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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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484
cccl_upstream/cudax/examples/stf/linear_algebra/06-pdgemm-stackable.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 An example that implements a tiled matrix product over multiple devices using CUBLAS
*/
#include <cstdlib>
#include "cuda/experimental/stf.cuh"
#include <nvtx3/nvToolsExt.h>
#define TILED
using namespace cuda::experimental::stf;
static std::unordered_map<int, cublasHandle_t> cublas_handles;
/* Get a CUBLAS handle valid on the current device, or initialize it lazily */
cublasHandle_t get_cublas_handle()
{
int dev;
cuda_safe_call(cudaGetDevice(&dev));
auto& result = cublas_handles[dev];
if (result == cublasHandle_t())
{ // not found, default value inserted
// Lazy initialization, and save the handle for future use
cuda_safe_call(cublasCreate(&result));
}
return result;
}
template <typename T>
class matrix
{
public:
matrix(stackable_ctx& ctx,
size_t NROWS,
size_t NCOLS,
size_t BLOCKSIZE_ROWS,
size_t BLOCKSIZE_COLS,
const char* _symbol = "matrix")
: h_array(nullptr)
, m(NROWS)
, n(NCOLS)
, mb(BLOCKSIZE_ROWS)
, nb(BLOCKSIZE_COLS)
, mt(0)
, nt(0)
, symbol(_symbol)
, ndevs(0)
, grid_p(0)
, grid_q(0)
{
assert(m % mb == 0);
assert(n % nb == 0);
const size_t s = m * n * sizeof(T);
// cuda_safe_call(cudaMallocHost(&h_array, m*n*sizeof(T)));
// fprintf(stderr, "Allocating %ld x %ld x %ld = %ld bytes (%f GB) on host for %s\n", m, n, sizeof(T), s,
// s / (1024.0 * 1024.0 * 1024.0), _symbol);
h_array = static_cast<T*>(malloc(s));
assert(h_array);
cuda_safe_call(cudaHostRegister(h_array, s, cudaHostRegisterPortable));
// Compute the number of blocks
mt = m / mb;
nt = n / nb;
handles.resize(mt * nt);
for (size_t colb = 0; colb < nt; colb++)
{
for (size_t rowb = 0; rowb < mt; rowb++)
{
T* addr_h = get_block_h(rowb, colb);
#ifdef TILED
// tiles are stored contiguously
const size_t ld = mb;
#else
const size_t ld = m;
#endif
std::ignore = ld; // avoid warning #177-D: variable "ld" was declared but never referenced
auto s = make_slice(addr_h, std::tuple{mb, nb}, ld);
auto tile = ctx.logical_data(s);
tile.set_write_back(false);
tile.set_symbol(std::string(symbol) + "_" + std::to_string(rowb) + "_" + std::to_string(colb));
handles[rowb + colb * mt] = std::move(tile);
}
}
cuda_safe_call(cudaGetDeviceCount(&ndevs));
for (int a = 1; a * a <= ndevs; a++)
{
if (ndevs % a == 0)
{
grid_p = a;
grid_q = ndevs / a;
}
}
assert(grid_p * grid_q == ndevs);
// std::cout << "FOUND " << ndevs << " DEVICES "
// << "p=" << grid_p << " q=" << grid_q << '\n';
}
~matrix()
{
if (h_array)
{
cuda_safe_call(cudaHostUnregister(h_array));
free(h_array);
}
}
// Disable copy and move operations - this is a resource-owning class used locally
matrix(const matrix&) = delete;
matrix& operator=(const matrix&) = delete;
matrix(matrix&&) = delete;
matrix& operator=(matrix&&) = delete;
void push(access_mode mode)
{
for (auto& h : handles)
{
h.push(mode);
}
}
int get_preferred_devid(int row, int col) const
{
return (row % grid_p) + (col % grid_q) * grid_p;
}
auto& get_handle(int row, int col)
{
return handles[row + col * mt];
}
auto& get_handle(int row, int col) const
{
return handles[row + col * mt];
}
size_t get_index(size_t row, size_t col) const
{
#ifdef TILED
// Find which tile contains this element
const int tile_row = static_cast<int>(row / mb);
const int tile_col = static_cast<int>(col / nb);
const size_t tile_size = mb * nb;
// Look for the index of the beginning of the tile
const size_t tile_start = (tile_row + mt * tile_col) * tile_size;
// Offset within the tile
const size_t offset = (row % mb) + (col % nb) * mb;
return tile_start + offset;
#else
return row + col * m;
#endif
}
T* get_block_h(int brow, int bcol)
{
const size_t index = get_index(brow * mb, bcol * nb);
return &h_array[index];
}
// Fill with func(Matrix*,row, col)
template <typename Fun>
void fill(stackable_ctx& ctx, Fun&& fun)
{
nvtxRangePushA("FILL");
// Fill blocks by blocks
for (size_t colb = 0; colb < nt; colb++)
{
for (size_t rowb = 0; rowb < mt; rowb++)
{
// Each task fills a block
auto& h = get_handle(rowb, colb);
int devid = get_preferred_devid(rowb, colb);
ctx.parallel_for(exec_place::device(devid), h.shape(), h.write()).set_symbol("INIT")->*
[=] _CCCL_DEVICE(size_t lrow, size_t lcol, auto sA) {
const size_t row = lrow + rowb * sA.extent(0);
const size_t col = lcol + colb * sA.extent(1);
sA(lrow, lcol) = fun(row, col);
};
}
}
nvtxRangePop();
}
T* h_array;
size_t m; // nrows
size_t n; // ncols
size_t mb; // block size (rows)
size_t nb; // block size (cols)
size_t mt; // numter of column blocks
size_t nt; // numter of row blocks
// abstract data handles
std::vector<stackable_logical_data<slice<T, 2>>> handles;
const char* symbol;
// for the mapping
int ndevs;
int grid_p, grid_q;
};
void DGEMM(
stackable_ctx& ctx,
cublasOperation_t transa,
cublasOperation_t transb,
double alpha,
const matrix<double>& A,
int A_row,
int A_col,
const matrix<double>& B,
int B_row,
int B_col,
double beta,
matrix<double>& C,
int C_row,
int C_col)
{
const auto dev = exec_place::device(C.get_preferred_devid(C_row, C_col));
auto t = ctx.task(
dev, A.get_handle(A_row, A_col).read(), B.get_handle(B_row, B_col).read(), C.get_handle(C_row, C_col).rw());
t.set_symbol("DGEMM");
t->*[&](cudaStream_t stream, auto tA, auto tB, auto tC) {
cuda_safe_call(cublasSetStream(get_cublas_handle(), stream));
int k = tA.extent(transa == CUBLAS_OP_N ? 1 : 0);
cuda_safe_call(cublasDgemm(
get_cublas_handle(),
transa,
transb,
tC.extent(0),
tC.extent(1),
k,
&alpha,
tA.data_handle(),
tA.stride(1),
tB.data_handle(),
tB.stride(1),
&beta,
tC.data_handle(),
tC.stride(1)));
};
}
void PDGEMM(stackable_ctx& ctx,
cublasOperation_t transa,
cublasOperation_t transb,
double alpha,
const matrix<double>& A,
const matrix<double>& B,
double beta,
matrix<double>& C)
{
for (size_t m = 0; m < C.mt; m++)
{
for (size_t n = 0; n < C.nt; n++)
{
//=========================================
// alpha*A*B does not contribute; scale C
//=========================================
const size_t inner_k = transa == CUBLAS_OP_N ? A.n : A.m;
if (alpha == 0.0 || inner_k == 0)
{
DGEMM(ctx, transa, transb, alpha, A, 0, 0, B, 0, 0, beta, C, static_cast<int>(m), static_cast<int>(n));
}
else if (transa == CUBLAS_OP_N)
{
//================================
// CUBLAS_OP_N / CUBLAS_OP_N
//================================
if (transb == CUBLAS_OP_N)
{
assert(A.nt == B.mt);
for (size_t k = 0; k < A.nt; k++)
{
const double zbeta = k == 0 ? beta : 1.0;
DGEMM(ctx,
transa,
transb,
alpha,
A,
static_cast<int>(m),
static_cast<int>(k),
B,
static_cast<int>(k),
static_cast<int>(n),
zbeta,
C,
static_cast<int>(m),
static_cast<int>(n));
}
}
//=====================================
// CUBLAS_OP_N / CUBLAS_OP_T
//=====================================
else
{
for (size_t k = 0; k < A.nt; k++)
{
const double zbeta = k == 0 ? beta : 1.0;
DGEMM(ctx,
transa,
transb,
alpha,
A,
static_cast<int>(m),
static_cast<int>(k),
B,
static_cast<int>(n),
static_cast<int>(k),
zbeta,
C,
static_cast<int>(m),
static_cast<int>(n));
}
}
}
else
{
//=====================================
// CUBLAS_OP_T / CUBLAS_OP_N
//=====================================
if (transb == CUBLAS_OP_N)
{
for (size_t k = 0; k < A.mt; k++)
{
const double zbeta = k == 0 ? beta : 1.0;
DGEMM(ctx,
transa,
transb,
alpha,
A,
static_cast<int>(k),
static_cast<int>(m),
B,
static_cast<int>(k),
static_cast<int>(n),
zbeta,
C,
static_cast<int>(m),
static_cast<int>(n));
}
}
//==========================================
// CUBLAS_OP_T / CUBLAS_OP_T
//==========================================
else
{
for (size_t k = 0; k < A.mt; k++)
{
const double zbeta = k == 0 ? beta : 1.0;
DGEMM(ctx,
transa,
transb,
alpha,
A,
static_cast<int>(k),
static_cast<int>(m),
B,
static_cast<int>(n),
static_cast<int>(k),
zbeta,
C,
static_cast<int>(m),
static_cast<int>(n));
}
}
}
}
}
}
void run(stackable_ctx& ctx, size_t N, size_t NB)
{
/// auto fixed_alloc = block_allocator<fixed_size_allocator>(ctx, NB * NB * sizeof(double));
// ctx.set_allocator(fixed_alloc);
// Set up CUBLAS and CUSOLVER
int ndevs;
cuda_safe_call(cudaGetDeviceCount(&ndevs));
/* Warm up allocators */
for (int d = 0; d < ndevs; d++)
{
auto lX = ctx.logical_data(shape_of<slice<double>>(1));
ctx.parallel_for(exec_place::device(d), lX.shape(), lX.write())->*[] _CCCL_DEVICE(size_t, auto) {};
}
/* Initializes CUBLAS on all devices */
for (int d = 0; d < ndevs; d++)
{
cuda_safe_call(cudaSetDevice(d));
get_cublas_handle();
}
matrix<double> A(ctx, N, N, NB, NB, "A");
matrix<double> B(ctx, N, N, NB, NB, "B");
matrix<double> C(ctx, N, N, NB, NB, "C");
// (Hilbert matrix + 2*N*Id)
auto hilbert = [=] _CCCL_HOST_DEVICE(size_t row, size_t col) {
return 1.0 / (col + row + 1.0) + 2.0 * N * (col == row);
};
A.fill(ctx, hilbert);
B.fill(ctx, hilbert);
C.fill(ctx, hilbert);
cudaEvent_t startEvent, stopEvent;
cuda_safe_call(cudaEventCreate(&startEvent));
cuda_safe_call(cudaEventCreate(&stopEvent));
cuda_safe_call(cudaEventRecord(startEvent, ctx.fence()));
ctx.push();
A.push(access_mode::read);
B.push(access_mode::read);
C.push(access_mode::rw);
PDGEMM(ctx, CUBLAS_OP_N, CUBLAS_OP_N, 1.0, A, B, -2.0, C);
ctx.pop();
cuda_safe_call(cudaEventRecord(stopEvent, ctx.fence()));
ctx.finalize();
float milliseconds;
cuda_safe_call(cudaEventElapsedTime(&milliseconds, startEvent, stopEvent));
const double gflops_pdgemm =
2.0 * (static_cast<double>(N) * static_cast<double>(N) * static_cast<double>(N)) / 1000000000.0;
::std::cout
<< "[PDDGEMM] ELAPSED: " << milliseconds << " ms, GFLOPS: " << gflops_pdgemm / (milliseconds / 1000.0) << '\n';
}
int main(int argc, char** argv)
{
size_t N = 4096;
size_t NB = 512;
if (argc > 1)
{
N = static_cast<size_t>(::std::atoi(argv[1]));
}
if (argc > 2)
{
NB = static_cast<size_t>(::std::atoi(argv[2]));
}
assert(N % NB == 0);
stackable_ctx ctx;
run(ctx, N, NB);
}