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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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73
cccl_upstream/cudax/examples/stf/01-axpy-cuda_kernel.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 AXPY kernel described using a cuda_kernel construct
*
*/
#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);
}
int main()
{
context 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);
}
double alpha = 3.14;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
/* Compute Y = Y + alpha X */
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};
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
assert(fabs(Y[i] - (Y0(i) + alpha * X0(i))) < 0.0001);
assert(fabs(X[i] - X0(i)) < 0.0001);
}
}

80
cccl_upstream/cudax/examples/stf/01-axpy-cuda_kernel_chain.cu
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@@ -1,80 +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 Example of task implementing a chain of CUDA kernels
*
*/
#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);
}
int main()
{
context 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);
}
double alpha = 3.14;
double beta = 4.5;
double gamma = -4.1;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
/* Compute Y = Y + alpha X, Y = Y + beta X and then Y = Y + gamma X */
ctx.cuda_kernel_chain(lX.read(), lY.rw())->*[&](auto dX, auto dY) {
// clang-format off
return std::vector<cuda_kernel_desc> {
{ axpy, 16, 128, 0, alpha, dX, dY },
{ axpy, 16, 128, 0, beta, dX, dY },
{ axpy, 16, 128, 0, gamma, dX, dY }
};
// clang-format on
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
assert(fabs(Y[i] - (Y0(i) + (alpha + beta + gamma) * X0(i))) < 0.0001);
assert(fabs(X[i] - X0(i)) < 0.0001);
}
}

62
cccl_upstream/cudax/examples/stf/01-axpy-launch.cu
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@@ -1,62 +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 Example of AXPY kernel implemented with the launch API
*/
#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()
{
context 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);
}
double alpha = 3.14;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
/* Compute Y = Y + alpha X */
ctx.launch(lX.read(), lY.rw())->*[=] _CCCL_DEVICE(auto t, auto dX, auto dY) {
for (auto ind : t.apply_partition(shape(dX)))
{
dY(ind) += alpha * dX(ind);
}
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
assert(fabs(Y[i] - (Y0(i) + alpha * X0(i))) < 0.0001);
assert(fabs(X[i] - X0(i)) < 0.0001);
}
}

61
cccl_upstream/cudax/examples/stf/01-axpy-parallel_for.cu
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@@ -1,61 +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 An AXPY kernel implemented using the parallel_for construct
*
*/
#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()
{
context 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);
}
double alpha = 3.14;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
/* Compute Y = Y + alpha X */
ctx.parallel_for(lY.shape(), lX.read(), lY.rw())->*[alpha] __device__(size_t i, auto dX, auto dY) {
dY(i) += alpha * dX(i);
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
assert(fabs(Y[i] - (Y0(i) + alpha * X0(i))) < 0.0001);
assert(fabs(X[i] - X0(i)) < 0.0001);
}
}

72
cccl_upstream/cudax/examples/stf/01-axpy.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 AXPY kernel implemented with CUDA kernel in a task
*
*/
#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);
}
int main()
{
context 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);
}
double alpha = 3.14;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
/* Compute Y = Y + alpha X */
ctx.task(lX.read(), lY.rw())->*[&](cudaStream_t s, auto dX, auto dY) {
axpy<<<16, 128, 0, s>>>(alpha, dX, dY);
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
assert(fabs(Y[i] - (Y0(i) + alpha * X0(i))) < 0.0001);
assert(fabs(X[i] - X0(i)) < 0.0001);
}
}

81
cccl_upstream/cudax/examples/stf/02-axpy-host_launch.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 AXPY kernel implemented with a task of the CUDA graph backend and
* a host callback
*
* The host_launch mechanism is also illustrated
*
*/
#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);
}
int main()
{
graph_ctx 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);
}
double alpha = 3.14;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
/* Compute Y = Y + alpha X */
ctx.task(lX.read(), lY.rw())->*[&](cudaStream_t s, auto dX, auto dY) {
axpy<<<16, 128, 0, s>>>(alpha, dX, dY);
};
/* Asynchronously check the result on the host */
ctx.host_launch(lX.read(), lY.read())->*[&](auto hX, auto hY) {
for (size_t ind = 0; ind < hX.extent(0); ind++)
{
// Y should be Y0 + alpha X0
EXPECT(fabs(hY(ind) - (Y0(ind) + alpha * X0(ind))) < 0.0001);
// X should be X0
EXPECT(fabs(hX(ind) - X0(ind)) < 0.0001);
}
};
ctx.finalize();
}

69
cccl_upstream/cudax/examples/stf/03-temporary-data.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 we can create temporary data from shapes, and use them in tasks
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
const int n = 4096;
int X[n];
int Y[n];
for (size_t i = 0; i < n; i++)
{
X[i] = 3 * i;
Y[i] = 2 * i - 3;
}
context ctx;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
// Select an odd number
int niter = 19;
assert(niter % 2 == 1);
for (int iter = 0; iter < niter; iter++)
{
// We here define a temporary vector with the same shape as X, for which there is no existing copy
// This data handle has a limited scope, so that it is automatically destroyed at each iteration of the loop
auto tmp = ctx.logical_data(lX.shape());
ctx.task(lY.rw(), lX.rw(), tmp.write())->*[](cudaStream_t s, auto sY, auto sX, auto sTMP) {
// We swap X and Y using TMP as temporary buffer
// TMP = X
cuda_safe_call(
cudaMemcpyAsync(sTMP.data_handle(), sX.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
// X = Y
cuda_safe_call(cudaMemcpyAsync(sX.data_handle(), sY.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
// Y = TMP
cuda_safe_call(
cudaMemcpyAsync(sY.data_handle(), sTMP.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
};
}
ctx.finalize();
// We have exchanged an odd number of times, so they must be inverted
for (size_t i = 0; i < n; i++)
{
assert(X[i] == 2 * i - 3);
assert(Y[i] == 3 * i);
}
}

79
cccl_upstream/cudax/examples/stf/04-fibonacci-run_once.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 of Fibonacci sequence illustrating how we can use
* dynamically created logical data and the run_once utility
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int fibo_ref(int n)
{
if (n < 2)
{
return n;
}
else
{
return fibo_ref(n - 1) + fibo_ref(n - 2);
}
}
__global__ void add(slice<int> out, const slice<const int> in1, const slice<const int> in2)
{
out(0) = in1(0) + in2(0);
}
__global__ void set(slice<int> out, int val)
{
out(0) = val;
}
logical_data<slice<int>> compute_fibo(context& ctx, int n)
{
// The result for a given value n is memoized in a logical_data that will be reused every time we compute the same
// value
return run_once(n)->*[&](int n) {
auto result = ctx.logical_data(shape_of<slice<int>>(1)).set_symbol(std::to_string(n));
if (n < 2)
{
ctx.task(result.write()).set_symbol("fibo" + std::to_string(n))->*[=](cudaStream_t s, auto sresult) {
set<<<1, 1, 0, s>>>(sresult, n);
};
}
else
{
auto fib2 = compute_fibo(ctx, n - 2);
auto fib1 = compute_fibo(ctx, n - 1);
ctx.task(fib1.read(), fib2.read(), result.write()).set_symbol("fibo" + std::to_string(n))
->*[=](cudaStream_t s, auto s1, auto s2, auto sresult) {
add<<<1, 1, 0, s>>>(sresult, s1, s2);
};
}
return result;
};
}
int main(int argc, char** argv)
{
int n = (argc > 1) ? atoi(argv[1]) : 4;
context ctx;
auto result = compute_fibo(ctx, n);
ctx.host_launch(result.read())->*[&](auto res) {
EXPECT(res(0) == fibo_ref(n));
};
ctx.finalize();
}

75
cccl_upstream/cudax/examples/stf/04-fibonacci.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 of Fibonacci sequence illustrating how we can use
* dynamically created logical data
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int fibo_ref(int n)
{
if (n < 2)
{
return n;
}
else
{
return fibo_ref(n - 1) + fibo_ref(n - 2);
}
}
__global__ void add(slice<int> out, const slice<const int> in1, const slice<const int> in2)
{
out(0) = in1(0) + in2(0);
}
__global__ void set(slice<int> out, int val)
{
out(0) = val;
}
logical_data<slice<int>> compute_fibo(context& ctx, int n)
{
auto out = ctx.logical_data(shape_of<slice<int>>(1));
if (n < 2)
{
ctx.task(out.write())->*[=](cudaStream_t s, auto sout) {
set<<<1, 1, 0, s>>>(sout, n);
};
}
else
{
auto fib1 = compute_fibo(ctx, n - 1);
auto fib2 = compute_fibo(ctx, n - 2);
ctx.task(fib1.read(), fib2.read(), out.write())->*[=](cudaStream_t s, auto s1, auto s2, auto sout) {
add<<<1, 1, 0, s>>>(sout, s1, s2);
};
}
return out;
}
int main(int argc, char** argv)
{
int n = (argc > 1) ? atoi(argv[1]) : 4;
context ctx; // = graph_ctx();
auto result = compute_fibo(ctx, n);
ctx.host_launch(result.read())->*[&](auto res) {
EXPECT(res(0) == fibo_ref(n));
};
ctx.finalize();
}

95
cccl_upstream/cudax/examples/stf/08-cub-reduce.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 Example of reduction implementing using CUB kernels
*/
#include <thrust/device_vector.h>
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
template <int BLOCK_THREADS, typename T>
__global__ void reduce(slice<const T> values, slice<T> partials, size_t nelems)
{
using namespace cub;
typedef BlockReduce<T, BLOCK_THREADS> BlockReduceT;
auto thread_id = BLOCK_THREADS * blockIdx.x + threadIdx.x;
// Local reduction
T local_sum = 0;
for (size_t ind = thread_id; ind < nelems; ind += blockDim.x * gridDim.x)
{
local_sum += values(ind);
}
__shared__ typename BlockReduceT::TempStorage temp_storage;
// Per-thread tile data
T result = BlockReduceT(temp_storage).Sum(local_sum);
if (threadIdx.x == 0)
{
partials(blockIdx.x) = result;
}
}
template <typename Ctx>
void run()
{
Ctx ctx;
const size_t N = 1024 * 16;
const size_t BLOCK_SIZE = 128;
const size_t num_blocks = 32;
int *X, ref_tot;
X = new int[N];
ref_tot = 0;
for (size_t ind = 0; ind < N; ind++)
{
X[ind] = rand() % N;
ref_tot += X[ind];
}
auto values = ctx.logical_data(X, {N});
auto partials = ctx.logical_data(shape_of<slice<int>>(num_blocks));
auto result = ctx.logical_data(shape_of<slice<int>>(1));
ctx.task(values.read(), partials.write(), result.write())->*[&](auto stream, auto values, auto partials, auto result) {
// reduce values into partials
reduce<BLOCK_SIZE, int><<<num_blocks, BLOCK_SIZE, 0, stream>>>(values, partials, N);
// reduce partials on a single block into result
reduce<BLOCK_SIZE, int><<<1, BLOCK_SIZE, 0, stream>>>(partials, result, num_blocks);
};
ctx.host_launch(result.read())->*[&](auto p) {
if (p(0) != ref_tot)
{
fprintf(stderr, "INCORRECT RESULT: p sum = %d, ref tot = %d\n", p(0), ref_tot);
abort();
}
};
ctx.finalize();
}
int main()
{
run<stream_ctx>();
run<graph_ctx>();
}

55
cccl_upstream/cudax/examples/stf/09-dot-reduce.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 Implementation of the DOT kernel using a reduce access mode
*
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
const size_t N = 16;
double X[N], Y[N];
double ref_res = 0.0;
for (size_t i = 0; i < N; i++)
{
X[i] = cos(double(i));
Y[i] = sin(double(i));
// Compute the reference result of the DOT product of X and Y
ref_res += X[i] * Y[i];
}
context ctx;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
auto lsum = ctx.logical_data(shape_of<scalar_view<double>>());
/* Compute sum(x_i * y_i)*/
ctx.parallel_for(lY.shape(), lX.read(), lY.read(), lsum.reduce(reducer::sum<double>{}))
->*[] __device__(size_t i, auto dX, auto dY, double& sum) {
sum += dX(i) * dY(i);
};
double res = ctx.wait(lsum);
ctx.finalize();
_CCCL_ASSERT(fabs(res - ref_res) < 0.0001, "Invalid result");
}

118
cccl_upstream/cudax/examples/stf/1f1b.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 Toy example to reproduce the asynchrony of a 1F1B pipeline
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
__global__ void forward(slice<int>, long long int clock_cnt)
{
long long int start_clock = clock64();
long long int clock_offset = 0;
while (clock_offset < clock_cnt)
{
clock_offset = clock64() - start_clock;
}
}
__global__ void backward(slice<int>, long long int clock_cnt)
{
long long int start_clock = clock64();
long long int clock_offset = 0;
while (clock_offset < clock_cnt)
{
clock_offset = clock64() - start_clock;
}
}
int main(int argc, char** argv)
{
context ctx;
// Use a graph context if the second argument is set and not null
if (argc > 2 && atoi(argv[2]))
{
ctx = graph_ctx();
}
int device;
cudaGetDevice(&device);
// cudaDevAttrClockRate: Peak clock frequency in kilohertz;
int clock_rate;
cudaDeviceGetAttribute(&clock_rate, cudaDevAttrClockRate, device);
auto occ_f = reserved::compute_occupancy(forward);
auto occ_b = reserved::compute_occupancy(backward);
int factor = 1;
if (argc > 1)
{
factor = atoi(argv[1]);
}
size_t num_batches = 8 * factor;
int num_devs = 8;
int real_devs;
cuda_safe_call(cudaGetDeviceCount(&real_devs));
std::vector<logical_data<slice<int>>> data;
for (size_t b = 0; b < num_batches; b++)
{
auto batch_data = ctx.logical_data(shape_of<slice<int>>(1024));
data.push_back(batch_data);
ctx.task(exec_place::device(0), data[b].write())->*[](cudaStream_t, auto) {
// Init ...
};
}
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
size_t niter = 10;
for (size_t iter = 0; iter < niter; iter++)
{
for (size_t b = 0; b < num_batches; b++)
{
for (int d = 0; d < num_devs; d++)
{
ctx.task(exec_place::device(d % real_devs), data[b].rw())->*[=](cudaStream_t s, auto bd) {
int ms = 10;
long long int clock_cnt = (long long int) (ms * clock_rate / factor);
forward<<<occ_f.min_grid_size, occ_f.block_size, 0, s>>>(bd, clock_cnt);
};
}
// }
//
// for (size_t b = 0; b < num_batches; b++) {
for (int d = num_devs; d-- > 0;)
{
ctx.task(exec_place::device(d % real_devs), data[b].rw())->*[=](cudaStream_t s, auto bd) {
int ms = 20;
long long int clock_cnt = (long long int) (ms * clock_rate / factor);
backward<<<occ_b.min_grid_size, occ_b.block_size, 0, s>>>(bd, clock_cnt);
};
}
}
/* We introduce a fence because the actual pipeline would introduce
* some all to all communication to update coefficients */
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
}
ctx.finalize();
return 0;
}

124
cccl_upstream/cudax/examples/stf/CMakeLists.txt
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@@ -1,124 +0,0 @@
set(
stf_example_sources
01-axpy.cu
01-axpy-cuda_kernel.cu
01-axpy-cuda_kernel_chain.cu
02-axpy-host_launch.cu
03-temporary-data.cu
04-fibonacci.cu
04-fibonacci-run_once.cu
08-cub-reduce.cu
axpy-annotated.cu
void_data_interface.cu
explicit_data_places.cu
partitioned_axpy.cu
thrust_zip_iterator.cu
1f1b.cu
)
# Examples which rely on code generation (parallel_for or launch)
set(
stf_example_codegen_sources
01-axpy-launch.cu
01-axpy-parallel_for.cu
binary_fhe.cu
binary_fhe_stackable.cu
09-dot-reduce.cu
cfd.cu
custom_data_interface.cu
fdtd_mgpu.cu
fdtd_while.cu
fdtd_repeat_n.cu
frozen_data_init.cu
graph_algorithms/degree_centrality.cu
graph_algorithms/jaccard.cu
graph_algorithms/pagerank.cu
graph_algorithms/pagerank_batched.cu
graph_algorithms/pagerank_while.cu
graph_algorithms/tricount.cu
graph_scope.cu
heat.cu
heat_mgpu.cu
jacobi.cu
jacobi_pfor.cu
jacobi_stackable.cu
jacobi_stackable_raii.cu
jacobi_update_cond.cu
launch_histogram.cu
launch_scan.cu
launch_sum.cu
launch_sum_cub.cu
linear_algebra/burger.cu
linear_algebra/burger_sensitivity.cu
linear_algebra/cg_csr.cu
linear_algebra/cg_csr_stackable.cu
logical_gates_composition.cu
mandelbrot.cu
parallel_for_2D.cu
pi.cu
scan.cu
sqrt_newton_stackable.cu
standalone-launches.cu
word_count.cu
word_count_reduce.cu
)
# Examples using CUBLAS, CUSOLVER...
set(
stf_example_mathlib_sources
linear_algebra/06-pdgemm.cu
linear_algebra/06-pdgemm-stackable.cu
linear_algebra/07-cholesky.cu
linear_algebra/07-potri.cu
linear_algebra/cg_dense_2D.cu
linear_algebra/strassen.cu
)
cccl_get_cudatoolkit()
## cudax_add_stf_example
#
# Add an stf example executable and register it with ctest.
#
# target_name_var: Variable name to overwrite with the name of the example
# target. Useful for modifying the example/target after creation.
# source: The source file for the example.
#
# Additional args are passed to cudax_stf_configure_target.
function(cudax_add_stf_example target_name_var source)
get_filename_component(dir ${source} DIRECTORY)
get_filename_component(filename ${source} NAME_WE)
if (dir)
set(filename "${dir}/${filename}")
endif()
string(REPLACE "/" "." example_name "stf/${filename}")
set(example_target cudax.example.${example_name})
cccl_add_executable(${example_target} SOURCES ${source} ADD_CTEST)
cudax_stf_configure_target(${example_target} ${ARGN})
target_link_libraries(
${example_target}
PRIVATE #
cudax.compiler_interface
cudax.examples.thrust
)
set(${target_name_var} ${example_target} PARENT_SCOPE)
endfunction()
foreach (source IN LISTS stf_example_sources)
cudax_add_stf_example(example_target "${source}")
endforeach()
if (cudax_ENABLE_CUDASTF_CODE_GENERATION)
foreach (source IN LISTS stf_example_codegen_sources)
cudax_add_stf_example(example_target "${source}")
endforeach()
endif()
if (cudax_ENABLE_CUDASTF_MATHLIBS)
foreach (source IN LISTS stf_example_mathlib_sources)
cudax_add_stf_example(example_target "${source}" LINK_MATHLIBS)
endforeach()
endif()

82
cccl_upstream/cudax/examples/stf/axpy-annotated.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 we can annotate tasks and logical data with debugging symbol
*
* CUDASTF_DOT_FILE=axpy.dot build/examples/axpy-annotated
*
* # Generate the visualization from this dot file in PDF or PNG format
* dot -Tpdf axpy.dot -o axpy.pdf
* dot -Tpng axpy.dot -o axpy.png
*
* # Generate visualization with events (for advanced users)
* CUDASTF_DOT_IGNORE_PREREQS=0 CUDASTF_DOT_FILE=axpy-with-events.dot build/examples/axpy-annotated
* dot -Tpng axpy-with-events.dot -o axpy-with-events.png
*
*/
#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);
}
int main()
{
context 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);
}
double alpha = 3.14;
auto lX = ctx.logical_data(X).set_symbol("X");
auto lY = ctx.logical_data(Y).set_symbol("Y");
/* Compute Y = Y + alpha X */
ctx.task(lX.read(), lY.rw()).set_symbol("axpy")->*[&](cudaStream_t s, auto dX, auto dY) {
axpy<<<16, 128, 0, s>>>(alpha, dX, dY);
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
assert(fabs(Y[i] - (Y0(i) + alpha * X0(i))) < 0.0001);
assert(fabs(X[i] - X0(i)) < 0.0001);
}
}

201
cccl_upstream/cudax/examples/stf/binary_fhe.cu
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@@ -1,201 +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 A toy example to illustrate how we can compose logical operations
* over encrypted data
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
class ciphertext;
class plaintext
{
public:
plaintext(const context& ctx)
: ctx(ctx)
{}
plaintext(context& ctx, std::vector<char> v)
: values(v)
, ctx(ctx)
{
l = ctx.logical_data(&values[0], values.size());
}
void set_symbol(std::string s)
{
l.set_symbol(s);
symbol = s;
}
std::string get_symbol() const
{
return symbol;
}
std::string symbol;
const logical_data<slice<char>>& data() const
{
return l;
}
logical_data<slice<char>>& data()
{
return l;
}
// This will asynchronously fill string s
void convert_to_vector(std::vector<char>& v)
{
ctx.host_launch(l.read()).set_symbol("to_vector")->*[&](auto dl) {
v.resize(dl.size());
for (size_t i = 0; i < dl.size(); i++)
{
v[i] = dl(i);
}
};
}
ciphertext encrypt() const;
logical_data<slice<char>> l;
private:
std::vector<char> values;
mutable context ctx;
};
class ciphertext
{
public:
ciphertext(const context& ctx)
: ctx(ctx)
{}
plaintext decrypt() const
{
plaintext p(ctx);
p.l = ctx.logical_data(shape_of<slice<char>>(l.shape().size()));
// fprintf(stderr, "Decrypting...\n");
ctx.parallel_for(l.shape(), l.read(), p.l.write()).set_symbol("decrypt")->*
[] _CCCL_DEVICE(size_t i, auto dctxt, auto dptxt) {
dptxt(i) = char((dctxt(i) >> 32));
// printf("DECRYPT %ld : %lx -> %x\n", i, dctxt(i), (int) dptxt(i));
};
return p;
}
ciphertext operator|(const ciphertext& other) const
{
ciphertext result(ctx);
result.l = ctx.logical_data(data().shape());
ctx.parallel_for(data().shape(), data().read(), other.data().read(), result.data().write()).set_symbol("OR")->*
[] _CCCL_DEVICE(size_t i, auto d_c1, auto d_c2, auto d_res) {
d_res(i) = d_c1(i) | d_c2(i);
};
return result;
}
ciphertext operator&(const ciphertext& other) const
{
ciphertext result(ctx);
result.l = ctx.logical_data(data().shape());
ctx.parallel_for(data().shape(), data().read(), other.data().read(), result.data().write()).set_symbol("AND")->*
[] _CCCL_DEVICE(size_t i, auto d_c1, auto d_c2, auto d_res) {
d_res(i) = d_c1(i) & d_c2(i);
};
return result;
}
ciphertext operator~() const
{
ciphertext result(ctx);
result.l = ctx.logical_data(data().shape());
ctx.parallel_for(data().shape(), data().read(), result.data().write()).set_symbol("NOT")->*
[] _CCCL_DEVICE(size_t i, auto d_c, auto d_res) {
d_res(i) = ~d_c(i);
};
return result;
}
const logical_data<slice<uint64_t>>& data() const
{
return l;
}
logical_data<slice<uint64_t>>& data()
{
return l;
}
logical_data<slice<uint64_t>> l;
private:
mutable context ctx;
};
ciphertext plaintext::encrypt() const
{
ciphertext c(ctx);
c.l = ctx.logical_data(shape_of<slice<uint64_t>>(l.shape().size()));
ctx.parallel_for(l.shape(), l.read(), c.l.write()).set_symbol("encrypt")->*
[] _CCCL_DEVICE(size_t i, auto dptxt, auto dctxt) {
// A super safe encryption !
dctxt(i) = ((uint64_t) (dptxt(i)) << 32 | 0x4);
};
return c;
}
template <typename T>
T circuit(const T& a, const T& b)
{
return (~((a | ~b) & (~a | b)));
}
int main()
{
context ctx;
std::vector<char> vA{3, 3, 2, 2, 17};
plaintext pA(ctx, vA);
pA.set_symbol("A");
std::vector<char> vB{1, 7, 7, 7, 49};
plaintext pB(ctx, vB);
pB.set_symbol("B");
auto eA = pA.encrypt();
auto eB = pB.encrypt();
auto out = circuit(eA, eB);
std::vector<char> v_out;
out.decrypt().convert_to_vector(v_out);
ctx.finalize();
for (size_t i = 0; i < v_out.size(); i++)
{
char expected = circuit(vA[i], vB[i]);
EXPECT(expected == v_out[i]);
}
}

240
cccl_upstream/cudax/examples/stf/binary_fhe_stackable.cu
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@@ -1,240 +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) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
/**
* @file
* @brief A toy example to illustrate how we can compose logical operations over encrypted data
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
#include <memory>
class ciphertext;
class plaintext
{
public:
plaintext(const stackable_ctx& ctx)
: ctx(ctx)
{}
plaintext(stackable_ctx& ctx, ::std::vector<char> v)
: values(mv(v))
, ctx(ctx)
, ld(ctx.logical_data(values.data(), values.size()))
{}
auto& set_symbol(const std::string& s)
{
ld.set_symbol(s);
symbol = s;
return *this;
}
const std::string& get_symbol() const
{
return symbol;
}
// This will asynchronously fill string s
void convert_to_vector(std::vector<char>& v)
{
ctx.host_launch(ld.read()).set_symbol("to_vector")->*[&](auto dl) {
v.resize(dl.size());
for (size_t i = 0; i < dl.size(); i++)
{
v[i] = dl(i);
}
};
}
ciphertext encrypt() const;
private:
std::vector<char> values;
mutable stackable_ctx ctx;
::std::string symbol;
public:
mutable stackable_logical_data<slice<char>> ld;
};
class ciphertext
{
public:
ciphertext() = default;
// We need a deep-copy semantic
ciphertext(const ciphertext& other)
: ctx(other.ctx)
, symbol(other.symbol)
{
copy_content(ctx, other, *this);
}
ciphertext(const stackable_ctx& ctx)
: ctx(ctx)
{}
ciphertext(ciphertext&&) = default;
ciphertext& operator=(ciphertext&&) = default;
static void copy_content(stackable_ctx& ctx, const ciphertext& src, ciphertext& dst)
{
dst.ld = ctx.logical_data(src.ld.shape());
ctx.parallel_for(src.ld.shape(), src.ld.read(), dst.ld.write()).set_symbol("copy")->*
[] __device__(size_t i, auto src, auto dst) {
dst(i) = src(i);
};
}
auto& set_symbol(std::string s)
{
ld.set_symbol(s);
symbol = mv(s);
return *this;
}
const std::string& get_symbol() const
{
return symbol;
}
plaintext decrypt() const
{
plaintext p(ctx);
p.ld = ctx.logical_data(shape_of<slice<char>>(ld.shape().size()));
ctx.parallel_for(ld.shape(), ld.read(), p.ld.write()).set_symbol("decrypt")->*
[] __device__(size_t i, auto cipher_data, auto plain_data) {
plain_data(i) = static_cast<char>(cipher_data(i) >> 32);
};
return p;
}
// Copy assignment operator
// We need a deep-copy semantic
ciphertext& operator=(const ciphertext& other)
{
if (this != &other)
{
ctx = other.ctx;
symbol = other.symbol;
copy_content(ctx, other, *this);
}
return *this;
}
ciphertext operator|(const ciphertext& other) const
{
ciphertext result(ctx);
result.ld = ctx.logical_data(ld.shape());
ctx.parallel_for(ld.shape(), ld.read(), other.ld.read(), result.ld.write()).set_symbol("OR")->*
[] __device__(size_t i, auto d_c1, auto d_c2, auto d_res) {
d_res(i) = d_c1(i) | d_c2(i);
};
return result;
}
ciphertext operator&(const ciphertext& other) const
{
ciphertext result(ctx);
result.ld = ctx.logical_data(ld.shape());
ctx.parallel_for(ld.shape(), ld.read(), other.ld.read(), result.ld.write()).set_symbol("AND")->*
[] __device__(size_t i, auto d_c1, auto d_c2, auto d_res) {
d_res(i) = d_c1(i) & d_c2(i);
};
return result;
}
ciphertext operator~() const
{
ciphertext result(ctx);
result.ld = ctx.logical_data(ld.shape());
ctx.parallel_for(ld.shape(), ld.read(), result.ld.write()).set_symbol("NOT")->*
[] __device__(size_t i, auto d_c, auto d_res) {
d_res(i) = ~d_c(i);
};
return result;
}
mutable stackable_logical_data<slice<uint64_t>> ld;
private:
mutable stackable_ctx ctx;
::std::string symbol;
};
ciphertext plaintext::encrypt() const
{
ciphertext c(ctx);
c.ld = ctx.logical_data(shape_of<slice<uint64_t>>(ld.shape().size()));
ctx.parallel_for(ld.shape(), ld.read(), c.ld.write()).set_symbol("encrypt")->*
[] __device__(size_t i, auto dptxt, auto dctxt) {
// A super safe encryption !
dctxt(i) = ((uint64_t) (dptxt(i)) << 32 | 0x4);
};
return c;
}
template <typename T>
T circuit(const T& a, const T& b)
{
return ~((a | ~b) & (~a | b));
}
int main()
{
stackable_ctx ctx;
const std::vector<char> vA{3, 3, 2, 2, 17};
plaintext pA(ctx, std::vector<char>(vA));
pA.set_symbol("A");
const std::vector<char> vB{1, 7, 7, 7, 49};
plaintext pB(ctx, std::vector<char>(vB));
pB.set_symbol("B");
auto s_encrypt = ctx.dot_section("encrypt");
auto eA = pA.encrypt().set_symbol("A");
auto eB = pB.encrypt().set_symbol("B");
s_encrypt.end();
ctx.push();
auto s_circuit = ctx.dot_section("circuit");
auto out = circuit(eA, eB);
s_circuit.end();
ctx.pop();
std::vector<char> v_out;
out.decrypt().convert_to_vector(v_out);
ctx.finalize();
for (size_t i = 0; i < v_out.size(); i++)
{
char expected = circuit(vA[i], vB[i]);
EXPECT(expected == v_out[i]);
}
}

480
cccl_upstream/cudax/examples/stf/cfd.cu
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@@ -1,480 +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 Simulation of a fluid over a regular grid with an implicit Jacobi solver
*/
#include <cuda/experimental/stf.cuh>
#include <chrono>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
using namespace std::chrono;
using namespace cuda::experimental::stf;
/* wall-clock time */
double gettime()
{
auto now = system_clock::now().time_since_epoch();
return duration_cast<duration<double>>(now).count();
}
void writeplotfile(int m, int n, int scale)
{
FILE* gnuplot = EXPECT(fopen("cfd.plt", "w"));
SCOPE(exit)
{
EXPECT(fclose(gnuplot) == 0);
};
fprintf(gnuplot,
"set terminal pngcairo\n"
"set output 'cfd_output.png'\n"
"set size square\n"
"set key off\n"
"unset xtics\n"
"unset ytics\n");
fprintf(gnuplot, "set xrange [%i:%i]\n", 1 - scale, m + scale);
fprintf(gnuplot, "set yrange [%i:%i]\n", 1 - scale, n + scale);
fprintf(gnuplot,
"plot \"colourmap.dat\" w rgbimage, \"velocity.dat\" u "
"1:2:(%d*0.75*$3/sqrt($3**2+$4**2)):(%d*0.75*$4/sqrt($3**2+$4**2)) with vectors lc rgb \"#7F7F7F\"",
scale,
scale);
// printf("\nWritten gnuplot script 'cfd.plt'\n");
}
double colfunc(double x)
{
double x1 = 0.2;
double x2 = 0.5;
double absx = fabs(x);
if (absx > x2)
{
return 0.0;
}
else if (absx < x1)
{
return 1.0;
}
else
{
return 1.0 - pow((absx - x1) / (x2 - x1), 2);
}
}
void hue2rgb(double hue, int& r, int& g, int& b)
{
int rgbmax = 255;
r = (int) (rgbmax * colfunc(hue - 1.0));
g = (int) (rgbmax * colfunc(hue - 0.5));
b = (int) (rgbmax * colfunc(hue));
}
void writedatafiles(context& ctx, logical_data<slice<double, 2>> lpsi, int m, int n, int scale)
{
auto lvel = ctx.logical_data(shape_of<slice<double, 3>>(m, n, 2)).set_symbol("vel");
auto lrgb = ctx.logical_data(shape_of<slice<int, 3>>(m, n, 3)).set_symbol("rgb");
ctx.host_launch(lpsi.read(), lvel.write(), lrgb.write()).set_symbol("writedatafiles")
->*[=](auto psi, auto vel, auto rgb) {
// printf("\n\nWriting data files ...\n");
// calculate velocities and hues
for (int i = 0; i < m; i++)
{
for (int j = 0; j < n; j++)
{
vel(i, j, 0) = (psi(i + 1, j + 2) - psi(i + 1, j)) / 2.0;
vel(i, j, 1) = -(psi(i + 2, j + 1) - psi(i, j + 1)) / 2.0;
double v1 = vel(i, j, 0);
double v2 = vel(i, j, 1);
double modvsq = v1 * v1 + v2 * v2;
double hue = pow(modvsq, 0.4);
hue2rgb(hue, rgb(i, j, 0), rgb(i, j, 1), rgb(i, j, 2));
}
}
// write data
FILE* cfile = EXPECT(fopen("colourmap.dat", "w"));
SCOPE(exit)
{
fclose(cfile);
};
FILE* vfile = EXPECT(fopen("velocity.dat", "w"));
SCOPE(exit)
{
fclose(vfile);
};
for (int i = 0; i < m; i++)
{
int ix = i + 1;
for (int j = 0; j < n; j++)
{
int iy = j + 1;
fprintf(cfile, "%i %i %i %i %i\n", ix, iy, rgb(i, j, 0), rgb(i, j, 1), rgb(i, j, 2));
if ((ix - 1) % scale == (scale - 1) / 2 && (iy - 1) % scale == (scale - 1) / 2)
{
fprintf(vfile, "%i %i %f %f\n", ix, iy, vel(i, j, 0), vel(i, j, 1));
}
}
}
// printf("... done!\n");
writeplotfile(m, n, scale);
};
}
void jacobistep(context& ctx, logical_data<slice<double, 2>> lpsinew, logical_data<slice<double, 2>> lpsi, int m, int n)
{
ctx.parallel_for(box<2>({1, m + 1}, {1, n + 1}), lpsinew.write(), lpsi.read()).set_symbol("jacobi_step")
->*[] __device__(size_t i, size_t j, auto psinew, auto psi) {
psinew(i, j) = 0.25 * (psi(i - 1, j) + psi(i + 1, j) + psi(i, j + 1) + psi(i, j - 1));
};
}
void jacobistepvort(
context& ctx,
logical_data<slice<double, 2>> lzetnew,
logical_data<slice<double, 2>> lpsinew,
logical_data<slice<double, 2>> lzet,
logical_data<slice<double, 2>> lpsi,
int m,
int n,
double re)
{
ctx.parallel_for(box<2>({1, m + 1}, {1, n + 1}), lpsinew.write(), lpsi.read(), lzet.read())
.set_symbol("jacobi_step_psi")
->*[] __device__(size_t i, size_t j, auto psinew, auto psi, auto zet) {
psinew(i, j) = 0.25 * (psi(i - 1, j) + psi(i + 1, j) + psi(i, j + 1) + psi(i, j - 1)) - zet(i, j);
};
ctx.parallel_for(box<2>({1, m + 1}, {1, n + 1}), lzetnew.write(), lzet.read(), lpsi.read())
.set_symbol("jacobi_step_zet")
->*[=] __device__(size_t i, size_t j, auto zetnew, auto zet, auto psi) {
zetnew(i, j) = 0.25 * (zet(i - 1, j) + zet(i + 1, j) + zet(i, j + 1) + zet(i, j - 1))
- re / 16.0
* ((psi(i, j + 1) - psi(i, j - 1)) * (zet(i + 1, j) - zet(i - 1, j))
- (psi(i + 1, j) - psi(i - 1, j)) * (zet(i, j + 1) - zet(i, j - 1)));
};
}
double deltasq(context& ctx, logical_data<slice<double, 2>> lnewarr, logical_data<slice<double, 2>> loldarr)
{
auto ldsq = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("tmp_accumulator");
ctx.parallel_for(lnewarr.shape(), ldsq.reduce(reducer::sum<double>{}), lnewarr.read(), loldarr.read())
.set_symbol("deltasq")
->*[] __device__(size_t i, size_t j, auto& dsq, auto newarr, auto oldarr) {
double tmp = newarr(i, j) - oldarr(i, j);
dsq += tmp * tmp;
};
return ctx.wait(ldsq);
}
void boundarypsi(context& ctx, logical_data<slice<double, 2>> lpsi, int m, int /*n*/, int b, int h, int w)
{
// BCs on bottom edge
ctx.parallel_for(box({b + 1, b + w}), lpsi.rw()).set_symbol("boundary_bottom")->*[=] __device__(size_t i, auto psi) {
psi(i, 0) = double(i - b);
};
ctx.parallel_for(box<1>({b + w, m + 1}), lpsi.rw()).set_symbol("boundary_bottom")->*[=] __device__(size_t i, auto psi) {
psi(i, 0) = double(w);
};
// BCS on RHS
ctx.parallel_for(box({1, h + 1}), lpsi.rw()).set_symbol("boundary_right")->*[=] __device__(size_t j, auto psi) {
psi(m + 1, j) = double(w);
};
ctx.parallel_for(box({h + 1, h + w}), lpsi.rw()).set_symbol("boundary_right")->*[=] __device__(size_t j, auto psi) {
psi(m + 1, j) = (double) (w - j + h);
};
}
void boundaryzet(context& ctx, logical_data<slice<double, 2>> lzet, logical_data<slice<double, 2>> lpsi, int m, int n)
{
// set top/bottom BCs:
ctx.parallel_for(box({1, m + 1}), lzet.rw(), lpsi.read()).set_symbol("boundary_topbottom")
->*[=] __device__(size_t i, auto zet, auto psi) {
zet(i, 0) = 2.0 * (psi(i, 1) - psi(i, 0));
zet(i, n + 1) = 2.0 * (psi(i, n) - psi(i, n + 1));
};
// set left and right BCs:
ctx.parallel_for(box({1, n + 1}), lzet.rw(), lpsi.read()).set_symbol("boundary_leftright")
->*[=] __device__(size_t j, auto zet, auto psi) {
zet(0, j) = 2.0 * (psi(1, j) - psi(0, j));
zet(m + 1, j) = 2.0 * (psi(m, j) - psi(m + 1, j));
};
}
int main(int argc, char** argv)
{
context ctx;
int printfreq = 10; // output frequency
double error = -1.0;
double tolerance = 0.0001; //-1.0; // 0.0001; //tolerance for convergence. <=0 means do not check
// command line arguments
int scalefactor = 1, numiter = 10;
double re = -1.0; // Reynold's number - must be less than 3.7
// simulation sizes
int bbase = 10;
int hbase = 15;
int wbase = 5;
int mbase = 32;
int nbase = 32;
int irrotational = 1, checkerr = 0;
// do we stop because of tolerance?
if (tolerance > 0)
{
checkerr = 1;
}
// check command line parameters and parse them
if (argc > 5)
{
printf("Usage: cfd <scale> <numiter> [reynolds] [use_graphs]\n");
return 0;
}
if (argc > 1)
{
scalefactor = atoi(argv[1]);
}
if (argc > 2)
{
numiter = atoi(argv[2]);
}
if (argc > 3)
{
re = atof(argv[3]);
irrotational = 0;
}
// Use a CUDA graph backend
if (argc > 4)
{
if (atoi(argv[4]) == 1)
{
ctx = graph_ctx();
}
fprintf(stderr, "Using %s backend.\n", ctx.to_string().c_str());
}
// if (!checkerr) {
// printf("Scale Factor = %i, iterations = %i\n", scalefactor, numiter);
// } else {
// printf("Scale Factor = %i, iterations = %i, tolerance= %g\n", scalefactor, numiter, tolerance);
// }
// if (irrotational) {
// printf("Irrotational flow\n");
// } else {
// printf("Reynolds number = %f\n", re);
// }
tolerance /= scalefactor;
// Calculate b, h & w and m & n
int b = bbase * scalefactor;
int h = hbase * scalefactor;
int w = wbase * scalefactor;
int m = mbase * scalefactor;
int n = nbase * scalefactor;
re /= scalefactor;
// printf("Running CFD on %d x %d grid in serial\n", m, n);
// main arrays and their temporary versions
logical_data<slice<double, 2>> lzet, lzettmp, lpsi, lpsitmp;
// allocate arrays
lpsi = ctx.logical_data(shape_of<slice<double, 2>>(m + 2, n + 2)).set_symbol("psi");
lpsitmp = ctx.logical_data(lpsi.shape()).set_symbol("psi_tmp");
// zero the psi array
ctx.parallel_for(lpsi.shape(), lpsi.write()).set_symbol("InitPsi")->*[] __device__(size_t i, size_t j, auto psi) {
psi(i, j) = 0.0;
};
if (!irrotational)
{
lzet = ctx.logical_data(lpsi.shape()).set_symbol("zet");
lzettmp = ctx.logical_data(lpsi.shape()).set_symbol("zet_tmp");
// zero the zeta array
ctx.parallel_for(lzet.shape(), lzet.write()).set_symbol("InitZet")->*[] __device__(size_t i, size_t j, auto zet) {
zet(i, j) = 0.0;
};
}
// set the psi boundary conditions
boundarypsi(ctx, lpsi, m, n, b, h, w);
// compute normalisation factor for error
auto lbnorm = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("bnorm");
nvtx_range r_norm("Compute_Normalization");
// bnorm = psi * psi
ctx.parallel_for(lpsi.shape(), lpsi.read(), lbnorm.reduce(reducer::sum<double>{}))
->*[] __device__(size_t i, size_t j, auto psi, auto& bnorm) {
bnorm += psi(i, j) * psi(i, j);
};
if (!irrotational)
{
// update zeta BCs that depend on psi
boundaryzet(ctx, lzet, lpsi, m, n);
// update normalisation
ctx.parallel_for(lzet.shape(), lzet.read(), lbnorm.reduce(reducer::sum<double>{}, no_init{}))
->*[] __device__(size_t i, size_t j, auto zet, auto& bnorm_zet) {
bnorm_zet += zet(i, j) * zet(i, j);
};
}
r_norm.end();
double bnorm = ctx.wait(lbnorm);
bnorm = sqrt(bnorm);
// begin iterative Jacobi loop
// printf("\nStarting main loop...\n\n");
double tstart = gettime();
nvtx_range r_iter("Overall_Iteration");
int iter = 1;
for (; iter <= numiter; iter++)
{
// calculate psi for next iteration
if (irrotational)
{
jacobistep(ctx, lpsitmp, lpsi, m, n);
}
else
{
jacobistepvort(ctx, lzettmp, lpsitmp, lzet, lpsi, m, n, re);
}
// calculate current error if required
bool compute_error = (iter == numiter) || (checkerr && (iter % printfreq == 0));
if (compute_error)
{
error = deltasq(ctx, lpsitmp, lpsi);
if (!irrotational)
{
error += deltasq(ctx, lzettmp, lzet);
}
error = sqrt(error);
error = error / bnorm;
if (checkerr && (error < tolerance))
{
// printf("Converged on iteration %d\n", iter);
break;
}
}
// copy back
ctx.parallel_for(box<2>({1, m + 1}, {1, n + 1}), lpsi.rw(), lpsitmp.read()).set_symbol("SwitchPsi")
->*[] __device__(size_t i, size_t j, auto psi, auto psitmp) {
psi(i, j) = psitmp(i, j);
};
if (!irrotational)
{
ctx.parallel_for(box<2>({1, m + 1}, {1, n + 1}), lzet.rw(), lzettmp.read()).set_symbol("SwitchZet")
->*[] __device__(size_t i, size_t j, auto zet, auto zettmp) {
zet(i, j) = zettmp(i, j);
};
}
if (!irrotational)
{
// update zeta BCs that depend on psi
boundaryzet(ctx, lzet, lpsi, m, n);
}
// if (iter % printfreq == 0) {
// if (!checkerr) {
// printf("Completed iteration %d\n", iter);
// } else {
// printf("Completed iteration %d, error = %g\n", iter, error);
// }
// }
}
r_iter.end();
if (iter > numiter)
{
iter = numiter;
}
double tstop = gettime();
double ttot = tstop - tstart;
double titer = ttot / (double) iter;
// output results
writedatafiles(ctx, lpsi, m, n, scalefactor);
ctx.finalize();
// print out some stats
// printf("\n... finished\n");
printf("After %d iterations, the error is %g\n", iter, error);
printf("Time for %d iterations was %g seconds\n", iter, ttot);
printf("Each iteration took %g seconds\n", titer);
// printf("... finished\n");
return 0;
}

327
cccl_upstream/cudax/examples/stf/custom_data_interface.cu
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@@ -1,327 +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 illustrates how to create a custom data interface and use them in tasks
*
*/
#include <cuda/std/array>
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
/**
* @brief A simple class describing a contiguous matrix of size (m, n)
*/
template <typename T>
class matrix
{
public:
matrix(size_t m, size_t n, T* base)
: m(m)
, n(n)
, base(base)
{}
__host__ __device__ T& operator()(size_t i, size_t j)
{
return base[i + j * m];
}
__host__ __device__ const T& operator()(size_t i, size_t j) const
{
return base[i + j * m];
}
size_t m, n;
T* base;
};
/**
* @brief defines the shape of a matrix
*
* Note that we specialize cuda::experimental::stf::shape_of to avoid ambiguous specialization
*
* @extends shape_of
*/
template <typename T>
class cuda::experimental::stf::shape_of<matrix<T>>
{
public:
/**
* @brief The default constructor.
*
* All `shape_of` specializations must define this constructor.
*/
shape_of() = default;
explicit shape_of(size_t m, size_t n)
: m(m)
, n(n)
{}
/**
* @name Copies a shape.
*
* All `shape_of` specializations must define this constructor.
*/
shape_of(const shape_of&) = default;
/**
* @brief Extracts the shape from a matrix
*
* @param M matrix to get the shape from
*
* All `shape_of` specializations must define this constructor.
*/
shape_of(const matrix<T>& M)
: shape_of<matrix<T>>(M.m, M.n)
{}
/// Mandatory method : defined the total number of elements in the shape
size_t size() const
{
return m * n;
}
using coords_t = ::cuda::std::array<size_t, 2>;
// This transforms a tuple of (shape, 1D index) into a coordinate
_CCCL_HOST_DEVICE coords_t index_to_coords(size_t index) const
{
return {index % m, index / m};
}
size_t m;
size_t n;
};
/**
* @brief Data interface to manipulate a matrix in the CUDA stream backend
*/
template <typename T>
class matrix_stream_interface : public stream_data_interface_simple<matrix<T>>
{
public:
using base = stream_data_interface_simple<matrix<T>>;
using typename base::shape_t;
/// Initialize from an existing matrix
matrix_stream_interface(matrix<T> m)
: base(std::move(m))
{}
/// Initialize from a shape of matrix
matrix_stream_interface(typename base::shape_t s)
: base(s)
{}
/// Copy the content of an instance to another instance
///
/// This implementation assumes that we have registered memory if one of the data place is the host
void stream_data_copy(
const data_place& dst_memory_node,
instance_id_t dst_instance_id,
const data_place& src_memory_node,
instance_id_t src_instance_id,
cudaStream_t stream) override
{
assert(src_memory_node != dst_memory_node);
cudaMemcpyKind kind = cudaMemcpyDeviceToDevice;
if (src_memory_node.is_host())
{
kind = cudaMemcpyHostToDevice;
}
if (dst_memory_node.is_host())
{
kind = cudaMemcpyDeviceToHost;
}
const matrix<T>& src_instance = this->instance(src_instance_id);
const matrix<T>& dst_instance = this->instance(dst_instance_id);
size_t sz = src_instance.m * src_instance.n * sizeof(T);
cuda_safe_call(cudaMemcpyAsync((void*) dst_instance.base, (void*) src_instance.base, sz, kind, stream));
}
/// allocate an instance on a specific data place
///
/// setting *s to a negative value informs CUDASTF that the allocation
/// failed, and that a memory reclaiming mechanism need to be performed.
void stream_data_allocate(
backend_ctx_untyped& /*unused*/,
const data_place& memory_node,
instance_id_t instance_id,
::std::ptrdiff_t& s,
void** /*unused*/,
cudaStream_t stream) override
{
matrix<T>& instance = this->instance(instance_id);
size_t sz = instance.m * instance.n * sizeof(T);
T* base_ptr;
if (memory_node.is_host())
{
// Fallback to a synchronous method as there is no asynchronous host allocation API
cuda_safe_call(cudaStreamSynchronize(stream));
cuda_safe_call(cudaHostAlloc(&base_ptr, sz, cudaHostAllocMapped));
}
else
{
cuda_safe_call(cudaMallocAsync(&base_ptr, sz, stream));
}
// By filling a positive number, we notify that the allocation was successful
s = sz;
instance.base = base_ptr;
}
/// deallocate an instance
void stream_data_deallocate(
backend_ctx_untyped& /*unused*/,
const data_place& memory_node,
instance_id_t instance_id,
void* /*unused*/,
cudaStream_t stream) override
{
matrix<T>& instance = this->instance(instance_id);
if (memory_node.is_host())
{
// Fallback to a synchronous method as there is no asynchronous host deallocation API
cuda_safe_call(cudaStreamSynchronize(stream));
cuda_safe_call(cudaFreeHost(instance.base));
}
else
{
cuda_safe_call(cudaFreeAsync(instance.base, stream));
}
}
/// Register the host memory associated to an instance of matrix
///
/// Note that this pin_host_memory method is not mandatory, but then it is
/// the responsibility of the user to only passed memory that is already
/// registered, and the allocation method on the host must allocate
/// registered memory too. Otherwise, copy methods need to be synchronous.
bool pin_host_memory(instance_id_t instance_id) override
{
matrix<T>& instance = this->instance(instance_id);
if (!instance.base)
{
return false;
}
cuda_safe_call(pin_memory(instance.base, instance.m * instance.n * sizeof(T)));
return true;
}
/// Unregister memory pinned by pin_host_memory
void unpin_host_memory(instance_id_t instance_id) override
{
matrix<T>& instance = this->instance(instance_id);
unpin_memory(instance.base);
}
};
/**
* @brief Define how the CUDA stream backend must manipulate a matrix
*
* Note that we specialize cuda::experimental::stf::shape_of to avoid ambiguous specialization
*
* @extends streamed_interface_of
*/
template <typename T>
struct cuda::experimental::stf::streamed_interface_of<matrix<T>>
{
using type = matrix_stream_interface<T>;
};
/**
* @brief A hash of the matrix
*/
template <typename T>
struct cuda::experimental::stf::hash<matrix<T>>
{
std::size_t operator()(matrix<T> const& m) const noexcept
{
// Combine hashes from the base address and sizes
return cuda::experimental::stf::hash_all(m.m, m.n, m.base);
}
};
template <typename T>
__global__ void kernel(matrix<T> M)
{
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
int nthreads_x = gridDim.x * blockDim.x;
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
int nthreads_y = gridDim.y * blockDim.y;
for (int x = tid_x; x < M.m; x += nthreads_x)
{
for (int y = tid_y; y < M.n; y += nthreads_y)
{
M(x, y) += -x + 7 * y;
}
}
}
int main()
{
stream_ctx ctx;
const size_t m = 8;
const size_t n = 10;
std::vector<int> v(m * n);
matrix<int> M(m, n, &v[0]);
// M(i,j) = 17 * i + 23 * j
for (size_t j = 0; j < n; j++)
{
for (size_t i = 0; i < m; i++)
{
M(i, j) = 17 * i + 23 * j;
}
}
auto lM = ctx.logical_data(M);
// M(i,j) += -i + 7*i
ctx.task(lM.rw())->*[](cudaStream_t s, auto dM) {
kernel<<<dim3(8, 8), dim3(8, 8), 0, s>>>(dM);
};
// M(i,j) += 2*i + 6*j
ctx.parallel_for(lM.shape(), lM.rw())->*[] _CCCL_DEVICE(size_t i, size_t j, auto dM) {
dM(i, j) += 2 * i + 6 * j;
};
ctx.finalize();
for (size_t j = 0; j < n; j++)
{
for (size_t i = 0; i < m; i++)
{
assert(M(i, j) == (17 * i + 23 * j) + (-i + 7 * j) + (2 * i + 6 * j));
}
}
}

87
cccl_upstream/cudax/examples/stf/explicit_data_places.cu
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@@ -1,87 +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 An AXPY kernel implemented with a task of the CUDA stream backend
* where the task accesses host memory from the device
*
*/
#include <cuda/experimental/stf.cuh>
#include <iostream>
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);
}
int main()
{
// Verify whether this device can access memory concurrently from CPU and GPU.
int dev;
cuda_safe_call(cudaGetDevice(&dev));
assert(dev >= 0);
cudaDeviceProp prop;
cuda_safe_call(cudaGetDeviceProperties(&prop, dev));
if (!prop.concurrentManagedAccess)
{
fprintf(stderr, "Concurrent CPU/GPU access not supported, skipping test.\n");
return 0;
}
stream_ctx 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);
}
double alpha = 3.14;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
/* Compute Y = Y + alpha X, but leave X on the host and access it with mapped memory */
ctx.task(lX.read(data_place::host()), lY.rw())->*[&](cudaStream_t s, auto dX, auto dY) {
axpy<<<16, 128, 0, s>>>(alpha, dX, dY);
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
assert(fabs(Y[i] - (Y0(i) + alpha * X0(i))) < 0.0001);
assert(fabs(X[i] - X0(i)) < 0.0001);
}
}

300
cccl_upstream/cudax/examples/stf/fdtd_mgpu.cu
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@@ -1,300 +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 An example solving Maxwell equations in 3D using FDTD on multiple devices
*/
#include <cuda/experimental/stf.cuh>
#include <stdlib.h>
using namespace cuda::experimental::stf;
// FIXME : MSVC has trouble with box constructors
#if !_CCCL_COMPILER(MSVC)
void write_vtk_2D(const std::string& filename, slice<const double, 3> Ez, double dx, double dy, double /*unused*/)
{
FILE* f = fopen(filename.c_str(), "w");
const size_t pos_z = Ez.extent(2) / 2;
const size_t nx = Ez.extent(0);
const size_t size = Ez.extent(0) * Ez.extent(1);
fprintf(f, "# vtk DataFile Version 3.0\n");
fprintf(f, "vtk output\n");
fprintf(f, "ASCII\n");
fprintf(f, "DATASET UNSTRUCTURED_GRID\n");
fprintf(f, "POINTS %ld float\n", 4 * size);
for (size_t y = 0; y < Ez.extent(1); y++)
{
for (size_t x = 0; x < Ez.extent(0); x++)
{
fprintf(f, "%lf %lf 0.0\n", dx * static_cast<float>(x + 0), dy * static_cast<float>(y + 0));
fprintf(f, "%lf %lf 0.0\n", dx * static_cast<float>(x + 1), dy * static_cast<float>(y + 0));
fprintf(f, "%lf %lf 0.0\n", dx * static_cast<float>(x + 1), dy * static_cast<float>(y + 1));
fprintf(f, "%lf %lf 0.0\n", dx * static_cast<float>(x + 0), dy * static_cast<float>(y + 1));
}
}
fprintf(f, "CELLS %ld %ld\n", size, 5 * size);
size_t cell_id = 0;
for (size_t y = 0; y < Ez.extent(1); y++)
{
for (size_t x = 0; x < Ez.extent(0); x++)
{
const size_t point_offset = cell_id * 4;
fprintf(f,
"4 %d %d %d %d\n",
(int) (point_offset + 0),
(int) (point_offset + 1),
(int) (point_offset + 2),
(int) (point_offset + 3));
cell_id++;
}
}
fprintf(f, "CELL_TYPES %ld\n", size);
for (size_t ii = 0; ii < size; ii++)
{
fprintf(f, "5\n");
}
fprintf(f, "CELL_DATA %ld\n", size);
fprintf(f, "SCALARS Ez double 1\n");
fprintf(f, "LOOKUP_TABLE default\n");
for (size_t y = 0; y < Ez.extent(1); y++)
{
for (size_t x = 0; x < Ez.extent(0); x++)
{
fprintf(f, "%lf\n", Ez(x, y, pos_z));
}
}
fclose(f);
}
// Define the source function
_CCCL_DEVICE double Source(double t, double x, double y, double z)
{
constexpr double pi = 3.14159265358979323846;
constexpr double freq = 1e9;
constexpr double omega = (2 * pi * freq);
constexpr double wavelength = 3e8 / freq;
constexpr double k = 2 * pi / wavelength;
return sin(k * x - omega * t);
}
#endif // !_CCCL_COMPILER(MSVC)
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if !_CCCL_COMPILER(MSVC)
context ctx;
// Initialize the time loop
size_t timesteps = 10;
if (argc > 1)
{
timesteps = (size_t) atol(argv[1]);
}
// No output by default
int output_freq = -1;
if (argc > 2)
{
output_freq = atoi(argv[2]);
}
// Default value : grid of all devices
exec_place where = exec_place::all_devices();
if (argc > 3)
{
switch (atoi(argv[3]))
{
case 0:
where = exec_place::host();
break;
case 1:
where = exec_place::current_device();
break;
case 2:
where = exec_place::all_devices();
break;
case 3:
where = exec_place::repeat(exec_place::current_device(), 8);
break;
default:
fprintf(stderr, "Invalid exec place argument\n");
abort();
}
fprintf(stderr, "Running on %s\n", where.to_string().c_str());
}
if (argc > 4)
{
int use_graph = atoi(argv[4]);
if (use_graph)
{
ctx = graph_ctx();
}
fprintf(stderr, "Use %s backend.\n", use_graph ? "graph" : "stream");
}
// Domain dimensions
const size_t SIZE_X = 100;
const size_t SIZE_Y = 100;
const size_t SIZE_Z = 100;
// Grid spacing
const double DX = 0.01;
const double DY = 0.01;
const double DZ = 0.01;
// Define the electric and magnetic fields
auto data_shape = shape_of<slice<double, 3>>(SIZE_X, SIZE_Y, SIZE_Z);
// One structured partition drives every task's decomposition AND the data
// placement: dimension 2 blocked over the grid of devices (change the spec
// entry to split any other dimension). Interior boxes below iterate each
// place's owned coordinates restricted to the box, and uneven sizes are
// handled by predication.
auto part = make_partition(dim4(SIZE_X, SIZE_Y, SIZE_Z), partition_spec{whole, whole, blocked<0>}, where.get_dims());
auto lEx = ctx.logical_data(data_shape);
auto lEy = ctx.logical_data(data_shape);
auto lEz = ctx.logical_data(data_shape);
auto lHx = ctx.logical_data(data_shape);
auto lHy = ctx.logical_data(data_shape);
auto lHz = ctx.logical_data(data_shape);
// Define the permittivity and permeability of the medium
auto lepsilon = ctx.logical_data(data_shape);
auto lmu = ctx.logical_data(data_shape);
const double EPSILON = 8.85e-12; // Permittivity of free space
const double MU = 1.256e-6; // Permeability of free space
// CFL condition DT <= min(DX, DY, DZ) * sqrt(epsilon_max * mu_max)
double DT = 0.25 * min(min(DX, DY), DZ) * sqrt(EPSILON * MU);
// Initialize E
ctx.parallel_for(part, where, data_shape, lEx.write(), lEy.write(), lEz.write())
->*[] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ex, auto Ey, auto Ez) {
Ex(i, j, k) = 0.0;
Ey(i, j, k) = 0.0;
Ez(i, j, k) = 0.0;
};
// Initialize H
ctx.parallel_for(part, where, data_shape, lHx.write(), lHy.write(), lHz.write())
->*[] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hx, auto Hy, auto Hz) {
Hx(i, j, k) = 0.0;
Hy(i, j, k) = 0.0;
Hz(i, j, k) = 0.0;
};
// Initialize permittivity and permeability fields
ctx.parallel_for(part, where, data_shape, lepsilon.write(), lmu.write())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto epsilon, auto mu) {
epsilon(i, j, k) = EPSILON;
mu(i, j, k) = MU;
};
// Set the source function at the center of the grid
const size_t center_x = SIZE_X / 2;
const size_t center_y = SIZE_Y / 2;
const size_t center_z = SIZE_Z / 2;
// Index shapes for the electric and magnetic fields
box Es({1ul, SIZE_X - 1}, {1ul, SIZE_Y - 1}, {1ul, SIZE_Z - 1});
box Hs({0ul, SIZE_X - 1}, {0ul, SIZE_Y - 1}, {0ul, SIZE_Z - 1});
ctx.repeat(timesteps)->*[&](context ctx, size_t n) {
// Update the electric fields
// Update Ex
ctx.parallel_for(part, where, Es, lEx.rw(), lHy.read(), lHz.read(), lepsilon.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ex, auto Hy, auto Hz, auto epsilon) {
Ex(i, j, k) = Ex(i, j, k)
+ (DT / (epsilon(i, j, k) * DX)) * (Hz(i, j, k) - Hz(i, j - 1, k) - Hy(i, j, k) + Hy(i, j, k - 1));
};
// Update Ey
ctx.parallel_for(part, where, Es, lEy.rw(), lHx.read(), lHz.read(), lepsilon.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ey, auto Hx, auto Hz, auto epsilon) {
Ey(i, j, k) = Ey(i, j, k)
+ (DT / (epsilon(i, j, k) * DY)) * (Hx(i, j, k) - Hx(i, j, k - 1) - Hz(i, j, k) + Hz(i - 1, j, k));
};
// Update Ez and inject the point source in the same volumetric pass
ctx.parallel_for(part, where, Es, lEz.rw(), lHx.read(), lHy.read(), lepsilon.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ez, auto Hx, auto Hy, auto epsilon) {
Ez(i, j, k) = Ez(i, j, k)
+ (DT / (epsilon(i, j, k) * DZ)) * (Hy(i, j, k) - Hy(i - 1, j, k) - Hx(i, j, k) + Hx(i, j - 1, k));
if (i == center_x && j == center_y && k == center_z)
{
Ez(i, j, k) += Source(n * DT, i * DX, j * DY, k * DZ);
}
};
// Update the magnetic fields
// Update Hx
ctx.parallel_for(part, where, Hs, lHx.rw(), lEy.read(), lEz.read(), lmu.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hx, auto Ey, auto Ez, auto mu) {
Hx(i, j, k) = Hx(i, j, k)
- (DT / (mu(i, j, k) * DY)) * (Ez(i, j + 1, k) - Ez(i, j, k) - Ey(i, j, k + 1) + Ey(i, j, k));
};
// Update Hy
ctx.parallel_for(part, where, Hs, lHy.rw(), lEx.read(), lEz.read(), lmu.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hy, auto Ex, auto Ez, auto mu) {
Hy(i, j, k) = Hy(i, j, k)
- (DT / (mu(i, j, k) * DZ)) * (Ex(i, j, k + 1) - Ex(i, j, k) - Ez(i + 1, j, k) + Ez(i, j, k));
};
// Update Hz
ctx.parallel_for(part, where, Hs, lHz.rw(), lEx.read(), lEy.read(), lmu.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hz, auto Ex, auto Ey, auto mu) {
Hz(i, j, k) = Hz(i, j, k)
- (DT / (mu(i, j, k) * DX)) * (Ey(i + 1, j, k) - Ey(i, j, k) - Ex(i, j + 1, k) + Ex(i, j, k));
};
if (output_freq > 0 && n % output_freq == 0)
{
ctx.host_launch(lEz.read())->*[=](auto Ez) {
// Output the electric field at the center of the grid
fprintf(stderr, "%ld\t%le\n", n, Ez(center_x, center_y, center_z));
std::string filename = "Ez" + std::to_string(n) + ".vtk";
// Dump a 2D slice of Ez in VTK
write_vtk_2D(filename, Ez, DX, DY, DZ);
};
}
};
ctx.finalize();
#endif // !_CCCL_COMPILER(MSVC)
}

186
cccl_upstream/cudax/examples/stf/fdtd_repeat_n.cu
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@@ -1,186 +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 FDTD example using the repeat_n helper function
*
* This shows how to refactor the original fdtd_while.cu example
* to use the new repeat_n helper for cleaner loop patterns.
*/
#include <cuda/experimental/stf.cuh>
#include <iostream>
#include <stdlib.h>
using namespace cuda::experimental::stf;
// Define the source function
_CCCL_DEVICE double Source(double t, double x, double y, double z)
{
constexpr double pi = 3.14159265358979323846;
constexpr double freq = 1e9;
constexpr double omega = (2 * pi * freq);
constexpr double wavelength = 3e8 / freq;
constexpr double k = 2 * pi / wavelength;
return sin(k * x - omega * t);
}
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
#else
stackable_ctx ctx;
// Initialize the time loop
size_t timesteps = 10;
if (argc > 1)
{
timesteps = (size_t) atol(argv[1]);
}
// Domain dimensions (smaller for this example)
const size_t SIZE_X = 50;
const size_t SIZE_Y = 50;
const size_t SIZE_Z = 50;
// Grid spacing
const double DX = 0.01;
const double DY = 0.01;
const double DZ = 0.01;
// Define the electric and magnetic fields
auto data_shape = shape_of<slice<double, 3>>(SIZE_X, SIZE_Y, SIZE_Z);
auto lEx = ctx.logical_data(data_shape);
auto lEy = ctx.logical_data(data_shape);
auto lEz = ctx.logical_data(data_shape);
auto lHx = ctx.logical_data(data_shape);
auto lHy = ctx.logical_data(data_shape);
auto lHz = ctx.logical_data(data_shape);
// Define the permittivity and permeability of the medium
auto lepsilon = ctx.logical_data(data_shape);
auto lmu = ctx.logical_data(data_shape);
const double EPSILON = 8.85e-12; // Permittivity of free space
const double MU = 1.256e-6; // Permeability of free space
// CFL condition DT <= min(DX, DY, DZ) * sqrt(epsilon_max * mu_max)
double DT = 0.25 * min(min(DX, DY), DZ) * sqrt(EPSILON * MU);
// Initialize E fields
ctx.parallel_for(data_shape, lEx.write(), lEy.write(), lEz.write())
->*[] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ex, auto Ey, auto Ez) {
Ex(i, j, k) = 0.0;
Ey(i, j, k) = 0.0;
Ez(i, j, k) = 0.0;
};
// Initialize H fields
ctx.parallel_for(data_shape, lHx.write(), lHy.write(), lHz.write())
->*[] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hx, auto Hy, auto Hz) {
Hx(i, j, k) = 0.0;
Hy(i, j, k) = 0.0;
Hz(i, j, k) = 0.0;
};
// Initialize permittivity and permeability fields
ctx.parallel_for(data_shape, lepsilon.write(), lmu.write())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto epsilon, auto mu) {
epsilon(i, j, k) = EPSILON;
mu(i, j, k) = MU;
};
// Set the source location
const size_t center_x = SIZE_X / 2;
const size_t center_y = SIZE_Y / 2;
const size_t center_z = SIZE_Z / 2;
// Index shapes for Electric fields, Magnetic fields, and the source
box Es({1ul, SIZE_X - 1}, {1ul, SIZE_Y - 1}, {1ul, SIZE_Z - 1});
box Hs({0ul, SIZE_X - 1}, {0ul, SIZE_Y - 1}, {0ul, SIZE_Z - 1});
box source_s({center_x, center_x + 1}, {center_y, center_y + 1}, {center_z, center_z + 1});
std::cout << "Running FDTD simulation for " << timesteps << " timesteps" << '\n';
std::cout << "Grid size: " << SIZE_X << "x" << SIZE_Y << "x" << SIZE_Z << '\n';
{
auto repeat_guard = ctx.repeat_graph_scope(timesteps);
// Update Ex
ctx.parallel_for(Es, lEx.rw(), lHy.read(), lHz.read(), lepsilon.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ex, auto Hy, auto Hz, auto epsilon) {
Ex(i, j, k) = Ex(i, j, k)
+ (DT / (epsilon(i, j, k) * DX)) * (Hz(i, j, k) - Hz(i, j - 1, k) - Hy(i, j, k) + Hy(i, j, k - 1));
};
// Update Ey
ctx.parallel_for(Es, lEy.rw(), lHx.read(), lHz.read(), lepsilon.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ey, auto Hx, auto Hz, auto epsilon) {
Ey(i, j, k) = Ey(i, j, k)
+ (DT / (epsilon(i, j, k) * DY)) * (Hx(i, j, k) - Hx(i, j, k - 1) - Hz(i, j, k) + Hz(i - 1, j, k));
};
// Update Ez
ctx.parallel_for(Es, lEz.rw(), lHx.read(), lHy.read(), lepsilon.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ez, auto Hx, auto Hy, auto epsilon) {
Ez(i, j, k) = Ez(i, j, k)
+ (DT / (epsilon(i, j, k) * DZ)) * (Hy(i, j, k) - Hy(i - 1, j, k) - Hx(i, j, k) + Hx(i, j - 1, k));
};
// Add the source function at the center of the grid
// Note: We could add a current iteration tracker if needed for time-dependent sources
ctx.parallel_for(source_s, lEz.rw())->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ez) {
// For simplicity, using a constant source in this example
// In the full version, you'd want to track the current timestep
Ez(i, j, k) = Ez(i, j, k) + 0.1 * sin(0.1 * (i + j + k));
};
// Update Hx
ctx.parallel_for(Hs, lHx.rw(), lEy.read(), lEz.read(), lmu.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hx, auto Ey, auto Ez, auto mu) {
Hx(i, j, k) = Hx(i, j, k)
- (DT / (mu(i, j, k) * DY)) * (Ez(i, j + 1, k) - Ez(i, j, k) - Ey(i, j, k + 1) + Ey(i, j, k));
};
// Update Hy
ctx.parallel_for(Hs, lHy.rw(), lEx.read(), lEz.read(), lmu.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hy, auto Ex, auto Ez, auto mu) {
Hy(i, j, k) = Hy(i, j, k)
- (DT / (mu(i, j, k) * DZ)) * (Ex(i, j, k + 1) - Ex(i, j, k) - Ez(i + 1, j, k) + Ez(i, j, k));
};
// Update Hz
ctx.parallel_for(Hs, lHz.rw(), lEx.read(), lEy.read(), lmu.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hz, auto Ex, auto Ey, auto mu) {
Hz(i, j, k) = Hz(i, j, k)
- (DT / (mu(i, j, k) * DX)) * (Ey(i + 1, j, k) - Ey(i, j, k) - Ex(i, j + 1, k) + Ex(i, j, k));
};
} // repeat_guard
// Print final result at center
ctx.host_launch(lEz.read())->*[=](auto Ez) {
std::cout << "Final Ez at center: " << Ez(center_x, center_y, center_z) << '\n';
};
ctx.finalize();
std::cout << "FDTD simulation completed!" << '\n';
return 0;
#endif
}

288
cccl_upstream/cudax/examples/stf/fdtd_while.cu
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@@ -1,288 +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 An example solving Maxwell equations in 3D using FDTD on multiple devices
*/
#include <cuda/experimental/stf.cuh>
#include <stdlib.h>
using namespace cuda::experimental::stf;
// FIXME : MSVC has trouble with box constructors
#if !_CCCL_COMPILER(MSVC)
void write_vtk_2D(const std::string& filename, slice<const double, 3> Ez, double dx, double dy, double /*unused*/)
{
FILE* f = fopen(filename.c_str(), "w");
const size_t pos_z = Ez.extent(2) / 2;
const size_t nx = Ez.extent(0);
const size_t size = Ez.extent(0) * Ez.extent(1);
fprintf(f, "# vtk DataFile Version 3.0\n");
fprintf(f, "vtk output\n");
fprintf(f, "ASCII\n");
fprintf(f, "DATASET UNSTRUCTURED_GRID\n");
fprintf(f, "POINTS %ld float\n", 4 * size);
for (size_t y = 0; y < Ez.extent(1); y++)
{
for (size_t x = 0; x < Ez.extent(0); x++)
{
fprintf(f, "%lf %lf 0.0\n", dx * static_cast<float>(x + 0), dy * static_cast<float>(y + 0));
fprintf(f, "%lf %lf 0.0\n", dx * static_cast<float>(x + 1), dy * static_cast<float>(y + 0));
fprintf(f, "%lf %lf 0.0\n", dx * static_cast<float>(x + 1), dy * static_cast<float>(y + 1));
fprintf(f, "%lf %lf 0.0\n", dx * static_cast<float>(x + 0), dy * static_cast<float>(y + 1));
}
}
fprintf(f, "CELLS %ld %ld\n", size, 5 * size);
size_t cell_id = 0;
for (size_t y = 0; y < Ez.extent(1); y++)
{
for (size_t x = 0; x < Ez.extent(0); x++)
{
const size_t point_offset = cell_id * 4;
fprintf(f,
"4 %d %d %d %d\n",
(int) (point_offset + 0),
(int) (point_offset + 1),
(int) (point_offset + 2),
(int) (point_offset + 3));
cell_id++;
}
}
fprintf(f, "CELL_TYPES %ld\n", size);
for (size_t ii = 0; ii < size; ii++)
{
fprintf(f, "5\n");
}
fprintf(f, "CELL_DATA %ld\n", size);
fprintf(f, "SCALARS Ez double 1\n");
fprintf(f, "LOOKUP_TABLE default\n");
for (size_t y = 0; y < Ez.extent(1); y++)
{
for (size_t x = 0; x < Ez.extent(0); x++)
{
fprintf(f, "%lf\n", Ez(x, y, pos_z));
}
}
fclose(f);
}
// Define the source function
_CCCL_DEVICE double Source(double t, double x, double y, double z)
{
constexpr double pi = 3.14159265358979323846;
constexpr double freq = 1e9;
constexpr double omega = (2 * pi * freq);
constexpr double wavelength = 3e8 / freq;
constexpr double k = 2 * pi / wavelength;
return sin(k * x - omega * t);
}
#endif // !_CCCL_COMPILER(MSVC)
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if !_CCCL_COMPILER(MSVC)
# if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
# else
stackable_ctx ctx;
// Initialize the time loop
size_t timesteps = 10;
if (argc > 1)
{
timesteps = (size_t) atol(argv[1]);
}
// No output by default
int output_freq = -1;
if (argc > 2)
{
output_freq = atoi(argv[2]);
}
// Domain dimensions
const size_t SIZE_X = 100;
const size_t SIZE_Y = 100;
const size_t SIZE_Z = 100;
// Grid spacing
const double DX = 0.01;
const double DY = 0.01;
const double DZ = 0.01;
// Define the electric and magnetic fields
auto data_shape = shape_of<slice<double, 3>>(SIZE_X, SIZE_Y, SIZE_Z);
auto lEx = ctx.logical_data(data_shape);
auto lEy = ctx.logical_data(data_shape);
auto lEz = ctx.logical_data(data_shape);
auto lHx = ctx.logical_data(data_shape);
auto lHy = ctx.logical_data(data_shape);
auto lHz = ctx.logical_data(data_shape);
// Define the permittivity and permeability of the medium
auto lepsilon = ctx.logical_data(data_shape);
auto lmu = ctx.logical_data(data_shape);
const double EPSILON = 8.85e-12; // Permittivity of free space
const double MU = 1.256e-6; // Permeability of free space
// CFL condition DT <= min(DX, DY, DZ) * sqrt(epsilon_max * mu_max)
double DT = 0.25 * min(min(DX, DY), DZ) * sqrt(EPSILON * MU);
// Initialize E
ctx.parallel_for(data_shape, lEx.write(), lEy.write(), lEz.write())
->*[] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ex, auto Ey, auto Ez) {
Ex(i, j, k) = 0.0;
Ey(i, j, k) = 0.0;
Ez(i, j, k) = 0.0;
};
// Initialize H
ctx.parallel_for(data_shape, lHx.write(), lHy.write(), lHz.write())
->*[] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hx, auto Hy, auto Hz) {
Hx(i, j, k) = 0.0;
Hy(i, j, k) = 0.0;
Hz(i, j, k) = 0.0;
};
// Initialize permittivity and permeability fields
ctx.parallel_for(data_shape, lepsilon.write(), lmu.write())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto epsilon, auto mu) {
epsilon(i, j, k) = EPSILON;
mu(i, j, k) = MU;
};
// Set the source function at the center of the grid
const size_t center_x = SIZE_X / 2;
const size_t center_y = SIZE_Y / 2;
const size_t center_z = SIZE_Z / 2;
/* Index shapes for Electric fields, Magnetic fields, and the indices where there is a source */
box Es({1ul, SIZE_X - 1}, {1ul, SIZE_Y - 1}, {1ul, SIZE_Z - 1});
box Hs({0ul, SIZE_X - 1}, {0ul, SIZE_Y - 1}, {0ul, SIZE_Z - 1});
box source_s({center_x, center_x + 1}, {center_y, center_y + 1}, {center_z, center_z + 1});
int iterations_per_graph = (output_freq == -1) ? timesteps : output_freq;
for (size_t n = 0; n < timesteps / iterations_per_graph; n++)
{
fprintf(stderr, "WHILE BLAAAAA...\n");
// Counter for while loop iterations
auto counter_shape = shape_of<scalar_view<int>>();
auto lcounter = ctx.logical_data(counter_shape);
// Initialize counter
ctx.parallel_for(box(1), lcounter.write())->*[=] __device__(size_t, auto counter) {
*counter = iterations_per_graph;
};
auto while_guard = ctx.while_graph_scope();
{
// Update the electric fields
// Update Ex
ctx.parallel_for(Es, lEx.rw(), lHy.read(), lHz.read(), lepsilon.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ex, auto Hy, auto Hz, auto epsilon) {
Ex(i, j, k) =
Ex(i, j, k)
+ (DT / (epsilon(i, j, k) * DX)) * (Hz(i, j, k) - Hz(i, j - 1, k) - Hy(i, j, k) + Hy(i, j, k - 1));
};
// Update Ey
ctx.parallel_for(Es, lEy.rw(), lHx.read(), lHz.read(), lepsilon.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ey, auto Hx, auto Hz, auto epsilon) {
Ey(i, j, k) =
Ey(i, j, k)
+ (DT / (epsilon(i, j, k) * DY)) * (Hx(i, j, k) - Hx(i, j, k - 1) - Hz(i, j, k) + Hz(i - 1, j, k));
};
// Update Ez
ctx.parallel_for(Es, lEz.rw(), lHx.read(), lHy.read(), lepsilon.read())
->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ez, auto Hx, auto Hy, auto epsilon) {
Ez(i, j, k) =
Ez(i, j, k)
+ (DT / (epsilon(i, j, k) * DZ)) * (Hy(i, j, k) - Hy(i - 1, j, k) - Hx(i, j, k) + Hx(i, j - 1, k));
};
// Add the source function at the center of the grid
ctx.parallel_for(source_s, lEz.rw())->*[=] _CCCL_DEVICE(size_t i, size_t j, size_t k, auto Ez) {
Ez(i, j, k) = Ez(i, j, k) + Source(n * DT, i * DX, j * DY, k * DZ);
};
// Update the magnetic fields
// Update Hx
ctx.parallel_for(Hs, lHx.rw(), lEy.read(), lEz.read(), lmu.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hx, auto Ey, auto Ez, auto mu) {
Hx(i, j, k) =
Hx(i, j, k) - (DT / (mu(i, j, k) * DY)) * (Ez(i, j + 1, k) - Ez(i, j, k) - Ey(i, j, k + 1) + Ey(i, j, k));
};
// Update Hy
ctx.parallel_for(Hs, lHy.rw(), lEx.read(), lEz.read(), lmu.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hy, auto Ex, auto Ez, auto mu) {
Hy(i, j, k) =
Hy(i, j, k) - (DT / (mu(i, j, k) * DZ)) * (Ex(i, j, k + 1) - Ex(i, j, k) - Ez(i + 1, j, k) + Ez(i, j, k));
};
// Update Hz
ctx.parallel_for(Hs, lHz.rw(), lEx.read(), lEy.read(), lmu.read())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, size_t k, auto Hz, auto Ex, auto Ey, auto mu) {
Hz(i, j, k) =
Hz(i, j, k) - (DT / (mu(i, j, k) * DX)) * (Ey(i + 1, j, k) - Ey(i, j, k) - Ex(i, j + 1, k) + Ex(i, j, k));
};
auto handle = while_guard.cond_handle();
ctx.parallel_for(box(1), lcounter.rw())->*[handle] __device__(size_t, auto counter) {
(*counter)--;
bool should_continue = (*counter > 0);
cudaGraphSetConditional(handle, should_continue);
};
} // end of the while pattern
if (output_freq > 0 && n % output_freq == 0)
{
ctx.host_launch(lEz.read())->*[=](auto Ez) {
// Output the electric field at the center of the grid
fprintf(stderr, "%ld\t%le\n", n, Ez(center_x, center_y, center_z));
std::string filename = "Ez" + std::to_string(n) + ".vtk";
// Dump a 2D slice of Ez in VTK
write_vtk_2D(filename, Ez, DX, DY, DZ);
};
}
}
ctx.finalize();
# endif // _CCCL_CTK_AT_LEAST(12, 4)
#endif // !_CCCL_COMPILER(MSVC)
}

53
cccl_upstream/cudax/examples/stf/frozen_data_init.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 Illustrate how we can use frozen data to initialize constant data
*
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
context ctx;
/* Create a piece of data that can be use many times without further synchronizations */
auto buffer = ctx.logical_data(shape_of<slice<double, 2>>(128, 64)).set_symbol("buffer");
ctx.parallel_for(buffer.shape(), buffer.write())->*[] __device__(size_t i, size_t j, auto b) {
b(i, j) = sin(-1.0 * i) + cos(2.0 * j);
};
auto frozen_buffer = ctx.freeze(buffer);
auto h_buf = frozen_buffer.get(data_place::host()).first;
auto d_buf = frozen_buffer.get(data_place::current_device()).first;
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
auto lX = ctx.logical_data(buffer.shape()).set_symbol("X");
ctx.parallel_for(lX.shape(), lX.write()).set_symbol("X=buf")->*[d_buf] __device__(size_t i, size_t j, auto x) {
x(i, j) = d_buf(i, j);
};
ctx.parallel_for(exec_place::host(), lX.shape(), lX.read()).set_symbol("check buf")
->*[h_buf](size_t i, size_t j, auto x) {
EXPECT(fabs(x(i, j) - h_buf(i, j)) < 0.0001);
};
// Make sure all tasks are done before unfreezing
frozen_buffer.unfreeze(ctx.fence());
ctx.finalize();
}

64
cccl_upstream/cudax/examples/stf/graph_algorithms/degree_centrality.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 Computes the Degree Centrality for each vertex within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
/**
* @brief Computes the Degree Centrality for each vertex.
*
* @param idx The index of the vertex for which Degree Centrality is being calculated.
* @param d_offsets Slice containing the offset vector of the CSR representation.
* @return The degree of each vertex.
*/
__device__ int degree_centrality(int idx, slice<const int> loffsets)
{
return loffsets[idx + 1] - loffsets[idx];
}
int main()
{
stream_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
// output degrees for each vertex
int num_vertices = offsets.size() - 1;
std::vector<int> degrees(num_vertices, 0);
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto ldegrees = ctx.logical_data(&degrees[0], degrees.size());
ctx.parallel_for(box(num_vertices), loffsets.read(), ldegrees.rw())
->*[] __device__(size_t idx, auto loffsets, auto ldegrees) {
ldegrees[idx] = degree_centrality(idx, loffsets);
};
ctx.finalize();
// for (int i = 0; i < num_vertices; ++i) {
// printf("Vertex %d: Degree Centrality = %d\n", i, degrees[i]);
// }
return 0;
}

137
cccl_upstream/cudax/examples/stf/graph_algorithms/jaccard.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 Computes the Jaccard Similarity for each vertex within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
// Performs Binary Search on a given array with start/end bounds and a lookup element
__device__ int binary_search(slice<const int> arr, int start, int end, int lookup)
{
while (start <= end)
{
int mid = start + (end - start) / 2;
if (arr[mid] == lookup)
{
return mid;
}
else if (arr[mid] < lookup)
{
start = mid + 1;
}
else
{
end = mid - 1;
}
}
return -1;
}
/**
* @brief Computes the intersection size of neighbors of two vertices.
*
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param u Index of the first vertex.
* @param v Index of the second vertex.
* @return The number of common neighbors (intersection size) of vertices u and v.
*/
__device__ int calculate_intersection_size(slice<const int> loffsets, slice<const int> lnonzeros, int u, int v)
{
int count = 0;
for (int i = loffsets[u]; i < loffsets[u + 1]; i++)
{
if (binary_search(lnonzeros, loffsets[v], loffsets[v + 1] - 1, lnonzeros[i]) != -1)
{
count++;
}
}
return count;
}
/**
* @brief Computes the union size of neighbors of two vertices.
*
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param u Index of the first vertex.
* @param v Index of the second vertex.
* @return The number of unique neighbors (union size) of vertices u and v.
*/
__device__ int calculate_union_size(slice<const int> loffsets, slice<const int> lnonzeros, int u, int v)
{
int count = (loffsets[u + 1] - loffsets[u]) + (loffsets[v + 1] - loffsets[v]);
for (int i = loffsets[u]; i < loffsets[u + 1]; i++)
{
if (binary_search(lnonzeros, loffsets[v], loffsets[v + 1] - 1, lnonzeros[i]) != -1)
{
count--;
}
}
return count;
}
int main()
{
stream_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
// output jaccard similarities for each vertex
int num_vertices = offsets.size() - 1;
std::vector<float> jaccard_similarities(num_vertices * num_vertices, 0.0f);
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto ljaccard_similarities = ctx.logical_data(&jaccard_similarities[0], jaccard_similarities.size());
ctx.parallel_for(box(num_vertices), loffsets.read(), lnonzeros.read(), ljaccard_similarities.rw())
->*[] __device__(size_t idx, auto loffsets, auto lnonzeros, auto ljaccard_similarities) {
for (int j = 0; j < loffsets.size() - 1; j++)
{
if (idx != j)
{
int intersection = calculate_intersection_size(loffsets, lnonzeros, idx, j);
int uni = calculate_union_size(loffsets, lnonzeros, idx, j);
if (uni > 0)
{
ljaccard_similarities[idx * (loffsets.size() - 1) + j] = static_cast<float>(intersection) / uni;
}
}
}
};
ctx.finalize();
for (int u = 0; u < num_vertices; u++)
{
for (int v = 0; v < num_vertices; v++)
{
if (u != v)
{
printf(
"Jaccard similarity between vertex %d and vertex %d: %f\n", u, v, jaccard_similarities[u * num_vertices + v]);
}
}
}
return 0;
}

121
cccl_upstream/cudax/examples/stf/graph_algorithms/pagerank.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 Computes the PageRank for vertices within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
/**
* @brief Calculates the PageRank for a given vertex.
*
* @param idx The index of the vertex for which PageRank is being calculated.
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param lpage_rank Slice containing current PageRank values for each vertex.
* @param lnew_page_rank Slice containing where new PageRank values will be stored.
* @param init_rank The initial PageRank value to be used in the calculation.
*/
__device__ void calculating_pagerank(
int idx,
const slice<const int>& loffsets,
const slice<const int>& lnonzeros,
const slice<const float>& lpage_rank,
slice<float>& lnew_page_rank,
float init_rank)
{
float rank_sum = 0.0;
for (int i = loffsets[idx]; i < loffsets[idx + 1]; i++)
{
int neighbor = lnonzeros[i];
int out_degree = loffsets[neighbor + 1] - loffsets[neighbor];
rank_sum += lpage_rank[neighbor] / out_degree;
}
lnew_page_rank[idx] = 0.85 * rank_sum + (1.0 - 0.85) * init_rank;
}
int main()
{
stream_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
int num_vertices = offsets.size() - 1;
float init_rank = 1.0f / num_vertices;
float tolerance = 1e-6f;
int NITER = 100;
// output pageranks for each vertex
std::vector<float> page_rank(num_vertices, init_rank);
std::vector<float> new_page_rank(num_vertices);
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto lpage_rank = ctx.logical_data(&page_rank[0], page_rank.size());
auto lnew_page_rank = ctx.logical_data(&new_page_rank[0], new_page_rank.size());
auto lmax_diff = ctx.logical_data(shape_of<scalar_view<float>>());
for (int iter = 0; iter < NITER; ++iter)
{
// Calculate Current Iteration PageRank
ctx.parallel_for(
box(num_vertices),
loffsets.read(),
lnonzeros.read(),
lpage_rank.rw(),
lnew_page_rank.rw(),
lmax_diff.reduce(reducer::maxval<float>{}))
->*[init_rank] __device__(
size_t idx, auto loffsets, auto lnonzeros, auto lpage_rank, auto lnew_page_rank, auto& max_diff) {
calculating_pagerank(idx, loffsets, lnonzeros, lpage_rank, lnew_page_rank, init_rank);
max_diff = ::std::max(max_diff, lnew_page_rank[idx] - lpage_rank[idx]);
};
// Reduce Error and Check for Convergence
bool converged = (ctx.wait(lmax_diff) < tolerance);
if (converged)
{
break;
}
// Update New PageRank Values
std::swap(lpage_rank, lnew_page_rank);
}
ctx.finalize();
/* CHECKING FOR ANSWER CORRECTNESS */
// sum of all page ranks should equal 1
double sum_pageranks = 0.0;
for (int64_t i = 0; i < num_vertices; i++)
{
sum_pageranks += page_rank[i];
}
printf("Page rank answer is %s.\n", abs(sum_pageranks - 1.0) < 0.001 ? "correct" : "not correct");
printf("PageRank Results:\n");
for (size_t i = 0; i < page_rank.size(); ++i)
{
printf("Vertex %zu: %f\n", i, page_rank[i]);
}
return 0;
}

203
cccl_upstream/cudax/examples/stf/graph_algorithms/pagerank_batched.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 Computes the PageRank for vertices within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
#if _CCCL_CTK_AT_LEAST(12, 4)
/**
* @brief Calculates the PageRank for a given vertex.
*
* @param idx The index of the vertex for which PageRank is being calculated.
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param lpage_rank Slice containing current PageRank values for each vertex.
* @param lnew_page_rank Slice containing where new PageRank values will be stored.
* @param lpersonalization Slice containing the personalization vector for each vertex.
*/
__device__ void calculating_pagerank(
int idx,
const slice<const int>& loffsets,
const slice<const int>& lnonzeros,
const slice<const float>& lpage_rank,
slice<float>& lnew_page_rank,
const slice<const float>& lpersonalization)
{
float rank_sum = 0.0;
for (int i = loffsets[idx]; i < loffsets[idx + 1]; i++)
{
int neighbor = lnonzeros[i];
int out_degree = loffsets[neighbor + 1] - loffsets[neighbor];
rank_sum += lpage_rank[neighbor] / out_degree;
}
lnew_page_rank[idx] = 0.85 * rank_sum + (1.0 - 0.85) * lpersonalization[idx];
}
/**
* @brief Computes PageRank using the power iteration method
*
* @param ctx The CUDASTF context
* @param loffsets Logical data for CSR offset vector
* @param lnonzeros Logical data for CSR non-zero elements vector
* @param lpage_rank Logical data for current PageRank values
* @param lpersonalization Logical data for personalization vector
* @param num_vertices Number of vertices in the graph
* @param NITER Maximum number of iterations
* @param tolerance Convergence tolerance
*/
void compute_pagerank(
stackable_ctx& ctx,
stackable_logical_data<slice<int>>& loffsets,
stackable_logical_data<slice<int>>& lnonzeros,
stackable_logical_data<slice<float>>& lpage_rank,
stackable_logical_data<slice<float>>& lpersonalization,
int num_vertices,
int NITER,
float tolerance)
{
// Create local temporary buffer and convergence tracking
auto lnew_page_rank = ctx.logical_data(lpage_rank.shape());
auto lmax_diff = ctx.logical_data(shape_of<scalar_view<float>>());
auto liter = ctx.logical_data(shape_of<scalar_view<int>>());
// Initialize iteration counter
ctx.parallel_for(box(1), liter.write())->*[] __device__(size_t, auto iter) {
*iter = 0;
};
{
auto while_guard = ctx.while_graph_scope();
// Calculate Current Iteration PageRank
ctx.parallel_for(
box(num_vertices),
loffsets.read(),
lnonzeros.read(),
lpage_rank.rw(),
lnew_page_rank.write(),
lpersonalization.read(),
lmax_diff.reduce(reducer::maxval<float>{}))
->*
[] __device__(
size_t idx,
auto loffsets,
auto lnonzeros,
auto lpage_rank,
auto lnew_page_rank,
auto lpersonalization,
auto& max_diff) {
calculating_pagerank(idx, loffsets, lnonzeros, lpage_rank, lnew_page_rank, lpersonalization);
max_diff = ::std::max(max_diff, lnew_page_rank[idx] - lpage_rank[idx]);
};
// Update PageRank Values
ctx.parallel_for(lpage_rank.shape(), lpage_rank.write(), lnew_page_rank.read())
->*[] __device__(size_t i, auto page_rank, auto new_page_rank) {
page_rank(i) = new_page_rank(i);
};
while_guard.update_cond(lmax_diff.read(), liter.rw())->*[NITER, tolerance] __device__(auto max_diff, auto iter) {
bool converged = (*max_diff < tolerance);
bool max_reached = ((*iter)++ >= NITER); // Maximum iteration limit
return !converged && !max_reached; // Continue if not converged and under limit
};
}
}
#endif // _CCCL_CTK_AT_LEAST(12, 4)
int main()
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving example: while_graph_scope is only available since CUDA 12.4.\n");
return 0;
#else
stackable_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
int num_vertices = offsets.size() - 1;
float init_rank = 1.0f / num_vertices;
float tolerance = 1e-6f;
int NITER = 100;
int num_personalization = 4;
::std::vector<stackable_logical_data<slice<float>>> lpage_rank_slices;
for (int i = 0; i < num_personalization; i++)
{
lpage_rank_slices.push_back(ctx.logical_data(shape_of<slice<float>>(num_vertices)));
}
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
loffsets.set_read_only();
lnonzeros.set_read_only();
{
auto scope = ctx.graph_scope();
for (int p = 0; p < num_personalization; p++)
{
// Initialize PageRank values to uniform distribution
ctx.parallel_for(lpage_rank_slices[p].shape(), lpage_rank_slices[p].write())
->*[init_rank] __device__(size_t i, auto page_rank) {
page_rank(i) = init_rank;
};
// Create personalization vector (uniform for this example)
auto lpersonalization = ctx.logical_data(shape_of<slice<float>>(num_vertices));
ctx.parallel_for(lpersonalization.shape(), lpersonalization.write())
->*[init_rank] __device__(size_t i, auto lpersonalization) {
lpersonalization(i) = init_rank;
};
compute_pagerank(ctx, loffsets, lnonzeros, lpage_rank_slices[p], lpersonalization, num_vertices, NITER, tolerance);
}
}
for (int p = 0; p < num_personalization; p++)
{
ctx.host_launch(lpage_rank_slices[p].read())->*[p, num_vertices] __host__(slice<const float> page_rank) {
double sum_pageranks = 0.0;
for (int64_t i = 0; i < num_vertices; i++)
{
sum_pageranks += page_rank[i];
}
printf("Page rank answer for personalization %d is %s.\n",
p,
abs(sum_pageranks - 1.0) < 0.001 ? "correct" : "not correct");
// Print first few results for verification
printf("Personalization %d - First 5 vertices: ", p);
for (size_t i = 0; i < std::min(5UL, page_rank.size()); ++i)
{
printf("%.6f ", page_rank[i]);
}
printf("\n");
};
}
ctx.finalize();
return 0;
#endif // !_CCCL_CTK_BELOW(12, 4)
}

135
cccl_upstream/cudax/examples/stf/graph_algorithms/pagerank_while.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 Computes the PageRank for vertices within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
/**
* @brief Calculates the PageRank for a given vertex.
*
* @param idx The index of the vertex for which PageRank is being calculated.
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param lpage_rank Slice containing current PageRank values for each vertex.
* @param lnew_page_rank Slice containing where new PageRank values will be stored.
* @param init_rank The initial PageRank value to be used in the calculation.
*/
__device__ void calculating_pagerank(
int idx,
const slice<const int>& loffsets,
const slice<const int>& lnonzeros,
const slice<const float>& lpage_rank,
slice<float>& lnew_page_rank,
float init_rank)
{
float rank_sum = 0.0;
for (int i = loffsets[idx]; i < loffsets[idx + 1]; i++)
{
int neighbor = lnonzeros[i];
int out_degree = loffsets[neighbor + 1] - loffsets[neighbor];
rank_sum += lpage_rank[neighbor] / out_degree;
}
lnew_page_rank[idx] = 0.85 * rank_sum + (1.0 - 0.85) * init_rank;
}
int main()
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving example: while_graph_scope is only available since CUDA 12.4.\n");
return 0;
#else
stackable_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
int num_vertices = offsets.size() - 1;
float init_rank = 1.0f / num_vertices;
float tolerance = 1e-6f;
int NITER = 100;
// output pageranks for each vertex
std::vector<float> page_rank(num_vertices, init_rank);
std::vector<float> new_page_rank(num_vertices);
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto lpage_rank = ctx.logical_data(&page_rank[0], page_rank.size());
auto lnew_page_rank = ctx.logical_data(&new_page_rank[0], new_page_rank.size());
auto lmax_diff = ctx.logical_data(shape_of<scalar_view<float>>());
auto liter = ctx.logical_data(shape_of<scalar_view<int>>());
// Initialize iteration counter
ctx.parallel_for(box(1), liter.write())->*[] __device__(size_t, auto iter) {
*iter = 0;
};
{
auto while_guard = ctx.while_graph_scope();
// Calculate Current Iteration PageRank
ctx.parallel_for(
box(num_vertices),
loffsets.read(),
lnonzeros.read(),
lpage_rank.rw(),
lnew_page_rank.rw(),
lmax_diff.reduce(reducer::maxval<float>{}))
->*[init_rank] __device__(
size_t idx, auto loffsets, auto lnonzeros, auto lpage_rank, auto lnew_page_rank, auto& max_diff) {
calculating_pagerank(idx, loffsets, lnonzeros, lpage_rank, lnew_page_rank, init_rank);
max_diff = ::std::max(max_diff, lnew_page_rank[idx] - lpage_rank[idx]);
};
// Update PageRank Values
ctx.parallel_for(lpage_rank.shape(), lpage_rank.write(), lnew_page_rank.read())
->*[] __device__(size_t i, auto page_rank, auto new_page_rank) {
page_rank(i) = new_page_rank(i);
};
while_guard.update_cond(lmax_diff.read(), liter.rw())->*[NITER, tolerance] __device__(auto max_diff, auto iter) {
bool converged = (*max_diff < tolerance);
bool max_reached = ((*iter)++ >= NITER); // Maximum iteration limit
return !converged && !max_reached; // Continue if not converged and under limit
};
}
ctx.finalize();
/* CHECKING FOR ANSWER CORRECTNESS */
// sum of all page ranks should equal 1
double sum_pageranks = 0.0;
for (int64_t i = 0; i < num_vertices; i++)
{
sum_pageranks += page_rank[i];
}
printf("Page rank answer is %s.\n", abs(sum_pageranks - 1.0) < 0.001 ? "correct" : "not correct");
printf("PageRank Results:\n");
for (size_t i = 0; i < page_rank.size(); ++i)
{
printf("Vertex %zu: %f\n", i, page_rank[i]);
}
return 0;
#endif // !_CCCL_CTK_BELOW(12, 4)
}

100
cccl_upstream/cudax/examples/stf/graph_algorithms/tricount.cu
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@@ -1,100 +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 Computes the total number of triangles within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
// Performs Binary Search on a given array with start/end bounds and a lookup element
__device__ int binary_search(slice<const int> arr, int start, int end, int lookup)
{
while (start <= end)
{
int mid = start + (end - start) / 2;
if (arr[mid] == lookup)
{
return mid;
}
else if (arr[mid] < lookup)
{
start = mid + 1;
}
else
{
end = mid - 1;
}
}
return -1;
}
/**
* @brief Computes the Triangle Counting for each vertex.
*
* @param idx The index of the vertex for which Triangle Counting is being calculated.
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @return The local triangle count for the vertex.
*/
__device__ unsigned long long int triangle_count(int idx, slice<const int> loffsets, slice<const int> lnonzeros)
{
int lcount = 0;
for (int i = loffsets[idx]; i < loffsets[idx + 1]; i++)
{
int v = lnonzeros[i];
for (int j = loffsets[idx]; j < loffsets[idx + 1]; j++)
{
int w = lnonzeros[j];
if (binary_search(lnonzeros, loffsets[v], loffsets[v + 1] - 1, w) != -1)
{
lcount++;
}
}
}
return lcount;
}
int main()
{
stream_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 0, 1, 2, 4, 5, 6, 8, 9, 10};
// edges in CSR format
std::vector<int> nonzeros = {0, 0, 0, 1, 1, 1, 0, 1, 1, 1};
int num_vertices = offsets.size() - 1;
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto ltotal_count = ctx.logical_data(shape_of<scalar_view<unsigned long long>>());
ctx.parallel_for(
box(num_vertices), loffsets.read(), lnonzeros.read(), ltotal_count.reduce(reducer::sum<unsigned long long>{}))
->*[] __device__(size_t idx, auto loffsets, auto lnonzeros, auto& total_count) {
total_count += triangle_count(idx, loffsets, lnonzeros);
};
auto total_count = ctx.wait(ltotal_count);
ctx.finalize();
printf("Number of triangles: %lld\n", total_count);
return 0;
}

105
cccl_upstream/cudax/examples/stf/graph_scope.cu
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@@ -1,105 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDASTF in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2022-2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
/**
* @file
* @brief Demonstration of graph_scope RAII usage styles
*
* This example shows different ways to use stackable_ctx::graph_scope_guard
* for automatic push/pop management in nested contexts.
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
stackable_ctx ctx;
int data[10] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
auto lA = ctx.logical_data(data);
// Style 1: Direct constructor (like std::lock_guard)
// This is the most idiomatic C++ style
{
stackable_ctx::graph_scope_guard scope{ctx}; // Direct constructor - push() called
auto temp = ctx.logical_data(lA.shape());
ctx.parallel_for(temp.shape(), temp.write(), lA.read())->*[] __device__(size_t i, auto temp, auto a) {
temp(i) = a(i) * 2;
};
ctx.parallel_for(lA.shape(), lA.write(), temp.read())->*[] __device__(size_t i, auto a, auto temp) {
a(i) = temp(i);
};
// pop() called automatically when scope goes out of scope
}
// Style 2: Factory method (convenience)
// Useful when you prefer auto type deduction
{
auto scope = ctx.graph_scope(); // Factory method - push() called
ctx.parallel_for(lA.shape(), lA.rw())->*[] __device__(size_t i, auto a) {
a(i) += 1;
};
// pop() called automatically
}
// Style 3: Direct constructor with explicit type alias
// Useful for readability in complex scenarios
{
using scope_t = stackable_ctx::graph_scope_guard;
scope_t scope{ctx}; // Explicit type - push() called
ctx.parallel_for(lA.shape(), lA.rw())->*[] __device__(size_t i, auto a) {
a(i) *= 3;
};
// pop() called automatically
}
// Style 4: Iterative pattern (like in stackable2.cu)
// Demonstrates repeated nested contexts
for (int iter = 0; iter < 3; iter++)
{
stackable_ctx::graph_scope_guard iteration{ctx}; // New scope each iteration
auto temp = ctx.logical_data(lA.shape());
// tmp = a
ctx.parallel_for(temp.shape(), temp.write(), lA.read())->*[] __device__(size_t i, auto temp, auto a) {
temp(i) = a(i);
};
// a++
ctx.parallel_for(lA.shape(), lA.rw())->*[] __device__(size_t i, auto a) {
a(i) += 1;
};
// tmp *= 2
ctx.parallel_for(temp.shape(), temp.rw())->*[] __device__(size_t i, auto temp) {
temp(i) *= 2;
};
// a += tmp
ctx.parallel_for(lA.shape(), temp.read(), lA.rw())->*[] __device__(size_t i, auto temp, auto a) {
a(i) += temp(i);
};
// pop() called automatically at end of iteration
}
ctx.finalize();
return 0;
}

126
cccl_upstream/cudax/examples/stf/heat.cu
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@@ -1,126 +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 An example solving heat equation with finite differences using the
* parallel_for construct.
*
* A multi-gpu version is shown in the heat_mgpu.cu example.
*
* This example also illustrate how to annotate resources with set_symbol
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
void dump_iter(slice<const double, 2> sUn, int iter)
{
/* Create a binary file in the PPM format */
char name[64];
snprintf(name, 64, "heat_%06d.ppm", iter);
FILE* f = fopen(name, "wb");
fprintf(f, "P6\n%zu %zu\n255\n", sUn.extent(0), sUn.extent(1));
for (size_t j = 0; j < sUn.extent(1); j++)
{
for (size_t i = 0; i < sUn.extent(0); i++)
{
int v = (int) (255.0 * sUn(i, j) / 100.0);
// we assume values between 0.0 and 100.0 : max value is in red,
// min is in blue
unsigned char color[3];
color[0] = static_cast<char>(v); /* red */
color[1] = static_cast<char>(0); /* green */
color[2] = static_cast<char>(255 - v); /* blue */
fwrite(color, 1, 3, f);
}
}
fclose(f);
}
int main()
{
context ctx;
const size_t N = 800;
auto lU = ctx.logical_data(shape_of<slice<double, 2>>(N, N));
auto lU1 = ctx.logical_data(lU.shape());
// Initialize the Un field with boundary conditions, and a disk at a lower
// temperature in the middle.
ctx.parallel_for(lU.shape(), lU.write())->*[=] _CCCL_DEVICE(size_t i, size_t j, auto U) {
double rad = U.extent(0) / 8.0;
double dx = (double) i - U.extent(0) / 2;
double dy = (double) j - U.extent(1) / 2;
U(i, j) = (dx * dx + dy * dy < rad * rad) ? 100.0 : 0.0;
/* Set up boundary conditions */
if (j == 0.0)
{
U(i, j) = 100.0;
}
if (j == U.extent(1) - 1)
{
U(i, j) = 0.0;
}
if (i == 0.0)
{
U(i, j) = 0.0;
}
if (i == U.extent(0) - 1)
{
U(i, j) = 0.0;
}
};
// diffusion constant
double a = 0.5;
double dx = 0.1;
double dy = 0.1;
double dx2 = dx * dx;
double dy2 = dy * dy;
// time step
double dt = dx2 * dy2 / (2.0 * a * (dx2 + dy2));
double c = a * dt;
int nsteps = 1000;
int image_freq = -1;
for (int iter = 0; iter < nsteps; iter++)
{
if (image_freq > 0 && iter % image_freq == 0)
{
// Dump Un in a PPM file
ctx.host_launch(lU.read())->*[=](auto U) {
dump_iter(U, iter);
};
}
// Update Un using Un1 value with a finite difference scheme
ctx.parallel_for(inner<1>(lU.shape()), lU.read(), lU1.write())
->*[=]
_CCCL_DEVICE(size_t i, size_t j, auto U, auto U1) {
U1(i, j) =
U(i, j)
+ c * ((U(i - 1, j) - 2 * U(i, j) + U(i + 1, j)) / dx2 + (U(i, j - 1) - 2 * U(i, j) + U(i, j + 1)) / dy2);
};
std::swap(lU, lU1);
}
ctx.finalize();
}

163
cccl_upstream/cudax/examples/stf/heat_mgpu.cu
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@@ -1,163 +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 An example solving heat equation with finite differences on multiple devices
*
* This example also illustrate how to annotate resources with set_symbol
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
void dump_iter(slice<const double, 2> sUn, int iter)
{
/* Create a binary file in the PPM format */
char name[64];
snprintf(name, 64, "heat_%06d.ppm", iter);
FILE* f = fopen(name, "wb");
fprintf(f, "P6\n%zu %zu\n255\n", sUn.extent(0), sUn.extent(1));
for (size_t j = 0; j < sUn.extent(1); j++)
{
for (size_t i = 0; i < sUn.extent(0); i++)
{
int v = (int) (255.0 * sUn(i, j) / 100.0);
// we assume values between 0.0 and 100.0 : max value is in red,
// min is in blue
unsigned char color[3];
color[0] = static_cast<char>(v); /* red */
color[1] = static_cast<char>(0); /* green */
color[2] = static_cast<char>(255 - v); /* blue */
fwrite(color, 1, 3, f);
}
}
fclose(f);
}
int main(int argc, char** argv)
{
context ctx;
size_t N = 1000;
int nsteps = 100;
int image_freq = -1;
if (argc > 1)
{
N = atol(argv[1]);
}
if (argc > 2)
{
nsteps = atoi(argv[2]);
}
if (argc > 3)
{
image_freq = atoi(argv[3]);
}
if (argc > 4)
{
int use_graphs = atoi(argv[4]);
if (use_graphs != 0)
{
ctx = graph_ctx();
}
}
auto lU = ctx.logical_data(shape_of<slice<double, 2>>(N, N));
auto lU1 = ctx.logical_data(lU.shape());
lU.set_symbol("U");
lU1.set_symbol("U1");
auto all_devs = exec_place::all_devices();
// Initialize the Un field with boundary conditions, and a disk at a lower
// temperature in the middle.
ctx.parallel_for(blocked_partition(), all_devs, lU.shape(), lU.write()).set_symbol("init")->*
[=] _CCCL_DEVICE(size_t i, size_t j, auto U) {
double rad = U.extent(0) / 8.0;
double dx = (double) i - U.extent(0) / 2;
double dy = (double) j - U.extent(1) / 2;
U(i, j) = (dx * dx + dy * dy < rad * rad) ? 100.0 : 0.0;
/* Set up boundary conditions */
if (j == 0.0)
{
U(i, j) = 100.0;
}
if (j == U.extent(1) - 1)
{
U(i, j) = 0.0;
}
if (i == 0.0)
{
U(i, j) = 0.0;
}
if (i == U.extent(0) - 1)
{
U(i, j) = 0.0;
}
};
// diffusion constant
double a = 0.5;
double dx = 0.1;
double dy = 0.1;
double dx2 = dx * dx;
double dy2 = dy * dy;
// time step
double dt = dx2 * dy2 / (2.0 * a * (dx2 + dy2));
double c = a * dt;
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
ctx.repeat(nsteps)->*[&](context ctx, size_t iter) {
if (image_freq > 0 && iter % image_freq == 0)
{
// Dump Un in a PPM file
ctx.host_launch(lU.read()).set_symbol("dump")->*[=](auto U) {
dump_iter(U, static_cast<int>(iter));
};
}
// Update Un using Un1 value with a finite difference scheme
ctx.parallel_for(blocked_partition(), all_devs, inner<1>(lU.shape()), lU.read(), lU1.write()).set_symbol("step")->*
[=] _CCCL_DEVICE(size_t i, size_t j, auto U, auto U1) {
U1(i, j) =
U(i, j)
+ c * ((U(i - 1, j) - 2 * U(i, j) + U(i + 1, j)) / dx2 + (U(i, j - 1) - 2 * U(i, j) + U(i, j + 1)) / dy2);
};
std::swap(lU, lU1);
};
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
ctx.finalize();
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
printf("Elapsed time: %f ms\n", elapsedTime);
}

153
cccl_upstream/cudax/examples/stf/jacobi.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 Jacobi method with launch
*
*/
#include <cuda/experimental/stf.cuh>
#include <iostream>
using namespace cuda::experimental::stf;
/* Implement atomicMax with a compare and swap */
_CCCL_DEVICE double atomicMax(double* address, double val)
{
unsigned long long int* address_as_ull = (unsigned long long int*) address;
unsigned long long int old = *address_as_ull, assumed;
do
{
assumed = old;
old = atomicCAS(address_as_ull, assumed, __double_as_longlong(fmax(val, __longlong_as_double(assumed))));
// Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN)
} while (assumed != old);
return __longlong_as_double(old);
}
template <typename thread_hierarchy_t>
_CCCL_DEVICE double reduce_max(thread_hierarchy_t& t, double local_max)
{
auto ti = t.inner();
slice<double> error = t.template storage<double>(0);
error(0) = 0.0;
t.sync();
// Note we do not use t.static_width(1) because t is a runtime variable so it
// cannot be used directly to statically evaluate the size.
__shared__ double block_max[thread_hierarchy_t::static_width(1)];
block_max[ti.rank()] = local_max;
for (size_t s = ti.size() / 2; s > 0; s /= 2)
{
if (ti.rank() < s)
{
block_max[ti.rank()] = fmax(block_max[ti.rank() + s], block_max[ti.rank()]);
}
ti.sync();
}
if (ti.rank() == 0)
{
atomicMax(&error(0), block_max[0]);
}
t.sync();
return error(0);
}
int main(int argc, char** argv)
{
context ctx;
size_t n = 4096;
size_t m = 4096;
size_t iter_max = 100;
double tol = 0.0000001;
if (argc > 2)
{
n = atol(argv[1]);
m = atol(argv[2]);
}
if (argc > 3)
{
iter_max = atoi(argv[3]);
}
if (argc > 4)
{
tol = atof(argv[4]);
}
auto lA = ctx.logical_data(shape_of<slice<double, 2>>(m, n));
auto lAnew = ctx.logical_data(lA.shape());
auto all_devs = exec_place::all_devices();
ctx.parallel_for(blocked_partition(), all_devs, lA.shape(), lA.write(), lAnew.write()).set_symbol("init")->*
[=] _CCCL_DEVICE(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = (i == j) ? 10.0 : -1.0;
};
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
auto spec = con(con<64>(), mem(sizeof(double)));
ctx.launch(spec, all_devs, lA.rw(), lAnew.write())->*[iter_max, tol, n, m] _CCCL_DEVICE(auto t, auto A, auto Anew) {
auto ti = t.inner();
for (size_t iter = 0; iter < iter_max; iter++)
{
// thread-local maximum error
double local_error = 0.0;
for (auto [i, j] : t.apply_partition(inner<1>(shape(A))))
{
Anew(i, j) = 0.25 * (A(i - 1, j) + A(i + 1, j) + A(i, j - 1) + A(i, j + 1));
local_error = fmax(local_error, fabs(A(i, j) - Anew(i, j)));
}
// compute the overall maximum error
double error = reduce_max(t, local_error);
/* Fill A with the new values */
for (auto [i, j] : t.apply_partition(shape(A)))
{
A(i, j) = Anew(i, j);
}
if (iter % 25 == 0 && t.rank() == 0)
{
printf("iter %zu : error %e (tol %e)\n", iter, error, tol);
}
}
};
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
ctx.finalize();
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
printf("Elapsed time: %f ms\n", elapsedTime);
}

95
cccl_upstream/cudax/examples/stf/jacobi_pfor.cu
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@@ -1,95 +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 Jacobi method with parallel_for
*
*/
#include <cuda/experimental/stf.cuh>
#include <iostream>
using namespace cuda::experimental::stf;
int main(int argc, char** argv)
{
context ctx;
size_t n = 4096;
size_t m = 4096;
double tol = 0.5;
size_t iter_max = 1000;
if (argc > 2)
{
n = atol(argv[1]);
m = atol(argv[2]);
}
if (argc > 3)
{
tol = atof(argv[3]);
}
if (argc > 4)
{
iter_max = atoi(argv[4]);
}
auto lA = ctx.logical_data(shape_of<slice<double, 2>>(m, n));
auto lAnew = ctx.logical_data(lA.shape());
ctx.parallel_for(lA.shape(), lA.write(), lAnew.write()).set_symbol("init")->*
[=] __device__(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = (i == j) ? 10.0 : -1.0;
Anew(i, j) = A(i, j);
};
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
auto lresidual = ctx.logical_data(shape_of<scalar_view<double>>());
size_t iter = 0;
do
{
ctx.parallel_for(inner<1>(lA.shape()), lA.read(), lAnew.rw(), lresidual.reduce(reducer::maxval<double>{}))
->*[] __device__(size_t i, size_t j, auto A, auto Anew, auto& residual) {
Anew(i, j) = 0.25 * (A(i - 1, j) + A(i + 1, j) + A(i, j - 1) + A(i, j + 1));
residual = ::std::max(residual, fabs(A(i, j) - Anew(i, j)));
};
ctx.parallel_for(inner<1>(lA.shape()), lA.rw(), lAnew.read())->*[] __device__(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = Anew(i, j);
};
iter++;
} while (ctx.wait(lresidual) > tol && iter < iter_max);
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
double final_residual = ctx.wait(lresidual);
printf("Converged after %ld iterations, residual = %lf\n", iter, final_residual);
ctx.finalize();
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
printf("Elapsed time: %f ms\n", elapsedTime);
}

97
cccl_upstream/cudax/examples/stf/jacobi_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 Jacobi method with parallel_for and graphs
*
*/
#include <cuda/experimental/stf.cuh>
#include <iostream>
using namespace cuda::experimental::stf;
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
#else
stackable_ctx ctx;
size_t n = 4096;
size_t m = 4096;
double tol = 0.1;
if (argc > 2)
{
n = atol(argv[1]);
m = atol(argv[2]);
}
if (argc > 3)
{
tol = atof(argv[3]);
}
auto lA = ctx.logical_data(shape_of<slice<double, 2>>(m, n));
auto lAnew = ctx.logical_data(lA.shape());
ctx.parallel_for(lA.shape(), lA.write(), lAnew.write()).set_symbol("init")->*
[=] __device__(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = (i == j) ? 1.0 : -1.0;
};
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
auto lconverged = ctx.logical_data(shape_of<scalar_view<bool>>());
size_t iter = 0;
// Creating a conditional handle but not using it in a conditional node can make the graph instantiation fail.
cudaGraphConditionalHandle handle;
ctx.push_while(&handle, 1, cudaGraphCondAssignDefault);
ctx.parallel_for(inner<1>(lA.shape()), lA.read(), lAnew.write(), lconverged.reduce(reducer::logical_and<bool>{}))
->*[tol] __device__(size_t i, size_t j, auto A, auto Anew, auto& converged) {
Anew(i, j) = 0.25 * (A(i - 1, j) + A(i + 1, j) + A(i, j - 1) + A(i, j + 1));
double error = fabs(A(i, j) - Anew(i, j));
converged = converged && (error < tol);
};
ctx.parallel_for(inner<1>(lA.shape()), lA.rw(), lAnew.read())->*[] __device__(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = Anew(i, j);
};
ctx.parallel_for(box(1), lconverged.read())->*[handle] __device__(size_t, auto converged) {
cudaGraphSetConditional(handle, !*converged);
};
ctx.pop();
fprintf(stderr, "ITER %zu: converged\n", iter++);
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
ctx.finalize();
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
printf("Elapsed time: %f ms\n", elapsedTime);
#endif
}

104
cccl_upstream/cudax/examples/stf/jacobi_stackable_raii.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 Jacobi method with a while scope guard and explicit management of the conditional handle
*
*/
#include <cuda/experimental/stf.cuh>
#include <iostream>
#include "cuda/experimental/__stf/stackable/stackable_ctx.cuh"
using namespace cuda::experimental::stf;
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
#else
stackable_ctx ctx;
size_t n = 4096;
size_t m = 4096;
double tol = 0.1;
if (argc > 2)
{
n = atol(argv[1]);
m = atol(argv[2]);
}
if (argc > 3)
{
tol = atof(argv[3]);
}
auto lA = ctx.logical_data(shape_of<slice<double, 2>>(m, n));
auto lAnew = ctx.logical_data(lA.shape());
ctx.parallel_for(lA.shape(), lA.write(), lAnew.write()).set_symbol("init")->*
[=] __device__(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = (i == j) ? 1.0 : -1.0;
};
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
size_t iter = 0;
auto lresidual = ctx.logical_data(shape_of<scalar_view<double>>());
{
auto while_guard = ctx.while_graph_scope();
ctx.parallel_for(inner<1>(lA.shape()), lA.read(), lAnew.write(), lresidual.reduce(reducer::maxval<double>{}))
->*[] __device__(size_t i, size_t j, auto A, auto Anew, auto& residual) {
Anew(i, j) = 0.25 * (A(i - 1, j) + A(i + 1, j) + A(i, j - 1) + A(i, j + 1));
double error = fabs(A(i, j) - Anew(i, j));
residual = error;
};
ctx.parallel_for(inner<1>(lA.shape()), lA.rw(), lAnew.read())->*[] __device__(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = Anew(i, j);
};
auto handle = while_guard.cond_handle();
ctx.parallel_for(box(1), lresidual.read())->*[handle, tol] __device__(size_t, auto residual) {
bool converged = (*residual < tol);
cudaGraphSetConditional(handle, !converged);
};
}
// Store final residual for verification
double final_residual = ctx.wait(lresidual);
fprintf(stderr, "ITER %zu: converged residual %e\n", iter++, final_residual);
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
ctx.finalize();
EXPECT(final_residual <= tol); // Algorithm should have converged within tolerance
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
printf("Elapsed time: %f ms\n", elapsedTime);
#endif
}

101
cccl_upstream/cudax/examples/stf/jacobi_update_cond.cu
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@@ -1,101 +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 Jacobi method using the update_cond helper for clean condition management
*/
#include <cuda/experimental/stf.cuh>
#include <iostream>
using namespace cuda::experimental::stf;
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
#else
stackable_ctx ctx;
size_t n = 4096;
size_t m = 4096;
double tol = 0.5;
int max_iter = 1000;
if (argc > 2)
{
n = atol(argv[1]);
m = atol(argv[2]);
}
if (argc > 3)
{
tol = atof(argv[3]);
}
if (argc > 4)
{
max_iter = atoi(argv[4]);
}
auto lA = ctx.logical_data(shape_of<slice<double, 2>>(m, n));
auto lAnew = ctx.logical_data(lA.shape());
auto lresidual = ctx.logical_data(shape_of<scalar_view<double>>());
auto liter = ctx.logical_data(shape_of<scalar_view<int>>());
ctx.parallel_for(lA.shape(), lA.write(), lAnew.write()).set_symbol("init")->*
[=] __device__(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = (i == j) ? 10.0 : -1.0;
Anew(i, j) = A(i, j);
};
// Initialize iteration counter
ctx.parallel_for(box(1), liter.write())->*[] __device__(size_t, auto iter) {
*iter = 0;
};
{
auto while_guard = ctx.while_graph_scope();
ctx.parallel_for(inner<1>(lA.shape()), lA.read(), lAnew.rw(), lresidual.reduce(reducer::maxval<double>()))
->*[tol] __device__(size_t i, size_t j, auto A, auto Anew, auto& residual) {
Anew(i, j) = 0.25 * (A(i - 1, j) + A(i + 1, j) + A(i, j - 1) + A(i, j + 1));
double error = fabs(A(i, j) - Anew(i, j));
residual = ::std::max(error, residual);
};
ctx.parallel_for(inner<1>(lA.shape()), lA.rw(), lAnew.read())->*[] __device__(size_t i, size_t j, auto A, auto Anew) {
A(i, j) = Anew(i, j);
};
while_guard.update_cond(lresidual.read(), liter.rw())->*[tol, max_iter] __device__(auto residual, auto iter) {
bool converged = (*residual < tol);
bool max_reached = ((*iter)++ >= max_iter); // Maximum iteration limit
return !converged && !max_reached; // Continue if not converged and under limit
};
}
int final_iterations = ctx.wait(liter);
double final_residual = ctx.wait(lresidual);
printf("Converged after %d iterations, residual = %lf\n", final_iterations, final_residual);
ctx.finalize();
EXPECT(final_residual <= tol);
EXPECT(final_iterations < max_iter);
return 0;
#endif
}

170
cccl_upstream/cudax/examples/stf/launch_histogram.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 A naive parallel histogram algorithm written with launch
*
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
__host__ __device__ double X0(int i)
{
return sin((double) i);
}
int main(int argc, char** argv)
{
stream_ctx ctx;
double lower_level = -1.0;
double upper_level = 1.0;
constexpr size_t num_levels = 21;
size_t N = 128 * 1024UL;
if (argc > 1)
{
N = size_t(atoll(argv[1]));
}
int check = 1;
if (argc > 2)
{
check = atoi(argv[2]);
}
// fprintf(stderr, "SIZE %s\n", pretty_print_bytes(N * sizeof(double)).c_str());
std::vector<double> X(N);
std::vector<size_t> histo(num_levels - 1);
for (size_t i = 0; i < N; i++)
{
X[i] = X0(i);
}
// If we were to register each part one by one, there could be pages which
// cross multiple parts, and the pinning operation would fail.
cuda_safe_call(cudaHostRegister(&X[0], N * sizeof(double), cudaHostRegisterPortable));
auto lX = ctx.logical_data(&X[0], N);
lX.set_symbol("X");
auto lhisto = ctx.logical_data(&histo[0], num_levels - 1);
lhisto.set_symbol("histogram");
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
constexpr size_t BLOCK_THREADS = 128;
// size_t NDEVS = 1;
// auto where = exec_place::repeat(exec_place::current_device(), NDEVS);
auto where = exec_place::current_device();
auto spec = con<8>(con(BLOCK_THREADS, mem((num_levels - 1) * sizeof(size_t))));
ctx.launch(spec, where, lX.read(), lhisto.write())->*[=] _CCCL_DEVICE(auto th, auto x, auto histo) {
size_t block_id = th.rank(0);
slice<size_t> smem_hist = th.template storage<size_t>(1);
assert(smem_hist.size() == (num_levels - 1));
/* Thread local histogram */
size_t local_hist[num_levels - 1];
for (size_t k = 0; k < num_levels - 1; k++)
{
local_hist[k] = 0;
smem_hist[k] = 0;
}
if (th.rank() == 0)
{
for (size_t k = 0; k < num_levels - 1; k++)
{
histo[k] = 0;
}
}
for (size_t i = th.rank(); i < x.size(); i += th.size())
{
double xi = x(i);
if (xi >= lower_level && xi < upper_level)
{
size_t bin = size_t(((num_levels - 1) * (xi - lower_level)) / (upper_level - lower_level));
local_hist[bin]++;
}
}
// smem was zero'ed
th.inner().sync();
/* Each thread contributes to an histogram in shared memory */
for (size_t k = 0; k < num_levels - 1; k++)
{
atomicAdd((unsigned long long*) &smem_hist[k], local_hist[k]);
}
// histo was zero'ed
th.sync();
if (th.inner().rank() == 0)
{
for (size_t k = 0; k < num_levels - 1; k++)
{
atomicAdd((unsigned long long*) &histo[k], smem_hist[k]);
}
}
};
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
ctx.finalize();
float ms = 0;
cuda_safe_call(cudaEventElapsedTime(&ms, start, stop));
// fprintf(stdout, "%zu %f ms\n", N / 1024 / 1024, ms);
if (check)
{
// fprintf(stderr, "Checking result...\n");
size_t refhist[num_levels - 1];
for (size_t i = 0; i < num_levels - 1; i++)
{
refhist[i] = 0;
}
for (size_t i = 0; i < N; i++)
{
double xi = X[i];
if (xi >= lower_level && xi < upper_level)
{
size_t bin = size_t(((num_levels - 1) * (xi - lower_level)) / (upper_level - lower_level));
refhist[bin]++;
}
}
// double dlevel = (upper_level - lower_level) / (num_levels - 1);
for (size_t i = 0; i < num_levels - 1; i++)
{
EXPECT(refhist[i] == histo[i]);
// fprintf(stderr, "[%lf:%lf[ %ld\n", lower_level + i * dlevel, lower_level + (i + 1) * dlevel, histo[i]);
}
}
}

162
cccl_upstream/cudax/examples/stf/launch_scan.cu
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@@ -1,162 +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 A parallel scan algorithm
*
*/
#include <cub/cub.cuh> // or equivalently <cub/device/device_scan.cuh>
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
__host__ __device__ double X0(int)
{
// return sin((double) i);
return 1.0;
}
int main(int argc, char** argv)
{
stream_ctx ctx;
// graph_ctx ctx;
size_t N = 128 * 1024UL * 1024UL;
if (argc > 1)
{
N = size_t(atoll(argv[1]));
}
int check = 0;
if (argc > 2)
{
check = atoi(argv[2]);
}
std::vector<double> X(N);
for (size_t i = 0; i < N; i++)
{
X[i] = X0(i);
}
auto lX = ctx.logical_data(&X[0], N);
// No need to move this back to the host if we do not check the result
if (!check)
{
lX.set_write_back(false);
}
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
constexpr size_t BLOCK_THREADS = 128;
constexpr size_t NBLOCKS = 8;
auto spec = con<NBLOCKS>(con<BLOCK_THREADS>(), mem(NBLOCKS * sizeof(double)));
// auto where = exec_place::repeat(exec_place::current_device(), NDEVS);
auto where = exec_place::current_device();
ctx.launch(spec, where, lX.rw())->*[=] _CCCL_DEVICE(auto th, auto x) {
const size_t block_id = th.rank(0);
const size_t tid = th.inner().rank();
// const size_t tid = th.rank(1, 0);
// Block-wide partials using static allocation
__shared__ double block_partial_sum[th.static_width(1)];
// Device-wide partial sums
slice<double> dev_partial_sum = th.template storage<double>(0);
/* Thread local prefix-sum */
const box<1> b = th.apply_partition(shape(x), std::tuple<blocked_partition, blocked_partition>());
for (size_t i = b.get_begin(0) + 1; i < b.get_end(0); i++)
{
x(i) += x(i - 1);
}
block_partial_sum[tid] = x(b.get_end(0) - 1);
th.inner().sync();
/* Block level : get partials sum across the different threads */
if (tid == 0)
{ // rank in scope block is 0
// Prefix sum on partial sums
for (size_t i = 1; i < BLOCK_THREADS; i++)
{
block_partial_sum[i] += block_partial_sum[i - 1];
}
dev_partial_sum[block_id] = block_partial_sum[BLOCK_THREADS - 1];
}
/* Reduce partial sums at device level : get sum across all blocks */
th.sync();
if (block_id == 0 && tid == 0)
{ // rank in scope 0
for (size_t i = 1; i < NBLOCKS; i++)
{
dev_partial_sum[i] += dev_partial_sum[i - 1];
// printf("SUMMED dev_partial_sum[%ld] = %f\n", i, dev_partial_sum[i]);
}
}
th.sync();
for (size_t i = b.get_begin(0); i < b.get_end(0); i++)
{
if (tid > 0)
{
x(i) += block_partial_sum[tid - 1];
}
if (block_id > 0)
{
x(i) += dev_partial_sum[block_id - 1];
}
}
};
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
ctx.finalize();
float ms = 0;
cuda_safe_call(cudaEventElapsedTime(&ms, start, stop));
printf("%s in %f ms (%g GB/s)\n",
pretty_print_bytes(N * sizeof(double)).c_str(),
ms,
double(N * sizeof(double) / 1024 / 1024) / ms);
if (check)
{
fprintf(stderr, "Checking result...\n");
EXPECT(fabs(X[0] - X0(0)) < 0.00001);
for (size_t i = 0; i < N; i++)
{
if (fabs(X[i] - X[i - 1] - X0(i)) > 0.00001)
{
fprintf(stderr, "I %zu X[i] %f (X[i] - X[i-1]) %f expect %f\n", i, X[i], (X[i] - X[i - 1]), X0(i));
}
EXPECT(fabs(X[i] - X[i - 1] - X0(i)) < 0.00001);
}
}
}

81
cccl_upstream/cudax/examples/stf/launch_sum.cu
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@@ -1,81 +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 A reduction kernel written using launch
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
double X0(int i)
{
return sin((double) i);
}
int main()
{
context ctx;
const size_t N = 128 * 1024 * 1024;
std::vector<double> X(N);
double sum = 0.0;
double ref_sum = 0.0;
for (size_t ind = 0; ind < N; ind++)
{
X[ind] = sin((double) ind);
ref_sum += X[ind];
}
auto lX = ctx.logical_data(&X[0], {N});
auto lsum = ctx.logical_data(&sum, {1});
auto number_devices = 1; //
auto where = exec_place::repeat(exec_place::device(0), number_devices);
auto spec = par<16>(con<32>());
ctx.launch(spec, where, lX.read(), lsum.rw())->*[] _CCCL_DEVICE(auto th, auto x, auto sum) {
// Each thread computes the sum of elements assigned to it
double local_sum = 0.0;
for (size_t i = th.rank(); i < x.size(); i += th.size())
{
local_sum += x(i);
}
auto ti = th.inner();
__shared__ double block_sum[th.static_width(1)];
block_sum[ti.rank()] = local_sum;
for (size_t s = ti.size() / 2; s > 0; s /= 2)
{
ti.sync();
if (ti.rank() < s)
{
block_sum[ti.rank()] += block_sum[ti.rank() + s];
}
}
if (ti.rank() == 0)
{
atomicAdd(&sum(0), block_sum[0]);
}
};
ctx.finalize();
EXPECT(fabs(sum - ref_sum) < 0.0001);
}

73
cccl_upstream/cudax/examples/stf/launch_sum_cub.cu
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@@ -1,73 +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 A reduction kernel written using launch and CUB
*/
#include <cub/cub.cuh>
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
double X0(int i)
{
return sin((double) i);
}
int main()
{
context ctx;
const size_t N = 128 * 1024 * 1024;
std::vector<double> X(N);
double sum = 0.0;
double ref_sum = 0.0;
for (size_t ind = 0; ind < N; ind++)
{
X[ind] = sin((double) ind);
ref_sum += X[ind];
}
auto lX = ctx.logical_data(&X[0], {N});
auto lsum = ctx.logical_data(&sum, {1});
auto number_devices = 2;
auto where = exec_place::repeat(exec_place::device(0), number_devices);
auto spec = par<32>(con<128>());
ctx.launch(spec, where, lX.read(), lsum.rw())->*[] _CCCL_DEVICE(auto th, auto x, auto sum) {
// Each thread computes the sum of elements assigned to it
double local_sum = 0.0;
for (auto ind : th.apply_partition(shape(x)))
{
local_sum += x(ind);
}
using BlockReduce = cub::BlockReduce<double, th.static_width(1)>;
__shared__ typename BlockReduce::TempStorage temp_storage;
double block_sum = BlockReduce(temp_storage).Sum(local_sum);
if (th.inner().rank() == 0)
{
atomicAdd(&sum(0), block_sum);
}
};
ctx.finalize();
EXPECT(fabs(sum - ref_sum) < 0.0001);
}

484
cccl_upstream/cudax/examples/stf/linear_algebra/06-pdgemm-stackable.cu
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@@ -1,484 +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 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);
}

399
cccl_upstream/cudax/examples/stf/linear_algebra/06-pdgemm.cu
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@@ -1,399 +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 An example that implements a tiled matrix product over multiple devices using CUBLAS
*
* This also illustrates how the same code base can be used both with a
* stream_ctx and a graph_ctx backend.
*/
#include <cuda/experimental/__stf/utility/nvtx.cuh>
#include <cuda/experimental/stf.cuh>
#define TILED
using namespace cuda::experimental::stf;
static std::unordered_map<exec_place, cublasHandle_t, hash<exec_place>> cublas_handles;
/* 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())
{
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;
}
template <typename T>
class matrix
{
public:
matrix(stream_ctx& ctx,
size_t NROWS,
size_t NCOLS,
size_t BLOCKSIZE_ROWS,
size_t BLOCKSIZE_COLS,
const char* _symbol = "matrix")
{
symbol = _symbol;
m = NROWS;
mb = BLOCKSIZE_ROWS;
n = NCOLS;
nb = BLOCKSIZE_COLS;
assert(m % mb == 0);
assert(n % nb == 0);
size_t s = ((size_t) m) * ((size_t) 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 = (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';
}
int get_preferred_devid(int row, int col)
{
return (row % grid_p) + (col % grid_q) * grid_p;
}
logical_data<slice<T, 2>>& get_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(stream_ctx& ctx, Fun&& fun)
{
nvtx_range r("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) {
size_t row = lrow + rowb * sA.extent(0);
size_t col = lcol + colb * sA.extent(1);
sA(lrow, lcol) = fun(row, col);
};
}
}
}
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<logical_data<slice<T, 2>>> handles;
const char* symbol;
// for the mapping
int ndevs;
int grid_p, grid_q;
};
void DGEMM(
stream_ctx& ctx,
cublasOperation_t transa,
cublasOperation_t transb,
double alpha,
matrix<double>& A,
int A_row,
int A_col,
matrix<double>& B,
int B_row,
int B_col,
double beta,
matrix<double>& C,
int C_row,
int C_col)
{
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(stream_ctx& ctx,
cublasOperation_t transa,
cublasOperation_t transb,
double alpha,
matrix<double>& A,
matrix<double>& B,
double beta,
matrix<double>& C)
{
nvtx_range r("PDGEMM");
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(ctx, 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)
{
assert(A.nt == B.mt);
for (size_t k = 0; k < A.nt; k++)
{
double zbeta = k == 0 ? beta : 1.0;
DGEMM(ctx, 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(ctx, 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(ctx, 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(ctx, transa, transb, alpha, A, k, m, B, n, k, zbeta, C, m, n);
}
}
}
}
}
}
void run(stream_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()));
PDGEMM(ctx, CUBLAS_OP_N, CUBLAS_OP_N, 1.0, A, B, -2.0, C);
cuda_safe_call(cudaEventRecord(stopEvent, ctx.fence()));
ctx.finalize();
float milliseconds;
cuda_safe_call(cudaEventElapsedTime(&milliseconds, startEvent, stopEvent));
double gflops_pdgemm = 2.0 * ((double) N * (double) N * (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 = atoi(argv[1]);
}
if (argc > 2)
{
NB = atoi(argv[2]);
}
assert(N % NB == 0);
stream_ctx ctx;
run(ctx, N, NB);
// // Also run using a graph context.
// ctx = graph_ctx();
// run(ctx, N, NB);
}

748
cccl_upstream/cudax/examples/stf/linear_algebra/07-cholesky.cu
View File

@@ -1,748 +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/utility/nvtx.cuh>
#include <cuda/experimental/stf.cuh>
#include <iostream>
#include <cublas_v2.h>
#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++)
{
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));
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")->*
[=] _CCCL_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>(size_t(Lwork_expected));
potrf_buffer.set_allocator(ctx.get_default_allocator());
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 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(),
C.handle(C_row, C_col).rw());
t.set_symbol("DGEMM");
t->*[transa, transb, alpha, beta](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 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)
{
auto guard = ctx.dot_section("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)
{
auto guard = ctx.dot_section("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)
{
auto guard = ctx.dot_section("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)
{
auto guard = ctx.dot_section("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())->*[] _CCCL_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 = [=] _CCCL_HOST_DEVICE(size_t row, size_t col) {
return 1.0 / (col + row + 1.0) + 2.0 * N * (col == row);
};
auto s = ctx.dot_section("fillA");
if (check_result)
{
Aref.fill(hilbert);
}
A.fill(hilbert);
s.end();
/* 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 = [] _CCCL_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(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(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.");
}
}
}

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cccl_upstream/cudax/examples/stf/linear_algebra/07-potri.cu
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372
cccl_upstream/cudax/examples/stf/linear_algebra/burger.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 Sparse conjugate gradient algorithm
*/
#include <cuda/experimental/stf.cuh>
#include <chrono>
#include <iostream>
#include <string>
#include <type_traits>
#include <vector>
#include "dot.cuh"
#include "newton_solver.cuh"
using namespace cuda::experimental::stf;
#if !_CCCL_CTK_BELOW(12, 4)
void build_full_csr_structure(size_t* row_offsets, size_t* col_indices, size_t N)
{
size_t nnz = 0;
row_offsets[0] = 0;
for (size_t row = 0; row < N; row++)
{
if (row == 0 || row == N - 1)
{
// Boundary rows: only diagonal entry (identity for BC: u[i] = prescribed_value)
col_indices[nnz++] = row;
}
else
{
// Interior rows: tridiagonal structure (left, center, right)
col_indices[nnz++] = row - 1; // left neighbor
col_indices[nnz++] = row; // center (diagonal)
col_indices[nnz++] = row + 1; // right neighbor
}
row_offsets[row + 1] = nnz;
}
}
template <typename ctx_t>
void assemble_jacobian_full(
ctx_t& ctx, vector_t<double> U, vector_t<double> values, size_t N, double h, double dt, double nu)
{
ctx.parallel_for(box(N), U.read(), values.write()).set_symbol("assemble_jacobian_full")
->*[N, h, dt, nu] __device__(size_t row, auto dU, auto dvalues) {
if (row == 0)
{
// Left boundary: u[0] = 0 (homogeneous Dirichlet)
// Jacobian row: [1, 0, 0, ..., 0]
size_t val_idx = 0; // First entry in CSR values array
dvalues[val_idx] = 1.0;
}
else if (row == N - 1)
{
// Right boundary: u[N-1] = 0 (homogeneous Dirichlet)
// Jacobian row: [0, ..., 0, 1]
size_t val_idx = 1 + 3 * (N - 2); // Last entry in CSR values array
dvalues[val_idx] = 1.0;
}
else
{
// Interior point: Burger's equation discretization
double u_i = dU[row];
double u_ip1 = dU[row + 1];
double u_im1 = dU[row - 1];
// Jacobian entries: ∂F_i/∂u_{i-1}, ∂F_i/∂u_i, ∂F_i/∂u_{i+1}
double left = -u_i / (2 * h) - nu / (h * h);
double center = 1.0 / dt + (u_ip1 - u_im1) / (2 * h) + 2.0 * nu / (h * h);
double right = u_i / (2 * h) - nu / (h * h);
// CSR indexing for interior row i: starts at 1 + 3*(i-1)
size_t val_idx = 1 + 3 * (row - 1);
dvalues[val_idx] = left; // ∂F_i/∂u_{i-1}
dvalues[val_idx + 1] = center; // ∂F_i/∂u_i
dvalues[val_idx + 2] = right; // ∂F_i/∂u_{i+1}
}
};
}
// residual: length N (full system including boundaries)
template <typename ctx_t, typename T>
void compute_residual_full(
ctx_t& ctx, vector_t<T> U, vector_t<T> U_prev, vector_t<T> residual, size_t N, double h, double dt, double nu)
{
ctx.parallel_for(box(N), residual.write(), U.read(), U_prev.read()).set_symbol("compute_residual_full")
->*[N, h, dt, nu] __device__(size_t i, auto dresidual, auto dU, auto dU_prev) {
if (i == 0)
{
// Left boundary condition: u[0] = 0
dresidual(i) = dU(i) - 0.0;
}
else if (i == N - 1)
{
// Right boundary condition: u[N-1] = 0
dresidual(i) = dU(i) - 0.0;
}
else
{
// Interior point: Burger's equation F_i = ∂u/∂t + u*∂u/∂x - nu*∂²u/∂x²
double u_i = dU(i);
double u_ip1 = dU(i + 1);
double u_im1 = dU(i - 1);
double term_time = (u_i - dU_prev(i)) / dt; // ∂u/∂t
double term_conv = u_i * (u_ip1 - u_im1) / (2 * h); // u * ∂u/∂x (nonlinear convection)
double term_diff = -nu * (u_im1 - 2 * u_i + u_ip1) / (h * h); // -nu * ∂²u/∂x²
dresidual(i) = term_time + term_conv + term_diff;
}
};
}
// Callback function objects for Burger's equation
struct BurgerResidualCallback
{
size_t N;
double h, dt, nu;
template <typename ctx_t>
void
operator()(ctx_t& ctx, const vector_t<double>& x, const vector_t<double>& x_prev, vector_t<double>& residual) const
{
compute_residual_full(ctx, x, x_prev, residual, N, h, dt, nu);
}
};
struct BurgerJacobianCallback
{
size_t N;
double h, dt, nu;
template <typename ctx_t>
void operator()(ctx_t& ctx, const vector_t<double>& x, vector_t<double>& jacobian_values) const
{
assemble_jacobian_full(ctx, x, jacobian_values, N, h, dt, nu);
}
};
// Initialize the solution output file (call once at simulation start)
void initialize_solution_file(const char* filename, size_t N, double h)
{
FILE* fp = fopen(filename, "w");
if (fp)
{
fprintf(fp, "# Burger equation solution - block format\n");
fprintf(fp, "# Each timestep is a separate block, separated by blank lines\n");
fprintf(fp, "# Format: x_coordinate u(x,t)\n");
fprintf(fp, "# Grid points: %zu, h=%.6e\n", N, h);
fprintf(fp,
"# Use in gnuplot: plot for [i=0:*] 'solution.dat' index i with lines title sprintf('step %%d', i*10)\n");
fprintf(fp, "#\n");
fclose(fp);
}
else
{
printf("Error: Could not create %s for writing\n", filename);
}
}
// Function to append timestep block to solution file (simple and reliable)
template <typename ctx_t>
void dump_solution(
ctx_t& ctx, vector_t<double>& U, size_t timestep, size_t N, double h, double dt, const char* filename = "solution.dat")
{
ctx.host_launch(U.read()).set_symbol("dump solution")->*[timestep, h, N, dt, filename](auto hU) {
FILE* fp = fopen(filename, "a"); // Simple append - no read/modify/write
if (fp)
{
fprintf(fp, "# Timestep %zu, t=%.6e\n", timestep, timestep * dt);
for (size_t i = 0; i < N; i++)
{
double x = i * h;
fprintf(fp, "%.10e %.10e\n", x, hU(i));
}
fprintf(fp, "\n"); // Blank line to separate datasets
fclose(fp);
printf("Appended timestep %zu (t=%.4e) to %s\n", timestep, timestep * dt, filename);
}
else
{
printf("Error: Could not open %s for appending\n", filename);
}
};
}
#endif
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
#else
// Usage: ./burger [N] [nsteps] [nu]
// N = Grid points (default: 100000)
// nsteps = Time steps (default: 10000)
// nu = Viscosity (default: 0.05, try 0.001 for shocks)
stackable_ctx ctx;
size_t N = 2560;
if (argc > 1)
{
N = atoi(argv[1]);
fprintf(stderr, "N = %zu\n", N);
}
size_t nsteps = 200;
if (argc > 2)
{
nsteps = atol(argv[2]);
fprintf(stderr, "nsteps = %ld\n", nsteps);
}
// Set reasonable parameters - implicit method allows larger time steps
double nu = 0.05; // Default viscosity
if (argc > 3)
{
nu = atof(argv[3]);
fprintf(stderr, "nu = %e\n", nu);
}
ssize_t output_freq = -1;
if (argc > 4)
{
output_freq = atoi(argv[4]);
fprintf(stderr, "output_freq %ld\n", output_freq);
}
// use_while = 0 => no while; 1 => while in CG, 2 => while in Newton and CG
int use_while = 2;
if (argc > 5)
{
use_while = atoi(argv[5]);
fprintf(stderr, "use_while = %d\n", use_while);
}
double h = 1.0 / (N - 1);
double dt_diffusion = 0.5 * h * h / nu; // Diffusion-limited time step
double dt_fixed = 0.001; // Fixed reasonable time step
double dt = std::max(dt_diffusion, dt_fixed); // Use larger of the two
// For very fine grids, cap the time step to prevent tiny steps
if (N > 10000)
{
dt = std::min(dt, 0.01); // Cap at 0.01 for large grids
}
double total_time = nsteps * dt;
fprintf(stderr, "=== Simulation Parameters ===\n");
fprintf(stderr, "Grid: N=%zu, h=%e\n", N, h);
fprintf(stderr, "Time: dt=%e, nsteps=%zu, total_time=%e\n", dt, nsteps, total_time);
fprintf(stderr, "Physics: nu=%e (viscosity)\n", nu);
fprintf(stderr, "Diffusion number: nu*dt/h^2 = %e\n", nu * dt / (h * h));
fprintf(stderr, "=============================\n");
// Full N×N system: boundary rows have 1 entry each, interior rows have 3 entries each
// Total: 2*1 + (N-2)*3 = 3*N - 4 non-zeros
size_t nz = 3 * N - 4;
size_t* row_offsets;
size_t* col_indices;
cuda_safe_call(cudaHostAlloc(&row_offsets, (N + 1) * sizeof(size_t), cudaHostAllocMapped));
cuda_safe_call(cudaHostAlloc(&col_indices, nz * sizeof(size_t), cudaHostAllocMapped));
build_full_csr_structure(row_offsets, col_indices, N);
auto csr_row_offsets = ctx.logical_data(make_slice(row_offsets, N + 1)).set_symbol("csr_row");
auto csr_col_ind = ctx.logical_data(make_slice(col_indices, nz)).set_symbol("csr_col");
auto csr_values = ctx.logical_data(shape_of<slice<double>>(nz)).set_symbol("csr_val");
auto U = ctx.logical_data(shape_of<slice<double>>(N)).set_symbol("U");
// This will prevent erroneous modifications and may allow access from concurrent graphs
csr_row_offsets.set_read_only();
csr_col_ind.set_read_only();
// Initial condition
ctx.parallel_for(U.shape(), U.write()).set_symbol("init conditions")->*[h, N] __device__(size_t i, auto dU) {
double x = i * h;
if (i == 0 || i == N - 1)
{
dU(i) = 0.0; // Homogeneous Dirichlet boundary conditions
}
else
{
dU(i) = sin(M_PI * x);
}
};
// Initialize solution output file
initialize_solution_file("solution.dat", N, h);
auto start = std::chrono::high_resolution_clock::now();
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
// Parameters are now set above with auto-scaling
size_t substeps = (output_freq > 0) ? output_freq : nsteps;
size_t outer_iterations = nsteps / substeps;
if (use_while == 2)
{
for (size_t outer = 0; outer < outer_iterations; outer++)
{
auto g = ctx.graph_scope();
// Repeat substeps inner iterations using STF repeat block
{
auto repeat_guard = ctx.repeat_graph_scope(substeps);
// Create callback function objects for Burger's equation
BurgerResidualCallback residual_callback{N, h, dt, nu};
BurgerJacobianCallback jacobian_callback{N, h, dt, nu};
// Solve the nonlinear system using generic Newton solver
newton_solver(ctx, U, csr_values, csr_row_offsets, csr_col_ind, residual_callback, jacobian_callback);
} // repeat_guard automatically manages the loop condition
// Dump solution after each substep block
size_t current_timestep = (outer + 1) * substeps;
dump_solution(ctx, U, current_timestep, N, h, dt);
}
}
else
{
for (size_t outer = 0; outer < outer_iterations; outer++)
{
// Repeat substeps inner iterations using STF repeat block
for (size_t substep = 0; substep < substeps; substep++)
{
// Create callback function objects for Burger's equation
BurgerResidualCallback residual_callback{N, h, dt, nu};
BurgerJacobianCallback jacobian_callback{N, h, dt, nu};
// Solve the nonlinear system using generic Newton solver
newton_solver_no_while(
ctx, U, csr_values, csr_row_offsets, csr_col_ind, residual_callback, jacobian_callback, use_while == 1);
} // repeat_guard automatically manages the loop condition
// Dump solution after each substep block
size_t current_timestep = (outer + 1) * substeps;
dump_solution(ctx, U, current_timestep, N, h, dt);
}
}
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
auto end = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();
std::cout << "Duration: " << duration << " milliseconds" << '\n';
ctx.finalize();
#endif
}

479
cccl_upstream/cudax/examples/stf/linear_algebra/burger_sensitivity.cu
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@@ -1,479 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDASTF in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2022-2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
/**
* @file
* @brief Sensitivity analysis for Burger equation - analyze shock formation vs viscosity
*/
#include <cuda/experimental/stf.cuh>
#include <algorithm>
#include <random>
#include <string>
#include <type_traits>
#include <vector>
#include "cg_solver.cuh"
#include "dot.cuh"
#include "newton_solver.cuh"
using namespace cuda::experimental::stf;
#if !_CCCL_CTK_BELOW(12, 4)
void build_tridiagonal_csr_structure(size_t* row_offsets, size_t* col_indices, size_t N)
{
size_t nnz = 0;
row_offsets[0] = 0;
for (size_t row = 0; row < N; row++)
{
if (row == 0 || row == N - 1)
{
// Boundary rows: only diagonal entry (identity for BC: u[i] = prescribed_value)
col_indices[nnz++] = row;
}
else
{
// Interior rows: tridiagonal structure (left, center, right)
col_indices[nnz++] = row - 1; // left neighbor
col_indices[nnz++] = row; // center (diagonal)
col_indices[nnz++] = row + 1; // right neighbor
}
row_offsets[row + 1] = nnz;
}
}
template <typename ctx_t>
void assemble_jacobian_full(
ctx_t& ctx, vector_t<double> U, vector_t<double> values, size_t N, double h, double dt, double nu)
{
ctx.parallel_for(box(N), U.read(), values.write()).set_symbol("assemble_jacobian_full")
->*[N, h, dt, nu] __device__(size_t row, auto dU, auto dvalues) {
if (row == 0)
{
// Left boundary: u[0] = 0 (homogeneous Dirichlet)
size_t val_idx = 0;
dvalues[val_idx] = 1.0;
}
else if (row == N - 1)
{
// Right boundary: u[N-1] = 0 (homogeneous Dirichlet)
size_t val_idx = 1 + 3 * (N - 2);
dvalues[val_idx] = 1.0;
}
else
{
// Interior point: Burger's equation discretization
double u_i = dU[row];
double u_ip1 = dU[row + 1];
double u_im1 = dU[row - 1];
// Jacobian entries: ∂F_i/∂u_{i-1}, ∂F_i/∂u_i, ∂F_i/∂u_{i+1}
double left = -u_i / (2 * h) - nu / (h * h);
double center = 1.0 / dt + (u_ip1 - u_im1) / (2 * h) + 2.0 * nu / (h * h);
double right = u_i / (2 * h) - nu / (h * h);
size_t val_idx = 1 + 3 * (row - 1);
dvalues[val_idx] = left;
dvalues[val_idx + 1] = center;
dvalues[val_idx + 2] = right;
}
};
}
template <typename ctx_t, typename T>
void compute_residual_full(
ctx_t& ctx, vector_t<T> U, vector_t<T> U_prev, vector_t<T> residual, size_t N, double h, double dt, double nu)
{
ctx.parallel_for(box(N), residual.write(), U.read(), U_prev.read()).set_symbol("compute_residual_full")
->*[N, h, dt, nu] __device__(size_t i, auto dresidual, auto dU, auto dU_prev) {
if (i == 0)
{
dresidual(i) = dU(i) - 0.0;
}
else if (i == N - 1)
{
dresidual(i) = dU(i) - 0.0;
}
else
{
// Interior point: Burger's equation F_i = ∂u/∂t + u*∂u/∂x - nu*∂²u/∂x²
double u_i = dU(i);
double u_ip1 = dU(i + 1);
double u_im1 = dU(i - 1);
double term_time = (u_i - dU_prev(i)) / dt;
double term_conv = u_i * (u_ip1 - u_im1) / (2 * h);
double term_diff = -nu * (u_im1 - 2 * u_i + u_ip1) / (h * h);
dresidual(i) = term_time + term_conv + term_diff;
}
};
}
// Shock detection: compute maximum gradient magnitude
template <typename ctx_t>
void detect_shock(
ctx_t& ctx, vector_t<double>& U, stackable_logical_data<scalar_view<double>>& max_gradient, size_t N, double h)
{
ctx.parallel_for(box(N - 1), U.read(), max_gradient.reduce(reducer::maxval<double>{})).set_symbol("detect_shock")
->*[h] __device__(size_t i, auto dU, double& dmax_grad) {
double gradient = fabs(dU(i + 1) - dU(i)) / h;
dmax_grad = fmax(dmax_grad, gradient);
};
}
// Callback function objects for Burger's equation
struct BurgerResidualCallback
{
size_t N;
double h, dt, nu;
template <typename ctx_t>
void
operator()(ctx_t& ctx, const vector_t<double>& x, const vector_t<double>& x_prev, vector_t<double>& residual) const
{
compute_residual_full(ctx, x, x_prev, residual, N, h, dt, nu);
}
};
struct BurgerJacobianCallback
{
size_t N;
double h, dt, nu;
template <typename ctx_t>
void operator()(ctx_t& ctx, const vector_t<double>& x, vector_t<double>& jacobian_values) const
{
assemble_jacobian_full(ctx, x, jacobian_values, N, h, dt, nu);
}
};
// Generate nu values around target with given distribution
std::vector<double> generate_nu_samples(double nu_target, double nu_std, size_t num_samples)
{
std::vector<double> nu_values;
std::random_device rd;
std::mt19937 gen(rd());
std::normal_distribution<double> dist(nu_target, nu_std);
// Generate samples and ensure they are positive
for (size_t i = 0; i < num_samples; ++i)
{
double nu_sample = dist(gen);
// Ensure nu > 0 for physical validity
if (nu_sample > 1e-6)
{
nu_values.push_back(nu_sample);
}
else
{
// Retry if we get non-physical values
i--;
}
}
// Sort for better output organization
std::sort(nu_values.begin(), nu_values.end());
return nu_values;
}
// Initialize sensitivity analysis output file
void initialize_sensitivity_file(const char* filename, double nu_target, double nu_std, size_t num_samples)
{
FILE* fp = fopen(filename, "w");
if (fp)
{
fprintf(fp, "# Burger equation sensitivity analysis\n");
fprintf(fp, "# Target nu: %.6e, std: %.6e, samples: %zu\n", nu_target, nu_std, num_samples);
fprintf(fp, "# Format: nu_value shock_time max_gradient final_time\n");
fprintf(fp, "# shock_time: time when max gradient exceeds threshold (or -1 if no shock)\n");
fprintf(fp, "# max_gradient: maximum gradient achieved\n");
fprintf(fp, "# final_time: total simulation time reached\n");
fprintf(fp, "#\n");
fclose(fp);
}
}
// Initialize shock solutions output file
void initialize_shock_file(const char* filename, double shock_threshold)
{
FILE* fp = fopen(filename, "w");
if (fp)
{
fprintf(fp, "# Burger equation shock solutions\n");
fprintf(fp, "# Solutions dumped when gradient exceeds threshold: %.1f\n", shock_threshold);
fprintf(fp, "# Each shock is a separate data block, separated by blank lines\n");
fprintf(fp, "# Block header: Sample ID, nu value, shock time, max gradient\n");
fprintf(fp, "# Block format: x_coordinate u(x,t_shock)\n");
fprintf(fp, "#\n");
fprintf(fp,
"# Use in gnuplot: plot for [i=0:*] 'shock_solutions.dat' index i with lines title sprintf('Sample %%d', "
"i+1)\n");
fprintf(fp, "#\n");
fclose(fp);
printf("Initialized shock solutions file: %s\n", filename);
}
}
// Dump solution when shock is detected
template <typename ctx_t>
void dump_shock_solution(
ctx_t& ctx,
vector_t<double>& U,
double nu,
double shock_time,
double max_gradient,
size_t sample_id,
size_t N,
double h,
const char* filename = "shock_solutions.dat")
{
ctx.host_launch(U.read()).set_symbol("dump shock solution")
->*
[nu, shock_time, max_gradient, sample_id, h, N, filename](auto hU) {
FILE* fp = fopen(filename, "a"); // Append to file
if (fp)
{
fprintf(
fp, "# Sample %zu: nu=%.6e, shock_time=%.6e, max_gradient=%.2f\n", sample_id, nu, shock_time, max_gradient);
fprintf(fp, "# Format: x_coordinate u(x,t_shock)\n");
for (size_t i = 0; i < N; i++)
{
double x = i * h;
fprintf(fp, "%.10e %.10e\n", x, hU(i));
}
fprintf(fp, "\n"); // Blank line to separate datasets
fclose(fp);
printf(" -> Solution saved to %s", filename);
}
else
{
printf(" -> Error: Could not save solution to %s", filename);
}
};
}
template <typename ctx_t>
void run_single_nu_simulation(
ctx_t& ctx,
double nu,
vector_t<double>& U,
vector_t<double>& csr_values,
const vector_t<size_t>& csr_row_offsets,
const vector_t<size_t>& csr_col_ind,
size_t N,
double h,
double dt,
double max_time,
double shock_threshold,
size_t sample_id,
double& shock_time,
double& max_gradient,
double& final_time)
{
// Reset solution to initial condition
ctx.parallel_for(U.shape(), U.write()).set_symbol("reset_initial_condition")->*[h, N] __device__(size_t i, auto dU) {
double x = i * h;
dU(i) = (i == 0 || i == N - 1) ? 0.0 : sin(M_PI * x);
};
auto current_time = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("current_time");
auto max_grad_global = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("max_grad_global");
auto shock_detected = ctx.logical_data(shape_of<scalar_view<int>>()).set_symbol("shock_detected");
// Initialize tracking variables
ctx.parallel_for(box(1), current_time.write(), max_grad_global.write(), shock_detected.write())
.set_symbol("init_tracking")
->*[] __device__(size_t i, auto dtime, auto dmax_grad, auto dshock) {
*dtime = 0.0;
*dmax_grad = 0.0;
*dshock = 0; // 0 = no shock, 1 = shock detected
};
// Time evolution loop with shock detection
{
auto while_guard = ctx.while_graph_scope();
// Create callback function objects
BurgerResidualCallback residual_callback{N, h, dt, nu};
BurgerJacobianCallback jacobian_callback{N, h, dt, nu};
// Solve the nonlinear system
newton_solver(ctx, U, csr_values, csr_row_offsets, csr_col_ind, residual_callback, jacobian_callback);
// Update time
ctx.parallel_for(box(1), current_time.rw()).set_symbol("update_time")->*[dt] __device__(size_t i, auto dtime) {
*dtime += dt;
};
// Detect shock by computing maximum gradient
auto current_grad = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("current_grad");
detect_shock(ctx, U, current_grad, N, h);
// Update global maximum gradient and check for shock
ctx.parallel_for(box(1), max_grad_global.rw(), current_grad.read(), shock_detected.rw())
.set_symbol("update_shock_detection")
->*[shock_threshold] __device__(size_t i, auto dmax_grad, auto dcurrent_grad, auto dshock) {
double grad = *dcurrent_grad;
if (grad > *dmax_grad)
{
*dmax_grad = grad;
}
if (grad > shock_threshold && *dshock == 0)
{
*dshock = 1; // First time shock threshold is exceeded
}
};
// Continue while time < max_time and no shock detected
while_guard.update_cond(current_time.read(), shock_detected.read())->*[max_time] __device__(auto dtime, auto dshock) {
return (*dtime < max_time) && (*dshock == 0);
};
}
// Extract results to host variables
ctx.host_launch(current_time.read(), max_grad_global.read(), shock_detected.read()).set_symbol("extract_results")
->*[&shock_time, &max_gradient, &final_time](auto htime, auto hmax_grad, auto hshock) {
final_time = *htime;
max_gradient = *hmax_grad;
shock_time = (*hshock == 1) ? *htime : -1.0; // -1 indicates no shock
if (shock_time > 0)
{
printf("shock at t=%.4f, max_grad=%.1f\n", shock_time, max_gradient);
}
else
{
printf("no shock, max_grad=%.1f\n", max_gradient);
}
};
// Dump solution if shock was detected
if (shock_time > 0)
{
dump_shock_solution(ctx, U, nu, shock_time, max_gradient, sample_id, N, h);
}
}
#endif
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
#else
// Usage: ./burger_sensitivity [N] [nu_target] [nu_std] [num_samples] [shock_threshold]
size_t N = 1000; // Smaller grid for sensitivity analysis
double nu_target = 0.02; // Target viscosity
double nu_std = 0.01; // Standard deviation for nu distribution
size_t num_samples = 20; // Number of nu samples to test
double shock_threshold = 15.0; // Gradient threshold to detect shock (du/dx magnitude)
if (argc > 1)
{
N = atoi(argv[1]);
}
if (argc > 2)
{
nu_target = atof(argv[2]);
}
if (argc > 3)
{
nu_std = atof(argv[3]);
}
if (argc > 4)
{
num_samples = atoi(argv[4]);
}
if (argc > 5)
{
shock_threshold = atof(argv[5]);
}
double h = 1.0 / (N - 1);
double dt = 0.001; // Fixed time step
double max_time = 2.0; // Maximum simulation time per sample
fprintf(stderr, "=== Sensitivity Analysis Parameters ===\n");
fprintf(stderr, "Grid: N=%zu, h=%e\n", N, h);
fprintf(stderr, "Viscosity: target=%e, std=%e, samples=%zu\n", nu_target, nu_std, num_samples);
fprintf(stderr, "Time: dt=%e, max_time=%e\n", dt, max_time);
fprintf(stderr, "Shock threshold: %.1f (gradient magnitude)\n", shock_threshold);
fprintf(stderr, "======================================\n");
stackable_ctx ctx;
// Generate nu samples
auto nu_values = generate_nu_samples(nu_target, nu_std, num_samples);
// Set up CSR structure
size_t nz = 3 * N - 4;
size_t* row_offsets;
size_t* col_indices;
cuda_safe_call(cudaHostAlloc(&row_offsets, (N + 1) * sizeof(size_t), cudaHostAllocMapped));
cuda_safe_call(cudaHostAlloc(&col_indices, nz * sizeof(size_t), cudaHostAllocMapped));
build_tridiagonal_csr_structure(row_offsets, col_indices, N);
auto csr_row_offsets = ctx.logical_data(make_slice(row_offsets, N + 1)).set_symbol("csr_row");
auto csr_col_ind = ctx.logical_data(make_slice(col_indices, nz)).set_symbol("csr_col");
csr_row_offsets.set_read_only();
csr_col_ind.set_read_only();
// Initialize output files
initialize_sensitivity_file("sensitivity_results.dat", nu_target, nu_std, num_samples);
initialize_shock_file("shock_solutions.dat", shock_threshold);
// Run sensitivity analysis
printf("Running sensitivity analysis with %zu samples...\n", num_samples);
{
auto g = ctx.graph_scope();
for (size_t i = 0; i < nu_values.size(); ++i)
{
double nu = nu_values[i];
double shock_time, max_gradient, final_time;
printf("Sample %zu/%zu: nu=%.6e... ", i + 1, nu_values.size(), nu);
fflush(stdout);
auto csr_values = ctx.logical_data(shape_of<slice<double>>(nz)).set_symbol("csr_val");
auto U = ctx.logical_data(shape_of<slice<double>>(N)).set_symbol("U");
run_single_nu_simulation(
ctx,
nu,
U,
csr_values,
csr_row_offsets,
csr_col_ind,
N,
h,
dt,
max_time,
shock_threshold,
i + 1,
shock_time,
max_gradient,
final_time);
}
}
ctx.finalize();
#endif
}

232
cccl_upstream/cudax/examples/stf/linear_algebra/cg_csr.cu
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@@ -1,232 +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 Sparse conjugate gradient algorithm
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
using vector_t = logical_data<slice<double>>;
using scalar_t = logical_data<scalar_view<double>>;
using context_t = context;
struct csr_matrix
{
csr_matrix(
context_t& ctx, size_t num_rows, size_t num_nonzeros, double* values, size_t* row_offsets, size_t* column_indices)
{
val_handle = ctx.logical_data(make_slice(values, num_nonzeros));
col_handle = ctx.logical_data(make_slice(column_indices, num_nonzeros));
row_handle = ctx.logical_data(make_slice(row_offsets, num_rows + 1));
}
/* Description of the CSR */
mutable logical_data<slice<double>> val_handle;
mutable logical_data<slice<size_t>> row_handle;
mutable logical_data<slice<size_t>> col_handle;
};
// Note that a and b might be the same logical data
void DOT(context_t& ctx, vector_t& a, vector_t& b, scalar_t& res)
{
ctx.parallel_for(a.shape(), a.read(), b.read(), res.reduce(reducer::sum<double>{}))
->*[] __device__(size_t i, auto da, auto db, double& dres) {
dres += da(i) * db(i);
};
};
void SPMV(context_t& ctx, csr_matrix& a, vector_t& x, vector_t& y)
{
ctx.parallel_for(y.shape(), a.val_handle.read(), a.col_handle.read(), a.row_handle.read(), x.read(), y.write())
->*[] _CCCL_DEVICE(size_t row, auto da_val, auto da_col, auto da_row, auto dx, auto dy) {
int row_start = da_row(row);
int row_end = da_row(row + 1);
double sum = 0.0;
for (int elt = row_start; elt < row_end; elt++)
{
sum += da_val(elt) * dx(da_col(elt));
}
dy(row) = sum;
};
}
/* genTridiag: generate a random tridiagonal symmetric matrix
from :
https://github.com/NVIDIA/cuda-samples/blob/master/Samples/4_CUDA_Libraries/conjugateGradientCudaGraphs/conjugateGradientCudaGraphs.cu
*/
void genTridiag(size_t* I, size_t* J, double* val, size_t N, size_t nz)
{
const double d = 2.0;
I[0] = 0, J[0] = 0, J[1] = 1;
val[0] = drand48() + d;
val[1] = drand48();
int start;
for (size_t i = 1; i < N; i++)
{
if (i > 1)
{
I[i] = I[i - 1] + 3;
}
else
{
I[1] = 2;
}
start = (i - 1) * 3 + 2;
J[start] = i - 1;
J[start + 1] = i;
if (i < N - 1)
{
J[start + 2] = i + 1;
}
val[start] = val[start - 1];
val[start + 1] = drand48() + d;
if (i < N - 1)
{
val[start + 2] = drand48();
}
}
I[N] = nz;
}
void cg_solver(context_t& ctx, csr_matrix& A, vector_t& X, vector_t& B)
{
// Initial guess X = 1
ctx.parallel_for(X.shape(), X.write())->*[] _CCCL_DEVICE(size_t i, auto dX) {
dX(i) = 1.0;
};
// Residual R initialized to B
auto R = ctx.logical_data(B.shape());
ctx.parallel_for(R.shape(), R.write(), B.read())->*[] _CCCL_DEVICE(size_t i, auto dR, auto dB) {
dR(i) = dB(i);
};
// R = R - A*X
auto Ax = ctx.logical_data(X.shape());
SPMV(ctx, A, X, Ax);
ctx.parallel_for(R.shape(), R.rw(), Ax.read())->*[] _CCCL_DEVICE(size_t i, auto dR, auto dAx) {
dR(i) -= dAx(i);
};
// P = R;
auto P = ctx.logical_data(R.shape());
ctx.parallel_for(P.shape(), P.write(), R.read())->*[] _CCCL_DEVICE(size_t i, auto dP, auto dR) {
dP(i) = dR(i);
};
// RSOLD = R'*R
auto rsold = ctx.logical_data(shape_of<scalar_view<double>>());
DOT(ctx, R, R, rsold);
const int MAXITER = X.shape().size();
for (int k = 0; k < MAXITER; k++)
{
// Ap = A*P
auto Ap = ctx.logical_data(P.shape());
SPMV(ctx, A, P, Ap);
// We don't compute alpha explicitly
// alpha = rsold / (p' * Ap);
auto pAp = ctx.logical_data(shape_of<scalar_view<double>>());
DOT(ctx, P, Ap, pAp);
// x = x + alpha * p;
ctx.parallel_for(X.shape(), X.rw(), rsold.read(), pAp.read(), P.read())
->*[] _CCCL_DEVICE(size_t i, auto dX, auto drsold, auto dpAp, auto dP) {
double alpha = (*drsold / *dpAp);
dX(i) += alpha * dP(i);
};
// r = r - alpha * Ap;
ctx.parallel_for(R.shape(), R.rw(), rsold.read(), pAp.read(), Ap.read())
->*[] _CCCL_DEVICE(size_t i, auto dR, auto drsold, auto dpAp, auto dAp) {
double alpha = (*drsold / *dpAp);
dR(i) -= alpha * dAp(i);
};
// rsnew = r' * r;
auto rsnew = ctx.logical_data(shape_of<scalar_view<double>>());
DOT(ctx, R, R, rsnew);
// Read the residual on the CPU, and halt the iterative process if we have converged
// (note that this will block the submission of tasks)
double err = ctx.wait(rsnew);
fprintf(stderr, "iter %d : residual %e\n", k, err);
if (err < 1e-10)
{
// We have converged
fprintf(stderr, "Successfully converged (err = %le)\n", err);
break;
}
// p = r + (rsnew / rsold) * p;
ctx.parallel_for(P.shape(), P.rw(), R.read(), rsnew.read(), rsold.read())
->*[] _CCCL_DEVICE(size_t i, auto dP, auto dR, auto drsnew, auto drsold) {
dP(i) = dR(i) + (*drsnew / *drsold) * dP(i);
};
// update old residual
ctx.parallel_for(box(1), rsold.write(), rsnew.read())->*[] _CCCL_DEVICE(size_t i, auto drsold, auto drsnew) {
*drsold = *drsnew;
};
}
}
int main(int argc, char** argv)
{
size_t N = 10485760;
context_t ctx;
if (argc > 1)
{
N = atoi(argv[1]);
fprintf(stderr, "N = %zu\n", N);
}
size_t nz = (N - 2) * 3 + 4;
size_t* row_offsets;
size_t* column_indices;
double* values;
cuda_safe_call(cudaHostAlloc(&row_offsets, (N + 1) * sizeof(size_t), cudaHostAllocMapped));
cuda_safe_call(cudaHostAlloc(&column_indices, nz * sizeof(size_t), cudaHostAllocMapped));
cuda_safe_call(cudaHostAlloc(&values, nz * sizeof(double), cudaHostAllocMapped));
// Generate a random matrix that is supposed to be invertible
genTridiag(row_offsets, column_indices, values, N, nz);
csr_matrix A(ctx, N, nz, values, row_offsets, column_indices);
auto X = ctx.logical_data(shape_of<slice<double>>(N));
auto B = ctx.logical_data(shape_of<slice<double>>(N));
// RHS
ctx.parallel_for(B.shape(), B.write())->*[] __device__(size_t i, auto dB) {
dB(i) = 1.0;
};
cg_solver(ctx, A, X, B);
ctx.finalize();
}

240
cccl_upstream/cudax/examples/stf/linear_algebra/cg_csr_stackable.cu
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@@ -1,240 +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 Sparse conjugate gradient algorithm
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
#if !_CCCL_CTK_BELOW(12, 4)
using vector_t = stackable_logical_data<slice<double>>;
using scalar_t = stackable_logical_data<scalar_view<double>>;
using context_t = stackable_ctx;
struct csr_matrix
{
csr_matrix(
context_t& ctx, size_t num_rows, size_t num_nonzeros, double* values, size_t* row_offsets, size_t* column_indices)
{
val_handle = ctx.logical_data(make_slice(values, num_nonzeros));
col_handle = ctx.logical_data(make_slice(column_indices, num_nonzeros));
row_handle = ctx.logical_data(make_slice(row_offsets, num_rows + 1));
val_handle.set_symbol("csr_val");
col_handle.set_symbol("csr_col");
row_handle.set_symbol("csr_row");
}
/* Description of the CSR */
mutable stackable_logical_data<slice<double>> val_handle;
mutable stackable_logical_data<slice<size_t>> row_handle;
mutable stackable_logical_data<slice<size_t>> col_handle;
};
// Note that a and b might be the same logical data
void DOT(context_t& ctx, vector_t& a, vector_t& b, scalar_t& res)
{
ctx.parallel_for(a.shape(), a.read(), b.read(), res.reduce(reducer::sum<double>{})).set_symbol("DOT")->*
[] __device__(size_t i, auto da, auto db, double& dres) {
dres += da(i) * db(i);
};
};
void SPMV(context_t& ctx, csr_matrix& a, vector_t& x, vector_t& y)
{
ctx.parallel_for(y.shape(), a.val_handle.read(), a.col_handle.read(), a.row_handle.read(), x.read(), y.write())
.set_symbol("SPMV")
->*[] _CCCL_DEVICE(size_t row, auto da_val, auto da_col, auto da_row, auto dx, auto dy) {
int row_start = da_row(row);
int row_end = da_row(row + 1);
double sum = 0.0;
for (int elt = row_start; elt < row_end; elt++)
{
sum += da_val(elt) * dx(da_col(elt));
}
dy(row) = sum;
};
}
/* genTridiag: generate a random tridiagonal symmetric matrix
from :
https://github.com/NVIDIA/cuda-samples/blob/master/Samples/4_CUDA_Libraries/conjugateGradientCudaGraphs/conjugateGradientCudaGraphs.cu
*/
void genTridiag(size_t* I, size_t* J, double* val, size_t N, size_t nz)
{
const double d = 2.0;
I[0] = 0, J[0] = 0, J[1] = 1;
val[0] = drand48() + d;
val[1] = drand48();
int start;
for (size_t i = 1; i < N; i++)
{
if (i > 1)
{
I[i] = I[i - 1] + 3;
}
else
{
I[1] = 2;
}
start = (i - 1) * 3 + 2;
J[start] = i - 1;
J[start + 1] = i;
if (i < N - 1)
{
J[start + 2] = i + 1;
}
val[start] = val[start - 1];
val[start + 1] = drand48() + d;
if (i < N - 1)
{
val[start + 2] = drand48();
}
}
I[N] = nz;
}
void cg_solver(context_t& ctx, csr_matrix& A, vector_t& X, vector_t& B)
{
// Initial guess X = 1
ctx.parallel_for(X.shape(), X.write()).set_symbol("init_guess")->*[] _CCCL_DEVICE(size_t i, auto dX) {
dX(i) = 1.0;
};
// Residual R initialized to B
auto R = ctx.logical_data(B.shape());
ctx.parallel_for(R.shape(), R.write(), B.read()).set_symbol("R=B")->*[] _CCCL_DEVICE(size_t i, auto dR, auto dB) {
dR(i) = dB(i);
};
// R = R - A*X
auto Ax = ctx.logical_data(X.shape()).set_symbol("Ax");
SPMV(ctx, A, X, Ax);
ctx.parallel_for(R.shape(), R.rw(), Ax.read()).set_symbol("R -= Ax")->*[] _CCCL_DEVICE(size_t i, auto dR, auto dAx) {
dR(i) -= dAx(i);
};
// P = R;
auto P = ctx.logical_data(R.shape()).set_symbol("P");
ctx.parallel_for(P.shape(), P.write(), R.read()).set_symbol("P=R")->*[] _CCCL_DEVICE(size_t i, auto dP, auto dR) {
dP(i) = dR(i);
};
// RSOLD = R'*R
auto rsold = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("rsold");
DOT(ctx, R, R, rsold);
{
auto while_guard = ctx.while_graph_scope();
// Ap = A*P
auto Ap = ctx.logical_data(P.shape()).set_symbol("Ap");
SPMV(ctx, A, P, Ap);
// We don't compute alpha explicitly
// alpha = rsold / (p' * Ap);
auto pAp = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("pAp");
DOT(ctx, P, Ap, pAp);
// x = x + alpha * p;
ctx.parallel_for(X.shape(), X.rw(), rsold.read(), pAp.read(), P.read()).set_symbol("X+=alpha*P")
->*[] _CCCL_DEVICE(size_t i, auto dX, auto drsold, auto dpAp, auto dP) {
double alpha = (*drsold / *dpAp);
dX(i) += alpha * dP(i);
};
// r = r - alpha * Ap;
ctx.parallel_for(R.shape(), R.rw(), rsold.read(), pAp.read(), Ap.read()).set_symbol("R-=alpha*Ap")
->*[] _CCCL_DEVICE(size_t i, auto dR, auto drsold, auto dpAp, auto dAp) {
double alpha = (*drsold / *dpAp);
dR(i) -= alpha * dAp(i);
};
// rsnew = r' * r;
auto rsnew = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("rsnew");
DOT(ctx, R, R, rsnew);
while_guard.update_cond(rsnew.read())->*[] __device__(auto drsnew) {
printf("RES %e\n", *drsnew);
bool converged = (*drsnew < 1e-13);
return !converged;
};
// p = r + (rsnew / rsold) * p;
ctx.parallel_for(P.shape(), P.rw(), R.read(), rsnew.read(), rsold.read()).set_symbol("P=r+(rsnew/rsold)*P")
->*[] _CCCL_DEVICE(size_t i, auto dP, auto dR, auto drsnew, auto drsold) {
dP(i) = dR(i) + (*drsnew / *drsold) * dP(i);
};
// update old residual
ctx.parallel_for(box(1), rsold.write(), rsnew.read()).set_symbol("update_rsold")
->*[] _CCCL_DEVICE(size_t i, auto drsold, auto drsnew) {
*drsold = *drsnew;
};
}
}
#endif
int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
#else
size_t N = 10485760;
context_t ctx;
if (argc > 1)
{
N = atoi(argv[1]);
fprintf(stderr, "N = %zu\n", N);
}
size_t nz = (N - 2) * 3 + 4;
size_t* row_offsets;
size_t* column_indices;
double* values;
cuda_safe_call(cudaHostAlloc(&row_offsets, (N + 1) * sizeof(size_t), cudaHostAllocMapped));
cuda_safe_call(cudaHostAlloc(&column_indices, nz * sizeof(size_t), cudaHostAllocMapped));
cuda_safe_call(cudaHostAlloc(&values, nz * sizeof(double), cudaHostAllocMapped));
// Generate a random matrix that is supposed to be invertible
genTridiag(row_offsets, column_indices, values, N, nz);
csr_matrix A(ctx, N, nz, values, row_offsets, column_indices);
auto X = ctx.logical_data(shape_of<slice<double>>(N)).set_symbol("X");
auto B = ctx.logical_data(shape_of<slice<double>>(N)).set_symbol("B");
// RHS
ctx.parallel_for(B.shape(), B.write()).set_symbol("B assembly")->*[] __device__(size_t i, auto dB) {
dB(i) = 1.0;
};
cg_solver(ctx, A, X, B);
ctx.finalize();
#endif
}

462
cccl_upstream/cudax/examples/stf/linear_algebra/cg_dense_2D.cu
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@@ -1,462 +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 Conjugate gradient for a tiled dense matrix
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
static cublasHandle_t cublas_handle;
stream_ctx ctx;
class matrix
{
public:
matrix(size_t N)
: N(N)
{
h_addr.reset(new double[N * N]);
cuda_safe_call(cudaHostRegister(h_addr.get(), N * N * sizeof(double), cudaHostRegisterPortable));
handle = ::std::make_shared<logical_data<slice<double, 2>>>(
ctx.logical_data(make_slice(h_addr.get(), std::tuple{N, N}, N)));
}
void fill(const std::function<double(int, int)>& f)
{
ctx.task(exec_place::host(), handle->write())->*[&f](cudaStream_t stream, auto ds) {
cuda_safe_call(cudaStreamSynchronize(stream));
for (size_t col = 0; col < ds.extent(1); col++)
{
for (size_t row = 0; row < ds.extent(0); row++)
{
ds(row, col) = f(row, col);
}
}
};
}
size_t N;
std::unique_ptr<double[]> h_addr;
std::shared_ptr<logical_data<slice<double, 2>>> handle;
};
class vector
{
public:
vector(size_t N, size_t _block_size, bool is_tmp = false)
: N(N)
, block_size(_block_size)
, nblocks((N + block_size - 1) / block_size)
{
handles.resize(nblocks);
if (is_tmp)
{
// There is no physical backing for this temporary vector
for (size_t b = 0; b < nblocks; b++)
{
size_t bs = std::min(N - block_size * b, block_size);
handles[b] = ::std::make_shared<logical_data<slice<double>>>(ctx.logical_data(shape_of<slice<double>>(bs)));
}
}
else
{
h_addr.reset(new double[N]);
cuda_safe_call(cudaHostRegister(h_addr.get(), N * sizeof(double), cudaHostRegisterPortable));
for (size_t b = 0; b < nblocks; b++)
{
size_t bs = std::min(N - block_size * b, block_size);
handles[b] =
::std::make_shared<logical_data<slice<double>>>(ctx.logical_data(make_slice(&h_addr[block_size * b], bs)));
}
}
}
// Copy constructor
vector(const vector& a)
: N(a.N)
, block_size(a.block_size)
, nblocks(a.nblocks)
{
handles.resize(nblocks);
for (size_t b = 0; b < nblocks; b++)
{
size_t bs = std::min(N - block_size * b, block_size);
handles[b] = ::std::make_shared<logical_data<slice<double>>>(ctx.logical_data(shape_of<slice<double>>(bs)));
ctx.task(handles[b]->write(), a.handles[b]->read())->*[bs](cudaStream_t stream, auto dthis, auto da) {
// There are likely much more efficient ways.
cuda_safe_call(cudaMemcpyAsync(
dthis.data_handle(), da.data_handle(), bs * sizeof(double), cudaMemcpyDeviceToDevice, stream));
};
}
}
void fill(const std::function<double(int)>& f)
{
size_t bs = block_size;
for (size_t b = 0; b < nblocks; b++)
{
ctx.task(exec_place::host(), handles[b]->write())->*[&f, b, bs](cudaStream_t stream, auto ds) {
cuda_safe_call(cudaStreamSynchronize(stream));
for (size_t local_row = 0; local_row < ds.extent(0); local_row++)
{
ds(local_row) = f(local_row + b * bs);
}
};
}
}
size_t N;
size_t block_size;
size_t nblocks;
mutable std::vector<std::shared_ptr<logical_data<slice<double>>>> handles;
std::unique_ptr<double[]> h_addr;
};
__global__ void scalar_div(const double* a, const double* b, double* c)
{
*c = *a / *b;
}
// A += B
__global__ void scalar_add(double* a, const double* b)
{
*a = *a + *b;
}
__global__ void scalar_minus(const double* a, double* res)
{
*res = -(*a);
}
class scalar
{
public:
scalar(bool is_tmp = false)
{
size_t s = sizeof(double);
if (is_tmp)
{
// There is no physical backing for this temporary vector
handle = ::std::make_shared<logical_data<slice<double>>>(ctx.logical_data(shape_of<slice<double>>(1)));
}
else
{
h_addr.reset(new double);
cuda_safe_call(cudaHostRegister(h_addr.get(), s, cudaHostRegisterPortable));
handle = ::std::make_shared<logical_data<slice<double>>>(ctx.logical_data(make_slice(h_addr.get(), 1)));
}
}
scalar(scalar&&) = default;
scalar& operator=(scalar&&) = default;
// Copy constructor
scalar(const scalar& a)
{
handle = ::std::make_shared<logical_data<slice<double>>>(ctx.logical_data(shape_of<slice<double>>(1)));
ctx.task(handle->write(), a.handle->read())->*[](cudaStream_t stream, auto dthis, auto da) {
// There are likely much more efficient ways.
cuda_safe_call(
cudaMemcpyAsync(dthis.data_handle(), da.data_handle(), sizeof(double), cudaMemcpyDeviceToDevice, stream));
};
}
scalar operator/(scalar const& rhs) const
{
// Submit a task that computes this/rhs
scalar res(true);
ctx.task(handle->read(), rhs.handle->read(), res.handle->write())
->*[](cudaStream_t stream, auto da, auto db, auto dres) {
scalar_div<<<1, 1, 0, stream>>>(da.data_handle(), db.data_handle(), dres.data_handle());
};
return res;
}
// this += rhs
scalar& operator+=(const scalar& rhs)
{
ctx.task(handle->rw(), rhs.handle->read())->*[](cudaStream_t stream, auto dthis, auto drhs) {
scalar_add<<<1, 1, 0, stream>>>(dthis.data_handle(), drhs.data_handle());
};
return *this;
}
scalar operator-() const
{
// Submit a task that computes -s
scalar res(true);
ctx.task(handle->read(), res.handle->write())->*[](cudaStream_t stream, auto dthis, auto dres) {
scalar_minus<<<1, 1, 0, stream>>>(dthis.data_handle(), dres.data_handle());
};
return res;
}
// Get value on the host
double get_value()
{
double val;
ctx.task(exec_place::host(), handle->read())->*[&val](cudaStream_t stream, auto ds) {
cuda_safe_call(cudaStreamSynchronize(stream));
val = ds(0);
};
return val;
}
mutable std::shared_ptr<logical_data<slice<double>>> handle;
std::unique_ptr<double> h_addr;
};
class scalar DOT(vector& a, class vector& b)
{
assert(a.nblocks == b.nblocks);
scalar global_res(true);
// Loop over all blocks,
for (size_t bid = 0; bid < a.nblocks; bid++)
{
scalar res(true);
// Note that it works even if a.handle == b.handle because they have the same access mode
ctx.task(a.handles[bid]->read(), b.handles[bid]->read(), res.handle->write())
->*[](cudaStream_t stream, auto da, auto db, auto dres) {
cuda_safe_call(cublasSetStream(cublas_handle, stream));
cuda_safe_call(cublasSetPointerMode(cublas_handle, CUBLAS_POINTER_MODE_DEVICE));
cuda_safe_call(
cublasDdot(cublas_handle, da.extent(0), da.data_handle(), 1, db.data_handle(), 1, dres.data_handle()));
};
if (bid == 0)
{
// First access requires an assignment because it was not initialized
global_res = std::move(res);
}
else
{
global_res += res;
}
}
return global_res;
};
// Y = Y + alpha * X
void AXPY(const class scalar& alpha, class vector& x, class vector& y)
{
assert(x.N == y.N);
assert(x.nblocks == y.nblocks);
for (size_t b = 0; b < x.nblocks; b++)
{
ctx.task(alpha.handle->read(), x.handles[b]->read(), y.handles[b]->rw())
->*
[](cudaStream_t stream, auto dalpha, auto dx, auto dy) {
auto nx = dx.extent(0);
cuda_safe_call(cublasSetStream(cublas_handle, stream));
cuda_safe_call(cublasSetPointerMode(cublas_handle, CUBLAS_POINTER_MODE_DEVICE));
cuda_safe_call(cublasDaxpy(cublas_handle, nx, dalpha.data_handle(), dx.data_handle(), 1, dy.data_handle(), 1));
};
}
};
// Y = alpha*Y + X
void SCALE_AXPY(const scalar& alpha, const class vector& x, class vector& y)
{
assert(x.N == y.N);
assert(x.nblocks == y.nblocks);
for (size_t b = 0; b < x.nblocks; b++)
{
ctx.task(alpha.handle->read(), x.handles[b]->read(), y.handles[b]->rw())
->*[](cudaStream_t stream, auto dalpha, auto dx, auto dy) {
cuda_safe_call(cublasSetStream(cublas_handle, stream));
auto nx = dx.extent(0);
// Y = alpha Y
cuda_safe_call(cublasSetPointerMode(cublas_handle, CUBLAS_POINTER_MODE_DEVICE));
cuda_safe_call(cublasDscal(cublas_handle, nx, dalpha.data_handle(), dy.data_handle(), 1));
// Y = Y + X
const double one = 1.0;
cuda_safe_call(cublasSetPointerMode(cublas_handle, CUBLAS_POINTER_MODE_HOST));
cuda_safe_call(cublasDaxpy(cublas_handle, nx, &one, dx.data_handle(), 1, dy.data_handle(), 1));
};
}
};
// y = alpha Ax + beta y
void GEMV(double alpha, class matrix& a, class vector& x, double beta, class vector& y)
{
assert(a.N == x.N);
assert(x.N == y.N);
size_t block_size = x.block_size;
assert(block_size == y.block_size);
for (size_t row_y = 0; row_y < y.nblocks; row_y++)
{
for (size_t row_x = 0; row_x < x.nblocks; row_x++)
{
double local_beta = (row_x == 0) ? beta : 1.0;
// If beta is null, then this is a write only mode
auto y_mode = local_beta == 0.0 ? access_mode::write : access_mode::rw;
ctx.task(a.handle->read(), x.handles[row_x]->read(), task_dep<slice<double>>(*(y.handles[row_y].get()), y_mode))
->*[alpha, local_beta, row_x, row_y, block_size](cudaStream_t stream, auto da, auto dx, auto dy) {
auto nx = dx.extent(0);
auto ny = dy.extent(0);
auto ldA = da.stride(1);
const double* Ablock = &da(row_y * block_size, row_x * block_size);
cuda_safe_call(cublasSetStream(cublas_handle, stream));
cuda_safe_call(cublasSetPointerMode(cublas_handle, CUBLAS_POINTER_MODE_HOST));
cuda_safe_call(cublasDgemv(
cublas_handle,
CUBLAS_OP_N,
ny,
nx,
&alpha,
Ablock,
ldA,
dx.data_handle(),
1,
&local_beta,
dy.data_handle(),
1));
};
}
}
}
void cg(matrix& A, vector& X, vector& B)
{
int N = A.N;
assert(N == X.N);
assert(N == B.N);
vector R = B;
// R = R - A*X
GEMV(-1.0, A, X, 1.0, R);
vector P = R;
// RSOLD = R'*R
scalar rsold = DOT(R, R);
int MAXITER = N;
if (getenv("MAXITER"))
{
MAXITER = atoi(getenv("MAXITER"));
}
for (int k = 0; k < MAXITER; k++)
{
vector Ap(N, P.block_size, true);
// Ap = A*P
GEMV(1.0, A, P, 0.0, Ap);
// alpha = rsold / (p' * Ap);
scalar alpha = rsold / DOT(P, Ap);
// x = x + alpha * p;
AXPY(alpha, P, X);
// r = r - alpha * Ap;
AXPY(-alpha, Ap, R);
// rsnew = r' * r;
scalar rsnew = DOT(R, R);
// Read the residual on the CPU, and halt the iterative process if we have converged
{
double err;
ctx.task(exec_place::host(), rsnew.handle->read())->*[&err](cudaStream_t stream, auto dres) {
cuda_safe_call(cudaStreamSynchronize(stream));
err = sqrt(dres(0));
};
if (err < 1e-10)
{
// We have converged
// fprintf(stderr, "Successfully converged (err = %le)\n", err);
break;
}
}
// p = r + (rsnew / rsold) * p;
SCALE_AXPY(rsnew / rsold, R, P);
rsold = std::move(rsnew);
}
}
int main(int argc, char** argv)
{
size_t N = 1024;
if (argc > 1)
{
N = atoi(argv[1]);
fprintf(stderr, "N = %zu\n", N);
}
size_t block_size = N / 4;
if (argc > 2)
{
block_size = atoi(argv[2]);
fprintf(stderr, "block_size = %zu\n", block_size);
}
// Do this lazily ?
cuda_safe_call(cublasCreate(&cublas_handle));
matrix A(N);
A.fill([&](int row, int col) {
return (1.0 / (row + col + 1) + (row == col ? 0.1 : 0.0));
});
vector B(N, block_size);
vector X(N, block_size);
B.fill([&](int /*unused*/) {
return 1.0;
});
X.fill([&](int /*unused*/) {
return 0.0;
});
cg(A, X, B);
ctx.finalize();
}

189
cccl_upstream/cudax/examples/stf/linear_algebra/cg_solver.cuh
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@@ -1,189 +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.
//
//===----------------------------------------------------------------------===//
#pragma once
/**
* @file
* @brief Sparse conjugate gradient algorithm
*/
#include <cuda/experimental/stf.cuh>
#include "dot.cuh"
using namespace cuda::experimental::stf;
#if !_CCCL_CTK_BELOW(12, 4)
template <typename ctx_t, typename T>
void cg_solver(ctx_t& ctx, csr_matrix<T>& A, vector_t<T>& X, vector_t<T>& B, double cg_tol = 1e-10, size_t max_cg = 1000)
{
// Initial guess X = 0 (better for Newton corrections)
ctx.parallel_for(X.shape(), X.write()).set_symbol("init_guess")->*[] _CCCL_DEVICE(size_t i, auto dX) {
dX(i) = 0.0;
};
// Residual R initialized to B
auto R = ctx.logical_data(B.shape()).set_symbol("R");
ctx.parallel_for(R.shape(), R.write(), B.read()).set_symbol("R=B")->*[] _CCCL_DEVICE(size_t i, auto dR, auto dB) {
dR(i) = dB(i);
};
// R = R - A*X
auto Ax = ctx.logical_data(X.shape()).set_symbol("Ax");
SPMV(ctx, A, X, Ax);
ctx.parallel_for(R.shape(), R.rw(), Ax.read()).set_symbol("R -= Ax")->*[] _CCCL_DEVICE(size_t i, auto dR, auto dAx) {
dR(i) -= dAx(i);
};
// P = R;
auto P = ctx.logical_data(R.shape()).set_symbol("P");
ctx.parallel_for(P.shape(), P.write(), R.read()).set_symbol("P=R")->*[] _CCCL_DEVICE(size_t i, auto dP, auto dR) {
dP(i) = dR(i);
};
// RSOLD = R'*R
auto rsold = ctx.logical_data(shape_of<scalar_view<T>>()).set_symbol("rsold");
DOT(ctx, R, R, rsold);
// CG iteration counter
auto cg_iter = ctx.logical_data(shape_of<scalar_view<int>>()).set_symbol("cg_iter");
ctx.parallel_for(box(1), cg_iter.write()).set_symbol("init_cg_iter")->*[] _CCCL_DEVICE(size_t i, auto diter) {
*diter = 0;
};
{
auto while_guard = ctx.while_graph_scope();
// Ap = A*P
auto Ap = ctx.logical_data(P.shape()).set_symbol("Ap");
SPMV(ctx, A, P, Ap);
// We don't compute alpha explicitly
// alpha = rsold / (p' * Ap);
auto pAp = ctx.logical_data(shape_of<scalar_view<T>>()).set_symbol("pAp");
DOT(ctx, P, Ap, pAp);
// x = x + alpha * p;
ctx.parallel_for(X.shape(), X.rw(), rsold.read(), pAp.read(), P.read()).set_symbol("X+=alpha*P")
->*[] _CCCL_DEVICE(size_t i, auto dX, auto drsold, auto dpAp, auto dP) {
T alpha = (*drsold / *dpAp);
dX(i) += alpha * dP(i);
};
// r = r - alpha * Ap;
ctx.parallel_for(R.shape(), R.rw(), rsold.read(), pAp.read(), Ap.read()).set_symbol("R-=alpha*Ap")
->*[] _CCCL_DEVICE(size_t i, auto dR, auto drsold, auto dpAp, auto dAp) {
T alpha = (*drsold / *dpAp);
dR(i) -= alpha * dAp(i);
};
// rsnew = r' * r;
auto rsnew = ctx.logical_data(shape_of<scalar_view<T>>()).set_symbol("rsnew");
DOT(ctx, R, R, rsnew);
while_guard.update_cond(rsnew.read(), cg_iter.rw())->*[cg_tol, max_cg] __device__(auto drsnew, auto diter) {
(*diter)++; // increment iteration counter
bool converged = (*drsnew < cg_tol * cg_tol);
// printf("CG iter %d: RES %e (tol=%e)\n", *diter, sqrt(*drsnew), cg_tol);
return !converged && (*diter < max_cg);
};
// p = r + (rsnew / rsold) * p;
ctx.parallel_for(P.shape(), P.rw(), R.read(), rsnew.read(), rsold.read()).set_symbol("P=r+(rsnew/rsold)*P")
->*[] _CCCL_DEVICE(size_t i, auto dP, auto dR, auto drsnew, auto drsold) {
dP(i) = dR(i) + (*drsnew / *drsold) * dP(i);
};
// update old residual
ctx.parallel_for(box(1), rsold.write(), rsnew.read()).set_symbol("update_rsold")
->*[] _CCCL_DEVICE(size_t i, auto drsold, auto drsnew) {
*drsold = *drsnew;
};
}
}
template <typename ctx_t, typename T>
void cg_solver_no_while(
ctx_t& ctx, csr_matrix<T>& A, vector_t<T>& X, vector_t<T>& B, double cg_tol = 1e-10, size_t max_cg = 1000)
{
// Initial guess X = 0 (better for Newton corrections)
ctx.parallel_for(X.shape(), X.write()).set_symbol("init_guess")->*[] _CCCL_DEVICE(size_t i, auto dX) {
dX(i) = 0.0;
};
// Residual R initialized to B
auto R = ctx.logical_data(B.shape()).set_symbol("R");
ctx.parallel_for(R.shape(), R.write(), B.read()).set_symbol("R=B")->*[] _CCCL_DEVICE(size_t i, auto dR, auto dB) {
dR(i) = dB(i);
};
// R = R - A*X
auto Ax = ctx.logical_data(X.shape()).set_symbol("Ax");
SPMV(ctx, A, X, Ax);
ctx.parallel_for(R.shape(), R.rw(), Ax.read()).set_symbol("R -= Ax")->*[] _CCCL_DEVICE(size_t i, auto dR, auto dAx) {
dR(i) -= dAx(i);
};
// P = R;
auto P = ctx.logical_data(R.shape()).set_symbol("P");
ctx.parallel_for(P.shape(), P.write(), R.read()).set_symbol("P=R")->*[] _CCCL_DEVICE(size_t i, auto dP, auto dR) {
dP(i) = dR(i);
};
// RSOLD = R'*R
auto rsold = ctx.logical_data(shape_of<scalar_view<T>>()).set_symbol("rsold");
DOT(ctx, R, R, rsold);
size_t iter = 0;
auto rsnew = ctx.logical_data(shape_of<scalar_view<T>>()).set_symbol("rsnew");
do
{
// Ap = A*P
auto Ap = ctx.logical_data(P.shape()).set_symbol("Ap");
SPMV(ctx, A, P, Ap);
// We don't compute alpha explicitly
// alpha = rsold / (p' * Ap);
auto pAp = ctx.logical_data(shape_of<scalar_view<T>>()).set_symbol("pAp");
DOT(ctx, P, Ap, pAp);
// x = x + alpha * p;
ctx.parallel_for(X.shape(), X.rw(), rsold.read(), pAp.read(), P.read()).set_symbol("X+=alpha*P")
->*[] _CCCL_DEVICE(size_t i, auto dX, auto drsold, auto dpAp, auto dP) {
T alpha = (*drsold / *dpAp);
dX(i) += alpha * dP(i);
};
// r = r - alpha * Ap;
ctx.parallel_for(R.shape(), R.rw(), rsold.read(), pAp.read(), Ap.read()).set_symbol("R-=alpha*Ap")
->*[] _CCCL_DEVICE(size_t i, auto dR, auto drsold, auto dpAp, auto dAp) {
T alpha = (*drsold / *dpAp);
dR(i) -= alpha * dAp(i);
};
// rsnew = r' * r;
DOT(ctx, R, R, rsnew);
// p = r + (rsnew / rsold) * p;
ctx.parallel_for(P.shape(), P.rw(), R.read(), rsnew.read(), rsold.read()).set_symbol("P=r+(rsnew/rsold)*P")
->*[] _CCCL_DEVICE(size_t i, auto dP, auto dR, auto drsnew, auto drsold) {
dP(i) = dR(i) + (*drsnew / *drsold) * dP(i);
};
// update old residual
ctx.parallel_for(box(1), rsold.write(), rsnew.read()).set_symbol("update_rsold")
->*[] _CCCL_DEVICE(size_t i, auto drsold, auto drsnew) {
*drsold = *drsnew;
};
} while ((++iter < max_cg) && (ctx.wait(rsnew) > cg_tol * cg_tol));
}
#endif

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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.
//
//===----------------------------------------------------------------------===//
#pragma once
//! \file
//! \brief DOT algorithm
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
template <typename T>
using vector_t = stackable_logical_data<slice<T>>;
template <typename T>
using scalar_t = stackable_logical_data<scalar_view<T>>;
template <typename T = double>
struct csr_matrix
{
csr_matrix(stackable_logical_data<slice<T>> _val_handle,
stackable_logical_data<slice<size_t>> _row_handle,
stackable_logical_data<slice<size_t>> _col_handle)
: val_handle(mv(_val_handle))
, row_handle(mv(_row_handle))
, col_handle(mv(_col_handle))
{}
/* Description of the CSR */
mutable stackable_logical_data<slice<T>> val_handle;
mutable stackable_logical_data<slice<size_t>> row_handle;
mutable stackable_logical_data<slice<size_t>> col_handle;
};
// Note that a and b might be the same logical data
template <typename ctx_t, typename T>
void DOT(ctx_t& ctx, vector_t<T>& a, vector_t<T>& b, scalar_t<T>& res)
{
ctx.parallel_for(a.shape(), a.read(), b.read(), res.reduce(reducer::sum<T>{})).set_symbol("DOT")->*
[] __device__(size_t i, auto da, auto db, T& dres) {
dres += da(i) * db(i);
};
};
template <typename ctx_t, typename T>
void SPMV(ctx_t& ctx, csr_matrix<T>& a, vector_t<T>& x, vector_t<T>& y)
{
ctx.parallel_for(y.shape(), a.val_handle.read(), a.col_handle.read(), a.row_handle.read(), x.read(), y.write())
.set_symbol("SPMV")
->*[] _CCCL_DEVICE(size_t row, auto da_val, auto da_col, auto da_row, auto dx, auto dy) {
int row_start = da_row(row);
int row_end = da_row(row + 1);
double sum = 0.0;
for (int elt = row_start; elt < row_end; elt++)
{
sum += da_val(elt) * dx(da_col(elt));
}
dy(row) = sum;
};
}

172
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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 Generic Newton Solver
*/
#include <cuda/experimental/stf.cuh>
#include "cg_solver.cuh"
#include "dot.cuh"
using namespace cuda::experimental::stf;
/**
* Generic Newton solver for nonlinear systems F(x) = 0
*
* @tparam ctx_t STF context type
* @tparam ResidualCallback Callback to compute residual F(x)
* @tparam JacobianCallback Callback to assemble Jacobian J = ∂F/∂x
*
* The callbacks must be callable with these signatures:
* - ResidualCallback: void fn(ctx_t&, const vector_t<double>& x, const vector_t<double>& x_prev, vector_t<double>&
* residual)
* - JacobianCallback: void fn(ctx_t&, const vector_t<double>& x, vector_t<double>& jacobian_values)
*/
template <typename ctx_t, typename ResidualCallback, typename JacobianCallback>
void newton_solver(
ctx_t& ctx,
vector_t<double>& U,
vector_t<double>& csr_values,
const vector_t<size_t>& csr_row_offsets,
const vector_t<size_t>& csr_col_ind,
ResidualCallback compute_residual_fn,
JacobianCallback assemble_jacobian_fn,
size_t max_newton = 20,
double newton_tol = 1e-10,
size_t max_cg = 100)
{
auto U_prev = ctx.logical_data(U.shape()).set_symbol("U_prev");
ctx.parallel_for(U.shape(), U_prev.write(), U.read()).set_symbol("init_guess")
->*[] __device__(size_t i, auto dU_prev, auto dU) {
dU_prev(i) = dU(i);
};
auto newton_norm2 = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("newton_norm2");
auto newton_iter = ctx.logical_data(shape_of<scalar_view<size_t>>()).set_symbol("newton_iter");
ctx.parallel_for(box(1), newton_iter.write()).set_symbol("init_newton_iter")->*[] _CCCL_DEVICE(size_t i, auto diter) {
*diter = 0;
};
{
auto while_guard = ctx.while_graph_scope();
auto residual = ctx.logical_data(U.shape()).set_symbol("residual");
auto delta = ctx.logical_data(U.shape()).set_symbol("delta");
// Compute residual F(U)
compute_residual_fn(ctx, U, U_prev, residual);
// Compute Newton residual norm for convergence check
DOT(ctx, residual, residual, newton_norm2);
// Assemble Jacobian J = ∂F/∂U
assemble_jacobian_fn(ctx, U, csr_values);
auto rhs = ctx.logical_data(U.shape()).set_symbol("rhs");
// Set up RHS: rhs = -F(U)
ctx.parallel_for(rhs.shape(), rhs.write(), residual.read()).set_symbol("rhs = -residual")
->*[] __device__(size_t i, auto drhs, auto dresidual) {
drhs(i) = -dresidual(i);
};
csr_matrix<double> A(csr_values, csr_row_offsets, csr_col_ind);
// Solve linear system: J * delta = -F(U)
double cg_tol = 1e-8;
cg_solver(ctx, A, delta, rhs, cg_tol, max_cg);
// Newton update: U = U + delta (no special boundary handling needed)
ctx.parallel_for(U.shape(), U.rw(), delta.read()).set_symbol("newton_update")
->*[] __device__(size_t i, auto dU, auto ddelta) {
dU(i) += ddelta(i);
};
while_guard.update_cond(newton_norm2.read(), newton_iter.rw())
->*[newton_tol, max_newton] __device__(auto dnorm2, auto diter) {
(*diter)++; // increment iteration counter
bool converged = (*dnorm2 < newton_tol * newton_tol);
return !converged && (*diter < max_newton);
};
}
}
template <typename ctx_t, typename ResidualCallback, typename JacobianCallback>
void newton_solver_no_while(
ctx_t& ctx,
vector_t<double>& U,
vector_t<double>& csr_values,
const vector_t<size_t>& csr_row_offsets,
const vector_t<size_t>& csr_col_ind,
ResidualCallback compute_residual_fn,
JacobianCallback assemble_jacobian_fn,
bool cg_use_while = false,
size_t max_newton = 20,
double newton_tol = 1e-10,
size_t max_cg = 100)
{
auto U_prev = ctx.logical_data(U.shape()).set_symbol("U_prev");
ctx.parallel_for(U.shape(), U_prev.write(), U.read()).set_symbol("init_guess")
->*[] __device__(size_t i, auto dU_prev, auto dU) {
dU_prev(i) = dU(i);
};
auto newton_norm2 = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("newton_norm2");
size_t iter = 0;
do
{
auto residual = ctx.logical_data(U.shape()).set_symbol("residual");
auto delta = ctx.logical_data(U.shape()).set_symbol("delta");
// Compute residual F(U)
compute_residual_fn(ctx, U, U_prev, residual);
// Compute Newton residual norm for convergence check
DOT(ctx, residual, residual, newton_norm2);
// Assemble Jacobian J = ∂F/∂U
assemble_jacobian_fn(ctx, U, csr_values);
auto rhs = ctx.logical_data(U.shape()).set_symbol("rhs");
// Set up RHS: rhs = -F(U)
ctx.parallel_for(rhs.shape(), rhs.write(), residual.read()).set_symbol("rhs = -residual")
->*[] __device__(size_t i, auto drhs, auto dresidual) {
drhs(i) = -dresidual(i);
};
csr_matrix<double> A(csr_values, csr_row_offsets, csr_col_ind);
// Solve linear system: J * delta = -F(U)
double cg_tol = 1e-8;
if (cg_use_while)
{
// fprintf(stderr, "NEWTON NO WHILE, CG WHILE.\n");
cg_solver(ctx, A, delta, rhs, cg_tol, max_cg);
}
else
{
// fprintf(stderr, "NEWTON NO WHILE, CG NO WHILE.\n");
cg_solver_no_while(ctx, A, delta, rhs, cg_tol, max_cg);
}
// Newton update: U = U + delta (no special boundary handling needed)
ctx.parallel_for(U.shape(), U.rw(), delta.read()).set_symbol("newton_update")
->*[] __device__(size_t i, auto dU, auto ddelta) {
dU(i) += ddelta(i);
};
} while ((++iter < max_newton) && ctx.wait(newton_norm2) > newton_tol * newton_tol);
}

489
cccl_upstream/cudax/examples/stf/linear_algebra/strassen.cu
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@@ -1,489 +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 Strassen matrix multiplication algorithm
*
* This demonstrates how CUDASTF helps combining many interdependent tasks and
* deal with temporary data.
*/
#include <cuda/experimental/stf.cuh>
static const size_t BLOCKSIZE = 1024;
using namespace cuda::experimental::stf;
using logical_matrix = logical_data<slice<double, 2>>;
inline size_t get_m(logical_matrix& s)
{
return s.shape().extent(0);
}
inline size_t get_n(logical_matrix& s)
{
return s.shape().extent(1);
}
// XXX global for the sake of simplicity, yet ...
static std::vector<cublasHandle_t> cublas_handle;
cublasHandle_t get_cublas_handle()
{
int dev;
cuda_safe_call(cudaGetDevice(&dev));
return cublas_handle[dev];
}
// C = AB
void MULT_CLASSIC(context& ctx, logical_matrix& A, logical_matrix& B, logical_matrix& C)
{
ctx.task(A.read(), B.read(), C.write()).set_symbol("MULT")->*[](cudaStream_t s, auto a, auto b, auto c) {
cuda_safe_call(cublasSetStream(get_cublas_handle(), s));
size_t N = a.extent(0);
const double zero = 0.0;
const double one = 1.0;
cuda_safe_call(cublasDgemm(
get_cublas_handle(),
CUBLAS_OP_N,
CUBLAS_OP_N,
N,
N,
N,
&one,
a.data_handle(),
a.stride(1),
b.data_handle(),
b.stride(1),
&zero,
c.data_handle(),
c.stride(1)));
};
}
// A = A + alpha B
template <typename T>
__global__ void add_kernel(int m, int n, T* A, int ld_A, T alpha, const T* B, int ld_B)
{
for (int idx = threadIdx.x + blockIdx.x * blockDim.x; idx < n; idx += blockDim.x * gridDim.x)
{
for (int idy = threadIdx.y + blockIdx.y * blockDim.y; idy < m; idy += blockDim.y * gridDim.y)
{
A[idy + idx * ld_A] += alpha * B[idy + idx * ld_B];
}
}
}
// Compute A = A + B
template <typename T>
void ADD(context& ctx, logical_matrix& A, T alpha, logical_matrix& B)
{
ctx.task(A.rw(), B.read()).set_symbol("ADD")->*[&](cudaStream_t s, auto a, auto b) {
int m_A = a.extent(0);
int n_A = a.extent(1);
int ld_A = a.stride(1);
int ld_B = b.stride(1);
T* addr_A = a.data_handle();
const T* addr_B = b.data_handle();
add_kernel<<<16, 16, 0, s>>>(m_A, n_A, addr_A, ld_A, alpha, addr_B, ld_B);
};
}
template <typename T>
__global__ void copy_kernel(int m, int n, const T* src, int ld_src, T* dst, int ld_dst)
{
for (int idx = threadIdx.x + blockIdx.x * blockDim.x; idx < n; idx += blockDim.x * gridDim.x)
{
for (int idy = threadIdx.y + blockIdx.y * blockDim.y; idy < m; idy += blockDim.y * gridDim.y)
{
dst[idy + idx * ld_dst] = src[idy + idx * ld_src];
}
}
}
// row and col = 0 or 1
template <typename T>
void COPY_TO_SUBMATRIX(context& ctx, logical_data<slice<T, 2>>& A, logical_data<slice<T, 2>>& subA, int row, int col)
{
// To copy to a subset, this is a write only access, so that we did not need a valid copy for subA before ...
ctx.task(A.read(), subA.write()).set_symbol("COPY_TO")->*[&](cudaStream_t s, auto a, auto subA) {
int ld_A = a.stride(1);
int ld_subA = subA.stride(1);
int m_subA = subA.extent(0);
int n_subA = subA.extent(1);
T* addr_subA = subA.data_handle();
const T* addr_A_base = a.data_handle();
const T* addr_A = addr_A_base + row * m_subA + col * n_subA * ld_A;
// subA = A_row,col
copy_kernel<<<16, 16, 0, s>>>(m_subA, n_subA, addr_A, ld_A, addr_subA, ld_subA);
};
}
template <typename T>
void COPY_FROM_SUBMATRICES(context& ctx, logical_data<slice<T, 2>>& A, logical_data<slice<T, 2>> subA[2][2])
{
// To copy to a subset, this is a write only access, so that we did not need a valid copy for subA before ...
// When copying from a subset to the whole matrix, we need a RW because we only modify a part of the matrix
ctx.task(A.write(), subA[0][0].read(), subA[0][1].read(), subA[1][0].read(), subA[1][1].read()).set_symbol("COPY_FROM")
->*[&](cudaStream_t s, auto a, auto a00, auto a01, auto a10, auto a11) {
int ld_A = a.stride(1);
T* addr_A_base = a.data_handle();
for (int col = 0; col < 2; col++)
{
for (int row = 0; row < 2; row++)
{
auto& subA = col == 0 ? (row == 0 ? a00 : a10) : (row == 0 ? a01 : a11);
int m_subA = subA.extent(0);
int n_subA = subA.extent(1);
int ld_subA = subA.stride(1);
const T* addr_subA = subA.data_handle();
T* addr_A = addr_A_base + row * m_subA + col * n_subA * ld_A;
// A_row,col= subA
copy_kernel<<<16, 16, 0, s>>>(m_subA, n_subA, addr_subA, ld_subA, addr_A, ld_A);
}
}
};
}
template <typename T>
void COPY_MATRIX(context& ctx, logical_data<slice<T, 2>>& dst, logical_data<slice<T, 2>>& src)
{
// This is a write only access, so that we did not need a valid copy for subA before ...
ctx.task(dst.write(), src.read()).set_symbol("COPY")->*[&](cudaStream_t s, auto d_dst, auto d_src) {
int ld_src = d_dst.stride(1);
int ld_dst = d_src.stride(1);
auto m = d_src.extent(0);
assert(m == d_dst.extent(0));
auto n = d_src.extent(1);
assert(n == d_dst.extent(1));
const T* addr_src = d_src.data_handle();
T* addr_dst = d_dst.data_handle();
copy_kernel<<<16, 16, 0, s>>>(m, n, addr_src, ld_src, addr_dst, ld_dst);
};
}
void MULT(context& ctx, logical_matrix& A, logical_matrix& B, logical_matrix& C);
void MULT_REC_NAIVE(context& ctx, logical_matrix& A, logical_matrix& B, logical_matrix& C)
{
logical_matrix subA[2][2], subB[2][2], subC[2][2];
size_t N = get_m(A);
assert(get_m(A) == get_n(A));
assert(get_m(B) == get_n(B));
assert(get_m(C) == get_n(C));
assert(N % 2 == 0);
size_t half_N = N / 2;
// These are TMP data which don't have a valid copy yet
for (int col = 0; col < 2; col++)
{
for (int row = 0; row < 2; row++)
{
subA[row][col] = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
subB[row][col] = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
subC[row][col] = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_TO_SUBMATRIX(ctx, A, subA[row][col], row, col);
COPY_TO_SUBMATRIX(ctx, B, subB[row][col], row, col);
}
}
for (int col = 0; col < 2; col++)
{
for (int row = 0; row < 2; row++)
{
for (int k = 0; k < 2; k++)
{
auto Ck = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
MULT(ctx, subA[row][k], subB[k][col], Ck);
ADD(ctx, subC[row][col], 1.0, Ck);
}
// C_row,col = subC[row][col]
COPY_FROM_SUBMATRICES(ctx, C, subC);
}
}
}
void MULT_STRASSEN(context& ctx, logical_matrix& A, logical_matrix& B, logical_matrix& C)
{
/*
* STRASSEN ALGORITHM
*
* M1 = (A00 + A11)(B00 + B11)
* M2 = (A10 + A11)B00
* M3 = A00(B01 - B11)
* M4 = A11(B10 - B00)
* M5 = (A00 + A01)B11
* M6 = (A10 - A00)(B00 + B01)
* M7 = (A01 - A11)(B10 + B11)
*
* C00 = M1 + M4 - M5 + M7
* C01 = M3 + M5
* C10 = M2 + M4
* C11 = M1 - M2 + M3 + M6
*
*/
size_t N = get_m(A);
assert(N % 2 == 0);
size_t half_N = N / 2;
logical_matrix subA[2][2], subB[2][2], subC[2][2];
auto M1 = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
auto M2 = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
auto M3 = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
auto M4 = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
auto M5 = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
auto M6 = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
auto M7 = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
assert(get_m(A) == get_n(A));
assert(get_m(B) == get_n(B));
assert(get_m(C) == get_n(C));
// These are TMP data which don't have a valid copy yet
for (int col = 0; col < 2; col++)
{
for (int row = 0; row < 2; row++)
{
subA[row][col] = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
subB[row][col] = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
subC[row][col] = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_TO_SUBMATRIX(ctx, A, subA[row][col], row, col);
COPY_TO_SUBMATRIX(ctx, B, subB[row][col], row, col);
}
}
// M1 = (A00 + A11)(B00 + B11)
{
auto left = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N)),
right = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_MATRIX(ctx, left, subA[0][0]);
ADD(ctx, left, 1.0, subA[1][1]);
COPY_MATRIX(ctx, right, subB[0][0]);
ADD(ctx, right, 1.0, subB[1][1]);
MULT(ctx, left, right, M1);
}
// M2 = (A10 + A11)B00
{
auto left = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_MATRIX(ctx, left, subA[1][0]);
ADD(ctx, left, 1.0, subA[1][1]);
MULT(ctx, left, subB[0][0], M2);
}
// M3 = A00(B01 - B11)
{
auto right = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_MATRIX(ctx, right, subB[0][1]);
ADD(ctx, right, -1.0, subB[1][1]);
MULT(ctx, subA[0][0], right, M3);
}
// M4 = A11(B10 - B00)
{
auto right = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_MATRIX(ctx, right, subB[1][0]);
ADD(ctx, right, -1.0, subB[0][0]);
MULT(ctx, subA[1][1], right, M4);
}
// M5 = (A00 + A01)B11
{
auto left = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_MATRIX(ctx, left, subA[0][0]);
ADD(ctx, left, 1.0, subA[0][1]);
MULT(ctx, left, subB[1][1], M5);
}
// M6 = (A10 - A00)(B00 + B01)
{
auto left = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N)),
right = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_MATRIX(ctx, left, subA[1][0]);
ADD(ctx, left, -1.0, subA[1][1]);
COPY_MATRIX(ctx, right, subB[0][0]);
ADD(ctx, right, 1.0, subB[0][1]);
MULT(ctx, left, right, M6);
}
// M7 = (A01 - A11)(B10 + B11)
{
auto left = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
auto right = ctx.logical_data(shape_of<slice<double, 2>>(half_N, half_N));
COPY_MATRIX(ctx, left, subA[0][1]);
ADD(ctx, left, -1.0, subA[1][1]);
COPY_MATRIX(ctx, right, subB[1][0]);
ADD(ctx, right, 1.0, subB[1][1]);
MULT(ctx, left, right, M7);
}
// C00 = M1 + M4 - M5 + M7
COPY_MATRIX(ctx, subC[0][0], M1);
ADD(ctx, subC[0][0], 1.0, M4);
ADD(ctx, subC[0][0], -1.0, M5);
ADD(ctx, subC[0][0], -1.0, M5);
ADD(ctx, subC[0][0], 1.0, M7);
// C01 = M3 + M5
COPY_MATRIX(ctx, subC[0][1], M3);
ADD(ctx, subC[0][1], 1.0, M5);
// C10 = M2 + M4
COPY_MATRIX(ctx, subC[1][0], M2);
ADD(ctx, subC[1][0], 1.0, M4);
// C11 = M1 - M2 + M3 + M6
COPY_MATRIX(ctx, subC[1][1], M1);
ADD(ctx, subC[1][1], -1.0, M2);
ADD(ctx, subC[1][1], 1.0, M3);
ADD(ctx, subC[1][1], 1.0, M6);
// Write back subsets of C to C
COPY_FROM_SUBMATRICES(ctx, C, subC);
}
void MULT(context& ctx, logical_matrix& A, logical_matrix& B, logical_matrix& C)
{
size_t N = get_m(A);
if (N <= BLOCKSIZE)
{
MULT_CLASSIC(ctx, A, B, C);
}
else
{
// MULT_REC_NAIVE(ctx, A, B, C);
MULT_STRASSEN(ctx, A, B, C);
}
}
void strassen_test(context& ctx, size_t N)
{
double* A = new double[N * N];
double* B = new double[N * N];
double* C = new double[N * N];
int ldA = N;
int ldB = N;
int ldC = N;
cuda_safe_call(cudaHostRegister(A, N * N * sizeof(double), cudaHostRegisterPortable));
cuda_safe_call(cudaHostRegister(B, N * N * sizeof(double), cudaHostRegisterPortable));
cuda_safe_call(cudaHostRegister(C, N * N * sizeof(double), cudaHostRegisterPortable));
for (size_t col = 0; col < N; col++)
{
for (size_t row = 0; row < N; row++)
{
A[row + N * col] = 1.0;
B[row + N * col] = -1.0;
C[row + N * col] = 0.0;
}
}
auto descA = ctx.logical_data(make_slice(A, std::tuple{N, N}, ldA)),
descB = ctx.logical_data(make_slice(B, std::tuple{N, N}, ldB)),
descC = ctx.logical_data(make_slice(C, std::tuple{N, N}, ldC));
descA.set_symbol("A");
descB.set_symbol("B");
descC.set_symbol("C");
std::chrono::steady_clock::time_point start, stop;
ctx.host_launch(descC.read())->*[&](auto /* ignored */) {
start = std::chrono::steady_clock::now();
};
MULT(ctx, descA, descB, descC);
ctx.host_launch(descC.read())->*[&](auto /* ignored */) {
stop = std::chrono::steady_clock::now();
};
ctx.finalize();
std::chrono::duration<double> duration = stop - start;
fprintf(stderr, "Elapsed: %.2lf ms\n", duration.count() * 1000.0);
}
int main(int argc, char** argv)
{
long N = 2 * BLOCKSIZE;
if (argc > 1)
{
N = atoi(argv[1]);
}
bool use_graphs = false;
if (argc > 2)
{
use_graphs = (atoi(argv[2]) > 0);
}
// Set up CUBLAS
int ndevs;
cuda_safe_call(cudaGetDeviceCount(&ndevs));
cublas_handle.resize(ndevs);
for (int d = 0; d < ndevs; d++)
{
cuda_safe_call(cudaSetDevice(d));
cuda_safe_call(cublasCreate(&cublas_handle[d]));
}
cuda_safe_call(cudaSetDevice(0));
context ctx;
if (use_graphs)
{
ctx = graph_ctx();
}
strassen_test(ctx, N);
}

73
cccl_upstream/cudax/examples/stf/logical_gates_composition.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 Composition of boolean operations applied on logical data
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
// z = AND(x,y)
logical_data<slice<int>> AND(context& ctx, logical_data<slice<int>> x, logical_data<slice<int>> y)
{
assert(x.shape().size() == y.shape().size());
auto z = ctx.logical_data(x.shape());
std::string symbol = "(" + x.get_symbol() + " & " + y.get_symbol() + ")";
z.set_symbol(symbol);
ctx.parallel_for(z.shape(), x.read(), y.read(), z.write()).set_symbol("AND")->*
[] __device__(size_t i, auto dx, auto dy, auto dz) {
dz(i) = dx(i) & dy(i);
};
return z;
}
// y = NOT(x)
logical_data<slice<int>> NOT(context& ctx, logical_data<slice<int>> x)
{
auto y = ctx.logical_data(x.shape());
std::string symbol = "( !" + x.get_symbol() + ")";
y.set_symbol(symbol);
ctx.parallel_for(y.shape(), x.read(), y.write()).set_symbol("NOT")->*[] __device__(size_t i, auto dx, auto dy) {
dy(i) = ~dx(i);
};
return y;
}
int main()
{
const size_t n = 12;
int X[n], Y[n], Z[n];
context ctx;
auto lX = ctx.logical_data(X);
auto lY = ctx.logical_data(Y);
auto lZ = ctx.logical_data(Z);
lX.set_symbol("X");
lY.set_symbol("Y");
lZ.set_symbol("Z");
auto lB = AND(ctx, AND(ctx, lX, lY), AND(ctx, lX, lZ));
auto lC = AND(ctx, NOT(ctx, AND(ctx, lB, NOT(ctx, lY))), lX);
ctx.finalize();
}

129
cccl_upstream/cudax/examples/stf/mandelbrot.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 A transparent multi-GPU implementation of Mandelbrot fractal using parallel_for
*/
#include <cuda/experimental/stf.cuh>
#include <fstream>
#include <iostream>
using namespace cuda::experimental::stf;
int main(int argc, char** argv)
{
context ctx;
// Image dimensions
size_t width = 2000;
size_t height = 1000;
// Complex plane boundaries
double xMin = -2.0;
double xMax = 1.0;
double yMin = -1.5;
double yMax = 1.5;
// Maximum number of iterations
int maxIterations = 256;
// Describe a 2D array of integers of size (width x height)
auto lbuffer = ctx.logical_data(shape_of<slice<int, 2>>(width, height));
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
// Compute each pixel
ctx.parallel_for(blocked_partition(), exec_place::all_devices(), lbuffer.shape(), lbuffer.write())
->*[=] _CCCL_DEVICE(size_t x, size_t y, auto buffer) {
// Map pixel coordinates to complex plane
// c = cr + i ci
double cr = x * (xMax - xMin) / width + xMin;
double ci = y * (yMax - yMin) / height + yMin;
// z = zr + i zi
double zr = 0.0;
double zi = 0.0;
int iterations = 0;
// Evaluate depth
while (zr * zr + zi * zi < 4 && iterations < maxIterations)
{
// compute : z = z * z + c;
//
// z = (zr + i zi) (zr + i zi) + cr + i ci
// z = zr zr - zi zi + 2 i zi zr + cr + i ci
// zr = (zr zr - zi zi + cr)
// zi = (2 zi zr + ci)
double zr_prev = zr;
double zi_prev = zi;
zr = zr_prev * zr_prev - zi_prev * zi_prev + cr;
zi = 2.0 * zr_prev * zi_prev + ci;
iterations++;
}
buffer(x, y) = iterations;
};
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
if (argc > 1)
{
auto fileName = std::string(argv[1]);
// Generate a PPM file from the buffer
ctx.host_launch(lbuffer.read())->*[&](auto buffer) {
std::ofstream imageFile(fileName, std::ios::binary);
if (!imageFile)
{
std::cerr << "Failed to create image file: " << fileName << '\n';
return;
}
imageFile << "P6\n";
imageFile << width << " " << height << "\n";
imageFile << "255\n";
for (size_t y = 0; y < height; y++)
{
for (size_t x = 0; x < width; x++)
{
int iterations = buffer(x, y);
// Convert iterations to RGB values
unsigned char r = (iterations % 8) * 32;
unsigned char g = (iterations % 16) * 16;
unsigned char b = (iterations % 32) * 8;
// Write pixel data to file
imageFile << r << g << b;
}
}
imageFile.close();
std::cout << "Mandelbrot image generated and saved as " << fileName << '\n';
};
}
ctx.finalize();
// Must call this first, see e.g.
// https://stackoverflow.com/questions/6551121/cuda-cudaeventelapsedtime-returns-device-not-ready-error
cuda_safe_call(cudaEventSynchronize(stop));
fprintf(stderr, "Mandelbrot took %.2f ms\n", cuda_try<cudaEventElapsedTime>(start, stop));
}

74
cccl_upstream/cudax/examples/stf/parallel_for_2D.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 using parallel_for on shapes with different dimensions
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
__host__ __device__ double x0(size_t i, size_t j)
{
return sin((double) (i - j));
}
__host__ __device__ double y0(size_t i, size_t j)
{
return cos((double) (i + j));
}
int main()
{
context ctx;
const size_t N = 16;
double X[2 * N * 2 * N];
double Y[N * N];
auto lx = ctx.logical_data(make_slice(&X[0], std::tuple{2 * N, 2 * N}, 2 * N));
auto ly = ctx.logical_data(make_slice(&Y[0], std::tuple{N, N}, N));
ctx.parallel_for(lx.shape(), lx.write())->*[=] _CCCL_DEVICE(size_t i, size_t j, auto sx) {
sx(i, j) = x0(i, j);
};
ctx.parallel_for(ly.shape(), lx.read(), ly.write())->*[=] _CCCL_DEVICE(size_t i, size_t j, auto sx, auto sy) {
sy(i, j) = y0(i, j);
for (size_t ii = 0; ii < 2; ii++)
{
for (size_t jj = 0; jj < 2; jj++)
{
sy(i, j) += sx(2 * i + ii, 2 * j + jj);
}
}
};
ctx.parallel_for(exec_place::host(), ly.shape(), ly.read())
->*[=] __host__(size_t i, size_t j, slice<const double, 2> sy) {
double expected = y0(i, j);
for (size_t ii = 0; ii < 2; ii++)
{
for (size_t jj = 0; jj < 2; jj++)
{
expected += x0(2 * i + ii, 2 * j + jj);
}
}
if (fabs(sy(i, j) - expected) > 0.001)
{
printf("sy(%zu, %zu) %f expect %f\n", i, j, sy(i, j), expected);
}
};
ctx.finalize();
}

143
cccl_upstream/cudax/examples/stf/partitioned_axpy.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) 2026 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
/**
* @file
*
* @brief AXPY over data distributed across the machine's devices with a
* structured partition specification
*
* The partition ("dimension 0, blocked over the grid of devices") is
* expressed once as a cute_partition. The same description is then used to:
*
* 1. EVALUATE the placement before committing any memory
* (evaluate_localized_placement: bytes per place, placement accuracy);
* 2. back a logical data with a composite data place, so STF tasks operate
* on memory whose pages physically live on the device that owns them;
* 3. perform a raw geometry-aware allocation (allocate_nd(data_dims, elemsize))
* outside of any STF context.
*
* Each place computes its own blocked portion (the idiomatic grid-task
* pattern), so no cross-device access is required; peer/mempool access setup
* is handled by the places machinery itself.
*/
#include <cuda/experimental/stf.cuh>
#include <cmath>
#include <cstdio>
using namespace cuda::experimental::stf;
__global__ void axpy(size_t start, size_t cnt, double a, const double* x, double* y)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int nthreads = gridDim.x * blockDim.x;
for (size_t i = tid; i < cnt; i += nthreads)
{
y[start + i] += a * x[start + i];
}
}
double X0(size_t i)
{
return sin((double) i);
}
double Y0(size_t i)
{
return cos((double) i);
}
int main()
{
// The places machinery enumerates the devices and sets up peer/mempool
// access between them; on a single-GPU machine this is one place.
auto all_devs = exec_place::all_devices();
const size_t nplaces = all_devs.get_dims().size();
const size_t N = 4 * 1024 * 1024;
// "Dimension 0, blocked over grid axis 0" - the per-dimension specification
auto part = make_partition(dim4(N), partition_spec{blocked<0>}, all_devs.get_dims());
// 1. Score the mapping before allocating anything
auto stats = evaluate_localized_placement(all_devs, part, sizeof(double));
printf("Placement over %zu place(s): %zu blocks in %zu allocations, accuracy %.1f%%\n",
nplaces,
stats.nblocks,
stats.nallocs,
100.0 * stats.accuracy());
for (const auto& entry : stats.bytes_per_place)
{
printf(" %s: %.2f MB\n", entry.first.c_str(), entry.second / (1024.0 * 1024.0));
}
// 2. Run STF tasks over logical data placed by the same policy
stream_ctx ctx;
::std::vector<double> X(N), Y(N);
for (size_t i = 0; i < N; i++)
{
X[i] = X0(i);
Y[i] = Y0(i);
}
auto lX = ctx.logical_data(&X[0], {N});
auto lY = ctx.logical_data(&Y[0], {N});
const double alpha = 3.14;
// The composite data place distributes instances across the grid with the
// classic blocked partitioner (the callback form of the same policy)
auto dist = data_place::composite(blocked_partition_custom<0>{}, all_devs);
// One task over the grid; each place computes its own blocked chunk
auto t = ctx.task(all_devs, lX.read(dist), lY.rw(dist));
t->*[&](auto, auto dX, auto dY) {
const size_t chunk = (N + nplaces - 1) / nplaces;
for (size_t i = 0; i < nplaces; i++)
{
const size_t start = i * chunk;
if (start >= N)
{
// With ceil-division chunks, trailing places may have no work
continue;
}
const size_t cnt = ::std::min(chunk, N - start);
auto active = t.activate_place(i);
axpy<<<128, 128, 0, t.get_stream(i)>>>(start, cnt, alpha, dX.data_handle(), dY.data_handle());
}
};
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
if (fabs(Y[i] - (Y0(i) + alpha * X0(i))) > 0.0001)
{
fprintf(stderr, "Verification FAILED at %zu\n", i);
return 1;
}
}
printf("STF task over composite-placed data: verified\n");
// 3. Raw geometry-aware allocation, no STF context involved
auto dp = ::cuda::experimental::places::make_composite_data_place(all_devs, part);
void* raw = dp.allocate_nd(dim4(N), sizeof(double));
auto* d_buf = static_cast<double*>(raw);
cuda_safe_call(cudaMemset(d_buf, 0, N * sizeof(double)));
cuda_safe_call(cudaDeviceSynchronize());
dp.deallocate(raw, N * sizeof(double));
printf("Raw shaped allocation on the partitioned place: OK\n");
return 0;
}

52
cccl_upstream/cudax/examples/stf/pi.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 Approximate pi using Monte Carlo method
*
*/
#include <cuda/experimental/stf.cuh>
#include <curand_kernel.h>
#include <stdio.h>
using namespace cuda::experimental::stf;
int main(int, char**)
{
context ctx;
auto lsum = ctx.logical_data(shape_of<scalar_view<size_t>>());
size_t N = 1000000;
ctx.parallel_for(box(N), lsum.reduce(reducer::sum<size_t>{}))->*[] __device__(size_t i, auto& sum) {
curandState local_state;
curand_init(1234, i, 0, &local_state);
double x = curand_uniform_double(&local_state); // Random x in [0, 1)
double y = curand_uniform_double(&local_state); // Random y in [0, 1)
// Count (x,y) coordinates which are within the unit circle
if (x * x + y * y <= 1.0)
{
sum++;
}
};
// We get the ratio of "shots" within the unit circle and the total number of
// "shots". The surface of the quarter of unit circle [0, 1) x [0, 1) is pi/4
auto res = ctx.wait(lsum);
double pi_val = (4.0 * res) / N;
ctx.finalize();
_CCCL_ASSERT(fabs(pi_val - 3.1415) < 0.1, "Invalid result");
}

204
cccl_upstream/cudax/examples/stf/scan.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 A parallel scan algorithm using CUB kernels
*
*/
#include <cub/cub.cuh> // or equivalently <cub/device/device_scan.cuh>
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
__host__ __device__ double X0(int i)
{
return sin((double) i);
}
/**
* @brief Performs an inclusive scan on a logical data slice using CUB.
*
* This function determines the temporary device storage requirements for a scan, allocates
* temporary storage, and then performs the scan using the CUB library. The scan is performed
* in place, modifying the input `logical_data` slice.
*
* @tparam Ctx The context type for data management and task execution.
* @tparam T The data type of the elements in the `logical_data` slice.
*
* @param ctx Reference to the context object.
* @param ld Reference to the `logical_data` object containing the data slice.
* @param dp The `data_place` enum specifying where the data should reside (e.g., CPU, GPU).
*/
template <typename Ctx, typename T>
void scan(Ctx& ctx, logical_data<slice<T>>& ld, data_place dp)
{
// Determine temporary device storage requirements
auto num_items = int(ld.shape().size());
size_t tmp_size = 0;
cub::DeviceScan::InclusiveSum(nullptr, tmp_size, (T*) nullptr, (T*) nullptr, num_items);
// fprintf(stderr, "SCAN %ld items TMP = %ld bytes\n", num_items, tmp_size);
logical_data<slice<char>> ltmp = ctx.logical_data(shape_of<slice<char>>(tmp_size)).set_symbol("tmp");
ctx.task(ld.rw(mv(dp)), ltmp.write()).set_symbol("scan " + ld.get_symbol())
->*[=](cudaStream_t stream, auto d, auto tmp) mutable {
T* buffer = d.data_handle();
cub::DeviceScan::InclusiveSum(tmp.data_handle(), tmp_size, buffer, buffer, num_items, stream);
};
}
int main(int argc, char** argv)
{
stream_ctx ctx;
// graph_ctx ctx;
// const size_t N = 128ULL*1024ULL*1024ULL;
size_t nmb = 128;
if (argc > 1)
{
nmb = atoi(argv[1]);
}
int check = 0;
if (argc > 2)
{
check = atoi(argv[2]);
}
const size_t N = nmb * 1024ULL * 1024ULL;
const int ndevs = cuda_try<cudaGetDeviceCount>();
const size_t NBLOCKS = 2 * ndevs;
size_t BLOCK_SIZE = (N + NBLOCKS - 1) / NBLOCKS;
auto fixed_alloc = block_allocator<fixed_size_allocator>(ctx, BLOCK_SIZE * sizeof(double));
ctx.set_allocator(fixed_alloc);
// dummy task to initialize the allocator XXX
{
auto ldummy = ctx.logical_data(shape_of<slice<double>>(NBLOCKS)).set_symbol("dummy");
ctx.task(ldummy.write(data_place::managed()))->*[](cudaStream_t, auto) {};
}
std::vector<double> X(N);
std::vector<logical_data<slice<double>>> lX(NBLOCKS);
logical_data<slice<double>> laux;
// If we were to register each part one by one, there could be pages which
// cross multiple parts, and the pinning operation would fail.
cuda_safe_call(cudaHostRegister(&X[0], N * sizeof(double), cudaHostRegisterPortable));
for (size_t b = 0; b < NBLOCKS; b++)
{
size_t start = b * BLOCK_SIZE;
size_t end = std::min(start + BLOCK_SIZE, N);
lX[b] = ctx.logical_data(&X[start], {end - start}).set_symbol("X_" + std::to_string(b));
// No need to move this back to the host if we do not check the result
if (!check)
{
lX[b].set_write_back(false);
}
}
for (size_t b = 0; b < NBLOCKS; b++)
{
cuda_safe_call(cudaSetDevice(b % ndevs));
size_t start = b * BLOCK_SIZE;
ctx.parallel_for(lX[b].shape(), lX[b].write())->*[=] _CCCL_DEVICE(size_t i, auto lx) {
lx(i) = X0(i + start);
};
}
cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
cudaEvent_t start, stop;
cuda_safe_call(cudaEventCreate(&start));
cuda_safe_call(cudaEventCreate(&stop));
cuda_safe_call(cudaEventRecord(start, ctx.fence()));
for (size_t k = 0; k < 100; k++)
{
// Create an auxiliary temporary buffer and blank it
laux = ctx.logical_data(shape_of<slice<double>>(NBLOCKS)).set_symbol("aux");
ctx.parallel_for(laux.shape(), laux.write(data_place::managed())).set_symbol("init_aux")
->*[] _CCCL_DEVICE(size_t i, auto aux) {
aux(i) = 0.0;
};
// Scan each block
for (size_t b = 0; b < NBLOCKS; b++)
{
cuda_safe_call(cudaSetDevice(b % ndevs));
scan(ctx, lX[b], data_place::device(b % ndevs));
}
for (size_t b = 0; b < NBLOCKS; b++)
{
// cuda_safe_call(cudaSetDevice(b % ndevs));
ctx.parallel_for(exec_place::device(0),
box({b, b + 1}),
lX[b].read(data_place::device(b % ndevs)),
laux.rw(data_place::managed()))
.set_symbol("store sum X_" + std::to_string(b))
->*[] _CCCL_DEVICE(size_t ind, auto Xb, auto aux) {
aux(ind) = Xb(Xb.extent(0) - 1);
};
}
// Prefix sum of the per-block sums
scan(ctx, laux, data_place::managed());
// Add partial sum of Xi to X(i+1)
for (size_t b = 1; b < NBLOCKS; b++)
{
cuda_safe_call(cudaSetDevice(b % ndevs));
ctx.parallel_for(lX[b].shape(), lX[b].rw(), laux.read(data_place::managed()))
.set_symbol("add X_" + std::to_string(b))
->*[=] _CCCL_DEVICE(size_t i, auto Xb, auto aux) {
Xb(i) += aux(b - 1);
};
}
}
cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
ctx.finalize();
float ms = 0;
cuda_safe_call(cudaEventElapsedTime(&ms, start, stop));
fprintf(stdout, "%zu %f ms\n", N / 1024 / 1024, ms);
if (check)
{
#if 0
for (size_t i = 0; i < N; i++) {
EXPECT(fabs(X[i] - expected_result[i]) < 0.00001);
}
#endif
#if 1
fprintf(stderr, "Checking result ...\n");
EXPECT(fabs(X[0] - X0(0)) < 0.00001);
for (size_t i = 0; i < N; i++)
{
EXPECT(fabs(X[i] - X[i - 1] - X0(i)) < 0.00001);
}
}
#endif
}

80
cccl_upstream/cudax/examples/stf/sqrt_newton_stackable.cu
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@@ -1,80 +0,0 @@
//===----------------------------------------------------------------------===//
//
// Part of CUDASTF in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2022-2025 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
/**
* @file
*
* @brief Compute square roots via Newton's method using while_graph_scope
*
* This is a minimal example of an iterative solver with convergence
* checking in a stackable context. Each iteration applies the
* Babylonian step x <- (x + S/x) / 2 and reduces the maximum
* absolute change across all elements. The while loop exits once
* the change drops below a tolerance.
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int main()
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving example: while_graph_scope requires CUDA 12.4+.\n");
return 0;
#else
stackable_ctx ctx;
constexpr size_t N = 1024;
constexpr double tol = 1e-12;
::std::vector<double> host_S(N);
::std::vector<double> host_X(N);
for (size_t i = 0; i < N; i++)
{
host_S[i] = 1.0 + static_cast<double>(i);
host_X[i] = host_S[i]; // initial guess x0 = S
}
auto lS = ctx.logical_data(make_slice(host_S.data(), N)).set_symbol("S");
lS.set_read_only();
auto lX = ctx.logical_data(make_slice(host_X.data(), N)).set_symbol("X");
auto lmax_err = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("max_err");
{
auto while_guard = ctx.while_graph_scope();
// Babylonian step: x = (x + S/x) / 2, reduce max |change|
ctx.parallel_for(box(N), lX.rw(), lS.read(), lmax_err.reduce(reducer::maxval<double>{}))
->*[] __device__(size_t i, auto x, auto s, auto& max_err) {
double x_old = x(i);
double x_new = 0.5 * (x_old + s(i) / x_old);
x(i) = x_new;
max_err = fabs(x_new - x_old);
};
while_guard.update_cond(lmax_err.read())->*[tol] __device__(auto max_err) {
return (*max_err > tol);
};
}
ctx.finalize();
for (size_t i = 0; i < N; i++)
{
double expected = sqrt(1.0 + static_cast<double>(i));
EXPECT(fabs(host_X[i] - expected) < 1e-8);
}
return 0;
#endif
}

77
cccl_upstream/cudax/examples/stf/standalone-launches.cu
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@@ -1,77 +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 test illustrates how we can use multiple reserved::launch in a single task on different pieces of data
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
int X0(int i)
{
return i * i + 12;
}
int main()
{
stream_ctx ctx;
const int N = 16;
int X[N], Y[N], Z[N];
for (size_t ind = 0; ind < N; ind++)
{
X[ind] = X0(ind);
Y[ind] = 0;
Z[ind] = 0;
}
auto handle_X = ctx.logical_data(X, {N});
auto handle_Y = ctx.logical_data(Y, {N});
auto handle_Z = ctx.logical_data(Z, {N});
ctx.task(handle_X.read(), handle_Y.write(), handle_Z.write())
->*[](cudaStream_t s, slice<const int> x, slice<int> y, slice<int> z) {
std::vector<cudaStream_t> streams;
streams.push_back(s);
auto spec = par(1024);
reserved::launch(spec, exec_place::current_device(), streams, std::tuple{x, y})
->*[] _CCCL_DEVICE(auto t, slice<const int> x, slice<int> y) {
size_t tid = t.rank();
size_t nthreads = t.size();
for (size_t ind = tid; ind < N; ind += nthreads)
{
y(ind) = 2 * x(ind);
}
};
reserved::launch(spec, exec_place::current_device(), streams, std::tuple{y, z})
->*[] _CCCL_DEVICE(auto t, slice<int> y, slice<int> z) {
size_t tid = t.rank();
size_t nthreads = t.size();
for (size_t ind = tid; ind < N; ind += nthreads)
{
z(ind) = 3 * y(ind);
}
};
};
ctx.finalize();
for (size_t ind = 0; ind < N; ind++)
{
assert(Y[ind] == 2 * X[ind]);
assert(Z[ind] == 3 * Y[ind]);
}
}

135
cccl_upstream/cudax/examples/stf/thrust_zip_iterator.cu
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@@ -1,135 +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 illustrates how we can convert Thrust iterators to
* logical data, and how to create thrust iterators from data instances in a
* task.
*/
#include <thrust/device_vector.h>
#include <thrust/functional.h>
#include <thrust/host_vector.h>
#include <thrust/iterator/zip_iterator.h>
#include <thrust/transform.h>
#include <cuda/experimental/stf.cuh>
#include <iostream>
using namespace cuda::experimental::stf;
// Functor to apply the transformation
struct my_transform_functor
{
__host__ __device__ int operator()(const cuda::std::tuple<int, char>& t) const
{
int a = cuda::std::get<0>(t);
char b = cuda::std::get<1>(t);
return a + static_cast<int>(b); // Example operation
}
};
/*
* How to use CUDASTF to manipulate data originally created using Thrust
*/
template <typename ZippedIt, typename OutIt>
void thrust_algorithm(context& ctx, ZippedIt& first, ZippedIt& last, OutIt& output, data_place data_location)
{
/*
* Interpret Thrust data structures as logical data
*/
size_t num_elements = cuda::std::distance(first, last);
// Extract underlying iterators from the zip iterator
auto itA = cuda::std::get<0>(first.get_iterator_tuple());
int* A = thrust::raw_pointer_cast(&(*itA));
auto itB = cuda::std::get<1>(first.get_iterator_tuple());
char* B = thrust::raw_pointer_cast(&(*itB));
int* C = thrust::raw_pointer_cast(output.data());
auto lA = ctx.logical_data(make_slice(A, num_elements), data_location);
auto lB = ctx.logical_data(make_slice(B, num_elements), data_location);
auto lC = ctx.logical_data(make_slice(C, num_elements), data_location);
/* Important : result C will only be valid once we finalize the context or introduce a task fence ! */
ctx.task(lA.read(), lB.read(), lC.write())->*[](cudaStream_t stream, auto dA, auto dB, auto dC) {
// Reconstruct a zipped iterator from the data instances passed to the lambda function
size_t num_elements = dA.size();
auto dfirst = thrust::make_zip_iterator(cuda::std::tuple(dA.data_handle(), dB.data_handle()));
auto dlast = dfirst + num_elements;
// Create a device pointer from the raw pointer
thrust::device_ptr<int> dout = thrust::device_pointer_cast(dC.data_handle());
thrust::transform(thrust::cuda::par_nosync.on(stream), dfirst, dlast, dout, my_transform_functor());
};
}
int main()
{
context ctx;
/*
* First create device vectors and zipped them
*/
thrust::device_vector<int> A(3);
thrust::device_vector<char> B(3);
thrust::device_vector<int> C(3);
A[0] = 10;
A[1] = 20;
A[2] = 30;
B[0] = 'x';
B[1] = 'y';
B[2] = 'z';
auto first = thrust::make_zip_iterator(cuda::std::tuple(A.begin(), B.begin()));
auto last = thrust::make_zip_iterator(cuda::std::tuple(A.end(), B.end()));
thrust_algorithm(ctx, first, last, C, data_place::current_device());
/*
* Use host data, and rely on CUDASTF for transfers
*/
thrust::host_vector<int> hA(3);
thrust::host_vector<char> hB(3);
thrust::host_vector<int> hC(3);
hA[0] = 10;
hA[1] = 20;
hA[2] = 30;
hB[0] = 'x';
hB[1] = 'y';
hB[2] = 'z';
auto hfirst = thrust::make_zip_iterator(cuda::std::tuple(hA.begin(), hB.begin()));
auto hlast = thrust::make_zip_iterator(cuda::std::tuple(hA.end(), hB.end()));
thrust_algorithm(ctx, hfirst, hlast, hC, data_place::host());
/* Before this, we cannot assume that the Thrust algorithms have been
* performed and/or that the results have been written back to their original
* location. */
ctx.finalize();
// Check results
for (size_t i = 0; i < 3; i++)
{
EXPECT(C[i] == (A[i] + static_cast<int>(B[i])));
EXPECT(hC[i] == (hA[i] + static_cast<int>(hB[i])));
}
return 0;
}

57
cccl_upstream/cudax/examples/stf/void_data_interface.cu
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@@ -1,57 +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 Illustrate how to use the void data interface
*
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
__global__ void dummy_kernel() {}
int main()
{
context ctx;
auto token = ctx.token();
ctx.task(token.write())->*[](cudaStream_t) {
};
void_interface sync;
auto token2 = ctx.logical_data(sync);
auto token3 = ctx.token();
ctx.task(token2.write(), token.read())->*[](cudaStream_t) {
};
// Do not pass useless arguments by removing void_interface arguments
// Note that the rw() access is possible even if there was no prior write()
// or actual underlying data.
ctx.task(token3.rw(), token.read())->*[](cudaStream_t) {
};
ctx.cuda_kernel(token3.rw())->*[]() {
return cuda_kernel_desc{dummy_kernel, 16, 128, 0};
};
EXPECT(token.is_void_interface());
EXPECT(token2.is_void_interface());
EXPECT(token3.is_void_interface());
ctx.finalize();
}

91
cccl_upstream/cudax/examples/stf/word_count.cu
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@@ -1,91 +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 Counting words in a text using a launch kernel
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
// determines whether the character is alphabetical
__host__ __device__ bool is_alpha(const char c)
{
return (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z');
}
int main()
{
// Paragraph from 'The Raven' by Edgar Allan Poe
// http://en.wikipedia.org/wiki/The_Raven
const char raw_input[] =
" But the raven, sitting lonely on the placid bust, spoke only,\n"
" That one word, as if his soul in that one word he did outpour.\n"
" Nothing further then he uttered - not a feather then he fluttered -\n"
" Till I scarcely more than muttered `Other friends have flown before -\n"
" On the morrow he will leave me, as my hopes have flown before.'\n"
" Then the bird said, `Nevermore.'\n";
context ctx;
auto ltext = ctx.logical_data(const_cast<char*>(&raw_input[0]), {sizeof(raw_input)});
int cnt = 0;
auto lcnt = ctx.logical_data(&cnt, {1});
auto number_devices = 2;
auto all_devs = exec_place::repeat(exec_place::device(0), number_devices);
auto spec = par(con(128));
ctx.launch(spec, all_devs, ltext.read(), lcnt.rw())->*[] _CCCL_DEVICE(auto th, auto text, auto cnt) {
int local_cnt = 0;
for (size_t i = th.rank(); i < text.size() - 1; i += th.size())
{
/* If the thread encounters the beginning of a new word, increment
* its local counter */
if (!is_alpha(text(i)) && is_alpha(text(i + 1)))
{
local_cnt++;
}
}
// Get a piece of shared memory, and zero it
__shared__ int block_cnt;
block_cnt = 0;
th.inner().sync();
// In every block, partial sums are gathered, and added to the result
// by the first thread of the block.
atomicAdd(&block_cnt, local_cnt);
th.inner().sync();
if (th.inner().rank() == 0)
{
atomicAdd(&cnt(0), block_cnt);
}
};
ctx.finalize();
int ref_cnt = 0;
for (size_t i = 0; i < sizeof(raw_input) - 1; i++)
{
if (!is_alpha(raw_input[i]) && is_alpha(raw_input[i + 1]))
{
ref_cnt++;
}
}
// fprintf(stderr, "Result : found %d words (expected %d)\n", cnt, ref_cnt);
EXPECT(cnt == ref_cnt);
}

68
cccl_upstream/cudax/examples/stf/word_count_reduce.cu
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@@ -1,68 +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 Counting words in a text using a launch kernel
*/
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
// determines whether the character is alphabetical
__host__ __device__ bool is_alpha(const char c)
{
return (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z');
}
int main()
{
// Paragraph from 'The Raven' by Edgar Allan Poe
// http://en.wikipedia.org/wiki/The_Raven
const char raw_input[] =
" But the raven, sitting lonely on the placid bust, spoke only,\n"
" That one word, as if his soul in that one word he did outpour.\n"
" Nothing further then he uttered - not a feather then he fluttered -\n"
" Till I scarcely more than muttered `Other friends have flown before -\n"
" On the morrow he will leave me, as my hopes have flown before.'\n"
" Then the bird said, `Nevermore.'\n";
context ctx;
size_t text_len = sizeof(raw_input);
auto ltext = ctx.logical_data(const_cast<char*>(&raw_input[0]), {text_len});
auto lcnt = ctx.logical_data(shape_of<scalar_view<int>>());
ctx.parallel_for(box(text_len - 1), ltext.read(), lcnt.reduce(reducer::sum<int>{}))
->*[] _CCCL_DEVICE(size_t i, auto text, int& s) {
/* When we have the beginning of a new word, increment the counter */
if (!is_alpha(text(i)) && is_alpha(text(i + 1)))
{
s++;
}
};
int cnt = ctx.wait(lcnt);
printf("Got %d words.\n", cnt);
ctx.finalize();
int ref_cnt = 0;
for (size_t i = 0; i < sizeof(raw_input) - 1; i++)
{
if (!is_alpha(raw_input[i]) && is_alpha(raw_input[i + 1]))
{
ref_cnt++;
}
}
_CCCL_ASSERT(cnt == ref_cnt, "Count mismatch");
}