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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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177
cccl_upstream/cudax/test/stf/stackable/graph_scope_test.cu
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@@ -1,177 +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) 2022-2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
/**
* @file
* @brief Test for graph_scope RAII functionality in stackable_ctx
*
* This test demonstrates and validates the graph_scope RAII wrapper
* for automatic push/pop management in nested contexts.
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
stackable_ctx ctx;
int input[1024];
for (size_t i = 0; i < 1024; i++)
{
input[i] = static_cast<int>(i);
}
auto data = ctx.logical_data(input).set_symbol("data");
// Test 1: Direct constructor style (lock_guard style) - most idiomatic
{
stackable_ctx::graph_scope_guard scope{ctx}; // push() called here
auto temp = ctx.logical_data(data.shape()).set_symbol("temp");
ctx.parallel_for(temp.shape(), temp.write(), data.read())->*[] __device__(size_t i, auto temp, auto data) {
temp(i) = data(i) * 2;
};
ctx.parallel_for(data.shape(), data.write(), temp.read())->*[] __device__(size_t i, auto data, auto temp) {
data(i) = temp(i) + 1;
};
// pop() called automatically when scope goes out of scope
}
// Test 2: Factory method style (convenience)
{
auto scope = ctx.graph_scope(); // push() called here
auto temp = ctx.logical_data(data.shape()).set_symbol("temp2");
ctx.parallel_for(temp.shape(), temp.write(), data.read())->*[] __device__(size_t i, auto temp, auto data) {
temp(i) = data(i) * 3;
};
ctx.parallel_for(data.shape(), data.write(), temp.read())->*[] __device__(size_t i, auto data, auto temp) {
data(i) = temp(i);
};
// pop() called automatically when scope goes out of scope
}
// Test 3: Nested scopes with direct constructor style
{
stackable_ctx::graph_scope_guard outer_scope{ctx}; // outer push
auto intermediate = ctx.logical_data(data.shape()).set_symbol("intermediate");
ctx.parallel_for(intermediate.shape(), intermediate.write(), data.read())
->*[] __device__(size_t i, auto inter, auto data) {
inter(i) = data(i) / 2;
};
{
stackable_ctx::graph_scope_guard inner_scope{ctx}; // inner push (nested)
auto temp = ctx.logical_data(data.shape()).set_symbol("nested_temp");
ctx.parallel_for(temp.shape(), temp.write(), intermediate.read())->*[] __device__(size_t i, auto temp, auto inter) {
temp(i) = inter(i) + 10;
};
ctx.parallel_for(data.shape(), data.write(), temp.read())->*[] __device__(size_t i, auto data, auto temp) {
data(i) = temp(i);
};
// inner pop() called automatically here
}
// outer pop() called automatically here
}
// Test 4: Iterative pattern (like stackable2.cu)
for (int iter = 0; iter < 3; iter++)
{
stackable_ctx::graph_scope_guard iteration{ctx}; // New scope each iteration
auto temp = ctx.logical_data(data.shape()).set_symbol("iter_temp");
// tmp = data
ctx.parallel_for(temp.shape(), temp.write(), data.read())->*[] __device__(size_t i, auto temp, auto data) {
temp(i) = data(i);
};
// data++
ctx.parallel_for(data.shape(), data.rw())->*[] __device__(size_t i, auto data) {
data(i) += 1;
};
// temp *= 2
ctx.parallel_for(temp.shape(), temp.rw())->*[] __device__(size_t i, auto temp) {
temp(i) *= 2;
};
// data += temp
ctx.parallel_for(data.shape(), temp.read(), data.rw())->*[] __device__(size_t i, auto temp, auto data) {
data(i) += temp(i);
};
// pop() called automatically at end of iteration
}
// Test 5: Mixed usage styles
{
// Outer scope using direct constructor
stackable_ctx::graph_scope_guard outer{ctx};
auto temp1 = ctx.logical_data(data.shape()).set_symbol("temp1");
{
// Inner scope using factory method
auto inner = ctx.graph_scope();
auto temp2 = ctx.logical_data(data.shape()).set_symbol("temp2");
ctx.parallel_for(temp2.shape(), temp2.write(), data.read())->*[] __device__(size_t i, auto temp2, auto data) {
temp2(i) = data(i) + 5;
};
ctx.parallel_for(temp1.shape(), temp1.write(), temp2.read())->*[] __device__(size_t i, auto temp1, auto temp2) {
temp1(i) = temp2(i) * 2;
};
// inner pop() automatically
}
ctx.parallel_for(data.shape(), data.write(), temp1.read())->*[] __device__(size_t i, auto data, auto temp1) {
data(i) = temp1(i);
};
// outer pop() automatically
}
ctx.finalize();
for (size_t i = 0; i < 1024; i++)
{
int expected = static_cast<int>(i);
expected = expected * 2 + 1;
expected = expected * 3;
expected = expected / 2 + 10;
for (int iter = 0; iter < 3; iter++)
{
expected = 3 * expected + 1;
}
expected = (expected + 5) * 2;
EXPECT(input[i] == expected);
}
return 0;
}