[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
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
@@ -1,210 +0,0 @@
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//===----------------------------------------------------------------------===//
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//
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// Part of CUDASTF in CUDA C++ Core Libraries,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
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#include <iostream>
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using namespace cuda::experimental::stf;
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static graph_ctx ctx;
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/*
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* DATA BLOCKS
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* | GHOSTS | DATA | GHOSTS |
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*/
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template <typename T>
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class data_block
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{
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public:
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data_block(size_t beg, size_t end, size_t ghost_size)
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: ghost_size(ghost_size)
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, block_size(end - beg + 1)
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, array(new T[block_size + 2 * ghost_size])
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, left_interface(new T[ghost_size])
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, right_interface(new T[ghost_size])
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, handle(ctx.logical_data(array.get(), block_size + 2 * ghost_size))
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, left_handle(ctx.logical_data(left_interface.get(), ghost_size))
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, right_handle(ctx.logical_data(right_interface.get(), ghost_size))
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{}
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T* get_array_in_task()
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{
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return handle.instance().data_handle();
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}
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T* get_array()
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{
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return array.get();
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}
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public:
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size_t ghost_size;
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size_t block_size;
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std::unique_ptr<T[]> array;
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std::unique_ptr<T[]> left_interface;
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std::unique_ptr<T[]> right_interface;
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// HANDLE = whole data + boundaries
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logical_data<slice<T>> handle;
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// A piece of data to store the left part of the block
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logical_data<slice<T>> left_handle;
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// A piece of data to store the right part of the block
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logical_data<slice<T>> right_handle;
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};
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template <typename T>
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__global__ void copy_kernel(size_t cnt, T* dst, const T* src)
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{
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for (int idx = threadIdx.x + blockIdx.x * blockDim.x; idx < cnt; idx += blockDim.x * gridDim.x)
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{
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dst[idx] = src[idx];
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}
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}
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template <typename T>
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__global__ void stencil_kernel(size_t cnt, size_t ghost_size, T* array, const T* array1)
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{
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for (int idx = threadIdx.x + blockIdx.x * blockDim.x; idx < cnt; idx += blockDim.x * gridDim.x)
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{
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int idx2 = idx + ghost_size;
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array[idx2] = 0.9 * array1[idx2] + 0.05 * array1[idx2 - 1] + 0.05 * array1[idx2 + 1];
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}
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}
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// bn1.array = bn.array
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template <typename T>
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void stencil(data_block<T>& bn, data_block<T>& bn1)
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{
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ctx.task(bn.handle.rw(), bn1.handle.read())
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->*[bs = bn.block_size, gs = bn.ghost_size](cudaStream_t stream, auto s1, auto s2) {
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stencil_kernel<<<256, 64, 0, stream>>>(bs, gs, s1.data_handle(), s2.data_handle());
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};
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}
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template <typename T>
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void update_inner_interfaces(data_block<T>& bn)
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{
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// LEFT
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ctx.task(bn.handle.read(), bn.left_handle.rw())->*[gs = bn.ghost_size](cudaStream_t stream, auto s1, auto s2) {
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copy_kernel<<<(gs > 64 ? 256 : 1), 64, 0, stream>>>(gs, s2.data_handle(), s1.data_handle() + gs);
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};
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// RIGHT
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ctx.task(bn.handle.read(), bn.right_handle.rw())
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->*[bs = bn.block_size, gs = bn.ghost_size](cudaStream_t stream, auto s1, auto s2) {
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copy_kernel<<<(gs > 64 ? 256 : 1), 64, 0, stream>>>(gs, s2.data_handle(), s1.data_handle() + bs);
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};
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}
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template <typename T>
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void update_outer_interfaces(data_block<T>& bn, data_block<T>& left, data_block<T>& right)
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{
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ctx.task(bn.handle.rw(), left.right_handle.read())->*[gs = bn.ghost_size](cudaStream_t stream, auto s1, auto s2) {
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copy_kernel<<<(gs > 64 ? 256 : 1), 64, 0, stream>>>(gs, s1.data_handle(), s2.data_handle());
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};
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ctx.task(bn.handle.rw(), right.left_handle.read())
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->*[bs = bn.block_size, gs = bn.ghost_size](cudaStream_t stream, auto s1, auto s2) {
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copy_kernel<<<(gs > 64 ? 256 : 1), 64, 0, stream>>>(gs, s1.data_handle() + gs + bs, s2.data_handle());
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};
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}
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// bn1.array = bn.array
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template <typename T>
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void copy_array(data_block<T>& bn, data_block<T>& bn1)
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{
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ctx.task(bn1.handle.rw(), bn.handle.read())
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->*[sz = bn.block_size + 2 * bn.ghost_size](cudaStream_t stream, auto s1, auto s2) {
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copy_kernel<<<256, 64, 0, stream>>>(sz, s1.data_handle(), s2.data_handle());
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};
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}
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int main(int argc, char** argv)
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{
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size_t NBLOCKS = 2;
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size_t BLOCK_SIZE = 1024 * 64;
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size_t TOTAL_SIZE = NBLOCKS * BLOCK_SIZE;
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std::vector<double> U0(NBLOCKS * BLOCK_SIZE);
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for (size_t idx = 0; idx < NBLOCKS * BLOCK_SIZE; idx++)
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{
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U0[idx] = (idx == 0) ? 1.0 : 0.0;
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}
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std::vector<data_block<double>> Un;
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std::vector<data_block<double>> Un1;
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// Create blocks and allocates host data
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for (size_t b = 0; b < NBLOCKS; b++)
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{
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int beg = b * BLOCK_SIZE;
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int end = (b + 1) * BLOCK_SIZE;
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Un.push_back(data_block<double>(beg, end, 1));
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Un1.push_back(data_block<double>(beg, end, 1));
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}
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// Fill blocks with initial values
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for (size_t b = 0; b < NBLOCKS; b++)
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{
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size_t beg = b * BLOCK_SIZE;
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// int end = (b+1)*BLOCK_SIZE;
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double* Un_vals = Un[b].get_array();
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double* Un1_vals = Un1[b].get_array();
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// Attention, unusual loop: index goes all through BLOCK_SIZE inclusive.
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for (size_t local_idx = 0; local_idx <= BLOCK_SIZE; local_idx++)
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{
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Un_vals[local_idx] = U0[(beg + local_idx - 1) % TOTAL_SIZE];
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Un1_vals[local_idx] = U0[(beg + local_idx - 1) % TOTAL_SIZE];
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}
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}
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// Create the graph - it starts out empty
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int NITER = 400;
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for (int iter = 0; iter < NITER; iter++)
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{
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// UPDATE Un from Un1
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for (size_t b = 0; b < NBLOCKS; b++)
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{
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stencil(Un[b], Un1[b]);
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}
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for (size_t b = 0; b < NBLOCKS; b++)
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{
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// Update the internal copies of the left and right boundaries
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update_inner_interfaces(Un[b]);
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}
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for (size_t b = 0; b < NBLOCKS; b++)
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{
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update_outer_interfaces(Un[b], Un[(b - 1 + NBLOCKS) % NBLOCKS], Un[(b + 1) % NBLOCKS]);
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}
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for (size_t b = 0; b < NBLOCKS; b++)
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{
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copy_array(Un[b], Un1[b]);
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}
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}
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ctx.submit();
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if (argc > 1)
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{
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std::cout << "Generating DOT output in " << argv[1] << '\n';
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ctx.print_to_dot(argv[1]);
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}
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ctx.finalize();
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}
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