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 %" PetscInt_FMT "\n", 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(deformation, "nearnullspace", (PetscObject *)&mnull));
442:         if (!mnull) PetscCall(PetscObjectQuery(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 %" PetscInt_FMT "\n", 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-%" PetscInt_FMT " coarsening on aggressive levels\n", 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 %" PetscInt_FMT "\n", 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 (%" PetscInt_FMT ") must be non-negative and < block size (%" PetscInt_FMT "), NB, can not use -mat_coarsen_strength_index with -mat_coarsen_strength_index",
686:                pc_gamg_agg->crs->strength_index[ii], 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 %" PetscInt_FMT " iterations\n", 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 %" PetscInt_FMT " out of range. nloc = %" PetscInt_FMT, lid, 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 %" PetscInt_FMT " != lid %" PetscInt_FMT " + my0 %" PetscInt_FMT, gid1, lid, 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 = 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 %" PetscInt_FMT " times???", 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 != 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=%" PetscInt_FMT ") adj in 'selected' lists - structurally unsymmetric matrix", hav);
1021:                 SETERRQ(PETSC_COMM_SELF, PETSC_ERR_PLIB, "found node %" PetscInt_FMT " times???", 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 = 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: }