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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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64
cccl_upstream/cudax/test/stf/hashtable/fusion.cu
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@@ -1,64 +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/stream/interfaces/hashtable_linearprobing.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
// Iterate over every item in the hashtableA, and add them to B
__global__ void gpu_merge_hashtable(hashtable A, const hashtable B)
{
unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
while (threadid < reserved::kHashTableCapacity)
{
if (B.addr[threadid].key != reserved::kEmpty)
{
uint32_t value = B.addr[threadid].value;
if (value != reserved::kEmpty)
{
// printf("INSERTING key %d value %d\n", pHashTableB[threadid].key, value);
A.insert(B.addr[threadid]);
}
}
threadid += blockDim.x * gridDim.x;
}
}
int main()
{
stream_ctx ctx;
hashtable A;
A.insert(reserved::KeyValue(107, 4));
A.insert(reserved::KeyValue(108, 6));
hashtable B;
B.insert(reserved::KeyValue(7, 14));
B.insert(reserved::KeyValue(8, 16));
auto lA = ctx.logical_data(A);
auto lB = ctx.logical_data(B);
ctx.task(lA.rw(), lB.read())->*[](auto stream, auto hA, auto hB) {
gpu_merge_hashtable<<<32, 128, 0, stream>>>(hA, hB);
cuda_safe_call(cudaGetLastError());
};
ctx.host_launch(lA.read())->*[](auto hA) {
EXPECT(hA.get(107) == 4);
EXPECT(hA.get(108) == 6);
EXPECT(hA.get(7) == 14);
EXPECT(hA.get(8) == 16);
};
ctx.finalize();
}

122
cccl_upstream/cudax/test/stf/hashtable/fusion_reduction.cu
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@@ -1,122 +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/stream/interfaces/hashtable_linearprobing.cuh>
#include <cuda/experimental/__stf/stream/reduction.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
__global__ void gpu_merge_hashtable(hashtable A, const hashtable B)
{
unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
while (threadid < B.get_capacity())
{
if (B.addr[threadid].key != reserved::kEmpty)
{
uint32_t value = B.addr[threadid].value;
if (value != reserved::kEmpty)
{
// printf("INSERTING key %d value %d\n", pHashTableB[threadid].key, value);
A.insert(B.addr[threadid]);
}
}
threadid += blockDim.x * gridDim.x;
}
}
void cpu_merge_hashtable(hashtable A, const hashtable B)
{
for (unsigned int i = 0; i < B.get_capacity(); i++)
{
if (B.addr[i].key != reserved::kEmpty)
{
uint32_t value = B.addr[i].value;
if (value != reserved::kEmpty)
{
// printf("INSERTING key %d value %d\n", pHashTableB[threadid].key, value);
A.insert(B.addr[i]);
}
}
}
}
class hashtable_fusion_t : public stream_reduction_operator<hashtable>
{
void op(const hashtable& in, hashtable& inout, const exec_place& e, cudaStream_t s) override
{
if (e.affine_data_place().is_host())
{
cuda_safe_call(cudaStreamSynchronize(s)); // TODO use a callback
cpu_merge_hashtable(inout, in);
}
else
{
gpu_merge_hashtable<<<32, 32, 0, s>>>(inout, in);
}
}
void init_op(hashtable& /*unused*/, const exec_place& /*unused*/, cudaStream_t /*unused*/) override
{
// This init operator is a no-op because hashtables are already
// initialized as empty tables
}
};
// A kernel to fill the hashtable with some fictitious values
__global__ void fill_table(size_t dev_id, size_t cnt, hashtable h)
{
unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int nthreads = blockDim.x * gridDim.x;
for (unsigned int i = threadid; i < cnt; i += nthreads)
{
uint32_t key = dev_id * 1000 + i;
uint32_t value = 2 * i;
reserved::KeyValue kvs(key, value);
h.insert(kvs);
}
}
int main()
{
stream_ctx ctx;
// Explicit capacity of 2048 entries
hashtable refh(2048);
auto h_handle = ctx.logical_data(refh);
auto fusion_op = std::make_shared<hashtable_fusion_t>();
for (size_t dev_id = 0; dev_id < 4; dev_id++)
{
ctx.task(h_handle.relaxed(fusion_op))->*[&](auto stream, auto h) {
EXPECT(h.get_capacity() == 2048);
fill_table<<<32, 32, 0, stream>>>(dev_id, 10, h);
};
}
ctx.host_launch(h_handle.read())->*[&](auto h) {
// Check that the table contains all values
for (size_t dev_id = 0; dev_id < 4; dev_id++)
{
for (unsigned i = 0; i < 10; i++)
{
uint32_t key = static_cast<uint32_t>(dev_id * 1000 + i);
uint32_t value = 2 * i;
EXPECT(h.get(key) == value);
}
}
};
ctx.finalize();
}

38
cccl_upstream/cudax/test/stf/hashtable/parallel_for.cu
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@@ -1,38 +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/stream/interfaces/hashtable_linearprobing.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
#include <cuda/experimental/__stf/utility/dimensions.cuh>
using namespace cuda::experimental::stf;
int main()
{
stream_ctx ctx;
// This constructor automatically initializes an empty hashtable on the host
hashtable h;
auto lh = ctx.logical_data(h);
ctx.parallel_for(box(16), lh.rw())->*[] _CCCL_DEVICE(size_t i, auto h) {
uint32_t key = 10 * i;
uint32_t value = 17 + i * 14;
h.insert(key, value);
};
ctx.finalize();
// Thanks to the write-back mechanism on lh, h has been updated
for (size_t i = 0; i < 16; i++)
{
assert(h.get(i * 10) == 17 + i * 14);
}
}

39
cccl_upstream/cudax/test/stf/hashtable/parallel_for_shape.cu
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@@ -1,39 +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/stream/interfaces/hashtable_linearprobing.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
#include <cuda/experimental/__stf/utility/dimensions.cuh>
using namespace cuda::experimental::stf;
int main()
{
stream_ctx ctx;
// Create a logical data from a shape of hashtable
auto lh = ctx.logical_data(shape_of<hashtable>());
// A write() access is needed because we initialized lh from a shape, so there is no reference copy
ctx.parallel_for(box(16), lh.write())->*[] _CCCL_DEVICE(size_t i, auto h) {
uint32_t key = 10 * i;
uint32_t value = 17 + i * 14;
h.insert(key, value);
};
ctx.host_launch(lh.rw())->*[](auto h) {
for (int i = 0; i < 16; i++)
{
EXPECT(h.get(i * 10) == 17 + i * 14);
}
};
ctx.finalize();
}

65
cccl_upstream/cudax/test/stf/hashtable/test.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.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/__stf/stream/interfaces/hashtable_linearprobing.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
// Insert the key/values in kvs into the hashtable
__global__ void gpu_hashtable_insert_kernel(hashtable h, const reserved::KeyValue* kvs, unsigned int numkvs)
{
unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
if (threadid < numkvs)
{
h.insert(kvs[threadid]);
}
}
void gpu_hashtable_insert(hashtable d_h, const reserved::KeyValue* device_kvs, unsigned int num_kvs, cudaStream_t stream)
{
// Have CUDA calculate the thread block size
const auto occ_res = reserved::compute_occupancy(gpu_hashtable_insert_kernel);
int threadblocksize = occ_res.block_size;
int gridsize = ((uint32_t) num_kvs + threadblocksize - 1) / threadblocksize;
// Insert all the keys into the hash table
gpu_hashtable_insert_kernel<<<gridsize, threadblocksize, 0, stream>>>(d_h, device_kvs, (uint32_t) num_kvs);
}
int main()
{
stream_ctx ctx;
// This constructor automatically initializes an empty hashtable on the host
hashtable h;
auto lh = ctx.logical_data(h);
// Create an array of values on the host
reserved::KeyValue kvs_array[16];
for (uint32_t i = 0; i < 16; i++)
{
kvs_array[i].key = i * 10;
kvs_array[i].value = 17 + i * 14;
}
auto h_kvs_array = ctx.logical_data(make_slice(&kvs_array[0], 16));
ctx.task(lh.rw(), h_kvs_array.read())->*[](auto stream, auto h, auto a) {
gpu_hashtable_insert(h, a.data_handle(), static_cast<uint32_t>(a.extent(0)), stream);
};
ctx.finalize();
// Thanks to the write-back mechanism on lh, h has been updated
for (size_t i = 0; i < 16; i++)
{
assert(h.get(i * 10) == 17 + i * 14);
}
}