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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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# vectorAdd - Vector Addition
## Description
This CUDA Runtime API sample is a very basic sample that implements element by element vector addition. It is the same as the sample illustrating Chapter 3 of the programming guide with some additions like error checking.
## Key Concepts
CUDA Runtime API, Vector Addition
## Supported SM Architectures
[SM 3.5 ](https://developer.nvidia.com/cuda-gpus) [SM 3.7 ](https://developer.nvidia.com/cuda-gpus) [SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus)
## Supported OSes
Linux, Windows
## Supported CPU Architecture
x86_64, ppc64le, armv7l
## CUDA APIs involved
### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaFree, cudaMalloc, cudaGetLastError, cudaMemcpy, cudaGetErrorString
## Prerequisites
Download and install the [CUDA Toolkit 11.6](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
## Build and Run
### Windows
The Windows samples are built using the Visual Studio IDE. Solution files (.sln) are provided for each supported version of Visual Studio, using the format:
```
*_vs<version>.sln - for Visual Studio <version>
```
Each individual sample has its own set of solution files in its directory:
To build/examine all the samples at once, the complete solution files should be used. To build/examine a single sample, the individual sample solution files should be used.
> **Note:** Some samples require that the Microsoft DirectX SDK (June 2010 or newer) be installed and that the VC++ directory paths are properly set up (**Tools > Options...**). Check DirectX Dependencies section for details."
### Linux
The Linux samples are built using makefiles. To use the makefiles, change the current directory to the sample directory you wish to build, and run make:
```
$ cd <sample_dir>
$ make
```
The samples makefiles can take advantage of certain options:
* **TARGET_ARCH=<arch>** - cross-compile targeting a specific architecture. Allowed architectures are x86_64, ppc64le, armv7l.
By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equivalent of setting TARGET_ARCH=x86_64.<br/>
`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=ppc64le` <br/> `$ make TARGET_ARCH=armv7l` <br/>
See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) for more details.
* **dbg=1** - build with debug symbols
```
$ make dbg=1
```
* **SMS="A B ..."** - override the SM architectures for which the sample will be built, where `"A B ..."` is a space-delimited list of SM architectures. For example, to generate SASS for SM 50 and SM 60, use `SMS="50 60"`.
```
$ make SMS="50 60"
```
* **HOST_COMPILER=<host_compiler>** - override the default g++ host compiler. See the [Linux Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements) for a list of supported host compilers.
```
$ make HOST_COMPILER=g++
```
## References (for more details)

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cccl_upstream/cudax/test/stf/cuda-samples/0_Introduction/vectorAdd/vectorAdd.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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/**
* Vector addition: C = A + B.
*
* This sample is a very basic sample that implements element by element
* vector addition. It is the same as the sample illustrating Chapter 2
* of the programming guide with some additions like error checking.
*/

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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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/**
* Vector addition: C = A + B.
*
* This sample is a very basic sample that implements element by element
* vector addition. It is the same as the sample illustrating Chapter 2
* of the programming guide with some additions like error checking.
*/
#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
using namespace cuda::experimental::stf;
/**
* CUDA Kernel Device code
*
* Computes the vector addition of A and B into C. The 3 vectors have the same
* number of elements numElements.
*/
__global__ void vectorAdd(const float* A, const float* B, float* C, int numElements)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < numElements)
{
C[i] = A[i] + B[i] + 0.0f;
}
}
template <typename Ctx>
void run()
{
Ctx ctx;
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;
// Print the vector length to be used, and compute its size
int numElements = 50000;
size_t size = numElements * sizeof(float);
// printf("[Vector addition of %d elements]\n", numElements);
// Allocate the host input vector A
float* h_A = (float*) malloc(size);
// Allocate the host input vector B
float* h_B = (float*) malloc(size);
// Allocate the host output vector C
float* h_C = (float*) malloc(size);
// Verify that allocations succeeded
if (h_A == NULL || h_B == NULL || h_C == NULL)
{
fprintf(stderr, "Failed to allocate host vectors!\n");
exit(EXIT_FAILURE);
}
// Initialize the host input vectors
for (int i = 0; i < numElements; ++i)
{
h_A[i] = rand() / (float) RAND_MAX;
h_B[i] = rand() / (float) RAND_MAX;
}
auto A_handle = ctx.logical_data(h_A, numElements);
auto B_handle = ctx.logical_data(h_B, numElements);
auto C_handle = ctx.logical_data(h_C, numElements);
ctx.task(A_handle.read(), B_handle.read(), C_handle.write())->*[&](cudaStream_t stream, auto d_A, auto d_B, auto d_C) {
// Launch the Vector Add CUDA Kernel
int threadsPerBlock = 256;
int blocksPerGrid = (numElements + threadsPerBlock - 1) / threadsPerBlock;
// printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock);
vectorAdd<<<blocksPerGrid, threadsPerBlock, 0, stream>>>(
d_A.data_handle(), d_B.data_handle(), d_C.data_handle(), numElements);
err = cudaGetLastError();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
};
ctx.host_launch(A_handle.read(), B_handle.read(), C_handle.read())->*[](auto hA, auto hB, auto hC) {
// Verify that the result vector is correct
for (int i = 0; i < hC.extent(0); ++i)
{
if (::std::abs(hA[i] + hB[i] - hC[i]) > 1e-5)
{
fprintf(stderr, "Result verification failed at element %d!\n", i);
exit(EXIT_FAILURE);
}
}
};
ctx.finalize();
// Free host memory
free(h_A);
free(h_B);
free(h_C);
}
/**
* Host main routine
*/
int main(void)
{
run<stream_ctx>();
run<graph_ctx>();
}

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/* Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/*
* This file demonstrates the usage of conditional graph nodes with
* a series of *simple* example graphs.
*
* For more information on conditional nodes, see the programming guide:
*
* https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#conditional-graph-nodes
*
*/
#include <cuda/experimental/stf.cuh>
#include <cassert>
#include <cstdio>
#include <cuda_runtime.h>
using namespace cuda::experimental::stf;
#if _CCCL_CTK_AT_LEAST(12, 4)
// This kernel will only be executed if the condition is true
__global__ void doWhileEmptyKernel(void)
{
printf("GPU: doWhileEmptyKernel()\n");
return;
}
__global__ void doWhileLoopKernel(char* dPtr, cudaGraphConditionalHandle handle)
{
if (--(*dPtr) == 0)
{
cudaGraphSetConditional(handle, 0);
}
printf("GPU: counter = %d\n", *dPtr);
}
void simpleDoWhileGraph(void)
{
cudaGraph_t graph;
cudaGraphExec_t graphExec;
cudaGraphNode_t conditionalNode;
// Allocate a byte of device memory to use as input
char* dPtr;
cuda_safe_call(cudaMalloc((void**) &dPtr, 1));
printf("simpleDoWhileGraph: Building graph...\n");
cuda_safe_call(cudaGraphCreate(&graph, 0));
cudaGraphConditionalHandle handle;
cuda_safe_call(cudaGraphConditionalHandleCreate(&handle, graph, 1, cudaGraphCondAssignDefault));
cudaGraphNodeParams cParams{};
cParams.type = cudaGraphNodeTypeConditional;
cParams.conditional.handle = handle;
cParams.conditional.type = cudaGraphCondTypeWhile;
cParams.conditional.size = 1;
# if _CCCL_CTK_AT_LEAST(13, 0)
cuda_safe_call(cudaGraphAddNode(&conditionalNode, graph, NULL, NULL, 0, &cParams));
# else
cuda_safe_call(cudaGraphAddNode(&conditionalNode, graph, NULL, 0, &cParams));
# endif
cudaGraph_t bodyGraph = cParams.conditional.phGraph_out[0];
cudaStream_t captureStream;
cuda_safe_call(cudaStreamCreate(&captureStream));
cuda_safe_call(
cudaStreamBeginCaptureToGraph(captureStream, bodyGraph, nullptr, nullptr, 0, cudaStreamCaptureModeGlobal));
doWhileEmptyKernel<<<1, 1, 0, captureStream>>>();
doWhileEmptyKernel<<<1, 1, 0, captureStream>>>();
doWhileLoopKernel<<<1, 1, 0, captureStream>>>(dPtr, handle);
cuda_safe_call(cudaStreamEndCapture(captureStream, nullptr));
cuda_safe_call(cudaStreamDestroy(captureStream));
cuda_safe_call(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0));
// Initialize device memory and launch the graph
cuda_safe_call(cudaMemset(dPtr, 10, 1)); // Set dPtr to 10
printf("Host: Launching graph with loop counter set to 10\n");
cuda_safe_call(cudaGraphLaunch(graphExec, 0));
cuda_safe_call(cudaDeviceSynchronize());
// Cleanup
cuda_safe_call(cudaGraphExecDestroy(graphExec));
cuda_safe_call(cudaGraphDestroy(graph));
cuda_safe_call(cudaFree(dPtr));
printf("simpleDoWhileGraph: Complete\n\n");
}
void stf_dowhile()
{
stackable_ctx ctx;
{
auto repeat_guard = ctx.repeat_graph_scope(10);
ctx.task()->*[](cudaStream_t stream) {
doWhileEmptyKernel<<<1, 1, 0, stream>>>();
doWhileEmptyKernel<<<1, 1, 0, stream>>>();
};
}
ctx.finalize();
printf("STF do while complete\n\n");
}
/*
* Create a graph containing a conditional while loop using stream capture.
* This demonstrates how to insert a conditional node into a stream which is
* being captured. The graph consists of a kernel node, A, followed by a
* conditional while node, B, followed by a kernel node, D. The conditional
* body is populated by a single kernel node, C:
*
* A -> B [ C ] -> D
*
* The same kernel will be used for both nodes A and C. This kernel will test
* a device memory location and set the condition when the location is non-zero.
* We must run the kernel before the loop as well as inside the loop in order
* to behave like a while loop as opposed to a do-while loop. We need to evaluate
* the device memory location before the conditional node is evaluated in order
* to set the condition variable properly. Because we're using a kernel upstream
* of the conditional node, there is no need to use the handle default value to
* initialize the conditional value.
*/
__global__ void capturedWhileKernel(char* dPtr, cudaGraphConditionalHandle handle)
{
printf("GPU: counter = %d\n", *dPtr);
if (*dPtr)
{
(*dPtr)--;
}
cudaGraphSetConditional(handle, *dPtr);
}
__global__ void capturedWhileEmptyKernel(void)
{
printf("GPU: capturedWhileEmptyKernel()\n");
return;
}
void capturedWhileGraph(void)
{
cudaGraph_t graph;
cudaGraphExec_t graphExec;
cudaStreamCaptureStatus status;
const cudaGraphNode_t* dependencies;
size_t numDependencies;
// Allocate a byte of device memory to use as input
char* dPtr;
cuda_safe_call(cudaMalloc((void**) &dPtr, 1));
printf("capturedWhileGraph: Building graph...\n");
cudaStream_t captureStream;
cuda_safe_call(cudaStreamCreate(&captureStream));
cuda_safe_call(cudaStreamBeginCapture(captureStream, cudaStreamCaptureModeGlobal));
// Obtain the handle of the graph
# if _CCCL_CTK_AT_LEAST(13, 0)
cuda_safe_call(cudaStreamGetCaptureInfo(captureStream, &status, NULL, &graph, &dependencies, NULL, &numDependencies));
# else
cuda_safe_call(cudaStreamGetCaptureInfo(captureStream, &status, NULL, &graph, &dependencies, &numDependencies));
# endif
// Create the conditional handle
cudaGraphConditionalHandle handle;
cuda_safe_call(cudaGraphConditionalHandleCreate(&handle, graph));
// Insert kernel node A
capturedWhileKernel<<<1, 1, 0, captureStream>>>(dPtr, handle);
// Obtain the handle for node A
# if _CCCL_CTK_AT_LEAST(13, 0)
cuda_safe_call(cudaStreamGetCaptureInfo(captureStream, &status, NULL, &graph, &dependencies, NULL, &numDependencies));
# else
cuda_safe_call(cudaStreamGetCaptureInfo(captureStream, &status, NULL, &graph, &dependencies, &numDependencies));
# endif
// Insert conditional node B
cudaGraphNode_t conditionalNode;
cudaGraphNodeParams cParams{};
cParams.type = cudaGraphNodeTypeConditional;
cParams.conditional.handle = handle;
cParams.conditional.type = cudaGraphCondTypeWhile;
cParams.conditional.size = 1;
# if _CCCL_CTK_AT_LEAST(13, 0)
cuda_safe_call(cudaGraphAddNode(&conditionalNode, graph, dependencies, NULL, numDependencies, &cParams));
# else
cuda_safe_call(cudaGraphAddNode(&conditionalNode, graph, dependencies, numDependencies, &cParams));
# endif
cudaGraph_t bodyGraph = cParams.conditional.phGraph_out[0];
// Update stream capture dependencies to account for the node we manually added
# if _CCCL_CTK_AT_LEAST(13, 0)
cuda_safe_call(
cudaStreamUpdateCaptureDependencies(captureStream, &conditionalNode, NULL, 1, cudaStreamSetCaptureDependencies));
# else
cuda_safe_call(
cudaStreamUpdateCaptureDependencies(captureStream, &conditionalNode, 1, cudaStreamSetCaptureDependencies));
# endif
// Insert kernel node D
capturedWhileEmptyKernel<<<1, 1, 0, captureStream>>>();
cuda_safe_call(cudaStreamEndCapture(captureStream, &graph));
cuda_safe_call(cudaStreamDestroy(captureStream));
// Populate conditional body graph using stream capture
cudaStream_t bodyStream;
cuda_safe_call(cudaStreamCreate(&bodyStream));
cuda_safe_call(
cudaStreamBeginCaptureToGraph(bodyStream, bodyGraph, nullptr, nullptr, 0, cudaStreamCaptureModeGlobal));
// Insert kernel node C
capturedWhileKernel<<<1, 1, 0, bodyStream>>>(dPtr, handle);
cuda_safe_call(cudaStreamEndCapture(bodyStream, nullptr));
cuda_safe_call(cudaStreamDestroy(bodyStream));
cuda_safe_call(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0));
// Initialize device memory and launch the graph
// Device memory is zero, so the conditional node will not execute
cuda_safe_call(cudaMemset(dPtr, 0, 1)); // Set dPtr to 0
printf("Host: Launching graph with loop counter set to 0\n");
cuda_safe_call(cudaGraphLaunch(graphExec, 0));
cuda_safe_call(cudaDeviceSynchronize());
// Initialize device memory and launch the graph
cuda_safe_call(cudaMemset(dPtr, 10, 1)); // Set dPtr to 10
printf("Host: Launching graph with loop counter set to 10\n");
cuda_safe_call(cudaGraphLaunch(graphExec, 0));
cuda_safe_call(cudaDeviceSynchronize());
// Cleanup
cuda_safe_call(cudaGraphExecDestroy(graphExec));
cuda_safe_call(cudaGraphDestroy(graph));
cuda_safe_call(cudaFree(dPtr));
printf("capturedWhileGraph: Complete\n\n");
}
void stf_dowhile_2()
{
stackable_ctx ctx;
{
// We force everything to be a CUDA graph
auto scope = ctx.graph_scope();
// We use a token to ensure that A, B(C) and D are serialized
auto t = ctx.token();
// A
ctx.task(t.rw())->*[](cudaStream_t stream) {
doWhileEmptyKernel<<<1, 1, 0, stream>>>();
};
// B
{
auto repeat_guard = ctx.repeat_graph_scope(10);
// C
ctx.task(t.rw())->*[](cudaStream_t stream) {
doWhileEmptyKernel<<<1, 1, 0, stream>>>();
};
}
// D
ctx.task(t.rw())->*[](cudaStream_t stream) {
doWhileEmptyKernel<<<1, 1, 0, stream>>>();
};
}
ctx.finalize();
}
void stf_dowhile_2_cuda_kernel()
{
stackable_ctx ctx;
{
// We force everything to be a CUDA graph
auto scope = ctx.graph_scope();
// We use a token to ensure that A, B(C) and D are serialized
auto t = ctx.token();
// A
ctx.cuda_kernel(t.rw())->*[]() {
return cuda_kernel_desc{doWhileEmptyKernel, 1, 1, 0};
};
// B
{
auto repeat_guard = ctx.repeat_graph_scope(10);
// C
ctx.cuda_kernel(t.rw())->*[]() {
return cuda_kernel_desc{doWhileEmptyKernel, 1, 1, 0};
};
}
// D
ctx.cuda_kernel(t.rw())->*[]() {
return cuda_kernel_desc{doWhileEmptyKernel, 1, 1, 0};
};
}
ctx.finalize();
}
#endif // _CCCL_CTK_AT_LEAST(12, 4)
int main(int, char**)
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
return 0;
#else
simpleDoWhileGraph();
stf_dowhile();
capturedWhileGraph();
stf_dowhile_2();
// same as stf_dowhile_2 but uses cuda_kernel
stf_dowhile_2_cuda_kernel();
return 0;
#endif // _CCCL_CTK_AT_LEAST(12, 4)
}

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# jacobiCudaGraphs - Jacobi CUDA Graphs
## Description
Demonstrates Instantiated CUDA Graph Update with Jacobi Iterative Method using cudaGraphExecKernelNodeSetParams() and cudaGraphExecUpdate() approach.
## Key Concepts
CUDA Graphs, Stream Capture, Instantiated CUDA Graph Update, Cooperative Groups
## Supported SM Architectures
[SM 3.5 ](https://developer.nvidia.com/cuda-gpus) [SM 3.7 ](https://developer.nvidia.com/cuda-gpus) [SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus)
## Supported OSes
Linux, Windows
## Supported CPU Architecture
x86_64, ppc64le, armv7l
## CUDA APIs involved
### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaGraphAddMemsetNode, cudaStreamCreateWithFlags, cudaMemcpyAsync, cudaMallocHost, cudaPitchedPtr, cudaGraphCreate, cudaMalloc, cudaPos, cudaGraphAddMemcpyNode, cudaStreamEndCapture, cudaGraphExecDestroy, cudaStreamBeginCapture, cudaGraphExecKernelNodeSetParams, cudaStreamSynchronize, cudaGraphLaunch, cudaFree, cudaGraphInstantiate, cudaExtent, cudaMemsetAsync, cudaFreeHost, cudaGraphAddKernelNode, cudaGraphExecUpdate
## Prerequisites
Download and install the [CUDA Toolkit 11.6](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
## Build and Run
### Windows
The Windows samples are built using the Visual Studio IDE. Solution files (.sln) are provided for each supported version of Visual Studio, using the format:
```
*_vs<version>.sln - for Visual Studio <version>
```
Each individual sample has its own set of solution files in its directory:
To build/examine all the samples at once, the complete solution files should be used. To build/examine a single sample, the individual sample solution files should be used.
> **Note:** Some samples require that the Microsoft DirectX SDK (June 2010 or newer) be installed and that the VC++ directory paths are properly set up (**Tools > Options...**). Check DirectX Dependencies section for details."
### Linux
The Linux samples are built using makefiles. To use the makefiles, change the current directory to the sample directory you wish to build, and run make:
```
$ cd <sample_dir>
$ make
```
The samples makefiles can take advantage of certain options:
* **TARGET_ARCH=<arch>** - cross-compile targeting a specific architecture. Allowed architectures are x86_64, ppc64le, armv7l.
By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equivalent of setting TARGET_ARCH=x86_64.<br/>
`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=ppc64le` <br/> `$ make TARGET_ARCH=armv7l` <br/>
See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) for more details.
* **dbg=1** - build with debug symbols
```
$ make dbg=1
```
* **SMS="A B ..."** - override the SM architectures for which the sample will be built, where `"A B ..."` is a space-delimited list of SM architectures. For example, to generate SASS for SM 50 and SM 60, use `SMS="50 60"`.
```
$ make SMS="50 60"
```
* **HOST_COMPILER=<host_compiler>** - override the default g++ host compiler. See the [Linux Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements) for a list of supported host compilers.
```
$ make HOST_COMPILER=g++
```
## References (for more details)

638
cccl_upstream/cudax/test/stf/cuda-samples/3_CUDA_Features/jacobiCudaGraphs/jacobi.cu
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@@ -1,638 +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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
// This sample demonstrates Instantiated CUDA Graph Update
// with Jacobi Iterative Method in 3 different methods:
// 1 - JacobiMethodGpuCudaGraphExecKernelSetParams() - CUDA Graph with
// cudaGraphExecKernelNodeSetParams() 2 - JacobiMethodGpuCudaGraphExecUpdate() -
// CUDA Graph with cudaGraphExecUpdate() 3 - JacobiMethodGpu() - Non CUDA Graph
// method
// Jacobi method on a linear system A*x = b,
// where A is diagonally dominant and the exact solution consists
// of all ones.
#include <cuda/experimental/__stf/utility/cuda_safe_call.cuh>
#include <cooperative_groups.h>
using cuda::experimental::stf::cuda_safe_call;
#define N_ROWS 512
namespace cg = cooperative_groups;
// 8 Rows of square-matrix A processed by each CTA.
// This can be max 32 and only power of 2 (i.e., 2/4/8/16/32).
#define ROWS_PER_CTA 8
#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600
#else
__device__ double atomicAdd(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(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);
}
#endif
// creates N_ROWS x N_ROWS matrix A with N_ROWS+1 on the diagonal and 1
// elsewhere. The elements of the right hand side b all equal 2*n, hence the
// exact solution x to A*x = b is a vector of ones.
void createLinearSystem(float* A, double* b)
{
int i, j;
for (i = 0; i < N_ROWS; i++)
{
b[i] = 2.0 * N_ROWS;
for (j = 0; j < N_ROWS; j++)
{
A[i * N_ROWS + j] = 1.0;
}
A[i * N_ROWS + i] = N_ROWS + 1.0;
}
}
static __global__ void
JacobiMethod(const float* A, const double* b, const float conv_threshold, double* x, double* x_new, double* sum)
{
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
__shared__ double x_shared[N_ROWS]; // N_ROWS == n
__shared__ double b_shared[ROWS_PER_CTA + 1];
for (int i = threadIdx.x; i < N_ROWS; i += blockDim.x)
{
x_shared[i] = x[i];
}
if (threadIdx.x < ROWS_PER_CTA)
{
int k = threadIdx.x;
for (int i = k + (blockIdx.x * ROWS_PER_CTA); (k < ROWS_PER_CTA) && (i < N_ROWS);
k += ROWS_PER_CTA, i += ROWS_PER_CTA)
{
b_shared[i % (ROWS_PER_CTA + 1)] = b[i];
}
}
cg::sync(cta);
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
for (int k = 0, i = blockIdx.x * ROWS_PER_CTA; (k < ROWS_PER_CTA) && (i < N_ROWS); k++, i++)
{
double rowThreadSum = 0.0;
for (int j = threadIdx.x; j < N_ROWS; j += blockDim.x)
{
rowThreadSum += (A[i * N_ROWS + j] * x_shared[j]);
}
for (int offset = tile32.size() / 2; offset > 0; offset /= 2)
{
rowThreadSum += tile32.shfl_down(rowThreadSum, offset);
}
if (tile32.thread_rank() == 0)
{
atomicAdd(&b_shared[i % (ROWS_PER_CTA + 1)], -rowThreadSum);
}
}
cg::sync(cta);
if (threadIdx.x < ROWS_PER_CTA)
{
cg::thread_block_tile<ROWS_PER_CTA> tile8 = cg::tiled_partition<ROWS_PER_CTA>(cta);
double temp_sum = 0.0;
int k = threadIdx.x;
for (int i = k + (blockIdx.x * ROWS_PER_CTA); (k < ROWS_PER_CTA) && (i < N_ROWS);
k += ROWS_PER_CTA, i += ROWS_PER_CTA)
{
double dx = b_shared[i % (ROWS_PER_CTA + 1)];
dx /= A[i * N_ROWS + i];
x_new[i] = (x_shared[i] + dx);
temp_sum += fabs(dx);
}
for (int offset = tile8.size() / 2; offset > 0; offset /= 2)
{
temp_sum += tile8.shfl_down(temp_sum, offset);
}
if (tile8.thread_rank() == 0)
{
atomicAdd(sum, temp_sum);
}
}
}
// Thread block size for finalError kernel should be multiple of 32
static __global__ void finalError(double* x, double* g_sum)
{
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
extern __shared__ double warpSum[];
double sum = 0.0;
int globalThreadId = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = globalThreadId; i < N_ROWS; i += blockDim.x * gridDim.x)
{
double d = x[i] - 1.0;
sum += fabs(d);
}
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
for (int offset = tile32.size() / 2; offset > 0; offset /= 2)
{
sum += tile32.shfl_down(sum, offset);
}
if (tile32.thread_rank() == 0)
{
warpSum[threadIdx.x / warpSize] = sum;
}
cg::sync(cta);
double blockSum = 0.0;
if (threadIdx.x < (blockDim.x / warpSize))
{
blockSum = warpSum[threadIdx.x];
}
if (threadIdx.x < 32)
{
for (int offset = tile32.size() / 2; offset > 0; offset /= 2)
{
blockSum += tile32.shfl_down(blockSum, offset);
}
if (tile32.thread_rank() == 0)
{
atomicAdd(g_sum, blockSum);
}
}
}
// Run the Jacobi method for A*x = b on GPU with CUDA Graph -
// cudaGraphExecKernelNodeSetParams().
double JacobiMethodGpuCudaGraphExecKernelSetParams(
const float* A,
const double* b,
float conv_threshold,
const int max_iter,
double* x,
double* x_new,
cudaStream_t stream)
{
// CTA size
dim3 nthreads(256, 1, 1);
// grid size
dim3 nblocks((N_ROWS / ROWS_PER_CTA) + 2, 1, 1);
cudaGraph_t graph;
cudaGraphExec_t graphExec = NULL;
double sum = 0.0;
double* d_sum = NULL;
cuda_safe_call(cudaMalloc(&d_sum, sizeof(double)));
std::vector<cudaGraphNode_t> nodeDependencies;
cudaGraphNode_t memcpyNode, jacobiKernelNode, memsetNode;
cudaMemcpy3DParms memcpyParams;
cudaMemsetParams memsetParams;
memsetParams.dst = (void*) d_sum;
memsetParams.value = 0;
memsetParams.pitch = 0;
// elementSize can be max 4 bytes, so we take sizeof(float) and width=2
memsetParams.elementSize = sizeof(float);
memsetParams.width = 2;
memsetParams.height = 1;
cuda_safe_call(cudaGraphCreate(&graph, 0));
cuda_safe_call(cudaGraphAddMemsetNode(&memsetNode, graph, NULL, 0, &memsetParams));
nodeDependencies.push_back(memsetNode);
cudaKernelNodeParams NodeParams0, NodeParams1;
NodeParams0.func = (void*) JacobiMethod;
NodeParams0.gridDim = nblocks;
NodeParams0.blockDim = nthreads;
NodeParams0.sharedMemBytes = 0;
void* kernelArgs0[6] = {
(void*) &A,
const_cast<double**>(&b),
const_cast<float*>(&conv_threshold),
(void*) &x,
(void*) &x_new,
(void*) &d_sum};
NodeParams0.kernelParams = kernelArgs0;
NodeParams0.extra = NULL;
cuda_safe_call(
cudaGraphAddKernelNode(&jacobiKernelNode, graph, nodeDependencies.data(), nodeDependencies.size(), &NodeParams0));
nodeDependencies.clear();
nodeDependencies.push_back(jacobiKernelNode);
memcpyParams.srcArray = NULL;
memcpyParams.srcPos = make_cudaPos(0, 0, 0);
memcpyParams.srcPtr = make_cudaPitchedPtr(d_sum, sizeof(double), 1, 1);
memcpyParams.dstArray = NULL;
memcpyParams.dstPos = make_cudaPos(0, 0, 0);
memcpyParams.dstPtr = make_cudaPitchedPtr(&sum, sizeof(double), 1, 1);
memcpyParams.extent = make_cudaExtent(sizeof(double), 1, 1);
memcpyParams.kind = cudaMemcpyDeviceToHost;
cuda_safe_call(
cudaGraphAddMemcpyNode(&memcpyNode, graph, nodeDependencies.data(), nodeDependencies.size(), &memcpyParams));
cuda_safe_call(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0));
NodeParams1.func = (void*) JacobiMethod;
NodeParams1.gridDim = nblocks;
NodeParams1.blockDim = nthreads;
NodeParams1.sharedMemBytes = 0;
void* kernelArgs1[6] = {
(void*) &A,
const_cast<double**>(&b),
const_cast<float*>(&conv_threshold),
(void*) &x_new,
(void*) &x,
(void*) &d_sum};
NodeParams1.kernelParams = kernelArgs1;
NodeParams1.extra = NULL;
int k = 0;
for (k = 0; k < max_iter; k++)
{
cuda_safe_call(
cudaGraphExecKernelNodeSetParams(graphExec, jacobiKernelNode, ((k & 1) == 0) ? &NodeParams0 : &NodeParams1));
cuda_safe_call(cudaGraphLaunch(graphExec, stream));
cuda_safe_call(cudaStreamSynchronize(stream));
if (sum <= conv_threshold)
{
cuda_safe_call(cudaMemsetAsync(d_sum, 0, sizeof(double), stream));
nblocks.x = (N_ROWS / nthreads.x) + 1;
size_t sharedMemSize = ((nthreads.x / 32) + 1) * sizeof(double);
if ((k & 1) == 0)
{
finalError<<<nblocks, nthreads, sharedMemSize, stream>>>(x_new, d_sum);
}
else
{
finalError<<<nblocks, nthreads, sharedMemSize, stream>>>(x, d_sum);
}
cuda_safe_call(cudaMemcpyAsync(&sum, d_sum, sizeof(double), cudaMemcpyDeviceToHost, stream));
cuda_safe_call(cudaStreamSynchronize(stream));
// printf("GPU iterations : %d\n", k + 1);
// printf("GPU error: %.3e\n", sum);
break;
}
}
cuda_safe_call(cudaFree(d_sum));
return sum;
}
// Run the Jacobi method for A*x = b on GPU with Instantiated CUDA Graph Update
// API - cudaGraphExecUpdate().
double JacobiMethodGpuCudaGraphExecUpdate(
const float* A,
const double* b,
const float conv_threshold,
const int max_iter,
double* x,
double* x_new,
cudaStream_t stream)
{
// CTA size
dim3 nthreads(256, 1, 1);
// grid size
dim3 nblocks((N_ROWS / ROWS_PER_CTA) + 2, 1, 1);
cudaGraph_t graph;
cudaGraphExec_t graphExec = NULL;
double sum = 0.0;
double* d_sum;
cuda_safe_call(cudaMalloc(&d_sum, sizeof(double)));
int k = 0;
for (k = 0; k < max_iter; k++)
{
cuda_safe_call(cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal));
cuda_safe_call(cudaMemsetAsync(d_sum, 0, sizeof(double), stream));
if ((k & 1) == 0)
{
JacobiMethod<<<nblocks, nthreads, 0, stream>>>(A, b, conv_threshold, x, x_new, d_sum);
}
else
{
JacobiMethod<<<nblocks, nthreads, 0, stream>>>(A, b, conv_threshold, x_new, x, d_sum);
}
cuda_safe_call(cudaMemcpyAsync(&sum, d_sum, sizeof(double), cudaMemcpyDeviceToHost, stream));
cuda_safe_call(cudaStreamEndCapture(stream, &graph));
if (graphExec == NULL)
{
cuda_safe_call(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0));
}
else
{
cudaGraphExecUpdateResult updateResult_out;
cuda_safe_call(cudaGraphExecUpdate(graphExec, graph, NULL, &updateResult_out));
if (updateResult_out != cudaGraphExecUpdateSuccess)
{
if (graphExec != NULL)
{
cuda_safe_call(cudaGraphExecDestroy(graphExec));
}
printf("k = %d graph update failed with error - %d\n", k, updateResult_out);
cuda_safe_call(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0));
}
}
cuda_safe_call(cudaGraphLaunch(graphExec, stream));
cuda_safe_call(cudaStreamSynchronize(stream));
if (sum <= conv_threshold)
{
cuda_safe_call(cudaMemsetAsync(d_sum, 0, sizeof(double), stream));
nblocks.x = (N_ROWS / nthreads.x) + 1;
size_t sharedMemSize = ((nthreads.x / 32) + 1) * sizeof(double);
if ((k & 1) == 0)
{
finalError<<<nblocks, nthreads, sharedMemSize, stream>>>(x_new, d_sum);
}
else
{
finalError<<<nblocks, nthreads, sharedMemSize, stream>>>(x, d_sum);
}
cuda_safe_call(cudaMemcpyAsync(&sum, d_sum, sizeof(double), cudaMemcpyDeviceToHost, stream));
cuda_safe_call(cudaStreamSynchronize(stream));
// printf("GPU iterations : %d\n", k + 1);
// printf("GPU error: %.3e\n", sum);
break;
}
}
cuda_safe_call(cudaFree(d_sum));
return sum;
}
// Run the Jacobi method for A*x = b on GPU without CUDA Graph.
double JacobiMethodGpu(
const float* A,
const double* b,
const float conv_threshold,
const int max_iter,
double* x,
double* x_new,
cudaStream_t stream)
{
// CTA size
dim3 nthreads(256, 1, 1);
// grid size
dim3 nblocks((N_ROWS / ROWS_PER_CTA) + 2, 1, 1);
double sum = 0.0;
double* d_sum;
cuda_safe_call(cudaMalloc(&d_sum, sizeof(double)));
int k = 0;
for (k = 0; k < max_iter; k++)
{
cuda_safe_call(cudaMemsetAsync(d_sum, 0, sizeof(double), stream));
if ((k & 1) == 0)
{
JacobiMethod<<<nblocks, nthreads, 0, stream>>>(A, b, conv_threshold, x, x_new, d_sum);
}
else
{
JacobiMethod<<<nblocks, nthreads, 0, stream>>>(A, b, conv_threshold, x_new, x, d_sum);
}
cuda_safe_call(cudaMemcpyAsync(&sum, d_sum, sizeof(double), cudaMemcpyDeviceToHost, stream));
cuda_safe_call(cudaStreamSynchronize(stream));
if (sum <= conv_threshold)
{
cuda_safe_call(cudaMemsetAsync(d_sum, 0, sizeof(double), stream));
nblocks.x = (N_ROWS / nthreads.x) + 1;
size_t sharedMemSize = ((nthreads.x / 32) + 1) * sizeof(double);
if ((k & 1) == 0)
{
finalError<<<nblocks, nthreads, sharedMemSize, stream>>>(x_new, d_sum);
}
else
{
finalError<<<nblocks, nthreads, sharedMemSize, stream>>>(x, d_sum);
}
cuda_safe_call(cudaMemcpyAsync(&sum, d_sum, sizeof(double), cudaMemcpyDeviceToHost, stream));
cuda_safe_call(cudaStreamSynchronize(stream));
// printf("GPU iterations : %d\n", k + 1);
// printf("GPU error: %.3e\n", sum);
break;
}
}
cuda_safe_call(cudaFree(d_sum));
return sum;
}
// Run the Jacobi method for A*x = b on CPU.
void JacobiMethodCPU(float* A, double* b, float conv_threshold, int max_iter, int* num_iter, double* x)
{
double* x_new;
x_new = (double*) calloc(N_ROWS, sizeof(double));
int k;
for (k = 0; k < max_iter; k++)
{
double sum = 0.0;
for (int i = 0; i < N_ROWS; i++)
{
double temp_dx = b[i];
for (int j = 0; j < N_ROWS; j++)
{
temp_dx -= A[i * N_ROWS + j] * x[j];
}
temp_dx /= A[i * N_ROWS + i];
x_new[i] += temp_dx;
sum += fabs(temp_dx);
}
for (int i = 0; i < N_ROWS; i++)
{
x[i] = x_new[i];
}
if (sum <= conv_threshold)
{
break;
}
}
*num_iter = k + 1;
free(x_new);
}
int main()
{
// if (checkCmdLineFlag(argc, (const char **)argv, "help")) {
// printf("Command line: jacobiCudaGraphs [-option]\n");
// printf("Valid options:\n");
// printf(
// "-gpumethod=<0,1 or 2> : 0 - [Default] "
// "JacobiMethodGpuCudaGraphExecKernelSetParams\n");
// printf(" : 1 - JacobiMethodGpuCudaGraphExecUpdate\n");
// printf(" : 2 - JacobiMethodGpu - Non CUDA Graph\n");
// printf("-device=device_num : cuda device id");
// printf("-help : Output a help message\n");
// exit(EXIT_SUCCESS);
// }
//
int gpumethod = 0;
// if (checkCmdLineFlag(argc, (const char **)argv, "gpumethod")) {
// gpumethod = getCmdLineArgumentInt(argc, (const char **)argv, "gpumethod");
//
// if (gpumethod < 0 || gpumethod > 2) {
// printf("Error: gpumethod must be 0 or 1 or 2, gpumethod=%d is invalid\n",
// gpumethod);
// exit(EXIT_SUCCESS);
// }
// }
// int dev = findCudaDevice(argc, (const char **)argv);
// int dev = 0;
double* b = NULL;
float* A = NULL;
cuda_safe_call(cudaMallocHost(&b, N_ROWS * sizeof(double)));
memset(b, 0, N_ROWS * sizeof(double));
cuda_safe_call(cudaMallocHost(&A, N_ROWS * N_ROWS * sizeof(float)));
memset(A, 0, N_ROWS * N_ROWS * sizeof(float));
createLinearSystem(A, b);
double* x = NULL;
// start with array of all zeroes
x = (double*) calloc(N_ROWS, sizeof(double));
float conv_threshold = 1.0e-2;
int max_iter = 4 * N_ROWS * N_ROWS;
int cnt = 0;
// // create timer
// StopWatchInterface *timerCPU = NULL, *timerGpu = NULL;
// sdkCreateTimer(&timerCPU);
//
// sdkStartTimer(&timerCPU);
JacobiMethodCPU(A, b, conv_threshold, max_iter, &cnt, x);
double sum = 0.0;
// Compute error
for (int i = 0; i < N_ROWS; i++)
{
double d = x[i] - 1.0;
sum += fabs(d);
}
// sdkStopTimer(&timerCPU);
// printf("CPU iterations : %d\n", cnt);
// printf("CPU error: %.3e\n", sum);
// printf("CPU Processing time: %f (ms)\n", sdkGetTimerValue(&timerCPU));
float* d_A;
double *d_b, *d_x, *d_x_new;
cudaStream_t stream1;
cuda_safe_call(cudaStreamCreateWithFlags(&stream1, cudaStreamNonBlocking));
cuda_safe_call(cudaMalloc(&d_b, sizeof(double) * N_ROWS));
cuda_safe_call(cudaMalloc(&d_A, sizeof(float) * N_ROWS * N_ROWS));
cuda_safe_call(cudaMalloc(&d_x, sizeof(double) * N_ROWS));
cuda_safe_call(cudaMalloc(&d_x_new, sizeof(double) * N_ROWS));
cuda_safe_call(cudaMemsetAsync(d_x, 0, sizeof(double) * N_ROWS, stream1));
cuda_safe_call(cudaMemsetAsync(d_x_new, 0, sizeof(double) * N_ROWS, stream1));
cuda_safe_call(cudaMemcpyAsync(d_A, A, sizeof(float) * N_ROWS * N_ROWS, cudaMemcpyHostToDevice, stream1));
cuda_safe_call(cudaMemcpyAsync(d_b, b, sizeof(double) * N_ROWS, cudaMemcpyHostToDevice, stream1));
// sdkCreateTimer(&timerGpu);
// sdkStartTimer(&timerGpu);
double sumGPU = 0.0;
if (gpumethod == 0)
{
sumGPU = JacobiMethodGpuCudaGraphExecKernelSetParams(d_A, d_b, conv_threshold, max_iter, d_x, d_x_new, stream1);
}
else if (gpumethod == 1)
{
sumGPU = JacobiMethodGpuCudaGraphExecUpdate(d_A, d_b, conv_threshold, max_iter, d_x, d_x_new, stream1);
}
else if (gpumethod == 2)
{
sumGPU = JacobiMethodGpu(d_A, d_b, conv_threshold, max_iter, d_x, d_x_new, stream1);
}
// sdkStopTimer(&timerGpu);
// printf("GPU Processing time: %f (ms)\n", sdkGetTimerValue(&timerGpu));
cuda_safe_call(cudaFree(d_b));
cuda_safe_call(cudaFree(d_A));
cuda_safe_call(cudaFree(d_x));
cuda_safe_call(cudaFree(d_x_new));
cuda_safe_call(cudaFreeHost(A));
cuda_safe_call(cudaFreeHost(b));
// printf("&&&& jacobiCudaGraphs %s\n", (fabs(sum - sumGPU) < conv_threshold) ? "PASSED" : "FAILED");
return (fabs(sum - sumGPU) < conv_threshold) ? EXIT_SUCCESS : EXIT_FAILURE;
}

376
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@@ -1,376 +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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
// Jacobi method on a linear system A*x = b,
// where A is diagonally dominant and the exact solution consists
// of all ones.
#include <cuda/experimental/__stf/internal/scalar_interface.cuh>
#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
#define N_ROWS 512
namespace cg = cooperative_groups;
using namespace cuda::experimental::stf;
// 8 Rows of square-matrix A processed by each CTA.
// This can be max 32 and only power of 2 (i.e., 2/4/8/16/32).
#define ROWS_PER_CTA 8
// creates N_ROWS x N_ROWS matrix A with N_ROWS+1 on the diagonal and 1
// elsewhere. The elements of the right hand side b all equal 2*n, hence the
// exact solution x to A*x = b is a vector of ones.
void createLinearSystem(float* A, double* b)
{
int i, j;
for (i = 0; i < N_ROWS; i++)
{
b[i] = 2.0 * N_ROWS;
for (j = 0; j < N_ROWS; j++)
{
A[i * N_ROWS + j] = 1.0;
}
A[i * N_ROWS + i] = N_ROWS + 1.0;
}
}
static __global__ void
JacobiMethod(const float* A, const double* b, const float conv_threshold, double* x, double* x_new, double* sum)
{
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
__shared__ double x_shared[N_ROWS]; // N_ROWS == n
__shared__ double b_shared[ROWS_PER_CTA + 1];
for (int i = threadIdx.x; i < N_ROWS; i += blockDim.x)
{
x_shared[i] = x[i];
}
if (threadIdx.x < ROWS_PER_CTA)
{
int k = threadIdx.x;
for (int i = k + (blockIdx.x * ROWS_PER_CTA); (k < ROWS_PER_CTA) && (i < N_ROWS);
k += ROWS_PER_CTA, i += ROWS_PER_CTA)
{
b_shared[i % (ROWS_PER_CTA + 1)] = b[i];
}
}
cg::sync(cta);
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
for (int k = 0, i = blockIdx.x * ROWS_PER_CTA; (k < ROWS_PER_CTA) && (i < N_ROWS); k++, i++)
{
double rowThreadSum = 0.0;
for (int j = threadIdx.x; j < N_ROWS; j += blockDim.x)
{
rowThreadSum += (A[i * N_ROWS + j] * x_shared[j]);
}
for (int offset = tile32.size() / 2; offset > 0; offset /= 2)
{
rowThreadSum += tile32.shfl_down(rowThreadSum, offset);
}
if (tile32.thread_rank() == 0)
{
atomicAdd(&b_shared[i % (ROWS_PER_CTA + 1)], -rowThreadSum);
}
}
cg::sync(cta);
if (threadIdx.x < ROWS_PER_CTA)
{
cg::thread_block_tile<ROWS_PER_CTA> tile8 = cg::tiled_partition<ROWS_PER_CTA>(cta);
double temp_sum = 0.0;
int k = threadIdx.x;
for (int i = k + (blockIdx.x * ROWS_PER_CTA); (k < ROWS_PER_CTA) && (i < N_ROWS);
k += ROWS_PER_CTA, i += ROWS_PER_CTA)
{
double dx = b_shared[i % (ROWS_PER_CTA + 1)];
dx /= A[i * N_ROWS + i];
x_new[i] = (x_shared[i] + dx);
temp_sum += fabs(dx);
}
for (int offset = tile8.size() / 2; offset > 0; offset /= 2)
{
temp_sum += tile8.shfl_down(temp_sum, offset);
}
if (tile8.thread_rank() == 0)
{
atomicAdd(sum, temp_sum);
}
}
}
// Thread block size for finalError kernel should be multiple of 32
static __global__ void finalError(const double* x, double* g_sum)
{
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
extern __shared__ double warpSum[];
double sum = 0.0;
int globalThreadId = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = globalThreadId; i < N_ROWS; i += blockDim.x * gridDim.x)
{
double d = x[i] - 1.0;
sum += fabs(d);
}
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
for (int offset = tile32.size() / 2; offset > 0; offset /= 2)
{
sum += tile32.shfl_down(sum, offset);
}
if (tile32.thread_rank() == 0)
{
warpSum[threadIdx.x / warpSize] = sum;
}
cg::sync(cta);
double blockSum = 0.0;
if (threadIdx.x < (blockDim.x / warpSize))
{
blockSum = warpSum[threadIdx.x];
}
if (threadIdx.x < 32)
{
for (int offset = tile32.size() / 2; offset > 0; offset /= 2)
{
blockSum += tile32.shfl_down(blockSum, offset);
}
if (tile32.thread_rank() == 0)
{
atomicAdd(g_sum, blockSum);
}
}
}
// Run the Jacobi method for A*x = b on GPU without CUDA Graph.
// double JacobiMethodGpu(const float *A, const double *b,
// const float conv_threshold, const int max_iter,
// double *x, double *x_new) {
double JacobiMethodGpu(
stream_ctx& ctx,
logical_data<slice<float>>& A_handle,
logical_data<slice<double>>& b_handle,
const float conv_threshold,
const int max_iter,
logical_data<slice<double>>& x_handle,
logical_data<slice<double>>& x_new_handle)
{
// CTA size
dim3 nthreads(256, 1, 1);
// grid size
dim3 nblocks((N_ROWS / ROWS_PER_CTA) + 2, 1, 1);
auto sum_handle = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("sum");
int k;
for (k = 0; k < max_iter; k++)
{
auto x_mode = (k & 1) == 0 ? access_mode::read : access_mode::rw;
auto x_new_mode = (k & 1) == 0 ? access_mode::rw : access_mode::read;
ctx.task(A_handle.read(),
b_handle.read(),
task_dep<slice<double>>(x_handle, x_mode),
task_dep<slice<double>>(x_new_handle, x_new_mode),
sum_handle.write())
.set_symbol("JacobiMethod")
->*[&](cudaStream_t stream, auto A, auto b, auto x, auto x_new, auto d_sum) {
cuda_try(cudaMemsetAsync(d_sum.addr, 0, sizeof(double), stream));
if ((k & 1) == 0)
{
JacobiMethod<<<nblocks, nthreads, 0, stream>>>(
A.data_handle(), b.data_handle(), conv_threshold, x.data_handle(), x_new.data_handle(), d_sum.addr);
}
else
{
JacobiMethod<<<nblocks, nthreads, 0, stream>>>(
A.data_handle(), b.data_handle(), conv_threshold, x_new.data_handle(), x.data_handle(), d_sum.addr);
}
};
if (ctx.wait(sum_handle) <= conv_threshold)
{
break;
}
}
auto final_x_handle = ((k & 1) == 0) ? &x_new_handle : &x_handle;
ctx.task(sum_handle.write(), final_x_handle->read()).set_symbol("finalError")
->*[&](cudaStream_t stream, auto d_sum, auto final_x) {
cuda_try(cudaMemsetAsync(d_sum.addr, 0, sizeof(double), stream));
nblocks.x = (N_ROWS / nthreads.x) + 1;
size_t sharedMemSize = ((nthreads.x / 32) + 1) * sizeof(double);
finalError<<<nblocks, nthreads, sharedMemSize, stream>>>(final_x.data_handle(), d_sum.addr);
};
return ctx.wait(sum_handle);
}
// Run the Jacobi method for A*x = b on CPU.
void JacobiMethodCPU(float* A, double* b, float conv_threshold, int max_iter, int* num_iter, double* x)
{
double* x_new = (double*) calloc(N_ROWS, sizeof(double));
SCOPE(exit)
{
free(x_new);
};
int k = 0;
for (; k < max_iter; k++)
{
double sum = 0.0;
for (int i = 0; i < N_ROWS; i++)
{
double temp_dx = b[i];
for (int j = 0; j < N_ROWS; j++)
{
temp_dx -= A[i * N_ROWS + j] * x[j];
}
temp_dx /= A[i * N_ROWS + i];
x_new[i] += temp_dx;
sum += fabs(temp_dx);
}
for (int i = 0; i < N_ROWS; i++)
{
x[i] = x_new[i];
}
if (sum <= conv_threshold)
{
break;
}
}
*num_iter = k + 1;
}
template <typename Ctx>
int run()
{
Ctx ctx;
double* b = cuda_try<cudaMallocHost<double>>(N_ROWS * sizeof(double), 0);
SCOPE(exit)
{
cuda_try(cudaFreeHost(b));
};
float* A = cuda_try<cudaMallocHost<float>>(N_ROWS * N_ROWS * sizeof(float), 0);
SCOPE(exit)
{
cuda_try(cudaFreeHost(A));
};
memset(b, 0, N_ROWS * sizeof(double));
memset(A, 0, N_ROWS * N_ROWS * sizeof(float));
createLinearSystem(A, b);
// start with array of all zeroes
double* x = (double*) calloc(N_ROWS, sizeof(double));
SCOPE(exit)
{
free(x);
};
auto A_handle = ctx.logical_data(A, N_ROWS * N_ROWS).set_symbol("A");
auto b_handle = ctx.logical_data(b, N_ROWS).set_symbol("b");
auto x_handle = ctx.logical_data(x, N_ROWS).set_symbol("x");
auto x_new_handle = ctx.logical_data(shape_of<slice<double>>(N_ROWS)).set_symbol("x_new");
float conv_threshold = 1.0e-2;
int max_iter = 4 * N_ROWS * N_ROWS;
int cnt = 0;
JacobiMethodCPU(A, b, conv_threshold, max_iter, &cnt, x);
double sum = 0.0;
// Compute error
for (int i = 0; i < N_ROWS; i++)
{
double d = x[i] - 1.0;
sum += fabs(d);
}
ctx.task(x_handle.write()).set_symbol("memset x")->*[&](cudaStream_t stream, auto d_x) {
cuda_try(cudaMemsetAsync(d_x.data_handle(), 0, sizeof(double) * N_ROWS, stream));
};
ctx.task(x_new_handle.write()).set_symbol("memset x_new")->*[](cudaStream_t stream, auto d_x_new) {
cuda_try(cudaMemsetAsync(d_x_new.data_handle(), 0, sizeof(double) * N_ROWS, stream));
};
double sumGPU = JacobiMethodGpu(ctx, A_handle, b_handle, conv_threshold, max_iter, x_handle, x_new_handle);
ctx.finalize();
if (fabs(sum - sumGPU) > conv_threshold)
{
printf("&&&& jacobiCudaGraphs FAILED\n");
return EXIT_FAILURE;
}
return 0;
}
int main()
{
return run<stream_ctx>();
// run<graph_ctx>();
}

521
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@@ -1,521 +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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/*
* This sample implements a conjugate gradient solver on multiple GPU using
* Unified Memory optimized prefetching and usage hints.
*
*/
// includes, system
#include <iostream>
#include <map>
#include <set>
#include <utility>
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
// Utilities and system includes
#include <cuda/experimental/__places/partitions/blocked_partition.cuh>
#include <cuda/experimental/stf.cuh>
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
using namespace cuda::experimental::stf;
namespace cg = cooperative_groups;
const char* sSDKname = "conjugateGradientMultiDeviceCG";
#define ENABLE_CPU_DEBUG_CODE 0
#define THREADS_PER_BLOCK 64
__device__ double grid_dot_result = 0.0;
/* genTridiag: generate a random tridiagonal symmetric matrix */
void genTridiag(slice<int> I, slice<int> J, slice<float> val, int N, int nz)
{
I(0) = 0, J(0) = 0, J(1) = 1;
val(0) = (float) rand() / RAND_MAX + 10.0f;
val(1) = (float) rand() / RAND_MAX;
int start;
for (int 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) = (float) rand() / RAND_MAX + 10.0f;
if (i < N - 1)
{
val(start + 2) = (float) rand() / RAND_MAX;
}
}
I(N) = nz;
}
// I - contains location of the given non-zero element in the row of the matrix
// J - contains location of the given non-zero element in the column of the
// matrix val - contains values of the given non-zero elements of the matrix
// inputVecX - input vector to be multiplied
// outputVecY - resultant vector
void cpuSpMV(int* I, int* J, float* val, int /*unused*/, int num_rows, float alpha, float* inputVecX, float* outputVecY)
{
for (int i = 0; i < num_rows; i++)
{
int num_elems_this_row = I[i + 1] - I[i];
float output = 0.0;
for (int j = 0; j < num_elems_this_row; j++)
{
output += alpha * val[I[i] + j] * inputVecX[J[I[i] + j]];
}
outputVecY[i] = output;
}
return;
}
float dotProduct(float* vecA, float* vecB, int size)
{
float result = 0.0;
for (int i = 0; i < size; i++)
{
result = result + (vecA[i] * vecB[i]);
}
return result;
}
void scaleVector(float* vec, float alpha, int size)
{
for (int i = 0; i < size; i++)
{
vec[i] = alpha * vec[i];
}
}
void saxpy(float* x, float* y, float a, int size)
{
for (int i = 0; i < size; i++)
{
y[i] = a * x[i] + y[i];
}
}
void cpuConjugateGrad(int* I, int* J, float* val, float* x, float* Ax, float* p, float* r, int nnz, int N, float tol)
{
int max_iter = 10000;
float alpha = 1.0;
float alpham1 = -1.0;
float r0 = 0.0, b, a, na;
cpuSpMV(I, J, val, nnz, N, alpha, x, Ax);
saxpy(Ax, r, alpham1, N);
float r1 = dotProduct(r, r, N);
int k = 1;
while (r1 > tol * tol && k <= max_iter)
{
if (k > 1)
{
b = r1 / r0;
scaleVector(p, b, N);
saxpy(r, p, alpha, N);
}
else
{
for (int i = 0; i < N; i++)
{
p[i] = r[i];
}
}
cpuSpMV(I, J, val, nnz, N, alpha, p, Ax);
float dot = dotProduct(p, Ax, N);
a = r1 / dot;
saxpy(p, x, a, N);
na = -a;
saxpy(Ax, r, na, N);
r0 = r1;
r1 = dotProduct(r, r, N);
printf("\nCPU code iteration = %3d, residual = %e\n", k, sqrt(r1));
k++;
}
}
template <typename thread_hierarchy_t>
__device__ void gpuSpMV(
slice<const int> I,
slice<const int> J,
slice<const float> val,
int nnz,
int num_rows,
float alpha,
slice<float> inputVecX,
slice<float> outputVecY,
const thread_hierarchy_t& t)
{
for (int i = t.rank(); i < num_rows; i += t.size())
{
int row_elem = I(i);
int next_row_elem = I(i + 1);
int num_elems_this_row = next_row_elem - row_elem;
float output = 0.0;
for (int j = 0; j < num_elems_this_row; j++)
{
output += alpha * val(row_elem + j) * inputVecX(J(row_elem + j));
}
outputVecY(i) = output;
}
}
template <typename thread_hierarchy_t>
__device__ void gpuSaxpy(slice<float> x, slice<float> y, float a, int size, const thread_hierarchy_t& t)
{
for (int i = t.rank(); i < size; i += t.size())
{
y(i) = a * x(i) + y(i);
}
}
template <typename thread_hierarchy_t>
__device__ double
gpuDotProduct(slice<float> vecA, slice<float> vecB, int size, double* dot_result, thread_hierarchy_t& t)
{
slice<double> tmp = t.template storage<double>(1);
cg::thread_block cta = cooperative_groups::this_thread_block();
double temp_sum = 0.0;
for (int i = t.rank(); i < size; i += t.size())
{
temp_sum += (double) (vecA(i) * vecB(i));
}
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
if (tile32.thread_rank() == 0)
{
tmp[tile32.meta_group_rank()] = temp_sum;
}
cta.sync();
if (tile32.meta_group_rank() == 0)
{
temp_sum = tile32.thread_rank() < tile32.meta_group_size() ? tmp[tile32.thread_rank()] : 0.0;
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
if (tile32.thread_rank() == 0)
{
atomicAdd(&grid_dot_result, temp_sum);
}
}
t.sync();
if (t.rank(0, -1) == 0)
{
atomicAdd_system(dot_result, grid_dot_result);
grid_dot_result = 0.0;
}
t.sync();
return *dot_result;
}
template <typename thread_hierarchy_t>
__device__ void gpuCopyVector(slice<float> srcA, slice<float> destB, int size, const thread_hierarchy_t& t)
{
for (int i = t.rank(); i < size; i += t.size())
{
destB(i) = srcA(i);
}
}
template <typename thread_hierarchy_t>
__device__ void
gpuScaleVectorAndSaxpy(slice<float> x, slice<float> y, float a, float scale, int size, const thread_hierarchy_t& t)
{
for (int i = t.rank(); i < size; i += t.size())
{
y(i) = a * x(i) + scale * y(i);
}
}
template <typename thread_hierarchy_t>
__device__ void multiGpuConjugateGradient(
thread_hierarchy_t t,
slice<const int> I,
slice<const int> J,
slice<const float> val,
slice<float> x,
slice<float> Ax,
slice<float> p,
slice<float> r,
double* dot_result,
int nnz,
int N,
float tol)
{
const int max_iter = 10000;
float alpha = 1.0;
float alpham1 = -1.0;
float r0 = 0.0, r1, b, a, na;
for (int i = t.rank(); i < N; i += t.size())
{
r[i] = 1.0;
x[i] = 0.0;
}
gpuSpMV(I, J, val, nnz, N, alpha, x, Ax, t);
gpuSaxpy(Ax, r, alpham1, N, t);
r1 = gpuDotProduct(r, r, N, dot_result, t);
int k = 1;
while (r1 > tol * tol && k <= max_iter)
{
if (k > 1)
{
b = r1 / r0;
gpuScaleVectorAndSaxpy(r, p, alpha, b, N, t);
}
else
{
gpuCopyVector(r, p, N, t);
}
gpuSpMV(I, J, val, nnz, N, alpha, p, Ax, t);
if (t.rank() == 0)
{
*dot_result = 0.0;
}
a = r1 / gpuDotProduct(p, Ax, N, dot_result, t);
gpuSaxpy(p, x, a, N, t);
na = -a;
gpuSaxpy(Ax, r, na, N, t);
r0 = r1;
if (t.rank() == 0)
{
*dot_result = 0.0;
}
r1 = gpuDotProduct(r, r, N, dot_result, t);
k++;
}
}
int main()
{
stream_ctx ctx;
#if 0
constexpr size_t kNumGpusRequired = 8;
#else
constexpr size_t kNumGpusRequired = 1;
#endif
int N = 0, nz = 0, *I = NULL, *J = NULL;
float* val = NULL;
const float tol = 1e-5f;
float* x;
float rhs = 1.0;
float r1;
float *r, *p, *Ax;
// printf("Starting [%s]...\n", sSDKname);
/* Generate a random tridiagonal symmetric matrix in CSR format */
N = 10485760 * 2;
nz = (N - 2) * 3 + 4;
I = (int*) malloc(sizeof(int) * (N + 1));
J = (int*) malloc(sizeof(int) * nz);
val = (float*) malloc(sizeof(float) * nz);
float* val_cpu = (float*) malloc(sizeof(float) * nz);
auto handle_I = ctx.logical_data(I, {(unsigned) (N + 1)});
auto handle_J = ctx.logical_data(J, {(unsigned) nz});
auto handle_val = ctx.logical_data(val, {(unsigned) nz});
ctx.host_launch(handle_I.write(), handle_J.write(), handle_val.write())->*[=](auto I, auto J, auto val) {
genTridiag(I, J, val, N, nz);
memcpy(val_cpu, val.data_handle(), sizeof(float) * nz);
};
double* dot_result = (double*) malloc(sizeof(double));
dot_result[0] = 0.0;
x = (float*) malloc(sizeof(float) * N);
r = (float*) malloc(sizeof(float) * N);
p = (float*) malloc(sizeof(float) * N);
Ax = (float*) malloc(sizeof(float) * N);
auto handle_r = ctx.logical_data(r, {(unsigned) N});
auto handle_p = ctx.logical_data(p, {(unsigned) N});
auto handle_Ax = ctx.logical_data(Ax, {(unsigned) N});
auto handle_x = ctx.logical_data(x, {(unsigned) N});
auto handle_dot_result = ctx.logical_data(dot_result, {(unsigned) 1});
// std::cout << "\nRunning on GPUs = " << kNumGpusRequired << '\n';
const int sMemSize = sizeof(double) * ((THREADS_PER_BLOCK / 32) + 1);
// auto all_devs = exec_place::repeat<blocked_partition>(exec_place::device(0), kNumGpusRequired);
auto all_devs = exec_place::n_devices(kNumGpusRequired);
/* The grid size is 0 and will be computed upon launch */
auto spec = con(con(THREADS_PER_BLOCK, mem(sMemSize)));
ctx.launch(
spec,
all_devs,
handle_I.read(),
handle_J.read(),
handle_val.read(),
handle_x.write(),
handle_Ax.write(),
handle_p.write(),
handle_r.write(),
handle_dot_result.write())
->*[=]
_CCCL_DEVICE(auto t,
slice<const int> I,
slice<const int> J,
slice<const float> val,
slice<float> x,
slice<float> Ax,
slice<float> p,
slice<float> r,
slice<double> dot_result) {
multiGpuConjugateGradient(t, I, J, val, x, Ax, p, r, dot_result.data_handle(), nz, N, tol);
};
ctx.finalize();
r1 = dot_result[0];
printf("GPU Final, residual = %e \n ", sqrt(r1));
#if ENABLE_CPU_DEBUG_CODE
float* Ax_cpu = (float*) malloc(sizeof(float) * N);
float* r_cpu = (float*) malloc(sizeof(float) * N);
float* p_cpu = (float*) malloc(sizeof(float) * N);
float* x_cpu = (float*) malloc(sizeof(float) * N);
for (int i = 0; i < N; i++)
{
r_cpu[i] = 1.0;
Ax_cpu[i] = x_cpu[i] = 0.0;
}
cpuConjugateGrad(I, J, val, x_cpu, Ax_cpu, p_cpu, r_cpu, nz, N, tol);
#endif
float rsum, diff, err = 0.0;
for (int i = 0; i < N; i++)
{
rsum = 0.0;
for (int j = I[i]; j < I[i + 1]; j++)
{
rsum += val_cpu[j] * x[J[j]];
}
diff = fabs(rsum - rhs);
if (diff > err)
{
err = diff;
}
}
#if ENABLE_CPU_DEBUG_CODE
free(Ax_cpu);
free(r_cpu);
free(p_cpu);
free(x_cpu);
#endif
printf("Test Summary: Error amount = %f \n", err);
fprintf(stdout, "&&&& conjugateGradientMultiDeviceCG %s\n", (sqrt(r1) < tol) ? "PASSED" : "FAILED");
exit((sqrt(r1) < tol) ? EXIT_SUCCESS : EXIT_FAILURE);
}

210
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@@ -1,210 +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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/*
* This sample evaluates fair call price for a
* given set of European options using Monte Carlo approach.
* See supplied whitepaper for more explanations.
*/
#include "MonteCarlo_gold.cu"
#include "MonteCarlo_kernel.cu"
////////////////////////////////////////////////////////////////////////////////
// Common functions
////////////////////////////////////////////////////////////////////////////////
float randFloat(float low, float high)
{
float t = (float) rand() / (float) RAND_MAX;
return (1.0f - t) * low + t * high;
}
/// Utility function to tweak problem size for small GPUs
int adjustProblemSize(int GPU_N, int default_nOptions)
{
int nOptions = default_nOptions;
for (int i = 0; i < GPU_N; i++)
{
cudaDeviceProp deviceProp;
cuda_safe_call(cudaGetDeviceProperties(&deviceProp, i));
int cudaCores = 80;
if (cudaCores <= 32)
{
nOptions = (nOptions < cudaCores / 2 ? nOptions : cudaCores / 2);
}
}
return nOptions;
}
int adjustGridSize(int GPUIndex, int defaultGridSize)
{
cudaDeviceProp deviceProp;
cuda_safe_call(cudaGetDeviceProperties(&deviceProp, GPUIndex));
int maxGridSize = deviceProp.multiProcessorCount * 40;
return ((defaultGridSize > maxGridSize) ? maxGridSize : defaultGridSize);
}
///////////////////////////////////////////////////////////////////////////////
// CPU reference functions
///////////////////////////////////////////////////////////////////////////////
extern "C" void MonteCarloCPU(TOptionValue& callValue, TOptionData optionData, float* h_Random, int pathN);
// Black-Scholes formula for call options
extern "C" void BlackScholesCall(float& CallResult, TOptionData optionData);
////////////////////////////////////////////////////////////////////////////////
// Single-threaded multi-GPU solver using STF
////////////////////////////////////////////////////////////////////////////////
static void multiSolver(TOptionPlan* plan, int nPlans)
{
stream_ctx ctx;
for (int i = 0; i < nPlans; i++)
{
cuda_safe_call(cudaSetDevice(plan[i].device));
initMonteCarloGPU(ctx, &plan[i]);
MonteCarloGPU(ctx, &plan[i]);
closeMonteCarloGPU(ctx, &plan[i]);
}
ctx.finalize();
}
///////////////////////////////////////////////////////////////////////////////
// Main program
///////////////////////////////////////////////////////////////////////////////
int main(int argc, char** argv)
{
int GPU_N;
cuda_safe_call(cudaGetDeviceCount(&GPU_N));
int nOptions = 8 * 1024;
nOptions = adjustProblemSize(GPU_N, nOptions);
int OPT_N = nOptions * GPU_N;
int PATH_N = 262144;
// Input data array
TOptionData* optionData = new TOptionData[OPT_N];
// Final GPU MC results
TOptionValue* callValueGPU = new TOptionValue[OPT_N];
//"Theoretical" call values by Black-Scholes formula
float* callValueBS = new float[OPT_N];
// Solver config
TOptionPlan* optionSolver = new TOptionPlan[GPU_N];
int i;
double delta, ref, sumDelta, sumRef, sumReserve;
srand(123);
for (i = 0; i < OPT_N; i++)
{
optionData[i].S = randFloat(5.0f, 50.0f);
optionData[i].X = randFloat(10.0f, 25.0f);
optionData[i].T = randFloat(1.0f, 5.0f);
optionData[i].R = 0.06f;
optionData[i].V = 0.10f;
callValueGPU[i].Expected = -1.0f;
callValueGPU[i].Confidence = -1.0f;
}
// Get option count for each GPU
for (i = 0; i < GPU_N; i++)
{
optionSolver[i].optionCount = OPT_N / GPU_N;
}
// Take into account cases with "odd" option counts
for (i = 0; i < (OPT_N % GPU_N); i++)
{
optionSolver[i].optionCount++;
}
// Assign GPU option ranges
int gpuBase = 0;
for (i = 0; i < GPU_N; i++)
{
optionSolver[i].device = i;
optionSolver[i].optionData = optionData + gpuBase;
optionSolver[i].callValue = callValueGPU + gpuBase;
optionSolver[i].pathN = PATH_N;
optionSolver[i].gridSize = adjustGridSize(optionSolver[i].device, optionSolver[i].optionCount);
gpuBase += optionSolver[i].optionCount;
}
multiSolver(optionSolver, GPU_N);
// Compare Monte Carlo and Black-Scholes results
sumDelta = 0;
sumRef = 0;
sumReserve = 0;
for (i = 0; i < OPT_N; i++)
{
BlackScholesCall(callValueBS[i], optionData[i]);
delta = fabs(callValueBS[i] - callValueGPU[i].Expected);
ref = callValueBS[i];
sumDelta += delta;
sumRef += fabs(ref);
if (delta > 1e-6)
{
sumReserve += callValueGPU[i].Confidence / delta;
}
}
sumReserve /= OPT_N;
delete[] optionSolver;
delete[] callValueBS;
delete[] callValueGPU;
delete[] optionData;
if (sumReserve <= 1.0f)
{
printf("Test failed!\n");
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}

109
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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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef MONTECARLO_COMMON_H
#define MONTECARLO_COMMON_H
#include <cuda/experimental/stf.cuh>
#include "curand_kernel.h"
#include "realtype.cuh"
using namespace cuda::experimental::stf;
////////////////////////////////////////////////////////////////////////////////
// Global types
////////////////////////////////////////////////////////////////////////////////
typedef struct
{
float S;
float X;
float T;
float R;
float V;
} TOptionData;
typedef struct
{
float Expected;
float Confidence;
} TOptionValue;
// Preprocessed input option data
typedef struct
{
real S;
real X;
real MuByT;
real VBySqrtT;
} __TOptionData;
// GPU outputs before CPU postprocessing
typedef struct
{
real Expected;
real Confidence;
} __TOptionValue;
typedef struct
{
// Device ID for multi-GPU version
int device;
// Option count for this plan
int optionCount;
// Host-side data source and result destination
TOptionData* optionData;
TOptionValue* callValue;
logical_data<slice<__TOptionData>> preproc_optionData_handle;
logical_data<slice<__TOptionValue>> callValue_handle;
// Temporary Host-side pinned memory for async + faster data transfers
__TOptionValue* h_CallValue;
// Host-side option data
void* h_OptionData;
// Random number generator states
logical_data<slice<curandState>> rngStates_handle;
// Pseudorandom samples count
int pathN;
int gridSize;
} TOptionPlan;
#endif

160
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@@ -1,160 +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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "MonteCarlo_common.cuh"
////////////////////////////////////////////////////////////////////////////////
// Black-Scholes formula for Monte Carlo results validation
////////////////////////////////////////////////////////////////////////////////
#define A1 0.31938153
#define A2 -0.356563782
#define A3 1.781477937
#define A4 -1.821255978
#define A5 1.330274429
#define RSQRT2PI 0.39894228040143267793994605993438
// Polynomial approximation of
// cumulative normal distribution function
double CND(double d)
{
double K = 1.0 / (1.0 + 0.2316419 * fabs(d));
double cnd = RSQRT2PI * exp(-0.5 * d * d) * (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5)))));
if (d > 0)
{
cnd = 1.0 - cnd;
}
return cnd;
}
// Black-Scholes formula for call value
extern "C" void BlackScholesCall(float& callValue, TOptionData optionData)
{
double S = optionData.S;
double X = optionData.X;
double T = optionData.T;
double R = optionData.R;
double V = optionData.V;
double sqrtT = sqrt(T);
double d1 = (log(S / X) + (R + 0.5 * V * V) * T) / (V * sqrtT);
double d2 = d1 - V * sqrtT;
double CNDD1 = CND(d1);
double CNDD2 = CND(d2);
double expRT = exp(-R * T);
callValue = (float) (S * CNDD1 - X * expRT * CNDD2);
}
////////////////////////////////////////////////////////////////////////////////
// CPU Monte Carlo
////////////////////////////////////////////////////////////////////////////////
static double CPU_endCallValue(double S, double X, double r, double MuByT, double VBySqrtT)
{
double callValue = S * exp(MuByT + VBySqrtT * r) - X;
return (callValue > 0) ? callValue : 0;
}
#define CURAND_SAFE_CALL(call) \
do \
{ \
const curandStatus_t err = (call); \
if (CURAND_STATUS_SUCCESS != err) \
{ \
int dev = -1; \
cudaGetDevice(&dev); \
fprintf(stderr, "%s:%u [device %d] CURAND error in call %s.\n", __FILE__, __LINE__, dev, #call); \
abort(); \
} \
} while (0)
extern "C" void MonteCarloCPU(TOptionValue& callValue, TOptionData optionData, float* h_Samples, int pathN)
{
const double S = optionData.S;
const double X = optionData.X;
const double T = optionData.T;
const double R = optionData.R;
const double V = optionData.V;
const double MuByT = (R - 0.5 * V * V) * T;
const double VBySqrtT = V * sqrt(T);
float* samples;
curandGenerator_t gen;
CURAND_SAFE_CALL(curandCreateGeneratorHost(&gen, CURAND_RNG_PSEUDO_DEFAULT));
unsigned long long seed = 1234ULL;
CURAND_SAFE_CALL(curandSetPseudoRandomGeneratorSeed(gen, seed));
if (h_Samples != NULL)
{
samples = h_Samples;
}
else
{
samples = (float*) malloc(pathN * sizeof(float));
CURAND_SAFE_CALL(curandGenerateNormal(gen, samples, pathN, 0.0, 1.0));
}
// for(int i=0; i<10; i++) printf("CPU sample = %f\n", samples[i]);
double sum = 0, sum2 = 0;
for (int pos = 0; pos < pathN; pos++)
{
double sample = samples[pos];
double callValue = CPU_endCallValue(S, X, sample, MuByT, VBySqrtT);
sum += callValue;
sum2 += callValue * callValue;
}
if (h_Samples == NULL)
{
free(samples);
}
CURAND_SAFE_CALL(curandDestroyGenerator(gen));
// Derive average from the total sum and discount by riskfree rate
callValue.Expected = (float) (exp(-R * T) * sum / (double) pathN);
// Standard deviation
double stdDev = sqrt(((double) pathN * sum2 - sum * sum) / ((double) pathN * (double) (pathN - 1)));
// Confidence width; in 95% of all cases theoretical value lies within these
// borders
callValue.Confidence = (float) (exp(-R * T) * 1.96 * stdDev / sqrt((double) pathN));
}

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@@ -1,246 +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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
////////////////////////////////////////////////////////////////////////////////
// Global types
////////////////////////////////////////////////////////////////////////////////
#include "MonteCarlo_reduction.cuh"
// This will output the proper error string when calling cudaGetLastError
#define getLastCudaError(msg) __getLastCudaError((msg), __FILE__, __LINE__)
namespace cg = cooperative_groups;
inline void __getLastCudaError(const char* errorMessage, const char* file, const int line)
{
cudaError_t err = cudaGetLastError();
if (cudaSuccess != err)
{
fprintf(stderr,
"%s(%i) : getLastCudaError() CUDA error:"
" %s : (%d) %s.\n",
file,
line,
errorMessage,
static_cast<int>(err),
cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
}
////////////////////////////////////////////////////////////////////////////////
// Internal GPU-side data structures
////////////////////////////////////////////////////////////////////////////////
#define MAX_OPTIONS (1024 * 1024)
////////////////////////////////////////////////////////////////////////////////
// Overloaded shortcut payoff functions for different precision modes
////////////////////////////////////////////////////////////////////////////////
__device__ inline float endCallValue(float S, float X, float r, float MuByT, float VBySqrtT)
{
float callValue = S * __expf(MuByT + VBySqrtT * r) - X;
return (callValue > 0.0F) ? callValue : 0.0F;
}
__device__ inline double endCallValue(double S, double X, double r, double MuByT, double VBySqrtT)
{
double callValue = S * exp(MuByT + VBySqrtT * r) - X;
return (callValue > 0.0) ? callValue : 0.0;
}
#define THREAD_N 256
////////////////////////////////////////////////////////////////////////////////
// This kernel computes the integral over all paths using a single thread block
// per option. It is fastest when the number of thread blocks times the work per
// block is high enough to keep the GPU busy.
////////////////////////////////////////////////////////////////////////////////
static __global__ void MonteCarloOneBlockPerOption(
curandState* __restrict rngStates,
const __TOptionData* __restrict d_OptionData,
__TOptionValue* __restrict d_CallValue,
int pathN,
int optionN)
{
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
const int SUM_N = THREAD_N;
__shared__ real s_SumCall[SUM_N];
__shared__ real s_Sum2Call[SUM_N];
// determine global thread id
int tid = threadIdx.x + blockIdx.x * blockDim.x;
// Copy random number state to local memory for efficiency
curandState localState = rngStates[tid];
for (int optionIndex = blockIdx.x; optionIndex < optionN; optionIndex += gridDim.x)
{
const real S = d_OptionData[optionIndex].S;
const real X = d_OptionData[optionIndex].X;
const real MuByT = d_OptionData[optionIndex].MuByT;
const real VBySqrtT = d_OptionData[optionIndex].VBySqrtT;
// Cycle through the entire samples array:
// derive end stock price for each path
// accumulate partial integrals into intermediate shared memory buffer
for (int iSum = threadIdx.x; iSum < SUM_N; iSum += blockDim.x)
{
__TOptionValue sumCall = {0, 0};
_CCCL_PRAGMA_UNROLL(8)
for (int i = iSum; i < pathN; i += SUM_N)
{
real r = curand_normal(&localState);
real callValue = endCallValue(S, X, r, MuByT, VBySqrtT);
sumCall.Expected += callValue;
sumCall.Confidence += callValue * callValue;
}
s_SumCall[iSum] = sumCall.Expected;
s_Sum2Call[iSum] = sumCall.Confidence;
}
// Reduce shared memory accumulators
// and write final result to global memory
cg::sync(cta);
sumReduce<real, SUM_N, THREAD_N>(s_SumCall, s_Sum2Call, cta, tile32, &d_CallValue[optionIndex]);
}
}
static __global__ void rngSetupStates(curandState* rngState, int device_id)
{
// determine global thread id
int tid = threadIdx.x + blockIdx.x * blockDim.x;
// Each threadblock gets different seed,
// Threads within a threadblock get different sequence numbers
curand_init(blockIdx.x + gridDim.x * device_id, threadIdx.x, 0, &rngState[tid]);
}
////////////////////////////////////////////////////////////////////////////////
// Host-side interface to GPU Monte Carlo
////////////////////////////////////////////////////////////////////////////////
template <typename Ctx>
void initMonteCarloGPU(Ctx& ctx, TOptionPlan* plan)
{
plan->h_OptionData = new __TOptionData[plan->optionCount];
plan->h_CallValue = new __TOptionValue[plan->optionCount];
cuda_safe_call(
cudaHostRegister(plan->h_OptionData, plan->optionCount * sizeof(__TOptionData), cudaHostRegisterPortable));
cuda_safe_call(
cudaHostRegister(plan->h_CallValue, plan->optionCount * sizeof(__TOptionValue), cudaHostRegisterPortable));
// Register this vector
plan->preproc_optionData_handle =
ctx.logical_data((__TOptionData*) plan->h_OptionData, plan->optionCount).set_symbol("preproc_optionData");
plan->callValue_handle =
ctx.logical_data((__TOptionValue*) plan->h_CallValue, plan->optionCount).set_symbol("callValue");
plan->rngStates_handle =
ctx.logical_data(shape_of<slice<curandState>>(plan->gridSize * THREAD_N)).set_symbol("rngStates");
cuda_safe_call(cudaSetDevice(plan->device));
// Allocate states for pseudo random number generators
ctx.task(plan->rngStates_handle.write()).set_symbol("rngSetupStates")->*[&](cudaStream_t stream, auto rngStates) {
cuda_safe_call(
cudaMemsetAsync(rngStates.data_handle(), 0, plan->gridSize * THREAD_N * sizeof(curandState), stream));
getLastCudaError("cudaMemsetAsync failed.\n");
rngSetupStates<<<plan->gridSize, THREAD_N, 0, stream>>>(rngStates.data_handle(), plan->device);
getLastCudaError("rngSetupStates kernel failed.\n");
};
}
// Compute statistics and deallocate internal device memory
template <typename Ctx>
void closeMonteCarloGPU(Ctx& ctx, TOptionPlan* plan)
{
ctx.host_launch(plan->callValue_handle.rw()).set_symbol("compute_stats")->*[&](auto h_CallValue) {
for (int i = 0; i < plan->optionCount; i++)
{
const double RT = plan->optionData[i].R * plan->optionData[i].T;
const double sum = h_CallValue.data_handle()[i].Expected;
const double sum2 = h_CallValue.data_handle()[i].Confidence;
const double pathN = plan->pathN;
plan->callValue[i].Expected = (float) (exp(-RT) * sum / pathN);
double stdDev = sqrt((pathN * sum2 - sum * sum) / (pathN * (pathN - 1)));
plan->callValue[i].Confidence = (float) (exp(-RT) * 1.96 * stdDev / sqrt(pathN));
}
};
}
// Main computations
template <typename Ctx>
void MonteCarloGPU(Ctx& ctx, TOptionPlan* plan)
{
if (plan->optionCount <= 0 || plan->optionCount > MAX_OPTIONS)
{
printf("MonteCarloGPU(): bad option count.\n");
return;
}
// Preprocess computations on the host
ctx.host_launch(plan->preproc_optionData_handle.rw()).set_symbol("preprocess")->*[&](auto h_preproc_OptionData) {
for (int i = 0; i < plan->optionCount; i++)
{
const double T = plan->optionData[i].T;
const double R = plan->optionData[i].R;
const double V = plan->optionData[i].V;
const double MuByT = (R - 0.5 * V * V) * T;
const double VBySqrtT = V * sqrt(T);
h_preproc_OptionData.data_handle()[i].S = (real) plan->optionData[i].S;
h_preproc_OptionData.data_handle()[i].X = (real) plan->optionData[i].X;
h_preproc_OptionData.data_handle()[i].MuByT = (real) MuByT;
h_preproc_OptionData.data_handle()[i].VBySqrtT = (real) VBySqrtT;
}
};
ctx.task(plan->preproc_optionData_handle.read(), plan->callValue_handle.write(), plan->rngStates_handle.rw())
.set_symbol("MonteCarloOneBlockPerOption")
->*[&](cudaStream_t stream, auto preproc_optionData, auto callValue_handle, auto rngStates) {
MonteCarloOneBlockPerOption<<<plan->gridSize, THREAD_N, 0, stream>>>(
rngStates.data_handle(),
preproc_optionData.data_handle(),
callValue_handle.data_handle(),
plan->pathN,
plan->optionCount);
getLastCudaError("MonteCarloOneBlockPerOption() execution failed\n");
};
}

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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef MONTECARLO_REDUCTION_CUH
#define MONTECARLO_REDUCTION_CUH
#include <cooperative_groups.h>
#include "MonteCarlo_common.cuh"
namespace cg = cooperative_groups;
////////////////////////////////////////////////////////////////////////////////
// This function calculates total sum for each of the two input arrays.
// SUM_N must be power of two
// Unrolling provides a bit of a performance improvement for small
// to medium path counts.
////////////////////////////////////////////////////////////////////////////////
template <class T, int SUM_N, int blockSize>
__device__ void
sumReduce(T* sum, T* sum2, cg::thread_block& cta, cg::thread_block_tile<32>& tile32, __TOptionValue* d_CallValue)
{
const int VEC = 32;
const int tid = cta.thread_rank();
T beta = sum[tid];
T beta2 = sum2[tid];
T temp, temp2;
for (int i = VEC / 2; i > 0; i >>= 1)
{
if (tile32.thread_rank() < i)
{
temp = sum[tid + i];
temp2 = sum2[tid + i];
beta += temp;
beta2 += temp2;
sum[tid] = beta;
sum2[tid] = beta2;
}
cg::sync(tile32);
}
cg::sync(cta);
if (tid == 0)
{
beta = 0;
beta2 = 0;
for (int i = 0; i < blockDim.x; i += VEC)
{
beta += sum[i];
beta2 += sum2[i];
}
__TOptionValue t = {beta, beta2};
*d_CallValue = t;
}
cg::sync(cta);
}
#endif

75
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# MonteCarloMultiGPU - Monte Carlo Option Pricing with Multi-GPU support
## Description
This sample evaluates fair call price for a given set of European options using the Monte Carlo approach, taking advantage of all CUDA-capable GPUs installed in the system. This sample use double precision hardware if a GTX 200 class GPU is present. The sample also takes advantage of CUDA 4.0 capability to supporting using a single CPU thread to control multiple GPUs
## Key Concepts
Random Number Generator, Computational Finance, CURAND Library
## Supported SM Architectures
[SM 3.5 ](https://developer.nvidia.com/cuda-gpus) [SM 3.7 ](https://developer.nvidia.com/cuda-gpus) [SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus)
## Supported OSes
Linux, Windows
## Supported CPU Architecture
x86_64, ppc64le, armv7l
## CUDA APIs involved
### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaMemset, cudaFree, cudaStreamDestroy, cudaEventRecord, cudaMallocHost, cudaStreamCreate, cudaEventCreate, cudaGetDeviceCount, cudaDeviceSynchronize, cudaEventSynchronize, cudaFreeHost, cudaMalloc, cudaEventDestroy, cudaSetDevice, cudaMemcpyAsync, cudaStreamSynchronize, cudaGetDeviceProperties
## Dependencies needed to build/run
[CURAND](../../../README.md#curand)
## Prerequisites
Download and install the [CUDA Toolkit 11.6](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
## Build and Run
### Windows
The Windows samples are built using the Visual Studio IDE. Solution files (.sln) are provided for each supported version of Visual Studio, using the format:
```
*_vs<version>.sln - for Visual Studio <version>
```
Each individual sample has its own set of solution files in its directory:
To build/examine all the samples at once, the complete solution files should be used. To build/examine a single sample, the individual sample solution files should be used.
> **Note:** Some samples require that the Microsoft DirectX SDK (June 2010 or newer) be installed and that the VC++ directory paths are properly set up (**Tools > Options...**). Check DirectX Dependencies section for details."
### Linux
The Linux samples are built using makefiles. To use the makefiles, change the current directory to the sample directory you wish to build, and run make:
```
$ cd <sample_dir>
$ make
```
The samples makefiles can take advantage of certain options:
* **TARGET_ARCH=<arch>** - cross-compile targeting a specific architecture. Allowed architectures are x86_64, ppc64le, armv7l.
By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equivalent of setting TARGET_ARCH=x86_64.<br/>
`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=ppc64le` <br/> `$ make TARGET_ARCH=armv7l` <br/>
See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) for more details.
* **dbg=1** - build with debug symbols
```
$ make dbg=1
```
* **SMS="A B ..."** - override the SM architectures for which the sample will be built, where `"A B ..."` is a space-delimited list of SM architectures. For example, to generate SASS for SM 50 and SM 60, use `SMS="50 60"`.
```
$ make SMS="50 60"
```
* **HOST_COMPILER=<host_compiler>** - override the default g++ host compiler. See the [Linux Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements) for a list of supported host compilers.
```
$ make HOST_COMPILER=g++
```
## References (for more details)
[whitepaper](./doc/MonteCarlo.pdf)

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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.
//
//===----------------------------------------------------------------------===//
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef REALTYPE_H
#define REALTYPE_H
// #define DOUBLE_PRECISION
#ifndef DOUBLE_PRECISION
typedef float real;
#else
typedef double real;
#endif
#endif