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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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58
cccl_upstream/cudax/test/stf/algorithm/algorithm_with_read.cu
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@@ -1,58 +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;
template <typename T>
void init(context& ctx, logical_data<T> l, int val)
{
ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
s(i) = val;
};
}
int main()
{
context ctx;
auto a = ctx.logical_data<int>(10000000);
auto b = ctx.logical_data<int>(10000000);
auto c = ctx.logical_data<int>(10000000);
auto d = ctx.logical_data<int>(10000000);
init(ctx, a, 12);
init(ctx, b, 35);
init(ctx, c, 42);
init(ctx, d, 17);
/* a += 1; a += b; */
auto fn = [](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
ctx.parallel_for(a.shape(), a.rw())->*[] __device__(size_t i, auto sa) {
sa(i) += 1;
};
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
};
algorithm alg;
for (size_t i = 0; i < 100; i++)
{
alg.run_as_task(fn, ctx, a.rw(), b.read());
alg.run_as_task(fn, ctx, a.rw(), c.read());
alg.run_as_task(fn, ctx, c.rw(), d.read());
alg.run_as_task(fn, ctx, d.rw(), a.read());
}
ctx.finalize();
}

108
cccl_upstream/cudax/test/stf/algorithm/graph_algorithms.cu
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@@ -1,108 +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;
template <typename ctx_t, typename T, typename T2>
void lib_call(ctx_t& ctx, logical_data<T> a, logical_data<T2> b)
{
nvtx_range r("lib_call");
// b *= 2
// a = a + b
// b = a
ctx.parallel_for(b.shape(), b.rw())->*[] __device__(size_t i, auto sb) {
sb(i) *= 2;
};
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
sb(i) = sa(i);
};
}
template <typename T>
void init(context& ctx, logical_data<T> l, int val)
{
ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
s(i) = val;
};
}
int main()
{
context ctx;
auto a = ctx.logical_data<int>(size_t(10000000));
auto b = ctx.logical_data<int>(size_t(10000000));
auto c = ctx.logical_data<int>(size_t(10000000));
auto d = ctx.logical_data<int>(size_t(10000000));
init(ctx, a, 12);
init(ctx, b, 35);
init(ctx, c, 42);
init(ctx, d, 42);
auto fn = [](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
ctx.parallel_for(b.shape(), b.rw())->*[] __device__(size_t i, auto sb) {
sb(i) *= 2;
};
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
sb(i) = sa(i);
};
};
algorithm alg;
{
nvtx_range r("run");
for (size_t i = 0; i < 100; i++)
{
ctx.task(a.rw(), b.rw())->*[&alg, &fn, &ctx](cudaStream_t stream, slice<int> sa, slice<int> sb) {
alg.run(fn, ctx, stream, sa, sb);
};
ctx.task(b.rw(), c.rw())->*[&alg, &fn, &ctx](cudaStream_t stream, slice<int> sb, slice<int> sc) {
alg.run(fn, ctx, stream, sb, sc);
};
ctx.task(c.rw(), d.rw())->*[&alg, &fn, &ctx](cudaStream_t stream, slice<int> sc, slice<int> sd) {
alg.run(fn, ctx, stream, sc, sd);
};
ctx.task(d.rw(), a.rw())->*[&alg, &fn, &ctx](cudaStream_t stream, slice<int> sd, slice<int> sa) {
alg.run(fn, ctx, stream, sd, sa);
};
}
}
{
nvtx_range r("run_as_task");
for (size_t i = 0; i < 100; i++)
{
alg.run_as_task(fn, ctx, a.rw(), b.rw());
alg.run_as_task(fn, ctx, a.rw(), c.rw());
alg.run_as_task(fn, ctx, c.rw(), d.rw());
alg.run_as_task(fn, ctx, d.rw(), a.rw());
}
}
ctx.finalize();
}

68
cccl_upstream/cudax/test/stf/algorithm/in_graph_ctx.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.cuh>
using namespace cuda::experimental::stf;
template <typename context_t, typename T>
void init(context_t& ctx, logical_data<T> l, int val)
{
ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
s(i) = val;
};
}
int main()
{
// context ctx = graph_ctx();
graph_ctx ctx;
auto a = ctx.logical_data<int>(size_t(1000000));
auto b = ctx.logical_data<int>(size_t(1000000));
auto c = ctx.logical_data<int>(size_t(1000000));
auto d = ctx.logical_data<int>(size_t(1000000));
init(ctx, a, 12);
init(ctx, b, 35);
init(ctx, c, 42);
init(ctx, d, 42);
auto fn = [](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
ctx.parallel_for(a.shape(), a.rw())->*[] __device__(size_t i, auto sa) {
sa(i) *= 3;
};
ctx.parallel_for(b.shape(), b.rw())->*[] __device__(size_t i, auto sb) {
sb(i) *= 2;
};
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
sb(i) = sa(i);
};
};
algorithm alg;
for (size_t i = 0; i < 5; i++)
{
alg.run_as_task(fn, ctx, a.rw(), b.rw());
alg.run_as_task(fn, ctx, a.rw(), c.rw());
alg.run_as_task(fn, ctx, c.rw(), d.rw());
alg.run_as_task(fn, ctx, d.rw(), a.rw());
}
ctx.finalize();
// cudaGraphDebugDotPrint(ctx.get_graph(), "pif.dot", 0);
}

67
cccl_upstream/cudax/test/stf/algorithm/nested.cu
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@@ -1,67 +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;
template <typename T>
void init(context& ctx, logical_data<T> l, int val)
{
ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
s(i) = val;
};
}
int main()
{
context ctx;
auto a = ctx.logical_data<int>(size_t(1000000));
auto b = ctx.logical_data<int>(size_t(1000000));
auto c = ctx.logical_data<int>(size_t(1000000));
auto d = ctx.logical_data<int>(size_t(1000000));
init(ctx, a, 12);
init(ctx, b, 35);
init(ctx, c, 42);
init(ctx, d, 17);
auto fn1 = [](context ctx, logical_data<slice<int>> a) {
ctx.parallel_for(a.shape(), a.rw())->*[] __device__(size_t i, auto sa) {
sa(i) += 1;
};
};
algorithm alg1;
auto fn2 = [&alg1, &fn1](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
alg1.run_as_task(fn1, ctx, a.rw());
alg1.run_as_task(fn1, ctx, b.rw());
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
sb(i) = sa(i);
};
};
algorithm alg2;
for (size_t i = 0; i < 100; i++)
{
alg2.run_as_task(fn2, ctx, a.rw(), b.rw());
alg2.run_as_task(fn2, ctx, a.rw(), c.rw());
alg2.run_as_task(fn2, ctx, c.rw(), d.rw());
alg2.run_as_task(fn2, ctx, d.rw(), a.rw());
}
ctx.finalize();
}