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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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75
cccl_upstream/cudax/test/stf/examples/01-axpy-launch-ranges-cg.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.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/__places/partitions/blocked_partition.cuh>
#include <cuda/experimental/__places/partitions/cyclic_shape.cuh>
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
double X0(int i)
{
return sin((double) i);
}
double Y0(int i)
{
return cos((double) i);
}
int main()
{
stream_ctx ctx;
const int N = 128;
double X[N], Y[N];
for (int ind = 0; ind < N; ind++)
{
X[ind] = X0(ind);
Y[ind] = Y0(ind);
}
const double alpha = 3.14;
auto handle_X = ctx.logical_data(X, {N});
auto handle_Y = ctx.logical_data(Y, {N});
auto number_devices = 4;
auto all_devs = exec_place::repeat(exec_place::device(0), number_devices);
auto spec = par(16 * 4, par(4));
ctx.launch(spec, all_devs, handle_X.read(), handle_Y.rw())->*[=] _CCCL_DEVICE(auto th, auto x, auto y) {
// Blocked partition among elements in the outer most level
auto outer_sh = blocked_partition::apply(shape(x), pos4(th.rank(0)), dim4(th.size(0)));
// Cyclic partition among elements in the remaining levels
auto inner_sh = cyclic_partition::apply(outer_sh, pos4(th.inner().rank()), dim4(th.inner().size()));
for (auto ind : inner_sh)
{
y(ind) += alpha * x(ind);
}
};
ctx.host_launch(handle_X.read(), handle_Y.read())->*[=](auto X, auto Y) {
for (int ind = 0; ind < N; ind++)
{
// Y should be Y0 + alpha X0
// fprintf(stderr, "Y[%ld] = %lf - expect %lf\n", ind, Y(ind), (Y0(ind) + alpha * X0(ind)));
EXPECT(fabs(Y(ind) - (Y0(ind) + alpha * X0(ind))) < 0.0001);
// X should be X0
EXPECT(fabs(X(ind) - X0(ind)) < 0.0001);
}
};
ctx.finalize();
}

119
cccl_upstream/cudax/test/stf/examples/01-axpy-places.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 This example illustrates how to use the task construct with grids of
* places and composite data places
*/
#include <cuda/experimental/__places/partitions/tiled_partition.cuh>
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
template <typename T>
__global__ void axpy(size_t start, size_t cnt, T a, const T* x, T* y)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int nthreads = gridDim.x * blockDim.x;
for (int ind = tid; ind < cnt; ind += nthreads)
{
y[ind + start] += a * x[ind + start];
}
}
double X0(size_t i)
{
return sin((double) i);
}
double Y0(size_t i)
{
return cos((double) i);
}
template <typename Ctx>
void run()
{
Ctx ctx;
const int N = 1024 * 1024 * 32;
double *X, *Y;
X = new double[N];
Y = new double[N];
SCOPE(exit)
{
delete[] X;
delete[] Y;
};
for (size_t ind = 0; ind < N; ind++)
{
X[ind] = X0(ind);
Y[ind] = Y0(ind);
}
// std::shared_ptr<execution_grid> all_devs = exec_place::all_devices();
// use grid [ 0 0 0 0 ] for debugging purpose
auto all_devs = exec_place::repeat(exec_place::device(0), 4);
// 512k doubles = 4MB (2 pages)
// A 1D blocking strategy over all devices with a block size of 32 and a round robin distribution of blocks across
// devices
// data_place cdp = data_place(exec_place::all_devices().as_grid().get_grid(),
// [](dim4 grid_dim, pos4 index_pos) { return pos4((index_pos.x / (512 * 1024ULL)) % grid_dim.x); });
data_place cdp = data_place::composite(tiled_partition<512 * 1024ULL>(), all_devs);
auto handle_X = ctx.logical_data(X, {N});
auto handle_Y = ctx.logical_data(Y, {N});
double alpha = 3.14;
/* Compute Y = Y + alpha X */
auto t = ctx.task(all_devs, handle_X.read(cdp), handle_Y.rw(cdp));
t->*[&](auto, auto sX, auto sY) {
size_t grid_size = t.grid_dims().size();
assert(N % grid_size == 0);
for (size_t i = 0; i < grid_size; i++)
{
auto active = t.activate_place(i);
axpy<<<16, 128, 0, t.get_stream(i)>>>(i * N / grid_size, N / grid_size, alpha, sX.data_handle(), sY.data_handle());
}
};
/* Check the result on the host */
ctx.host_launch(handle_X.read(), handle_Y.read())->*[&](auto sX, auto sY) {
for (size_t ind = 0; ind < N; ind++)
{
// Y should be Y0 + alpha X0
EXPECT(fabs(sY(ind) - (Y0(ind) + alpha * X0(ind))) < 0.0001);
// X should be X0
EXPECT(fabs(sX(ind) - X0(ind)) < 0.0001);
}
};
ctx.finalize();
}
int main()
{
run<stream_ctx>();
// Disabled until composite data places are implemented with graphs
// run<graph_ctx>();
}

256
cccl_upstream/cudax/test/stf/examples/05-stencil-no-copy.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.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
/*
* DATA BLOCKS
* | GHOSTS | DATA | GHOSTS |
*/
template <typename T>
class data_block
{
public:
data_block(stream_ctx& ctx, size_t beg, size_t end, size_t GHOST_SIZE)
: beg(beg)
, end(end)
, block_size(end - beg)
, ghost_size(GHOST_SIZE)
, array(std::vector<T>(block_size + 2 * ghost_size))
, handle(ctx.logical_data(&array[0], block_size + 2 * ghost_size))
{}
public:
size_t beg;
size_t end;
size_t block_size;
size_t ghost_size;
int dev_id;
private:
std::vector<T> array;
public:
// HANDLE = whole data + boundaries
logical_data<slice<T>> handle;
};
template <typename T>
T check_sum(stream_ctx& ctx, data_block<T>& bn)
{
T sum = 0.0;
auto t = ctx.task(exec_place::host(), bn.handle.read());
t->*[&](cudaStream_t stream, auto h_center) {
cuda_safe_call(cudaStreamSynchronize(stream));
for (size_t offset = bn.ghost_size; offset < bn.ghost_size + bn.block_size; offset++)
{
sum += h_center.data_handle()[offset];
}
};
return sum;
}
// array and array1 have a size of (cnt + 2*ghost_size)
template <typename T>
__global__ void stencil_kernel(size_t cnt, size_t ghost_size, T* array, const T* array1)
{
for (size_t idx = threadIdx.x + blockIdx.x * blockDim.x; idx < cnt; idx += blockDim.x * gridDim.x)
{
size_t idx2 = idx + ghost_size;
array[idx2] = 0.9 * array1[idx2] + 0.05 * array1[idx2 - 1] + 0.05 * array1[idx2 + 1];
}
}
template <typename T>
void stencil(stream_ctx& ctx, data_block<T>& bn, data_block<T>& bn1)
{
int dev = bn.dev_id;
auto t = ctx.task(exec_place::device(dev), bn.handle.rw(), bn1.handle.read());
t->*[&](cudaStream_t stream, auto bn_array, auto bn1_array) {
stencil_kernel<T>
<<<32, 64, 0, stream>>>(bn.block_size, bn.ghost_size, bn_array.data_handle(), bn1_array.data_handle());
};
}
template <typename T>
__global__ void copy_kernel(size_t cnt, T* dst, const T* src)
{
for (size_t idx = threadIdx.x + blockIdx.x * blockDim.x; idx < cnt; idx += blockDim.x * gridDim.x)
{
dst[idx] = src[idx];
}
}
template <typename T>
void copy_task(
stream_ctx& ctx,
size_t cnt,
logical_data<slice<T>>& dst,
size_t offset_dst,
int dst_dev,
logical_data<slice<T>>& src,
size_t offset_src,
int src_dev)
{
auto t = ctx.task(exec_place::device(dst_dev), dst.rw(), src.read(data_place::device(src_dev)));
t->*[&](cudaStream_t stream, auto dst_array, auto src_array) {
int nblocks = (cnt > 64) ? 32 : 1;
copy_kernel<T>
<<<nblocks, 64, 0, stream>>>(cnt, dst_array.data_handle() + offset_dst, src_array.data_handle() + offset_src);
};
}
// Copy left/right handles from neighbours to the array
template <typename T>
void update_halo(stream_ctx& ctx, data_block<T>& bn, data_block<T>& left, data_block<T>& right)
{
size_t gs = bn.ghost_size;
size_t bs = bn.block_size;
// Copy the bn.ghost_size last computed items in "left" (outside the halo)
copy_task<T>(ctx, gs, bn.handle, 0, bn.dev_id, left.handle, bs, left.dev_id);
// Copy the bn.ghost_size first computed items (outside the halo)
copy_task<T>(ctx, gs, bn.handle, gs + bs, bn.dev_id, right.handle, gs, right.dev_id);
}
// Copy inner part of bn into bn1
template <typename T>
void copy_inner(stream_ctx& ctx, data_block<T>& bn1, data_block<T>& bn)
{
size_t gs = bn.ghost_size;
size_t bs = bn.block_size;
int dev_id = bn.dev_id;
// Copy the bn.ghost_size last computed items in "left" (outside the halo)
copy_task<T>(ctx, bs, bn1.handle, gs, dev_id, bn.handle, gs, dev_id);
}
int main(int argc, char** argv)
{
int ndevs;
cuda_safe_call(cudaGetDeviceCount(&ndevs));
stream_ctx ctx;
int NITER = 500;
size_t NBLOCKS = 4 * ndevs;
size_t BLOCK_SIZE = 2048 * 1024;
if (argc > 1)
{
NITER = atoi(argv[1]);
}
if (argc > 2)
{
NBLOCKS = atoi(argv[2]);
}
const size_t GHOST_SIZE = 1;
size_t TOTAL_SIZE = NBLOCKS * BLOCK_SIZE;
double* U0 = new double[NBLOCKS * BLOCK_SIZE];
for (size_t idx = 0; idx < NBLOCKS * BLOCK_SIZE; idx++)
{
U0[idx] = (idx == 0) ? 1.0 : 0.0;
}
std::vector<data_block<double>> Un;
std::vector<data_block<double>> Un1;
// Create blocks and allocates host data
for (size_t b = 0; b < NBLOCKS; b++)
{
size_t beg = b * BLOCK_SIZE;
size_t end = (b + 1) * BLOCK_SIZE;
Un.emplace_back(ctx, beg, end, 1ull);
Un1.emplace_back(ctx, beg, end, 1ull);
}
for (size_t b = 0; b < NBLOCKS; b++)
{
Un[b].dev_id = b % ndevs;
Un1[b].dev_id = b % ndevs;
}
// Fill blocks with initial values. For the sake of simplicity, we are
// using a synchronization primitive and host code, but this could have
// been written asynchronously using host callbacks.
for (size_t b = 0; b < NBLOCKS; b++)
{
size_t beg = b * BLOCK_SIZE;
auto t = ctx.task(exec_place::host(), Un[b].handle.rw(), Un1[b].handle.rw());
t->*[&](cudaStream_t stream, auto Un_vals, auto Un1_vals) {
cuda_safe_call(cudaStreamSynchronize(stream));
for (size_t local_idx = 0; local_idx < BLOCK_SIZE; local_idx++)
{
double val = U0[(beg + local_idx + TOTAL_SIZE) % TOTAL_SIZE];
Un1_vals.data_handle()[local_idx + GHOST_SIZE] = val;
Un_vals.data_handle()[local_idx + GHOST_SIZE] = val;
}
};
}
for (int iter = 0; iter < NITER; iter++)
{
for (size_t b = 0; b < NBLOCKS; b++)
{
update_halo(ctx, Un1[b], Un[(b - 1 + NBLOCKS) % NBLOCKS], Un[(b + 1) % NBLOCKS]);
}
// UPDATE Un from Un1
for (size_t b = 0; b < NBLOCKS; b++)
{
stencil(ctx, Un[b], Un1[b]);
}
#if 0
// We make sure that the total sum of elements remains constant
if (iter % 250 == 0)
{
double sum = 0.0;
for (size_t b = 0; b < NBLOCKS; b++)
{
sum += check_sum(ctx, Un[b]);
}
// fprintf(stderr, "iter %d : CHECK SUM = %e\n", iter, sum);
}
#endif
for (size_t b = 0; b < NBLOCKS; b++)
{
// Copy inner part of Un into Un1
copy_inner(ctx, Un[b], Un1[b]);
}
}
// In this stencil, the sum of the elements is supposed to be a constant
double sum = 0.0;
for (size_t b = 0; b < NBLOCKS; b++)
{
sum += check_sum(ctx, Un[b]);
}
double err = fabs(sum - 1.0);
EXPECT(err < 0.0001);
ctx.finalize();
}

92
cccl_upstream/cudax/test/stf/examples/05-stencil-places.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.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/__places/partitions/tiled_partition.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
template <typename T>
__global__ void stencil_kernel(slice<T> Un, slice<const T> Un1)
{
size_t N = Un.extent(0);
for (size_t i = threadIdx.x + blockIdx.x * blockDim.x; i < N; i += blockDim.x * gridDim.x)
{
Un(i) = 0.9 * Un1(i) + 0.05 * Un1((i + N - 1) % N) + 0.05 * Un1((i + 1) % N);
}
}
int main(int argc, char** argv)
{
stream_ctx ctx;
int NITER = 500;
int NBLOCKS = 20;
const size_t BLOCK_SIZE = 2048 * 1024;
if (argc > 1)
{
NITER = atoi(argv[1]);
}
if (argc > 2)
{
NBLOCKS = atoi(argv[2]);
}
const size_t TOTAL_SIZE = NBLOCKS * BLOCK_SIZE;
double* Un = new double[TOTAL_SIZE];
double* Un1 = new double[TOTAL_SIZE];
for (size_t idx = 0; idx < TOTAL_SIZE; idx++)
{
Un[idx] = (idx == 0) ? 1.0 : 0.0;
Un1[idx] = Un[idx];
}
auto lUn = ctx.logical_data(make_slice(Un, TOTAL_SIZE));
auto lUn1 = ctx.logical_data(make_slice(Un1, TOTAL_SIZE));
// std::shared_ptr<execution_grid> all_devs = exec_place::all_devices();
// use grid [ 0 0 0 0 ] for debugging purpose
auto all_devs = exec_place::repeat(exec_place::device(0), 4);
data_place cdp = data_place::composite(tiled_partition<BLOCK_SIZE>(), all_devs);
for (int iter = 0; iter < NITER; iter++)
{
// UPDATE Un from Un1
ctx.task(lUn.rw(cdp), lUn1.read(cdp))->*[&](auto stream, auto sUn, auto sUn1) {
stencil_kernel<double><<<32, 128, 0, stream>>>(sUn, sUn1);
};
// We make sure that the total sum of elements remains constant
if (iter % 250 == 0)
{
double sum = 0.0;
ctx.task(exec_place::host(), lUn.read())->*[&](auto stream, auto sUn) {
cuda_safe_call(cudaStreamSynchronize(stream));
for (size_t offset = 0; offset < TOTAL_SIZE; offset++)
{
sum += sUn(offset);
}
};
// TODO add an assertion to check whether sum is close enough to 1.0
// fprintf(stderr, "iter %d : CHECK SUM = %e\n", iter, sum);
}
std::swap(lUn, lUn1);
}
ctx.finalize();
}

265
cccl_upstream/cudax/test/stf/examples/05-stencil.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.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
static stream_ctx ctx;
/*
* DATA BLOCKS
* | GHOSTS | DATA | GHOSTS |
*/
template <typename T>
class data_block
{
public:
data_block(size_t beg, size_t end, size_t GHOST_SIZE)
: beg(beg)
, end(end)
, block_size(end - beg)
, ghost_size(GHOST_SIZE)
, array(std::vector<T>(block_size + 2 * ghost_size))
, left_interface(std::vector<T>(ghost_size))
, right_interface(std::vector<T>(ghost_size))
, handle(ctx.logical_data(&array[0], block_size + 2 * ghost_size))
, left_handle(ctx.logical_data(&left_interface[0], ghost_size))
, right_handle(ctx.logical_data(&right_interface[0], ghost_size))
{}
T check_sum()
{
T sum = 0.0;
ctx.task(exec_place::host(), handle.read())->*[&](cudaStream_t stream, auto sn) {
cuda_safe_call(cudaStreamSynchronize(stream));
const T* h_center = sn.data_handle();
for (size_t offset = ghost_size; offset < ghost_size + block_size; offset++)
{
sum += h_center[offset];
}
};
return sum;
}
public:
size_t beg;
size_t end;
size_t block_size;
size_t ghost_size;
int preferred_device;
private:
std::vector<T> array;
std::vector<T> left_interface;
std::vector<T> right_interface;
public:
// HANDLE = whole data + boundaries
logical_data<slice<T>> handle;
// A piece of data to store the left part of the block
logical_data<slice<T>> left_handle;
// A piece of data to store the right part of the block
logical_data<slice<T>> right_handle;
};
// array and array1 have a size of (cnt + 2*ghost_size)
template <typename T>
__global__ void stencil_kernel(size_t cnt, size_t ghost_size, T* array, const T* array1)
{
for (size_t idx = threadIdx.x + blockIdx.x * blockDim.x; idx < cnt; idx += blockDim.x * gridDim.x)
{
size_t idx2 = idx + ghost_size;
array[idx2] = 0.9 * array1[idx2] + 0.05 * array1[idx2 - 1] + 0.05 * array1[idx2 + 1];
}
}
// bn1.array = bn.array
template <typename T>
void stencil(data_block<T>& bn, data_block<T>& bn1)
{
int dev = bn.preferred_device;
ctx.task(exec_place::device(dev), bn.handle.rw(), bn1.handle.read())->*[&](cudaStream_t stream, auto sN, auto sN1) {
stencil_kernel<T><<<32, 64, 0, stream>>>(bn.block_size, bn.ghost_size, sN.data_handle(), sN1.data_handle());
};
}
template <typename T>
__global__ void copy_kernel(size_t cnt, T* dst, const T* src)
{
for (size_t idx = threadIdx.x + blockIdx.x * blockDim.x; idx < cnt; idx += blockDim.x * gridDim.x)
{
dst[idx] = src[idx];
}
}
template <typename T>
void copy_task(
size_t cnt, logical_data<slice<T>>& dst, size_t offset_dst, logical_data<slice<T>>& src, size_t offset_src, int dev)
{
ctx.task(exec_place::device(dev), dst.rw(), src.read())->*[&](cudaStream_t stream, auto dstS, auto srcS) {
int nblocks = (cnt > 64) ? 32 : 1;
copy_kernel<T><<<nblocks, 64, 0, stream>>>(cnt, dstS.data_handle() + offset_dst, srcS.data_handle() + offset_src);
};
}
template <typename T>
void update_inner_interfaces(data_block<T>& bn)
{
// LEFT
copy_task<T>(bn.ghost_size, bn.left_handle, 0, bn.handle, bn.ghost_size, bn.preferred_device);
// RIGHT
copy_task<T>(bn.ghost_size, bn.right_handle, 0, bn.handle, bn.block_size, bn.preferred_device);
}
// Copy left/right handles from neighbours to the array
template <typename T>
void update_outer_interfaces(data_block<T>& bn, data_block<T>& left, data_block<T>& right)
{
// update_outer_interface_left
copy_task<T>(bn.ghost_size, bn.handle, 0, left.right_handle, 0, bn.preferred_device);
// update_outer_interface_right
copy_task<T>(bn.ghost_size, bn.handle, bn.ghost_size + bn.block_size, right.left_handle, 0, bn.preferred_device);
}
// bn1.array = bn.array
template <typename T>
void copy_array(data_block<T>& bn, data_block<T>& bn1)
{
assert(bn.preferred_device == bn1.preferred_device);
copy_task<T>(bn.block_size + 2 * bn.ghost_size, bn1.handle, 0, bn.handle, 0, bn.preferred_device);
}
int main(int argc, char** argv)
{
int NITER = 500;
size_t NBLOCKS = 4;
size_t BLOCK_SIZE = 2048 * 1024;
if (argc > 1)
{
NITER = atoi(argv[1]);
}
if (argc > 2)
{
NBLOCKS = atoi(argv[2]);
}
const size_t GHOST_SIZE = 1;
size_t TOTAL_SIZE = NBLOCKS * BLOCK_SIZE;
int ndevs;
cuda_safe_call(cudaGetDeviceCount(&ndevs));
// fprintf(stderr, "GOT %d devices\n", ndevs);
double* U0 = new double[NBLOCKS * BLOCK_SIZE];
for (size_t idx = 0; idx < NBLOCKS * BLOCK_SIZE; idx++)
{
U0[idx] = (idx == 0) ? 1.0 : 0.0;
}
std::vector<data_block<double>> Un;
std::vector<data_block<double>> Un1;
// Create blocks and allocates host data
for (size_t b = 0; b < NBLOCKS; b++)
{
size_t beg = b * BLOCK_SIZE;
size_t end = (b + 1) * BLOCK_SIZE;
Un.emplace_back(beg, end, 1ull);
Un1.emplace_back(beg, end, 1ull);
}
for (size_t b = 0; b < NBLOCKS; b++)
{
Un[b].preferred_device = b % ndevs;
Un1[b].preferred_device = b % ndevs;
}
// Fill blocks with initial values. For the sake of simplicity, we are
// using a synchronization primitive and host code, but this could have
// been written asynchronously using host callbacks.
for (size_t b = 0; b < NBLOCKS; b++)
{
size_t beg = b * BLOCK_SIZE;
ctx.task(exec_place::host(), Un1[b].handle.rw())->*[&](cudaStream_t stream, auto sUn1) {
cuda_safe_call(cudaStreamSynchronize(stream));
double* Un1_vals = sUn1.data_handle();
for (size_t local_idx = 0; local_idx < BLOCK_SIZE; local_idx++)
{
Un1_vals[local_idx + GHOST_SIZE] = U0[(beg + local_idx + TOTAL_SIZE) % TOTAL_SIZE];
}
};
}
for (int iter = 0; iter < NITER; iter++)
{
for (size_t b = 0; b < NBLOCKS; b++)
{
// Update the internal copies of the left and right boundaries
update_inner_interfaces(Un1[b]);
}
for (size_t b = 0; b < NBLOCKS; b++)
{
// Apply ghost cells from neighbours to put then in the "center" array
update_outer_interfaces(Un1[b], Un1[(b - 1 + NBLOCKS) % NBLOCKS], Un1[(b + 1) % NBLOCKS]);
}
// UPDATE Un from Un1
for (size_t b = 0; b < NBLOCKS; b++)
{
stencil(Un[b], Un1[b]);
}
for (size_t b = 0; b < NBLOCKS; b++)
{
// Save Un into Un1
copy_array(Un[b], Un1[b]);
}
#if 0
// We make sure that the total sum of elements remains constant
if (iter % 250 == 0)
{
double check_sum = 0.0;
for (size_t b = 0; b < NBLOCKS; b++)
{
check_sum += Un[b].check_sum();
}
// fprintf(stderr, "iter %d : CHECK SUM = %e\n", iter, check_sum);
}
#endif
}
// In this stencil, the sum of the elements is supposed to be a constant
double check_sum = 0.0;
for (size_t b = 0; b < NBLOCKS; b++)
{
check_sum += Un[b].check_sum();
}
double err = fabs(check_sum - 1.0);
EXPECT(err < 0.0001);
ctx.finalize();
}

114
cccl_upstream/cudax/test/stf/examples/05-stencil2d-places.cu
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@@ -1,114 +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/__places/partitions/tiled_partition.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
#include <cuda/experimental/__stf/utility/pretty_print.cuh>
using namespace cuda::experimental::stf;
template <typename T>
__global__ void stencil2D_kernel(slice<T, 2> sUn, slice<const T, 2> sUn1)
{
size_t N = sUn.extent(0);
for (size_t i = threadIdx.x + blockIdx.x * blockDim.x; i < N; i += blockDim.x * gridDim.x)
{
for (size_t j = 0; j < N; j++)
{
sUn(j, i) = 0.8 * sUn1(j, i) + 0.05 * sUn1(j, (i + 1) % N) + 0.05 * sUn1(j, (i - 1 + N) % N)
+ 0.05 * sUn1((j + 1) % N, i) + 0.05 * sUn1((j - 1 + N) % N, i);
}
}
}
int main(int argc, char** argv)
{
stream_ctx ctx;
size_t NITER = 500;
size_t N = 1000;
bool vtk_dump = false;
if (argc > 1)
{
NITER = atoi(argv[1]);
}
if (argc > 2)
{
N = atoi(argv[2]);
}
if (argc > 3)
{
int val = atoi(argv[3]);
vtk_dump = (val == 1);
}
size_t TOTAL_SIZE = N * N;
double* Un = new double[TOTAL_SIZE];
double* Un1 = new double[TOTAL_SIZE];
for (size_t idx = 0; idx < TOTAL_SIZE; idx++)
{
Un[idx] = (idx == 0) ? 1.0 : 0.0;
Un1[idx] = Un[idx];
}
auto lUn = ctx.logical_data(make_slice(Un, std::tuple{N, N}, N));
auto lUn1 = ctx.logical_data(make_slice(Un1, std::tuple{N, N}, N));
// std::shared_ptr<execution_grid> all_devs = exec_place::all_devices();
// use grid [ 0 0 0 0 ] for debugging purpose
auto all_devs = exec_place::repeat(exec_place::device(0), 4);
// Partition over the vector of processor along the y-axis of the data domain
// TODO implement the proper tiled_partitioning along y !
data_place cdp = data_place::composite(tiled_partition<128>(), all_devs);
for (size_t iter = 0; iter < NITER; iter++)
{
// UPDATE Un from Un1
ctx.task(lUn.rw(cdp), lUn1.read(cdp))->*[&](auto stream, auto sUn, auto sUn1) {
stencil2D_kernel<double><<<32, 128, 0, stream>>>(sUn, sUn1);
};
// We make sure that the total sum of elements remains constant
if (iter % 250 == 0)
{
double sum = 0.0;
ctx.task(exec_place::host(), lUn.read())->*[&](auto stream, auto sUn) {
cuda_safe_call(cudaStreamSynchronize(stream));
for (size_t j = 0; j < N; j++)
{
for (size_t i = 0; i < N; i++)
{
sum += sUn(j, i);
}
}
if (vtk_dump)
{
char str[32];
snprintf(str, 32, "Un_%05zu.vtk", iter);
mdspan_to_vtk(sUn, std::string(str));
}
};
// fprintf(stderr, "iter %d : CHECK SUM = %e\n", iter, sum);
}
std::swap(lUn, lUn1);
}
ctx.finalize();
}

750
cccl_upstream/cudax/test/stf/examples/07-cholesky-redux.cu
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@@ -1,750 +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 This example implements a Cholesky decomposition over multiple devices using CUBLAS and CUSOLVER
*
* It also illustrates how we can use CUDASTF to allocate temporary data for CUSOLVER in CUDASTF tasks
*/
#include <cuda/experimental/__stf/stream/interfaces/slice_reduction_ops.cuh>
#include <cuda/experimental/__stf/utility/nvtx.cuh>
#include <cuda/experimental/stf.cuh>
#include <iostream>
#define TILED
using namespace cuda::experimental::stf;
// Global for the sake of simplicity !
stream_ctx ctx;
/* Get a CUBLAS handle valid on the current execution place, or initialize it lazily */
cublasHandle_t& get_cublas_handle(const exec_place& ep = exec_place::current_device())
{
static std::unordered_map<exec_place, cublasHandle_t, hash<exec_place>> cublas_handles;
auto& result = cublas_handles[ep];
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;
}
/* Get a CUSOLVER handle valid on the current execution place, or initialize it lazily */
cusolverDnHandle_t& get_cusolver_handle(const exec_place& ep = exec_place::current_device())
{
static std::unordered_map<exec_place, cusolverDnHandle_t, hash<exec_place>> cusolver_handles;
auto& result = cusolver_handles[ep];
if (result == cusolverDnHandle_t())
{ // not found, default value inserted
// Lazy initialization, and save the handle for future use
cuda_safe_call(cusolverDnCreate(&result));
}
return result;
}
template <typename T>
class matrix
{
public:
matrix(int NROWS, int NCOLS, int BLOCKSIZE_ROWS, int BLOCKSIZE_COLS, bool is_sym, const char* _symbol = "matrix")
{
symbol = _symbol;
sym_matrix = is_sym;
m = NROWS;
mb = BLOCKSIZE_ROWS;
n = NCOLS;
nb = BLOCKSIZE_COLS;
assert(m % mb == 0);
assert(n % nb == 0);
// 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.resize(m * n);
cuda_safe_call(cudaHostRegister(&h_array[0], h_array.size() * sizeof(T), cudaHostRegisterPortable));
// Compute the number of blocks
mt = m / mb;
nt = n / nb;
handles.resize(mt * nt);
for (size_t colb = 0; colb < nt; colb++)
{
size_t low_rowb = sym_matrix ? colb : 0;
for (size_t rowb = low_rowb; rowb < mt; rowb++)
{
T* addr_h = get_block_h(rowb, colb);
auto& h = handle(rowb, colb);
#ifdef TILED
// tiles are stored contiguously
size_t ld = mb;
#else
size_t ld = m;
#endif
std::ignore = ld; // work around bug in compiler
h = ctx.logical_data(make_slice(addr_h, std::tuple{mb, nb}, ld));
h.set_symbol(std::string(symbol) + "_" + std::to_string(rowb) + "_" + std::to_string(colb));
h.set_write_back(false);
}
}
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';
}
int get_preferred_devid(int row, int col)
{
return (row % grid_p) + (col % grid_q) * grid_p;
}
auto& handle(int row, int col)
{
return handles[row + col * mt];
}
size_t get_index(size_t row, size_t col)
{
#ifdef TILED
// Find which tile contains this element
int tile_row = row / mb;
int tile_col = col / nb;
size_t tile_size = mb * nb;
// Look for the index of the beginning of the tile
size_t tile_start = (tile_row + mt * tile_col) * tile_size;
// Offset within the tile
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)
{
size_t index = get_index(brow * mb, bcol * nb);
return &h_array[index];
}
// Fill with func(Matrix*,row, col)
template <typename Fun>
void fill(Fun&& fun)
{
nvtx_range r("fill");
// Fill blocks by blocks
for (size_t colb = 0; colb < nt; colb++)
{
size_t low_rowb = sym_matrix ? colb : 0;
for (size_t rowb = low_rowb; rowb < mt; rowb++)
{
// Each task fills a block
auto& h = handle(rowb, colb);
int devid = get_preferred_devid(rowb, colb);
ctx.parallel_for(exec_place::device(devid), h.shape(), h.write()).set_symbol("INIT")->*
[=] __device__(size_t lrow, size_t lcol, auto sA) {
size_t row = lrow + rowb * sA.extent(0);
size_t col = lcol + colb * sA.extent(1);
sA(lrow, lcol) = fun(row, col);
};
}
}
}
std::vector<T> h_array;
size_t m; // nrows
size_t n; // ncols
// Is this a sym matrix ? (lower assumed)
bool sym_matrix;
size_t mb; // block size (rows)
size_t nb; // block size (cols)
size_t mt; // number of column blocks
size_t nt; // number of row blocks
// abstract data handles
std::vector<logical_data<slice<double, 2>>> handles;
const char* symbol;
// for the mapping
int ndevs;
int grid_p, grid_q;
};
void DPOTRF(cublasFillMode_t uplo, class matrix<double>& A, int A_row, int A_col)
{
auto& Akk = A.handle(A_row, A_col);
size_t m_akk = Akk.shape().extent(0);
// Note that the handle may be different from the actual handle...
int Lwork_expected;
cuda_safe_call(cusolverDnDpotrf_bufferSize(get_cusolver_handle(), uplo, m_akk, nullptr, 0, &Lwork_expected));
auto potrf_buffer = ctx.logical_data<double>(Lwork_expected);
auto devInfo = ctx.logical_data(shape_of<slice<int>>(1));
auto t =
ctx.task(exec_place::device(A.get_preferred_devid(A_row, A_col)), Akk.rw(), potrf_buffer.write(), devInfo.write());
t.set_symbol("DPOTRF");
t->*[uplo](cudaStream_t s, auto sAkk, auto buffer, auto info) {
auto& h = get_cusolver_handle();
cuda_safe_call(cusolverDnSetStream(h, s));
cuda_safe_call(cusolverDnDpotrf(
h,
uplo,
sAkk.extent(0),
sAkk.data_handle(),
sAkk.stride(1),
buffer.data_handle(),
buffer.extent(0),
info.data_handle()));
};
}
void DGEMM(
cublasOperation_t transa,
cublasOperation_t transb,
double alpha,
class matrix<double>& A,
int A_row,
int A_col,
class matrix<double>& B,
int B_row,
int B_col,
double beta,
class matrix<double>& C,
int C_row,
int C_col)
{
auto redux_op = std::make_shared<slice_reduction_op_sum<double, 2>>();
// If beta == 1.0 (we assume this is exactly 1.0), then this operation is
// an accumulation with the add operator
auto dep_c = (beta == 1.0) ? C.handle(C_row, C_col).relaxed(redux_op) : C.handle(C_row, C_col).rw();
auto t = ctx.task(exec_place::device(A.get_preferred_devid(C_row, C_col)),
A.handle(A_row, A_col).read(),
B.handle(B_row, B_col).read(),
dep_c);
t.set_symbol("DGEMM");
t->*[transa, transb, alpha, beta](cudaStream_t s, auto sA, auto sB, auto sC) {
EXPECT(sC.data_handle() != nullptr);
auto& h = get_cublas_handle();
cuda_safe_call(cublasSetStream(h, s));
auto k = (transa == CUBLAS_OP_N) ? sA.extent(1) : sA.extent(0);
cuda_safe_call(cublasDgemm(
h,
transa,
transb,
sC.extent(0),
sC.extent(1),
k,
&alpha,
sA.data_handle(),
sA.stride(1),
sB.data_handle(),
sB.stride(1),
&beta,
sC.data_handle(),
sC.stride(1)));
};
}
void DSYRK(
cublasFillMode_t uplo,
cublasOperation_t trans,
double alpha,
class matrix<double>& A,
int A_row,
int A_col,
double beta,
class matrix<double>& C,
int C_row,
int C_col)
{
auto t = ctx.task(exec_place::device(A.get_preferred_devid(C_row, C_col)),
A.handle(A_row, A_col).read(),
C.handle(C_row, C_col).rw());
t.set_symbol("DSYRK");
t->*[uplo, trans, alpha, beta](cudaStream_t s, auto sA, auto sC) {
auto& h = get_cublas_handle();
cuda_safe_call(cublasSetStream(h, s));
// number of rows of matrix op(A) and C
auto n = sC.extent(0);
// number of columns of matrix op(A)
auto k = (trans == CUBLAS_OP_N) ? sA.extent(1) : sA.extent(0);
cuda_safe_call(
cublasDsyrk(h, uplo, trans, n, k, &alpha, sA.data_handle(), sA.stride(1), &beta, sC.data_handle(), sC.stride(1)));
};
}
void DTRSM(
cublasSideMode_t side,
cublasFillMode_t uplo,
cublasOperation_t transa,
cublasDiagType_t diag,
double alpha,
class matrix<double>& A,
int A_row,
int A_col,
class matrix<double>& B,
int B_row,
int B_col)
{
auto t = ctx.task(exec_place::device(A.get_preferred_devid(B_row, B_col)),
A.handle(A_row, A_col).read(),
B.handle(B_row, B_col).rw());
t.set_symbol("DTRSM");
t->*[side, uplo, transa, diag, alpha](cudaStream_t s, auto sA, auto sB) {
auto& h = get_cublas_handle();
cuda_safe_call(cublasSetStream(h, s));
cuda_safe_call(cublasDtrsm(
h,
side,
uplo,
transa,
diag,
sB.extent(0),
sB.extent(1),
&alpha,
sA.data_handle(),
sA.stride(1),
sB.data_handle(),
sB.stride(1)));
};
}
void PDNRM2_HOST(matrix<double>* A, double* result)
{
#ifdef HAVE_DOT
reserved::dot::set_current_color("red");
#endif
for (size_t rowb = 0; rowb < A->mt; rowb++)
{
for (size_t colb = 0; colb < A->nt; colb++)
{
ctx.host_launch(A->handle(rowb, colb).read())->*[=](auto sA) {
double res2 = 0.0;
for (size_t col = 0; col < sA.extent(1); col++)
{
for (size_t row = 0; row < sA.extent(0); row++)
{
double v = sA(row, col);
res2 += v * v;
}
}
*result += res2;
};
}
}
}
void PDPOTRF(matrix<double>& A)
{
nvtx_range r("PDPOTRF");
#ifdef HAVE_DOT
reserved::dot::set_current_color("yellow");
#endif
assert(A.m == A.n);
assert(A.mt == A.nt);
int NBLOCKS = A.mt;
assert(A.mb == A.nb);
cuda_safe_call(cudaSetDevice(0));
for (int K = 0; K < NBLOCKS; K++)
{
int dev_akk = A.get_preferred_devid(K, K);
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(K, K)));
DPOTRF(CUBLAS_FILL_MODE_LOWER, A, K, K);
for (int row = K + 1; row < NBLOCKS; row++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(row, K)));
DTRSM(CUBLAS_SIDE_RIGHT, CUBLAS_FILL_MODE_LOWER, CUBLAS_OP_T, CUBLAS_DIAG_NON_UNIT, 1.0, A, K, K, A, row, K);
for (int col = K + 1; col < row; col++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(row, col)));
DGEMM(CUBLAS_OP_N, CUBLAS_OP_T, -1.0, A, row, K, A, col, K, 1.0, A, row, col);
}
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(row, row)));
DSYRK(CUBLAS_FILL_MODE_LOWER, CUBLAS_OP_N, -1.0, A, row, K, 1.0, A, row, row);
}
}
cuda_safe_call(cudaSetDevice(0));
}
// Algorithm from PLASMA
void PDTRSM(cublasSideMode_t side,
cublasFillMode_t uplo,
cublasOperation_t trans,
cublasDiagType_t diag,
double alpha,
class matrix<double>& A,
class matrix<double>& B)
{
nvtx_range r("PDTRSM");
// std::cout << "[PDTRSM] START B MT " << B.mt << " NT " << B.nt << '\n';
if (side == CUBLAS_SIDE_LEFT)
{
if (uplo == CUBLAS_FILL_MODE_UPPER)
{
// TODO
assert(0);
abort();
}
else
{
//===========================================
// CUBLAS_SIDE_LEFT / CUBLAS_FILL_MODE_LOWER / CUBLAS_OP_N
//===========================================
if (trans == CUBLAS_OP_N)
{
for (size_t k = 0; k < B.mt; k++)
{
double lalpha = k == 0 ? alpha : 1.0;
for (size_t n = 0; n < B.nt; n++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(k, k)));
DTRSM(side, uplo, trans, diag, lalpha, A, k, k, B, k, n);
}
for (size_t m = k + 1; m < B.mt; m++)
{
for (size_t n = 0; n < B.nt; n++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(m, k)));
DGEMM(CUBLAS_OP_N, CUBLAS_OP_N, -1.0, A, m, k, B, k, n, lalpha, B, m, n);
}
}
}
}
//================================================
// CUBLAS_SIDE_LEFT / CUBLAS_FILL_MODE_LOWER / CUBLAS_OP_[C|T]
//================================================
else
{
for (size_t k = 0; k < B.mt; k++)
{
double lalpha = k == 0 ? alpha : 1.0;
for (size_t n = 0; n < B.nt; n++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(B.mt - k - 1, B.mt - k - 1)));
DTRSM(side, uplo, trans, diag, lalpha, A, B.mt - k - 1, B.mt - k - 1, B, B.mt - k - 1, n);
}
for (size_t m = k + 1; m < B.mt; m++)
{
for (size_t n = 0; n < B.nt; n++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(B.mt - k - 1, B.mt - 1 - m)));
DGEMM(
trans, CUBLAS_OP_N, -1.0, A, B.mt - k - 1, B.mt - 1 - m, B, B.mt - k - 1, n, lalpha, B, B.mt - 1 - m, n);
}
}
}
}
}
}
else
{
// TODO
abort();
}
cuda_safe_call(cudaSetDevice(0));
// std::cout << "[PDTRSM] END" << '\n';
}
void PDPOTRS(matrix<double>& A, class matrix<double>& B, cublasFillMode_t uplo)
{
nvtx_range r("PDPOTRS");
#ifdef HAVE_DOT
reserved::dot::set_current_color("green");
#endif
// std::cout << "[PDPOTRS] START" << '\n';
// Call the parallel functions.
PDTRSM(
CUBLAS_SIDE_LEFT, uplo, uplo == CUBLAS_FILL_MODE_UPPER ? CUBLAS_OP_T : CUBLAS_OP_N, CUBLAS_DIAG_NON_UNIT, 1.0, A, B);
#ifdef HAVE_DOT
reserved::dot::set_current_color("darkgreen");
#endif
PDTRSM(
CUBLAS_SIDE_LEFT, uplo, uplo == CUBLAS_FILL_MODE_UPPER ? CUBLAS_OP_N : CUBLAS_OP_T, CUBLAS_DIAG_NON_UNIT, 1.0, A, B);
// std::cout << "[PDPOTRS] END" << '\n';
}
/*****************************************************************************
* Parallel tile matrix-matrix
*multiplication.
* @see plasma_omp_dgemm
******************************************************************************/
void PDGEMM(cublasOperation_t transa,
cublasOperation_t transb,
double alpha,
class matrix<double>& A,
class matrix<double>& B,
double beta,
class matrix<double>& C)
{
nvtx_range r("PDGEMM");
#ifdef HAVE_DOT
reserved::dot::set_current_color("blue");
#endif
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
//=========================================
int inner_k = transa == CUBLAS_OP_N ? A.n : A.m;
if (alpha == 0.0 || inner_k == 0)
{
DGEMM(transa, transb, alpha, A, 0, 0, B, 0, 0, beta, C, m, n);
}
else if (transa == CUBLAS_OP_N)
{
//================================
// CUBLAS_OP_N / CUBLAS_OP_N
//================================
if (transb == CUBLAS_OP_N)
{
for (size_t k = 0; k < A.nt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(transa, transb, alpha, A, m, k, B, k, n, zbeta, C, m, n);
}
}
//=====================================
// CUBLAS_OP_N / CUBLAS_OP_T
//=====================================
else
{
for (size_t k = 0; k < A.nt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(transa, transb, alpha, A, m, k, B, n, k, zbeta, C, m, n);
}
}
}
else
{
//=====================================
// CUBLAS_OP_T / CUBLAS_OP_N
//=====================================
if (transb == CUBLAS_OP_N)
{
for (size_t k = 0; k < A.mt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(transa, transb, alpha, A, k, m, B, k, n, zbeta, C, m, n);
}
}
//==========================================
// CUBLAS_OP_T / CUBLAS_OP_T
//==========================================
else
{
for (size_t k = 0; k < A.mt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(transa, transb, alpha, A, k, m, B, n, k, zbeta, C, m, n);
}
}
}
}
}
}
int main(int argc, char** argv)
{
int N = 1024;
int NB = 128;
if (argc > 1)
{
N = atoi(argv[1]);
}
if (argc > 2)
{
NB = atoi(argv[2]);
}
int check_result = 1;
if (getenv("CHECK_RESULT"))
{
check_result = atoi(getenv("CHECK_RESULT"));
}
assert(N % NB == 0);
// Use pools of preallocated blocks
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));
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())->*[] __device__(size_t, auto) {};
cuda_safe_call(cudaSetDevice(d));
get_cublas_handle();
get_cusolver_handle();
}
cuda_safe_call(cudaSetDevice(0));
matrix<double> A(N, N, NB, NB, true, "A");
matrix<double> Aref(N, N, NB, NB, false, "Aref");
// (Hilbert matrix + 2*N*Id) to have a diagonal dominant matrix
auto hilbert = [=] __host__ __device__(size_t row, size_t col) {
return 1.0 / (col + row + 1.0) + 2.0 * N * (col == row);
};
if (check_result)
{
Aref.fill(hilbert);
}
A.fill(hilbert);
/* Right-hand side */
matrix<double> B_potrs(N, 1, NB, 1, false, "B");
matrix<double> Bref_potrs(N, 1, NB, 1, false, "Bref");
if (check_result)
{
auto rhs_vals = [] __host__ __device__(size_t row, size_t /*unused*/) { return 1.0 * (row + 1); };
B_potrs.fill(rhs_vals);
Bref_potrs.fill(rhs_vals);
}
// // Compute ||Bref||
double Bref_nrm2 = 0.0;
double res_nrm2 = 0.0;
if (check_result)
{
PDNRM2_HOST(&Bref_potrs, &Bref_nrm2);
}
cudaEvent_t startEvent_pdpotrf, stopEvent_pdpotrf;
float milliseconds_pdpotrf = 0;
// for (size_t row = 0; row < A.mt; row++)
// {
// for (size_t col = 0; col <= row; col++)
// {
// cuda_safe_call(cudaSetDevice(A.get_preferred_devid(row, col)));
// NOOP(A, row, col);
// }
// }
cuda_safe_call(cudaSetDevice(0));
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
cuda_safe_call(cudaEventCreate(&startEvent_pdpotrf));
cuda_safe_call(cudaEventCreate(&stopEvent_pdpotrf));
cuda_safe_call(cudaEventRecord(startEvent_pdpotrf, ctx.fence()));
PDPOTRF(A);
cuda_safe_call(cudaSetDevice(0));
cuda_safe_call(cudaEventRecord(stopEvent_pdpotrf, ctx.fence()));
/*
* POTRS
*/
if (check_result)
{
// Solve AX = B and put the result in B
PDPOTRS(A, B_potrs, CUBLAS_FILL_MODE_LOWER);
// Compute (AX - B)
// Bref = (Aref*B - Bref)
PDGEMM(CUBLAS_OP_N, CUBLAS_OP_N, 1.0, Aref, B_potrs, -1.0, Bref_potrs);
// Compute ||AX - B|| = ||Bref||
PDNRM2_HOST(&Bref_potrs, &res_nrm2);
}
ctx.finalize();
cuda_safe_call(cudaEventElapsedTime(&milliseconds_pdpotrf, startEvent_pdpotrf, stopEvent_pdpotrf));
double gflops_pdpotrf = 1.0 / 3.0 * ((double) N * (double) N * (double) N) / (1000000000.0);
std::cout << "[PDPOTRF] ELAPSED: " << milliseconds_pdpotrf
<< " ms, GFLOPS: " << gflops_pdpotrf / (milliseconds_pdpotrf / 1000.0) << '\n';
if (check_result)
{
if (double residual = sqrt(res_nrm2) / sqrt(Bref_nrm2); residual >= 0.01)
{
std::cerr << "[POTRS] ||AX - B|| : " << sqrt(res_nrm2) << '\n';
std::cerr << "[POTRS] ||B|| : " << sqrt(Bref_nrm2) << '\n';
std::cerr << "[POTRS] RESIDUAL (||AX - B||/||B||) : " << residual << '\n';
assert(!"Algorithm did not converge.");
}
}
}

693
cccl_upstream/cudax/test/stf/examples/07-cholesky-unified.cu
View File

@@ -1,693 +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/stream_ctx.cuh>
#include <cuda/experimental/__stf/utility/nvtx.cuh>
#define TILED
using namespace cuda::experimental::stf;
// The backend used in this example only depends on that type
using backend_type = stream_ctx;
// using backend_type = graph_ctx;
// Global for the sake of simplicity !
backend_type ctx;
/* Get a CUBLAS handle valid on the current execution place, or initialize it lazily */
cublasHandle_t& get_cublas_handle(const exec_place& ep = exec_place::current_device())
{
static std::unordered_map<exec_place, cublasHandle_t, hash<exec_place>> cublas_handles;
auto& result = cublas_handles[ep];
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;
}
/* Get a CUSOLVER handle valid on the current execution place, or initialize it lazily */
cusolverDnHandle_t& get_cusolver_handle(const exec_place& ep = exec_place::current_device())
{
static std::unordered_map<exec_place, cusolverDnHandle_t, hash<exec_place>> cusolver_handles;
auto& result = cusolver_handles[ep];
if (result == cusolverDnHandle_t())
{ // not found, default value inserted
// Lazy initialization, and save the handle for future use
cuda_safe_call(cusolverDnCreate(&result));
}
return result;
}
template <typename T>
class matrix
{
public:
matrix(int NROWS, int NCOLS, int BLOCKSIZE_ROWS, int BLOCKSIZE_COLS, bool is_sym, const char* _symbol = "matrix")
{
symbol = _symbol;
sym_matrix = is_sym;
m = NROWS;
mb = BLOCKSIZE_ROWS;
n = NCOLS;
nb = BLOCKSIZE_COLS;
assert(m % mb == 0);
assert(n % nb == 0);
// 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.resize(m * n);
cuda_safe_call(cudaHostRegister(&h_array[0], h_array.size() * sizeof(T), cudaHostRegisterPortable));
// Compute the number of blocks
mt = m / mb;
nt = n / nb;
handles.resize(mt * nt);
for (size_t colb = 0; colb < nt; colb++)
{
int low_rowb = sym_matrix ? colb : 0;
for (size_t rowb = low_rowb; rowb < mt; rowb++)
{
T* addr_h = get_block_h(rowb, colb);
auto& h = handle(rowb, colb);
#ifdef TILED
// tiles are stored contiguously
size_t ld = mb;
#else
size_t ld = m;
#endif
std::ignore = ld; // work around bug in compiler
h = ctx.logical_data(make_slice(addr_h, std::tuple{mb, nb}, ld));
h.set_symbol(std::string(symbol) + "_" + std::to_string(rowb) + "_" + std::to_string(colb));
}
}
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';
}
int get_preferred_devid(int row, int col)
{
return (row % grid_p) + (col % grid_q) * grid_p;
}
auto& handle(int row, int col)
{
return handles[row + col * mt];
}
size_t get_index(size_t row, size_t col)
{
#ifdef TILED
// Find which tile contains this element
int tile_row = row / mb;
int tile_col = col / nb;
size_t tile_size = mb * nb;
// Look for the index of the beginning of the tile
size_t tile_start = (tile_row + mt * tile_col) * tile_size;
// Offset within the tile
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)
{
size_t index = get_index(brow * mb, bcol * nb);
return &h_array[index];
}
// Fill with func(Matrix*,row, col)
template <typename Fun>
void fill(Fun&& fun)
{
// Fill blocks by blocks
for (size_t colb = 0; colb < nt; colb++)
{
size_t low_rowb = sym_matrix ? colb : 0;
for (size_t rowb = low_rowb; rowb < mt; rowb++)
{
// Each task fills a block
ctx.host_launch(handle(rowb, colb).write())->*[=, self = this](auto sA) {
for (size_t lcol = 0; lcol < sA.extent(1); lcol++)
{
size_t col = lcol + colb * sA.extent(1);
for (size_t lrow = 0; lrow < sA.extent(0); lrow++)
{
size_t row = lrow + rowb * sA.extent(0);
sA(lrow, lcol) = fun(*self, row, col);
}
}
};
}
}
}
std::vector<T> h_array;
size_t m; // nrows
size_t n; // ncols
// Is this a sym matrix ? (lower assumed)
bool sym_matrix;
size_t mb; // block size (rows)
size_t nb; // block size (cols)
size_t mt; // number of column blocks
size_t nt; // number of row blocks
// abstract data handles
std::vector<logical_data<slice<double, 2>>> handles;
const char* symbol;
// for the mapping
int ndevs;
int grid_p, grid_q;
};
void DPOTRF(cublasFillMode_t uplo, class matrix<double>& A, int A_row, int A_col)
{
auto& Akk = A.handle(A_row, A_col);
size_t m_akk = Akk.shape().extent(0);
// Note that the handle may be different from the actual handle...
int Lwork_expected;
cuda_safe_call(cusolverDnDpotrf_bufferSize(get_cusolver_handle(), uplo, m_akk, nullptr, 0, &Lwork_expected));
auto potrf_buffer = ctx.logical_data<double>(Lwork_expected);
auto devInfo = ctx.logical_data(shape_of<slice<int>>(1));
auto t = ctx.task(Akk.rw(), potrf_buffer.write(), devInfo.write());
// t.set_symbol("DPOTRF");
t->*[&](cudaStream_t s, auto sAkk, auto buffer, auto info) {
auto& h = get_cusolver_handle();
cuda_safe_call(cusolverDnSetStream(h, s));
cuda_safe_call(cusolverDnDpotrf(
h,
uplo,
sAkk.extent(0),
sAkk.data_handle(),
sAkk.stride(1),
buffer.data_handle(),
buffer.extent(0),
info.data_handle()));
};
}
void DGEMM(
cublasOperation_t transa,
cublasOperation_t transb,
double alpha,
class matrix<double>& A,
int A_row,
int A_col,
class matrix<double>& B,
int B_row,
int B_col,
double beta,
class matrix<double>& C,
int C_row,
int C_col)
{
auto ignored = get_cublas_handle();
auto t = ctx.task(A.handle(A_row, A_col).read(), B.handle(B_row, B_col).read(), C.handle(C_row, C_col).rw());
// t.set_symbol("DGEMM");
t->*[&](cudaStream_t s, auto sA, auto sB, auto sC) {
auto& h = get_cublas_handle();
cuda_safe_call(cublasSetStream(h, s));
auto k = (transa == CUBLAS_OP_N) ? sA.extent(1) : sA.extent(0);
cuda_safe_call(cublasDgemm(
h,
transa,
transb,
sC.extent(0),
sC.extent(1),
k,
&alpha,
sA.data_handle(),
sA.stride(1),
sB.data_handle(),
sB.stride(1),
&beta,
sC.data_handle(),
sC.stride(1)));
};
}
void DSYRK(
cublasFillMode_t uplo,
cublasOperation_t trans,
double alpha,
class matrix<double>& A,
int A_row,
int A_col,
double beta,
class matrix<double>& C,
int C_row,
int C_col)
{
auto ignored = get_cublas_handle();
auto t = ctx.task(A.handle(A_row, A_col).read(), C.handle(C_row, C_col).rw());
// t.set_symbol("DSYRK");
t->*[&](cudaStream_t s, auto sA, auto sC) {
auto& h = get_cublas_handle();
cuda_safe_call(cublasSetStream(h, s));
// number of rows of matrix op(A) and C
auto n = sC.extent(0);
// number of columns of matrix op(A)
auto k = (trans == CUBLAS_OP_N) ? sA.extent(1) : sA.extent(0);
cuda_safe_call(
cublasDsyrk(h, uplo, trans, n, k, &alpha, sA.data_handle(), sA.stride(1), &beta, sC.data_handle(), sC.stride(1)));
};
}
void DTRSM(
cublasSideMode_t side,
cublasFillMode_t uplo,
cublasOperation_t transa,
cublasDiagType_t diag,
double alpha,
class matrix<double>& A,
int A_row,
int A_col,
class matrix<double>& B,
int B_row,
int B_col)
{
auto ignored = get_cublas_handle();
auto t = ctx.task(A.handle(A_row, A_col).read(), B.handle(B_row, B_col).rw());
// t.set_symbol("DTRSM");
t->*[&](cudaStream_t s, auto sA, auto sB) {
auto& h = get_cublas_handle();
cuda_safe_call(cublasSetStream(h, s));
cuda_safe_call(cublasDtrsm(
h,
side,
uplo,
transa,
diag,
sB.extent(0),
sB.extent(1),
&alpha,
sA.data_handle(),
sA.stride(1),
sB.data_handle(),
sB.stride(1)));
};
}
void PDNRM2_HOST(matrix<double>* A, double* result)
{
#ifdef HAVE_DOT
reserved::dot::set_current_color("red");
#endif
for (size_t rowb = 0; rowb < A->mt; rowb++)
{
for (size_t colb = 0; colb < A->nt; colb++)
{
ctx.host_launch(A->handle(rowb, colb).read())->*[=](auto sA) {
double res2 = 0.0;
for (size_t col = 0; col < sA.extent(1); col++)
{
for (size_t row = 0; row < sA.extent(0); row++)
{
double v = sA(row, col);
res2 += v * v;
}
}
*result += res2;
};
}
}
}
void PDPOTRF(matrix<double>& A)
{
nvtx_range r("PDPOTRF");
#ifdef HAVE_DOT
reserved::dot::set_current_color("yellow");
#endif
assert(A.m == A.n);
assert(A.mt == A.nt);
int NBLOCKS = A.mt;
assert(A.mb == A.nb);
cuda_safe_call(cudaSetDevice(0));
for (int K = 0; K < NBLOCKS; K++)
{
int dev_akk = A.get_preferred_devid(K, K);
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(K, K)));
DPOTRF(CUBLAS_FILL_MODE_LOWER, A, K, K);
for (int row = K + 1; row < NBLOCKS; row++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(row, K)));
DTRSM(CUBLAS_SIDE_RIGHT, CUBLAS_FILL_MODE_LOWER, CUBLAS_OP_T, CUBLAS_DIAG_NON_UNIT, 1.0, A, K, K, A, row, K);
for (int col = K + 1; col < row; col++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(row, col)));
DGEMM(CUBLAS_OP_N, CUBLAS_OP_T, -1.0, A, row, K, A, col, K, 1.0, A, row, col);
}
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(row, row)));
DSYRK(CUBLAS_FILL_MODE_LOWER, CUBLAS_OP_N, -1.0, A, row, K, 1.0, A, row, row);
}
}
cuda_safe_call(cudaSetDevice(0));
}
// Algorithm from PLASMA
void PDTRSM(cublasSideMode_t side,
cublasFillMode_t uplo,
cublasOperation_t trans,
cublasDiagType_t diag,
double alpha,
class matrix<double>& A,
class matrix<double>& B)
{
nvtx_range r("PDTRSM");
// std::cout << "[PDTRSM] START B MT " << B.mt << " NT " << B.nt << '\n';
if (side == CUBLAS_SIDE_LEFT)
{
if (uplo == CUBLAS_FILL_MODE_UPPER)
{
// TODO
assert(0);
abort();
}
else
{
//===========================================
// CUBLAS_SIDE_LEFT / CUBLAS_FILL_MODE_LOWER / CUBLAS_OP_N
//===========================================
if (trans == CUBLAS_OP_N)
{
for (size_t k = 0; k < B.mt; k++)
{
double lalpha = k == 0 ? alpha : 1.0;
for (size_t n = 0; n < B.nt; n++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(k, k)));
DTRSM(side, uplo, trans, diag, lalpha, A, k, k, B, k, n);
}
for (size_t m = k + 1; m < B.mt; m++)
{
for (size_t n = 0; n < B.nt; n++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(m, k)));
DGEMM(CUBLAS_OP_N, CUBLAS_OP_N, -1.0, A, m, k, B, k, n, lalpha, B, m, n);
}
}
}
}
//================================================
// CUBLAS_SIDE_LEFT / CUBLAS_FILL_MODE_LOWER / CUBLAS_OP_[C|T]
//================================================
else
{
for (size_t k = 0; k < B.mt; k++)
{
double lalpha = k == 0 ? alpha : 1.0;
for (size_t n = 0; n < B.nt; n++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(B.mt - k - 1, B.mt - k - 1)));
DTRSM(side, uplo, trans, diag, lalpha, A, B.mt - k - 1, B.mt - k - 1, B, B.mt - k - 1, n);
}
for (size_t m = k + 1; m < B.mt; m++)
{
for (size_t n = 0; n < B.nt; n++)
{
cuda_safe_call(cudaSetDevice(A.get_preferred_devid(B.mt - k - 1, B.mt - 1 - m)));
DGEMM(
trans, CUBLAS_OP_N, -1.0, A, B.mt - k - 1, B.mt - 1 - m, B, B.mt - k - 1, n, lalpha, B, B.mt - 1 - m, n);
}
}
}
}
}
}
else
{
// TODO
abort();
}
cuda_safe_call(cudaSetDevice(0));
// std::cout << "[PDTRSM] END" << '\n';
}
void PDPOTRS(matrix<double>& A, class matrix<double>& B, cublasFillMode_t uplo)
{
nvtx_range r("PDPOTRS");
#ifdef HAVE_DOT
reserved::dot::set_current_color("green");
#endif
// std::cout << "[PDPOTRS] START" << '\n';
// Call the parallel functions.
PDTRSM(
CUBLAS_SIDE_LEFT, uplo, uplo == CUBLAS_FILL_MODE_UPPER ? CUBLAS_OP_T : CUBLAS_OP_N, CUBLAS_DIAG_NON_UNIT, 1.0, A, B);
#ifdef HAVE_DOT
reserved::dot::set_current_color("darkgreen");
#endif
PDTRSM(
CUBLAS_SIDE_LEFT, uplo, uplo == CUBLAS_FILL_MODE_UPPER ? CUBLAS_OP_N : CUBLAS_OP_T, CUBLAS_DIAG_NON_UNIT, 1.0, A, B);
// std::cout << "[PDPOTRS] END" << '\n';
}
/*****************************************************************************
* Parallel tile matrix-matrix
*multiplication.
* @see plasma_omp_dgemm
******************************************************************************/
void PDGEMM(cublasOperation_t transa,
cublasOperation_t transb,
double alpha,
class matrix<double>& A,
class matrix<double>& B,
double beta,
class matrix<double>& C)
{
nvtx_range r("PDGEMM");
#ifdef HAVE_DOT
reserved::dot::set_current_color("blue");
#endif
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
//=========================================
int inner_k = transa == CUBLAS_OP_N ? A.n : A.m;
if (alpha == 0.0 || inner_k == 0)
{
DGEMM(transa, transb, alpha, A, 0, 0, B, 0, 0, beta, C, m, n);
}
else if (transa == CUBLAS_OP_N)
{
//================================
// CUBLAS_OP_N / CUBLAS_OP_N
//================================
if (transb == CUBLAS_OP_N)
{
for (size_t k = 0; k < A.nt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(transa, transb, alpha, A, m, k, B, k, n, zbeta, C, m, n);
}
}
//=====================================
// CUBLAS_OP_N / CUBLAS_OP_T
//=====================================
else
{
for (size_t k = 0; k < A.nt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(transa, transb, alpha, A, m, k, B, n, k, zbeta, C, m, n);
}
}
}
else
{
//=====================================
// CUBLAS_OP_T / CUBLAS_OP_N
//=====================================
if (transb == CUBLAS_OP_N)
{
for (size_t k = 0; k < A.mt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(transa, transb, alpha, A, k, m, B, k, n, zbeta, C, m, n);
}
}
//==========================================
// CUBLAS_OP_T / CUBLAS_OP_T
//==========================================
else
{
for (size_t k = 0; k < A.mt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(transa, transb, alpha, A, k, m, B, n, k, zbeta, C, m, n);
}
}
}
}
}
}
int main(int argc, char** argv)
{
int N = 1024;
int NB = 128;
if (argc > 1)
{
N = atoi(argv[1]);
}
if (argc > 2)
{
NB = atoi(argv[2]);
}
assert(N % NB == 0);
// Set up CUBLAS and CUSOLVER
int ndevs;
cuda_safe_call(cudaGetDeviceCount(&ndevs));
cuda_safe_call(cudaSetDevice(0));
matrix<double> A(N, N, NB, NB, true, "A");
matrix<double> Aref(N, N, NB, NB, false, "Aref");
// (Hilbert matrix + 2*N*Id) to have a diagonal dominant matrix
auto hilbert = [](matrix<double>& mat, int row, int col) {
return 1.0 / (col + row + 1.0) + 2.0 * mat.n * (col == row);
};
Aref.fill(hilbert);
A.fill(hilbert);
/* Right-hand side */
matrix<double> B_potrs(N, 1, NB, 1, false, "B");
matrix<double> Bref_potrs(N, 1, NB, 1, false, "Bref");
auto rhs_vals = [](matrix<double>& /*unused*/, int row, int /*unused*/) {
return 1.0 * (row + 1);
};
B_potrs.fill(rhs_vals);
Bref_potrs.fill(rhs_vals);
int check_result = 1;
if (getenv("CHECK_RESULT"))
{
check_result = atoi(getenv("CHECK_RESULT"));
}
// // Compute ||Bref||
double Bref_nrm2 = 0.0;
double res_nrm2 = 0.0;
if (check_result)
{
PDNRM2_HOST(&Bref_potrs, &Bref_nrm2);
}
// for (size_t row = 0; row < A.mt; row++)
// {
// for (size_t col = 0; col <= row; col++)
// {
// cuda_safe_call(cudaSetDevice(A.get_preferred_devid(row, col)));
// NOOP(A, row, col);
// }
// }
PDPOTRF(A);
/*
* POTRS
*/
if (check_result)
{
// Solve AX = B and put the result in B
PDPOTRS(A, B_potrs, CUBLAS_FILL_MODE_LOWER);
// Compute (AX - B)
// Bref = (Aref*B - Bref)
PDGEMM(CUBLAS_OP_N, CUBLAS_OP_N, 1.0, Aref, B_potrs, -1.0, Bref_potrs);
// Compute ||AX - B|| = ||Bref||
PDNRM2_HOST(&Bref_potrs, &res_nrm2);
}
ctx.finalize();
if (check_result)
{
double residual = sqrt(res_nrm2) / sqrt(Bref_nrm2);
// std::cout << "[POTRS] ||AX - B|| : " << sqrt(res_nrm2) << '\n';
// std::cout << "[POTRS] ||B|| : " << sqrt(Bref_nrm2) << '\n';
// std::cout << "[POTRS] RESIDUAL (||AX - B||/||B||) : " << residual << '\n';
assert(residual < 0.01);
}
return 0;
}

129
cccl_upstream/cudax/test/stf/examples/09-nbody-algorithm.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.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/stf.cuh>
#include <random>
using namespace cuda::experimental::stf;
struct body
{
// mass
double mass;
// position
double pos[3];
// speed
double vel[3];
// acceleration
double acc[3];
};
int main()
{
constexpr double kSofteningSquared = 1e-3;
constexpr double kG = 6.67259e-11;
size_t BODY_CNT = 4096;
double dt = 0.1;
size_t NITER = 25;
context ctx;
std::vector<body> particles;
particles.resize(BODY_CNT);
// Initialize particles
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(-1.0, 1.0);
for (auto& p : particles)
{
p.mass = 1.0;
p.pos[0] = dis(gen);
p.pos[1] = dis(gen);
p.pos[2] = dis(gen);
p.vel[0] = dis(gen);
p.vel[1] = dis(gen);
p.vel[2] = dis(gen);
p.acc[0] = 0.0;
p.acc[1] = 0.0;
p.acc[2] = 0.0;
}
auto h_particles = ctx.logical_data(make_slice(&particles[0], BODY_CNT));
auto fn = [dt](context ctx, logical_data<slice<body>> h_particles) {
// Compute accelerations
ctx.parallel_for(h_particles.shape(), h_particles.rw())->*[=] _CCCL_DEVICE __host__(size_t i, slice<body> p) {
double acc[3];
for (size_t k = 0; k < 3; k++)
{
acc[k] = p(i).acc[k];
}
for (size_t j = 0; j < p.extent(0); j++)
{
if (i != j)
{
double d[3];
for (size_t k = 0; k < 3; k++)
{
d[k] = p(j).pos[k] - p(i).pos[k];
}
double dist = d[0] * d[0] + d[1] * d[1] + d[2] * d[2] + kSofteningSquared;
double dist_inv = 1.0 / sqrt(dist);
for (size_t k = 0; k < 3; k++)
{
acc[k] += d[k] * kG * p(j).mass * dist_inv * dist_inv * dist_inv;
}
}
}
for (size_t k = 0; k < 3; k++)
{
p(i).acc[k] = acc[k];
}
};
// Update velocity and positions
ctx.parallel_for(h_particles.shape(), h_particles.rw())->*[=] __host__ __device__(size_t i, slice<body> p) {
for (size_t k = 0; k < 3; k++)
{
p(i).vel[k] += p(i).acc[k] * dt;
}
for (size_t k = 0; k < 3; k++)
{
p(i).pos[k] += p(i).vel[k] * dt;
}
for (size_t k = 0; k < 3; k++)
{
p(i).acc[k] = 0.0;
}
};
};
algorithm one_iter;
for (size_t iter = 0; iter < NITER; iter++)
{
// fprintf(stderr, "ITER %ld\n", iter);
one_iter.run_as_task(fn, ctx, h_particles.rw());
}
ctx.finalize();
}

283
cccl_upstream/cudax/test/stf/examples/09-nbody-blocked.cu
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@@ -1,283 +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.cuh>
#include <random>
using namespace cuda::experimental::stf;
struct body
{
// mass
double mass;
// position
double pos[3];
// speed
double vel[3];
};
// Function to write VTK file for a single time step
void writeVTKFile(context& ctx,
const std::string& filename,
size_t BLOCK_SIZE,
size_t BODY_CNT,
std::vector<logical_data<slice<body>>> parts)
{
std::ofstream outfile(filename);
if (!outfile)
{
std::cerr << "Error opening file: " << filename << '\n';
return;
}
outfile << "# vtk DataFile Version 4.2\n";
outfile << "Position Data\n";
outfile << "ASCII\n";
outfile << "DATASET UNSTRUCTURED_GRID\n";
outfile << "POINTS " << BODY_CNT << " float\n";
std::vector<double> dump(3 * BODY_CNT);
for (size_t b = 0; b < parts.size(); b++)
{
ctx.task(exec_place::host(), parts[b].read())->*[&](cudaStream_t s, slice<const body> p) {
cuda_safe_call(cudaStreamSynchronize(s));
for (size_t i = 0; i < p.size(); i++)
{
for (size_t k = 0; k < 3; k++)
{
dump[3 * (i + b * BLOCK_SIZE) + k] = p(i).pos[k];
}
}
};
}
for (size_t p = 0; p < BODY_CNT; p++)
{
outfile << dump[3 * p] << " " << dump[3 * p + 1] << " " << dump[3 * p + 2] << "\n";
}
outfile.close();
}
void load_input_file(std::string filename, std::vector<body>& particles)
{
std::ifstream infile(filename);
if (!infile)
{
std::cerr << "Error opening file." << '\n';
abort();
return;
}
double mass, posX, posY, posZ, velX, velY, velZ;
size_t ind = 0;
// Loop until we reach the end of the file
while (infile >> mass >> posX >> posY >> posZ >> velX >> velY >> velZ)
{
body p;
p.mass = mass;
p.pos[0] = posX;
p.pos[1] = posY;
p.pos[2] = posZ;
p.vel[0] = velX;
p.vel[1] = velY;
p.vel[2] = velZ;
// // Display first bodies
// if (ind < 10) {
// fprintf(stderr, "body xyz %e %e %e dxyz %e %e %e m %e\n", p.pos[0], p.pos[1], p.pos[2], p.vel[0],
// p.vel[1], p.vel[2], p.mass);
//}
ind++;
particles.push_back(p);
}
fprintf(stderr, "Loaded %zu bodies from %s...\n", ind, filename.c_str());
}
int main(int argc, char** argv)
{
constexpr double kSofteningSquared = 1e-9;
// constexpr double kG = 6.67259e-11;
constexpr double kG = 1.0;
size_t BODY_CNT = 128ULL * 1024ULL;
size_t BLOCK_SIZE = 16 * 1024ULL;
std::vector<body> particles;
// Initialize particles
if (argc > 1)
{
// Get dataset from file
std::string filename = argv[1];
load_input_file(filename, particles);
BODY_CNT = particles.size();
BLOCK_SIZE = (BODY_CNT + 7) / 8;
}
else
{
// Random distribution
BODY_CNT = 32ULL * 1024ULL;
particles.resize(BODY_CNT);
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(-1.0, 1.0);
for (auto& p : particles)
{
p.mass = 1.0;
p.pos[0] = dis(gen);
p.pos[1] = dis(gen);
p.pos[2] = dis(gen);
p.vel[0] = dis(gen);
p.vel[1] = dis(gen);
p.vel[2] = dis(gen);
}
}
cuda_safe_call(cudaHostRegister(&particles[0], BODY_CNT * sizeof(body), cudaHostRegisterPortable));
double dt = 0.005;
size_t NITER = 7; // 7000;
context ctx;
std::vector<logical_data<slice<body>>> parts;
// Accelerations
std::vector<logical_data<slice<double, 2>>> acc_parts;
size_t block_cnt = (BODY_CNT + BLOCK_SIZE - 1) / BLOCK_SIZE;
for (size_t i = 0; i < block_cnt; i++)
{
size_t first = i * BLOCK_SIZE;
size_t last = std::min((i + 1) * BLOCK_SIZE, BODY_CNT);
auto p_i = ctx.logical_data(make_slice(&particles[first], last - first));
parts.push_back(p_i);
auto acc_p_i = ctx.logical_data(shape_of<slice<double, 2>>(last - first, 3));
acc_parts.push_back(acc_p_i);
}
int ngpus;
cuda_safe_call(cudaGetDeviceCount(&ngpus));
cudaEvent_t start;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
for (size_t iter = 0; iter < NITER; iter++)
{
// Initialize acceleration to 0
for (size_t b = 0; b < block_cnt; b++)
{
ctx.launch(exec_place::device(b % ngpus), acc_parts[b].write())
//.set_symbol("init_acc")
->*[=] _CCCL_DEVICE(auto t, slice<double, 2> acc) {
for (size_t i = t.rank(); i < acc.extent(0); i += t.size())
{
for (size_t k = 0; k < 3; k++)
{
acc(i, k) = 0.0;
}
}
};
}
// Compute accelerations
for (size_t b = 0; b < block_cnt; b++)
{
for (size_t b_other = 0; b_other < block_cnt; b_other++)
{
ctx.launch(exec_place::device(b % ngpus), parts[b].read(), parts[b_other].read(), acc_parts[b].rw())
//.set_symbol("compute_acc")
->*[=] _CCCL_DEVICE(auto t, slice<const body> p, slice<const body> p_other, slice<double, 2> acc) {
for (size_t i = t.rank(); i < p.extent(0); i += t.size())
{
for (size_t j = 0; j < p_other.extent(0); j++)
{
if ((b * BLOCK_SIZE + i) != (b_other * BLOCK_SIZE + j))
{
double d[3];
for (size_t k = 0; k < 3; k++)
{
d[k] = p_other(j).pos[k] - p(i).pos[k];
}
double dist = d[0] * d[0] + d[1] * d[1] + d[2] * d[2] + kSofteningSquared;
double dist_inv = 1.0 / sqrt(dist);
for (size_t k = 0; k < 3; k++)
{
acc(i, k) += d[k] * kG * p_other(j).mass * dist_inv * dist_inv * dist_inv;
}
}
}
}
};
}
}
for (size_t b = 0; b < block_cnt; b++)
{
// Update velocity and positions
ctx.launch(exec_place::device(b % ngpus), parts[b].rw(), acc_parts[b].read())
//.set_symbol("update")
->*[=] _CCCL_DEVICE(auto t, slice<body> p, slice<const double, 2> acc) {
for (size_t i = t.rank(); i < p.extent(0); i += t.size())
{
for (size_t k = 0; k < 3; k++)
{
p(i).vel[k] += acc(i, k) * dt;
}
for (size_t k = 0; k < 3; k++)
{
p(i).pos[k] += p(i).vel[k] * dt;
}
}
};
}
// Write the VTK file for this time step
const char* dump_freq_str = getenv("DUMP_FREQ");
if (dump_freq_str && iter % atoi(dump_freq_str) == 0)
{
std::string filename = "time_step_" + std::to_string(iter) + ".vtk";
writeVTKFile(ctx, filename, BLOCK_SIZE, BODY_CNT, parts);
}
}
cudaEvent_t stop;
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
ctx.finalize();
float elapsed;
cuda_safe_call(cudaEventElapsedTime(&elapsed, start, stop));
// rough approximation !
double FLOP_COUNT = 21.0 * (1.0 * BODY_CNT) * (1.0 * BODY_CNT) * NITER;
printf("NBODY: elapsed %f ms, %f GFLOPS\n", elapsed, FLOP_COUNT / elapsed / 1000000.0);
}

122
cccl_upstream/cudax/test/stf/examples/09-nbody.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.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/stf.cuh>
#include <random>
using namespace cuda::experimental::stf;
struct body
{
// mass
double mass;
// position
double pos[3];
// speed
double vel[3];
// acceleration
double acc[3];
};
int main()
{
constexpr double kSofteningSquared = 1e-3;
constexpr double kG = 6.67259e-11;
size_t BODY_CNT = 4096;
double dt = 0.1;
size_t NITER = 25;
context ctx = graph_ctx();
std::vector<body> particles;
particles.resize(BODY_CNT);
// Initialize particles
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(-1.0, 1.0);
for (auto& p : particles)
{
p.mass = 1.0;
p.pos[0] = dis(gen);
p.pos[1] = dis(gen);
p.pos[2] = dis(gen);
p.vel[0] = dis(gen);
p.vel[1] = dis(gen);
p.vel[2] = dis(gen);
p.acc[0] = 0.0;
p.acc[1] = 0.0;
p.acc[2] = 0.0;
}
auto h_particles = ctx.logical_data(make_slice(&particles[0], BODY_CNT));
ctx.repeat(NITER)->*[&](context ctx, size_t) {
// Compute accelerations
ctx.parallel_for(h_particles.shape(), h_particles.rw())->*[=] _CCCL_DEVICE __host__(size_t i, slice<body> p) {
double acc[3];
for (size_t k = 0; k < 3; k++)
{
acc[k] = p(i).acc[k];
}
for (size_t j = 0; j < p.extent(0); j++)
{
if (i != j)
{
double d[3];
for (size_t k = 0; k < 3; k++)
{
d[k] = p(j).pos[k] - p(i).pos[k];
}
double dist = d[0] * d[0] + d[1] * d[1] + d[2] * d[2] + kSofteningSquared;
double dist_inv = 1.0 / sqrt(dist);
for (size_t k = 0; k < 3; k++)
{
acc[k] += d[k] * kG * p(j).mass * dist_inv * dist_inv * dist_inv;
}
}
}
for (size_t k = 0; k < 3; k++)
{
p(i).acc[k] = acc[k];
}
};
// Update velocity and positions
ctx.parallel_for(h_particles.shape(), h_particles.rw())->*[=] __host__ __device__(size_t i, slice<body> p) {
for (size_t k = 0; k < 3; k++)
{
p(i).vel[k] += p(i).acc[k] * dt;
}
for (size_t k = 0; k < 3; k++)
{
p(i).pos[k] += p(i).vel[k] * dt;
}
for (size_t k = 0; k < 3; k++)
{
p(i).acc[k] = 0.0;
}
};
};
ctx.finalize();
}

123
cccl_upstream/cudax/test/stf/examples/cuda_kernels_driver.cu
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@@ -1,123 +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 Test that the cuda_kernel construct works with global kernels, CUfunction and CUkernel entries.
*
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
__global__ void axpy(double a, slice<const double> x, slice<double> y)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int nthreads = gridDim.x * blockDim.x;
for (int i = tid; i < x.size(); i += nthreads)
{
y(i) += a * x(i);
}
}
double X0(int i)
{
return sin((double) i);
}
double Y0(int i)
{
return cos((double) i);
}
void test(bool is_graph)
{
context ctx;
if (is_graph)
{
ctx = graph_ctx();
}
const size_t N = 16;
double X[N], Y[N];
for (size_t i = 0; i < N; i++)
{
X[i] = X0(i);
Y[i] = Y0(i);
}
// Number of times we have applied the axpy kernel
int num_axpy = 0;
double alpha = 3.14;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
// runtime global kernel
ctx.cuda_kernel(lX.read(), lY.rw())->*[&](auto dX, auto dY) {
// axpy<<<16, 128, 0, ...>>>(alpha, dX, dY)
return cuda_kernel_desc{axpy, 16, 128, 0, alpha, dX, dY};
};
num_axpy++;
// CUfunction driver API
CUfunction axpy_fun;
cuda_safe_call(cudaGetFuncBySymbol(&axpy_fun, (void*) axpy));
ctx.cuda_kernel(lX.read(), lY.rw())->*[&](auto dX, auto dY) {
return cuda_kernel_desc{axpy_fun, 16, 128, 0, alpha, dX, dY};
};
num_axpy++;
#if _CCCL_CTK_AT_LEAST(12, 1)
// CUkernel driver API
CUkernel axpy_kernel;
cuda_safe_call(cudaGetKernel(&axpy_kernel, (void*) axpy));
ctx.cuda_kernel(lX.read(), lY.rw())->*[&](auto dX, auto dY) {
return cuda_kernel_desc{axpy_kernel, 16, 128, 0, alpha, dX, dY};
};
num_axpy++;
#endif
/* Some extra sanity checks, we put this in a dummy task to get access to dX and dY values */
ctx.task(lX.read(), lY.rw())->*[&](auto, auto dX, auto dY) {
int nregs = cuda_kernel_desc{axpy, 16, 128, 0, alpha, dX, dY}.get_num_registers();
int nregs_fun = cuda_kernel_desc{axpy_fun, 16, 128, 0, alpha, dX, dY}.get_num_registers();
_CCCL_ASSERT(nregs == nregs_fun, "invalid value");
#if _CCCL_CTK_AT_LEAST(12, 1)
int nregs_kernel = cuda_kernel_desc{axpy_kernel, 16, 128, 0, alpha, dX, dY}.get_num_registers();
_CCCL_ASSERT(nregs == nregs_kernel, "invalid value");
#endif
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
_CCCL_ASSERT(fabs(Y[i] - (Y0(i) + num_axpy * alpha * X0(i))) < 0.0001, "Invalid result");
_CCCL_ASSERT(fabs(X[i] - X0(i)) < 0.0001, "Invalid result");
}
}
int main()
{
// stream context
test(false);
// graph context
test(true);
}