mirror of
https://github.com/NVIDIA/cuda-samples.git
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419 lines
14 KiB
Plaintext
419 lines
14 KiB
Plaintext
/* 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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/*
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* This sample implements a conjugate gradient solver on GPU
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* using CUBLAS and CUSPARSE with CUDA Graphs
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*
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*/
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// includes, system
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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/* Using updated (v2) interfaces to cublas */
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#include <cublas_v2.h>
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#include <cuda_runtime.h>
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#include <cusparse.h>
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// Utilities and system includes
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#include <helper_cuda.h> // helper function CUDA error checking and initialization
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#include <helper_functions.h> // helper for shared functions common to CUDA Samples
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const char *sSDKname = "conjugateGradientCudaGraphs";
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#ifndef WITH_GRAPH
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#define WITH_GRAPH 1
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#endif
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/* genTridiag: generate a random tridiagonal symmetric matrix */
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void genTridiag(int *I, int *J, float *val, int N, int nz) {
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I[0] = 0, J[0] = 0, J[1] = 1;
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val[0] = (float)rand() / RAND_MAX + 10.0f;
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val[1] = (float)rand() / RAND_MAX;
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int start;
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for (int i = 1; i < N; i++) {
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if (i > 1) {
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I[i] = I[i - 1] + 3;
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} else {
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I[1] = 2;
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}
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start = (i - 1) * 3 + 2;
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J[start] = i - 1;
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J[start + 1] = i;
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if (i < N - 1) {
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J[start + 2] = i + 1;
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}
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val[start] = val[start - 1];
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val[start + 1] = (float)rand() / RAND_MAX + 10.0f;
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if (i < N - 1) {
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val[start + 2] = (float)rand() / RAND_MAX;
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}
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}
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I[N] = nz;
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}
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__global__ void initVectors(float *rhs, float *x, int N) {
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size_t gid = blockIdx.x * blockDim.x + threadIdx.x;
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for (size_t i = gid; i < N; i += gridDim.x * blockDim.x) {
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rhs[i] = 1.0;
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x[i] = 0.0;
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}
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}
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__global__ void r1_div_x(float *r1, float *r0, float *b) {
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int gid = blockIdx.x * blockDim.x + threadIdx.x;
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if (gid == 0) {
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b[0] = r1[0] / r0[0];
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}
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}
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__global__ void a_minus(float *a, float *na) {
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int gid = blockIdx.x * blockDim.x + threadIdx.x;
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if (gid == 0) {
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na[0] = -(a[0]);
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}
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}
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int main(int argc, char **argv) {
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int N = 0, nz = 0, *I = NULL, *J = NULL;
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float *val = NULL;
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const float tol = 1e-5f;
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const int max_iter = 10000;
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float *x;
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float *rhs;
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float r1;
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int *d_col, *d_row;
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float *d_val, *d_x;
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float *d_r, *d_p, *d_Ax;
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int k;
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float alpha, beta, alpham1;
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cudaStream_t stream1, streamForGraph;
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// This will pick the best possible CUDA capable device
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cudaDeviceProp deviceProp;
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int devID = findCudaDevice(argc, (const char **)argv);
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if (devID < 0) {
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printf("exiting...\n");
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exit(EXIT_SUCCESS);
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}
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
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// Statistics about the GPU device
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printf(
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"> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
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deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
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/* Generate a random tridiagonal symmetric matrix in CSR format */
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N = 1048576;
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nz = (N - 2) * 3 + 4;
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I = (int *)malloc(sizeof(int) * (N + 1));
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J = (int *)malloc(sizeof(int) * nz);
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val = (float *)malloc(sizeof(float) * nz);
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genTridiag(I, J, val, N, nz);
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x = (float *)malloc(sizeof(float) * N);
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rhs = (float *)malloc(sizeof(float) * N);
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for (int i = 0; i < N; i++) {
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rhs[i] = 1.0;
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x[i] = 0.0;
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}
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/* Get handle to the CUBLAS context */
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cublasHandle_t cublasHandle = 0;
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cublasStatus_t cublasStatus;
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cublasStatus = cublasCreate(&cublasHandle);
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checkCudaErrors(cublasStatus);
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/* Get handle to the CUSPARSE context */
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cusparseHandle_t cusparseHandle = 0;
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cusparseStatus_t cusparseStatus;
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cusparseStatus = cusparseCreate(&cusparseHandle);
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checkCudaErrors(cusparseStatus);
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checkCudaErrors(cudaStreamCreate(&stream1));
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checkCudaErrors(cudaMalloc((void **)&d_col, nz * sizeof(int)));
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checkCudaErrors(cudaMalloc((void **)&d_row, (N + 1) * sizeof(int)));
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checkCudaErrors(cudaMalloc((void **)&d_val, nz * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_x, N * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_r, N * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_p, N * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_Ax, N * sizeof(float)));
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float *d_r1, *d_r0, *d_dot, *d_a, *d_na, *d_b;
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checkCudaErrors(cudaMalloc((void **)&d_r1, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_r0, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_dot, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_a, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_na, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(float)));
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/* Wrap raw data into cuSPARSE generic API objects */
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cusparseSpMatDescr_t matA = NULL;
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checkCudaErrors(cusparseCreateCsr(
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&matA, N, N, nz, d_row, d_col, d_val, CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F));
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cusparseDnVecDescr_t vecx = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecx, N, d_x, CUDA_R_32F));
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cusparseDnVecDescr_t vecp = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecp, N, d_p, CUDA_R_32F));
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cusparseDnVecDescr_t vecAx = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecAx, N, d_Ax, CUDA_R_32F));
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/* Allocate workspace for cuSPARSE */
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size_t bufferSize = 0;
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checkCudaErrors(cusparseSpMV_bufferSize(
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cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecx,
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&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &bufferSize));
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void *buffer = NULL;
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checkCudaErrors(cudaMalloc(&buffer, bufferSize));
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cusparseMatDescr_t descr = 0;
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checkCudaErrors(cusparseCreateMatDescr(&descr));
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checkCudaErrors(cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL));
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checkCudaErrors(cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO));
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int numBlocks = 0, blockSize = 0;
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checkCudaErrors(
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cudaOccupancyMaxPotentialBlockSize(&numBlocks, &blockSize, initVectors));
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checkCudaErrors(cudaMemcpyAsync(d_col, J, nz * sizeof(int),
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cudaMemcpyHostToDevice, stream1));
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checkCudaErrors(cudaMemcpyAsync(d_row, I, (N + 1) * sizeof(int),
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cudaMemcpyHostToDevice, stream1));
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checkCudaErrors(cudaMemcpyAsync(d_val, val, nz * sizeof(float),
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cudaMemcpyHostToDevice, stream1));
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initVectors<<<numBlocks, blockSize, 0, stream1>>>(d_r, d_x, N);
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alpha = 1.0;
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alpham1 = -1.0;
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beta = 0.0;
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checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
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checkCudaErrors(cusparseSpMV(
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cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecx,
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&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &buffer));
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checkCudaErrors(cublasSetStream(cublasHandle, stream1));
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checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpham1, d_Ax, 1, d_r, 1));
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checkCudaErrors(
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cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE));
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checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
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k = 1;
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// First Iteration when k=1 starts
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checkCudaErrors(cublasScopy(cublasHandle, N, d_r, 1, d_p, 1));
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checkCudaErrors(cusparseSpMV(
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cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecp,
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&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &buffer));
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checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
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r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_dot, d_a);
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checkCudaErrors(cublasSaxpy(cublasHandle, N, d_a, d_p, 1, d_x, 1));
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a_minus<<<1, 1, 0, stream1>>>(d_a, d_na);
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checkCudaErrors(cublasSaxpy(cublasHandle, N, d_na, d_Ax, 1, d_r, 1));
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checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float),
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cudaMemcpyDeviceToDevice, stream1));
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checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
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checkCudaErrors(cudaMemcpyAsync(&r1, d_r1, sizeof(float),
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cudaMemcpyDeviceToHost, stream1));
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checkCudaErrors(cudaStreamSynchronize(stream1));
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printf("iteration = %3d, residual = %e\n", k, sqrt(r1));
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// First Iteration when k=1 ends
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k++;
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#if WITH_GRAPH
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cudaGraph_t initGraph;
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checkCudaErrors(cudaStreamCreate(&streamForGraph));
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checkCudaErrors(cublasSetStream(cublasHandle, stream1));
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checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
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checkCudaErrors(cudaStreamBeginCapture(stream1, cudaStreamCaptureModeGlobal));
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r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_r0, d_b);
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cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
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checkCudaErrors(cublasSscal(cublasHandle, N, d_b, d_p, 1));
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cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_HOST);
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checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_r, 1, d_p, 1));
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cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
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checkCudaErrors(
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cusparseSetPointerMode(cusparseHandle, CUSPARSE_POINTER_MODE_HOST));
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checkCudaErrors(cusparseSpMV(
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cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecp,
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&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &buffer));
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checkCudaErrors(cudaMemsetAsync(d_dot, 0, sizeof(float), stream1));
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checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
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r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_dot, d_a);
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checkCudaErrors(cublasSaxpy(cublasHandle, N, d_a, d_p, 1, d_x, 1));
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a_minus<<<1, 1, 0, stream1>>>(d_a, d_na);
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checkCudaErrors(cublasSaxpy(cublasHandle, N, d_na, d_Ax, 1, d_r, 1));
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checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float),
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cudaMemcpyDeviceToDevice, stream1));
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checkCudaErrors(cudaMemsetAsync(d_r1, 0, sizeof(float), stream1));
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checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
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checkCudaErrors(cudaMemcpyAsync((float *)&r1, d_r1, sizeof(float),
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cudaMemcpyDeviceToHost, stream1));
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checkCudaErrors(cudaStreamEndCapture(stream1, &initGraph));
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cudaGraphExec_t graphExec;
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checkCudaErrors(cudaGraphInstantiate(&graphExec, initGraph, NULL, NULL, 0));
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#endif
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checkCudaErrors(cublasSetStream(cublasHandle, stream1));
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checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
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while (r1 > tol * tol && k <= max_iter) {
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#if WITH_GRAPH
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checkCudaErrors(cudaGraphLaunch(graphExec, streamForGraph));
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checkCudaErrors(cudaStreamSynchronize(streamForGraph));
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#else
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r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_r0, d_b);
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cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
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checkCudaErrors(cublasSscal(cublasHandle, N, d_b, d_p, 1));
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cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_HOST);
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checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_r, 1, d_p, 1));
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checkCudaErrors(cusparseSpMV(
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cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecp,
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&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &buffer));
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cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
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checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
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r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_dot, d_a);
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checkCudaErrors(cublasSaxpy(cublasHandle, N, d_a, d_p, 1, d_x, 1));
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a_minus<<<1, 1, 0, stream1>>>(d_a, d_na);
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checkCudaErrors(cublasSaxpy(cublasHandle, N, d_na, d_Ax, 1, d_r, 1));
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checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float),
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cudaMemcpyDeviceToDevice, stream1));
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checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
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checkCudaErrors(cudaMemcpyAsync((float *)&r1, d_r1, sizeof(float),
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cudaMemcpyDeviceToHost, stream1));
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checkCudaErrors(cudaStreamSynchronize(stream1));
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#endif
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printf("iteration = %3d, residual = %e\n", k, sqrt(r1));
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k++;
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}
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#if WITH_GRAPH
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checkCudaErrors(cudaMemcpyAsync(x, d_x, N * sizeof(float),
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cudaMemcpyDeviceToHost, streamForGraph));
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checkCudaErrors(cudaStreamSynchronize(streamForGraph));
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#else
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checkCudaErrors(cudaMemcpyAsync(x, d_x, N * sizeof(float),
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cudaMemcpyDeviceToHost, stream1));
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checkCudaErrors(cudaStreamSynchronize(stream1));
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#endif
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float rsum, diff, err = 0.0;
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for (int i = 0; i < N; i++) {
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rsum = 0.0;
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for (int j = I[i]; j < I[i + 1]; j++) {
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rsum += val[j] * x[J[j]];
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}
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diff = fabs(rsum - rhs[i]);
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if (diff > err) {
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err = diff;
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}
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}
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#if WITH_GRAPH
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checkCudaErrors(cudaGraphExecDestroy(graphExec));
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checkCudaErrors(cudaGraphDestroy(initGraph));
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checkCudaErrors(cudaStreamDestroy(streamForGraph));
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#endif
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checkCudaErrors(cudaStreamDestroy(stream1));
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cusparseDestroy(cusparseHandle);
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cublasDestroy(cublasHandle);
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if (matA ) { checkCudaErrors(cusparseDestroySpMat(matA)); }
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if (vecx ) { checkCudaErrors(cusparseDestroyDnVec(vecx)); }
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if (vecAx ) { checkCudaErrors(cusparseDestroyDnVec(vecAx)); }
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if (vecp ) { checkCudaErrors(cusparseDestroyDnVec(vecp)); }
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free(I);
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free(J);
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free(val);
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free(x);
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free(rhs);
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cudaFree(d_col);
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cudaFree(d_row);
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cudaFree(d_val);
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cudaFree(d_x);
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cudaFree(d_r);
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cudaFree(d_p);
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cudaFree(d_Ax);
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printf("Test Summary: Error amount = %f\n", err);
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exit((k <= max_iter) ? 0 : 1);
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}
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