When trying to compile OpenACC code with GCC-9.3.0 (g++) configured with --enable-languages=c,c++,lto --disable-multilib the following code does not use multiple cores, whereas if the same code is compiled with the pgc++ compiler it does use multiple cores.
g++ compilation: g++ -lgomp -Ofast -o jsolve -fopenacc jsolvec.cpp
pgc++ compilation: pgc++ -o jsolvec.exe jsolvec.cpp -fast -Minfo=opt -ta=multicore
Code from OpenACC Tutorial1/solver https://github.com/OpenACCuserGroup/openacc-users-group.git:
// Jacobi iterative method for solving a system of linear equations// This is guaranteed to converge if the matrix is diagonally dominant,// so we artificially force the matrix to be diagonally dominant.// See https://en.wikipedia.org/wiki/Jacobi_method//// We solve for vector x in Ax = b// Rewrite the matrix A as a// lower triangular (L),// upper triangular (U),// and diagonal matrix (D).//// Ax = (L + D + U)x = b//// rearrange to get: Dx = b - (L+U)x --> x = (b-(L+U)x)/D//// we can do this iteratively: x_new = (b-(L+U)x_old)/D// build with TYPE=double (default) or TYPE=float// build with TOLERANCE=0.001 (default) or TOLERANCE= any other value// three arguments:// vector size// maximum iteration count// frequency of printing the residual (every n-th iteration)#include <cmath>#include <omp.h>#include <cstdlib>#include <iostream>#include <iomanip>using std::cout;#ifndef TYPE#define TYPE double#endif#define TOLERANCE 0.001voidinit_simple_diag_dom(int nsize, TYPE* A){ int i, j; // In a diagonally-dominant matrix, the diagonal element // is greater than the sum of the other elements in the row. // Scale the matrix so the sum of the row elements is close to one. for (i = 0; i < nsize; ++i) { TYPE sum; sum = (TYPE)0; for (j = 0; j < nsize; ++j) { TYPE x; x = (rand() % 23) / (TYPE)1000; A[i*nsize + j] = x; sum += x; } // Fill diagonal element with the sum A[i*nsize + i] += sum; // scale the row so the final matrix is almost an identity matrix for (j = 0; j < nsize; j++) A[i*nsize + j] /= sum; }} // init_simple_diag_domintmain(int argc, char **argv){ int nsize; // A[nsize][nsize] int i, j, iters, max_iters, riter; double start_time, elapsed_time; TYPE residual, err, chksum; TYPE *A, *b, *x1, *x2, *xnew, *xold, *xtmp; // set matrix dimensions and allocate memory for matrices nsize = 0; if (argc > 1) nsize = atoi(argv[1]); if (nsize <= 0) nsize = 1000; max_iters = 0; if (argc > 2) max_iters = atoi(argv[2]); if (max_iters <= 0) max_iters = 5000; riter = 0; if (argc > 3) riter = atoi(argv[3]); if (riter <= 0) riter = 200; cout << "nsize = "<< nsize << ", max_iters = "<< max_iters << "\n"; A = new TYPE[nsize*nsize]; b = new TYPE[nsize]; x1 = new TYPE[nsize]; x2 = new TYPE[nsize]; // generate a diagonally dominant matrix init_simple_diag_dom(nsize, A); // zero the x vectors, random values to the b vector for (i = 0; i < nsize; i++) { x1[i] = (TYPE)0.0; x2[i] = (TYPE)0.0; b[i] = (TYPE)(rand() % 51) / 100.0; } start_time = omp_get_wtime(); // // jacobi iterative solver // residual = TOLERANCE + 1.0; iters = 0; xnew = x1; // swap these pointers in each iteration xold = x2; while ((residual > TOLERANCE) && (iters < max_iters)) {++iters; // swap input and output vectors xtmp = xnew; xnew = xold; xold = xtmp; #pragma acc parallel loop for (i = 0; i < nsize; ++i) { TYPE rsum = (TYPE)0; #pragma acc loop reduction(+:rsum) for (j = 0; j < nsize; ++j) { if (i != j) rsum += A[i*nsize + j] * xold[j]; } xnew[i] = (b[i] - rsum) / A[i*nsize + i]; } // // test convergence, sqrt(sum((xnew-xold)**2)) // residual = 0.0; #pragma acc parallel loop reduction(+:residual) for (i = 0; i < nsize; i++) { TYPE dif; dif = xnew[i] - xold[i]; residual += dif * dif; } residual = sqrt((double)residual); if (iters % riter == 0 ) cout << "Iteration "<< iters << ", residual is "<< residual << "\n"; } elapsed_time = omp_get_wtime() - start_time; cout << "\nConverged after "<< iters << " iterations and "<< elapsed_time << " seconds, residual is "<< residual << "\n"; // // test answer by multiplying my computed value of x by // the input A matrix and comparing the result with the // input b vector. // err = (TYPE)0.0; chksum = (TYPE)0.0; for (i = 0; i < nsize; i++) { TYPE tmp; xold[i] = (TYPE)0.0; for (j = 0; j < nsize; j++) xold[i] += A[i*nsize + j] * xnew[j]; tmp = xold[i] - b[i]; chksum += xnew[i]; err += tmp * tmp; } err = sqrt((double)err); cout << "Solution error is "<< err << "\n"; if (err > TOLERANCE) cout << "****** Final Solution Out of Tolerance ******\n"<< err << "> "<< TOLERANCE << "\n"; delete A; delete b; delete x1; delete x2; return 0;}