Actual source code: mg.c
petsc-3.14.1 2020-11-03
2: /*
3: Defines the multigrid preconditioner interface.
4: */
5: #include <petsc/private/pcmgimpl.h>
6: #include <petscdm.h>
7: PETSC_INTERN PetscErrorCode PCPreSolveChangeRHS(PC,PetscBool*);
9: /*
10: Contains the list of registered coarse space construction routines
11: */
12: PetscFunctionList PCMGCoarseList = NULL;
14: PetscErrorCode PCMGMCycle_Private(PC pc,PC_MG_Levels **mglevelsin,PCRichardsonConvergedReason *reason)
15: {
16: PC_MG *mg = (PC_MG*)pc->data;
17: PC_MG_Levels *mgc,*mglevels = *mglevelsin;
19: PetscInt cycles = (mglevels->level == 1) ? 1 : (PetscInt) mglevels->cycles;
22: if (mglevels->eventsmoothsolve) {PetscLogEventBegin(mglevels->eventsmoothsolve,0,0,0,0);}
23: KSPSolve(mglevels->smoothd,mglevels->b,mglevels->x); /* pre-smooth */
24: KSPCheckSolve(mglevels->smoothd,pc,mglevels->x);
25: if (mglevels->eventsmoothsolve) {PetscLogEventEnd(mglevels->eventsmoothsolve,0,0,0,0);}
26: if (mglevels->level) { /* not the coarsest grid */
27: if (mglevels->eventresidual) {PetscLogEventBegin(mglevels->eventresidual,0,0,0,0);}
28: (*mglevels->residual)(mglevels->A,mglevels->b,mglevels->x,mglevels->r);
29: if (mglevels->eventresidual) {PetscLogEventEnd(mglevels->eventresidual,0,0,0,0);}
31: /* if on finest level and have convergence criteria set */
32: if (mglevels->level == mglevels->levels-1 && mg->ttol && reason) {
33: PetscReal rnorm;
34: VecNorm(mglevels->r,NORM_2,&rnorm);
35: if (rnorm <= mg->ttol) {
36: if (rnorm < mg->abstol) {
37: *reason = PCRICHARDSON_CONVERGED_ATOL;
38: PetscInfo2(pc,"Linear solver has converged. Residual norm %g is less than absolute tolerance %g\n",(double)rnorm,(double)mg->abstol);
39: } else {
40: *reason = PCRICHARDSON_CONVERGED_RTOL;
41: PetscInfo2(pc,"Linear solver has converged. Residual norm %g is less than relative tolerance times initial residual norm %g\n",(double)rnorm,(double)mg->ttol);
42: }
43: return(0);
44: }
45: }
47: mgc = *(mglevelsin - 1);
48: if (mglevels->eventinterprestrict) {PetscLogEventBegin(mglevels->eventinterprestrict,0,0,0,0);}
49: MatRestrict(mglevels->restrct,mglevels->r,mgc->b);
50: if (mglevels->eventinterprestrict) {PetscLogEventEnd(mglevels->eventinterprestrict,0,0,0,0);}
51: VecSet(mgc->x,0.0);
52: while (cycles--) {
53: PCMGMCycle_Private(pc,mglevelsin-1,reason);
54: }
55: if (mglevels->eventinterprestrict) {PetscLogEventBegin(mglevels->eventinterprestrict,0,0,0,0);}
56: MatInterpolateAdd(mglevels->interpolate,mgc->x,mglevels->x,mglevels->x);
57: if (mglevels->eventinterprestrict) {PetscLogEventEnd(mglevels->eventinterprestrict,0,0,0,0);}
58: if (mglevels->eventsmoothsolve) {PetscLogEventBegin(mglevels->eventsmoothsolve,0,0,0,0);}
59: KSPSolve(mglevels->smoothu,mglevels->b,mglevels->x); /* post smooth */
60: KSPCheckSolve(mglevels->smoothu,pc,mglevels->x);
61: if (mglevels->eventsmoothsolve) {PetscLogEventEnd(mglevels->eventsmoothsolve,0,0,0,0);}
62: }
63: return(0);
64: }
66: static PetscErrorCode PCApplyRichardson_MG(PC pc,Vec b,Vec x,Vec w,PetscReal rtol,PetscReal abstol, PetscReal dtol,PetscInt its,PetscBool zeroguess,PetscInt *outits,PCRichardsonConvergedReason *reason)
67: {
68: PC_MG *mg = (PC_MG*)pc->data;
69: PC_MG_Levels **mglevels = mg->levels;
71: PC tpc;
72: PetscBool changeu,changed;
73: PetscInt levels = mglevels[0]->levels,i;
76: /* When the DM is supplying the matrix then it will not exist until here */
77: for (i=0; i<levels; i++) {
78: if (!mglevels[i]->A) {
79: KSPGetOperators(mglevels[i]->smoothu,&mglevels[i]->A,NULL);
80: PetscObjectReference((PetscObject)mglevels[i]->A);
81: }
82: }
84: KSPGetPC(mglevels[levels-1]->smoothd,&tpc);
85: PCPreSolveChangeRHS(tpc,&changed);
86: KSPGetPC(mglevels[levels-1]->smoothu,&tpc);
87: PCPreSolveChangeRHS(tpc,&changeu);
88: if (!changed && !changeu) {
89: VecDestroy(&mglevels[levels-1]->b);
90: mglevels[levels-1]->b = b;
91: } else { /* if the smoother changes the rhs during PreSolve, we cannot use the input vector */
92: if (!mglevels[levels-1]->b) {
93: Vec *vec;
95: KSPCreateVecs(mglevels[levels-1]->smoothd,1,&vec,0,NULL);
96: mglevels[levels-1]->b = *vec;
97: PetscFree(vec);
98: }
99: VecCopy(b,mglevels[levels-1]->b);
100: }
101: mglevels[levels-1]->x = x;
103: mg->rtol = rtol;
104: mg->abstol = abstol;
105: mg->dtol = dtol;
106: if (rtol) {
107: /* compute initial residual norm for relative convergence test */
108: PetscReal rnorm;
109: if (zeroguess) {
110: VecNorm(b,NORM_2,&rnorm);
111: } else {
112: (*mglevels[levels-1]->residual)(mglevels[levels-1]->A,b,x,w);
113: VecNorm(w,NORM_2,&rnorm);
114: }
115: mg->ttol = PetscMax(rtol*rnorm,abstol);
116: } else if (abstol) mg->ttol = abstol;
117: else mg->ttol = 0.0;
119: /* since smoother is applied to full system, not just residual we need to make sure that smoothers don't
120: stop prematurely due to small residual */
121: for (i=1; i<levels; i++) {
122: KSPSetTolerances(mglevels[i]->smoothu,0,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT);
123: if (mglevels[i]->smoothu != mglevels[i]->smoothd) {
124: /* For Richardson the initial guess is nonzero since it is solving in each cycle the original system not just applying as a preconditioner */
125: KSPSetInitialGuessNonzero(mglevels[i]->smoothd,PETSC_TRUE);
126: KSPSetTolerances(mglevels[i]->smoothd,0,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT);
127: }
128: }
130: *reason = (PCRichardsonConvergedReason)0;
131: for (i=0; i<its; i++) {
132: PCMGMCycle_Private(pc,mglevels+levels-1,reason);
133: if (*reason) break;
134: }
135: if (!*reason) *reason = PCRICHARDSON_CONVERGED_ITS;
136: *outits = i;
137: if (!changed && !changeu) mglevels[levels-1]->b = NULL;
138: return(0);
139: }
141: PetscErrorCode PCReset_MG(PC pc)
142: {
143: PC_MG *mg = (PC_MG*)pc->data;
144: PC_MG_Levels **mglevels = mg->levels;
146: PetscInt i,c,n;
149: if (mglevels) {
150: n = mglevels[0]->levels;
151: for (i=0; i<n-1; i++) {
152: VecDestroy(&mglevels[i+1]->r);
153: VecDestroy(&mglevels[i]->b);
154: VecDestroy(&mglevels[i]->x);
155: MatDestroy(&mglevels[i+1]->restrct);
156: MatDestroy(&mglevels[i+1]->interpolate);
157: MatDestroy(&mglevels[i+1]->inject);
158: VecDestroy(&mglevels[i+1]->rscale);
159: }
160: /* this is not null only if the smoother on the finest level
161: changes the rhs during PreSolve */
162: VecDestroy(&mglevels[n-1]->b);
164: for (i=0; i<n; i++) {
165: if (mglevels[i]->coarseSpace) for (c = 0; c < mg->Nc; ++c) {VecDestroy(&mglevels[i]->coarseSpace[c]);}
166: PetscFree(mglevels[i]->coarseSpace);
167: mglevels[i]->coarseSpace = NULL;
168: MatDestroy(&mglevels[i]->A);
169: if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
170: KSPReset(mglevels[i]->smoothd);
171: }
172: KSPReset(mglevels[i]->smoothu);
173: }
174: mg->Nc = 0;
175: }
176: return(0);
177: }
179: PetscErrorCode PCMGSetLevels_MG(PC pc,PetscInt levels,MPI_Comm *comms)
180: {
182: PC_MG *mg = (PC_MG*)pc->data;
183: MPI_Comm comm;
184: PC_MG_Levels **mglevels = mg->levels;
185: PCMGType mgtype = mg->am;
186: PetscInt mgctype = (PetscInt) PC_MG_CYCLE_V;
187: PetscInt i;
188: PetscMPIInt size;
189: const char *prefix;
190: PC ipc;
191: PetscInt n;
196: if (mg->nlevels == levels) return(0);
197: PetscObjectGetComm((PetscObject)pc,&comm);
198: if (mglevels) {
199: mgctype = mglevels[0]->cycles;
200: /* changing the number of levels so free up the previous stuff */
201: PCReset_MG(pc);
202: n = mglevels[0]->levels;
203: for (i=0; i<n; i++) {
204: if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
205: KSPDestroy(&mglevels[i]->smoothd);
206: }
207: KSPDestroy(&mglevels[i]->smoothu);
208: PetscFree(mglevels[i]);
209: }
210: PetscFree(mg->levels);
211: }
213: mg->nlevels = levels;
215: PetscMalloc1(levels,&mglevels);
216: PetscLogObjectMemory((PetscObject)pc,levels*(sizeof(PC_MG*)));
218: PCGetOptionsPrefix(pc,&prefix);
220: mg->stageApply = 0;
221: for (i=0; i<levels; i++) {
222: PetscNewLog(pc,&mglevels[i]);
224: mglevels[i]->level = i;
225: mglevels[i]->levels = levels;
226: mglevels[i]->cycles = mgctype;
227: mg->default_smoothu = 2;
228: mg->default_smoothd = 2;
229: mglevels[i]->eventsmoothsetup = 0;
230: mglevels[i]->eventsmoothsolve = 0;
231: mglevels[i]->eventresidual = 0;
232: mglevels[i]->eventinterprestrict = 0;
234: if (comms) comm = comms[i];
235: KSPCreate(comm,&mglevels[i]->smoothd);
236: KSPSetErrorIfNotConverged(mglevels[i]->smoothd,pc->erroriffailure);
237: PetscObjectIncrementTabLevel((PetscObject)mglevels[i]->smoothd,(PetscObject)pc,levels-i);
238: KSPSetOptionsPrefix(mglevels[i]->smoothd,prefix);
239: PetscObjectComposedDataSetInt((PetscObject) mglevels[i]->smoothd, PetscMGLevelId, mglevels[i]->level);
240: if (i || levels == 1) {
241: char tprefix[128];
243: KSPSetType(mglevels[i]->smoothd,KSPCHEBYSHEV);
244: KSPSetConvergenceTest(mglevels[i]->smoothd,KSPConvergedSkip,NULL,NULL);
245: KSPSetNormType(mglevels[i]->smoothd,KSP_NORM_NONE);
246: KSPGetPC(mglevels[i]->smoothd,&ipc);
247: PCSetType(ipc,PCSOR);
248: KSPSetTolerances(mglevels[i]->smoothd,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT, mg->default_smoothd);
250: sprintf(tprefix,"mg_levels_%d_",(int)i);
251: KSPAppendOptionsPrefix(mglevels[i]->smoothd,tprefix);
252: } else {
253: KSPAppendOptionsPrefix(mglevels[0]->smoothd,"mg_coarse_");
255: /* coarse solve is (redundant) LU by default; set shifttype NONZERO to avoid annoying zero-pivot in LU preconditioner */
256: KSPSetType(mglevels[0]->smoothd,KSPPREONLY);
257: KSPGetPC(mglevels[0]->smoothd,&ipc);
258: MPI_Comm_size(comm,&size);
259: if (size > 1) {
260: PCSetType(ipc,PCREDUNDANT);
261: } else {
262: PCSetType(ipc,PCLU);
263: }
264: PCFactorSetShiftType(ipc,MAT_SHIFT_INBLOCKS);
265: }
266: PetscLogObjectParent((PetscObject)pc,(PetscObject)mglevels[i]->smoothd);
268: mglevels[i]->smoothu = mglevels[i]->smoothd;
269: mg->rtol = 0.0;
270: mg->abstol = 0.0;
271: mg->dtol = 0.0;
272: mg->ttol = 0.0;
273: mg->cyclesperpcapply = 1;
274: }
275: mg->levels = mglevels;
276: PCMGSetType(pc,mgtype);
277: return(0);
278: }
280: /*@C
281: PCMGSetLevels - Sets the number of levels to use with MG.
282: Must be called before any other MG routine.
284: Logically Collective on PC
286: Input Parameters:
287: + pc - the preconditioner context
288: . levels - the number of levels
289: - comms - optional communicators for each level; this is to allow solving the coarser problems
290: on smaller sets of processors.
292: Level: intermediate
294: Notes:
295: If the number of levels is one then the multigrid uses the -mg_levels prefix
296: for setting the level options rather than the -mg_coarse prefix.
298: .seealso: PCMGSetType(), PCMGGetLevels()
299: @*/
300: PetscErrorCode PCMGSetLevels(PC pc,PetscInt levels,MPI_Comm *comms)
301: {
307: PetscTryMethod(pc,"PCMGSetLevels_C",(PC,PetscInt,MPI_Comm*),(pc,levels,comms));
308: return(0);
309: }
312: PetscErrorCode PCDestroy_MG(PC pc)
313: {
315: PC_MG *mg = (PC_MG*)pc->data;
316: PC_MG_Levels **mglevels = mg->levels;
317: PetscInt i,n;
320: PCReset_MG(pc);
321: if (mglevels) {
322: n = mglevels[0]->levels;
323: for (i=0; i<n; i++) {
324: if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
325: KSPDestroy(&mglevels[i]->smoothd);
326: }
327: KSPDestroy(&mglevels[i]->smoothu);
328: PetscFree(mglevels[i]);
329: }
330: PetscFree(mg->levels);
331: }
332: PetscFree(pc->data);
333: PetscObjectComposeFunction((PetscObject)pc,"PCGetInterpolations_C",NULL);
334: PetscObjectComposeFunction((PetscObject)pc,"PCGetCoarseOperators_C",NULL);
335: return(0);
336: }
340: extern PetscErrorCode PCMGACycle_Private(PC,PC_MG_Levels**);
341: extern PetscErrorCode PCMGFCycle_Private(PC,PC_MG_Levels**);
342: extern PetscErrorCode PCMGKCycle_Private(PC,PC_MG_Levels**);
344: /*
345: PCApply_MG - Runs either an additive, multiplicative, Kaskadic
346: or full cycle of multigrid.
348: Note:
349: A simple wrapper which calls PCMGMCycle(),PCMGACycle(), or PCMGFCycle().
350: */
351: static PetscErrorCode PCApply_MG(PC pc,Vec b,Vec x)
352: {
353: PC_MG *mg = (PC_MG*)pc->data;
354: PC_MG_Levels **mglevels = mg->levels;
356: PC tpc;
357: PetscInt levels = mglevels[0]->levels,i;
358: PetscBool changeu,changed;
361: if (mg->stageApply) {PetscLogStagePush(mg->stageApply);}
362: /* When the DM is supplying the matrix then it will not exist until here */
363: for (i=0; i<levels; i++) {
364: if (!mglevels[i]->A) {
365: KSPGetOperators(mglevels[i]->smoothu,&mglevels[i]->A,NULL);
366: PetscObjectReference((PetscObject)mglevels[i]->A);
367: }
368: }
370: KSPGetPC(mglevels[levels-1]->smoothd,&tpc);
371: PCPreSolveChangeRHS(tpc,&changed);
372: KSPGetPC(mglevels[levels-1]->smoothu,&tpc);
373: PCPreSolveChangeRHS(tpc,&changeu);
374: if (!changeu && !changed) {
375: VecDestroy(&mglevels[levels-1]->b);
376: mglevels[levels-1]->b = b;
377: } else { /* if the smoother changes the rhs during PreSolve, we cannot use the input vector */
378: if (!mglevels[levels-1]->b) {
379: Vec *vec;
381: KSPCreateVecs(mglevels[levels-1]->smoothd,1,&vec,0,NULL);
382: mglevels[levels-1]->b = *vec;
383: PetscFree(vec);
384: }
385: VecCopy(b,mglevels[levels-1]->b);
386: }
387: mglevels[levels-1]->x = x;
389: if (mg->am == PC_MG_MULTIPLICATIVE) {
390: VecSet(x,0.0);
391: for (i=0; i<mg->cyclesperpcapply; i++) {
392: PCMGMCycle_Private(pc,mglevels+levels-1,NULL);
393: }
394: } else if (mg->am == PC_MG_ADDITIVE) {
395: PCMGACycle_Private(pc,mglevels);
396: } else if (mg->am == PC_MG_KASKADE) {
397: PCMGKCycle_Private(pc,mglevels);
398: } else {
399: PCMGFCycle_Private(pc,mglevels);
400: }
401: if (mg->stageApply) {PetscLogStagePop();}
402: if (!changeu && !changed) mglevels[levels-1]->b = NULL;
403: return(0);
404: }
407: PetscErrorCode PCSetFromOptions_MG(PetscOptionItems *PetscOptionsObject,PC pc)
408: {
409: PetscErrorCode ierr;
410: PetscInt levels,cycles;
411: PetscBool flg, flg2;
412: PC_MG *mg = (PC_MG*)pc->data;
413: PC_MG_Levels **mglevels;
414: PCMGType mgtype;
415: PCMGCycleType mgctype;
416: PCMGGalerkinType gtype;
419: levels = PetscMax(mg->nlevels,1);
420: PetscOptionsHead(PetscOptionsObject,"Multigrid options");
421: PetscOptionsInt("-pc_mg_levels","Number of Levels","PCMGSetLevels",levels,&levels,&flg);
422: if (!flg && !mg->levels && pc->dm) {
423: DMGetRefineLevel(pc->dm,&levels);
424: levels++;
425: mg->usedmfornumberoflevels = PETSC_TRUE;
426: }
427: PCMGSetLevels(pc,levels,NULL);
428: mglevels = mg->levels;
430: mgctype = (PCMGCycleType) mglevels[0]->cycles;
431: PetscOptionsEnum("-pc_mg_cycle_type","V cycle or for W-cycle","PCMGSetCycleType",PCMGCycleTypes,(PetscEnum)mgctype,(PetscEnum*)&mgctype,&flg);
432: if (flg) {
433: PCMGSetCycleType(pc,mgctype);
434: }
435: gtype = mg->galerkin;
436: PetscOptionsEnum("-pc_mg_galerkin","Use Galerkin process to compute coarser operators","PCMGSetGalerkin",PCMGGalerkinTypes,(PetscEnum)gtype,(PetscEnum*)>ype,&flg);
437: if (flg) {
438: PCMGSetGalerkin(pc,gtype);
439: }
440: flg2 = PETSC_FALSE;
441: PetscOptionsBool("-pc_mg_adapt_interp","Adapt interpolation using some coarse space","PCMGSetAdaptInterpolation",PETSC_FALSE,&flg2,&flg);
442: if (flg) {PCMGSetAdaptInterpolation(pc, flg2);}
443: PetscOptionsInt("-pc_mg_adapt_interp_n","Size of the coarse space for adaptive interpolation","PCMGSetCoarseSpace",mg->Nc,&mg->Nc,&flg);
444: PetscOptionsEnum("-pc_mg_adapt_interp_coarse_space","Type of coarse space: polynomial, harmonic, eigenvector, generalized_eigenvector","PCMGSetAdaptCoarseSpaceType",PCMGCoarseSpaceTypes,(PetscEnum)mg->coarseSpaceType,(PetscEnum*)&mg->coarseSpaceType,&flg);
445: PetscOptionsBool("-pc_mg_mesp_monitor","Monitor the multilevel eigensolver","PCMGSetAdaptInterpolation",PETSC_FALSE,&mg->mespMonitor,&flg);
446: flg = PETSC_FALSE;
447: PetscOptionsBool("-pc_mg_distinct_smoothup","Create separate smoothup KSP and append the prefix _up","PCMGSetDistinctSmoothUp",PETSC_FALSE,&flg,NULL);
448: if (flg) {
449: PCMGSetDistinctSmoothUp(pc);
450: }
451: mgtype = mg->am;
452: PetscOptionsEnum("-pc_mg_type","Multigrid type","PCMGSetType",PCMGTypes,(PetscEnum)mgtype,(PetscEnum*)&mgtype,&flg);
453: if (flg) {
454: PCMGSetType(pc,mgtype);
455: }
456: if (mg->am == PC_MG_MULTIPLICATIVE) {
457: PetscOptionsInt("-pc_mg_multiplicative_cycles","Number of cycles for each preconditioner step","PCMGMultiplicativeSetCycles",mg->cyclesperpcapply,&cycles,&flg);
458: if (flg) {
459: PCMGMultiplicativeSetCycles(pc,cycles);
460: }
461: }
462: flg = PETSC_FALSE;
463: PetscOptionsBool("-pc_mg_log","Log times for each multigrid level","None",flg,&flg,NULL);
464: if (flg) {
465: PetscInt i;
466: char eventname[128];
468: levels = mglevels[0]->levels;
469: for (i=0; i<levels; i++) {
470: sprintf(eventname,"MGSetup Level %d",(int)i);
471: PetscLogEventRegister(eventname,((PetscObject)pc)->classid,&mglevels[i]->eventsmoothsetup);
472: sprintf(eventname,"MGSmooth Level %d",(int)i);
473: PetscLogEventRegister(eventname,((PetscObject)pc)->classid,&mglevels[i]->eventsmoothsolve);
474: if (i) {
475: sprintf(eventname,"MGResid Level %d",(int)i);
476: PetscLogEventRegister(eventname,((PetscObject)pc)->classid,&mglevels[i]->eventresidual);
477: sprintf(eventname,"MGInterp Level %d",(int)i);
478: PetscLogEventRegister(eventname,((PetscObject)pc)->classid,&mglevels[i]->eventinterprestrict);
479: }
480: }
482: #if defined(PETSC_USE_LOG)
483: {
484: const char *sname = "MG Apply";
485: PetscStageLog stageLog;
486: PetscInt st;
488: PetscLogGetStageLog(&stageLog);
489: for (st = 0; st < stageLog->numStages; ++st) {
490: PetscBool same;
492: PetscStrcmp(stageLog->stageInfo[st].name, sname, &same);
493: if (same) mg->stageApply = st;
494: }
495: if (!mg->stageApply) {
496: PetscLogStageRegister(sname, &mg->stageApply);
497: }
498: }
499: #endif
500: }
501: PetscOptionsTail();
502: /* Check option consistency */
503: PCMGGetGalerkin(pc, >ype);
504: PCMGGetAdaptInterpolation(pc, &flg);
505: if (flg && (gtype >= PC_MG_GALERKIN_NONE)) SETERRQ(PetscObjectComm((PetscObject) pc), PETSC_ERR_ARG_INCOMP, "Must use Galerkin coarse operators when adapting the interpolator");
506: return(0);
507: }
509: const char *const PCMGTypes[] = {"MULTIPLICATIVE","ADDITIVE","FULL","KASKADE","PCMGType","PC_MG",NULL};
510: const char *const PCMGCycleTypes[] = {"invalid","v","w","PCMGCycleType","PC_MG_CYCLE",NULL};
511: const char *const PCMGGalerkinTypes[] = {"both","pmat","mat","none","external","PCMGGalerkinType","PC_MG_GALERKIN",NULL};
512: const char *const PCMGCoarseSpaceTypes[] = {"polynomial","harmonic","eigenvector","generalized_eigenvector","PCMGCoarseSpaceType","PCMG_POLYNOMIAL",NULL};
514: #include <petscdraw.h>
515: PetscErrorCode PCView_MG(PC pc,PetscViewer viewer)
516: {
517: PC_MG *mg = (PC_MG*)pc->data;
518: PC_MG_Levels **mglevels = mg->levels;
520: PetscInt levels = mglevels ? mglevels[0]->levels : 0,i;
521: PetscBool iascii,isbinary,isdraw;
524: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
525: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
526: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
527: if (iascii) {
528: const char *cyclename = levels ? (mglevels[0]->cycles == PC_MG_CYCLE_V ? "v" : "w") : "unknown";
529: PetscViewerASCIIPrintf(viewer," type is %s, levels=%D cycles=%s\n", PCMGTypes[mg->am],levels,cyclename);
530: if (mg->am == PC_MG_MULTIPLICATIVE) {
531: PetscViewerASCIIPrintf(viewer," Cycles per PCApply=%d\n",mg->cyclesperpcapply);
532: }
533: if (mg->galerkin == PC_MG_GALERKIN_BOTH) {
534: PetscViewerASCIIPrintf(viewer," Using Galerkin computed coarse grid matrices\n");
535: } else if (mg->galerkin == PC_MG_GALERKIN_PMAT) {
536: PetscViewerASCIIPrintf(viewer," Using Galerkin computed coarse grid matrices for pmat\n");
537: } else if (mg->galerkin == PC_MG_GALERKIN_MAT) {
538: PetscViewerASCIIPrintf(viewer," Using Galerkin computed coarse grid matrices for mat\n");
539: } else if (mg->galerkin == PC_MG_GALERKIN_EXTERNAL) {
540: PetscViewerASCIIPrintf(viewer," Using externally compute Galerkin coarse grid matrices\n");
541: } else {
542: PetscViewerASCIIPrintf(viewer," Not using Galerkin computed coarse grid matrices\n");
543: }
544: if (mg->view){
545: (*mg->view)(pc,viewer);
546: }
547: for (i=0; i<levels; i++) {
548: if (!i) {
549: PetscViewerASCIIPrintf(viewer,"Coarse grid solver -- level -------------------------------\n",i);
550: } else {
551: PetscViewerASCIIPrintf(viewer,"Down solver (pre-smoother) on level %D -------------------------------\n",i);
552: }
553: PetscViewerASCIIPushTab(viewer);
554: KSPView(mglevels[i]->smoothd,viewer);
555: PetscViewerASCIIPopTab(viewer);
556: if (i && mglevels[i]->smoothd == mglevels[i]->smoothu) {
557: PetscViewerASCIIPrintf(viewer,"Up solver (post-smoother) same as down solver (pre-smoother)\n");
558: } else if (i) {
559: PetscViewerASCIIPrintf(viewer,"Up solver (post-smoother) on level %D -------------------------------\n",i);
560: PetscViewerASCIIPushTab(viewer);
561: KSPView(mglevels[i]->smoothu,viewer);
562: PetscViewerASCIIPopTab(viewer);
563: }
564: }
565: } else if (isbinary) {
566: for (i=levels-1; i>=0; i--) {
567: KSPView(mglevels[i]->smoothd,viewer);
568: if (i && mglevels[i]->smoothd != mglevels[i]->smoothu) {
569: KSPView(mglevels[i]->smoothu,viewer);
570: }
571: }
572: } else if (isdraw) {
573: PetscDraw draw;
574: PetscReal x,w,y,bottom,th;
575: PetscViewerDrawGetDraw(viewer,0,&draw);
576: PetscDrawGetCurrentPoint(draw,&x,&y);
577: PetscDrawStringGetSize(draw,NULL,&th);
578: bottom = y - th;
579: for (i=levels-1; i>=0; i--) {
580: if (!mglevels[i]->smoothu || (mglevels[i]->smoothu == mglevels[i]->smoothd)) {
581: PetscDrawPushCurrentPoint(draw,x,bottom);
582: KSPView(mglevels[i]->smoothd,viewer);
583: PetscDrawPopCurrentPoint(draw);
584: } else {
585: w = 0.5*PetscMin(1.0-x,x);
586: PetscDrawPushCurrentPoint(draw,x+w,bottom);
587: KSPView(mglevels[i]->smoothd,viewer);
588: PetscDrawPopCurrentPoint(draw);
589: PetscDrawPushCurrentPoint(draw,x-w,bottom);
590: KSPView(mglevels[i]->smoothu,viewer);
591: PetscDrawPopCurrentPoint(draw);
592: }
593: PetscDrawGetBoundingBox(draw,NULL,&bottom,NULL,NULL);
594: bottom -= th;
595: }
596: }
597: return(0);
598: }
600: #include <petsc/private/kspimpl.h>
602: /*
603: Calls setup for the KSP on each level
604: */
605: PetscErrorCode PCSetUp_MG(PC pc)
606: {
607: PC_MG *mg = (PC_MG*)pc->data;
608: PC_MG_Levels **mglevels = mg->levels;
610: PetscInt i,n;
611: PC cpc;
612: PetscBool dump = PETSC_FALSE,opsset,use_amat,missinginterpolate = PETSC_FALSE;
613: Mat dA,dB;
614: Vec tvec;
615: DM *dms;
616: PetscViewer viewer = NULL;
617: PetscBool dAeqdB = PETSC_FALSE, needRestricts = PETSC_FALSE;
620: if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Must set MG levels with PCMGSetLevels() before setting up");
621: n = mglevels[0]->levels;
622: /* FIX: Move this to PCSetFromOptions_MG? */
623: if (mg->usedmfornumberoflevels) {
624: PetscInt levels;
625: DMGetRefineLevel(pc->dm,&levels);
626: levels++;
627: if (levels > n) { /* the problem is now being solved on a finer grid */
628: PCMGSetLevels(pc,levels,NULL);
629: n = levels;
630: PCSetFromOptions(pc); /* it is bad to call this here, but otherwise will never be called for the new hierarchy */
631: mglevels = mg->levels;
632: }
633: }
634: KSPGetPC(mglevels[0]->smoothd,&cpc);
637: /* If user did not provide fine grid operators OR operator was not updated since last global KSPSetOperators() */
638: /* so use those from global PC */
639: /* Is this what we always want? What if user wants to keep old one? */
640: KSPGetOperatorsSet(mglevels[n-1]->smoothd,NULL,&opsset);
641: if (opsset) {
642: Mat mmat;
643: KSPGetOperators(mglevels[n-1]->smoothd,NULL,&mmat);
644: if (mmat == pc->pmat) opsset = PETSC_FALSE;
645: }
647: if (!opsset) {
648: PCGetUseAmat(pc,&use_amat);
649: if (use_amat) {
650: PetscInfo(pc,"Using outer operators to define finest grid operator \n because PCMGGetSmoother(pc,nlevels-1,&ksp);KSPSetOperators(ksp,...); was not called.\n");
651: KSPSetOperators(mglevels[n-1]->smoothd,pc->mat,pc->pmat);
652: } else {
653: PetscInfo(pc,"Using matrix (pmat) operators to define finest grid operator \n because PCMGGetSmoother(pc,nlevels-1,&ksp);KSPSetOperators(ksp,...); was not called.\n");
654: KSPSetOperators(mglevels[n-1]->smoothd,pc->pmat,pc->pmat);
655: }
656: }
658: for (i=n-1; i>0; i--) {
659: if (!(mglevels[i]->interpolate || mglevels[i]->restrct)) {
660: missinginterpolate = PETSC_TRUE;
661: continue;
662: }
663: }
665: KSPGetOperators(mglevels[n-1]->smoothd,&dA,&dB);
666: if (dA == dB) dAeqdB = PETSC_TRUE;
667: if ((mg->galerkin == PC_MG_GALERKIN_NONE) || (((mg->galerkin == PC_MG_GALERKIN_PMAT) || (mg->galerkin == PC_MG_GALERKIN_MAT)) && !dAeqdB)) {
668: needRestricts = PETSC_TRUE; /* user must compute either mat, pmat, or both so must restrict x to coarser levels */
669: }
672: /*
673: Skipping if user has provided all interpolation/restriction needed (since DM might not be able to produce them (when coming from SNES/TS)
674: Skipping for galerkin==2 (externally managed hierarchy such as ML and GAMG). Cleaner logic here would be great. Wrap ML/GAMG as DMs?
675: */
676: if (missinginterpolate && pc->dm && mg->galerkin != PC_MG_GALERKIN_EXTERNAL && !pc->setupcalled) {
677: /* construct the interpolation from the DMs */
678: Mat p;
679: Vec rscale;
680: PetscMalloc1(n,&dms);
681: dms[n-1] = pc->dm;
682: /* Separately create them so we do not get DMKSP interference between levels */
683: for (i=n-2; i>-1; i--) {DMCoarsen(dms[i+1],MPI_COMM_NULL,&dms[i]);}
684: /*
685: Force the mat type of coarse level operator to be AIJ because usually we want to use LU for coarse level.
686: Notice that it can be overwritten by -mat_type because KSPSetUp() reads command line options.
687: But it is safe to use -dm_mat_type.
689: The mat type should not be hardcoded like this, we need to find a better way.
690: DMSetMatType(dms[0],MATAIJ);
691: */
692: for (i=n-2; i>-1; i--) {
693: DMKSP kdm;
694: PetscBool dmhasrestrict, dmhasinject;
695: KSPSetDM(mglevels[i]->smoothd,dms[i]);
696: if (!needRestricts) {KSPSetDMActive(mglevels[i]->smoothd,PETSC_FALSE);}
697: if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
698: KSPSetDM(mglevels[i]->smoothu,dms[i]);
699: if (!needRestricts) {KSPSetDMActive(mglevels[i]->smoothu,PETSC_FALSE);}
700: }
701: DMGetDMKSPWrite(dms[i],&kdm);
702: /* Ugly hack so that the next KSPSetUp() will use the RHS that we set. A better fix is to change dmActive to take
703: * a bitwise OR of computing the matrix, RHS, and initial iterate. */
704: kdm->ops->computerhs = NULL;
705: kdm->rhsctx = NULL;
706: if (!mglevels[i+1]->interpolate) {
707: DMCreateInterpolation(dms[i],dms[i+1],&p,&rscale);
708: PCMGSetInterpolation(pc,i+1,p);
709: if (rscale) {PCMGSetRScale(pc,i+1,rscale);}
710: VecDestroy(&rscale);
711: MatDestroy(&p);
712: }
713: DMHasCreateRestriction(dms[i],&dmhasrestrict);
714: if (dmhasrestrict && !mglevels[i+1]->restrct){
715: DMCreateRestriction(dms[i],dms[i+1],&p);
716: PCMGSetRestriction(pc,i+1,p);
717: MatDestroy(&p);
718: }
719: DMHasCreateInjection(dms[i],&dmhasinject);
720: if (dmhasinject && !mglevels[i+1]->inject){
721: DMCreateInjection(dms[i],dms[i+1],&p);
722: PCMGSetInjection(pc,i+1,p);
723: MatDestroy(&p);
724: }
725: }
727: for (i=n-2; i>-1; i--) {DMDestroy(&dms[i]);}
728: PetscFree(dms);
729: }
731: if (pc->dm && !pc->setupcalled) {
732: /* finest smoother also gets DM but it is not active, independent of whether galerkin==PC_MG_GALERKIN_EXTERNAL */
733: KSPSetDM(mglevels[n-1]->smoothd,pc->dm);
734: KSPSetDMActive(mglevels[n-1]->smoothd,PETSC_FALSE);
735: if (mglevels[n-1]->smoothd != mglevels[n-1]->smoothu) {
736: KSPSetDM(mglevels[n-1]->smoothu,pc->dm);
737: KSPSetDMActive(mglevels[n-1]->smoothu,PETSC_FALSE);
738: }
739: }
741: if (mg->galerkin < PC_MG_GALERKIN_NONE) {
742: Mat A,B;
743: PetscBool doA = PETSC_FALSE,doB = PETSC_FALSE;
744: MatReuse reuse = MAT_INITIAL_MATRIX;
746: if ((mg->galerkin == PC_MG_GALERKIN_PMAT) || (mg->galerkin == PC_MG_GALERKIN_BOTH)) doB = PETSC_TRUE;
747: if ((mg->galerkin == PC_MG_GALERKIN_MAT) || ((mg->galerkin == PC_MG_GALERKIN_BOTH) && (dA != dB))) doA = PETSC_TRUE;
748: if (pc->setupcalled) reuse = MAT_REUSE_MATRIX;
749: for (i=n-2; i>-1; i--) {
750: if (!mglevels[i+1]->restrct && !mglevels[i+1]->interpolate) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Must provide interpolation or restriction for each MG level except level 0");
751: if (!mglevels[i+1]->interpolate) {
752: PCMGSetInterpolation(pc,i+1,mglevels[i+1]->restrct);
753: }
754: if (!mglevels[i+1]->restrct) {
755: PCMGSetRestriction(pc,i+1,mglevels[i+1]->interpolate);
756: }
757: if (reuse == MAT_REUSE_MATRIX) {
758: KSPGetOperators(mglevels[i]->smoothd,&A,&B);
759: }
760: if (doA) {
761: MatGalerkin(mglevels[i+1]->restrct,dA,mglevels[i+1]->interpolate,reuse,1.0,&A);
762: }
763: if (doB) {
764: MatGalerkin(mglevels[i+1]->restrct,dB,mglevels[i+1]->interpolate,reuse,1.0,&B);
765: }
766: /* the management of the PetscObjectReference() and PetscObjecDereference() below is rather delicate */
767: if (!doA && dAeqdB) {
768: if (reuse == MAT_INITIAL_MATRIX) {PetscObjectReference((PetscObject)B);}
769: A = B;
770: } else if (!doA && reuse == MAT_INITIAL_MATRIX) {
771: KSPGetOperators(mglevels[i]->smoothd,&A,NULL);
772: PetscObjectReference((PetscObject)A);
773: }
774: if (!doB && dAeqdB) {
775: if (reuse == MAT_INITIAL_MATRIX) {PetscObjectReference((PetscObject)A);}
776: B = A;
777: } else if (!doB && reuse == MAT_INITIAL_MATRIX) {
778: KSPGetOperators(mglevels[i]->smoothd,NULL,&B);
779: PetscObjectReference((PetscObject)B);
780: }
781: if (reuse == MAT_INITIAL_MATRIX) {
782: KSPSetOperators(mglevels[i]->smoothd,A,B);
783: PetscObjectDereference((PetscObject)A);
784: PetscObjectDereference((PetscObject)B);
785: }
786: dA = A;
787: dB = B;
788: }
789: }
792: /* Adapt interpolation matrices */
793: if (mg->adaptInterpolation) {
794: mg->Nc = mg->Nc < 0 ? 6 : mg->Nc; /* Default to 6 modes */
795: for (i = 0; i < n; ++i) {
796: PCMGComputeCoarseSpace_Internal(pc, i, mg->coarseSpaceType, mg->Nc, !i ? NULL : mglevels[i-1]->coarseSpace, &mglevels[i]->coarseSpace);
797: if (i) {PCMGAdaptInterpolator_Internal(pc, i, mglevels[i-1]->smoothu, mglevels[i]->smoothu, mg->Nc, mglevels[i-1]->coarseSpace, mglevels[i]->coarseSpace);}
798: }
799: for (i = n-2; i > -1; --i) {
800: PCMGRecomputeLevelOperators_Internal(pc, i);
801: }
802: }
804: if (needRestricts && pc->dm) {
805: for (i=n-2; i>=0; i--) {
806: DM dmfine,dmcoarse;
807: Mat Restrict,Inject;
808: Vec rscale;
809: KSPGetDM(mglevels[i+1]->smoothd,&dmfine);
810: KSPGetDM(mglevels[i]->smoothd,&dmcoarse);
811: PCMGGetRestriction(pc,i+1,&Restrict);
812: PCMGGetRScale(pc,i+1,&rscale);
813: PCMGGetInjection(pc,i+1,&Inject);
814: DMRestrict(dmfine,Restrict,rscale,Inject,dmcoarse);
815: }
816: }
818: if (!pc->setupcalled) {
819: for (i=0; i<n; i++) {
820: KSPSetFromOptions(mglevels[i]->smoothd);
821: }
822: for (i=1; i<n; i++) {
823: if (mglevels[i]->smoothu && (mglevels[i]->smoothu != mglevels[i]->smoothd)) {
824: KSPSetFromOptions(mglevels[i]->smoothu);
825: }
826: }
827: /* insure that if either interpolation or restriction is set the other other one is set */
828: for (i=1; i<n; i++) {
829: PCMGGetInterpolation(pc,i,NULL);
830: PCMGGetRestriction(pc,i,NULL);
831: }
832: for (i=0; i<n-1; i++) {
833: if (!mglevels[i]->b) {
834: Vec *vec;
835: KSPCreateVecs(mglevels[i]->smoothd,1,&vec,0,NULL);
836: PCMGSetRhs(pc,i,*vec);
837: VecDestroy(vec);
838: PetscFree(vec);
839: }
840: if (!mglevels[i]->r && i) {
841: VecDuplicate(mglevels[i]->b,&tvec);
842: PCMGSetR(pc,i,tvec);
843: VecDestroy(&tvec);
844: }
845: if (!mglevels[i]->x) {
846: VecDuplicate(mglevels[i]->b,&tvec);
847: PCMGSetX(pc,i,tvec);
848: VecDestroy(&tvec);
849: }
850: }
851: if (n != 1 && !mglevels[n-1]->r) {
852: /* PCMGSetR() on the finest level if user did not supply it */
853: Vec *vec;
854: KSPCreateVecs(mglevels[n-1]->smoothd,1,&vec,0,NULL);
855: PCMGSetR(pc,n-1,*vec);
856: VecDestroy(vec);
857: PetscFree(vec);
858: }
859: }
861: if (pc->dm) {
862: /* need to tell all the coarser levels to rebuild the matrix using the DM for that level */
863: for (i=0; i<n-1; i++) {
864: if (mglevels[i]->smoothd->setupstage != KSP_SETUP_NEW) mglevels[i]->smoothd->setupstage = KSP_SETUP_NEWMATRIX;
865: }
866: }
868: for (i=1; i<n; i++) {
869: if (mglevels[i]->smoothu == mglevels[i]->smoothd || mg->am == PC_MG_FULL || mg->am == PC_MG_KASKADE || mg->cyclesperpcapply > 1){
870: /* if doing only down then initial guess is zero */
871: KSPSetInitialGuessNonzero(mglevels[i]->smoothd,PETSC_TRUE);
872: }
873: if (mglevels[i]->eventsmoothsetup) {PetscLogEventBegin(mglevels[i]->eventsmoothsetup,0,0,0,0);}
874: KSPSetUp(mglevels[i]->smoothd);
875: if (mglevels[i]->smoothd->reason == KSP_DIVERGED_PC_FAILED) {
876: pc->failedreason = PC_SUBPC_ERROR;
877: }
878: if (mglevels[i]->eventsmoothsetup) {PetscLogEventEnd(mglevels[i]->eventsmoothsetup,0,0,0,0);}
879: if (!mglevels[i]->residual) {
880: Mat mat;
881: KSPGetOperators(mglevels[i]->smoothd,&mat,NULL);
882: PCMGSetResidual(pc,i,PCMGResidualDefault,mat);
883: }
884: }
885: for (i=1; i<n; i++) {
886: if (mglevels[i]->smoothu && mglevels[i]->smoothu != mglevels[i]->smoothd) {
887: Mat downmat,downpmat;
889: /* check if operators have been set for up, if not use down operators to set them */
890: KSPGetOperatorsSet(mglevels[i]->smoothu,&opsset,NULL);
891: if (!opsset) {
892: KSPGetOperators(mglevels[i]->smoothd,&downmat,&downpmat);
893: KSPSetOperators(mglevels[i]->smoothu,downmat,downpmat);
894: }
896: KSPSetInitialGuessNonzero(mglevels[i]->smoothu,PETSC_TRUE);
897: if (mglevels[i]->eventsmoothsetup) {PetscLogEventBegin(mglevels[i]->eventsmoothsetup,0,0,0,0);}
898: KSPSetUp(mglevels[i]->smoothu);
899: if (mglevels[i]->smoothu->reason == KSP_DIVERGED_PC_FAILED) {
900: pc->failedreason = PC_SUBPC_ERROR;
901: }
902: if (mglevels[i]->eventsmoothsetup) {PetscLogEventEnd(mglevels[i]->eventsmoothsetup,0,0,0,0);}
903: }
904: }
906: if (mglevels[0]->eventsmoothsetup) {PetscLogEventBegin(mglevels[0]->eventsmoothsetup,0,0,0,0);}
907: KSPSetUp(mglevels[0]->smoothd);
908: if (mglevels[0]->smoothd->reason == KSP_DIVERGED_PC_FAILED) {
909: pc->failedreason = PC_SUBPC_ERROR;
910: }
911: if (mglevels[0]->eventsmoothsetup) {PetscLogEventEnd(mglevels[0]->eventsmoothsetup,0,0,0,0);}
913: /*
914: Dump the interpolation/restriction matrices plus the
915: Jacobian/stiffness on each level. This allows MATLAB users to
916: easily check if the Galerkin condition A_c = R A_f R^T is satisfied.
918: Only support one or the other at the same time.
919: */
920: #if defined(PETSC_USE_SOCKET_VIEWER)
921: PetscOptionsGetBool(((PetscObject)pc)->options,((PetscObject)pc)->prefix,"-pc_mg_dump_matlab",&dump,NULL);
922: if (dump) viewer = PETSC_VIEWER_SOCKET_(PetscObjectComm((PetscObject)pc));
923: dump = PETSC_FALSE;
924: #endif
925: PetscOptionsGetBool(((PetscObject)pc)->options,((PetscObject)pc)->prefix,"-pc_mg_dump_binary",&dump,NULL);
926: if (dump) viewer = PETSC_VIEWER_BINARY_(PetscObjectComm((PetscObject)pc));
928: if (viewer) {
929: for (i=1; i<n; i++) {
930: MatView(mglevels[i]->restrct,viewer);
931: }
932: for (i=0; i<n; i++) {
933: KSPGetPC(mglevels[i]->smoothd,&pc);
934: MatView(pc->mat,viewer);
935: }
936: }
937: return(0);
938: }
940: /* -------------------------------------------------------------------------------------*/
942: PetscErrorCode PCMGGetLevels_MG(PC pc, PetscInt *levels)
943: {
944: PC_MG *mg = (PC_MG *) pc->data;
947: *levels = mg->nlevels;
948: return(0);
949: }
951: /*@
952: PCMGGetLevels - Gets the number of levels to use with MG.
954: Not Collective
956: Input Parameter:
957: . pc - the preconditioner context
959: Output parameter:
960: . levels - the number of levels
962: Level: advanced
964: .seealso: PCMGSetLevels()
965: @*/
966: PetscErrorCode PCMGGetLevels(PC pc,PetscInt *levels)
967: {
973: *levels = 0;
974: PetscTryMethod(pc,"PCMGGetLevels_C",(PC,PetscInt*),(pc,levels));
975: return(0);
976: }
978: /*@
979: PCMGSetType - Determines the form of multigrid to use:
980: multiplicative, additive, full, or the Kaskade algorithm.
982: Logically Collective on PC
984: Input Parameters:
985: + pc - the preconditioner context
986: - form - multigrid form, one of PC_MG_MULTIPLICATIVE, PC_MG_ADDITIVE,
987: PC_MG_FULL, PC_MG_KASKADE
989: Options Database Key:
990: . -pc_mg_type <form> - Sets <form>, one of multiplicative,
991: additive, full, kaskade
993: Level: advanced
995: .seealso: PCMGSetLevels()
996: @*/
997: PetscErrorCode PCMGSetType(PC pc,PCMGType form)
998: {
999: PC_MG *mg = (PC_MG*)pc->data;
1004: mg->am = form;
1005: if (form == PC_MG_MULTIPLICATIVE) pc->ops->applyrichardson = PCApplyRichardson_MG;
1006: else pc->ops->applyrichardson = NULL;
1007: return(0);
1008: }
1010: /*@
1011: PCMGGetType - Determines the form of multigrid to use:
1012: multiplicative, additive, full, or the Kaskade algorithm.
1014: Logically Collective on PC
1016: Input Parameter:
1017: . pc - the preconditioner context
1019: Output Parameter:
1020: . type - one of PC_MG_MULTIPLICATIVE, PC_MG_ADDITIVE,PC_MG_FULL, PC_MG_KASKADE
1023: Level: advanced
1025: .seealso: PCMGSetLevels()
1026: @*/
1027: PetscErrorCode PCMGGetType(PC pc,PCMGType *type)
1028: {
1029: PC_MG *mg = (PC_MG*)pc->data;
1033: *type = mg->am;
1034: return(0);
1035: }
1037: /*@
1038: PCMGSetCycleType - Sets the type cycles to use. Use PCMGSetCycleTypeOnLevel() for more
1039: complicated cycling.
1041: Logically Collective on PC
1043: Input Parameters:
1044: + pc - the multigrid context
1045: - n - either PC_MG_CYCLE_V or PC_MG_CYCLE_W
1047: Options Database Key:
1048: . -pc_mg_cycle_type <v,w> - provide the cycle desired
1050: Level: advanced
1052: .seealso: PCMGSetCycleTypeOnLevel()
1053: @*/
1054: PetscErrorCode PCMGSetCycleType(PC pc,PCMGCycleType n)
1055: {
1056: PC_MG *mg = (PC_MG*)pc->data;
1057: PC_MG_Levels **mglevels = mg->levels;
1058: PetscInt i,levels;
1063: if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ORDER,"Must set MG levels with PCMGSetLevels() before calling");
1064: levels = mglevels[0]->levels;
1065: for (i=0; i<levels; i++) mglevels[i]->cycles = n;
1066: return(0);
1067: }
1069: /*@
1070: PCMGMultiplicativeSetCycles - Sets the number of cycles to use for each preconditioner step
1071: of multigrid when PCMGType of PC_MG_MULTIPLICATIVE is used
1073: Logically Collective on PC
1075: Input Parameters:
1076: + pc - the multigrid context
1077: - n - number of cycles (default is 1)
1079: Options Database Key:
1080: . -pc_mg_multiplicative_cycles n
1082: Level: advanced
1084: Notes:
1085: This is not associated with setting a v or w cycle, that is set with PCMGSetCycleType()
1087: .seealso: PCMGSetCycleTypeOnLevel(), PCMGSetCycleType()
1088: @*/
1089: PetscErrorCode PCMGMultiplicativeSetCycles(PC pc,PetscInt n)
1090: {
1091: PC_MG *mg = (PC_MG*)pc->data;
1096: mg->cyclesperpcapply = n;
1097: return(0);
1098: }
1100: PetscErrorCode PCMGSetGalerkin_MG(PC pc,PCMGGalerkinType use)
1101: {
1102: PC_MG *mg = (PC_MG*)pc->data;
1105: mg->galerkin = use;
1106: return(0);
1107: }
1109: /*@
1110: PCMGSetGalerkin - Causes the coarser grid matrices to be computed from the
1111: finest grid via the Galerkin process: A_i-1 = r_i * A_i * p_i
1113: Logically Collective on PC
1115: Input Parameters:
1116: + pc - the multigrid context
1117: - use - one of PC_MG_GALERKIN_BOTH,PC_MG_GALERKIN_PMAT,PC_MG_GALERKIN_MAT, or PC_MG_GALERKIN_NONE
1119: Options Database Key:
1120: . -pc_mg_galerkin <both,pmat,mat,none>
1122: Level: intermediate
1124: Notes:
1125: Some codes that use PCMG such as PCGAMG use Galerkin internally while constructing the hierarchy and thus do not
1126: use the PCMG construction of the coarser grids.
1128: .seealso: PCMGGetGalerkin(), PCMGGalerkinType
1130: @*/
1131: PetscErrorCode PCMGSetGalerkin(PC pc,PCMGGalerkinType use)
1132: {
1137: PetscTryMethod(pc,"PCMGSetGalerkin_C",(PC,PCMGGalerkinType),(pc,use));
1138: return(0);
1139: }
1141: /*@
1142: PCMGGetGalerkin - Checks if Galerkin multigrid is being used, i.e.
1143: A_i-1 = r_i * A_i * p_i
1145: Not Collective
1147: Input Parameter:
1148: . pc - the multigrid context
1150: Output Parameter:
1151: . galerkin - one of PC_MG_GALERKIN_BOTH,PC_MG_GALERKIN_PMAT,PC_MG_GALERKIN_MAT, PC_MG_GALERKIN_NONE, or PC_MG_GALERKIN_EXTERNAL
1153: Level: intermediate
1155: .seealso: PCMGSetGalerkin(), PCMGGalerkinType
1157: @*/
1158: PetscErrorCode PCMGGetGalerkin(PC pc,PCMGGalerkinType *galerkin)
1159: {
1160: PC_MG *mg = (PC_MG*)pc->data;
1164: *galerkin = mg->galerkin;
1165: return(0);
1166: }
1168: PetscErrorCode PCMGSetAdaptInterpolation_MG(PC pc, PetscBool adapt)
1169: {
1170: PC_MG *mg = (PC_MG *) pc->data;
1173: mg->adaptInterpolation = adapt;
1174: return(0);
1175: }
1177: PetscErrorCode PCMGGetAdaptInterpolation_MG(PC pc, PetscBool *adapt)
1178: {
1179: PC_MG *mg = (PC_MG *) pc->data;
1182: *adapt = mg->adaptInterpolation;
1183: return(0);
1184: }
1186: /*@
1187: PCMGSetAdaptInterpolation - Adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.
1189: Logically Collective on PC
1191: Input Parameters:
1192: + pc - the multigrid context
1193: - adapt - flag for adaptation of the interpolator
1195: Options Database Keys:
1196: + -pc_mg_adapt_interp - Turn on adaptation
1197: . -pc_mg_adapt_interp_n <int> - The number of modes to use, should be divisible by dimension
1198: - -pc_mg_adapt_interp_coarse_space <type> - The type of coarse space: polynomial, harmonic, eigenvector, generalized_eigenvector
1200: Level: intermediate
1202: .keywords: MG, set, Galerkin
1203: .seealso: PCMGGetAdaptInterpolation(), PCMGSetGalerkin()
1204: @*/
1205: PetscErrorCode PCMGSetAdaptInterpolation(PC pc, PetscBool adapt)
1206: {
1211: PetscTryMethod(pc,"PCMGSetAdaptInterpolation_C",(PC,PetscBool),(pc,adapt));
1212: return(0);
1213: }
1215: /*@
1216: PCMGGetAdaptInterpolation - Get the flag to adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.
1218: Not collective
1220: Input Parameter:
1221: . pc - the multigrid context
1223: Output Parameter:
1224: . adapt - flag for adaptation of the interpolator
1226: Level: intermediate
1228: .keywords: MG, set, Galerkin
1229: .seealso: PCMGSetAdaptInterpolation(), PCMGSetGalerkin()
1230: @*/
1231: PetscErrorCode PCMGGetAdaptInterpolation(PC pc, PetscBool *adapt)
1232: {
1238: PetscUseMethod(pc,"PCMGGetAdaptInterpolation_C",(PC,PetscBool*),(pc,adapt));
1239: return(0);
1240: }
1242: /*@
1243: PCMGSetNumberSmooth - Sets the number of pre and post-smoothing steps to use
1244: on all levels. Use PCMGDistinctSmoothUp() to create separate up and down smoothers if you want different numbers of
1245: pre- and post-smoothing steps.
1247: Logically Collective on PC
1249: Input Parameters:
1250: + mg - the multigrid context
1251: - n - the number of smoothing steps
1253: Options Database Key:
1254: . -mg_levels_ksp_max_it <n> - Sets number of pre and post-smoothing steps
1256: Level: advanced
1258: Notes:
1259: this does not set a value on the coarsest grid, since we assume that
1260: there is no separate smooth up on the coarsest grid.
1262: .seealso: PCMGSetDistinctSmoothUp()
1263: @*/
1264: PetscErrorCode PCMGSetNumberSmooth(PC pc,PetscInt n)
1265: {
1266: PC_MG *mg = (PC_MG*)pc->data;
1267: PC_MG_Levels **mglevels = mg->levels;
1269: PetscInt i,levels;
1274: if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ORDER,"Must set MG levels with PCMGSetLevels() before calling");
1275: levels = mglevels[0]->levels;
1277: for (i=1; i<levels; i++) {
1278: KSPSetTolerances(mglevels[i]->smoothu,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT,n);
1279: KSPSetTolerances(mglevels[i]->smoothd,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT,n);
1280: mg->default_smoothu = n;
1281: mg->default_smoothd = n;
1282: }
1283: return(0);
1284: }
1286: /*@
1287: PCMGSetDistinctSmoothUp - sets the up (post) smoother to be a separate KSP from the down (pre) smoother on all levels
1288: and adds the suffix _up to the options name
1290: Logically Collective on PC
1292: Input Parameters:
1293: . pc - the preconditioner context
1295: Options Database Key:
1296: . -pc_mg_distinct_smoothup
1298: Level: advanced
1300: Notes:
1301: this does not set a value on the coarsest grid, since we assume that
1302: there is no separate smooth up on the coarsest grid.
1304: .seealso: PCMGSetNumberSmooth()
1305: @*/
1306: PetscErrorCode PCMGSetDistinctSmoothUp(PC pc)
1307: {
1308: PC_MG *mg = (PC_MG*)pc->data;
1309: PC_MG_Levels **mglevels = mg->levels;
1311: PetscInt i,levels;
1312: KSP subksp;
1316: if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ORDER,"Must set MG levels with PCMGSetLevels() before calling");
1317: levels = mglevels[0]->levels;
1319: for (i=1; i<levels; i++) {
1320: const char *prefix = NULL;
1321: /* make sure smoother up and down are different */
1322: PCMGGetSmootherUp(pc,i,&subksp);
1323: KSPGetOptionsPrefix(mglevels[i]->smoothd,&prefix);
1324: KSPSetOptionsPrefix(subksp,prefix);
1325: KSPAppendOptionsPrefix(subksp,"up_");
1326: }
1327: return(0);
1328: }
1330: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
1331: PetscErrorCode PCGetInterpolations_MG(PC pc,PetscInt *num_levels,Mat *interpolations[])
1332: {
1333: PC_MG *mg = (PC_MG*)pc->data;
1334: PC_MG_Levels **mglevels = mg->levels;
1335: Mat *mat;
1336: PetscInt l;
1340: if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Must set MG levels before calling");
1341: PetscMalloc1(mg->nlevels,&mat);
1342: for (l=1; l< mg->nlevels; l++) {
1343: mat[l-1] = mglevels[l]->interpolate;
1344: PetscObjectReference((PetscObject)mat[l-1]);
1345: }
1346: *num_levels = mg->nlevels;
1347: *interpolations = mat;
1348: return(0);
1349: }
1351: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
1352: PetscErrorCode PCGetCoarseOperators_MG(PC pc,PetscInt *num_levels,Mat *coarseOperators[])
1353: {
1354: PC_MG *mg = (PC_MG*)pc->data;
1355: PC_MG_Levels **mglevels = mg->levels;
1356: PetscInt l;
1357: Mat *mat;
1361: if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Must set MG levels before calling");
1362: PetscMalloc1(mg->nlevels,&mat);
1363: for (l=0; l<mg->nlevels-1; l++) {
1364: KSPGetOperators(mglevels[l]->smoothd,NULL,&(mat[l]));
1365: PetscObjectReference((PetscObject)mat[l]);
1366: }
1367: *num_levels = mg->nlevels;
1368: *coarseOperators = mat;
1369: return(0);
1370: }
1372: /*@C
1373: PCMGRegisterCoarseSpaceConstructor - Adds a method to the PCMG package for coarse space construction.
1375: Not collective
1377: Input Parameters:
1378: + name - name of the constructor
1379: - function - constructor routine
1381: Notes:
1382: Calling sequence for the routine:
1383: $ my_csp(PC pc, PetscInt l, DM dm, KSP smooth, PetscInt Nc, const Vec initGuess[], Vec **coarseSp)
1384: $ pc - The PC object
1385: $ l - The multigrid level, 0 is the coarse level
1386: $ dm - The DM for this level
1387: $ smooth - The level smoother
1388: $ Nc - The size of the coarse space
1389: $ initGuess - Basis for an initial guess for the space
1390: $ coarseSp - A basis for the computed coarse space
1392: Level: advanced
1394: .seealso: PCMGGetCoarseSpaceConstructor(), PCRegister()
1395: @*/
1396: PetscErrorCode PCMGRegisterCoarseSpaceConstructor(const char name[], PetscErrorCode (*function)(PC, PetscInt, DM, KSP, PetscInt, const Vec[], Vec **))
1397: {
1401: PCInitializePackage();
1402: PetscFunctionListAdd(&PCMGCoarseList,name,function);
1403: return(0);
1404: }
1406: /*@C
1407: PCMGGetCoarseSpaceConstructor - Returns the given coarse space construction method.
1409: Not collective
1411: Input Parameter:
1412: . name - name of the constructor
1414: Output Parameter:
1415: . function - constructor routine
1417: Notes:
1418: Calling sequence for the routine:
1419: $ my_csp(PC pc, PetscInt l, DM dm, KSP smooth, PetscInt Nc, const Vec initGuess[], Vec **coarseSp)
1420: $ pc - The PC object
1421: $ l - The multigrid level, 0 is the coarse level
1422: $ dm - The DM for this level
1423: $ smooth - The level smoother
1424: $ Nc - The size of the coarse space
1425: $ initGuess - Basis for an initial guess for the space
1426: $ coarseSp - A basis for the computed coarse space
1428: Level: advanced
1430: .seealso: PCMGRegisterCoarseSpaceConstructor(), PCRegister()
1431: @*/
1432: PetscErrorCode PCMGGetCoarseSpaceConstructor(const char name[], PetscErrorCode (**function)(PC, PetscInt, DM, KSP, PetscInt, const Vec[], Vec **))
1433: {
1437: PetscFunctionListFind(PCMGCoarseList,name,function);
1438: return(0);
1439: }
1441: /* ----------------------------------------------------------------------------------------*/
1443: /*MC
1444: PCMG - Use multigrid preconditioning. This preconditioner requires you provide additional
1445: information about the coarser grid matrices and restriction/interpolation operators.
1447: Options Database Keys:
1448: + -pc_mg_levels <nlevels> - number of levels including finest
1449: . -pc_mg_cycle_type <v,w> - provide the cycle desired
1450: . -pc_mg_type <additive,multiplicative,full,kaskade> - multiplicative is the default
1451: . -pc_mg_log - log information about time spent on each level of the solver
1452: . -pc_mg_distinct_smoothup - configure up (after interpolation) and down (before restriction) smoothers separately (with different options prefixes)
1453: . -pc_mg_galerkin <both,pmat,mat,none> - use Galerkin process to compute coarser operators, i.e. Acoarse = R A R'
1454: . -pc_mg_multiplicative_cycles - number of cycles to use as the preconditioner (defaults to 1)
1455: . -pc_mg_dump_matlab - dumps the matrices for each level and the restriction/interpolation matrices
1456: to the Socket viewer for reading from MATLAB.
1457: - -pc_mg_dump_binary - dumps the matrices for each level and the restriction/interpolation matrices
1458: to the binary output file called binaryoutput
1460: Notes:
1461: If one uses a Krylov method such GMRES or CG as the smoother then one must use KSPFGMRES, KSPGCR, or KSPRICHARDSON as the outer Krylov method
1463: When run with a single level the smoother options are used on that level NOT the coarse grid solver options
1465: When run with KSPRICHARDSON the convergence test changes slightly if monitor is turned on. The iteration count may change slightly. This
1466: is because without monitoring the residual norm is computed WITHIN each multigrid cycle on the finest level after the pre-smoothing
1467: (because the residual has just been computed for the multigrid algorithm and is hence available for free) while with monitoring the
1468: residual is computed at the end of each cycle.
1470: Level: intermediate
1472: .seealso: PCCreate(), PCSetType(), PCType (for list of available types), PC, PCMGType, PCEXOTIC, PCGAMG, PCML, PCHYPRE
1473: PCMGSetLevels(), PCMGGetLevels(), PCMGSetType(), PCMGSetCycleType(),
1474: PCMGSetDistinctSmoothUp(), PCMGGetCoarseSolve(), PCMGSetResidual(), PCMGSetInterpolation(),
1475: PCMGSetRestriction(), PCMGGetSmoother(), PCMGGetSmootherUp(), PCMGGetSmootherDown(),
1476: PCMGSetCycleTypeOnLevel(), PCMGSetRhs(), PCMGSetX(), PCMGSetR()
1477: M*/
1479: PETSC_EXTERN PetscErrorCode PCCreate_MG(PC pc)
1480: {
1481: PC_MG *mg;
1485: PetscNewLog(pc,&mg);
1486: pc->data = (void*)mg;
1487: mg->nlevels = -1;
1488: mg->am = PC_MG_MULTIPLICATIVE;
1489: mg->galerkin = PC_MG_GALERKIN_NONE;
1490: mg->adaptInterpolation = PETSC_FALSE;
1491: mg->Nc = -1;
1492: mg->eigenvalue = -1;
1494: pc->useAmat = PETSC_TRUE;
1496: pc->ops->apply = PCApply_MG;
1497: pc->ops->setup = PCSetUp_MG;
1498: pc->ops->reset = PCReset_MG;
1499: pc->ops->destroy = PCDestroy_MG;
1500: pc->ops->setfromoptions = PCSetFromOptions_MG;
1501: pc->ops->view = PCView_MG;
1503: PetscObjectComposedDataRegister(&mg->eigenvalue);
1504: PetscObjectComposeFunction((PetscObject)pc,"PCMGSetGalerkin_C",PCMGSetGalerkin_MG);
1505: PetscObjectComposeFunction((PetscObject)pc,"PCMGGetLevels_C",PCMGGetLevels_MG);
1506: PetscObjectComposeFunction((PetscObject)pc,"PCMGSetLevels_C",PCMGSetLevels_MG);
1507: PetscObjectComposeFunction((PetscObject)pc,"PCGetInterpolations_C",PCGetInterpolations_MG);
1508: PetscObjectComposeFunction((PetscObject)pc,"PCGetCoarseOperators_C",PCGetCoarseOperators_MG);
1509: PetscObjectComposeFunction((PetscObject)pc,"PCMGSetAdaptInterpolation_C",PCMGSetAdaptInterpolation_MG);
1510: PetscObjectComposeFunction((PetscObject)pc,"PCMGGetAdaptInterpolation_C",PCMGGetAdaptInterpolation_MG);
1511: return(0);
1512: }