Actual source code: agg.c

      1: /*
      2:  GAMG geometric-algebric multigrid PC - Mark Adams 2011
      3:  */

      5: #include <../src/ksp/pc/impls/gamg/gamg.h>
      6: #include <petscblaslapack.h>
      7: #include <petscdm.h>
      8: #include <petsc/private/kspimpl.h>

     10: typedef struct {
     11:   PetscInt   nsmooths;                     // number of smoothing steps to construct prolongation
     12:   PetscInt   aggressive_coarsening_levels; // number of aggressive coarsening levels (square or MISk)
     13:   PetscInt   aggressive_mis_k;             // the k in MIS-k
     14:   PetscBool  use_aggressive_square_graph;
     15:   PetscBool  use_minimum_degree_ordering;
     16:   PetscBool  use_low_mem_filter;
     17:   PetscBool  graph_symmetrize;
     18:   MatCoarsen crs;
     19: } PC_GAMG_AGG;

     21: /*@
     22:   PCGAMGSetNSmooths - Set number of smoothing steps (1 is typical) used to construct the prolongation operator

     24:   Logically Collective

     26:   Input Parameters:
     27: + pc - the preconditioner context
     28: - n  - the number of smooths

     30:   Options Database Key:
     31: . -pc_gamg_agg_nsmooths <nsmooth, default=1> - number of smoothing steps to use

     33:   Level: intermediate

     35:   Note:
     36:   This is a different concept from the number smoothing steps used during the linear solution process which
     37:   can be set with `-mg_levels_ksp_max_it`

     39:   Developer Note:
     40:   This should be named `PCGAMGAGGSetNSmooths()`.

     42: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), [](ch_ksp), `PCMG`, `PCGAMG`
     43: @*/
     44: PetscErrorCode PCGAMGSetNSmooths(PC pc, PetscInt n)
     45: {
     46:   PetscFunctionBegin;
     49:   PetscTryMethod(pc, "PCGAMGSetNSmooths_C", (PC, PetscInt), (pc, n));
     50:   PetscFunctionReturn(PETSC_SUCCESS);
     51: }

     53: static PetscErrorCode PCGAMGSetNSmooths_AGG(PC pc, PetscInt n)
     54: {
     55:   PC_MG       *mg          = (PC_MG *)pc->data;
     56:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
     57:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

     59:   PetscFunctionBegin;
     60:   pc_gamg_agg->nsmooths = n;
     61:   PetscFunctionReturn(PETSC_SUCCESS);
     62: }

     64: /*@
     65:   PCGAMGSetAggressiveLevels -  Use aggressive coarsening on first n levels

     67:   Logically Collective

     69:   Input Parameters:
     70: + pc - the preconditioner context
     71: - n  - 0, 1 or more

     73:   Options Database Key:
     74: . -pc_gamg_aggressive_coarsening <n,default = 1> - Number of levels on which to square the graph on before aggregating it

     76:   Level: intermediate

     78: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), [](ch_ksp), `PCGAMG`, `PCGAMGSetThreshold()`, `PCGAMGMISkSetAggressive()`, `PCGAMGSetAggressiveSquareGraph()`, `PCGAMGMISkSetMinDegreeOrdering()`, `PCGAMGSetLowMemoryFilter()`
     79: @*/
     80: PetscErrorCode PCGAMGSetAggressiveLevels(PC pc, PetscInt n)
     81: {
     82:   PetscFunctionBegin;
     85:   PetscTryMethod(pc, "PCGAMGSetAggressiveLevels_C", (PC, PetscInt), (pc, n));
     86:   PetscFunctionReturn(PETSC_SUCCESS);
     87: }

     89: /*@
     90:   PCGAMGMISkSetAggressive - Number (k) distance in MIS coarsening (>2 is 'aggressive')

     92:   Logically Collective

     94:   Input Parameters:
     95: + pc - the preconditioner context
     96: - n  - 1 or more (default = 2)

     98:   Options Database Key:
     99: . -pc_gamg_aggressive_mis_k <n,default=2> - Number (k) distance in MIS coarsening (>2 is 'aggressive')

    101:   Level: intermediate

    103: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), [](ch_ksp), `PCGAMG`, `PCGAMGSetThreshold()`, `PCGAMGSetAggressiveLevels()`, `PCGAMGSetAggressiveSquareGraph()`, `PCGAMGMISkSetMinDegreeOrdering()`, `PCGAMGSetLowMemoryFilter()`
    104: @*/
    105: PetscErrorCode PCGAMGMISkSetAggressive(PC pc, PetscInt n)
    106: {
    107:   PetscFunctionBegin;
    110:   PetscTryMethod(pc, "PCGAMGMISkSetAggressive_C", (PC, PetscInt), (pc, n));
    111:   PetscFunctionReturn(PETSC_SUCCESS);
    112: }

    114: /*@
    115:   PCGAMGSetAggressiveSquareGraph - Use graph square A'A for aggressive coarsening, old method

    117:   Logically Collective

    119:   Input Parameters:
    120: + pc - the preconditioner context
    121: - b  - default false - MIS-k is faster

    123:   Options Database Key:
    124: . -pc_gamg_aggressive_square_graph <bool,default=false> - Use square graph (A'A) or MIS-k (k=2) for aggressive coarsening

    126:   Level: intermediate

    128: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), [](ch_ksp), `PCGAMG`, `PCGAMGSetThreshold()`, `PCGAMGSetAggressiveLevels()`, `PCGAMGMISkSetAggressive()`, `PCGAMGMISkSetMinDegreeOrdering()`, `PCGAMGSetLowMemoryFilter()`
    129: @*/
    130: PetscErrorCode PCGAMGSetAggressiveSquareGraph(PC pc, PetscBool b)
    131: {
    132:   PetscFunctionBegin;
    135:   PetscTryMethod(pc, "PCGAMGSetAggressiveSquareGraph_C", (PC, PetscBool), (pc, b));
    136:   PetscFunctionReturn(PETSC_SUCCESS);
    137: }

    139: /*@
    140:   PCGAMGMISkSetMinDegreeOrdering - Use minimum degree ordering in greedy MIS algorithm

    142:   Logically Collective

    144:   Input Parameters:
    145: + pc - the preconditioner context
    146: - b  - default true

    148:   Options Database Key:
    149: . -pc_gamg_mis_k_minimum_degree_ordering <bool,default=true> - Use minimum degree ordering in greedy MIS algorithm

    151:   Level: intermediate

    153: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), [](ch_ksp), `PCGAMG`, `PCGAMGSetThreshold()`, `PCGAMGSetAggressiveLevels()`, `PCGAMGMISkSetAggressive()`, `PCGAMGSetAggressiveSquareGraph()`, `PCGAMGSetLowMemoryFilter()`
    154: @*/
    155: PetscErrorCode PCGAMGMISkSetMinDegreeOrdering(PC pc, PetscBool b)
    156: {
    157:   PetscFunctionBegin;
    160:   PetscTryMethod(pc, "PCGAMGMISkSetMinDegreeOrdering_C", (PC, PetscBool), (pc, b));
    161:   PetscFunctionReturn(PETSC_SUCCESS);
    162: }

    164: /*@
    165:   PCGAMGSetLowMemoryFilter - Use low memory graph/matrix filter

    167:   Logically Collective

    169:   Input Parameters:
    170: + pc - the preconditioner context
    171: - b  - default false

    173:   Options Database Key:
    174: . -pc_gamg_low_memory_threshold_filter <bool,default=false> - Use low memory graph/matrix filter

    176:   Level: intermediate

    178: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), `PCGAMG`, `PCGAMGSetThreshold()`, `PCGAMGSetAggressiveLevels()`,
    179:   `PCGAMGMISkSetAggressive()`, `PCGAMGSetAggressiveSquareGraph()`, `PCGAMGMISkSetMinDegreeOrdering()`
    180: @*/
    181: PetscErrorCode PCGAMGSetLowMemoryFilter(PC pc, PetscBool b)
    182: {
    183:   PetscFunctionBegin;
    186:   PetscTryMethod(pc, "PCGAMGSetLowMemoryFilter_C", (PC, PetscBool), (pc, b));
    187:   PetscFunctionReturn(PETSC_SUCCESS);
    188: }

    190: /*@
    191:   PCGAMGSetGraphSymmetrize - Set the flag to symmetrize the graph used in coarsening

    193:   Logically Collective

    195:   Input Parameters:
    196: + pc - the preconditioner context
    197: - b  - default false

    199:   Options Database Key:
    200: . -pc_gamg_graph_symmetrize <bool,default=false> - Symmetrize the graph

    202:   Level: intermediate

    204: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), `PCGAMG`, `PCGAMGSetThreshold()`, `PCGAMGSetAggressiveLevels()`,
    205:   `PCGAMGMISkSetAggressive()`, `PCGAMGSetAggressiveSquareGraph()`, `PCGAMGMISkSetMinDegreeOrdering()`
    206: @*/
    207: PetscErrorCode PCGAMGSetGraphSymmetrize(PC pc, PetscBool b)
    208: {
    209:   PetscFunctionBegin;
    212:   PetscTryMethod(pc, "PCGAMGSetGraphSymmetrize_C", (PC, PetscBool), (pc, b));
    213:   PetscFunctionReturn(PETSC_SUCCESS);
    214: }

    216: static PetscErrorCode PCGAMGSetAggressiveLevels_AGG(PC pc, PetscInt n)
    217: {
    218:   PC_MG       *mg          = (PC_MG *)pc->data;
    219:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    220:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

    222:   PetscFunctionBegin;
    223:   pc_gamg_agg->aggressive_coarsening_levels = n;
    224:   PetscFunctionReturn(PETSC_SUCCESS);
    225: }

    227: static PetscErrorCode PCGAMGMISkSetAggressive_AGG(PC pc, PetscInt n)
    228: {
    229:   PC_MG       *mg          = (PC_MG *)pc->data;
    230:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    231:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

    233:   PetscFunctionBegin;
    234:   pc_gamg_agg->aggressive_mis_k = n;
    235:   PetscFunctionReturn(PETSC_SUCCESS);
    236: }

    238: static PetscErrorCode PCGAMGSetAggressiveSquareGraph_AGG(PC pc, PetscBool b)
    239: {
    240:   PC_MG       *mg          = (PC_MG *)pc->data;
    241:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    242:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

    244:   PetscFunctionBegin;
    245:   pc_gamg_agg->use_aggressive_square_graph = b;
    246:   PetscFunctionReturn(PETSC_SUCCESS);
    247: }

    249: static PetscErrorCode PCGAMGSetLowMemoryFilter_AGG(PC pc, PetscBool b)
    250: {
    251:   PC_MG       *mg          = (PC_MG *)pc->data;
    252:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    253:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

    255:   PetscFunctionBegin;
    256:   pc_gamg_agg->use_low_mem_filter = b;
    257:   PetscFunctionReturn(PETSC_SUCCESS);
    258: }

    260: static PetscErrorCode PCGAMGSetGraphSymmetrize_AGG(PC pc, PetscBool b)
    261: {
    262:   PC_MG       *mg          = (PC_MG *)pc->data;
    263:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    264:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

    266:   PetscFunctionBegin;
    267:   pc_gamg_agg->graph_symmetrize = b;
    268:   PetscFunctionReturn(PETSC_SUCCESS);
    269: }

    271: static PetscErrorCode PCGAMGMISkSetMinDegreeOrdering_AGG(PC pc, PetscBool b)
    272: {
    273:   PC_MG       *mg          = (PC_MG *)pc->data;
    274:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    275:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

    277:   PetscFunctionBegin;
    278:   pc_gamg_agg->use_minimum_degree_ordering = b;
    279:   PetscFunctionReturn(PETSC_SUCCESS);
    280: }

    282: static PetscErrorCode PCSetFromOptions_GAMG_AGG(PC pc, PetscOptionItems *PetscOptionsObject)
    283: {
    284:   PC_MG       *mg          = (PC_MG *)pc->data;
    285:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    286:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;
    287:   PetscBool    n_aggressive_flg, old_sq_provided = PETSC_FALSE, new_sq_provided = PETSC_FALSE, new_sqr_graph = pc_gamg_agg->use_aggressive_square_graph;
    288:   PetscInt     nsq_graph_old = 0;

    290:   PetscFunctionBegin;
    291:   PetscOptionsHeadBegin(PetscOptionsObject, "GAMG-AGG options");
    292:   PetscCall(PetscOptionsInt("-pc_gamg_agg_nsmooths", "number of smoothing steps to construct prolongation, usually 1", "PCGAMGSetNSmooths", pc_gamg_agg->nsmooths, &pc_gamg_agg->nsmooths, NULL));
    293:   // aggressive coarsening logic with deprecated -pc_gamg_square_graph
    294:   PetscCall(PetscOptionsInt("-pc_gamg_aggressive_coarsening", "Number of aggressive coarsening (MIS-2) levels from finest", "PCGAMGSetAggressiveLevels", pc_gamg_agg->aggressive_coarsening_levels, &pc_gamg_agg->aggressive_coarsening_levels, &n_aggressive_flg));
    295:   if (!n_aggressive_flg)
    296:     PetscCall(PetscOptionsInt("-pc_gamg_square_graph", "Number of aggressive coarsening (MIS-2) levels from finest (deprecated alias for -pc_gamg_aggressive_coarsening)", "PCGAMGSetAggressiveLevels", nsq_graph_old, &nsq_graph_old, &old_sq_provided));
    297:   PetscCall(PetscOptionsBool("-pc_gamg_aggressive_square_graph", "Use square graph (A'A) or MIS-k (k=2) for aggressive coarsening", "PCGAMGSetAggressiveSquareGraph", new_sqr_graph, &pc_gamg_agg->use_aggressive_square_graph, &new_sq_provided));
    298:   if (!new_sq_provided && old_sq_provided) {
    299:     pc_gamg_agg->aggressive_coarsening_levels = nsq_graph_old; // could be zero
    300:     pc_gamg_agg->use_aggressive_square_graph  = PETSC_TRUE;
    301:   }
    302:   if (new_sq_provided && old_sq_provided)
    303:     PetscCall(PetscInfo(pc, "Warning: both -pc_gamg_square_graph and -pc_gamg_aggressive_coarsening are used. -pc_gamg_square_graph is deprecated, Number of aggressive levels is %d\n", (int)pc_gamg_agg->aggressive_coarsening_levels));
    304:   PetscCall(PetscOptionsBool("-pc_gamg_mis_k_minimum_degree_ordering", "Use minimum degree ordering for greedy MIS", "PCGAMGMISkSetMinDegreeOrdering", pc_gamg_agg->use_minimum_degree_ordering, &pc_gamg_agg->use_minimum_degree_ordering, NULL));
    305:   PetscCall(PetscOptionsBool("-pc_gamg_low_memory_threshold_filter", "Use the (built-in) low memory graph/matrix filter", "PCGAMGSetLowMemoryFilter", pc_gamg_agg->use_low_mem_filter, &pc_gamg_agg->use_low_mem_filter, NULL));
    306:   PetscCall(PetscOptionsInt("-pc_gamg_aggressive_mis_k", "Number of levels of multigrid to use.", "PCGAMGMISkSetAggressive", pc_gamg_agg->aggressive_mis_k, &pc_gamg_agg->aggressive_mis_k, NULL));
    307:   PetscCall(PetscOptionsBool("-pc_gamg_graph_symmetrize", "Symmetrize graph for coarsening", "PCGAMGSetGraphSymmetrize", pc_gamg_agg->graph_symmetrize, &pc_gamg_agg->graph_symmetrize, NULL));
    308:   PetscOptionsHeadEnd();
    309:   PetscFunctionReturn(PETSC_SUCCESS);
    310: }

    312: static PetscErrorCode PCDestroy_GAMG_AGG(PC pc)
    313: {
    314:   PC_MG       *mg          = (PC_MG *)pc->data;
    315:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    316:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

    318:   PetscFunctionBegin;
    319:   PetscCall(MatCoarsenDestroy(&pc_gamg_agg->crs));
    320:   PetscCall(PetscFree(pc_gamg->subctx));
    321:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetNSmooths_C", NULL));
    322:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetAggressiveLevels_C", NULL));
    323:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGMISkSetAggressive_C", NULL));
    324:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGMISkSetMinDegreeOrdering_C", NULL));
    325:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetLowMemoryFilter_C", NULL));
    326:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetAggressiveSquareGraph_C", NULL));
    327:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetGraphSymmetrize_C", NULL));
    328:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSetCoordinates_C", NULL));
    329:   PetscFunctionReturn(PETSC_SUCCESS);
    330: }

    332: /*
    333:    PCSetCoordinates_AGG

    335:    Collective

    337:    Input Parameter:
    338:    . pc - the preconditioner context
    339:    . ndm - dimension of data (used for dof/vertex for Stokes)
    340:    . a_nloc - number of vertices local
    341:    . coords - [a_nloc][ndm] - interleaved coordinate data: {x_0, y_0, z_0, x_1, y_1, ...}
    342: */

    344: static PetscErrorCode PCSetCoordinates_AGG(PC pc, PetscInt ndm, PetscInt a_nloc, PetscReal *coords)
    345: {
    346:   PC_MG   *mg      = (PC_MG *)pc->data;
    347:   PC_GAMG *pc_gamg = (PC_GAMG *)mg->innerctx;
    348:   PetscInt arrsz, kk, ii, jj, nloc, ndatarows, ndf;
    349:   Mat      mat = pc->pmat;

    351:   PetscFunctionBegin;
    354:   nloc = a_nloc;

    356:   /* SA: null space vectors */
    357:   PetscCall(MatGetBlockSize(mat, &ndf));               /* this does not work for Stokes */
    358:   if (coords && ndf == 1) pc_gamg->data_cell_cols = 1; /* scalar w/ coords and SA (not needed) */
    359:   else if (coords) {
    360:     PetscCheck(ndm <= ndf, PETSC_COMM_SELF, PETSC_ERR_PLIB, "degrees of motion %" PetscInt_FMT " > block size %" PetscInt_FMT, ndm, ndf);
    361:     pc_gamg->data_cell_cols = (ndm == 2 ? 3 : 6); /* displacement elasticity */
    362:     if (ndm != ndf) PetscCheck(pc_gamg->data_cell_cols == ndf, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Don't know how to create null space for ndm=%" PetscInt_FMT ", ndf=%" PetscInt_FMT ".  Use MatSetNearNullSpace().", ndm, ndf);
    363:   } else pc_gamg->data_cell_cols = ndf; /* no data, force SA with constant null space vectors */
    364:   pc_gamg->data_cell_rows = ndatarows = ndf;
    365:   PetscCheck(pc_gamg->data_cell_cols > 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "pc_gamg->data_cell_cols %" PetscInt_FMT " <= 0", pc_gamg->data_cell_cols);
    366:   arrsz = nloc * pc_gamg->data_cell_rows * pc_gamg->data_cell_cols;

    368:   if (!pc_gamg->data || (pc_gamg->data_sz != arrsz)) {
    369:     PetscCall(PetscFree(pc_gamg->data));
    370:     PetscCall(PetscMalloc1(arrsz + 1, &pc_gamg->data));
    371:   }
    372:   /* copy data in - column-oriented */
    373:   for (kk = 0; kk < nloc; kk++) {
    374:     const PetscInt M    = nloc * pc_gamg->data_cell_rows; /* stride into data */
    375:     PetscReal     *data = &pc_gamg->data[kk * ndatarows]; /* start of cell */

    377:     if (pc_gamg->data_cell_cols == 1) *data = 1.0;
    378:     else {
    379:       /* translational modes */
    380:       for (ii = 0; ii < ndatarows; ii++) {
    381:         for (jj = 0; jj < ndatarows; jj++) {
    382:           if (ii == jj) data[ii * M + jj] = 1.0;
    383:           else data[ii * M + jj] = 0.0;
    384:         }
    385:       }

    387:       /* rotational modes */
    388:       if (coords) {
    389:         if (ndm == 2) {
    390:           data += 2 * M;
    391:           data[0] = -coords[2 * kk + 1];
    392:           data[1] = coords[2 * kk];
    393:         } else {
    394:           data += 3 * M;
    395:           data[0]         = 0.0;
    396:           data[M + 0]     = coords[3 * kk + 2];
    397:           data[2 * M + 0] = -coords[3 * kk + 1];
    398:           data[1]         = -coords[3 * kk + 2];
    399:           data[M + 1]     = 0.0;
    400:           data[2 * M + 1] = coords[3 * kk];
    401:           data[2]         = coords[3 * kk + 1];
    402:           data[M + 2]     = -coords[3 * kk];
    403:           data[2 * M + 2] = 0.0;
    404:         }
    405:       }
    406:     }
    407:   }
    408:   pc_gamg->data_sz = arrsz;
    409:   PetscFunctionReturn(PETSC_SUCCESS);
    410: }

    412: /*
    413:    PCSetData_AGG - called if data is not set with PCSetCoordinates.
    414:       Looks in Mat for near null space.
    415:       Does not work for Stokes

    417:   Input Parameter:
    418:    . pc -
    419:    . a_A - matrix to get (near) null space out of.
    420: */
    421: static PetscErrorCode PCSetData_AGG(PC pc, Mat a_A)
    422: {
    423:   PC_MG       *mg      = (PC_MG *)pc->data;
    424:   PC_GAMG     *pc_gamg = (PC_GAMG *)mg->innerctx;
    425:   MatNullSpace mnull;

    427:   PetscFunctionBegin;
    428:   PetscCall(MatGetNearNullSpace(a_A, &mnull));
    429:   if (!mnull) {
    430:     DM dm;

    432:     PetscCall(PCGetDM(pc, &dm));
    433:     if (!dm) PetscCall(MatGetDM(a_A, &dm));
    434:     if (dm) {
    435:       PetscObject deformation;
    436:       PetscInt    Nf;

    438:       PetscCall(DMGetNumFields(dm, &Nf));
    439:       if (Nf) {
    440:         PetscCall(DMGetField(dm, 0, NULL, &deformation));
    441:         PetscCall(PetscObjectQuery((PetscObject)deformation, "nearnullspace", (PetscObject *)&mnull));
    442:         if (!mnull) PetscCall(PetscObjectQuery((PetscObject)deformation, "nullspace", (PetscObject *)&mnull));
    443:       }
    444:     }
    445:   }

    447:   if (!mnull) {
    448:     PetscInt bs, NN, MM;

    450:     PetscCall(MatGetBlockSize(a_A, &bs));
    451:     PetscCall(MatGetLocalSize(a_A, &MM, &NN));
    452:     PetscCheck(MM % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MM %" PetscInt_FMT " must be divisible by bs %" PetscInt_FMT, MM, bs);
    453:     PetscCall(PCSetCoordinates_AGG(pc, bs, MM / bs, NULL));
    454:   } else {
    455:     PetscReal         *nullvec;
    456:     PetscBool          has_const;
    457:     PetscInt           i, j, mlocal, nvec, bs;
    458:     const Vec         *vecs;
    459:     const PetscScalar *v;

    461:     PetscCall(MatGetLocalSize(a_A, &mlocal, NULL));
    462:     PetscCall(MatNullSpaceGetVecs(mnull, &has_const, &nvec, &vecs));
    463:     for (i = 0; i < nvec; i++) {
    464:       PetscCall(VecGetLocalSize(vecs[i], &j));
    465:       PetscCheck(j == mlocal, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Attached null space vector size %" PetscInt_FMT " != matrix size %" PetscInt_FMT, j, mlocal);
    466:     }
    467:     pc_gamg->data_sz = (nvec + !!has_const) * mlocal;
    468:     PetscCall(PetscMalloc1((nvec + !!has_const) * mlocal, &nullvec));
    469:     if (has_const)
    470:       for (i = 0; i < mlocal; i++) nullvec[i] = 1.0;
    471:     for (i = 0; i < nvec; i++) {
    472:       PetscCall(VecGetArrayRead(vecs[i], &v));
    473:       for (j = 0; j < mlocal; j++) nullvec[(i + !!has_const) * mlocal + j] = PetscRealPart(v[j]);
    474:       PetscCall(VecRestoreArrayRead(vecs[i], &v));
    475:     }
    476:     pc_gamg->data           = nullvec;
    477:     pc_gamg->data_cell_cols = (nvec + !!has_const);
    478:     PetscCall(MatGetBlockSize(a_A, &bs));
    479:     pc_gamg->data_cell_rows = bs;
    480:   }
    481:   PetscFunctionReturn(PETSC_SUCCESS);
    482: }

    484: /*
    485:   formProl0 - collect null space data for each aggregate, do QR, put R in coarse grid data and Q in P_0

    487:   Input Parameter:
    488:    . agg_llists - list of arrays with aggregates -- list from selected vertices of aggregate unselected vertices
    489:    . bs - row block size
    490:    . nSAvec - column bs of new P
    491:    . my0crs - global index of start of locals
    492:    . data_stride - bs*(nloc nodes + ghost nodes) [data_stride][nSAvec]
    493:    . data_in[data_stride*nSAvec] - local data on fine grid
    494:    . flid_fgid[data_stride/bs] - make local to global IDs, includes ghosts in 'locals_llist'

    496:   Output Parameter:
    497:    . a_data_out - in with fine grid data (w/ghosts), out with coarse grid data
    498:    . a_Prol - prolongation operator
    499: */
    500: static PetscErrorCode formProl0(PetscCoarsenData *agg_llists, PetscInt bs, PetscInt nSAvec, PetscInt my0crs, PetscInt data_stride, PetscReal data_in[], const PetscInt flid_fgid[], PetscReal **a_data_out, Mat a_Prol)
    501: {
    502:   PetscInt        Istart, my0, Iend, nloc, clid, flid = 0, aggID, kk, jj, ii, mm, nSelected, minsz, nghosts, out_data_stride;
    503:   MPI_Comm        comm;
    504:   PetscReal      *out_data;
    505:   PetscCDIntNd   *pos;
    506:   PCGAMGHashTable fgid_flid;

    508:   PetscFunctionBegin;
    509:   PetscCall(PetscObjectGetComm((PetscObject)a_Prol, &comm));
    510:   PetscCall(MatGetOwnershipRange(a_Prol, &Istart, &Iend));
    511:   nloc = (Iend - Istart) / bs;
    512:   my0  = Istart / bs;
    513:   PetscCheck((Iend - Istart) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Iend %" PetscInt_FMT " - Istart %" PetscInt_FMT " must be divisible by bs %" PetscInt_FMT, Iend, Istart, bs);
    514:   Iend /= bs;
    515:   nghosts = data_stride / bs - nloc;

    517:   PetscCall(PCGAMGHashTableCreate(2 * nghosts + 1, &fgid_flid));
    518:   for (kk = 0; kk < nghosts; kk++) PetscCall(PCGAMGHashTableAdd(&fgid_flid, flid_fgid[nloc + kk], nloc + kk));

    520:   /* count selected -- same as number of cols of P */
    521:   for (nSelected = mm = 0; mm < nloc; mm++) {
    522:     PetscBool ise;

    524:     PetscCall(PetscCDIsEmptyAt(agg_llists, mm, &ise));
    525:     if (!ise) nSelected++;
    526:   }
    527:   PetscCall(MatGetOwnershipRangeColumn(a_Prol, &ii, &jj));
    528:   PetscCheck((ii / nSAvec) == my0crs, PETSC_COMM_SELF, PETSC_ERR_PLIB, "ii %" PetscInt_FMT " /nSAvec %" PetscInt_FMT "  != my0crs %" PetscInt_FMT, ii, nSAvec, my0crs);
    529:   PetscCheck(nSelected == (jj - ii) / nSAvec, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nSelected %" PetscInt_FMT " != (jj %" PetscInt_FMT " - ii %" PetscInt_FMT ")/nSAvec %" PetscInt_FMT, nSelected, jj, ii, nSAvec);

    531:   /* aloc space for coarse point data (output) */
    532:   out_data_stride = nSelected * nSAvec;

    534:   PetscCall(PetscMalloc1(out_data_stride * nSAvec, &out_data));
    535:   for (ii = 0; ii < out_data_stride * nSAvec; ii++) out_data[ii] = PETSC_MAX_REAL;
    536:   *a_data_out = out_data; /* output - stride nSelected*nSAvec */

    538:   /* find points and set prolongation */
    539:   minsz = 100;
    540:   for (mm = clid = 0; mm < nloc; mm++) {
    541:     PetscCall(PetscCDCountAt(agg_llists, mm, &jj));
    542:     if (jj > 0) {
    543:       const PetscInt lid = mm, cgid = my0crs + clid;
    544:       PetscInt       cids[100]; /* max bs */
    545:       PetscBLASInt   asz, M, N, INFO;
    546:       PetscBLASInt   Mdata, LDA, LWORK;
    547:       PetscScalar   *qqc, *qqr, *TAU, *WORK;
    548:       PetscInt      *fids;
    549:       PetscReal     *data;

    551:       PetscCall(PetscBLASIntCast(jj, &asz));
    552:       PetscCall(PetscBLASIntCast(asz * bs, &M));
    553:       PetscCall(PetscBLASIntCast(nSAvec, &N));
    554:       PetscCall(PetscBLASIntCast(M + ((N - M > 0) ? N - M : 0), &Mdata));
    555:       PetscCall(PetscBLASIntCast(Mdata, &LDA));
    556:       PetscCall(PetscBLASIntCast(N * bs, &LWORK));
    557:       /* count agg */
    558:       if (asz < minsz) minsz = asz;

    560:       /* get block */
    561:       PetscCall(PetscMalloc5(Mdata * N, &qqc, M * N, &qqr, N, &TAU, LWORK, &WORK, M, &fids));

    563:       aggID = 0;
    564:       PetscCall(PetscCDGetHeadPos(agg_llists, lid, &pos));
    565:       while (pos) {
    566:         PetscInt gid1;

    568:         PetscCall(PetscCDIntNdGetID(pos, &gid1));
    569:         PetscCall(PetscCDGetNextPos(agg_llists, lid, &pos));

    571:         if (gid1 >= my0 && gid1 < Iend) flid = gid1 - my0;
    572:         else {
    573:           PetscCall(PCGAMGHashTableFind(&fgid_flid, gid1, &flid));
    574:           PetscCheck(flid >= 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot find gid1 in table");
    575:         }
    576:         /* copy in B_i matrix - column-oriented */
    577:         data = &data_in[flid * bs];
    578:         for (ii = 0; ii < bs; ii++) {
    579:           for (jj = 0; jj < N; jj++) {
    580:             PetscReal d = data[jj * data_stride + ii];

    582:             qqc[jj * Mdata + aggID * bs + ii] = d;
    583:           }
    584:         }
    585:         /* set fine IDs */
    586:         for (kk = 0; kk < bs; kk++) fids[aggID * bs + kk] = flid_fgid[flid] * bs + kk;
    587:         aggID++;
    588:       }

    590:       /* pad with zeros */
    591:       for (ii = asz * bs; ii < Mdata; ii++) {
    592:         for (jj = 0; jj < N; jj++, kk++) qqc[jj * Mdata + ii] = .0;
    593:       }

    595:       /* QR */
    596:       PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF));
    597:       PetscCallBLAS("LAPACKgeqrf", LAPACKgeqrf_(&Mdata, &N, qqc, &LDA, TAU, WORK, &LWORK, &INFO));
    598:       PetscCall(PetscFPTrapPop());
    599:       PetscCheck(INFO == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "xGEQRF error");
    600:       /* get R - column-oriented - output B_{i+1} */
    601:       {
    602:         PetscReal *data = &out_data[clid * nSAvec];

    604:         for (jj = 0; jj < nSAvec; jj++) {
    605:           for (ii = 0; ii < nSAvec; ii++) {
    606:             PetscCheck(data[jj * out_data_stride + ii] == PETSC_MAX_REAL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "data[jj*out_data_stride + ii] != %e", (double)PETSC_MAX_REAL);
    607:             if (ii <= jj) data[jj * out_data_stride + ii] = PetscRealPart(qqc[jj * Mdata + ii]);
    608:             else data[jj * out_data_stride + ii] = 0.;
    609:           }
    610:         }
    611:       }

    613:       /* get Q - row-oriented */
    614:       PetscCallBLAS("LAPACKorgqr", LAPACKorgqr_(&Mdata, &N, &N, qqc, &LDA, TAU, WORK, &LWORK, &INFO));
    615:       PetscCheck(INFO == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "xORGQR error arg %" PetscBLASInt_FMT, -INFO);

    617:       for (ii = 0; ii < M; ii++) {
    618:         for (jj = 0; jj < N; jj++) qqr[N * ii + jj] = qqc[jj * Mdata + ii];
    619:       }

    621:       /* add diagonal block of P0 */
    622:       for (kk = 0; kk < N; kk++) { cids[kk] = N * cgid + kk; /* global col IDs in P0 */ }
    623:       PetscCall(MatSetValues(a_Prol, M, fids, N, cids, qqr, INSERT_VALUES));
    624:       PetscCall(PetscFree5(qqc, qqr, TAU, WORK, fids));
    625:       clid++;
    626:     } /* coarse agg */
    627:   } /* for all fine nodes */
    628:   PetscCall(MatAssemblyBegin(a_Prol, MAT_FINAL_ASSEMBLY));
    629:   PetscCall(MatAssemblyEnd(a_Prol, MAT_FINAL_ASSEMBLY));
    630:   PetscCall(PCGAMGHashTableDestroy(&fgid_flid));
    631:   PetscFunctionReturn(PETSC_SUCCESS);
    632: }

    634: static PetscErrorCode PCView_GAMG_AGG(PC pc, PetscViewer viewer)
    635: {
    636:   PC_MG       *mg          = (PC_MG *)pc->data;
    637:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    638:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

    640:   PetscFunctionBegin;
    641:   PetscCall(PetscViewerASCIIPrintf(viewer, "      AGG specific options\n"));
    642:   PetscCall(PetscViewerASCIIPrintf(viewer, "        Number of levels of aggressive coarsening %d\n", (int)pc_gamg_agg->aggressive_coarsening_levels));
    643:   if (pc_gamg_agg->aggressive_coarsening_levels > 0) {
    644:     PetscCall(PetscViewerASCIIPrintf(viewer, "        %s aggressive coarsening\n", !pc_gamg_agg->use_aggressive_square_graph ? "MIS-k" : "Square graph"));
    645:     if (!pc_gamg_agg->use_aggressive_square_graph) PetscCall(PetscViewerASCIIPrintf(viewer, "        MIS-%d coarsening on aggressive levels\n", (int)pc_gamg_agg->aggressive_mis_k));
    646:   }
    647:   PetscCall(PetscViewerASCIIPushTab(viewer));
    648:   PetscCall(PetscViewerASCIIPushTab(viewer));
    649:   PetscCall(PetscViewerASCIIPushTab(viewer));
    650:   PetscCall(PetscViewerASCIIPushTab(viewer));
    651:   if (pc_gamg_agg->crs) PetscCall(MatCoarsenView(pc_gamg_agg->crs, viewer));
    652:   else PetscCall(PetscViewerASCIIPrintf(viewer, "Coarsening algorithm not yet selected\n"));
    653:   PetscCall(PetscViewerASCIIPopTab(viewer));
    654:   PetscCall(PetscViewerASCIIPopTab(viewer));
    655:   PetscCall(PetscViewerASCIIPopTab(viewer));
    656:   PetscCall(PetscViewerASCIIPopTab(viewer));
    657:   PetscCall(PetscViewerASCIIPrintf(viewer, "        Number smoothing steps to construct prolongation %d\n", (int)pc_gamg_agg->nsmooths));
    658:   PetscFunctionReturn(PETSC_SUCCESS);
    659: }

    661: static PetscErrorCode PCGAMGCreateGraph_AGG(PC pc, Mat Amat, Mat *a_Gmat)
    662: {
    663:   PC_MG          *mg          = (PC_MG *)pc->data;
    664:   PC_GAMG        *pc_gamg     = (PC_GAMG *)mg->innerctx;
    665:   PC_GAMG_AGG    *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;
    666:   const PetscReal vfilter     = pc_gamg->threshold[pc_gamg->current_level];
    667:   PetscBool       ishem, ismis;
    668:   const char     *prefix;
    669:   MatInfo         info0, info1;
    670:   PetscInt        bs;

    672:   PetscFunctionBegin;
    673:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_COARSEN], 0, 0, 0, 0));
    674:   /* Note: depending on the algorithm that will be used for computing the coarse grid points this should pass PETSC_TRUE or PETSC_FALSE as the first argument */
    675:   /* MATCOARSENHEM requires numerical weights for edges so ensure they are computed */
    676:   PetscCall(MatCoarsenDestroy(&pc_gamg_agg->crs));
    677:   PetscCall(MatCoarsenCreate(PetscObjectComm((PetscObject)pc), &pc_gamg_agg->crs));
    678:   PetscCall(PetscObjectGetOptionsPrefix((PetscObject)pc, &prefix));
    679:   PetscCall(PetscObjectSetOptionsPrefix((PetscObject)pc_gamg_agg->crs, prefix));
    680:   PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)pc_gamg_agg->crs, "pc_gamg_"));
    681:   PetscCall(MatCoarsenSetFromOptions(pc_gamg_agg->crs));
    682:   PetscCall(MatGetBlockSize(Amat, &bs));
    683:   // check for valid indices wrt bs
    684:   for (int ii = 0; ii < pc_gamg_agg->crs->strength_index_size; ii++) {
    685:     PetscCheck(pc_gamg_agg->crs->strength_index[ii] >= 0 && pc_gamg_agg->crs->strength_index[ii] < bs, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONG, "Indices (%d) must be non-negative and < block size (%d), NB, can not use -mat_coarsen_strength_index with -mat_coarsen_strength_index",
    686:                (int)pc_gamg_agg->crs->strength_index[ii], (int)bs);
    687:   }
    688:   PetscCall(PetscObjectTypeCompare((PetscObject)pc_gamg_agg->crs, MATCOARSENHEM, &ishem));
    689:   if (ishem) {
    690:     if (pc_gamg_agg->aggressive_coarsening_levels) PetscCall(PetscInfo(pc, "HEM and aggressive coarsening ignored: HEM using %d iterations\n", (int)pc_gamg_agg->crs->max_it));
    691:     pc_gamg_agg->aggressive_coarsening_levels = 0;                                         // aggressive and HEM does not make sense
    692:     PetscCall(MatCoarsenSetMaximumIterations(pc_gamg_agg->crs, pc_gamg_agg->crs->max_it)); // for code coverage
    693:     PetscCall(MatCoarsenSetThreshold(pc_gamg_agg->crs, vfilter));                          // for code coverage
    694:   } else {
    695:     PetscCall(PetscObjectTypeCompare((PetscObject)pc_gamg_agg->crs, MATCOARSENMIS, &ismis));
    696:     if (ismis && pc_gamg_agg->aggressive_coarsening_levels && !pc_gamg_agg->use_aggressive_square_graph) {
    697:       PetscCall(PetscInfo(pc, "MIS and aggressive coarsening and no square graph: force square graph\n"));
    698:       pc_gamg_agg->use_aggressive_square_graph = PETSC_TRUE;
    699:     }
    700:   }
    701:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_COARSEN], 0, 0, 0, 0));
    702:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_GRAPH], 0, 0, 0, 0));
    703:   PetscCall(MatGetInfo(Amat, MAT_LOCAL, &info0)); /* global reduction */

    705:   if (ishem || pc_gamg_agg->use_low_mem_filter) {
    706:     PetscCall(MatCreateGraph(Amat, pc_gamg_agg->graph_symmetrize, (vfilter >= 0 || ishem) ? PETSC_TRUE : PETSC_FALSE, vfilter, pc_gamg_agg->crs->strength_index_size, pc_gamg_agg->crs->strength_index, a_Gmat));
    707:   } else {
    708:     // make scalar graph, symmetrize if not known to be symmetric, scale, but do not filter (expensive)
    709:     PetscCall(MatCreateGraph(Amat, pc_gamg_agg->graph_symmetrize, PETSC_TRUE, -1, pc_gamg_agg->crs->strength_index_size, pc_gamg_agg->crs->strength_index, a_Gmat));
    710:     if (vfilter >= 0) {
    711:       PetscInt           Istart, Iend, ncols, nnz0, nnz1, NN, MM, nloc;
    712:       Mat                tGmat, Gmat = *a_Gmat;
    713:       MPI_Comm           comm;
    714:       const PetscScalar *vals;
    715:       const PetscInt    *idx;
    716:       PetscInt          *d_nnz, *o_nnz, kk, *garray = NULL, *AJ, maxcols = 0;
    717:       MatScalar         *AA; // this is checked in graph
    718:       PetscBool          isseqaij;
    719:       Mat                a, b, c;
    720:       MatType            jtype;

    722:       PetscCall(PetscObjectGetComm((PetscObject)Gmat, &comm));
    723:       PetscCall(PetscObjectBaseTypeCompare((PetscObject)Gmat, MATSEQAIJ, &isseqaij));
    724:       PetscCall(MatGetType(Gmat, &jtype));
    725:       PetscCall(MatCreate(comm, &tGmat));
    726:       PetscCall(MatSetType(tGmat, jtype));

    728:       /* TODO GPU: this can be called when filter = 0 -> Probably provide MatAIJThresholdCompress that compresses the entries below a threshold?
    729:         Also, if the matrix is symmetric, can we skip this
    730:         operation? It can be very expensive on large matrices. */

    732:       // global sizes
    733:       PetscCall(MatGetSize(Gmat, &MM, &NN));
    734:       PetscCall(MatGetOwnershipRange(Gmat, &Istart, &Iend));
    735:       nloc = Iend - Istart;
    736:       PetscCall(PetscMalloc2(nloc, &d_nnz, nloc, &o_nnz));
    737:       if (isseqaij) {
    738:         a = Gmat;
    739:         b = NULL;
    740:       } else {
    741:         Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;

    743:         a      = d->A;
    744:         b      = d->B;
    745:         garray = d->garray;
    746:       }
    747:       /* Determine upper bound on non-zeros needed in new filtered matrix */
    748:       for (PetscInt row = 0; row < nloc; row++) {
    749:         PetscCall(MatGetRow(a, row, &ncols, NULL, NULL));
    750:         d_nnz[row] = ncols;
    751:         if (ncols > maxcols) maxcols = ncols;
    752:         PetscCall(MatRestoreRow(a, row, &ncols, NULL, NULL));
    753:       }
    754:       if (b) {
    755:         for (PetscInt row = 0; row < nloc; row++) {
    756:           PetscCall(MatGetRow(b, row, &ncols, NULL, NULL));
    757:           o_nnz[row] = ncols;
    758:           if (ncols > maxcols) maxcols = ncols;
    759:           PetscCall(MatRestoreRow(b, row, &ncols, NULL, NULL));
    760:         }
    761:       }
    762:       PetscCall(MatSetSizes(tGmat, nloc, nloc, MM, MM));
    763:       PetscCall(MatSetBlockSizes(tGmat, 1, 1));
    764:       PetscCall(MatSeqAIJSetPreallocation(tGmat, 0, d_nnz));
    765:       PetscCall(MatMPIAIJSetPreallocation(tGmat, 0, d_nnz, 0, o_nnz));
    766:       PetscCall(MatSetOption(tGmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
    767:       PetscCall(PetscFree2(d_nnz, o_nnz));
    768:       PetscCall(PetscMalloc2(maxcols, &AA, maxcols, &AJ));
    769:       nnz0 = nnz1 = 0;
    770:       for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
    771:         for (PetscInt row = 0, grow = Istart, ncol_row, jj; row < nloc; row++, grow++) {
    772:           PetscCall(MatGetRow(c, row, &ncols, &idx, &vals));
    773:           for (ncol_row = jj = 0; jj < ncols; jj++, nnz0++) {
    774:             PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
    775:             if (PetscRealPart(sv) > vfilter) {
    776:               PetscInt cid = idx[jj] + Istart; //diag

    778:               nnz1++;
    779:               if (c != a) cid = garray[idx[jj]];
    780:               AA[ncol_row] = vals[jj];
    781:               AJ[ncol_row] = cid;
    782:               ncol_row++;
    783:             }
    784:           }
    785:           PetscCall(MatRestoreRow(c, row, &ncols, &idx, &vals));
    786:           PetscCall(MatSetValues(tGmat, 1, &grow, ncol_row, AJ, AA, INSERT_VALUES));
    787:         }
    788:       }
    789:       PetscCall(PetscFree2(AA, AJ));
    790:       PetscCall(MatAssemblyBegin(tGmat, MAT_FINAL_ASSEMBLY));
    791:       PetscCall(MatAssemblyEnd(tGmat, MAT_FINAL_ASSEMBLY));
    792:       PetscCall(MatPropagateSymmetryOptions(Gmat, tGmat)); /* Normal Mat options are not relevant ? */
    793:       PetscCall(PetscInfo(pc, "\t %g%% nnz after filtering, with threshold %g, %g nnz ave. (N=%" PetscInt_FMT ", max row size %" PetscInt_FMT "\n", (!nnz0) ? 1. : 100. * (double)nnz1 / (double)nnz0, (double)vfilter, (!nloc) ? 1. : (double)nnz0 / (double)nloc, MM, maxcols));
    794:       PetscCall(MatViewFromOptions(tGmat, NULL, "-mat_filter_graph_view"));
    795:       PetscCall(MatDestroy(&Gmat));
    796:       *a_Gmat = tGmat;
    797:     }
    798:   }

    800:   PetscCall(MatGetInfo(*a_Gmat, MAT_LOCAL, &info1)); /* global reduction */
    801:   if (info0.nz_used > 0) PetscCall(PetscInfo(pc, "Filtering left %g %% edges in graph (%e %e)\n", 100.0 * info1.nz_used * (double)(bs * bs) / info0.nz_used, info0.nz_used, info1.nz_used));
    802:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_GRAPH], 0, 0, 0, 0));
    803:   PetscFunctionReturn(PETSC_SUCCESS);
    804: }

    806: typedef PetscInt    NState;
    807: static const NState NOT_DONE = -2;
    808: static const NState DELETED  = -1;
    809: static const NState REMOVED  = -3;
    810: #define IS_SELECTED(s) (s != DELETED && s != NOT_DONE && s != REMOVED)

    812: /*
    813:    fixAggregatesWithSquare - greedy grab of with G1 (unsquared graph) -- AIJ specific -- change to fixAggregatesWithSquare -- TODD
    814:      - AGG-MG specific: clears singletons out of 'selected_2'

    816:    Input Parameter:
    817:    . Gmat_2 - global matrix of squared graph (data not defined)
    818:    . Gmat_1 - base graph to grab with base graph
    819:    Input/Output Parameter:
    820:    . aggs_2 - linked list of aggs with gids)
    821: */
    822: static PetscErrorCode fixAggregatesWithSquare(PC pc, Mat Gmat_2, Mat Gmat_1, PetscCoarsenData *aggs_2)
    823: {
    824:   PetscBool      isMPI;
    825:   Mat_SeqAIJ    *matA_1, *matB_1 = NULL;
    826:   MPI_Comm       comm;
    827:   PetscInt       lid, *ii, *idx, ix, Iend, my0, kk, n, j;
    828:   Mat_MPIAIJ    *mpimat_2 = NULL, *mpimat_1 = NULL;
    829:   const PetscInt nloc = Gmat_2->rmap->n;
    830:   PetscScalar   *cpcol_1_state, *cpcol_2_state, *cpcol_2_par_orig, *lid_parent_gid;
    831:   PetscInt      *lid_cprowID_1 = NULL;
    832:   NState        *lid_state;
    833:   Vec            ghost_par_orig2;
    834:   PetscMPIInt    rank;

    836:   PetscFunctionBegin;
    837:   PetscCall(PetscObjectGetComm((PetscObject)Gmat_2, &comm));
    838:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
    839:   PetscCall(MatGetOwnershipRange(Gmat_1, &my0, &Iend));

    841:   /* get submatrices */
    842:   PetscCall(PetscStrbeginswith(((PetscObject)Gmat_1)->type_name, MATMPIAIJ, &isMPI));
    843:   PetscCall(PetscInfo(pc, "isMPI = %s\n", isMPI ? "yes" : "no"));
    844:   PetscCall(PetscMalloc3(nloc, &lid_state, nloc, &lid_parent_gid, nloc, &lid_cprowID_1));
    845:   for (lid = 0; lid < nloc; lid++) lid_cprowID_1[lid] = -1;
    846:   if (isMPI) {
    847:     /* grab matrix objects */
    848:     mpimat_2 = (Mat_MPIAIJ *)Gmat_2->data;
    849:     mpimat_1 = (Mat_MPIAIJ *)Gmat_1->data;
    850:     matA_1   = (Mat_SeqAIJ *)mpimat_1->A->data;
    851:     matB_1   = (Mat_SeqAIJ *)mpimat_1->B->data;

    853:     /* force compressed row storage for B matrix in AuxMat */
    854:     PetscCall(MatCheckCompressedRow(mpimat_1->B, matB_1->nonzerorowcnt, &matB_1->compressedrow, matB_1->i, Gmat_1->rmap->n, -1.0));
    855:     for (ix = 0; ix < matB_1->compressedrow.nrows; ix++) {
    856:       PetscInt lid = matB_1->compressedrow.rindex[ix];

    858:       PetscCheck(lid <= nloc && lid >= -1, PETSC_COMM_SELF, PETSC_ERR_USER, "lid %d out of range. nloc = %d", (int)lid, (int)nloc);
    859:       if (lid != -1) lid_cprowID_1[lid] = ix;
    860:     }
    861:   } else {
    862:     PetscBool isAIJ;

    864:     PetscCall(PetscStrbeginswith(((PetscObject)Gmat_1)->type_name, MATSEQAIJ, &isAIJ));
    865:     PetscCheck(isAIJ, PETSC_COMM_SELF, PETSC_ERR_USER, "Require AIJ matrix.");
    866:     matA_1 = (Mat_SeqAIJ *)Gmat_1->data;
    867:   }
    868:   if (nloc > 0) { PetscCheck(!matB_1 || matB_1->compressedrow.use, PETSC_COMM_SELF, PETSC_ERR_PLIB, "matB_1 && !matB_1->compressedrow.use: PETSc bug???"); }
    869:   /* get state of locals and selected gid for deleted */
    870:   for (lid = 0; lid < nloc; lid++) {
    871:     lid_parent_gid[lid] = -1.0;
    872:     lid_state[lid]      = DELETED;
    873:   }

    875:   /* set lid_state */
    876:   for (lid = 0; lid < nloc; lid++) {
    877:     PetscCDIntNd *pos;

    879:     PetscCall(PetscCDGetHeadPos(aggs_2, lid, &pos));
    880:     if (pos) {
    881:       PetscInt gid1;

    883:       PetscCall(PetscCDIntNdGetID(pos, &gid1));
    884:       PetscCheck(gid1 == lid + my0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "gid1 %d != lid %d + my0 %d", (int)gid1, (int)lid, (int)my0);
    885:       lid_state[lid] = gid1;
    886:     }
    887:   }

    889:   /* map local to selected local, DELETED means a ghost owns it */
    890:   for (lid = 0; lid < nloc; lid++) {
    891:     NState state = lid_state[lid];

    893:     if (IS_SELECTED(state)) {
    894:       PetscCDIntNd *pos;

    896:       PetscCall(PetscCDGetHeadPos(aggs_2, lid, &pos));
    897:       while (pos) {
    898:         PetscInt gid1;

    900:         PetscCall(PetscCDIntNdGetID(pos, &gid1));
    901:         PetscCall(PetscCDGetNextPos(aggs_2, lid, &pos));
    902:         if (gid1 >= my0 && gid1 < Iend) lid_parent_gid[gid1 - my0] = (PetscScalar)(lid + my0);
    903:       }
    904:     }
    905:   }
    906:   /* get 'cpcol_1/2_state' & cpcol_2_par_orig - uses mpimat_1/2->lvec for temp space */
    907:   if (isMPI) {
    908:     Vec tempVec;

    910:     /* get 'cpcol_1_state' */
    911:     PetscCall(MatCreateVecs(Gmat_1, &tempVec, NULL));
    912:     for (kk = 0, j = my0; kk < nloc; kk++, j++) {
    913:       PetscScalar v = (PetscScalar)lid_state[kk];

    915:       PetscCall(VecSetValues(tempVec, 1, &j, &v, INSERT_VALUES));
    916:     }
    917:     PetscCall(VecAssemblyBegin(tempVec));
    918:     PetscCall(VecAssemblyEnd(tempVec));
    919:     PetscCall(VecScatterBegin(mpimat_1->Mvctx, tempVec, mpimat_1->lvec, INSERT_VALUES, SCATTER_FORWARD));
    920:     PetscCall(VecScatterEnd(mpimat_1->Mvctx, tempVec, mpimat_1->lvec, INSERT_VALUES, SCATTER_FORWARD));
    921:     PetscCall(VecGetArray(mpimat_1->lvec, &cpcol_1_state));
    922:     /* get 'cpcol_2_state' */
    923:     PetscCall(VecScatterBegin(mpimat_2->Mvctx, tempVec, mpimat_2->lvec, INSERT_VALUES, SCATTER_FORWARD));
    924:     PetscCall(VecScatterEnd(mpimat_2->Mvctx, tempVec, mpimat_2->lvec, INSERT_VALUES, SCATTER_FORWARD));
    925:     PetscCall(VecGetArray(mpimat_2->lvec, &cpcol_2_state));
    926:     /* get 'cpcol_2_par_orig' */
    927:     for (kk = 0, j = my0; kk < nloc; kk++, j++) {
    928:       PetscScalar v = (PetscScalar)lid_parent_gid[kk];

    930:       PetscCall(VecSetValues(tempVec, 1, &j, &v, INSERT_VALUES));
    931:     }
    932:     PetscCall(VecAssemblyBegin(tempVec));
    933:     PetscCall(VecAssemblyEnd(tempVec));
    934:     PetscCall(VecDuplicate(mpimat_2->lvec, &ghost_par_orig2));
    935:     PetscCall(VecScatterBegin(mpimat_2->Mvctx, tempVec, ghost_par_orig2, INSERT_VALUES, SCATTER_FORWARD));
    936:     PetscCall(VecScatterEnd(mpimat_2->Mvctx, tempVec, ghost_par_orig2, INSERT_VALUES, SCATTER_FORWARD));
    937:     PetscCall(VecGetArray(ghost_par_orig2, &cpcol_2_par_orig));

    939:     PetscCall(VecDestroy(&tempVec));
    940:   } /* ismpi */
    941:   for (lid = 0; lid < nloc; lid++) {
    942:     NState state = lid_state[lid];

    944:     if (IS_SELECTED(state)) {
    945:       /* steal locals */
    946:       ii  = matA_1->i;
    947:       n   = ii[lid + 1] - ii[lid];
    948:       idx = matA_1->j + ii[lid];
    949:       for (j = 0; j < n; j++) {
    950:         PetscInt lidj   = idx[j], sgid;
    951:         NState   statej = lid_state[lidj];

    953:         if (statej == DELETED && (sgid = (PetscInt)PetscRealPart(lid_parent_gid[lidj])) != lid + my0) { /* steal local */
    954:           lid_parent_gid[lidj] = (PetscScalar)(lid + my0);                                              /* send this if sgid is not local */
    955:           if (sgid >= my0 && sgid < Iend) {                                                             /* I'm stealing this local from a local sgid */
    956:             PetscInt      hav = 0, slid = sgid - my0, gidj = lidj + my0;
    957:             PetscCDIntNd *pos, *last = NULL;

    959:             /* looking for local from local so id_llist_2 works */
    960:             PetscCall(PetscCDGetHeadPos(aggs_2, slid, &pos));
    961:             while (pos) {
    962:               PetscInt gid;

    964:               PetscCall(PetscCDIntNdGetID(pos, &gid));
    965:               if (gid == gidj) {
    966:                 PetscCheck(last, PETSC_COMM_SELF, PETSC_ERR_PLIB, "last cannot be null");
    967:                 PetscCall(PetscCDRemoveNextNode(aggs_2, slid, last));
    968:                 PetscCall(PetscCDAppendNode(aggs_2, lid, pos));
    969:                 hav = 1;
    970:                 break;
    971:               } else last = pos;
    972:               PetscCall(PetscCDGetNextPos(aggs_2, slid, &pos));
    973:             }
    974:             if (hav != 1) {
    975:               PetscCheck(hav, PETSC_COMM_SELF, PETSC_ERR_PLIB, "failed to find adj in 'selected' lists - structurally unsymmetric matrix");
    976:               SETERRQ(PETSC_COMM_SELF, PETSC_ERR_PLIB, "found node %d times???", (int)hav);
    977:             }
    978:           } else { /* I'm stealing this local, owned by a ghost */
    979:             PetscCheck(sgid == -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Mat has an un-symmetric graph. Use '-%spc_gamg_sym_graph true' to symmetrize the graph or '-%spc_gamg_threshold -1' if the matrix is structurally symmetric.",
    980:                        ((PetscObject)pc)->prefix ? ((PetscObject)pc)->prefix : "", ((PetscObject)pc)->prefix ? ((PetscObject)pc)->prefix : "");
    981:             PetscCall(PetscCDAppendID(aggs_2, lid, lidj + my0));
    982:           }
    983:         }
    984:       } /* local neighbors */
    985:     } else if (state == DELETED /* && lid_cprowID_1 */) {
    986:       PetscInt sgidold = (PetscInt)PetscRealPart(lid_parent_gid[lid]);

    988:       /* see if I have a selected ghost neighbor that will steal me */
    989:       if ((ix = lid_cprowID_1[lid]) != -1) {
    990:         ii  = matB_1->compressedrow.i;
    991:         n   = ii[ix + 1] - ii[ix];
    992:         idx = matB_1->j + ii[ix];
    993:         for (j = 0; j < n; j++) {
    994:           PetscInt cpid   = idx[j];
    995:           NState   statej = (NState)PetscRealPart(cpcol_1_state[cpid]);

    997:           if (IS_SELECTED(statej) && sgidold != (PetscInt)statej) { /* ghost will steal this, remove from my list */
    998:             lid_parent_gid[lid] = (PetscScalar)statej;              /* send who selected */
    999:             if (sgidold >= my0 && sgidold < Iend) {                 /* this was mine */
    1000:               PetscInt      hav = 0, oldslidj = sgidold - my0;
    1001:               PetscCDIntNd *pos, *last        = NULL;

    1003:               /* remove from 'oldslidj' list */
    1004:               PetscCall(PetscCDGetHeadPos(aggs_2, oldslidj, &pos));
    1005:               while (pos) {
    1006:                 PetscInt gid;

    1008:                 PetscCall(PetscCDIntNdGetID(pos, &gid));
    1009:                 if (lid + my0 == gid) {
    1010:                   /* id_llist_2[lastid] = id_llist_2[flid];   /\* remove lid from oldslidj list *\/ */
    1011:                   PetscCheck(last, PETSC_COMM_SELF, PETSC_ERR_PLIB, "last cannot be null");
    1012:                   PetscCall(PetscCDRemoveNextNode(aggs_2, oldslidj, last));
    1013:                   /* ghost (PetscScalar)statej will add this later */
    1014:                   hav = 1;
    1015:                   break;
    1016:                 } else last = pos;
    1017:                 PetscCall(PetscCDGetNextPos(aggs_2, oldslidj, &pos));
    1018:               }
    1019:               if (hav != 1) {
    1020:                 PetscCheck(hav, PETSC_COMM_SELF, PETSC_ERR_PLIB, "failed to find (hav=%d) adj in 'selected' lists - structurally unsymmetric matrix", (int)hav);
    1021:                 SETERRQ(PETSC_COMM_SELF, PETSC_ERR_PLIB, "found node %d times???", (int)hav);
    1022:               }
    1023:             } else {
    1024:               /* TODO: ghosts remove this later */
    1025:             }
    1026:           }
    1027:         }
    1028:       }
    1029:     } /* selected/deleted */
    1030:   } /* node loop */

    1032:   if (isMPI) {
    1033:     PetscScalar    *cpcol_2_parent, *cpcol_2_gid;
    1034:     Vec             tempVec, ghostgids2, ghostparents2;
    1035:     PetscInt        cpid, nghost_2;
    1036:     PCGAMGHashTable gid_cpid;

    1038:     PetscCall(VecGetSize(mpimat_2->lvec, &nghost_2));
    1039:     PetscCall(MatCreateVecs(Gmat_2, &tempVec, NULL));

    1041:     /* get 'cpcol_2_parent' */
    1042:     for (kk = 0, j = my0; kk < nloc; kk++, j++) { PetscCall(VecSetValues(tempVec, 1, &j, &lid_parent_gid[kk], INSERT_VALUES)); }
    1043:     PetscCall(VecAssemblyBegin(tempVec));
    1044:     PetscCall(VecAssemblyEnd(tempVec));
    1045:     PetscCall(VecDuplicate(mpimat_2->lvec, &ghostparents2));
    1046:     PetscCall(VecScatterBegin(mpimat_2->Mvctx, tempVec, ghostparents2, INSERT_VALUES, SCATTER_FORWARD));
    1047:     PetscCall(VecScatterEnd(mpimat_2->Mvctx, tempVec, ghostparents2, INSERT_VALUES, SCATTER_FORWARD));
    1048:     PetscCall(VecGetArray(ghostparents2, &cpcol_2_parent));

    1050:     /* get 'cpcol_2_gid' */
    1051:     for (kk = 0, j = my0; kk < nloc; kk++, j++) {
    1052:       PetscScalar v = (PetscScalar)j;

    1054:       PetscCall(VecSetValues(tempVec, 1, &j, &v, INSERT_VALUES));
    1055:     }
    1056:     PetscCall(VecAssemblyBegin(tempVec));
    1057:     PetscCall(VecAssemblyEnd(tempVec));
    1058:     PetscCall(VecDuplicate(mpimat_2->lvec, &ghostgids2));
    1059:     PetscCall(VecScatterBegin(mpimat_2->Mvctx, tempVec, ghostgids2, INSERT_VALUES, SCATTER_FORWARD));
    1060:     PetscCall(VecScatterEnd(mpimat_2->Mvctx, tempVec, ghostgids2, INSERT_VALUES, SCATTER_FORWARD));
    1061:     PetscCall(VecGetArray(ghostgids2, &cpcol_2_gid));
    1062:     PetscCall(VecDestroy(&tempVec));

    1064:     /* look for deleted ghosts and add to table */
    1065:     PetscCall(PCGAMGHashTableCreate(2 * nghost_2 + 1, &gid_cpid));
    1066:     for (cpid = 0; cpid < nghost_2; cpid++) {
    1067:       NState state = (NState)PetscRealPart(cpcol_2_state[cpid]);

    1069:       if (state == DELETED) {
    1070:         PetscInt sgid_new = (PetscInt)PetscRealPart(cpcol_2_parent[cpid]);
    1071:         PetscInt sgid_old = (PetscInt)PetscRealPart(cpcol_2_par_orig[cpid]);

    1073:         if (sgid_old == -1 && sgid_new != -1) {
    1074:           PetscInt gid = (PetscInt)PetscRealPart(cpcol_2_gid[cpid]);

    1076:           PetscCall(PCGAMGHashTableAdd(&gid_cpid, gid, cpid));
    1077:         }
    1078:       }
    1079:     }

    1081:     /* look for deleted ghosts and see if they moved - remove it */
    1082:     for (lid = 0; lid < nloc; lid++) {
    1083:       NState state = lid_state[lid];

    1085:       if (IS_SELECTED(state)) {
    1086:         PetscCDIntNd *pos, *last = NULL;

    1088:         /* look for deleted ghosts and see if they moved */
    1089:         PetscCall(PetscCDGetHeadPos(aggs_2, lid, &pos));
    1090:         while (pos) {
    1091:           PetscInt gid;

    1093:           PetscCall(PetscCDIntNdGetID(pos, &gid));
    1094:           if (gid < my0 || gid >= Iend) {
    1095:             PetscCall(PCGAMGHashTableFind(&gid_cpid, gid, &cpid));
    1096:             if (cpid != -1) {
    1097:               /* a moved ghost - */
    1098:               /* id_llist_2[lastid] = id_llist_2[flid];    /\* remove 'flid' from list *\/ */
    1099:               PetscCall(PetscCDRemoveNextNode(aggs_2, lid, last));
    1100:             } else last = pos;
    1101:           } else last = pos;

    1103:           PetscCall(PetscCDGetNextPos(aggs_2, lid, &pos));
    1104:         } /* loop over list of deleted */
    1105:       } /* selected */
    1106:     }
    1107:     PetscCall(PCGAMGHashTableDestroy(&gid_cpid));

    1109:     /* look at ghosts, see if they changed - and it */
    1110:     for (cpid = 0; cpid < nghost_2; cpid++) {
    1111:       PetscInt sgid_new = (PetscInt)PetscRealPart(cpcol_2_parent[cpid]);

    1113:       if (sgid_new >= my0 && sgid_new < Iend) { /* this is mine */
    1114:         PetscInt      gid      = (PetscInt)PetscRealPart(cpcol_2_gid[cpid]);
    1115:         PetscInt      slid_new = sgid_new - my0, hav = 0;
    1116:         PetscCDIntNd *pos;

    1118:         /* search for this gid to see if I have it */
    1119:         PetscCall(PetscCDGetHeadPos(aggs_2, slid_new, &pos));
    1120:         while (pos) {
    1121:           PetscInt gidj;

    1123:           PetscCall(PetscCDIntNdGetID(pos, &gidj));
    1124:           PetscCall(PetscCDGetNextPos(aggs_2, slid_new, &pos));

    1126:           if (gidj == gid) {
    1127:             hav = 1;
    1128:             break;
    1129:           }
    1130:         }
    1131:         if (hav != 1) {
    1132:           /* insert 'flidj' into head of llist */
    1133:           PetscCall(PetscCDAppendID(aggs_2, slid_new, gid));
    1134:         }
    1135:       }
    1136:     }
    1137:     PetscCall(VecRestoreArray(mpimat_1->lvec, &cpcol_1_state));
    1138:     PetscCall(VecRestoreArray(mpimat_2->lvec, &cpcol_2_state));
    1139:     PetscCall(VecRestoreArray(ghostparents2, &cpcol_2_parent));
    1140:     PetscCall(VecRestoreArray(ghostgids2, &cpcol_2_gid));
    1141:     PetscCall(VecDestroy(&ghostgids2));
    1142:     PetscCall(VecDestroy(&ghostparents2));
    1143:     PetscCall(VecDestroy(&ghost_par_orig2));
    1144:   }
    1145:   PetscCall(PetscFree3(lid_state, lid_parent_gid, lid_cprowID_1));
    1146:   PetscFunctionReturn(PETSC_SUCCESS);
    1147: }

    1149: /*
    1150:    PCGAMGCoarsen_AGG - supports squaring the graph (deprecated) and new graph for
    1151:      communication of QR data used with HEM and MISk coarsening

    1153:   Input Parameter:
    1154:    . a_pc - this

    1156:   Input/Output Parameter:
    1157:    . a_Gmat1 - graph to coarsen (in), graph off processor edges for QR gather scatter (out)

    1159:   Output Parameter:
    1160:    . agg_lists - list of aggregates

    1162: */
    1163: static PetscErrorCode PCGAMGCoarsen_AGG(PC a_pc, Mat *a_Gmat1, PetscCoarsenData **agg_lists)
    1164: {
    1165:   PC_MG       *mg          = (PC_MG *)a_pc->data;
    1166:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    1167:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;
    1168:   Mat          Gmat2, Gmat1 = *a_Gmat1; /* aggressive graph */
    1169:   IS           perm;
    1170:   PetscInt     Istart, Iend, Ii, nloc, bs, nn;
    1171:   PetscInt    *permute, *degree;
    1172:   PetscBool   *bIndexSet;
    1173:   PetscReal    hashfact;
    1174:   PetscInt     iSwapIndex;
    1175:   PetscRandom  random;
    1176:   MPI_Comm     comm;

    1178:   PetscFunctionBegin;
    1179:   PetscCall(PetscObjectGetComm((PetscObject)Gmat1, &comm));
    1180:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_COARSEN], 0, 0, 0, 0));
    1181:   PetscCall(MatGetLocalSize(Gmat1, &nn, NULL));
    1182:   PetscCall(MatGetBlockSize(Gmat1, &bs));
    1183:   PetscCheck(bs == 1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "bs %" PetscInt_FMT " must be 1", bs);
    1184:   nloc = nn / bs;
    1185:   /* get MIS aggs - randomize */
    1186:   PetscCall(PetscMalloc2(nloc, &permute, nloc, &degree));
    1187:   PetscCall(PetscCalloc1(nloc, &bIndexSet));
    1188:   for (Ii = 0; Ii < nloc; Ii++) permute[Ii] = Ii;
    1189:   PetscCall(PetscRandomCreate(PETSC_COMM_SELF, &random));
    1190:   PetscCall(MatGetOwnershipRange(Gmat1, &Istart, &Iend));
    1191:   for (Ii = 0; Ii < nloc; Ii++) {
    1192:     PetscInt nc;

    1194:     PetscCall(MatGetRow(Gmat1, Istart + Ii, &nc, NULL, NULL));
    1195:     degree[Ii] = nc;
    1196:     PetscCall(MatRestoreRow(Gmat1, Istart + Ii, &nc, NULL, NULL));
    1197:   }
    1198:   for (Ii = 0; Ii < nloc; Ii++) {
    1199:     PetscCall(PetscRandomGetValueReal(random, &hashfact));
    1200:     iSwapIndex = (PetscInt)(hashfact * nloc) % nloc;
    1201:     if (!bIndexSet[iSwapIndex] && iSwapIndex != Ii) {
    1202:       PetscInt iTemp = permute[iSwapIndex];

    1204:       permute[iSwapIndex]   = permute[Ii];
    1205:       permute[Ii]           = iTemp;
    1206:       iTemp                 = degree[iSwapIndex];
    1207:       degree[iSwapIndex]    = degree[Ii];
    1208:       degree[Ii]            = iTemp;
    1209:       bIndexSet[iSwapIndex] = PETSC_TRUE;
    1210:     }
    1211:   }
    1212:   // apply minimum degree ordering -- NEW
    1213:   if (pc_gamg_agg->use_minimum_degree_ordering) { PetscCall(PetscSortIntWithArray(nloc, degree, permute)); }
    1214:   PetscCall(PetscFree(bIndexSet));
    1215:   PetscCall(PetscRandomDestroy(&random));
    1216:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nloc, permute, PETSC_USE_POINTER, &perm));
    1217:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_MIS], 0, 0, 0, 0));
    1218:   // square graph
    1219:   if (pc_gamg->current_level < pc_gamg_agg->aggressive_coarsening_levels && pc_gamg_agg->use_aggressive_square_graph) {
    1220:     PetscCall(PCGAMGSquareGraph_GAMG(a_pc, Gmat1, &Gmat2));
    1221:   } else Gmat2 = Gmat1;
    1222:   // switch to old MIS-1 for square graph
    1223:   if (pc_gamg->current_level < pc_gamg_agg->aggressive_coarsening_levels) {
    1224:     if (!pc_gamg_agg->use_aggressive_square_graph) PetscCall(MatCoarsenMISKSetDistance(pc_gamg_agg->crs, pc_gamg_agg->aggressive_mis_k)); // hardwire to MIS-2
    1225:     else PetscCall(MatCoarsenSetType(pc_gamg_agg->crs, MATCOARSENMIS));                                                                   // old MIS -- side effect
    1226:   } else if (pc_gamg_agg->use_aggressive_square_graph && pc_gamg_agg->aggressive_coarsening_levels > 0) {                                 // we reset the MIS
    1227:     const char *prefix;

    1229:     PetscCall(PetscObjectGetOptionsPrefix((PetscObject)a_pc, &prefix));
    1230:     PetscCall(PetscObjectSetOptionsPrefix((PetscObject)pc_gamg_agg->crs, prefix));
    1231:     PetscCall(MatCoarsenSetFromOptions(pc_gamg_agg->crs)); // get the default back on non-aggressive levels when square graph switched to old MIS
    1232:   }
    1233:   PetscCall(MatCoarsenSetAdjacency(pc_gamg_agg->crs, Gmat2));
    1234:   PetscCall(MatCoarsenSetStrictAggs(pc_gamg_agg->crs, PETSC_TRUE));
    1235:   PetscCall(MatCoarsenSetGreedyOrdering(pc_gamg_agg->crs, perm));
    1236:   PetscCall(MatCoarsenApply(pc_gamg_agg->crs));
    1237:   PetscCall(MatCoarsenGetData(pc_gamg_agg->crs, agg_lists)); /* output */

    1239:   PetscCall(ISDestroy(&perm));
    1240:   PetscCall(PetscFree2(permute, degree));
    1241:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_MIS], 0, 0, 0, 0));

    1243:   if (Gmat2 != Gmat1) { // square graph, we need ghosts for selected
    1244:     PetscCoarsenData *llist = *agg_lists;

    1246:     PetscCall(fixAggregatesWithSquare(a_pc, Gmat2, Gmat1, *agg_lists));
    1247:     PetscCall(MatDestroy(&Gmat1));
    1248:     *a_Gmat1 = Gmat2;                          /* output */
    1249:     PetscCall(PetscCDSetMat(llist, *a_Gmat1)); /* Need a graph with ghosts here */
    1250:   }
    1251:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_COARSEN], 0, 0, 0, 0));
    1252:   PetscFunctionReturn(PETSC_SUCCESS);
    1253: }

    1255: /*
    1256:  PCGAMGConstructProlongator_AGG

    1258:  Input Parameter:
    1259:  . pc - this
    1260:  . Amat - matrix on this fine level
    1261:  . Graph - used to get ghost data for nodes in
    1262:  . agg_lists - list of aggregates
    1263:  Output Parameter:
    1264:  . a_P_out - prolongation operator to the next level
    1265:  */
    1266: static PetscErrorCode PCGAMGConstructProlongator_AGG(PC pc, Mat Amat, PetscCoarsenData *agg_lists, Mat *a_P_out)
    1267: {
    1268:   PC_MG         *mg      = (PC_MG *)pc->data;
    1269:   PC_GAMG       *pc_gamg = (PC_GAMG *)mg->innerctx;
    1270:   const PetscInt col_bs  = pc_gamg->data_cell_cols;
    1271:   PetscInt       Istart, Iend, nloc, ii, jj, kk, my0, nLocalSelected, bs;
    1272:   Mat            Gmat, Prol;
    1273:   PetscMPIInt    size;
    1274:   MPI_Comm       comm;
    1275:   PetscReal     *data_w_ghost;
    1276:   PetscInt       myCrs0, nbnodes = 0, *flid_fgid;
    1277:   MatType        mtype;

    1279:   PetscFunctionBegin;
    1280:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
    1281:   PetscCheck(col_bs >= 1, comm, PETSC_ERR_PLIB, "Column bs cannot be less than 1");
    1282:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_PROL], 0, 0, 0, 0));
    1283:   PetscCallMPI(MPI_Comm_size(comm, &size));
    1284:   PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
    1285:   PetscCall(MatGetBlockSize(Amat, &bs));
    1286:   nloc = (Iend - Istart) / bs;
    1287:   my0  = Istart / bs;
    1288:   PetscCheck((Iend - Istart) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "(Iend %" PetscInt_FMT " - Istart %" PetscInt_FMT ") not divisible by bs %" PetscInt_FMT, Iend, Istart, bs);
    1289:   PetscCall(PetscCDGetMat(agg_lists, &Gmat)); // get auxiliary matrix for ghost edges for size > 1

    1291:   /* get 'nLocalSelected' */
    1292:   for (ii = 0, nLocalSelected = 0; ii < nloc; ii++) {
    1293:     PetscBool ise;

    1295:     /* filter out singletons 0 or 1? */
    1296:     PetscCall(PetscCDIsEmptyAt(agg_lists, ii, &ise));
    1297:     if (!ise) nLocalSelected++;
    1298:   }

    1300:   /* create prolongator, create P matrix */
    1301:   PetscCall(MatGetType(Amat, &mtype));
    1302:   PetscCall(MatCreate(comm, &Prol));
    1303:   PetscCall(MatSetSizes(Prol, nloc * bs, nLocalSelected * col_bs, PETSC_DETERMINE, PETSC_DETERMINE));
    1304:   PetscCall(MatSetBlockSizes(Prol, bs, col_bs)); // should this be before MatSetSizes?
    1305:   PetscCall(MatSetType(Prol, mtype));
    1306: #if PetscDefined(HAVE_DEVICE)
    1307:   PetscBool flg;
    1308:   PetscCall(MatBoundToCPU(Amat, &flg));
    1309:   PetscCall(MatBindToCPU(Prol, flg));
    1310:   if (flg) PetscCall(MatSetBindingPropagates(Prol, PETSC_TRUE));
    1311: #endif
    1312:   PetscCall(MatSeqAIJSetPreallocation(Prol, col_bs, NULL));
    1313:   PetscCall(MatMPIAIJSetPreallocation(Prol, col_bs, NULL, col_bs, NULL));

    1315:   /* can get all points "removed" */
    1316:   PetscCall(MatGetSize(Prol, &kk, &ii));
    1317:   if (!ii) {
    1318:     PetscCall(PetscInfo(pc, "%s: No selected points on coarse grid\n", ((PetscObject)pc)->prefix));
    1319:     PetscCall(MatDestroy(&Prol));
    1320:     *a_P_out = NULL; /* out */
    1321:     PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_PROL], 0, 0, 0, 0));
    1322:     PetscFunctionReturn(PETSC_SUCCESS);
    1323:   }
    1324:   PetscCall(PetscInfo(pc, "%s: New grid %" PetscInt_FMT " nodes\n", ((PetscObject)pc)->prefix, ii / col_bs));
    1325:   PetscCall(MatGetOwnershipRangeColumn(Prol, &myCrs0, &kk));

    1327:   PetscCheck((kk - myCrs0) % col_bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "(kk %" PetscInt_FMT " -myCrs0 %" PetscInt_FMT ") not divisible by col_bs %" PetscInt_FMT, kk, myCrs0, col_bs);
    1328:   myCrs0 = myCrs0 / col_bs;
    1329:   PetscCheck((kk / col_bs - myCrs0) == nLocalSelected, PETSC_COMM_SELF, PETSC_ERR_PLIB, "(kk %" PetscInt_FMT "/col_bs %" PetscInt_FMT " - myCrs0 %" PetscInt_FMT ") != nLocalSelected %" PetscInt_FMT ")", kk, col_bs, myCrs0, nLocalSelected);

    1331:   /* create global vector of data in 'data_w_ghost' */
    1332:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_PROLA], 0, 0, 0, 0));
    1333:   if (size > 1) { /* get ghost null space data */
    1334:     PetscReal *tmp_gdata, *tmp_ldata, *tp2;

    1336:     PetscCall(PetscMalloc1(nloc, &tmp_ldata));
    1337:     for (jj = 0; jj < col_bs; jj++) {
    1338:       for (kk = 0; kk < bs; kk++) {
    1339:         PetscInt         ii, stride;
    1340:         const PetscReal *tp = PetscSafePointerPlusOffset(pc_gamg->data, jj * bs * nloc + kk);

    1342:         for (ii = 0; ii < nloc; ii++, tp += bs) tmp_ldata[ii] = *tp;

    1344:         PetscCall(PCGAMGGetDataWithGhosts(Gmat, 1, tmp_ldata, &stride, &tmp_gdata));

    1346:         if (!jj && !kk) { /* now I know how many total nodes - allocate TODO: move below and do in one 'col_bs' call */
    1347:           PetscCall(PetscMalloc1(stride * bs * col_bs, &data_w_ghost));
    1348:           nbnodes = bs * stride;
    1349:         }
    1350:         tp2 = PetscSafePointerPlusOffset(data_w_ghost, jj * bs * stride + kk);
    1351:         for (ii = 0; ii < stride; ii++, tp2 += bs) *tp2 = tmp_gdata[ii];
    1352:         PetscCall(PetscFree(tmp_gdata));
    1353:       }
    1354:     }
    1355:     PetscCall(PetscFree(tmp_ldata));
    1356:   } else {
    1357:     nbnodes      = bs * nloc;
    1358:     data_w_ghost = (PetscReal *)pc_gamg->data;
    1359:   }

    1361:   /* get 'flid_fgid' TODO - move up to get 'stride' and do get null space data above in one step (jj loop) */
    1362:   if (size > 1) {
    1363:     PetscReal *fid_glid_loc, *fiddata;
    1364:     PetscInt   stride;

    1366:     PetscCall(PetscMalloc1(nloc, &fid_glid_loc));
    1367:     for (kk = 0; kk < nloc; kk++) fid_glid_loc[kk] = (PetscReal)(my0 + kk);
    1368:     PetscCall(PCGAMGGetDataWithGhosts(Gmat, 1, fid_glid_loc, &stride, &fiddata));
    1369:     PetscCall(PetscMalloc1(stride, &flid_fgid)); /* copy real data to in */
    1370:     for (kk = 0; kk < stride; kk++) flid_fgid[kk] = (PetscInt)fiddata[kk];
    1371:     PetscCall(PetscFree(fiddata));

    1373:     PetscCheck(stride == nbnodes / bs, PETSC_COMM_SELF, PETSC_ERR_PLIB, "stride %" PetscInt_FMT " != nbnodes %" PetscInt_FMT "/bs %" PetscInt_FMT, stride, nbnodes, bs);
    1374:     PetscCall(PetscFree(fid_glid_loc));
    1375:   } else {
    1376:     PetscCall(PetscMalloc1(nloc, &flid_fgid));
    1377:     for (kk = 0; kk < nloc; kk++) flid_fgid[kk] = my0 + kk;
    1378:   }
    1379:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_PROLA], 0, 0, 0, 0));
    1380:   /* get P0 */
    1381:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_PROLB], 0, 0, 0, 0));
    1382:   {
    1383:     PetscReal *data_out = NULL;

    1385:     PetscCall(formProl0(agg_lists, bs, col_bs, myCrs0, nbnodes, data_w_ghost, flid_fgid, &data_out, Prol));
    1386:     PetscCall(PetscFree(pc_gamg->data));

    1388:     pc_gamg->data           = data_out;
    1389:     pc_gamg->data_cell_rows = col_bs;
    1390:     pc_gamg->data_sz        = col_bs * col_bs * nLocalSelected;
    1391:   }
    1392:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_PROLB], 0, 0, 0, 0));
    1393:   if (size > 1) PetscCall(PetscFree(data_w_ghost));
    1394:   PetscCall(PetscFree(flid_fgid));

    1396:   *a_P_out = Prol; /* out */
    1397:   PetscCall(MatViewFromOptions(Prol, NULL, "-pc_gamg_agg_view_initial_prolongation"));

    1399:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_PROL], 0, 0, 0, 0));
    1400:   PetscFunctionReturn(PETSC_SUCCESS);
    1401: }

    1403: /*
    1404:    PCGAMGOptimizeProlongator_AGG - given the initial prolongator optimizes it by smoothed aggregation pc_gamg_agg->nsmooths times

    1406:   Input Parameter:
    1407:    . pc - this
    1408:    . Amat - matrix on this fine level
    1409:  In/Output Parameter:
    1410:    . a_P - prolongation operator to the next level
    1411: */
    1412: static PetscErrorCode PCGAMGOptimizeProlongator_AGG(PC pc, Mat Amat, Mat *a_P)
    1413: {
    1414:   PC_MG       *mg          = (PC_MG *)pc->data;
    1415:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
    1416:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;
    1417:   PetscInt     jj;
    1418:   Mat          Prol = *a_P;
    1419:   MPI_Comm     comm;
    1420:   KSP          eksp;
    1421:   Vec          bb, xx;
    1422:   PC           epc;
    1423:   PetscReal    alpha, emax, emin;

    1425:   PetscFunctionBegin;
    1426:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
    1427:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_OPT], 0, 0, 0, 0));

    1429:   /* compute maximum singular value of operator to be used in smoother */
    1430:   if (0 < pc_gamg_agg->nsmooths) {
    1431:     /* get eigen estimates */
    1432:     if (pc_gamg->emax > 0) {
    1433:       emin = pc_gamg->emin;
    1434:       emax = pc_gamg->emax;
    1435:     } else {
    1436:       const char *prefix;

    1438:       PetscCall(MatCreateVecs(Amat, &bb, NULL));
    1439:       PetscCall(MatCreateVecs(Amat, &xx, NULL));
    1440:       PetscCall(KSPSetNoisy_Private(bb));

    1442:       PetscCall(KSPCreate(comm, &eksp));
    1443:       PetscCall(KSPSetNestLevel(eksp, pc->kspnestlevel));
    1444:       PetscCall(PCGetOptionsPrefix(pc, &prefix));
    1445:       PetscCall(KSPSetOptionsPrefix(eksp, prefix));
    1446:       PetscCall(KSPAppendOptionsPrefix(eksp, "pc_gamg_esteig_"));
    1447:       {
    1448:         PetscBool isset, sflg;

    1450:         PetscCall(MatIsSPDKnown(Amat, &isset, &sflg));
    1451:         if (isset && sflg) PetscCall(KSPSetType(eksp, KSPCG));
    1452:       }
    1453:       PetscCall(KSPSetErrorIfNotConverged(eksp, pc->erroriffailure));
    1454:       PetscCall(KSPSetNormType(eksp, KSP_NORM_NONE));

    1456:       PetscCall(KSPSetInitialGuessNonzero(eksp, PETSC_FALSE));
    1457:       PetscCall(KSPSetOperators(eksp, Amat, Amat));

    1459:       PetscCall(KSPGetPC(eksp, &epc));
    1460:       PetscCall(PCSetType(epc, PCJACOBI)); /* smoother in smoothed agg. */

    1462:       PetscCall(KSPSetTolerances(eksp, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT, 10)); // 10 is safer, but 5 is often fine, can override with -pc_gamg_esteig_ksp_max_it -mg_levels_ksp_chebyshev_esteig 0,0.25,0,1.2

    1464:       PetscCall(KSPSetFromOptions(eksp));
    1465:       PetscCall(KSPSetComputeSingularValues(eksp, PETSC_TRUE));
    1466:       PetscCall(KSPSolve(eksp, bb, xx));
    1467:       PetscCall(KSPCheckSolve(eksp, pc, xx));

    1469:       PetscCall(KSPComputeExtremeSingularValues(eksp, &emax, &emin));
    1470:       PetscCall(PetscInfo(pc, "%s: Smooth P0: max eigen=%e min=%e PC=%s\n", ((PetscObject)pc)->prefix, (double)emax, (double)emin, PCJACOBI));
    1471:       PetscCall(VecDestroy(&xx));
    1472:       PetscCall(VecDestroy(&bb));
    1473:       PetscCall(KSPDestroy(&eksp));
    1474:     }
    1475:     if (pc_gamg->use_sa_esteig) {
    1476:       mg->min_eigen_DinvA[pc_gamg->current_level] = emin;
    1477:       mg->max_eigen_DinvA[pc_gamg->current_level] = emax;
    1478:       PetscCall(PetscInfo(pc, "%s: Smooth P0: level %" PetscInt_FMT ", cache spectra %g %g\n", ((PetscObject)pc)->prefix, pc_gamg->current_level, (double)emin, (double)emax));
    1479:     } else {
    1480:       mg->min_eigen_DinvA[pc_gamg->current_level] = 0;
    1481:       mg->max_eigen_DinvA[pc_gamg->current_level] = 0;
    1482:     }
    1483:   } else {
    1484:     mg->min_eigen_DinvA[pc_gamg->current_level] = 0;
    1485:     mg->max_eigen_DinvA[pc_gamg->current_level] = 0;
    1486:   }

    1488:   /* smooth P0 */
    1489:   if (pc_gamg_agg->nsmooths > 0) {
    1490:     Vec diag;

    1492:     /* TODO: Set a PCFailedReason and exit the building of the AMG preconditioner */
    1493:     PetscCheck(emax != 0.0, PetscObjectComm((PetscObject)pc), PETSC_ERR_PLIB, "Computed maximum singular value as zero");

    1495:     PetscCall(MatCreateVecs(Amat, &diag, NULL));
    1496:     PetscCall(MatGetDiagonal(Amat, diag)); /* effectively PCJACOBI */
    1497:     PetscCall(VecReciprocal(diag));

    1499:     for (jj = 0; jj < pc_gamg_agg->nsmooths; jj++) {
    1500:       Mat tMat;

    1502:       PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_OPTSM], 0, 0, 0, 0));
    1503:       /*
    1504:         Smooth aggregation on the prolongator

    1506:         P_{i} := (I - 1.4/emax D^{-1}A) P_i\{i-1}
    1507:       */
    1508:       PetscCall(PetscLogEventBegin(petsc_gamg_setup_matmat_events[pc_gamg->current_level][2], 0, 0, 0, 0));
    1509:       PetscCall(MatMatMult(Amat, Prol, MAT_INITIAL_MATRIX, PETSC_CURRENT, &tMat));
    1510:       PetscCall(PetscLogEventEnd(petsc_gamg_setup_matmat_events[pc_gamg->current_level][2], 0, 0, 0, 0));
    1511:       PetscCall(MatProductClear(tMat));
    1512:       PetscCall(MatDiagonalScale(tMat, diag, NULL));

    1514:       /* TODO: Document the 1.4 and don't hardwire it in this routine */
    1515:       alpha = -1.4 / emax;
    1516:       PetscCall(MatAYPX(tMat, alpha, Prol, SUBSET_NONZERO_PATTERN));
    1517:       PetscCall(MatDestroy(&Prol));
    1518:       Prol = tMat;
    1519:       PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_OPTSM], 0, 0, 0, 0));
    1520:     }
    1521:     PetscCall(VecDestroy(&diag));
    1522:   }
    1523:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_OPT], 0, 0, 0, 0));
    1524:   PetscCall(MatViewFromOptions(Prol, NULL, "-pc_gamg_agg_view_prolongation"));
    1525:   *a_P = Prol;
    1526:   PetscFunctionReturn(PETSC_SUCCESS);
    1527: }

    1529: /*MC
    1530:   PCGAMGAGG - Smooth aggregation, {cite}`vanek1996algebraic`, {cite}`vanek2001convergence`, variant of PETSc's algebraic multigrid (`PCGAMG`) preconditioner

    1532:   Options Database Keys:
    1533: + -pc_gamg_agg_nsmooths <nsmooth, default=1> - number of smoothing steps to use with smooth aggregation to construct prolongation
    1534: . -pc_gamg_aggressive_coarsening <n,default=1> - number of aggressive coarsening (MIS-2) levels from finest.
    1535: . -pc_gamg_aggressive_square_graph <bool,default=false> - Use square graph (A'A) or MIS-k (k=2) for aggressive coarsening
    1536: . -pc_gamg_mis_k_minimum_degree_ordering <bool,default=true> - Use minimum degree ordering in greedy MIS algorithm
    1537: . -pc_gamg_pc_gamg_asm_hem_aggs <n,default=0> - Number of HEM aggregation steps for ASM smoother
    1538: - -pc_gamg_aggressive_mis_k <n,default=2> - Number (k) distance in MIS coarsening (>2 is 'aggressive')

    1540:   Level: intermediate

    1542:   Notes:
    1543:   To obtain good performance for `PCGAMG` for vector valued problems you must
    1544:   call `MatSetBlockSize()` to indicate the number of degrees of freedom per grid point.
    1545:   Call `MatSetNearNullSpace()` (or `PCSetCoordinates()` if solving the equations of elasticity) to indicate the near null space of the operator

    1547:   The many options for `PCMG` and `PCGAMG` such as controlling the smoothers on each level etc. also work for `PCGAMGAGG`

    1549: .seealso: `PCGAMG`, [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), [](ch_ksp), `PCCreate()`, `PCSetType()`,
    1550:           `MatSetBlockSize()`, `PCMGType`, `PCSetCoordinates()`, `MatSetNearNullSpace()`, `PCGAMGSetType()`,
    1551:           `PCGAMGAGG`, `PCGAMGGEO`, `PCGAMGCLASSICAL`, `PCGAMGSetProcEqLim()`, `PCGAMGSetCoarseEqLim()`, `PCGAMGSetRepartition()`, `PCGAMGRegister()`,
    1552:           `PCGAMGSetReuseInterpolation()`, `PCGAMGASMSetUseAggs()`, `PCGAMGSetParallelCoarseGridSolve()`, `PCGAMGSetNlevels()`, `PCGAMGSetThreshold()`,
    1553:           `PCGAMGGetType()`, `PCGAMGSetUseSAEstEig()`
    1554: M*/
    1555: PetscErrorCode PCCreateGAMG_AGG(PC pc)
    1556: {
    1557:   PC_MG       *mg      = (PC_MG *)pc->data;
    1558:   PC_GAMG     *pc_gamg = (PC_GAMG *)mg->innerctx;
    1559:   PC_GAMG_AGG *pc_gamg_agg;

    1561:   PetscFunctionBegin;
    1562:   /* create sub context for SA */
    1563:   PetscCall(PetscNew(&pc_gamg_agg));
    1564:   pc_gamg->subctx = pc_gamg_agg;

    1566:   pc_gamg->ops->setfromoptions = PCSetFromOptions_GAMG_AGG;
    1567:   pc_gamg->ops->destroy        = PCDestroy_GAMG_AGG;
    1568:   /* reset does not do anything; setup not virtual */

    1570:   /* set internal function pointers */
    1571:   pc_gamg->ops->creategraph       = PCGAMGCreateGraph_AGG;
    1572:   pc_gamg->ops->coarsen           = PCGAMGCoarsen_AGG;
    1573:   pc_gamg->ops->prolongator       = PCGAMGConstructProlongator_AGG;
    1574:   pc_gamg->ops->optprolongator    = PCGAMGOptimizeProlongator_AGG;
    1575:   pc_gamg->ops->createdefaultdata = PCSetData_AGG;
    1576:   pc_gamg->ops->view              = PCView_GAMG_AGG;

    1578:   pc_gamg_agg->nsmooths                     = 1;
    1579:   pc_gamg_agg->aggressive_coarsening_levels = 1;
    1580:   pc_gamg_agg->use_aggressive_square_graph  = PETSC_TRUE;
    1581:   pc_gamg_agg->use_minimum_degree_ordering  = PETSC_FALSE;
    1582:   pc_gamg_agg->use_low_mem_filter           = PETSC_FALSE;
    1583:   pc_gamg_agg->aggressive_mis_k             = 2;
    1584:   pc_gamg_agg->graph_symmetrize             = PETSC_TRUE;

    1586:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetNSmooths_C", PCGAMGSetNSmooths_AGG));
    1587:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetAggressiveLevels_C", PCGAMGSetAggressiveLevels_AGG));
    1588:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetAggressiveSquareGraph_C", PCGAMGSetAggressiveSquareGraph_AGG));
    1589:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGMISkSetMinDegreeOrdering_C", PCGAMGMISkSetMinDegreeOrdering_AGG));
    1590:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetLowMemoryFilter_C", PCGAMGSetLowMemoryFilter_AGG));
    1591:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGMISkSetAggressive_C", PCGAMGMISkSetAggressive_AGG));
    1592:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetGraphSymmetrize_C", PCGAMGSetGraphSymmetrize_AGG));
    1593:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSetCoordinates_C", PCSetCoordinates_AGG));
    1594:   PetscFunctionReturn(PETSC_SUCCESS);
    1595: }