mirror of
https://github.com/NVIDIA/cuda-samples.git
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216 lines
8.1 KiB
C++
216 lines
8.1 KiB
C++
/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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// This sample demonstrates Instantiated CUDA Graph Update
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// with Jacobi Iterative Method in 3 different methods:
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// 1 - JacobiMethodGpuCudaGraphExecKernelSetParams() - CUDA Graph with
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// cudaGraphExecKernelNodeSetParams() 2 - JacobiMethodGpuCudaGraphExecUpdate() -
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// CUDA Graph with cudaGraphExecUpdate() 3 - JacobiMethodGpu() - Non CUDA Graph
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// method
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// Jacobi method on a linear system A*x = b,
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// where A is diagonally dominant and the exact solution consists
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// of all ones.
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// The dimension N_ROWS is included in jacobi.h
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#include <cuda_runtime.h>
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#include <helper_cuda.h>
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#include <helper_timer.h>
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include "jacobi.h"
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// Run the Jacobi method for A*x = b on GPU with CUDA Graph -
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// cudaGraphExecKernelNodeSetParams().
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extern double JacobiMethodGpuCudaGraphExecKernelSetParams(
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const float *A, const double *b, const float conv_threshold,
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const int max_iter, double *x, double *x_new, cudaStream_t stream);
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// Run the Jacobi method for A*x = b on GPU with Instantiated CUDA Graph Update
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// API - cudaGraphExecUpdate().
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extern double JacobiMethodGpuCudaGraphExecUpdate(
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const float *A, const double *b, const float conv_threshold,
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const int max_iter, double *x, double *x_new, cudaStream_t stream);
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// Run the Jacobi method for A*x = b on GPU without CUDA Graph.
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extern double JacobiMethodGpu(const float *A, const double *b,
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const float conv_threshold, const int max_iter,
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double *x, double *x_new, cudaStream_t stream);
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// creates N_ROWS x N_ROWS matrix A with N_ROWS+1 on the diagonal and 1
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// elsewhere. The elements of the right hand side b all equal 2*n, hence the
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// exact solution x to A*x = b is a vector of ones.
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void createLinearSystem(float *A, double *b);
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// Run the Jacobi method for A*x = b on CPU.
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void JacobiMethodCPU(float *A, double *b, float conv_threshold, int max_iter,
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int *numit, double *x);
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int main(int argc, char **argv) {
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if (checkCmdLineFlag(argc, (const char **)argv, "help")) {
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printf("Command line: jacobiCudaGraphs [-option]\n");
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printf("Valid options:\n");
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printf(
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"-gpumethod=<0,1 or 2> : 0 - [Default] "
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"JacobiMethodGpuCudaGraphExecKernelSetParams\n");
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printf(" : 1 - JacobiMethodGpuCudaGraphExecUpdate\n");
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printf(" : 2 - JacobiMethodGpu - Non CUDA Graph\n");
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printf("-device=device_num : cuda device id");
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printf("-help : Output a help message\n");
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exit(EXIT_SUCCESS);
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}
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int gpumethod = 0;
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if (checkCmdLineFlag(argc, (const char **)argv, "gpumethod")) {
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gpumethod = getCmdLineArgumentInt(argc, (const char **)argv, "gpumethod");
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if (gpumethod < 0 || gpumethod > 2) {
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printf("Error: gpumethod must be 0 or 1 or 2, gpumethod=%d is invalid\n",
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gpumethod);
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exit(EXIT_SUCCESS);
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}
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}
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int dev = findCudaDevice(argc, (const char **)argv);
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double *b = NULL;
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float *A = NULL;
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checkCudaErrors(cudaMallocHost(&b, N_ROWS * sizeof(double)));
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memset(b, 0, N_ROWS * sizeof(double));
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checkCudaErrors(cudaMallocHost(&A, N_ROWS * N_ROWS * sizeof(float)));
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memset(A, 0, N_ROWS * N_ROWS * sizeof(float));
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createLinearSystem(A, b);
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double *x = NULL;
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// start with array of all zeroes
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x = (double *)calloc(N_ROWS, sizeof(double));
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float conv_threshold = 1.0e-2;
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int max_iter = 4 * N_ROWS * N_ROWS;
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int cnt = 0;
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// create timer
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StopWatchInterface *timerCPU = NULL, *timerGpu = NULL;
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sdkCreateTimer(&timerCPU);
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sdkStartTimer(&timerCPU);
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JacobiMethodCPU(A, b, conv_threshold, max_iter, &cnt, x);
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double sum = 0.0;
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// Compute error
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for (int i = 0; i < N_ROWS; i++) {
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double d = x[i] - 1.0;
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sum += fabs(d);
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}
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sdkStopTimer(&timerCPU);
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printf("CPU iterations : %d\n", cnt);
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printf("CPU error : %.3e\n", sum);
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printf("CPU Processing time: %f (ms)\n", sdkGetTimerValue(&timerCPU));
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float *d_A;
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double *d_b, *d_x, *d_x_new;
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cudaStream_t stream1;
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checkCudaErrors(cudaStreamCreateWithFlags(&stream1, cudaStreamNonBlocking));
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checkCudaErrors(cudaMalloc(&d_b, sizeof(double) * N_ROWS));
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checkCudaErrors(cudaMalloc(&d_A, sizeof(float) * N_ROWS * N_ROWS));
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checkCudaErrors(cudaMalloc(&d_x, sizeof(double) * N_ROWS));
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checkCudaErrors(cudaMalloc(&d_x_new, sizeof(double) * N_ROWS));
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checkCudaErrors(cudaMemsetAsync(d_x, 0, sizeof(double) * N_ROWS, stream1));
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checkCudaErrors(
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cudaMemsetAsync(d_x_new, 0, sizeof(double) * N_ROWS, stream1));
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checkCudaErrors(cudaMemcpyAsync(d_A, A, sizeof(float) * N_ROWS * N_ROWS,
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cudaMemcpyHostToDevice, stream1));
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checkCudaErrors(cudaMemcpyAsync(d_b, b, sizeof(double) * N_ROWS,
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cudaMemcpyHostToDevice, stream1));
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sdkCreateTimer(&timerGpu);
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sdkStartTimer(&timerGpu);
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double sumGPU = 0.0;
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if (gpumethod == 0) {
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sumGPU = JacobiMethodGpuCudaGraphExecKernelSetParams(
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d_A, d_b, conv_threshold, max_iter, d_x, d_x_new, stream1);
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} else if (gpumethod == 1) {
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sumGPU = JacobiMethodGpuCudaGraphExecUpdate(
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d_A, d_b, conv_threshold, max_iter, d_x, d_x_new, stream1);
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} else if (gpumethod == 2) {
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sumGPU = JacobiMethodGpu(d_A, d_b, conv_threshold, max_iter, d_x, d_x_new,
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stream1);
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}
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sdkStopTimer(&timerGpu);
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printf("GPU Processing time: %f (ms)\n", sdkGetTimerValue(&timerGpu));
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checkCudaErrors(cudaFree(d_b));
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checkCudaErrors(cudaFree(d_A));
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checkCudaErrors(cudaFree(d_x));
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checkCudaErrors(cudaFree(d_x_new));
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checkCudaErrors(cudaFreeHost(A));
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checkCudaErrors(cudaFreeHost(b));
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printf("&&&& jacobiCudaGraphs %s\n",
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(fabs(sum - sumGPU) < conv_threshold) ? "PASSED" : "FAILED");
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return (fabs(sum - sumGPU) < conv_threshold) ? EXIT_SUCCESS : EXIT_FAILURE;
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}
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void createLinearSystem(float *A, double *b) {
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int i, j;
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for (i = 0; i < N_ROWS; i++) {
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b[i] = 2.0 * N_ROWS;
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for (j = 0; j < N_ROWS; j++) A[i * N_ROWS + j] = 1.0;
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A[i * N_ROWS + i] = N_ROWS + 1.0;
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}
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}
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void JacobiMethodCPU(float *A, double *b, float conv_threshold, int max_iter,
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int *num_iter, double *x) {
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double *x_new;
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x_new = (double *)calloc(N_ROWS, sizeof(double));
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int k;
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for (k = 0; k < max_iter; k++) {
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double sum = 0.0;
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for (int i = 0; i < N_ROWS; i++) {
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double temp_dx = b[i];
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for (int j = 0; j < N_ROWS; j++) temp_dx -= A[i * N_ROWS + j] * x[j];
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temp_dx /= A[i * N_ROWS + i];
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x_new[i] += temp_dx;
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sum += fabs(temp_dx);
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}
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for (int i = 0; i < N_ROWS; i++) x[i] = x_new[i];
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if (sum <= conv_threshold) break;
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}
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*num_iter = k + 1;
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free(x_new);
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}
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