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Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.

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This commit is contained in:
Rob Armstrong
2025-03-27 10:30:07 -07:00
parent 2cd58fbc9a
commit ceab6e8bcc
782 changed files with 107230 additions and 106548 deletions
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468
Samples/4_CUDA_Libraries/conjugateGradientPrecond/main.cpp
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@@ -44,99 +44,90 @@
// includes, system
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
// CUDA Runtime
#include <cuda_runtime.h>
// Using updated (v2) interfaces for CUBLAS and CUSPARSE
#include <cusparse.h>
#include <cublas_v2.h>
#include <cusparse.h>
// Utilities and system includes
#include <helper_functions.h> // shared functions common to CUDA Samples
#include <helper_cuda.h> // CUDA error checking
#include <helper_cuda.h> // CUDA error checking
#include <helper_functions.h> // shared functions common to CUDA Samples
const char *sSDKname = "conjugateGradientPrecond";
const char *sSDKname = "conjugateGradientPrecond";
/*
* Generate a matrix representing a second order regular Laplacian operator
* on a 2D domain in Compressed Sparse Row format.
*/
void genLaplace(int *row_ptr, int *col_ind, float *val, int M, int N, int nz,
float *rhs)
void genLaplace(int *row_ptr, int *col_ind, float *val, int M, int N, int nz, float *rhs)
{
assert(M==N);
int n=(int)sqrt((double)N);
assert(n*n==N);
assert(M == N);
int n = (int)sqrt((double)N);
assert(n * n == N);
printf("laplace dimension = %d\n", n);
int idx = 0;
// loop over degrees of freedom
for (int i = 0; i < N; i++)
{
for (int i = 0; i < N; i++) {
int ix = i % n;
int iy = i / n;
row_ptr[i] = idx;
// up
if (iy > 0)
{
val[idx] = 1.0;
if (iy > 0) {
val[idx] = 1.0;
col_ind[idx] = i - n;
idx++;
}
else
{
else {
rhs[i] -= 1.0;
}
// left
if (ix > 0) {
val[idx] = 1.0;
val[idx] = 1.0;
col_ind[idx] = i - 1;
idx++;
} else {
}
else {
rhs[i] -= 0.0;
}
// center
val[idx] = -4.0;
val[idx] = -4.0;
col_ind[idx] = i;
idx++;
//right
if (ix < n - 1)
{
val[idx] = 1.0;
// right
if (ix < n - 1) {
val[idx] = 1.0;
col_ind[idx] = i + 1;
idx++;
}
else
{
else {
rhs[i] -= 0.0;
}
// down
if (iy < n - 1)
{
val[idx] = 1.0;
if (iy < n - 1) {
val[idx] = 1.0;
col_ind[idx] = i + n;
idx++;
}
else
{
else {
rhs[i] -= 0.0;
}
}
row_ptr[N] = idx;
}
/*
@@ -145,41 +136,41 @@ void genLaplace(int *row_ptr, int *col_ind, float *val, int M, int N, int nz,
* b) using an Incomplete Cholesky preconditioner, and
* c) using an ILU0 preconditioner.
*/
int main(int argc, char **argv){
const int max_iter = 1000;
int k, M = 0, N = 0, nz = 0, *I = NULL, *J = NULL;
int *d_col, *d_row;
int qatest = 0;
const float tol = 1e-12f;
float *x, *rhs;
float r0, r1, alpha, beta;
float *d_val, *d_x;
float *d_zm1, *d_zm2, *d_rm2;
float *d_r, *d_p, *d_omega, *d_y;
float *val = NULL;
float *d_valsILU0;
float rsum, diff, err = 0.0;
float qaerr1, qaerr2 = 0.0;
float dot, numerator, denominator, nalpha;
const float floatone = 1.0;
int main(int argc, char **argv)
{
const int max_iter = 1000;
int k, M = 0, N = 0, nz = 0, *I = NULL, *J = NULL;
int *d_col, *d_row;
int qatest = 0;
const float tol = 1e-12f;
float *x, *rhs;
float r0, r1, alpha, beta;
float *d_val, *d_x;
float *d_zm1, *d_zm2, *d_rm2;
float *d_r, *d_p, *d_omega, *d_y;
float *val = NULL;
float *d_valsILU0;
float rsum, diff, err = 0.0;
float qaerr1, qaerr2 = 0.0;
float dot, numerator, denominator, nalpha;
const float floatone = 1.0;
const float floatzero = 0.0;
int nErrors = 0;
printf("conjugateGradientPrecond starting...\n");
/* QA testing mode */
if (checkCmdLineFlag(argc, (const char **)argv, "qatest")) {
qatest = 1;
}
/* QA testing mode */
if (checkCmdLineFlag(argc, (const char **)argv, "qatest")) {
qatest = 1;
}
/* This will pick the best possible CUDA capable device */
cudaDeviceProp deviceProp;
int devID = findCudaDevice(argc, (const char **)argv);
int devID = findCudaDevice(argc, (const char **)argv);
printf("GPU selected Device ID = %d \n", devID);
if (devID < 0)
{
if (devID < 0) {
printf("Invalid GPU device %d selected, exiting...\n", devID);
exit(EXIT_SUCCESS);
}
@@ -188,21 +179,22 @@ int main(int argc, char **argv){
/* Statistics about the GPU device */
printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
deviceProp.multiProcessorCount,
deviceProp.major,
deviceProp.minor);
/* Generate a Laplace matrix in CSR (Compressed Sparse Row) format */
M = N = 16384;
nz = 5 * N - 4 * (int)sqrt((double)N);
I = (int *)malloc(sizeof(int) * (N + 1)); // csr row pointers for matrix A
J = (int *)malloc(sizeof(int) * nz); // csr column indices for matrix A
val = (float *)malloc(sizeof(float) * nz); // csr values for matrix A
x = (float *)malloc(sizeof(float) * N);
rhs = (float *)malloc(sizeof(float) * N);
nz = 5 * N - 4 * (int)sqrt((double)N);
I = (int *)malloc(sizeof(int) * (N + 1)); // csr row pointers for matrix A
J = (int *)malloc(sizeof(int) * nz); // csr column indices for matrix A
val = (float *)malloc(sizeof(float) * nz); // csr values for matrix A
x = (float *)malloc(sizeof(float) * N);
rhs = (float *)malloc(sizeof(float) * N);
for (int i = 0; i < N; i++)
{
rhs[i] = 0.0; // Initialize RHS
x[i] = 0.0; // Initial solution approximation
for (int i = 0; i < N; i++) {
rhs[i] = 0.0; // Initialize RHS
x[i] = 0.0; // Initial solution approximation
}
genLaplace(I, J, val, M, N, nz, rhs);
@@ -236,7 +228,7 @@ int main(int argc, char **argv){
checkCudaErrors(cudaMalloc((void **)&d_rm2, (N) * sizeof(float)));
/* Wrap raw data into cuSPARSE generic API objects */
cusparseDnVecDescr_t vecp = NULL, vecX=NULL, vecY = NULL, vecR = NULL, vecZM1=NULL;
cusparseDnVecDescr_t vecp = NULL, vecX = NULL, vecY = NULL, vecR = NULL, vecZM1 = NULL;
checkCudaErrors(cusparseCreateDnVec(&vecp, N, d_p, CUDA_R_32F));
checkCudaErrors(cusparseCreateDnVec(&vecX, N, d_x, CUDA_R_32F));
checkCudaErrors(cusparseCreateDnVec(&vecY, N, d_y, CUDA_R_32F));
@@ -246,18 +238,12 @@ int main(int argc, char **argv){
checkCudaErrors(cusparseCreateDnVec(&vecomega, N, d_omega, CUDA_R_32F));
/* Initialize problem data */
checkCudaErrors(cudaMemcpy(
d_col, J, nz * sizeof(int), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(
d_row, I, (N + 1) * sizeof(int), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(
d_val, val, nz * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(
d_val, val, nz * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(
d_x, x, N*sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(
d_r, rhs, N * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_col, J, nz * sizeof(int), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_row, I, (N + 1) * sizeof(int), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_val, val, nz * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_val, val, nz * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_x, x, N * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_r, rhs, N * sizeof(float), cudaMemcpyHostToDevice));
cusparseSpMatDescr_t matA = NULL;
cusparseSpMatDescr_t matM_lower, matM_upper;
@@ -266,73 +252,108 @@ int main(int argc, char **argv){
cusparseFillMode_t fill_upper = CUSPARSE_FILL_MODE_UPPER;
cusparseDiagType_t diag_non_unit = CUSPARSE_DIAG_TYPE_NON_UNIT;
checkCudaErrors(cusparseCreateCsr(
&matA, N, N, nz, d_row, d_col, d_val, CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F));
checkCudaErrors(cusparseCreateCsr(&matA,
N,
N,
nz,
d_row,
d_col,
d_val,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO,
CUDA_R_32F));
/* Copy A data to ILU(0) vals as input*/
checkCudaErrors(cudaMemcpy(
d_valsILU0, d_val, nz*sizeof(float), cudaMemcpyDeviceToDevice));
//Lower Part
checkCudaErrors( cusparseCreateCsr(&matM_lower, N, N, nz, d_row, d_col, d_valsILU0,
CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F) );
checkCudaErrors(cudaMemcpy(d_valsILU0, d_val, nz * sizeof(float), cudaMemcpyDeviceToDevice));
checkCudaErrors( cusparseSpMatSetAttribute(matM_lower,
CUSPARSE_SPMAT_FILL_MODE,
&fill_lower, sizeof(fill_lower)) );
checkCudaErrors( cusparseSpMatSetAttribute(matM_lower,
CUSPARSE_SPMAT_DIAG_TYPE,
&diag_unit, sizeof(diag_unit)) );
// Lower Part
checkCudaErrors(cusparseCreateCsr(&matM_lower,
N,
N,
nz,
d_row,
d_col,
d_valsILU0,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO,
CUDA_R_32F));
checkCudaErrors(cusparseSpMatSetAttribute(matM_lower, CUSPARSE_SPMAT_FILL_MODE, &fill_lower, sizeof(fill_lower)));
checkCudaErrors(cusparseSpMatSetAttribute(matM_lower, CUSPARSE_SPMAT_DIAG_TYPE, &diag_unit, sizeof(diag_unit)));
// M_upper
checkCudaErrors( cusparseCreateCsr(&matM_upper, N, N, nz, d_row, d_col, d_valsILU0,
CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F) );
checkCudaErrors( cusparseSpMatSetAttribute(matM_upper,
CUSPARSE_SPMAT_FILL_MODE,
&fill_upper, sizeof(fill_upper)) );
checkCudaErrors( cusparseSpMatSetAttribute(matM_upper,
CUSPARSE_SPMAT_DIAG_TYPE,
&diag_non_unit,
sizeof(diag_non_unit)) );
checkCudaErrors(cusparseCreateCsr(&matM_upper,
N,
N,
nz,
d_row,
d_col,
d_valsILU0,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_32I,
CUSPARSE_INDEX_BASE_ZERO,
CUDA_R_32F));
checkCudaErrors(cusparseSpMatSetAttribute(matM_upper, CUSPARSE_SPMAT_FILL_MODE, &fill_upper, sizeof(fill_upper)));
checkCudaErrors(
cusparseSpMatSetAttribute(matM_upper, CUSPARSE_SPMAT_DIAG_TYPE, &diag_non_unit, sizeof(diag_non_unit)));
/* Create ILU(0) info object */
int bufferSizeLU = 0;
size_t bufferSizeMV, bufferSizeL, bufferSizeU;
void* d_bufferLU, *d_bufferMV, *d_bufferL, *d_bufferU;
void *d_bufferLU, *d_bufferMV, *d_bufferL, *d_bufferU;
cusparseSpSVDescr_t spsvDescrL, spsvDescrU;
cusparseMatDescr_t matLU;
cusparseMatDescr_t matLU;
csrilu02Info_t infoILU = NULL;
checkCudaErrors(cusparseCreateCsrilu02Info(&infoILU));
checkCudaErrors( cusparseCreateMatDescr(&matLU) );
checkCudaErrors( cusparseSetMatType(matLU, CUSPARSE_MATRIX_TYPE_GENERAL) );
checkCudaErrors( cusparseSetMatIndexBase(matLU, CUSPARSE_INDEX_BASE_ZERO) );
checkCudaErrors(cusparseCreateMatDescr(&matLU));
checkCudaErrors(cusparseSetMatType(matLU, CUSPARSE_MATRIX_TYPE_GENERAL));
checkCudaErrors(cusparseSetMatIndexBase(matLU, CUSPARSE_INDEX_BASE_ZERO));
/* Allocate workspace for cuSPARSE */
checkCudaErrors(cusparseSpMV_bufferSize(
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone, matA,
vecp, &floatzero, vecomega, CUDA_R_32F, CUSPARSE_SPMV_ALG_DEFAULT,
&bufferSizeMV));
checkCudaErrors( cudaMalloc(&d_bufferMV, bufferSizeMV) );
checkCudaErrors(cusparseSpMV_bufferSize(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matA,
vecp,
&floatzero,
vecomega,
CUDA_R_32F,
CUSPARSE_SPMV_ALG_DEFAULT,
&bufferSizeMV));
checkCudaErrors(cudaMalloc(&d_bufferMV, bufferSizeMV));
checkCudaErrors(cusparseScsrilu02_bufferSize(
cusparseHandle, N, nz, matLU, d_val, d_row, d_col, infoILU, &bufferSizeLU));
checkCudaErrors( cudaMalloc(&d_bufferLU, bufferSizeLU) );
checkCudaErrors(
cusparseScsrilu02_bufferSize(cusparseHandle, N, nz, matLU, d_val, d_row, d_col, infoILU, &bufferSizeLU));
checkCudaErrors(cudaMalloc(&d_bufferLU, bufferSizeLU));
checkCudaErrors( cusparseSpSV_createDescr(&spsvDescrL) );
checkCudaErrors(cusparseSpSV_bufferSize(
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone, matM_lower, vecR, vecX, CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT, spsvDescrL, &bufferSizeL));
checkCudaErrors( cudaMalloc(&d_bufferL, bufferSizeL) );
checkCudaErrors(cusparseSpSV_createDescr(&spsvDescrL));
checkCudaErrors(cusparseSpSV_bufferSize(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matM_lower,
vecR,
vecX,
CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT,
spsvDescrL,
&bufferSizeL));
checkCudaErrors(cudaMalloc(&d_bufferL, bufferSizeL));
checkCudaErrors( cusparseSpSV_createDescr(&spsvDescrU) );
checkCudaErrors( cusparseSpSV_bufferSize(
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone, matM_upper, vecR, vecX, CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT, spsvDescrU, &bufferSizeU));
checkCudaErrors( cudaMalloc(&d_bufferU, bufferSizeU) );
checkCudaErrors(cusparseSpSV_createDescr(&spsvDescrU));
checkCudaErrors(cusparseSpSV_bufferSize(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matM_upper,
vecR,
vecX,
CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT,
spsvDescrU,
&bufferSizeU));
checkCudaErrors(cudaMalloc(&d_bufferU, bufferSizeU));
/* Conjugate gradient without preconditioning.
------------------------------------------
@@ -341,61 +362,58 @@ int main(int argc, char **argv){
"Matrix Computations 3rd ed.", Section 10.2.6 */
printf("Convergence of CG without preconditioning: \n");
k = 0;
k = 0;
r0 = 0;
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1));
while (r1 > tol * tol && k <= max_iter)
{
while (r1 > tol * tol && k <= max_iter) {
k++;
if (k == 1)
{
if (k == 1) {
checkCudaErrors(cublasScopy(cublasHandle, N, d_r, 1, d_p, 1));
}
else
{
else {
beta = r1 / r0;
checkCudaErrors(cublasSscal(cublasHandle, N, &beta, d_p, 1));
checkCudaErrors(cublasSaxpy(
cublasHandle, N, &floatone, d_r, 1, d_p, 1));
checkCudaErrors(cublasSaxpy(cublasHandle, N, &floatone, d_r, 1, d_p, 1));
}
checkCudaErrors(cusparseSpMV(
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone, matA,
vecp, &floatzero, vecomega, CUDA_R_32F, CUSPARSE_SPMV_ALG_DEFAULT,
d_bufferMV));
checkCudaErrors(cusparseSpMV(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matA,
vecp,
&floatzero,
vecomega,
CUDA_R_32F,
CUSPARSE_SPMV_ALG_DEFAULT,
d_bufferMV));
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &dot));
alpha = r1 / dot;
checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1));
nalpha = -alpha;
checkCudaErrors(cublasSaxpy(
cublasHandle, N, &nalpha, d_omega, 1, d_r, 1));
checkCudaErrors(cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1));
r0 = r1;
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1));
}
printf(" iteration = %3d, residual = %e \n", k, sqrt(r1));
checkCudaErrors(cudaMemcpy(
x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost));
checkCudaErrors(cudaMemcpy(x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost));
/* check result */
err = 0.0;
for (int i = 0; i < N; i++)
{
for (int i = 0; i < N; i++) {
rsum = 0.0;
for (int j = I[i]; j < I[i + 1]; j++)
{
for (int j = I[i]; j < I[i + 1]; j++) {
rsum += val[j] * x[J[j]];
}
diff = fabs(rsum - rhs[i]);
if (diff > err)
{
if (diff > err) {
err = diff;
}
}
@@ -404,21 +422,17 @@ int main(int argc, char **argv){
nErrors += (k > max_iter) ? 1 : 0;
qaerr1 = err;
if (0)
{
if (0) {
// output result in matlab-style array
int n = (int)sqrt((double)N);
printf("a = [ ");
for (int iy = 0; iy < n; iy++)
{
for (int ix = 0; ix < n; ix++)
{
for (int iy = 0; iy < n; iy++) {
for (int ix = 0; ix < n; ix++) {
printf(" %f ", x[iy * n + ix]);
}
if (iy == n - 1)
{
if (iy == n - 1) {
printf(" ]");
}
@@ -436,110 +450,117 @@ int main(int argc, char **argv){
/* Perform analysis for ILU(0) */
checkCudaErrors(cusparseScsrilu02_analysis(
cusparseHandle, N, nz, descr, d_valsILU0, d_row, d_col, infoILU,
CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_bufferLU));
cusparseHandle, N, nz, descr, d_valsILU0, d_row, d_col, infoILU, CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_bufferLU));
/* generate the ILU(0) factors */
checkCudaErrors(cusparseScsrilu02(
cusparseHandle, N, nz, matLU, d_valsILU0, d_row, d_col, infoILU,
CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_bufferLU));
cusparseHandle, N, nz, matLU, d_valsILU0, d_row, d_col, infoILU, CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_bufferLU));
/* perform triangular solve analysis */
checkCudaErrors(cusparseSpSV_analysis(
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone,
matM_lower, vecR, vecX, CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT, spsvDescrL, d_bufferL));
checkCudaErrors(cusparseSpSV_analysis(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matM_lower,
vecR,
vecX,
CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT,
spsvDescrL,
d_bufferL));
checkCudaErrors(cusparseSpSV_analysis(
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone,
matM_upper, vecR, vecX, CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT, spsvDescrU, d_bufferU));
checkCudaErrors(cusparseSpSV_analysis(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matM_upper,
vecR,
vecX,
CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT,
spsvDescrU,
d_bufferU));
/* reset the initial guess of the solution to zero */
for (int i = 0; i < N; i++)
{
for (int i = 0; i < N; i++) {
x[i] = 0.0;
}
checkCudaErrors(cudaMemcpy(
d_r, rhs, N * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(
d_x, x, N * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_r, rhs, N * sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_x, x, N * sizeof(float), cudaMemcpyHostToDevice));
k = 0;
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1));
while (r1 > tol * tol && k <= max_iter)
{
while (r1 > tol * tol && k <= max_iter) {
// preconditioner application: d_zm1 = U^-1 L^-1 d_r
checkCudaErrors(cusparseSpSV_solve(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone,
matM_lower, vecR, vecY, CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT,
spsvDescrL) );
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matM_lower,
vecR,
vecY,
CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT,
spsvDescrL));
checkCudaErrors(cusparseSpSV_solve(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone, matM_upper,
vecY, vecZM1,
CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT,
spsvDescrU));
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matM_upper,
vecY,
vecZM1,
CUDA_R_32F,
CUSPARSE_SPSV_ALG_DEFAULT,
spsvDescrU));
k++;
if (k == 1)
{
if (k == 1) {
checkCudaErrors(cublasScopy(cublasHandle, N, d_zm1, 1, d_p, 1));
}
else
{
checkCudaErrors(cublasSdot(
cublasHandle, N, d_r, 1, d_zm1, 1, &numerator));
checkCudaErrors(cublasSdot(
cublasHandle, N, d_rm2, 1, d_zm2, 1, &denominator));
else {
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_zm1, 1, &numerator));
checkCudaErrors(cublasSdot(cublasHandle, N, d_rm2, 1, d_zm2, 1, &denominator));
beta = numerator / denominator;
checkCudaErrors(cublasSscal(cublasHandle, N, &beta, d_p, 1));
checkCudaErrors(cublasSaxpy(
cublasHandle, N, &floatone, d_zm1, 1, d_p, 1));
checkCudaErrors(cublasSaxpy(cublasHandle, N, &floatone, d_zm1, 1, d_p, 1));
}
checkCudaErrors(cusparseSpMV(
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &floatone, matA,
vecp, &floatzero, vecomega, CUDA_R_32F, CUSPARSE_SPMV_ALG_DEFAULT,
d_bufferMV));
checkCudaErrors(cublasSdot(
cublasHandle, N, d_r, 1, d_zm1, 1, &numerator));
checkCudaErrors(cublasSdot(
cublasHandle, N, d_p, 1, d_omega, 1, &denominator));
checkCudaErrors(cusparseSpMV(cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
&floatone,
matA,
vecp,
&floatzero,
vecomega,
CUDA_R_32F,
CUSPARSE_SPMV_ALG_DEFAULT,
d_bufferMV));
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_zm1, 1, &numerator));
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &denominator));
alpha = numerator / denominator;
checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1));
checkCudaErrors(cublasScopy(cublasHandle, N, d_r, 1, d_rm2, 1));
checkCudaErrors(cublasScopy(cublasHandle, N, d_zm1, 1, d_zm2, 1));
nalpha = -alpha;
checkCudaErrors(cublasSaxpy(
cublasHandle, N, &nalpha, d_omega, 1, d_r, 1));
checkCudaErrors(cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1));
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1));
}
printf(" iteration = %3d, residual = %e \n", k, sqrt(r1));
checkCudaErrors(cudaMemcpy(
x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost));
checkCudaErrors(cudaMemcpy(x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost));
/* check result */
err = 0.0;
for (int i = 0; i < N; i++)
{
for (int i = 0; i < N; i++) {
rsum = 0.0;
for (int j = I[i]; j < I[i + 1]; j++)
{
for (int j = I[i]; j < I[i + 1]; j++) {
rsum += val[j] * x[J[j]];
}
diff = fabs(rsum - rhs[i]);
if (diff > err)
{
if (diff > err) {
err = diff;
}
}
@@ -601,8 +622,5 @@ int main(int argc, char **argv){
printf("Test Summary:\n");
printf(" Counted total of %d errors\n", nErrors);
printf(" qaerr1 = %f qaerr2 = %f\n\n", fabs(qaerr1), fabs(qaerr2));
exit((nErrors == 0 &&fabs(qaerr1) < 1e-5 && fabs(qaerr2) < 1e-5
? EXIT_SUCCESS
: EXIT_FAILURE));
exit((nErrors == 0 && fabs(qaerr1) < 1e-5 && fabs(qaerr2) < 1e-5 ? EXIT_SUCCESS : EXIT_FAILURE));
}