447 lines
16 KiB
C++

/*
* Copyright 2015 NVIDIA Corporation. All rights reserved.
*
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*
*/
#include <assert.h>
#include <ctype.h>
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "cusolverSp.h"
#include "cusolverSp_LOWLEVEL_PREVIEW.h"
#include "helper_cuda.h"
#include "helper_cusolver.h"
template <typename T_ELEM>
int loadMMSparseMatrix(char *filename,
char elem_type,
bool csrFormat,
int *m,
int *n,
int *nnz,
T_ELEM **aVal,
int **aRowInd,
int **aColInd,
int extendSymMatrix);
void UsageSP(void)
{
printf("<options>\n");
printf("-h : display this help\n");
printf("-file=<filename> : filename containing a matrix in MM format\n");
printf("-device=<device_id> : <device_id> if want to run on specific GPU\n");
exit(0);
}
void parseCommandLineArguments(int argc, char *argv[], struct testOpts &opts)
{
memset(&opts, 0, sizeof(opts));
if (checkCmdLineFlag(argc, (const char **)argv, "-h")) {
UsageSP();
}
if (checkCmdLineFlag(argc, (const char **)argv, "file")) {
char *fileName = 0;
getCmdLineArgumentString(argc, (const char **)argv, "file", &fileName);
if (fileName) {
opts.sparse_mat_filename = fileName;
}
else {
printf("\nIncorrect filename passed to -file \n ");
UsageSP();
}
}
}
int main(int argc, char *argv[])
{
struct testOpts opts;
cusolverSpHandle_t cusolverSpH = NULL; // reordering, permutation and 1st LU factorization
cusparseHandle_t cusparseH = NULL; // residual evaluation
cudaStream_t stream = NULL;
cusparseMatDescr_t descrA = NULL; // A is a base-0 general matrix
csrcholInfoHost_t h_info = NULL; // opaque info structure for LU with parital pivoting
csrcholInfo_t d_info = NULL; // opaque info structure for LU with parital pivoting
int rowsA = 0; // number of rows of A
int colsA = 0; // number of columns of A
int nnzA = 0; // number of nonzeros of A
int baseA = 0; // base index in CSR format
// CSR(A) from I/O
int *h_csrRowPtrA = NULL; // <int> n+1
int *h_csrColIndA = NULL; // <int> nnzA
double *h_csrValA = NULL; // <double> nnzA
double *h_x = NULL; // <double> n, x = A \ b
double *h_b = NULL; // <double> n, b = ones(m,1)
double *h_r = NULL; // <double> n, r = b - A*x
size_t size_internal = 0;
size_t size_chol = 0; // size of working space for csrlu
void *buffer_cpu = NULL; // working space for Cholesky
void *buffer_gpu = NULL; // working space for Cholesky
int *d_csrRowPtrA = NULL; // <int> n+1
int *d_csrColIndA = NULL; // <int> nnzA
double *d_csrValA = NULL; // <double> nnzA
double *d_x = NULL; // <double> n, x = A \ b
double *d_b = NULL; // <double> n, a copy of h_b
double *d_r = NULL; // <double> n, r = b - A*x
// the constants used in residual evaluation, r = b - A*x
const double minus_one = -1.0;
const double one = 1.0;
// the constant used in cusolverSp
// singularity is -1 if A is invertible under tol
// tol determines the condition of singularity
int singularity = 0;
const double tol = 1.e-14;
double x_inf = 0.0; // |x|
double r_inf = 0.0; // |r|
double A_inf = 0.0; // |A|
int errors = 0;
parseCommandLineArguments(argc, argv, opts);
findCudaDevice(argc, (const char **)argv);
if (opts.sparse_mat_filename == NULL) {
opts.sparse_mat_filename = sdkFindFilePath("lap2D_5pt_n100.mtx", argv[0]);
if (opts.sparse_mat_filename != NULL)
printf("Using default input file [%s]\n", opts.sparse_mat_filename);
else
printf("Could not find lap2D_5pt_n100.mtx\n");
}
else {
printf("Using input file [%s]\n", opts.sparse_mat_filename);
}
printf("step 1: read matrix market format\n");
if (opts.sparse_mat_filename) {
if (loadMMSparseMatrix<double>(opts.sparse_mat_filename,
'd',
true,
&rowsA,
&colsA,
&nnzA,
&h_csrValA,
&h_csrRowPtrA,
&h_csrColIndA,
true)) {
return 1;
}
baseA = h_csrRowPtrA[0]; // baseA = {0,1}
}
else {
fprintf(stderr, "Error: input matrix is not provided\n");
return 1;
}
if (rowsA != colsA) {
fprintf(stderr, "Error: only support square matrix\n");
return 1;
}
printf("sparse matrix A is %d x %d with %d nonzeros, base=%d\n", rowsA, colsA, nnzA, baseA);
checkCudaErrors(cusolverSpCreate(&cusolverSpH));
checkCudaErrors(cusparseCreate(&cusparseH));
checkCudaErrors(cudaStreamCreate(&stream));
checkCudaErrors(cusolverSpSetStream(cusolverSpH, stream));
checkCudaErrors(cusparseSetStream(cusparseH, stream));
checkCudaErrors(cusparseCreateMatDescr(&descrA));
checkCudaErrors(cusparseSetMatType(descrA, CUSPARSE_MATRIX_TYPE_GENERAL));
if (baseA) {
checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ONE));
}
else {
checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO));
}
h_x = (double *)malloc(sizeof(double) * colsA);
h_b = (double *)malloc(sizeof(double) * rowsA);
h_r = (double *)malloc(sizeof(double) * rowsA);
assert(NULL != h_x);
assert(NULL != h_b);
assert(NULL != h_r);
checkCudaErrors(cudaMalloc((void **)&d_csrRowPtrA, sizeof(int) * (rowsA + 1)));
checkCudaErrors(cudaMalloc((void **)&d_csrColIndA, sizeof(int) * nnzA));
checkCudaErrors(cudaMalloc((void **)&d_csrValA, sizeof(double) * nnzA));
checkCudaErrors(cudaMalloc((void **)&d_x, sizeof(double) * colsA));
checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(double) * rowsA));
checkCudaErrors(cudaMalloc((void **)&d_r, sizeof(double) * rowsA));
for (int row = 0; row < rowsA; row++) {
h_b[row] = 1.0;
}
printf("step 2: create opaque info structure\n");
checkCudaErrors(cusolverSpCreateCsrcholInfoHost(&h_info));
printf("step 3: analyze chol(A) to know structure of L\n");
checkCudaErrors(
cusolverSpXcsrcholAnalysisHost(cusolverSpH, rowsA, nnzA, descrA, h_csrRowPtrA, h_csrColIndA, h_info));
printf("step 4: workspace for chol(A)\n");
checkCudaErrors(cusolverSpDcsrcholBufferInfoHost(
cusolverSpH, rowsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA, h_info, &size_internal, &size_chol));
if (buffer_cpu) {
free(buffer_cpu);
}
buffer_cpu = (void *)malloc(sizeof(char) * size_chol);
assert(NULL != buffer_cpu);
printf("step 5: compute A = L*L^T \n");
checkCudaErrors(cusolverSpDcsrcholFactorHost(
cusolverSpH, rowsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA, h_info, buffer_cpu));
printf("step 6: check if the matrix is singular \n");
checkCudaErrors(cusolverSpDcsrcholZeroPivotHost(cusolverSpH, h_info, tol, &singularity));
if (0 <= singularity) {
fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity);
return 1;
}
printf("step 7: solve A*x = b \n");
checkCudaErrors(cusolverSpDcsrcholSolveHost(cusolverSpH, rowsA, h_b, h_x, h_info, buffer_cpu));
printf("step 8: evaluate residual r = b - A*x (result on CPU)\n");
// use GPU gemv to compute r = b - A*x
checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_r, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_x, h_x, sizeof(double) * colsA, cudaMemcpyHostToDevice));
/* Wrap raw data into cuSPARSE generic API objects */
cusparseSpMatDescr_t matA = NULL;
if (baseA) {
checkCudaErrors(cusparseCreateCsr(&matA,
rowsA,
colsA,
nnzA,
d_csrRowPtrA,
d_csrColIndA,
d_csrValA,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ONE,
CUDA_R_64F));
}
else {
checkCudaErrors(cusparseCreateCsr(&matA,
rowsA,
colsA,
nnzA,
d_csrRowPtrA,
d_csrColIndA,
d_csrValA,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO,
CUDA_R_64F));
}
cusparseDnVecDescr_t vecx = NULL;
checkCudaErrors(cusparseCreateDnVec(&vecx, colsA, d_x, CUDA_R_64F));
cusparseDnVecDescr_t vecAx = NULL;
checkCudaErrors(cusparseCreateDnVec(&vecAx, rowsA, d_r, CUDA_R_64F));
/* Allocate workspace for cuSPARSE */
size_t bufferSize = 0;
checkCudaErrors(cusparseSpMV_bufferSize(cusparseH,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&minus_one,
matA,
vecx,
&one,
vecAx,
CUDA_R_64F,
CUSPARSE_SPMV_ALG_DEFAULT,
&bufferSize));
void *buffer = NULL;
checkCudaErrors(cudaMalloc(&buffer, bufferSize));
checkCudaErrors(cusparseSpMV(cusparseH,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&minus_one,
matA,
vecx,
&one,
vecAx,
CUDA_R_64F,
CUSPARSE_SPMV_ALG_DEFAULT,
buffer));
checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost));
x_inf = vec_norminf(colsA, h_x);
r_inf = vec_norminf(rowsA, h_r);
A_inf = csr_mat_norminf(rowsA, colsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA);
printf("(CPU) |b - A*x| = %E \n", r_inf);
printf("(CPU) |A| = %E \n", A_inf);
printf("(CPU) |x| = %E \n", x_inf);
printf("(CPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf));
printf("step 9: create opaque info structure\n");
checkCudaErrors(cusolverSpCreateCsrcholInfo(&d_info));
checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_b, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice));
printf("step 10: analyze chol(A) to know structure of L\n");
checkCudaErrors(cusolverSpXcsrcholAnalysis(cusolverSpH, rowsA, nnzA, descrA, d_csrRowPtrA, d_csrColIndA, d_info));
printf("step 11: workspace for chol(A)\n");
checkCudaErrors(cusolverSpDcsrcholBufferInfo(
cusolverSpH, rowsA, nnzA, descrA, d_csrValA, d_csrRowPtrA, d_csrColIndA, d_info, &size_internal, &size_chol));
if (buffer_gpu) {
checkCudaErrors(cudaFree(buffer_gpu));
}
checkCudaErrors(cudaMalloc(&buffer_gpu, sizeof(char) * size_chol));
printf("step 12: compute A = L*L^T \n");
checkCudaErrors(cusolverSpDcsrcholFactor(
cusolverSpH, rowsA, nnzA, descrA, d_csrValA, d_csrRowPtrA, d_csrColIndA, d_info, buffer_gpu));
printf("step 13: check if the matrix is singular \n");
checkCudaErrors(cusolverSpDcsrcholZeroPivot(cusolverSpH, d_info, tol, &singularity));
if (0 <= singularity) {
fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity);
return 1;
}
printf("step 14: solve A*x = b \n");
checkCudaErrors(cusolverSpDcsrcholSolve(cusolverSpH, rowsA, d_b, d_x, d_info, buffer_gpu));
checkCudaErrors(cudaMemcpy(d_r, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice));
checkCudaErrors(cusparseSpMV(cusparseH,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&minus_one,
matA,
vecx,
&one,
vecAx,
CUDA_R_64F,
CUSPARSE_SPMV_ALG_DEFAULT,
buffer));
checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost));
r_inf = vec_norminf(rowsA, h_r);
printf("(GPU) |b - A*x| = %E \n", r_inf);
printf("(GPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf));
if (cusolverSpH) {
checkCudaErrors(cusolverSpDestroy(cusolverSpH));
}
if (cusparseH) {
checkCudaErrors(cusparseDestroy(cusparseH));
}
if (stream) {
checkCudaErrors(cudaStreamDestroy(stream));
}
if (descrA) {
checkCudaErrors(cusparseDestroyMatDescr(descrA));
}
if (h_info) {
checkCudaErrors(cusolverSpDestroyCsrcholInfoHost(h_info));
}
if (d_info) {
checkCudaErrors(cusolverSpDestroyCsrcholInfo(d_info));
}
if (matA) {
checkCudaErrors(cusparseDestroySpMat(matA));
}
if (vecx) {
checkCudaErrors(cusparseDestroyDnVec(vecx));
}
if (vecAx) {
checkCudaErrors(cusparseDestroyDnVec(vecAx));
}
if (h_csrValA) {
free(h_csrValA);
}
if (h_csrRowPtrA) {
free(h_csrRowPtrA);
}
if (h_csrColIndA) {
free(h_csrColIndA);
}
if (h_x) {
free(h_x);
}
if (h_b) {
free(h_b);
}
if (h_r) {
free(h_r);
}
if (buffer_cpu) {
free(buffer_cpu);
}
if (buffer_gpu) {
checkCudaErrors(cudaFree(buffer_gpu));
}
if (d_csrValA) {
checkCudaErrors(cudaFree(d_csrValA));
}
if (d_csrRowPtrA) {
checkCudaErrors(cudaFree(d_csrRowPtrA));
}
if (d_csrColIndA) {
checkCudaErrors(cudaFree(d_csrColIndA));
}
if (d_x) {
checkCudaErrors(cudaFree(d_x));
}
if (d_b) {
checkCudaErrors(cudaFree(d_b));
}
if (d_r) {
checkCudaErrors(cudaFree(d_r));
}
return 0;
}