/* Solve Ax=b using the conjugate gradient method a) without any preconditioning, 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 *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; } /* This will pick the best possible CUDA capable device */ cudaDeviceProp deviceProp; int devID = findCudaDevice(argc, (const char **)argv); printf("GPU selected Device ID = %d \n", devID); if (devID < 0) { printf("Invalid GPU device %d selected, exiting...\n", devID); exit(EXIT_SUCCESS); } checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID)); /* 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); int version = (deviceProp.major * 0x10 + deviceProp.minor); if (version < 0x11) { printf("%s: requires a minimum CUDA compute 1.1 capability\n", sSDKname); // cudaDeviceReset causes the driver to clean up all state. While // not mandatory in normal operation, it is good practice. It is also // needed to ensure correct operation when the application is being // profiled. Calling cudaDeviceReset causes all profile data to be // flushed before the application exits cudaDeviceReset(); exit(EXIT_SUCCESS); } /* Generate a random tridiagonal symmetric 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); for (int i = 0; i < N; i++) { rhs[i] = 0.0; // Initialize RHS x[i] = 0.0; // Initial approximation of solution } genLaplace(I, J, val, M, N, nz, rhs); /* Create CUBLAS context */ cublasHandle_t cublasHandle = 0; cublasStatus_t cublasStatus; cublasStatus = cublasCreate(&cublasHandle); checkCudaErrors(cublasStatus); /* Create CUSPARSE context */ cusparseHandle_t cusparseHandle = 0; cusparseStatus_t cusparseStatus; cusparseStatus = cusparseCreate(&cusparseHandle); checkCudaErrors(cusparseStatus); /* Description of the A matrix*/ cusparseMatDescr_t descr = 0; cusparseStatus = cusparseCreateMatDescr(&descr); checkCudaErrors(cusparseStatus); /* Define the properties of the matrix */ cusparseSetMatType(descr,CUSPARSE_MATRIX_TYPE_GENERAL); cusparseSetMatIndexBase(descr,CUSPARSE_INDEX_BASE_ZERO); /* Allocate required memory */ checkCudaErrors(cudaMalloc((void **)&d_col, nz*sizeof(int))); checkCudaErrors(cudaMalloc((void **)&d_row, (N+1)*sizeof(int))); checkCudaErrors(cudaMalloc((void **)&d_val, nz*sizeof(float))); checkCudaErrors(cudaMalloc((void **)&d_x, N*sizeof(float))); checkCudaErrors(cudaMalloc((void **)&d_y, N*sizeof(float))); checkCudaErrors(cudaMalloc((void **)&d_r, N*sizeof(float))); checkCudaErrors(cudaMalloc((void **)&d_p, N*sizeof(float))); checkCudaErrors(cudaMalloc((void **)&d_omega, N*sizeof(float))); cudaMemcpy(d_col, J, nz*sizeof(int), cudaMemcpyHostToDevice); cudaMemcpy(d_row, I, (N+1)*sizeof(int), cudaMemcpyHostToDevice); cudaMemcpy(d_val, val, nz*sizeof(float), cudaMemcpyHostToDevice); cudaMemcpy(d_x, x, N*sizeof(float), cudaMemcpyHostToDevice); cudaMemcpy(d_r, rhs, N*sizeof(float), cudaMemcpyHostToDevice); /* Conjugate gradient without preconditioning. ------------------------------------------ Follows the description by Golub & Van Loan, "Matrix Computations 3rd ed.", Section 10.2.6 */ printf("Convergence of conjugate gradient without preconditioning: \n"); k = 0; r0 = 0; cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1); while (r1 > tol*tol && k <= max_iter) { k++; if (k == 1) { cublasScopy(cublasHandle, N, d_r, 1, d_p, 1); } else { beta = r1/r0; cublasSscal(cublasHandle, N, &beta, d_p, 1); cublasSaxpy(cublasHandle, N, &floatone, d_r, 1, d_p, 1) ; } cusparseScsrmv(cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nz, &floatone, descr, d_val, d_row, d_col, d_p, &floatzero, d_omega); cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &dot); alpha = r1/dot; cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1); nalpha = -alpha; cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1); r0 = r1; cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1); } printf(" iteration = %3d, residual = %e \n", k, sqrt(r1)); cudaMemcpy(x, d_x, N*sizeof(float), cudaMemcpyDeviceToHost); /* check result */ 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[j]*x[J[j]]; } diff = fabs(rsum - rhs[i]); if (diff > err) { err = diff; } } printf(" Convergence Test: %s \n", (k <= max_iter) ? "OK" : "FAIL"); nErrors += (k > max_iter) ? 1 : 0; qaerr1 = err; 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++) { printf(" %f ", x[iy*n+ix]); } if (iy == n-1) { printf(" ]"); } printf("\n"); } } /* Preconditioned Conjugate Gradient using ILU. -------------------------------------------- Follows the description by Golub & Van Loan, "Matrix Computations 3rd ed.", Algorithm 10.3.1 */ printf("\nConvergence of conjugate gradient using incomplete LU preconditioning: \n"); int nzILU0 = 2*N-1; valsILU0 = (float *) malloc(nz*sizeof(float)); checkCudaErrors(cudaMalloc((void **)&d_valsILU0, nz*sizeof(float))); checkCudaErrors(cudaMalloc((void **)&d_zm1, (N)*sizeof(float))); checkCudaErrors(cudaMalloc((void **)&d_zm2, (N)*sizeof(float))); checkCudaErrors(cudaMalloc((void **)&d_rm2, (N)*sizeof(float))); /* create the analysis info object for the A matrix */ cusparseSolveAnalysisInfo_t infoA = 0; cusparseStatus = cusparseCreateSolveAnalysisInfo(&infoA); checkCudaErrors(cusparseStatus); /* Perform the analysis for the Non-Transpose case */ cusparseStatus = cusparseScsrsv_analysis(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, nz, descr, d_val, d_row, d_col, infoA); checkCudaErrors(cusparseStatus); /* Copy A data to ILU0 vals as input*/ cudaMemcpy(d_valsILU0, d_val, nz*sizeof(float), cudaMemcpyDeviceToDevice); /* generate the Incomplete LU factor H for the matrix A using cudsparseScsrilu0 */ cusparseStatus = cusparseScsrilu0(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, descr, d_valsILU0, d_row, d_col, infoA); checkCudaErrors(cusparseStatus); /* Create info objects for the ILU0 preconditioner */ cusparseSolveAnalysisInfo_t info_u; cusparseCreateSolveAnalysisInfo(&info_u); cusparseMatDescr_t descrL = 0; cusparseStatus = cusparseCreateMatDescr(&descrL); cusparseSetMatType(descrL,CUSPARSE_MATRIX_TYPE_GENERAL); cusparseSetMatIndexBase(descrL,CUSPARSE_INDEX_BASE_ZERO); cusparseSetMatFillMode(descrL, CUSPARSE_FILL_MODE_LOWER); cusparseSetMatDiagType(descrL, CUSPARSE_DIAG_TYPE_UNIT); cusparseMatDescr_t descrU = 0; cusparseStatus = cusparseCreateMatDescr(&descrU); cusparseSetMatType(descrU,CUSPARSE_MATRIX_TYPE_GENERAL); cusparseSetMatIndexBase(descrU,CUSPARSE_INDEX_BASE_ZERO); cusparseSetMatFillMode(descrU, CUSPARSE_FILL_MODE_UPPER); cusparseSetMatDiagType(descrU, CUSPARSE_DIAG_TYPE_NON_UNIT); cusparseStatus = cusparseScsrsv_analysis(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, nz, descrU, d_val, d_row, d_col, info_u); /* reset the initial guess of the solution to zero */ 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)); k = 0; cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1); while (r1 > tol*tol && k <= max_iter) { // Forward Solve, we can re-use infoA since the sparsity pattern of A matches that of L cusparseStatus = cusparseScsrsv_solve(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, &floatone, descrL, d_valsILU0, d_row, d_col, infoA, d_r, d_y); checkCudaErrors(cusparseStatus); // Back Substitution cusparseStatus = cusparseScsrsv_solve(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, &floatone, descrU, d_valsILU0, d_row, d_col, info_u, d_y, d_zm1); checkCudaErrors(cusparseStatus); k++; if (k == 1) { cublasScopy(cublasHandle, N, d_zm1, 1, d_p, 1); } else { cublasSdot(cublasHandle, N, d_r, 1, d_zm1, 1, &numerator); cublasSdot(cublasHandle, N, d_rm2, 1, d_zm2, 1, &denominator); beta = numerator/denominator; cublasSscal(cublasHandle, N, &beta, d_p, 1); cublasSaxpy(cublasHandle, N, &floatone, d_zm1, 1, d_p, 1) ; } cusparseScsrmv(cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nzILU0, &floatone, descrU, d_val, d_row, d_col, d_p, &floatzero, d_omega); cublasSdot(cublasHandle, N, d_r, 1, d_zm1, 1, &numerator); cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &denominator); alpha = numerator / denominator; cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1); cublasScopy(cublasHandle, N, d_r, 1, d_rm2, 1); cublasScopy(cublasHandle, N, d_zm1, 1, d_zm2, 1); nalpha = -alpha; cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1); cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1); } printf(" iteration = %3d, residual = %e \n", k, sqrt(r1)); cudaMemcpy(x, d_x, N*sizeof(float), cudaMemcpyDeviceToHost); /* check result */ 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[j]*x[J[j]]; } diff = fabs(rsum - rhs[i]); if (diff > err) { err = diff; } } printf(" Convergence Test: %s \n", (k <= max_iter) ? "OK" : "FAIL"); nErrors += (k > max_iter) ? 1 : 0; qaerr2 = err; /* Destroy parameters */ cusparseDestroySolveAnalysisInfo(infoA); cusparseDestroySolveAnalysisInfo(info_u); /* Destroy contexts */ cusparseDestroy(cusparseHandle); cublasDestroy(cublasHandle); /* Free device memory */ free(I); free(J); free(val); free(x); free(rhs); free(valsILU0); cudaFree(d_col); cudaFree(d_row); cudaFree(d_val); cudaFree(d_x); cudaFree(d_y); cudaFree(d_r); cudaFree(d_p); cudaFree(d_omega); cudaFree(d_valsILU0); cudaFree(d_zm1); cudaFree(d_zm2); cudaFree(d_rm2); // cudaDeviceReset causes the driver to clean up all state. While // not mandatory in normal operation, it is good practice. It is also // needed to ensure correct operation when the application is being // profiled. Calling cudaDeviceReset causes all profile data to be // flushed before the application exits cudaDeviceReset(); 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)); }
magma_int_t magma_scustomicsetup( magma_s_matrix A, magma_s_matrix b, magma_s_preconditioner *precond, magma_queue_t queue ) { magma_int_t info = 0; cusparseHandle_t cusparseHandle=NULL; cusparseMatDescr_t descrL=NULL; cusparseMatDescr_t descrU=NULL; magma_s_matrix hA={Magma_CSR}; char preconditionermatrix[255]; snprintf( preconditionermatrix, sizeof(preconditionermatrix), "precondL.mtx" ); CHECK( magma_s_csr_mtx( &hA, preconditionermatrix , queue) ); // for CUSPARSE CHECK( magma_smtransfer( hA, &precond->M, Magma_CPU, Magma_DEV , queue )); // copy the matrix to precond->L and (transposed) to precond->U CHECK( magma_smtransfer(precond->M, &(precond->L), Magma_DEV, Magma_DEV, queue )); CHECK( magma_smtranspose( precond->L, &(precond->U), queue )); // extract the diagonal of L into precond->d CHECK( magma_sjacobisetup_diagscal( precond->L, &precond->d, queue )); CHECK( magma_svinit( &precond->work1, Magma_DEV, hA.num_rows, 1, MAGMA_S_ZERO, queue )); // extract the diagonal of U into precond->d2 CHECK( magma_sjacobisetup_diagscal( precond->U, &precond->d2, queue )); CHECK( magma_svinit( &precond->work2, Magma_DEV, hA.num_rows, 1, MAGMA_S_ZERO, queue )); // CUSPARSE context // CHECK_CUSPARSE( cusparseCreate( &cusparseHandle )); CHECK_CUSPARSE( cusparseCreateMatDescr( &descrL )); CHECK_CUSPARSE( cusparseSetMatType( descrL, CUSPARSE_MATRIX_TYPE_TRIANGULAR )); CHECK_CUSPARSE( cusparseSetMatDiagType( descrL, CUSPARSE_DIAG_TYPE_NON_UNIT )); CHECK_CUSPARSE( cusparseSetMatIndexBase( descrL, CUSPARSE_INDEX_BASE_ZERO )); CHECK_CUSPARSE( cusparseSetMatFillMode( descrL, CUSPARSE_FILL_MODE_LOWER )); CHECK_CUSPARSE( cusparseCreateSolveAnalysisInfo( &precond->cuinfoL )); CHECK_CUSPARSE( cusparseScsrsv_analysis( cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, precond->M.num_rows, precond->M.nnz, descrL, precond->M.val, precond->M.row, precond->M.col, precond->cuinfoL )); CHECK_CUSPARSE( cusparseCreateMatDescr( &descrU )); CHECK_CUSPARSE( cusparseSetMatType( descrU, CUSPARSE_MATRIX_TYPE_TRIANGULAR )); CHECK_CUSPARSE( cusparseSetMatDiagType( descrU, CUSPARSE_DIAG_TYPE_NON_UNIT )); CHECK_CUSPARSE( cusparseSetMatIndexBase( descrU, CUSPARSE_INDEX_BASE_ZERO )); CHECK_CUSPARSE( cusparseSetMatFillMode( descrU, CUSPARSE_FILL_MODE_LOWER )); CHECK_CUSPARSE( cusparseCreateSolveAnalysisInfo( &precond->cuinfoU )); CHECK_CUSPARSE( cusparseScsrsv_analysis( cusparseHandle, CUSPARSE_OPERATION_TRANSPOSE, precond->M.num_rows, precond->M.nnz, descrU, precond->M.val, precond->M.row, precond->M.col, precond->cuinfoU )); cleanup: cusparseDestroy( cusparseHandle ); cusparseDestroyMatDescr( descrL ); cusparseDestroyMatDescr( descrU ); cusparseHandle=NULL; descrL=NULL; descrU=NULL; magma_smfree( &hA, queue ); return info; }