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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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72
cccl_upstream/cudax/test/stf/freeze/constant_logical_data.cu
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@@ -1,72 +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.
//
//===----------------------------------------------------------------------===//
/**
* @file
*
* @brief Helper to build constant data based on frozen logical data
*
*/
#include <cuda/experimental/__stf/utility/constant_logical_data.cuh>
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
stream_ctx ctx;
const int N = 16;
/* Create a constant value */
auto ld_cst = ctx.logical_data(shape_of<slice<int>>(N));
ctx.parallel_for(ld_cst.shape(), ld_cst.write())->*[] __device__(size_t i, slice<int> res) {
res(i) = 18 * i - 9;
};
auto cst = constant_logical_data(ctx, mv(ld_cst));
int X[N];
for (int i = 0; i < N; i++)
{
X[i] = 5 * i - 3;
}
auto lX = ctx.logical_data(X).set_symbol("X");
for (size_t iter = 0; iter < 4; iter++)
{
auto cst_slice = cst.get();
ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X+=cst")->*[cst_slice] __device__(size_t i, auto x) {
x(i) += cst_slice(i);
};
auto cst2 = run_once()->*[&]() {
auto ld = ctx.logical_data(shape_of<slice<int>>(N));
ctx.parallel_for(ld.shape(), ld.write())->*[] __device__(size_t i, slice<int> res) {
res(i) = 4 * i - 2;
};
return constant_logical_data(ctx, mv(ld));
};
auto cst2_slice = cst2.get();
ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X+=cst2")->*[cst2_slice] __device__(size_t i, auto x) {
x(i) += cst2_slice(i);
};
}
ctx.finalize();
for (int i = 0; i < N; i++)
{
EXPECT(X[i] == (5 * i - 3) + 4 * (18 * i - 9) + 4 * (4 * i - 2));
}
}

82
cccl_upstream/cudax/test/stf/freeze/freeze.cu
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@@ -1,82 +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.
//
//===----------------------------------------------------------------------===//
/**
* @file
*
* @brief Freeze data in read-only fashion
*
*/
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
int X0(int i)
{
return 17 * i + 45;
}
__global__ void print(slice<int> s)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int nthreads = gridDim.x * blockDim.x;
for (int i = tid; i < s.size(); i += nthreads)
{
printf("%d %d\n", i, s(i));
}
}
int main()
{
stream_ctx ctx;
cudaStream_t stream = ctx.pick_stream();
const int N = 16;
int X[N];
for (int i = 0; i < N; i++)
{
X[i] = X0(i);
}
auto lX = ctx.logical_data(X).set_symbol("X");
auto lY = ctx.logical_data(lX.shape()).set_symbol("Y");
ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X=2X")->*[] __device__(size_t i, auto x) {
x(i) *= 2;
};
auto fx = ctx.freeze(lX);
auto dX = fx.get(data_place::current_device(), stream);
print<<<8, 4, 0, stream>>>(dX);
ctx.parallel_for(lX.shape(), lX.read(), lY.write()).set_symbol("Y=X")->*[] __device__(size_t i, auto x, auto y) {
y(i) = x(i);
};
fx.unfreeze(stream);
ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X+=1")->*[] __device__(size_t i, auto x) {
x(i) += 1;
};
ctx.finalize();
for (int i = 0; i < N; i++)
{
EXPECT(X[i] == 2 * X0(i) + 1);
}
}

86
cccl_upstream/cudax/test/stf/freeze/freeze_rw.cu
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@@ -1,86 +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.
//
//===----------------------------------------------------------------------===//
/**
* @file
*
* @brief Freeze data in read-only fashion
*
*/
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
int X0(int i)
{
return 17 * i + 45;
}
__global__ void mult(slice<int> s, int val)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int nthreads = gridDim.x * blockDim.x;
for (int i = tid; i < s.size(); i += nthreads)
{
s(i) *= val;
}
}
int main()
{
stream_ctx ctx;
cudaStream_t stream = ctx.pick_stream();
const int N = 16;
int X[N];
for (int i = 0; i < N; i++)
{
X[i] = X0(i);
}
auto lX = ctx.logical_data(X).set_symbol("X");
auto lY = ctx.logical_data(lX.shape()).set_symbol("Y");
for (int k = 0; k < 4; k++)
{
auto fx = ctx.freeze(lX, access_mode::rw, data_place::current_device());
_CCCL_ASSERT(fx.get_access_mode() == access_mode::rw, "invalid access mode");
auto dX = fx.get(data_place::current_device(), stream);
mult<<<8, 4, 0, stream>>>(dX, 4);
fx.unfreeze(stream);
ctx.parallel_for(lX.shape(), lX.read(), lY.write()).set_symbol("Y=X")->*[] __device__(size_t i, auto x, auto y) {
y(i) = x(i);
};
ctx.parallel_for(lX.shape(), lY.rw()).set_symbol("Y+=1")->*[] __device__(size_t i, auto y) {
y(i) += 1;
};
// ctx.host_launch(lX.read(), lY.read())->*[](auto x, auto y) {
// for (int i = 0; i < x.size(); i++) {
// EXPECT(x(i) == 2*X0(i) + 4);
// }
//
// for (int i = 0; i < y.size(); i++) {
// EXPECT(y(i) == 2*X0(i) + 4);
// }
// };
}
ctx.finalize();
}

74
cccl_upstream/cudax/test/stf/freeze/freeze_untyped_rw.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.
//
//===----------------------------------------------------------------------===//
//! \file
//!
//! \brief Freeze data and store it as a frozen_logical_data_untyped object
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
int X0(int i)
{
return 17 * i + 45;
}
__global__ void mult(slice<int> s, int val)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int nthreads = gridDim.x * blockDim.x;
for (int i = tid; i < s.size(); i += nthreads)
{
s(i) *= val;
}
}
int main()
{
stream_ctx ctx;
cudaStream_t stream = ctx.pick_stream();
const int N = 16;
int X[N];
for (int i = 0; i < N; i++)
{
X[i] = X0(i);
}
auto lX = ctx.logical_data(X).set_symbol("X");
auto lY = ctx.logical_data(lX.shape()).set_symbol("Y");
for (int k = 0; k < 4; k++)
{
logical_data_untyped lX_untyped = lX;
auto fx = ctx.freeze(lX_untyped, access_mode::rw, data_place::current_device());
_CCCL_ASSERT(fx.get_access_mode() == access_mode::rw, "invalid access mode");
auto dX = fx.template get<slice<int>>(data_place::current_device(), stream);
mult<<<8, 4, 0, stream>>>(dX, 4);
fx.unfreeze(stream);
ctx.parallel_for(lX.shape(), lX.read(), lY.write()).set_symbol("Y=X")->*[] __device__(size_t i, auto x, auto y) {
y(i) = x(i);
};
ctx.parallel_for(lX.shape(), lY.rw()).set_symbol("Y+=1")->*[] __device__(size_t i, auto y) {
y(i) += 1;
};
}
ctx.finalize();
}

65
cccl_upstream/cudax/test/stf/freeze/freeze_write_back.cu
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@@ -1,65 +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.
//
//===----------------------------------------------------------------------===//
//! \file
//!
//! \brief Ensure write-back is working on logical data alias made by freezing another one
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
context ctx;
int array[1024];
for (size_t i = 0; i < 1024; i++)
{
array[i] = 2 - i * i;
}
auto lA = ctx.logical_data(array).set_symbol("A");
auto stream = ctx.pick_stream();
graph_ctx gctx(stream);
// Create an alias for lA in the graph by freezing it and creating a new
// local logical data in the graph.
auto fa = ctx.freeze(lA, access_mode::rw, data_place::current_device());
auto inst = fa.get(data_place::current_device(), stream);
auto glA = gctx.logical_data(inst, data_place::current_device());
gctx.parallel_for(glA.shape(), glA.rw())->*[] __device__(size_t i, auto a) {
a(i) += 4 * i;
};
// force to move to a different place, and probably to allocate another copy
// on the host. This tests if the write-back mechanism works from the host to
// the device when destroying the alias logical data glA.
gctx.host_launch(glA.rw())->*[](auto a) {
for (size_t i = 0; i < 1024; i++)
{
a(i) *= 2;
}
};
gctx.finalize();
fa.unfreeze(stream);
ctx.finalize();
for (size_t i = 0; i < 1024; i++)
{
EXPECT(array[i] == 2 * (2 - i * i + 4 * i));
}
}

98
cccl_upstream/cudax/test/stf/freeze/task_fence.cu
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@@ -1,98 +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.
//
//===----------------------------------------------------------------------===//
/**
* @file
*
* @brief Freeze data in read-only fashion
*
*/
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
int X0(int i)
{
return 17 * i + 45;
}
__global__ void print(slice<int> s)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int nthreads = gridDim.x * blockDim.x;
for (int i = tid; i < s.size(); i += nthreads)
{
printf("%d %d\n", i, s(i));
}
}
int main()
{
stream_ctx ctx;
const int N = 16;
int X[N];
for (int i = 0; i < N; i++)
{
X[i] = X0(i);
}
auto lX = ctx.logical_data(X).set_symbol("X");
auto lY = ctx.logical_data(lX.shape()).set_symbol("Y");
ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X=2X")->*[] __device__(size_t i, auto x) {
x(i) *= 2;
};
// test 1 : implicit sync of gets
{
auto fx = ctx.freeze(lX);
auto [dX, _] = fx.get(data_place::current_device());
// the stream returned by fence should depend on the get operation
auto stream2 = ctx.fence();
print<<<8, 4, 0, stream2>>>(dX);
ctx.parallel_for(lX.shape(), lX.read(), lY.write()).set_symbol("Y=X")->*[] __device__(size_t i, auto x, auto y) {
y(i) = x(i);
};
fx.unfreeze(stream2);
}
// test 2 : unfreeze with no events due to user sync
{
auto fx = ctx.freeze(lX);
auto [dX, _] = fx.get(data_place::current_device());
// the stream returned by fence should depend on the get operation
auto stream2 = ctx.fence();
print<<<8, 4, 0, stream2>>>(dX);
// We synchronize so there is nothing to depend on anymore
cudaStreamSynchronize(stream2);
fx.unfreeze(event_list());
}
ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X+=1")->*[] __device__(size_t i, auto x) {
x(i) += 1;
};
ctx.finalize();
for (int i = 0; i < N; i++)
{
EXPECT(X[i] == 2 * X0(i) + 1);
}
}

37
cccl_upstream/cudax/test/stf/freeze/token.cu
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//===----------------------------------------------------------------------===//
//
// 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.
//
//===----------------------------------------------------------------------===//
/**
* @file
*
* @brief Freeze token
*
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
context ctx;
auto ltoken = ctx.token();
auto ftoken = ctx.freeze(ltoken);
cudaStream_t stream = ctx.pick_stream();
// This makes any future operations in this CUDA stream depend on the
// availability of the token
[[maybe_unused]] auto dtoken = ftoken.get(data_place::current_device(), stream);
ftoken.unfreeze(stream);
ctx.finalize();
}