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> - the flag

 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> - the flag

 76:   Level: intermediate

 78:   Note:
 79:   By default, aggressive coarsening squares the matrix (computes $ A^T A$) before coarsening. Calling `PCGAMGSetAggressiveSquareGraph()` with a value of `PETSC_FALSE` changes the aggressive coarsening strategy to use MIS-k, see `PCGAMGMISkSetAggressive()`.

 81: .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()`
 82: @*/
 83: PetscErrorCode PCGAMGSetAggressiveLevels(PC pc, PetscInt n)
 84: {
 85:   PetscFunctionBegin;
 88:   PetscTryMethod(pc, "PCGAMGSetAggressiveLevels_C", (PC, PetscInt), (pc, n));
 89:   PetscFunctionReturn(PETSC_SUCCESS);
 90: }

 92: /*@
 93:   PCGAMGMISkSetAggressive - Number (k) distance in MIS coarsening (>2 is 'aggressive')

 95:   Logically Collective

 97:   Input Parameters:
 98: + pc - the preconditioner context
 99: - n  - 1 or more (default = 2)

101:   Options Database Key:
102: . -pc_gamg_aggressive_mis_k <n,default=2> - the flag

104:   Level: intermediate

106: .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()`
107: @*/
108: PetscErrorCode PCGAMGMISkSetAggressive(PC pc, PetscInt n)
109: {
110:   PetscFunctionBegin;
113:   PetscTryMethod(pc, "PCGAMGMISkSetAggressive_C", (PC, PetscInt), (pc, n));
114:   PetscFunctionReturn(PETSC_SUCCESS);
115: }

117: /*@
118:   PCGAMGSetAggressiveSquareGraph - Use graph square, $A^T A$, for aggressive coarsening. Coarsening is slower than the alternative (MIS-2), which is faster and uses less memory

120:   Logically Collective

122:   Input Parameters:
123: + pc - the preconditioner context
124: - b  - default true

126:   Options Database Key:
127: . -pc_gamg_aggressive_square_graph <bool,default=true> - the flag

129:   Level: intermediate

131:   Notes:
132:   If `b` is `PETSC_FALSE` then MIS-k is used for aggressive coarsening, see `PCGAMGMISkSetAggressive()`

134:   Squaring the matrix to perform the aggressive coarsening is slower and requires more memory than using MIS-k, but may result in a better preconditioner
135:   that converges faster.

137: .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()`
138: @*/
139: PetscErrorCode PCGAMGSetAggressiveSquareGraph(PC pc, PetscBool b)
140: {
141:   PetscFunctionBegin;
144:   PetscTryMethod(pc, "PCGAMGSetAggressiveSquareGraph_C", (PC, PetscBool), (pc, b));
145:   PetscFunctionReturn(PETSC_SUCCESS);
146: }

148: /*@
149:   PCGAMGMISkSetMinDegreeOrdering - Use minimum degree ordering in greedy MIS algorithm

151:   Logically Collective

153:   Input Parameters:
154: + pc - the preconditioner context
155: - b  - default false

157:   Options Database Key:
158: . -pc_gamg_mis_k_minimum_degree_ordering <bool,default=false> - the flag

160:   Level: intermediate

162: .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()`
163: @*/
164: PetscErrorCode PCGAMGMISkSetMinDegreeOrdering(PC pc, PetscBool b)
165: {
166:   PetscFunctionBegin;
169:   PetscTryMethod(pc, "PCGAMGMISkSetMinDegreeOrdering_C", (PC, PetscBool), (pc, b));
170:   PetscFunctionReturn(PETSC_SUCCESS);
171: }

173: /*@
174:   PCGAMGSetLowMemoryFilter - Use low memory graph/matrix filter

176:   Logically Collective

178:   Input Parameters:
179: + pc - the preconditioner context
180: - b  - default false

182:   Options Database Key:
183: . -pc_gamg_low_memory_threshold_filter <bool,default=false> - the flag

185:   Level: intermediate

187: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), `PCGAMG`, `PCGAMGSetThreshold()`, `PCGAMGSetAggressiveLevels()`,
188:   `PCGAMGMISkSetAggressive()`, `PCGAMGSetAggressiveSquareGraph()`, `PCGAMGMISkSetMinDegreeOrdering()`
189: @*/
190: PetscErrorCode PCGAMGSetLowMemoryFilter(PC pc, PetscBool b)
191: {
192:   PetscFunctionBegin;
195:   PetscTryMethod(pc, "PCGAMGSetLowMemoryFilter_C", (PC, PetscBool), (pc, b));
196:   PetscFunctionReturn(PETSC_SUCCESS);
197: }

199: /*@
200:   PCGAMGSetGraphSymmetrize - Symmetrize graph used for coarsening. Defaults to true, but if matrix has symmetric attribute, then not needed since the graph is already known to be symmetric

202:   Logically Collective

204:   Input Parameters:
205: + pc - the preconditioner context
206: - b  - default true

208:   Options Database Key:
209: . -pc_gamg_graph_symmetrize <bool,default=true> - the flag

211:   Level: intermediate

213: .seealso: [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), `PCGAMG`, `PCGAMGSetThreshold()`, `PCGAMGSetAggressiveLevels()`, `MatCreateGraph()`,
214:   `PCGAMGMISkSetAggressive()`, `PCGAMGSetAggressiveSquareGraph()`, `PCGAMGMISkSetMinDegreeOrdering()`
215: @*/
216: PetscErrorCode PCGAMGSetGraphSymmetrize(PC pc, PetscBool b)
217: {
218:   PetscFunctionBegin;
221:   PetscTryMethod(pc, "PCGAMGSetGraphSymmetrize_C", (PC, PetscBool), (pc, b));
222:   PetscFunctionReturn(PETSC_SUCCESS);
223: }

225: static PetscErrorCode PCGAMGSetAggressiveLevels_AGG(PC pc, PetscInt n)
226: {
227:   PC_MG       *mg          = (PC_MG *)pc->data;
228:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
229:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

231:   PetscFunctionBegin;
232:   pc_gamg_agg->aggressive_coarsening_levels = n;
233:   PetscFunctionReturn(PETSC_SUCCESS);
234: }

236: static PetscErrorCode PCGAMGMISkSetAggressive_AGG(PC pc, PetscInt n)
237: {
238:   PC_MG       *mg          = (PC_MG *)pc->data;
239:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
240:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

242:   PetscFunctionBegin;
243:   pc_gamg_agg->aggressive_mis_k = n;
244:   PetscFunctionReturn(PETSC_SUCCESS);
245: }

247: static PetscErrorCode PCGAMGSetAggressiveSquareGraph_AGG(PC pc, PetscBool b)
248: {
249:   PC_MG       *mg          = (PC_MG *)pc->data;
250:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
251:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

253:   PetscFunctionBegin;
254:   pc_gamg_agg->use_aggressive_square_graph = b;
255:   PetscFunctionReturn(PETSC_SUCCESS);
256: }

258: static PetscErrorCode PCGAMGSetLowMemoryFilter_AGG(PC pc, PetscBool b)
259: {
260:   PC_MG       *mg          = (PC_MG *)pc->data;
261:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
262:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

264:   PetscFunctionBegin;
265:   pc_gamg_agg->use_low_mem_filter = b;
266:   PetscFunctionReturn(PETSC_SUCCESS);
267: }

269: static PetscErrorCode PCGAMGSetGraphSymmetrize_AGG(PC pc, PetscBool b)
270: {
271:   PC_MG       *mg          = (PC_MG *)pc->data;
272:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
273:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

275:   PetscFunctionBegin;
276:   pc_gamg_agg->graph_symmetrize = b;
277:   PetscFunctionReturn(PETSC_SUCCESS);
278: }

280: static PetscErrorCode PCGAMGMISkSetMinDegreeOrdering_AGG(PC pc, PetscBool b)
281: {
282:   PC_MG       *mg          = (PC_MG *)pc->data;
283:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
284:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

286:   PetscFunctionBegin;
287:   pc_gamg_agg->use_minimum_degree_ordering = b;
288:   PetscFunctionReturn(PETSC_SUCCESS);
289: }

291: static PetscErrorCode PCSetFromOptions_GAMG_AGG(PC pc, PetscOptionItems PetscOptionsObject)
292: {
293:   PC_MG       *mg          = (PC_MG *)pc->data;
294:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
295:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;
296:   PetscBool    n_aggressive_flg, old_sq_provided = PETSC_FALSE, new_sq_provided = PETSC_FALSE, new_sqr_graph = pc_gamg_agg->use_aggressive_square_graph;
297:   PetscInt     nsq_graph_old = 0;

299:   PetscFunctionBegin;
300:   PetscOptionsHeadBegin(PetscOptionsObject, "GAMG-AGG options");
301:   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));
302:   // aggressive coarsening logic with deprecated -pc_gamg_square_graph
303:   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));
304:   if (!n_aggressive_flg)
305:     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));
306:   PetscCall(PetscOptionsBool("-pc_gamg_aggressive_square_graph", "Use square graph $ (A^T A)$ for aggressive coarsening, if false, MIS-k (k=2) is used, see PCGAMGMISkSetAggressive()", "PCGAMGSetAggressiveSquareGraph", new_sqr_graph, &pc_gamg_agg->use_aggressive_square_graph, &new_sq_provided));
307:   if (!new_sq_provided && old_sq_provided) {
308:     pc_gamg_agg->aggressive_coarsening_levels = nsq_graph_old; // could be zero
309:     pc_gamg_agg->use_aggressive_square_graph  = PETSC_TRUE;
310:   }
311:   if (new_sq_provided && old_sq_provided)
312:     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 %" PetscInt_FMT "\n", pc_gamg_agg->aggressive_coarsening_levels));
313:   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));
314:   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));
315:   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));
316:   PetscCall(PetscOptionsBool("-pc_gamg_graph_symmetrize", "Symmetrize graph for coarsening", "PCGAMGSetGraphSymmetrize", pc_gamg_agg->graph_symmetrize, &pc_gamg_agg->graph_symmetrize, NULL));
317:   PetscOptionsHeadEnd();
318:   PetscFunctionReturn(PETSC_SUCCESS);
319: }

321: static PetscErrorCode PCDestroy_GAMG_AGG(PC pc)
322: {
323:   PC_MG       *mg          = (PC_MG *)pc->data;
324:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
325:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

327:   PetscFunctionBegin;
328:   PetscCall(MatCoarsenDestroy(&pc_gamg_agg->crs));
329:   PetscCall(PetscFree(pc_gamg->subctx));
330:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetNSmooths_C", NULL));
331:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetAggressiveLevels_C", NULL));
332:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGMISkSetAggressive_C", NULL));
333:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGMISkSetMinDegreeOrdering_C", NULL));
334:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetLowMemoryFilter_C", NULL));
335:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetAggressiveSquareGraph_C", NULL));
336:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetGraphSymmetrize_C", NULL));
337:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSetCoordinates_C", NULL));
338:   PetscFunctionReturn(PETSC_SUCCESS);
339: }

341: /*
342:    PCSetCoordinates_AGG

344:    Collective

346:    Input Parameter:
347:    . pc - the preconditioner context
348:    . ndm - dimension of data (used for dof/vertex for Stokes)
349:    . a_nloc - number of vertices local
350:    . coords - [a_nloc][ndm] - interleaved coordinate data: {x_0, y_0, z_0, x_1, y_1, ...}
351: */

353: static PetscErrorCode PCSetCoordinates_AGG(PC pc, PetscInt ndm, PetscInt a_nloc, PetscReal *coords)
354: {
355:   PC_MG   *mg      = (PC_MG *)pc->data;
356:   PC_GAMG *pc_gamg = (PC_GAMG *)mg->innerctx;
357:   PetscInt arrsz, kk, ii, jj, nloc, ndatarows, ndf;
358:   Mat      mat = pc->pmat;

360:   PetscFunctionBegin;
363:   nloc = a_nloc;

365:   /* SA: null space vectors */
366:   PetscCall(MatGetBlockSize(mat, &ndf));               /* this does not work for Stokes */
367:   if (coords && ndf == 1) pc_gamg->data_cell_cols = 1; /* scalar w/ coords and SA (not needed) */
368:   else if (coords) {
369:     PetscCheck(ndm <= ndf, PETSC_COMM_SELF, PETSC_ERR_PLIB, "degrees of motion %" PetscInt_FMT " > block size %" PetscInt_FMT, ndm, ndf);
370:     pc_gamg->data_cell_cols = (ndm == 2 ? 3 : 6); /* displacement elasticity */
371:     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);
372:   } else pc_gamg->data_cell_cols = ndf; /* no data, force SA with constant null space vectors */
373:   pc_gamg->data_cell_rows = ndatarows = ndf;
374:   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);
375:   arrsz = nloc * pc_gamg->data_cell_rows * pc_gamg->data_cell_cols;

377:   if (!pc_gamg->data || (pc_gamg->data_sz != arrsz)) {
378:     PetscCall(PetscFree(pc_gamg->data));
379:     PetscCall(PetscMalloc1(arrsz + 1, &pc_gamg->data));
380:   }
381:   /* copy data in - column-oriented */
382:   for (kk = 0; kk < nloc; kk++) {
383:     const PetscInt M    = nloc * pc_gamg->data_cell_rows; /* stride into data */
384:     PetscReal     *data = &pc_gamg->data[kk * ndatarows]; /* start of cell */

386:     if (pc_gamg->data_cell_cols == 1) *data = 1.0;
387:     else {
388:       /* translational modes */
389:       for (ii = 0; ii < ndatarows; ii++) {
390:         for (jj = 0; jj < ndatarows; jj++) {
391:           if (ii == jj) data[ii * M + jj] = 1.0;
392:           else data[ii * M + jj] = 0.0;
393:         }
394:       }

396:       /* rotational modes */
397:       if (coords) {
398:         if (ndm == 2) {
399:           data += 2 * M;
400:           data[0] = -coords[2 * kk + 1];
401:           data[1] = coords[2 * kk];
402:         } else {
403:           data += 3 * M;
404:           data[0]         = 0.0;
405:           data[M + 0]     = coords[3 * kk + 2];
406:           data[2 * M + 0] = -coords[3 * kk + 1];
407:           data[1]         = -coords[3 * kk + 2];
408:           data[M + 1]     = 0.0;
409:           data[2 * M + 1] = coords[3 * kk];
410:           data[2]         = coords[3 * kk + 1];
411:           data[M + 2]     = -coords[3 * kk];
412:           data[2 * M + 2] = 0.0;
413:         }
414:       }
415:     }
416:   }
417:   pc_gamg->data_sz = arrsz;
418:   PetscFunctionReturn(PETSC_SUCCESS);
419: }

421: /*
422:    PCSetData_AGG - called if data is not set with PCSetCoordinates.
423:       Looks in Mat for near null space.
424:       Does not work for Stokes

426:   Input Parameter:
427:    . pc -
428:    . a_A - matrix to get (near) null space out of.
429: */
430: static PetscErrorCode PCSetData_AGG(PC pc, Mat a_A)
431: {
432:   PC_MG       *mg      = (PC_MG *)pc->data;
433:   PC_GAMG     *pc_gamg = (PC_GAMG *)mg->innerctx;
434:   MatNullSpace mnull;

436:   PetscFunctionBegin;
437:   PetscCall(MatGetNearNullSpace(a_A, &mnull));
438:   if (!mnull) {
439:     DM dm;

441:     PetscCall(PCGetDM(pc, &dm));
442:     if (!dm) PetscCall(MatGetDM(a_A, &dm));
443:     if (dm) {
444:       PetscObject deformation;
445:       PetscInt    Nf;

447:       PetscCall(DMGetNumFields(dm, &Nf));
448:       if (Nf) {
449:         PetscCall(DMGetField(dm, 0, NULL, &deformation));
450:         if (deformation) {
451:           PetscCall(PetscObjectQuery(deformation, "nearnullspace", (PetscObject *)&mnull));
452:           if (!mnull) PetscCall(PetscObjectQuery(deformation, "nullspace", (PetscObject *)&mnull));
453:         }
454:       }
455:     }
456:   }

458:   if (!mnull) {
459:     PetscInt bs, NN, MM;

461:     PetscCall(MatGetBlockSize(a_A, &bs));
462:     PetscCall(MatGetLocalSize(a_A, &MM, &NN));
463:     PetscCheck(MM % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MM %" PetscInt_FMT " must be divisible by bs %" PetscInt_FMT, MM, bs);
464:     PetscCall(PCSetCoordinates_AGG(pc, bs, MM / bs, NULL));
465:   } else {
466:     PetscReal         *nullvec;
467:     PetscBool          has_const;
468:     PetscInt           i, j, mlocal, nvec, bs;
469:     const Vec         *vecs;
470:     const PetscScalar *v;

472:     PetscCall(MatGetLocalSize(a_A, &mlocal, NULL));
473:     PetscCall(MatNullSpaceGetVecs(mnull, &has_const, &nvec, &vecs));
474:     for (i = 0; i < nvec; i++) {
475:       PetscCall(VecGetLocalSize(vecs[i], &j));
476:       PetscCheck(j == mlocal, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Attached null space vector size %" PetscInt_FMT " != matrix size %" PetscInt_FMT, j, mlocal);
477:     }
478:     pc_gamg->data_sz = (nvec + !!has_const) * mlocal;
479:     PetscCall(PetscMalloc1((nvec + !!has_const) * mlocal, &nullvec));
480:     if (has_const)
481:       for (i = 0; i < mlocal; i++) nullvec[i] = 1.0;
482:     for (i = 0; i < nvec; i++) {
483:       PetscCall(VecGetArrayRead(vecs[i], &v));
484:       for (j = 0; j < mlocal; j++) nullvec[(i + !!has_const) * mlocal + j] = PetscRealPart(v[j]);
485:       PetscCall(VecRestoreArrayRead(vecs[i], &v));
486:     }
487:     pc_gamg->data           = nullvec;
488:     pc_gamg->data_cell_cols = (nvec + !!has_const);
489:     PetscCall(MatGetBlockSize(a_A, &bs));
490:     pc_gamg->data_cell_rows = bs;
491:   }
492:   PetscFunctionReturn(PETSC_SUCCESS);
493: }

495: /*
496:   formProl0 - collect null space data for each aggregate, do QR, put R in coarse grid data and Q in P_0

498:   Input Parameter:
499:    . agg_llists - list of arrays with aggregates -- list from selected vertices of aggregate unselected vertices
500:    . bs - row block size
501:    . nSAvec - column bs of new P
502:    . my0crs - global index of start of locals
503:    . data_stride - bs*(nloc nodes + ghost nodes) [data_stride][nSAvec]
504:    . data_in[data_stride*nSAvec] - local data on fine grid
505:    . flid_fgid[data_stride/bs] - make local to global IDs, includes ghosts in 'locals_llist'

507:   Output Parameter:
508:    . a_data_out - in with fine grid data (w/ghosts), out with coarse grid data
509:    . a_Prol - prolongation operator
510: */
511: 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)
512: {
513:   PetscInt      Istart, my0, Iend, nloc, clid, flid = 0, aggID, kk, jj, ii, mm, nSelected, minsz, nghosts, out_data_stride;
514:   MPI_Comm      comm;
515:   PetscReal    *out_data;
516:   PetscCDIntNd *pos;
517:   PetscHMapI    fgid_flid;

519:   PetscFunctionBegin;
520:   PetscCall(PetscObjectGetComm((PetscObject)a_Prol, &comm));
521:   PetscCall(MatGetOwnershipRange(a_Prol, &Istart, &Iend));
522:   nloc = (Iend - Istart) / bs;
523:   my0  = Istart / bs;
524:   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);
525:   Iend /= bs;
526:   nghosts = data_stride / bs - nloc;

528:   PetscCall(PetscHMapICreateWithSize(2 * nghosts + 1, &fgid_flid));

530:   for (kk = 0; kk < nghosts; kk++) PetscCall(PetscHMapISet(fgid_flid, flid_fgid[nloc + kk], nloc + kk));

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

536:     PetscCall(PetscCDIsEmptyAt(agg_llists, mm, &ise));
537:     if (!ise) nSelected++;
538:   }
539:   PetscCall(MatGetOwnershipRangeColumn(a_Prol, &ii, &jj));
540:   PetscCheck((ii / nSAvec) == my0crs, PETSC_COMM_SELF, PETSC_ERR_PLIB, "ii %" PetscInt_FMT " /nSAvec %" PetscInt_FMT "  != my0crs %" PetscInt_FMT, ii, nSAvec, my0crs);
541:   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);

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

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

550:   /* find points and set prolongation */
551:   minsz = 100;
552:   for (mm = clid = 0; mm < nloc; mm++) {
553:     PetscCall(PetscCDCountAt(agg_llists, mm, &jj));
554:     if (jj > 0) {
555:       const PetscInt lid = mm, cgid = my0crs + clid;
556:       PetscInt       cids[100]; /* max bs */
557:       PetscBLASInt   asz, M, N, INFO;
558:       PetscBLASInt   Mdata, LDA, LWORK;
559:       PetscScalar   *qqc, *qqr, *TAU, *WORK;
560:       PetscInt      *fids;
561:       PetscReal     *data;

563:       PetscCall(PetscBLASIntCast(jj, &asz));
564:       PetscCall(PetscBLASIntCast(asz * bs, &M));
565:       PetscCall(PetscBLASIntCast(nSAvec, &N));
566:       PetscCall(PetscBLASIntCast(M + ((N - M > 0) ? N - M : 0), &Mdata));
567:       PetscCall(PetscBLASIntCast(Mdata, &LDA));
568:       PetscCall(PetscBLASIntCast(N * bs, &LWORK));
569:       /* count agg */
570:       if (asz < minsz) minsz = asz;

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

575:       aggID = 0;
576:       PetscCall(PetscCDGetHeadPos(agg_llists, lid, &pos));
577:       while (pos) {
578:         PetscInt gid1;

580:         PetscCall(PetscCDIntNdGetID(pos, &gid1));
581:         PetscCall(PetscCDGetNextPos(agg_llists, lid, &pos));

583:         if (gid1 >= my0 && gid1 < Iend) flid = gid1 - my0;
584:         else {
585:           PetscCall(PetscHMapIGet(fgid_flid, gid1, &flid));
586:           PetscCheck(flid >= 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot find gid1 in table");
587:         }
588:         /* copy in B_i matrix - column-oriented */
589:         data = &data_in[flid * bs];
590:         for (ii = 0; ii < bs; ii++) {
591:           for (jj = 0; jj < N; jj++) {
592:             PetscReal d = data[jj * data_stride + ii];

594:             qqc[jj * Mdata + aggID * bs + ii] = d;
595:           }
596:         }
597:         /* set fine IDs */
598:         for (kk = 0; kk < bs; kk++) fids[aggID * bs + kk] = flid_fgid[flid] * bs + kk;
599:         aggID++;
600:       }

602:       /* pad with zeros */
603:       for (ii = asz * bs; ii < Mdata; ii++) {
604:         for (jj = 0; jj < N; jj++, kk++) qqc[jj * Mdata + ii] = .0;
605:       }

607:       /* QR */
608:       PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF));
609:       PetscCallBLAS("LAPACKgeqrf", LAPACKgeqrf_(&Mdata, &N, qqc, &LDA, TAU, WORK, &LWORK, &INFO));
610:       PetscCall(PetscFPTrapPop());
611:       PetscCheck(INFO == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "xGEQRF error");
612:       /* get R - column-oriented - output B_{i+1} */
613:       {
614:         PetscReal *data = &out_data[clid * nSAvec];

616:         for (jj = 0; jj < nSAvec; jj++) {
617:           for (ii = 0; ii < nSAvec; ii++) {
618:             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);
619:             if (ii <= jj) data[jj * out_data_stride + ii] = PetscRealPart(qqc[jj * Mdata + ii]);
620:             else data[jj * out_data_stride + ii] = 0.;
621:           }
622:         }
623:       }

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

629:       for (ii = 0; ii < M; ii++) {
630:         for (jj = 0; jj < N; jj++) qqr[N * ii + jj] = qqc[jj * Mdata + ii];
631:       }

633:       /* add diagonal block of P0 */
634:       for (kk = 0; kk < N; kk++) cids[kk] = N * cgid + kk; /* global col IDs in P0 */
635:       PetscCall(MatSetValues(a_Prol, M, fids, N, cids, qqr, INSERT_VALUES));
636:       PetscCall(PetscFree5(qqc, qqr, TAU, WORK, fids));
637:       clid++;
638:     } /* coarse agg */
639:   } /* for all fine nodes */
640:   PetscCall(MatAssemblyBegin(a_Prol, MAT_FINAL_ASSEMBLY));
641:   PetscCall(MatAssemblyEnd(a_Prol, MAT_FINAL_ASSEMBLY));
642:   PetscCall(PetscHMapIDestroy(&fgid_flid));
643:   PetscFunctionReturn(PETSC_SUCCESS);
644: }

646: static PetscErrorCode PCView_GAMG_AGG(PC pc, PetscViewer viewer)
647: {
648:   PC_MG       *mg          = (PC_MG *)pc->data;
649:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
650:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;

652:   PetscFunctionBegin;
653:   PetscCall(PetscViewerASCIIPrintf(viewer, "      AGG specific options\n"));
654:   PetscCall(PetscViewerASCIIPrintf(viewer, "        Number of levels of aggressive coarsening %" PetscInt_FMT "\n", pc_gamg_agg->aggressive_coarsening_levels));
655:   if (pc_gamg_agg->aggressive_coarsening_levels > 0) {
656:     PetscCall(PetscViewerASCIIPrintf(viewer, "        %s aggressive coarsening\n", !pc_gamg_agg->use_aggressive_square_graph ? "MIS-k" : "Square graph"));
657:     if (!pc_gamg_agg->use_aggressive_square_graph) PetscCall(PetscViewerASCIIPrintf(viewer, "        MIS-%" PetscInt_FMT " coarsening on aggressive levels\n", pc_gamg_agg->aggressive_mis_k));
658:   }
659:   PetscCall(PetscViewerASCIIPushTab(viewer));
660:   PetscCall(PetscViewerASCIIPushTab(viewer));
661:   PetscCall(PetscViewerASCIIPushTab(viewer));
662:   PetscCall(PetscViewerASCIIPushTab(viewer));
663:   if (pc_gamg_agg->crs) PetscCall(MatCoarsenView(pc_gamg_agg->crs, viewer));
664:   else PetscCall(PetscViewerASCIIPrintf(viewer, "Coarsening algorithm not yet selected\n"));
665:   PetscCall(PetscViewerASCIIPopTab(viewer));
666:   PetscCall(PetscViewerASCIIPopTab(viewer));
667:   PetscCall(PetscViewerASCIIPopTab(viewer));
668:   PetscCall(PetscViewerASCIIPopTab(viewer));
669:   PetscCall(PetscViewerASCIIPrintf(viewer, "        Number smoothing steps to construct prolongation %" PetscInt_FMT "\n", pc_gamg_agg->nsmooths));
670:   PetscFunctionReturn(PETSC_SUCCESS);
671: }

673: static PetscErrorCode PCGAMGCreateGraph_AGG(PC pc, Mat Amat, Mat *a_Gmat)
674: {
675:   PC_MG          *mg          = (PC_MG *)pc->data;
676:   PC_GAMG        *pc_gamg     = (PC_GAMG *)mg->innerctx;
677:   PC_GAMG_AGG    *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;
678:   const PetscReal vfilter     = pc_gamg->threshold[pc_gamg->current_level];
679:   PetscBool       ishem, ismis;
680:   const char     *prefix;
681:   MatInfo         info0, info1;
682:   PetscInt        bs;

684:   PetscFunctionBegin;
685:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_COARSEN], 0, 0, 0, 0));
686:   /* 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 */
687:   /* MATCOARSENHEM requires numerical weights for edges so ensure they are computed */
688:   PetscCall(MatCoarsenDestroy(&pc_gamg_agg->crs));
689:   PetscCall(MatCoarsenCreate(PetscObjectComm((PetscObject)pc), &pc_gamg_agg->crs));
690:   PetscCall(PetscObjectGetOptionsPrefix((PetscObject)pc, &prefix));
691:   PetscCall(PetscObjectSetOptionsPrefix((PetscObject)pc_gamg_agg->crs, prefix));
692:   PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)pc_gamg_agg->crs, "pc_gamg_"));
693:   PetscCall(MatCoarsenSetFromOptions(pc_gamg_agg->crs));
694:   PetscCall(MatGetBlockSize(Amat, &bs));
695:   // check for valid indices wrt bs
696:   for (int ii = 0; ii < pc_gamg_agg->crs->strength_index_size; ii++) {
697:     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 (%" PetscInt_FMT ") must be non-negative and < block size (%" PetscInt_FMT "), NB, can not use -mat_coarsen_strength_index with -mat_coarsen_strength_index",
698:                pc_gamg_agg->crs->strength_index[ii], bs);
699:   }
700:   PetscCall(PetscObjectTypeCompare((PetscObject)pc_gamg_agg->crs, MATCOARSENHEM, &ishem));
701:   if (ishem) {
702:     if (pc_gamg_agg->aggressive_coarsening_levels) PetscCall(PetscInfo(pc, "HEM and aggressive coarsening ignored: HEM using %" PetscInt_FMT " iterations\n", pc_gamg_agg->crs->max_it));
703:     pc_gamg_agg->aggressive_coarsening_levels = 0;                                         // aggressive and HEM does not make sense
704:     PetscCall(MatCoarsenSetMaximumIterations(pc_gamg_agg->crs, pc_gamg_agg->crs->max_it)); // for code coverage
705:     PetscCall(MatCoarsenSetThreshold(pc_gamg_agg->crs, vfilter));                          // for code coverage
706:   } else {
707:     PetscCall(PetscObjectTypeCompare((PetscObject)pc_gamg_agg->crs, MATCOARSENMIS, &ismis));
708:     if (ismis && pc_gamg_agg->aggressive_coarsening_levels && !pc_gamg_agg->use_aggressive_square_graph) {
709:       PetscCall(PetscInfo(pc, "MIS and aggressive coarsening and no square graph: force square graph\n"));
710:       pc_gamg_agg->use_aggressive_square_graph = PETSC_TRUE;
711:     }
712:   }
713:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_COARSEN], 0, 0, 0, 0));
714:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_GRAPH], 0, 0, 0, 0));
715:   PetscCall(MatGetInfo(Amat, MAT_LOCAL, &info0)); /* global reduction */

717:   if (ishem || pc_gamg_agg->use_low_mem_filter) {
718:     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));
719:   } else {
720:     // make scalar graph, symmetrize if not known to be symmetric, scale, but do not filter (expensive)
721:     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));
722:     if (vfilter >= 0) {
723:       PetscInt           Istart, Iend, ncols, nnz0, nnz1, NN, MM, nloc;
724:       Mat                tGmat, Gmat = *a_Gmat;
725:       MPI_Comm           comm;
726:       const PetscScalar *vals;
727:       const PetscInt    *idx;
728:       PetscInt          *d_nnz, *o_nnz, kk, *garray = NULL, *AJ, maxcols = 0;
729:       MatScalar         *AA; // this is checked in graph
730:       PetscBool          isseqaij;
731:       Mat                a, b, c;
732:       MatType            jtype;

734:       PetscCall(PetscObjectGetComm((PetscObject)Gmat, &comm));
735:       PetscCall(PetscObjectBaseTypeCompare((PetscObject)Gmat, MATSEQAIJ, &isseqaij));
736:       PetscCall(MatGetType(Gmat, &jtype));
737:       PetscCall(MatCreate(comm, &tGmat));
738:       PetscCall(MatSetType(tGmat, jtype));

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

744:       // global sizes
745:       PetscCall(MatGetSize(Gmat, &MM, &NN));
746:       PetscCall(MatGetOwnershipRange(Gmat, &Istart, &Iend));
747:       nloc = Iend - Istart;
748:       PetscCall(PetscMalloc2(nloc, &d_nnz, nloc, &o_nnz));
749:       if (isseqaij) {
750:         a = Gmat;
751:         b = NULL;
752:       } else {
753:         Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;

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

790:               nnz1++;
791:               if (c != a) cid = garray[idx[jj]];
792:               AA[ncol_row] = vals[jj];
793:               AJ[ncol_row] = cid;
794:               ncol_row++;
795:             }
796:           }
797:           PetscCall(MatRestoreRow(c, row, &ncols, &idx, &vals));
798:           PetscCall(MatSetValues(tGmat, 1, &grow, ncol_row, AJ, AA, INSERT_VALUES));
799:         }
800:       }
801:       PetscCall(PetscFree2(AA, AJ));
802:       PetscCall(MatAssemblyBegin(tGmat, MAT_FINAL_ASSEMBLY));
803:       PetscCall(MatAssemblyEnd(tGmat, MAT_FINAL_ASSEMBLY));
804:       PetscCall(MatPropagateSymmetryOptions(Gmat, tGmat)); /* Normal Mat options are not relevant ? */
805:       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));
806:       PetscCall(MatViewFromOptions(tGmat, NULL, "-mat_filter_graph_view"));
807:       PetscCall(MatDestroy(&Gmat));
808:       *a_Gmat = tGmat;
809:     }
810:   }

812:   PetscCall(MatGetInfo(*a_Gmat, MAT_LOCAL, &info1)); /* global reduction */
813:   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));
814:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_GRAPH], 0, 0, 0, 0));
815:   PetscFunctionReturn(PETSC_SUCCESS);
816: }

818: typedef PetscInt    NState;
819: static const NState NOT_DONE = -2;
820: static const NState DELETED  = -1;
821: static const NState REMOVED  = -3;
822: #define IS_SELECTED(s) (s != DELETED && s != NOT_DONE && s != REMOVED)

824: /*
825:    fixAggregatesWithSquare - greedy grab of with G1 (unsquared graph) -- AIJ specific -- change to fixAggregatesWithSquare -- TODD
826:      - AGG-MG specific: clears singletons out of 'selected_2'

828:    Input Parameter:
829:    . Gmat_2 - global matrix of squared graph (data not defined)
830:    . Gmat_1 - base graph to grab with base graph
831:    Input/Output Parameter:
832:    . aggs_2 - linked list of aggs with gids)
833: */
834: static PetscErrorCode fixAggregatesWithSquare(PC pc, Mat Gmat_2, Mat Gmat_1, PetscCoarsenData *aggs_2)
835: {
836:   PetscBool      isMPI;
837:   Mat_SeqAIJ    *matA_1, *matB_1 = NULL;
838:   MPI_Comm       comm;
839:   PetscInt       lid, *ii, *idx, ix, Iend, my0, kk, n, j;
840:   Mat_MPIAIJ    *mpimat_2 = NULL, *mpimat_1 = NULL;
841:   const PetscInt nloc = Gmat_2->rmap->n;
842:   PetscScalar   *cpcol_1_state, *cpcol_2_state, *cpcol_2_par_orig, *lid_parent_gid;
843:   PetscInt      *lid_cprowID_1 = NULL;
844:   NState        *lid_state;
845:   Vec            ghost_par_orig2;
846:   PetscMPIInt    rank;

848:   PetscFunctionBegin;
849:   PetscCall(PetscObjectGetComm((PetscObject)Gmat_2, &comm));
850:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
851:   PetscCall(MatGetOwnershipRange(Gmat_1, &my0, &Iend));

853:   /* get submatrices */
854:   PetscCall(PetscStrbeginswith(((PetscObject)Gmat_1)->type_name, MATMPIAIJ, &isMPI));
855:   PetscCall(PetscInfo(pc, "isMPI = %s\n", isMPI ? "yes" : "no"));
856:   PetscCall(PetscMalloc3(nloc, &lid_state, nloc, &lid_parent_gid, nloc, &lid_cprowID_1));
857:   for (lid = 0; lid < nloc; lid++) lid_cprowID_1[lid] = -1;
858:   if (isMPI) {
859:     /* grab matrix objects */
860:     mpimat_2 = (Mat_MPIAIJ *)Gmat_2->data;
861:     mpimat_1 = (Mat_MPIAIJ *)Gmat_1->data;
862:     matA_1   = (Mat_SeqAIJ *)mpimat_1->A->data;
863:     matB_1   = (Mat_SeqAIJ *)mpimat_1->B->data;

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

870:       PetscCheck(lid <= nloc && lid >= -1, PETSC_COMM_SELF, PETSC_ERR_USER, "lid %" PetscInt_FMT " out of range. nloc = %" PetscInt_FMT, lid, nloc);
871:       if (lid != -1) lid_cprowID_1[lid] = ix;
872:     }
873:   } else {
874:     PetscBool isAIJ;

876:     PetscCall(PetscStrbeginswith(((PetscObject)Gmat_1)->type_name, MATSEQAIJ, &isAIJ));
877:     PetscCheck(isAIJ, PETSC_COMM_SELF, PETSC_ERR_USER, "Require AIJ matrix.");
878:     matA_1 = (Mat_SeqAIJ *)Gmat_1->data;
879:   }
880:   if (nloc > 0) PetscCheck(!matB_1 || matB_1->compressedrow.use, PETSC_COMM_SELF, PETSC_ERR_PLIB, "matB_1 && !matB_1->compressedrow.use: PETSc bug???");
881:   /* get state of locals and selected gid for deleted */
882:   for (lid = 0; lid < nloc; lid++) {
883:     lid_parent_gid[lid] = -1.0;
884:     lid_state[lid]      = DELETED;
885:   }

887:   /* set lid_state */
888:   for (lid = 0; lid < nloc; lid++) {
889:     PetscCDIntNd *pos;

891:     PetscCall(PetscCDGetHeadPos(aggs_2, lid, &pos));
892:     if (pos) {
893:       PetscInt gid1;

895:       PetscCall(PetscCDIntNdGetID(pos, &gid1));
896:       PetscCheck(gid1 == lid + my0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "gid1 %" PetscInt_FMT " != lid %" PetscInt_FMT " + my0 %" PetscInt_FMT, gid1, lid, my0);
897:       lid_state[lid] = gid1;
898:     }
899:   }

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

905:     if (IS_SELECTED(state)) {
906:       PetscCDIntNd *pos;

908:       PetscCall(PetscCDGetHeadPos(aggs_2, lid, &pos));
909:       while (pos) {
910:         PetscInt gid1;

912:         PetscCall(PetscCDIntNdGetID(pos, &gid1));
913:         PetscCall(PetscCDGetNextPos(aggs_2, lid, &pos));
914:         if (gid1 >= my0 && gid1 < Iend) lid_parent_gid[gid1 - my0] = (PetscScalar)(lid + my0);
915:       }
916:     }
917:   }
918:   /* get 'cpcol_1/2_state' & cpcol_2_par_orig - uses mpimat_1/2->lvec for temp space */
919:   if (isMPI) {
920:     Vec tempVec;

922:     /* get 'cpcol_1_state' */
923:     PetscCall(MatCreateVecs(Gmat_1, &tempVec, NULL));
924:     for (kk = 0, j = my0; kk < nloc; kk++, j++) {
925:       PetscScalar v = (PetscScalar)lid_state[kk];

927:       PetscCall(VecSetValues(tempVec, 1, &j, &v, INSERT_VALUES));
928:     }
929:     PetscCall(VecAssemblyBegin(tempVec));
930:     PetscCall(VecAssemblyEnd(tempVec));
931:     PetscCall(VecScatterBegin(mpimat_1->Mvctx, tempVec, mpimat_1->lvec, INSERT_VALUES, SCATTER_FORWARD));
932:     PetscCall(VecScatterEnd(mpimat_1->Mvctx, tempVec, mpimat_1->lvec, INSERT_VALUES, SCATTER_FORWARD));
933:     PetscCall(VecGetArray(mpimat_1->lvec, &cpcol_1_state));
934:     /* get 'cpcol_2_state' */
935:     PetscCall(VecScatterBegin(mpimat_2->Mvctx, tempVec, mpimat_2->lvec, INSERT_VALUES, SCATTER_FORWARD));
936:     PetscCall(VecScatterEnd(mpimat_2->Mvctx, tempVec, mpimat_2->lvec, INSERT_VALUES, SCATTER_FORWARD));
937:     PetscCall(VecGetArray(mpimat_2->lvec, &cpcol_2_state));
938:     /* get 'cpcol_2_par_orig' */
939:     for (kk = 0, j = my0; kk < nloc; kk++, j++) {
940:       PetscScalar v = lid_parent_gid[kk];

942:       PetscCall(VecSetValues(tempVec, 1, &j, &v, INSERT_VALUES));
943:     }
944:     PetscCall(VecAssemblyBegin(tempVec));
945:     PetscCall(VecAssemblyEnd(tempVec));
946:     PetscCall(VecDuplicate(mpimat_2->lvec, &ghost_par_orig2));
947:     PetscCall(VecScatterBegin(mpimat_2->Mvctx, tempVec, ghost_par_orig2, INSERT_VALUES, SCATTER_FORWARD));
948:     PetscCall(VecScatterEnd(mpimat_2->Mvctx, tempVec, ghost_par_orig2, INSERT_VALUES, SCATTER_FORWARD));
949:     PetscCall(VecGetArray(ghost_par_orig2, &cpcol_2_par_orig));

951:     PetscCall(VecDestroy(&tempVec));
952:   } /* ismpi */
953:   for (lid = 0; lid < nloc; lid++) {
954:     NState state = lid_state[lid];

956:     if (IS_SELECTED(state)) {
957:       /* steal locals */
958:       ii  = matA_1->i;
959:       n   = ii[lid + 1] - ii[lid];
960:       idx = matA_1->j + ii[lid];
961:       for (j = 0; j < n; j++) {
962:         PetscInt lidj   = idx[j], sgid;
963:         NState   statej = lid_state[lidj];

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

971:             /* looking for local from local so id_llist_2 works */
972:             PetscCall(PetscCDGetHeadPos(aggs_2, slid, &pos));
973:             while (pos) {
974:               PetscInt gid;

976:               PetscCall(PetscCDIntNdGetID(pos, &gid));
977:               if (gid == gidj) {
978:                 PetscCheck(last, PETSC_COMM_SELF, PETSC_ERR_PLIB, "last cannot be null");
979:                 PetscCall(PetscCDRemoveNextNode(aggs_2, slid, last));
980:                 PetscCall(PetscCDAppendNode(aggs_2, lid, pos));
981:                 hav = 1;
982:                 break;
983:               } else last = pos;
984:               PetscCall(PetscCDGetNextPos(aggs_2, slid, &pos));
985:             }
986:             if (hav != 1) {
987:               PetscCheck(hav, PETSC_COMM_SELF, PETSC_ERR_PLIB, "failed to find adj in 'selected' lists - structurally unsymmetric matrix");
988:               SETERRQ(PETSC_COMM_SELF, PETSC_ERR_PLIB, "found node %" PetscInt_FMT " times???", hav);
989:             }
990:           } else { /* I'm stealing this local, owned by a ghost */
991:             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.",
992:                        ((PetscObject)pc)->prefix ? ((PetscObject)pc)->prefix : "", ((PetscObject)pc)->prefix ? ((PetscObject)pc)->prefix : "");
993:             PetscCall(PetscCDAppendID(aggs_2, lid, lidj + my0));
994:           }
995:         }
996:       } /* local neighbors */
997:     } else if (state == DELETED /* && lid_cprowID_1 */) {
998:       PetscInt sgidold = (PetscInt)PetscRealPart(lid_parent_gid[lid]);

1000:       /* see if I have a selected ghost neighbor that will steal me */
1001:       if ((ix = lid_cprowID_1[lid]) != -1) {
1002:         ii  = matB_1->compressedrow.i;
1003:         n   = ii[ix + 1] - ii[ix];
1004:         idx = matB_1->j + ii[ix];
1005:         for (j = 0; j < n; j++) {
1006:           PetscInt cpid   = idx[j];
1007:           NState   statej = (NState)PetscRealPart(cpcol_1_state[cpid]);

1009:           if (IS_SELECTED(statej) && sgidold != statej) { /* ghost will steal this, remove from my list */
1010:             lid_parent_gid[lid] = (PetscScalar)statej;    /* send who selected */
1011:             if (sgidold >= my0 && sgidold < Iend) {       /* this was mine */
1012:               PetscInt      hav = 0, oldslidj = sgidold - my0;
1013:               PetscCDIntNd *pos, *last        = NULL;

1015:               /* remove from 'oldslidj' list */
1016:               PetscCall(PetscCDGetHeadPos(aggs_2, oldslidj, &pos));
1017:               while (pos) {
1018:                 PetscInt gid;

1020:                 PetscCall(PetscCDIntNdGetID(pos, &gid));
1021:                 if (lid + my0 == gid) {
1022:                   /* id_llist_2[lastid] = id_llist_2[flid];   /\* remove lid from oldslidj list *\/ */
1023:                   PetscCheck(last, PETSC_COMM_SELF, PETSC_ERR_PLIB, "last cannot be null");
1024:                   PetscCall(PetscCDRemoveNextNode(aggs_2, oldslidj, last));
1025:                   /* ghost (PetscScalar)statej will add this later */
1026:                   hav = 1;
1027:                   break;
1028:                 } else last = pos;
1029:                 PetscCall(PetscCDGetNextPos(aggs_2, oldslidj, &pos));
1030:               }
1031:               if (hav != 1) {
1032:                 PetscCheck(hav, PETSC_COMM_SELF, PETSC_ERR_PLIB, "failed to find (hav=%" PetscInt_FMT ") adj in 'selected' lists - structurally unsymmetric matrix", hav);
1033:                 SETERRQ(PETSC_COMM_SELF, PETSC_ERR_PLIB, "found node %" PetscInt_FMT " times???", hav);
1034:               }
1035:             } else {
1036:               /* TODO: ghosts remove this later */
1037:             }
1038:           }
1039:         }
1040:       }
1041:     } /* selected/deleted */
1042:   } /* node loop */

1044:   if (isMPI) {
1045:     PetscScalar *cpcol_2_parent, *cpcol_2_gid;
1046:     Vec          tempVec, ghostgids2, ghostparents2;
1047:     PetscInt     cpid, nghost_2;
1048:     PetscHMapI   gid_cpid;

1050:     PetscCall(VecGetSize(mpimat_2->lvec, &nghost_2));
1051:     PetscCall(MatCreateVecs(Gmat_2, &tempVec, NULL));

1053:     /* get 'cpcol_2_parent' */
1054:     for (kk = 0, j = my0; kk < nloc; kk++, j++) PetscCall(VecSetValues(tempVec, 1, &j, &lid_parent_gid[kk], INSERT_VALUES));
1055:     PetscCall(VecAssemblyBegin(tempVec));
1056:     PetscCall(VecAssemblyEnd(tempVec));
1057:     PetscCall(VecDuplicate(mpimat_2->lvec, &ghostparents2));
1058:     PetscCall(VecScatterBegin(mpimat_2->Mvctx, tempVec, ghostparents2, INSERT_VALUES, SCATTER_FORWARD));
1059:     PetscCall(VecScatterEnd(mpimat_2->Mvctx, tempVec, ghostparents2, INSERT_VALUES, SCATTER_FORWARD));
1060:     PetscCall(VecGetArray(ghostparents2, &cpcol_2_parent));

1062:     /* get 'cpcol_2_gid' */
1063:     for (kk = 0, j = my0; kk < nloc; kk++, j++) {
1064:       PetscScalar v = (PetscScalar)j;

1066:       PetscCall(VecSetValues(tempVec, 1, &j, &v, INSERT_VALUES));
1067:     }
1068:     PetscCall(VecAssemblyBegin(tempVec));
1069:     PetscCall(VecAssemblyEnd(tempVec));
1070:     PetscCall(VecDuplicate(mpimat_2->lvec, &ghostgids2));
1071:     PetscCall(VecScatterBegin(mpimat_2->Mvctx, tempVec, ghostgids2, INSERT_VALUES, SCATTER_FORWARD));
1072:     PetscCall(VecScatterEnd(mpimat_2->Mvctx, tempVec, ghostgids2, INSERT_VALUES, SCATTER_FORWARD));
1073:     PetscCall(VecGetArray(ghostgids2, &cpcol_2_gid));
1074:     PetscCall(VecDestroy(&tempVec));

1076:     /* look for deleted ghosts and add to table */
1077:     PetscCall(PetscHMapICreateWithSize(2 * nghost_2 + 1, &gid_cpid));
1078:     for (cpid = 0; cpid < nghost_2; cpid++) {
1079:       NState state = (NState)PetscRealPart(cpcol_2_state[cpid]);

1081:       if (state == DELETED) {
1082:         PetscInt sgid_new = (PetscInt)PetscRealPart(cpcol_2_parent[cpid]);
1083:         PetscInt sgid_old = (PetscInt)PetscRealPart(cpcol_2_par_orig[cpid]);

1085:         if (sgid_old == -1 && sgid_new != -1) {
1086:           PetscInt gid = (PetscInt)PetscRealPart(cpcol_2_gid[cpid]);

1088:           PetscCall(PetscHMapISet(gid_cpid, gid, cpid));
1089:         }
1090:       }
1091:     }

1093:     /* look for deleted ghosts and see if they moved - remove it */
1094:     for (lid = 0; lid < nloc; lid++) {
1095:       NState state = lid_state[lid];

1097:       if (IS_SELECTED(state)) {
1098:         PetscCDIntNd *pos, *last = NULL;

1100:         /* look for deleted ghosts and see if they moved */
1101:         PetscCall(PetscCDGetHeadPos(aggs_2, lid, &pos));
1102:         while (pos) {
1103:           PetscInt gid;

1105:           PetscCall(PetscCDIntNdGetID(pos, &gid));
1106:           if (gid < my0 || gid >= Iend) {
1107:             PetscCall(PetscHMapIGet(gid_cpid, gid, &cpid));
1108:             if (cpid != -1) {
1109:               /* a moved ghost - */
1110:               /* id_llist_2[lastid] = id_llist_2[flid];    /\* remove 'flid' from list *\/ */
1111:               PetscCall(PetscCDRemoveNextNode(aggs_2, lid, last));
1112:             } else last = pos;
1113:           } else last = pos;

1115:           PetscCall(PetscCDGetNextPos(aggs_2, lid, &pos));
1116:         } /* loop over list of deleted */
1117:       } /* selected */
1118:     }
1119:     PetscCall(PetscHMapIDestroy(&gid_cpid));

1121:     /* look at ghosts, see if they changed - and it */
1122:     for (cpid = 0; cpid < nghost_2; cpid++) {
1123:       PetscInt sgid_new = (PetscInt)PetscRealPart(cpcol_2_parent[cpid]);

1125:       if (sgid_new >= my0 && sgid_new < Iend) { /* this is mine */
1126:         PetscInt      gid      = (PetscInt)PetscRealPart(cpcol_2_gid[cpid]);
1127:         PetscInt      slid_new = sgid_new - my0, hav = 0;
1128:         PetscCDIntNd *pos;

1130:         /* search for this gid to see if I have it */
1131:         PetscCall(PetscCDGetHeadPos(aggs_2, slid_new, &pos));
1132:         while (pos) {
1133:           PetscInt gidj;

1135:           PetscCall(PetscCDIntNdGetID(pos, &gidj));
1136:           PetscCall(PetscCDGetNextPos(aggs_2, slid_new, &pos));

1138:           if (gidj == gid) {
1139:             hav = 1;
1140:             break;
1141:           }
1142:         }
1143:         if (hav != 1) {
1144:           /* insert 'flidj' into head of llist */
1145:           PetscCall(PetscCDAppendID(aggs_2, slid_new, gid));
1146:         }
1147:       }
1148:     }
1149:     PetscCall(VecRestoreArray(mpimat_1->lvec, &cpcol_1_state));
1150:     PetscCall(VecRestoreArray(mpimat_2->lvec, &cpcol_2_state));
1151:     PetscCall(VecRestoreArray(ghostparents2, &cpcol_2_parent));
1152:     PetscCall(VecRestoreArray(ghostgids2, &cpcol_2_gid));
1153:     PetscCall(VecDestroy(&ghostgids2));
1154:     PetscCall(VecDestroy(&ghostparents2));
1155:     PetscCall(VecDestroy(&ghost_par_orig2));
1156:   }
1157:   PetscCall(PetscFree3(lid_state, lid_parent_gid, lid_cprowID_1));
1158:   PetscFunctionReturn(PETSC_SUCCESS);
1159: }

1161: /*
1162:    PCGAMGCoarsen_AGG - supports squaring the graph (deprecated) and new graph for
1163:      communication of QR data used with HEM and MISk coarsening

1165:   Input Parameter:
1166:    . a_pc - this

1168:   Input/Output Parameter:
1169:    . a_Gmat1 - graph to coarsen (in), graph off processor edges for QR gather scatter (out)

1171:   Output Parameter:
1172:    . agg_lists - list of aggregates

1174: */
1175: static PetscErrorCode PCGAMGCoarsen_AGG(PC a_pc, Mat *a_Gmat1, PetscCoarsenData **agg_lists)
1176: {
1177:   PC_MG       *mg          = (PC_MG *)a_pc->data;
1178:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
1179:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;
1180:   Mat          Gmat2, Gmat1 = *a_Gmat1; /* aggressive graph */
1181:   IS           perm;
1182:   PetscInt     Istart, Iend, Ii, nloc, bs, nn;
1183:   PetscInt    *permute, *degree;
1184:   PetscBool   *bIndexSet;
1185:   PetscReal    hashfact;
1186:   PetscInt     iSwapIndex;
1187:   PetscRandom  random;
1188:   MPI_Comm     comm;

1190:   PetscFunctionBegin;
1191:   PetscCall(PetscObjectGetComm((PetscObject)Gmat1, &comm));
1192:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_COARSEN], 0, 0, 0, 0));
1193:   PetscCall(MatGetLocalSize(Gmat1, &nn, NULL));
1194:   PetscCall(MatGetBlockSize(Gmat1, &bs));
1195:   PetscCheck(bs == 1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "bs %" PetscInt_FMT " must be 1", bs);
1196:   nloc = nn / bs;
1197:   /* get MIS aggs - randomize */
1198:   PetscCall(PetscMalloc2(nloc, &permute, nloc, &degree));
1199:   PetscCall(PetscCalloc1(nloc, &bIndexSet));
1200:   for (Ii = 0; Ii < nloc; Ii++) permute[Ii] = Ii;
1201:   PetscCall(PetscRandomCreate(PETSC_COMM_SELF, &random));
1202:   PetscCall(MatGetOwnershipRange(Gmat1, &Istart, &Iend));
1203:   for (Ii = 0; Ii < nloc; Ii++) {
1204:     PetscInt nc;

1206:     PetscCall(MatGetRow(Gmat1, Istart + Ii, &nc, NULL, NULL));
1207:     degree[Ii] = nc;
1208:     PetscCall(MatRestoreRow(Gmat1, Istart + Ii, &nc, NULL, NULL));
1209:   }
1210:   for (Ii = 0; Ii < nloc; Ii++) {
1211:     PetscCall(PetscRandomGetValueReal(random, &hashfact));
1212:     iSwapIndex = (PetscInt)(hashfact * nloc) % nloc;
1213:     if (!bIndexSet[iSwapIndex] && iSwapIndex != Ii) {
1214:       PetscInt iTemp = permute[iSwapIndex];

1216:       permute[iSwapIndex]   = permute[Ii];
1217:       permute[Ii]           = iTemp;
1218:       iTemp                 = degree[iSwapIndex];
1219:       degree[iSwapIndex]    = degree[Ii];
1220:       degree[Ii]            = iTemp;
1221:       bIndexSet[iSwapIndex] = PETSC_TRUE;
1222:     }
1223:   }
1224:   // apply minimum degree ordering -- NEW
1225:   if (pc_gamg_agg->use_minimum_degree_ordering) PetscCall(PetscSortIntWithArray(nloc, degree, permute));
1226:   PetscCall(PetscFree(bIndexSet));
1227:   PetscCall(PetscRandomDestroy(&random));
1228:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nloc, permute, PETSC_USE_POINTER, &perm));
1229:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_MIS], 0, 0, 0, 0));
1230:   // square graph
1231:   if (pc_gamg->current_level < pc_gamg_agg->aggressive_coarsening_levels && pc_gamg_agg->use_aggressive_square_graph) PetscCall(PCGAMGSquareGraph_GAMG(a_pc, Gmat1, &Gmat2));
1232:   else Gmat2 = Gmat1;
1233:   // switch to old MIS-1 for square graph
1234:   if (pc_gamg->current_level < pc_gamg_agg->aggressive_coarsening_levels) {
1235:     if (!pc_gamg_agg->use_aggressive_square_graph) PetscCall(MatCoarsenMISKSetDistance(pc_gamg_agg->crs, pc_gamg_agg->aggressive_mis_k)); // hardwire to MIS-2
1236:     else PetscCall(MatCoarsenSetType(pc_gamg_agg->crs, MATCOARSENMIS));                                                                   // old MIS -- side effect
1237:   } else if (pc_gamg_agg->use_aggressive_square_graph && pc_gamg_agg->aggressive_coarsening_levels > 0) {                                 // we reset the MIS
1238:     const char *prefix;

1240:     PetscCall(PetscObjectGetOptionsPrefix((PetscObject)a_pc, &prefix));
1241:     PetscCall(PetscObjectSetOptionsPrefix((PetscObject)pc_gamg_agg->crs, prefix));
1242:     PetscCall(MatCoarsenSetFromOptions(pc_gamg_agg->crs)); // get the default back on non-aggressive levels when square graph switched to old MIS
1243:   }
1244:   PetscCall(MatCoarsenSetAdjacency(pc_gamg_agg->crs, Gmat2));
1245:   PetscCall(MatCoarsenSetStrictAggs(pc_gamg_agg->crs, PETSC_TRUE));
1246:   PetscCall(MatCoarsenSetGreedyOrdering(pc_gamg_agg->crs, perm));
1247:   PetscCall(MatCoarsenApply(pc_gamg_agg->crs));
1248:   PetscCall(MatCoarsenGetData(pc_gamg_agg->crs, agg_lists)); /* output */

1250:   PetscCall(ISDestroy(&perm));
1251:   PetscCall(PetscFree2(permute, degree));
1252:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_MIS], 0, 0, 0, 0));

1254:   if (Gmat2 != Gmat1) { // square graph, we need ghosts for selected
1255:     PetscCoarsenData *llist = *agg_lists;

1257:     PetscCall(fixAggregatesWithSquare(a_pc, Gmat2, Gmat1, *agg_lists));
1258:     PetscCall(MatDestroy(&Gmat1));
1259:     *a_Gmat1 = Gmat2;                          /* output */
1260:     PetscCall(PetscCDSetMat(llist, *a_Gmat1)); /* Need a graph with ghosts here */
1261:   }
1262:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_COARSEN], 0, 0, 0, 0));
1263:   PetscFunctionReturn(PETSC_SUCCESS);
1264: }

1266: /*
1267:  PCGAMGConstructProlongator_AGG

1269:  Input Parameter:
1270:  . pc - this
1271:  . Amat - matrix on this fine level
1272:  . Graph - used to get ghost data for nodes in
1273:  . agg_lists - list of aggregates
1274:  Output Parameter:
1275:  . a_P_out - prolongation operator to the next level
1276:  */
1277: static PetscErrorCode PCGAMGConstructProlongator_AGG(PC pc, Mat Amat, PetscCoarsenData *agg_lists, Mat *a_P_out)
1278: {
1279:   PC_MG         *mg      = (PC_MG *)pc->data;
1280:   PC_GAMG       *pc_gamg = (PC_GAMG *)mg->innerctx;
1281:   const PetscInt col_bs  = pc_gamg->data_cell_cols;
1282:   PetscInt       Istart, Iend, nloc, ii, jj, kk, my0, nLocalSelected, bs;
1283:   Mat            Gmat, Prol;
1284:   PetscMPIInt    size;
1285:   MPI_Comm       comm;
1286:   PetscReal     *data_w_ghost;
1287:   PetscInt       myCrs0, nbnodes = 0, *flid_fgid;
1288:   MatType        mtype;

1290:   PetscFunctionBegin;
1291:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1292:   PetscCheck(col_bs >= 1, comm, PETSC_ERR_PLIB, "Column bs cannot be less than 1");
1293:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_PROL], 0, 0, 0, 0));
1294:   PetscCallMPI(MPI_Comm_size(comm, &size));
1295:   PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
1296:   PetscCall(MatGetBlockSize(Amat, &bs));
1297:   nloc = (Iend - Istart) / bs;
1298:   my0  = Istart / bs;
1299:   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);
1300:   PetscCall(PetscCDGetMat(agg_lists, &Gmat)); // get auxiliary matrix for ghost edges for size > 1

1302:   /* get 'nLocalSelected' */
1303:   for (ii = 0, nLocalSelected = 0; ii < nloc; ii++) {
1304:     PetscBool ise;

1306:     /* filter out singletons 0 or 1? */
1307:     PetscCall(PetscCDIsEmptyAt(agg_lists, ii, &ise));
1308:     if (!ise) nLocalSelected++;
1309:   }

1311:   /* create prolongator, create P matrix */
1312:   PetscCall(MatGetType(Amat, &mtype));
1313:   PetscCall(MatCreate(comm, &Prol));
1314:   PetscCall(MatSetSizes(Prol, nloc * bs, nLocalSelected * col_bs, PETSC_DETERMINE, PETSC_DETERMINE));
1315:   PetscCall(MatSetBlockSizes(Prol, bs, col_bs)); // should this be before MatSetSizes?
1316:   PetscCall(MatSetType(Prol, mtype));
1317: #if PetscDefined(HAVE_DEVICE)
1318:   PetscBool flg;
1319:   PetscCall(MatBoundToCPU(Amat, &flg));
1320:   PetscCall(MatBindToCPU(Prol, flg));
1321:   if (flg) PetscCall(MatSetBindingPropagates(Prol, PETSC_TRUE));
1322: #endif
1323:   PetscCall(MatSeqAIJSetPreallocation(Prol, col_bs, NULL));
1324:   PetscCall(MatMPIAIJSetPreallocation(Prol, col_bs, NULL, col_bs, NULL));

1326:   /* can get all points "removed" */
1327:   PetscCall(MatGetSize(Prol, &kk, &ii));
1328:   if (!ii) {
1329:     PetscCall(PetscInfo(pc, "%s: No selected points on coarse grid\n", ((PetscObject)pc)->prefix));
1330:     PetscCall(MatDestroy(&Prol));
1331:     *a_P_out = NULL; /* out */
1332:     PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_PROL], 0, 0, 0, 0));
1333:     PetscFunctionReturn(PETSC_SUCCESS);
1334:   }
1335:   PetscCall(PetscInfo(pc, "%s: New grid %" PetscInt_FMT " nodes\n", ((PetscObject)pc)->prefix, ii / col_bs));
1336:   PetscCall(MatGetOwnershipRangeColumn(Prol, &myCrs0, &kk));

1338:   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);
1339:   myCrs0 = myCrs0 / col_bs;
1340:   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);

1342:   /* create global vector of data in 'data_w_ghost' */
1343:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_PROLA], 0, 0, 0, 0));
1344:   if (size > 1) { /* get ghost null space data */
1345:     PetscReal *tmp_gdata, *tmp_ldata, *tp2;

1347:     PetscCall(PetscMalloc1(nloc, &tmp_ldata));
1348:     for (jj = 0; jj < col_bs; jj++) {
1349:       for (kk = 0; kk < bs; kk++) {
1350:         PetscInt         ii, stride;
1351:         const PetscReal *tp = PetscSafePointerPlusOffset(pc_gamg->data, jj * bs * nloc + kk);

1353:         for (ii = 0; ii < nloc; ii++, tp += bs) tmp_ldata[ii] = *tp;

1355:         PetscCall(PCGAMGGetDataWithGhosts(Gmat, 1, tmp_ldata, &stride, &tmp_gdata));

1357:         if (!jj && !kk) { /* now I know how many total nodes - allocate TODO: move below and do in one 'col_bs' call */
1358:           PetscCall(PetscMalloc1(stride * bs * col_bs, &data_w_ghost));
1359:           nbnodes = bs * stride;
1360:         }
1361:         tp2 = PetscSafePointerPlusOffset(data_w_ghost, jj * bs * stride + kk);
1362:         for (ii = 0; ii < stride; ii++, tp2 += bs) *tp2 = tmp_gdata[ii];
1363:         PetscCall(PetscFree(tmp_gdata));
1364:       }
1365:     }
1366:     PetscCall(PetscFree(tmp_ldata));
1367:   } else {
1368:     nbnodes      = bs * nloc;
1369:     data_w_ghost = pc_gamg->data;
1370:   }

1372:   /* get 'flid_fgid' TODO - move up to get 'stride' and do get null space data above in one step (jj loop) */
1373:   if (size > 1) {
1374:     PetscReal *fid_glid_loc, *fiddata;
1375:     PetscInt   stride;

1377:     PetscCall(PetscMalloc1(nloc, &fid_glid_loc));
1378:     for (kk = 0; kk < nloc; kk++) fid_glid_loc[kk] = (PetscReal)(my0 + kk);
1379:     PetscCall(PCGAMGGetDataWithGhosts(Gmat, 1, fid_glid_loc, &stride, &fiddata));
1380:     PetscCall(PetscMalloc1(stride, &flid_fgid)); /* copy real data to in */
1381:     for (kk = 0; kk < stride; kk++) flid_fgid[kk] = (PetscInt)fiddata[kk];
1382:     PetscCall(PetscFree(fiddata));

1384:     PetscCheck(stride == nbnodes / bs, PETSC_COMM_SELF, PETSC_ERR_PLIB, "stride %" PetscInt_FMT " != nbnodes %" PetscInt_FMT "/bs %" PetscInt_FMT, stride, nbnodes, bs);
1385:     PetscCall(PetscFree(fid_glid_loc));
1386:   } else {
1387:     PetscCall(PetscMalloc1(nloc, &flid_fgid));
1388:     for (kk = 0; kk < nloc; kk++) flid_fgid[kk] = my0 + kk;
1389:   }
1390:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_PROLA], 0, 0, 0, 0));
1391:   /* get P0 */
1392:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_PROLB], 0, 0, 0, 0));
1393:   {
1394:     PetscReal *data_out = NULL;

1396:     PetscCall(formProl0(agg_lists, bs, col_bs, myCrs0, nbnodes, data_w_ghost, flid_fgid, &data_out, Prol));
1397:     PetscCall(PetscFree(pc_gamg->data));

1399:     pc_gamg->data           = data_out;
1400:     pc_gamg->data_cell_rows = col_bs;
1401:     pc_gamg->data_sz        = col_bs * col_bs * nLocalSelected;
1402:   }
1403:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_PROLB], 0, 0, 0, 0));
1404:   if (size > 1) PetscCall(PetscFree(data_w_ghost));
1405:   PetscCall(PetscFree(flid_fgid));

1407:   *a_P_out = Prol; /* out */
1408:   PetscCall(MatViewFromOptions(Prol, NULL, "-pc_gamg_agg_view_initial_prolongation"));

1410:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_PROL], 0, 0, 0, 0));
1411:   PetscFunctionReturn(PETSC_SUCCESS);
1412: }

1414: /*
1415:    PCGAMGOptimizeProlongator_AGG - given the initial prolongator optimizes it by smoothed aggregation pc_gamg_agg->nsmooths times

1417:   Input Parameter:
1418:    . pc - this
1419:    . Amat - matrix on this fine level
1420:  In/Output Parameter:
1421:    . a_P - prolongation operator to the next level
1422: */
1423: static PetscErrorCode PCGAMGOptimizeProlongator_AGG(PC pc, Mat Amat, Mat *a_P)
1424: {
1425:   PC_MG       *mg          = (PC_MG *)pc->data;
1426:   PC_GAMG     *pc_gamg     = (PC_GAMG *)mg->innerctx;
1427:   PC_GAMG_AGG *pc_gamg_agg = (PC_GAMG_AGG *)pc_gamg->subctx;
1428:   PetscInt     jj;
1429:   Mat          Prol = *a_P;
1430:   MPI_Comm     comm;
1431:   KSP          eksp;
1432:   Vec          bb, xx;
1433:   PC           epc;
1434:   PetscReal    alpha, emax, emin;

1436:   PetscFunctionBegin;
1437:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1438:   PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_OPT], 0, 0, 0, 0));

1440:   /* compute maximum singular value of operator to be used in smoother */
1441:   if (0 < pc_gamg_agg->nsmooths) {
1442:     /* get eigen estimates */
1443:     if (pc_gamg->emax > 0) {
1444:       emin = pc_gamg->emin;
1445:       emax = pc_gamg->emax;
1446:     } else {
1447:       const char *prefix;

1449:       PetscCall(MatCreateVecs(Amat, &bb, NULL));
1450:       PetscCall(MatCreateVecs(Amat, &xx, NULL));
1451:       PetscCall(KSPSetNoisy_Private(Amat, bb));

1453:       PetscCall(KSPCreate(comm, &eksp));
1454:       PetscCall(KSPSetNestLevel(eksp, pc->kspnestlevel));
1455:       PetscCall(PCGetOptionsPrefix(pc, &prefix));
1456:       PetscCall(KSPSetOptionsPrefix(eksp, prefix));
1457:       PetscCall(KSPAppendOptionsPrefix(eksp, "pc_gamg_esteig_"));
1458:       {
1459:         PetscBool isset, sflg;

1461:         PetscCall(MatIsSPDKnown(Amat, &isset, &sflg));
1462:         if (isset && sflg) PetscCall(KSPSetType(eksp, KSPCG));
1463:       }
1464:       PetscCall(KSPSetErrorIfNotConverged(eksp, pc->erroriffailure));
1465:       PetscCall(KSPSetNormType(eksp, KSP_NORM_NONE));

1467:       PetscCall(KSPSetInitialGuessNonzero(eksp, PETSC_FALSE));
1468:       PetscCall(KSPSetOperators(eksp, Amat, Amat));

1470:       PetscCall(KSPGetPC(eksp, &epc));
1471:       PetscCall(PCSetType(epc, PCJACOBI)); /* smoother in smoothed agg. */

1473:       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

1475:       PetscCall(KSPSetFromOptions(eksp));
1476:       PetscCall(KSPSetComputeSingularValues(eksp, PETSC_TRUE));
1477:       PetscCall(KSPSolve(eksp, bb, xx));
1478:       PetscCall(KSPCheckSolve(eksp, pc, xx));

1480:       PetscCall(KSPComputeExtremeSingularValues(eksp, &emax, &emin));
1481:       PetscCall(PetscInfo(pc, "%s: Smooth P0: max eigen=%e min=%e PC=%s\n", ((PetscObject)pc)->prefix, (double)emax, (double)emin, PCJACOBI));
1482:       PetscCall(VecDestroy(&xx));
1483:       PetscCall(VecDestroy(&bb));
1484:       PetscCall(KSPDestroy(&eksp));
1485:     }
1486:     if (pc_gamg->use_sa_esteig) {
1487:       mg->min_eigen_DinvA[pc_gamg->current_level] = emin;
1488:       mg->max_eigen_DinvA[pc_gamg->current_level] = emax;
1489:       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));
1490:     } else {
1491:       mg->min_eigen_DinvA[pc_gamg->current_level] = 0;
1492:       mg->max_eigen_DinvA[pc_gamg->current_level] = 0;
1493:     }
1494:   } else {
1495:     mg->min_eigen_DinvA[pc_gamg->current_level] = 0;
1496:     mg->max_eigen_DinvA[pc_gamg->current_level] = 0;
1497:   }

1499:   /* smooth P0 */
1500:   if (pc_gamg_agg->nsmooths > 0) {
1501:     Vec diag;

1503:     /* TODO: Set a PCFailedReason and exit the building of the AMG preconditioner */
1504:     PetscCheck(emax != 0.0, PetscObjectComm((PetscObject)pc), PETSC_ERR_PLIB, "Computed maximum singular value as zero");

1506:     PetscCall(MatCreateVecs(Amat, &diag, NULL));
1507:     PetscCall(MatGetDiagonal(Amat, diag)); /* effectively PCJACOBI */
1508:     PetscCall(VecReciprocal(diag));

1510:     for (jj = 0; jj < pc_gamg_agg->nsmooths; jj++) {
1511:       Mat tMat;

1513:       PetscCall(PetscLogEventBegin(petsc_gamg_setup_events[GAMG_OPTSM], 0, 0, 0, 0));
1514:       /*
1515:         Smooth aggregation on the prolongator

1517:         P_{i} := (I - 1.4/emax D^{-1}A) P_i\{i-1}
1518:       */
1519:       PetscCall(PetscLogEventBegin(petsc_gamg_setup_matmat_events[pc_gamg->current_level][2], 0, 0, 0, 0));
1520:       PetscCall(MatMatMult(Amat, Prol, MAT_INITIAL_MATRIX, PETSC_CURRENT, &tMat));
1521:       PetscCall(PetscLogEventEnd(petsc_gamg_setup_matmat_events[pc_gamg->current_level][2], 0, 0, 0, 0));
1522:       PetscCall(MatProductClear(tMat));
1523:       PetscCall(MatDiagonalScale(tMat, diag, NULL));

1525:       /* TODO: Document the 1.4 and don't hardwire it in this routine */
1526:       alpha = -1.4 / emax;
1527:       PetscCall(MatAYPX(tMat, alpha, Prol, SUBSET_NONZERO_PATTERN));
1528:       PetscCall(MatDestroy(&Prol));
1529:       Prol = tMat;
1530:       PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_OPTSM], 0, 0, 0, 0));
1531:     }
1532:     PetscCall(VecDestroy(&diag));
1533:   }
1534:   PetscCall(PetscLogEventEnd(petsc_gamg_setup_events[GAMG_OPT], 0, 0, 0, 0));
1535:   PetscCall(MatViewFromOptions(Prol, NULL, "-pc_gamg_agg_view_prolongation"));
1536:   *a_P = Prol;
1537:   PetscFunctionReturn(PETSC_SUCCESS);
1538: }

1540: /*MC
1541:   PCGAMGAGG - Smooth aggregation, {cite}`vanek1996algebraic`, {cite}`vanek2001convergence`, variant of PETSc's algebraic multigrid (`PCGAMG`) preconditioner

1543:   Options Database Keys:
1544: + -pc_gamg_agg_nsmooths <nsmooth, default=1> - number of smoothing steps to use with smooth aggregation to construct prolongation
1545: . -pc_gamg_aggressive_coarsening <n,default=1> - number of aggressive coarsening (MIS-2) levels from finest.
1546: . -pc_gamg_aggressive_square_graph <bool,default=true> - Use square graph (A'A), alternative is MIS-k (k=2), for aggressive coarsening
1547: . -pc_gamg_mis_k_minimum_degree_ordering <bool,default=false> - Use minimum degree ordering in greedy MIS algorithm
1548: . -pc_gamg_pc_gamg_asm_hem_aggs <n,default=0> - Number of HEM aggregation steps for ASM smoother
1549: - -pc_gamg_aggressive_mis_k <n,default=2> - Number (k) distance in MIS coarsening (>2 is 'aggressive')

1551:   Level: intermediate

1553:   Notes:
1554:   To obtain good performance for `PCGAMG` for vector valued problems you must
1555:   call `MatSetBlockSize()` to indicate the number of degrees of freedom per grid point.
1556:   Call `MatSetNearNullSpace()` (or `PCSetCoordinates()` if solving the equations of elasticity) to indicate the near null space of the operator

1558:   The many options for `PCMG` and `PCGAMG` such as controlling the smoothers on each level etc. also work for `PCGAMGAGG`

1560: .seealso: `PCGAMG`, [the Users Manual section on PCGAMG](sec_amg), [the Users Manual section on PCMG](sec_mg), [](ch_ksp), `PCCreate()`, `PCSetType()`,
1561:           `MatSetBlockSize()`, `PCMGType`, `PCSetCoordinates()`, `MatSetNearNullSpace()`, `PCGAMGSetType()`,
1562:           `PCGAMGAGG`, `PCGAMGGEO`, `PCGAMGCLASSICAL`, `PCGAMGSetProcEqLim()`, `PCGAMGSetCoarseEqLim()`, `PCGAMGSetRepartition()`, `PCGAMGRegister()`,
1563:           `PCGAMGSetReuseInterpolation()`, `PCGAMGASMSetUseAggs()`, `PCGAMGSetParallelCoarseGridSolve()`, `PCGAMGSetNlevels()`, `PCGAMGSetThreshold()`,
1564:           `PCGAMGGetType()`, `PCGAMGSetUseSAEstEig()`
1565: M*/
1566: PetscErrorCode PCCreateGAMG_AGG(PC pc)
1567: {
1568:   PC_MG       *mg      = (PC_MG *)pc->data;
1569:   PC_GAMG     *pc_gamg = (PC_GAMG *)mg->innerctx;
1570:   PC_GAMG_AGG *pc_gamg_agg;

1572:   PetscFunctionBegin;
1573:   /* create sub context for SA */
1574:   PetscCall(PetscNew(&pc_gamg_agg));
1575:   pc_gamg->subctx = pc_gamg_agg;

1577:   pc_gamg->ops->setfromoptions = PCSetFromOptions_GAMG_AGG;
1578:   pc_gamg->ops->destroy        = PCDestroy_GAMG_AGG;
1579:   /* reset does not do anything; setup not virtual */

1581:   /* set internal function pointers */
1582:   pc_gamg->ops->creategraph       = PCGAMGCreateGraph_AGG;
1583:   pc_gamg->ops->coarsen           = PCGAMGCoarsen_AGG;
1584:   pc_gamg->ops->prolongator       = PCGAMGConstructProlongator_AGG;
1585:   pc_gamg->ops->optprolongator    = PCGAMGOptimizeProlongator_AGG;
1586:   pc_gamg->ops->createdefaultdata = PCSetData_AGG;
1587:   pc_gamg->ops->view              = PCView_GAMG_AGG;

1589:   pc_gamg_agg->nsmooths                     = 1;
1590:   pc_gamg_agg->aggressive_coarsening_levels = 1;
1591:   pc_gamg_agg->use_aggressive_square_graph  = PETSC_TRUE;
1592:   pc_gamg_agg->use_minimum_degree_ordering  = PETSC_FALSE;
1593:   pc_gamg_agg->use_low_mem_filter           = PETSC_FALSE;
1594:   pc_gamg_agg->aggressive_mis_k             = 2;
1595:   pc_gamg_agg->graph_symmetrize             = PETSC_TRUE;

1597:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetNSmooths_C", PCGAMGSetNSmooths_AGG));
1598:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetAggressiveLevels_C", PCGAMGSetAggressiveLevels_AGG));
1599:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetAggressiveSquareGraph_C", PCGAMGSetAggressiveSquareGraph_AGG));
1600:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGMISkSetMinDegreeOrdering_C", PCGAMGMISkSetMinDegreeOrdering_AGG));
1601:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetLowMemoryFilter_C", PCGAMGSetLowMemoryFilter_AGG));
1602:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGMISkSetAggressive_C", PCGAMGMISkSetAggressive_AGG));
1603:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGAMGSetGraphSymmetrize_C", PCGAMGSetGraphSymmetrize_AGG));
1604:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSetCoordinates_C", PCSetCoordinates_AGG));
1605:   PetscFunctionReturn(PETSC_SUCCESS);
1606: }