 2)
{
block_size = atoi(argv[2]);
}
// At most 1 buffer is allocated at the same time
setenv("MAX_ALLOC_CNT", "1", 0);
graph_ctx ctx;
::std::vector<logical_data<slice<char>>> handles(nblocks);
char* h_buffer = new char[nblocks * block_size];
for (int i = 0; i < nblocks; i++)
{
handles[i] = ctx.logical_data(make_slice(&h_buffer[i * block_size], block_size));
handles[i].set_symbol("D_" + std::to_string(i));
}
// We only 2 buffers, we are forced to reuse the buffer from D0 for D2
for (int i = 0; i < 3; i++)
{
ctx.task(handles[i % nblocks].rw())->*[&](cudaStream_t s, auto /*unused*/) {
kernel<<<1, 1, 0, s>>>();
};
}
ctx.submit();
if (argc > 3)
{
std::cout << "Generating DOT output in " << argv[3] << '\n';
ctx.print_to_dot(argv[1]);
}
ctx.finalize();
#endif // !_CCCL_COMPILER(MSVC)
}

68
cccl_upstream/cudax/test/stf/reclaiming/graph_2.cu
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@@ -1,68 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDASTF 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) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <iostream>
using namespace cuda::experimental::stf;
__global__ void kernel()
{
// No-op
}
int main(int argc, char** argv)
{
int nblocks = 4;
size_t block_size = 1024 * 1024;
if (argc > 1)
{
nblocks = atoi(argv[1]);
}
if (argc > 2)
{
block_size = atoi(argv[2]);
}
// At most 1 buffer is allocated at the same time
setenv("MAX_ALLOC_CNT", "2", 1);
graph_ctx ctx;
::std::vector<logical_data<slice<char>>> handles(nblocks);
std::vector<char> h_buffer(nblocks * block_size);
for (int i = 0; i < nblocks; i++)
{
handles[i] = ctx.logical_data(make_slice(&h_buffer[i * block_size], block_size));
handles[i].set_symbol("D_" + std::to_string(i));
}
// We only 2 buffers, we are forced to reuse the buffer from D0 for D2
for (int i = 0; i < 3; i++)
{
ctx.task(handles[i % nblocks].read(), handles[(i + 1) % nblocks].rw())
->*[&](cudaStream_t s, auto /*unused*/, auto /*unused*/) {
kernel<<<1, 1, 0, s>>>();
};
}
ctx.submit();
if (argc > 3)
{
std::cout << "Generating DOT output in " << argv[3] << '\n';
ctx.print_to_dot(argv[3]);
}
ctx.finalize();
}

79
cccl_upstream/cudax/test/stf/reclaiming/graph_real_oom.cu
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@@ -1,79 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDASTF 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) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <iostream>
using namespace cuda::experimental::stf;
__global__ void dummy() {}
int main(int argc, char** argv)
{
const int dev_id = 0;
cuda_safe_call(cudaSetDevice(dev_id));
cudaDeviceProp prop;
cuda_safe_call(cudaGetDeviceProperties(&prop, dev_id));
const size_t total_mem_ref = prop.totalGlobalMem;
size_t free_mem, total_mem;
cuda_safe_call(cudaMemGetInfo(&free_mem, &total_mem));
std::cout << "Device memory: " << total_mem_ref / (1024 * 1024.) << " MB"
<< " FREE/TOTAL=" << free_mem / (1024 * 1024.) << "/" << total_mem / (1024 * 1024.) << '\n';
// Warning: this should represent 6% of device's available memory
size_t block_size = free_mem / 32;
int nblocks = 2;
// We preallocate most available device memory to avoid being limited by host memory.
void* wasted_mem;
size_t wasted_size = block_size * 29;
cuda_safe_call(cudaMalloc(&wasted_mem, wasted_size));
std::cout << "Wasted: " << wasted_size / (1024 * 1024.) << " MB" << '\n';
graph_ctx ctx;
std::vector<logical_data<slice<char>>> handles(nblocks);
char* h_buffer = new char[nblocks * block_size];
for (int i = 0; i < 2; i++)
{
handles[i] = ctx.logical_data(make_slice(&h_buffer[i * block_size], block_size));
handles[i].set_symbol("D_" + std::to_string(i));
}
// There can only be a single handle on the device at the same time
for (int i = 0; i < nblocks; i++)
{
ctx.task(handles[i % nblocks].rw())->*[](cudaStream_t stream, auto /*unused*/) {
dummy<<<1, 1, 0, stream>>>();
};
}
// Checking the amount of memory actually available now
cuda_safe_call(cudaMemGetInfo(&free_mem, &total_mem));
std::cout << "Device memory: FREE/TOTAL=" << free_mem / (1024 * 1024.) << "/" << total_mem / (1024 * 1024.) << '\n';
ctx.submit();
if (argc > 1)
{
std::cout << "Generating DOT output in " << argv[1] << '\n';
ctx.print_to_dot(argv[1]);
}
ctx.finalize();
}

145
cccl_upstream/cudax/test/stf/reclaiming/stream.cu
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@@ -1,145 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDASTF 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) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
__global__ void kernel(int i, slice<char> buf)
{
buf[0] = (char) i;
}
static __global__ void cuda_sleep_kernel(long long int clock_cnt)
{
long long int start_clock = clock64();
long long int clock_offset = 0;
while (clock_offset < clock_cnt)
{
clock_offset = clock64() - start_clock;
}
}
void cuda_sleep(double ms, cudaStream_t stream)
{
int device;
cudaGetDevice(&device);
// cudaDevAttrClockRate: Peak clock frequency in kilohertz;
int clock_rate;
cudaDeviceGetAttribute(&clock_rate, cudaDevAttrClockRate, device);
long long int clock_cnt = (long long int) (ms * clock_rate);
cuda_sleep_kernel<<<1, 1, 0, stream>>>(clock_cnt);
}
// Dummy allocator which only allocates a single block on a single device
//
// The first allocation succeeds, next attempt will fail until buffer is
// deallocated.
class one_block_allocator : public block_allocator_interface
{
public:
one_block_allocator() = default;
public:
// Note that this allocates memory immediately, so we just do not modify the event list and ignore it
void* allocate(backend_ctx_untyped&, const data_place& memory_node, ::std::ptrdiff_t& s, event_list&) override
{
if (busy)
{
s = -s;
return nullptr;
}
EXPECT(memory_node.is_device());
if (!base)
{
cuda_safe_call(cudaMalloc(&base, s));
}
busy = true;
return base;
}
void deallocate(backend_ctx_untyped&, const data_place&, event_list&, void*, size_t) override
{
EXPECT(busy);
busy = false;
}
event_list deinit(backend_ctx_untyped&) override
{
return event_list();
}
std::string to_string() const override
{
return "dummy";
}
private:
// We have a single block, so we keep its address, and a flag to indicate
// if it's busy
void* base = nullptr;
bool busy = false;
};
int main(int argc, char** argv)
{
int nblocks = 4;
size_t block_size = 1024 * 1024;
if (argc > 1)
{
nblocks = atoi(argv[1]);
}
if (argc > 2)
{
block_size = atoi(argv[2]);
}
stream_ctx ctx;
auto dummy_alloc = block_allocator<one_block_allocator>(ctx);
ctx.set_allocator(dummy_alloc);
::std::vector<logical_data<slice<char>>> handles(nblocks);
EXPECT(nblocks > 0);
EXPECT(block_size > 0);
char* h_buffer = new char[nblocks * block_size];
for (int i = 0; i < nblocks; i++)
{
handles[i] = ctx.logical_data(make_slice(&h_buffer[i * block_size], block_size));
handles[i].set_symbol("D_" + std::to_string(i));
}
// We only 2 buffers, we are forced to reuse the buffer from D0 for D2
for (int i = 0; i < nblocks; i++)
{
ctx.task(handles[i % nblocks].rw())->*[&](cudaStream_t s, auto buf) {
// Wait 100ms to have a more stressful asynchronous execution
cuda_sleep(100, s);
kernel<<<1, 1, 0, s>>>(i, buf);
};
}
ctx.finalize();
for (int i = 0; i < nblocks; i++)
{
EXPECT(h_buffer[block_size * i] == i);
}
}