Actual source code: ex50.c

      1: /*   DMDA/KSP solving a system of linear equations.
      2:      Poisson equation in 2D:

      4:      div(grad p) = f,  0 < x,y < 1
      5:      with
      6:        forcing function f = -cos(m*pi*x)*cos(n*pi*y),
      7:        Neumann boundary conditions
      8:         dp/dx = 0 for x = 0, x = 1.
      9:         dp/dy = 0 for y = 0, y = 1.

     11:      Contributed by Michael Boghosian <boghmic@iit.edu>, 2008,
     12:          based on petsc/src/ksp/ksp/tutorials/ex29.c and ex32.c

     14:      Compare to ex66.c

     16:      Example of Usage:
     17:           ./ex50 -da_grid_x 3 -da_grid_y 3 -pc_type mg -da_refine 3 -ksp_monitor -ksp_view -dm_view draw -draw_pause -1
     18:           ./ex50 -da_grid_x 100 -da_grid_y 100 -pc_type mg  -pc_mg_levels 1 -mg_levels_0_pc_type ilu -mg_levels_0_pc_factor_levels 1 -ksp_monitor -ksp_view
     19:           ./ex50 -da_grid_x 100 -da_grid_y 100 -pc_type mg -pc_mg_levels 1 -mg_levels_0_pc_type lu -mg_levels_0_pc_factor_shift_type NONZERO -ksp_monitor
     20:           mpiexec -n 4 ./ex50 -da_grid_x 3 -da_grid_y 3 -pc_type mg -da_refine 10 -ksp_monitor -ksp_view -log_view
     21: */

     23: static char help[] = "Solves 2D Poisson equation using multigrid.\n\n";

     25: #include <petscdm.h>
     26: #include <petscdmda.h>
     27: #include <petscksp.h>
     28: #include <petscsys.h>
     29: #include <petscvec.h>

     31: extern PetscErrorCode ComputeJacobian(KSP, Mat, Mat, void *);
     32: extern PetscErrorCode ComputeRHS(KSP, Vec, void *);

     34: typedef struct {
     35:   PetscScalar uu, tt;
     36: } UserContext;

     38: int main(int argc, char **argv)
     39: {
     40:   KSP         ksp;
     41:   DM          da;
     42:   UserContext user;

     44:   PetscFunctionBeginUser;
     45:   PetscCall(PetscInitialize(&argc, &argv, NULL, help));
     46:   PetscCall(KSPCreate(PETSC_COMM_WORLD, &ksp));
     47:   PetscCall(DMDACreate2d(PETSC_COMM_WORLD, DM_BOUNDARY_NONE, DM_BOUNDARY_NONE, DMDA_STENCIL_STAR, 11, 11, PETSC_DECIDE, PETSC_DECIDE, 1, 1, NULL, NULL, &da));
     48:   PetscCall(DMSetFromOptions(da));
     49:   PetscCall(DMSetUp(da));
     50:   PetscCall(KSPSetDM(ksp, (DM)da));
     51:   PetscCall(DMSetApplicationContext(da, &user));

     53:   user.uu = 1.0;
     54:   user.tt = 1.0;

     56:   PetscCall(KSPSetComputeRHS(ksp, ComputeRHS, &user));
     57:   PetscCall(KSPSetComputeOperators(ksp, ComputeJacobian, &user));
     58:   PetscCall(KSPSetFromOptions(ksp));
     59:   PetscCall(KSPSolve(ksp, NULL, NULL));

     61:   PetscCall(DMDestroy(&da));
     62:   PetscCall(KSPDestroy(&ksp));
     63:   PetscCall(PetscFinalize());
     64:   return 0;
     65: }

     67: PetscErrorCode ComputeRHS(KSP ksp, Vec b, PetscCtx ctx)
     68: {
     69:   UserContext  *user = (UserContext *)ctx;
     70:   PetscInt      i, j, M, N, xm, ym, xs, ys;
     71:   PetscScalar   Hx, Hy, pi, uu, tt;
     72:   PetscScalar **array;
     73:   DM            da;
     74:   MatNullSpace  nullspace;

     76:   PetscFunctionBeginUser;
     77:   PetscCall(KSPGetDM(ksp, &da));
     78:   PetscCall(DMDAGetInfo(da, 0, &M, &N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
     79:   uu = user->uu;
     80:   tt = user->tt;
     81:   pi = 4 * atan(1.0);
     82:   Hx = 1.0 / (PetscReal)M;
     83:   Hy = 1.0 / (PetscReal)N;

     85:   PetscCall(DMDAGetCorners(da, &xs, &ys, 0, &xm, &ym, 0)); /* Fine grid */
     86:   PetscCall(DMDAVecGetArray(da, b, &array));
     87:   for (j = ys; j < ys + ym; j++) {
     88:     for (i = xs; i < xs + xm; i++) array[j][i] = -PetscCosScalar(uu * pi * ((PetscReal)i + 0.5) * Hx) * PetscCosScalar(tt * pi * ((PetscReal)j + 0.5) * Hy) * Hx * Hy;
     89:   }
     90:   PetscCall(DMDAVecRestoreArray(da, b, &array));
     91:   PetscCall(VecAssemblyBegin(b));
     92:   PetscCall(VecAssemblyEnd(b));

     94:   /* force right-hand side to be consistent for singular matrix */
     95:   /* note this is really a hack, normally the model would provide you with a consistent right handside */
     96:   PetscCall(MatNullSpaceCreate(PETSC_COMM_WORLD, PETSC_TRUE, 0, 0, &nullspace));
     97:   PetscCall(MatNullSpaceRemove(nullspace, b));
     98:   PetscCall(MatNullSpaceDestroy(&nullspace));
     99:   PetscFunctionReturn(PETSC_SUCCESS);
    100: }

    102: PetscErrorCode ComputeJacobian(KSP ksp, Mat J, Mat jac, PetscCtx ctx)
    103: {
    104:   PetscInt     i, j, M, N, xm, ym, xs, ys, num, numi, numj;
    105:   PetscScalar  v[5], Hx, Hy, HydHx, HxdHy;
    106:   MatStencil   row, col[5];
    107:   DM           da;
    108:   MatNullSpace nullspace;

    110:   PetscFunctionBeginUser;
    111:   PetscCall(KSPGetDM(ksp, &da));
    112:   PetscCall(DMDAGetInfo(da, 0, &M, &N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
    113:   Hx    = 1.0 / (PetscReal)M;
    114:   Hy    = 1.0 / (PetscReal)N;
    115:   HxdHy = Hx / Hy;
    116:   HydHx = Hy / Hx;
    117:   PetscCall(DMDAGetCorners(da, &xs, &ys, 0, &xm, &ym, 0));
    118:   for (j = ys; j < ys + ym; j++) {
    119:     for (i = xs; i < xs + xm; i++) {
    120:       row.i = i;
    121:       row.j = j;

    123:       if (i == 0 || j == 0 || i == M - 1 || j == N - 1) {
    124:         num  = 0;
    125:         numi = 0;
    126:         numj = 0;
    127:         if (j != 0) {
    128:           v[num]     = -HxdHy;
    129:           col[num].i = i;
    130:           col[num].j = j - 1;
    131:           num++;
    132:           numj++;
    133:         }
    134:         if (i != 0) {
    135:           v[num]     = -HydHx;
    136:           col[num].i = i - 1;
    137:           col[num].j = j;
    138:           num++;
    139:           numi++;
    140:         }
    141:         if (i != M - 1) {
    142:           v[num]     = -HydHx;
    143:           col[num].i = i + 1;
    144:           col[num].j = j;
    145:           num++;
    146:           numi++;
    147:         }
    148:         if (j != N - 1) {
    149:           v[num]     = -HxdHy;
    150:           col[num].i = i;
    151:           col[num].j = j + 1;
    152:           num++;
    153:           numj++;
    154:         }
    155:         v[num]     = (PetscReal)numj * HxdHy + (PetscReal)numi * HydHx;
    156:         col[num].i = i;
    157:         col[num].j = j;
    158:         num++;
    159:         PetscCall(MatSetValuesStencil(jac, 1, &row, num, col, v, INSERT_VALUES));
    160:       } else {
    161:         v[0]     = -HxdHy;
    162:         col[0].i = i;
    163:         col[0].j = j - 1;
    164:         v[1]     = -HydHx;
    165:         col[1].i = i - 1;
    166:         col[1].j = j;
    167:         v[2]     = 2.0 * (HxdHy + HydHx);
    168:         col[2].i = i;
    169:         col[2].j = j;
    170:         v[3]     = -HydHx;
    171:         col[3].i = i + 1;
    172:         col[3].j = j;
    173:         v[4]     = -HxdHy;
    174:         col[4].i = i;
    175:         col[4].j = j + 1;
    176:         PetscCall(MatSetValuesStencil(jac, 1, &row, 5, col, v, INSERT_VALUES));
    177:       }
    178:     }
    179:   }
    180:   PetscCall(MatAssemblyBegin(jac, MAT_FINAL_ASSEMBLY));
    181:   PetscCall(MatAssemblyEnd(jac, MAT_FINAL_ASSEMBLY));

    183:   PetscCall(MatNullSpaceCreate(PETSC_COMM_WORLD, PETSC_TRUE, 0, 0, &nullspace));
    184:   PetscCall(MatSetNullSpace(J, nullspace));
    185:   PetscCall(MatNullSpaceDestroy(&nullspace));
    186:   PetscFunctionReturn(PETSC_SUCCESS);
    187: }

    189: /*TEST

    191:    build:
    192:       requires: !complex !single

    194:    test:
    195:       args: -pc_type mg -pc_mg_type full -ksp_type cg -ksp_monitor_short -da_refine 3 -mg_coarse_pc_type svd -ksp_view

    197:    test:
    198:       suffix: 2
    199:       nsize: 4
    200:       args: -pc_type mg -pc_mg_type full -ksp_type cg -ksp_monitor_short -da_refine 3 -mg_coarse_pc_type redundant -mg_coarse_redundant_pc_type svd -ksp_view

    202:    test:
    203:       suffix: 3
    204:       nsize: 2
    205:       args: -pc_type mg -pc_mg_type full -ksp_monitor_short -da_refine 5 -mg_coarse_ksp_type cg -mg_coarse_ksp_converged_reason -mg_coarse_ksp_rtol 1e-2 -mg_coarse_ksp_max_it 5 -mg_coarse_pc_type none -pc_mg_levels 2 -ksp_type pipefgmres -ksp_pipefgmres_shift 1.5

    207:    test:
    208:       suffix: tut_1
    209:       nsize: 1
    210:       args: -da_grid_x 4 -da_grid_y 4 -mat_view

    212:    test:
    213:       suffix: tut_2
    214:       requires: superlu_dist parmetis
    215:       nsize: 4
    216:       args: -da_grid_x 120 -da_grid_y 120 -pc_type lu -pc_factor_mat_solver_type superlu_dist -ksp_monitor -ksp_view

    218:    test:
    219:       suffix: tut_3
    220:       nsize: 4
    221:       args: -da_grid_x 1025 -da_grid_y 1025 -pc_type mg -pc_mg_levels 9 -ksp_monitor

    223: TEST*/