Actual source code: ml.c

  1: #define PETSCKSP_DLL

  3: /* 
  4:    Provides an interface to the ML 4.0 smoothed Aggregation 
  5: */
 6:  #include private/pcimpl.h
 7:  #include src/ksp/pc/impls/mg/mgimpl.h
 8:  #include src/mat/impls/aij/seq/aij.h
 9:  #include src/mat/impls/aij/mpi/mpiaij.h

 11: #include <math.h>
 13: #include "ml_config.h"
 14: #include "ml_include.h"

 17: /* The context (data structure) at each grid level */
 18: typedef struct {
 19:   Vec        x,b,r;           /* global vectors */
 20:   Mat        A,P,R;
 21:   KSP        ksp;
 22: } GridCtx;

 24: /* The context used to input PETSc matrix into ML at fine grid */
 25: typedef struct {
 26:   Mat          A;      /* Petsc matrix in aij format */
 27:   Mat          Aloc;   /* local portion of A to be used by ML */
 28:   Vec          x,y;
 29:   ML_Operator  *mlmat;
 30:   PetscScalar  *pwork; /* tmp array used by PetscML_comm() */
 31: } FineGridCtx;

 33: /* The context associates a ML matrix with a PETSc shell matrix */
 34: typedef struct {
 35:   Mat          A;       /* PETSc shell matrix associated with mlmat */
 36:   ML_Operator  *mlmat;  /* ML matrix assorciated with A */
 37:   Vec          y;
 38: } Mat_MLShell;

 40: /* Private context for the ML preconditioner */
 41: typedef struct {
 42:   ML             *ml_object;
 43:   ML_Aggregate   *agg_object;
 44:   GridCtx        *gridctx;
 45:   FineGridCtx    *PetscMLdata;
 46:   PetscInt       Nlevels,MaxNlevels,MaxCoarseSize,CoarsenScheme;
 47:   PetscReal      Threshold,DampingFactor;
 48:   PetscTruth     SpectralNormScheme_Anorm;
 49:   PetscMPIInt    size; /* size of communicator for pc->pmat */
 50:   PetscErrorCode (*PCSetUp)(PC);
 51:   PetscErrorCode (*PCDestroy)(PC);
 52: } PC_ML;

 55:                           int allocated_space,int columns[],double values[],int row_lengths[]);

 67: /* -------------------------------------------------------------------------- */
 68: /*
 69:    PCSetUp_ML - Prepares for the use of the ML preconditioner
 70:                     by setting data structures and options.   

 72:    Input Parameter:
 73: .  pc - the preconditioner context

 75:    Application Interface Routine: PCSetUp()

 77:    Notes:
 78:    The interface routine PCSetUp() is not usually called directly by
 79:    the user, but instead is called by PCApply() if necessary.
 80: */
 84: PetscErrorCode PCSetUp_ML(PC pc)
 85: {
 86:   PetscErrorCode  ierr;
 87:   PetscMPIInt     size;
 88:   FineGridCtx     *PetscMLdata;
 89:   ML              *ml_object;
 90:   ML_Aggregate    *agg_object;
 91:   ML_Operator     *mlmat;
 92:   PetscInt        nlocal_allcols,Nlevels,mllevel,level,level1,m,fine_level;
 93:   Mat             A,Aloc;
 94:   GridCtx         *gridctx;
 95:   PC_ML           *pc_ml=PETSC_NULL;
 96:   PetscContainer  container;
 97:   MatReuse        reuse = MAT_INITIAL_MATRIX;

100:   PetscObjectQuery((PetscObject)pc,"PC_ML",(PetscObject *)&container);
101:   if (container) {
102:     PetscContainerGetPointer(container,(void **)&pc_ml);
103:   } else {
104:     SETERRQ(PETSC_ERR_ARG_NULL,"Container does not exit");
105:   }

107:   if (pc->setupcalled){
108:     if (pc->flag == SAME_NONZERO_PATTERN){
109:       reuse = MAT_REUSE_MATRIX;
110:       PetscMLdata = pc_ml->PetscMLdata;
111:       gridctx     = pc_ml->gridctx;
112:       /* ML objects cannot be reused */
113:       ML_Destroy(&pc_ml->ml_object);
114:       ML_Aggregate_Destroy(&pc_ml->agg_object);
115:     } else {
116:       PC_ML           *pc_ml_new = PETSC_NULL;
117:       PetscContainer  container_new;
118:       PetscNew(PC_ML,&pc_ml_new);
119:       PetscLogObjectMemory(pc,sizeof(PC_ML));
120:       PetscContainerCreate(PETSC_COMM_SELF,&container_new);
121:       PetscContainerSetPointer(container_new,pc_ml_new);
122:       PetscContainerSetUserDestroy(container_new,PetscContainerDestroy_PC_ML);
123:       PetscObjectCompose((PetscObject)pc,"PC_ML",(PetscObject)container_new);

125:       PetscMemcpy(pc_ml_new,pc_ml,sizeof(PC_ML));
126:       PetscContainerDestroy(container);
127:       pc_ml = pc_ml_new;
128:     }
129:   }
130: 
131:   /* setup special features of PCML */
132:   /*--------------------------------*/
133:   /* covert A to Aloc to be used by ML at fine grid */
134:   A = pc->pmat;
135:   MPI_Comm_size(A->comm,&size);
136:   pc_ml->size = size;
137:   if (size > 1){
138:     if (reuse) Aloc = PetscMLdata->Aloc;
139:     MatConvert_MPIAIJ_ML(A,PETSC_NULL,reuse,&Aloc);
140:   } else {
141:     Aloc = A;
142:   }

144:   /* create and initialize struct 'PetscMLdata' */
145:   if (!reuse){
146:     PetscNew(FineGridCtx,&PetscMLdata);
147:     pc_ml->PetscMLdata = PetscMLdata;
148:     PetscMalloc((Aloc->cmap.n+1)*sizeof(PetscScalar),&PetscMLdata->pwork);

150:     VecCreate(PETSC_COMM_SELF,&PetscMLdata->x);
151:     VecSetSizes(PetscMLdata->x,Aloc->cmap.n,Aloc->cmap.n);
152:     VecSetType(PetscMLdata->x,VECSEQ);

154:     VecCreate(PETSC_COMM_SELF,&PetscMLdata->y);
155:     VecSetSizes(PetscMLdata->y,A->rmap.n,PETSC_DECIDE);
156:     VecSetType(PetscMLdata->y,VECSEQ);
157:   }
158:   PetscMLdata->A    = A;
159:   PetscMLdata->Aloc = Aloc;
160: 
161:   /* create ML discretization matrix at fine grid */
162:   /* ML requires input of fine-grid matrix. It determines nlevels. */
163:   MatGetSize(Aloc,&m,&nlocal_allcols);
164:   ML_Create(&ml_object,pc_ml->MaxNlevels);
165:   pc_ml->ml_object = ml_object;
166:   ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata);
167:   ML_Set_Amatrix_Getrow(ml_object,0,PetscML_getrow,PetscML_comm,nlocal_allcols);
168:   ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec);
169: 
170:   /* aggregation */
171:   ML_Aggregate_Create(&agg_object);
172:   pc_ml->agg_object = agg_object;
173: 
174:   ML_Aggregate_Set_MaxCoarseSize(agg_object,pc_ml->MaxCoarseSize);
175:   /* set options */
176:   switch (pc_ml->CoarsenScheme) {
177:   case 1:
178:     ML_Aggregate_Set_CoarsenScheme_Coupled(agg_object);break;
179:   case 2:
180:     ML_Aggregate_Set_CoarsenScheme_MIS(agg_object);break;
181:   case 3:
182:     ML_Aggregate_Set_CoarsenScheme_METIS(agg_object);break;
183:   }
184:   ML_Aggregate_Set_Threshold(agg_object,pc_ml->Threshold);
185:   ML_Aggregate_Set_DampingFactor(agg_object,pc_ml->DampingFactor);
186:   if (pc_ml->SpectralNormScheme_Anorm){
187:     ML_Aggregate_Set_SpectralNormScheme_Anorm(agg_object);
188:   }

190:   Nlevels = ML_Gen_MGHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object);
191:   if (Nlevels<=0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Nlevels %d must > 0",Nlevels);
192:   if (pc->setupcalled && pc_ml->Nlevels != Nlevels) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"previous Nlevels %D and current Nlevels %d must be same", pc_ml->Nlevels,Nlevels);
193:   pc_ml->Nlevels = Nlevels;
194:   if (!pc->setupcalled){
195:     PCMGSetLevels(pc,Nlevels,PETSC_NULL);
196:   PCSetFromOptions_MG(pc); /* should be called in PCSetFromOptions_ML(), but cannot be called prior to PCMGSetLevels() */
197:   }
198: 
199:   if (!reuse){
200:     PetscMalloc(Nlevels*sizeof(GridCtx),&gridctx);
201:     pc_ml->gridctx = gridctx;
202:   }
203:   fine_level = Nlevels - 1;

205:   /* wrap ML matrices by PETSc shell matrices at coarsened grids. 
206:      Level 0 is the finest grid for ML, but coarsest for PETSc! */
207:   gridctx[fine_level].A = A;
208: 
209:   level = fine_level - 1;
210:   if (size == 1){ /* convert ML P, R and A into seqaij format */
211:     for (mllevel=1; mllevel<Nlevels; mllevel++){
212:       mlmat = &(ml_object->Pmat[mllevel]);
213:       MatWrapML_SeqAIJ(mlmat,reuse,&gridctx[level].P);
214:       mlmat = &(ml_object->Rmat[mllevel-1]);
215:       MatWrapML_SeqAIJ(mlmat,reuse,&gridctx[level].R);
216: 
217:       mlmat = &(ml_object->Amat[mllevel]);
218:       if (reuse){
219:         /* ML matrix A changes sparse pattern although PETSc A doesn't, thus gridctx[level].A must be recreated! */
220:         MatDestroy(gridctx[level].A);
221:       }
222:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
223:       level--;
224:     }
225:   } else { /* convert ML P and R into shell format, ML A into mpiaij format */
226:     for (mllevel=1; mllevel<Nlevels; mllevel++){
227:       mlmat  = &(ml_object->Pmat[mllevel]);
228:       MatWrapML_SHELL(mlmat,reuse,&gridctx[level].P);
229:       mlmat  = &(ml_object->Rmat[mllevel-1]);
230:       MatWrapML_SHELL(mlmat,reuse,&gridctx[level].R);

232:       mlmat  = &(ml_object->Amat[mllevel]);
233:       if (reuse){
234:         MatDestroy(gridctx[level].A);
235:       }
236:       MatWrapML_MPIAIJ(mlmat,&gridctx[level].A);
237:       level--;
238:     }
239:   }

241:   /* create vectors and ksp at all levels */
242:   if (!reuse){
243:     for (level=0; level<fine_level; level++){
244:       level1 = level + 1;
245:       VecCreate(gridctx[level].A->comm,&gridctx[level].x);
246:       VecSetSizes(gridctx[level].x,gridctx[level].A->cmap.n,PETSC_DECIDE);
247:       VecSetType(gridctx[level].x,VECMPI);
248:       PCMGSetX(pc,level,gridctx[level].x);
249: 
250:       VecCreate(gridctx[level].A->comm,&gridctx[level].b);
251:       VecSetSizes(gridctx[level].b,gridctx[level].A->rmap.n,PETSC_DECIDE);
252:       VecSetType(gridctx[level].b,VECMPI);
253:       PCMGSetRhs(pc,level,gridctx[level].b);
254: 
255:       VecCreate(gridctx[level1].A->comm,&gridctx[level1].r);
256:       VecSetSizes(gridctx[level1].r,gridctx[level1].A->rmap.n,PETSC_DECIDE);
257:       VecSetType(gridctx[level1].r,VECMPI);
258:       PCMGSetR(pc,level1,gridctx[level1].r);

260:       if (level == 0){
261:         PCMGGetCoarseSolve(pc,&gridctx[level].ksp);
262:       } else {
263:         PCMGGetSmoother(pc,level,&gridctx[level].ksp);
264:       }
265:     }
266:     PCMGGetSmoother(pc,fine_level,&gridctx[fine_level].ksp);
267:   }

269:   /* create coarse level and the interpolation between the levels */
270:   for (level=0; level<fine_level; level++){
271:     level1 = level + 1;
272:     PCMGSetInterpolation(pc,level1,gridctx[level].P);
273:     PCMGSetRestriction(pc,level1,gridctx[level].R);
274:     if (level > 0){
275:       PCMGSetResidual(pc,level,PCMGDefaultResidual,gridctx[level].A);
276:     }
277:     KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A,DIFFERENT_NONZERO_PATTERN);
278:   }
279:   PCMGSetResidual(pc,fine_level,PCMGDefaultResidual,gridctx[fine_level].A);
280:   KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A,DIFFERENT_NONZERO_PATTERN);
281: 
282:   /* now call PCSetUp_MG()         */
283:   /*-------------------------------*/
284:   (*pc_ml->PCSetUp)(pc);
285:   return(0);
286: }

290: PetscErrorCode PetscContainerDestroy_PC_ML(void *ptr)
291: {
292:   PetscErrorCode  ierr;
293:   PC_ML           *pc_ml = (PC_ML*)ptr;
294:   PetscInt        level,fine_level=pc_ml->Nlevels-1;

297:   if (pc_ml->size > 1){MatDestroy(pc_ml->PetscMLdata->Aloc);}
298:   ML_Aggregate_Destroy(&pc_ml->agg_object);
299:   ML_Destroy(&pc_ml->ml_object);

301:   PetscFree(pc_ml->PetscMLdata->pwork);
302:   if (pc_ml->PetscMLdata->x){VecDestroy(pc_ml->PetscMLdata->x);}
303:   if (pc_ml->PetscMLdata->y){VecDestroy(pc_ml->PetscMLdata->y);}
304:   PetscFree(pc_ml->PetscMLdata);

306:   for (level=0; level<fine_level; level++){
307:     if (pc_ml->gridctx[level].A){MatDestroy(pc_ml->gridctx[level].A);}
308:     if (pc_ml->gridctx[level].P){MatDestroy(pc_ml->gridctx[level].P);}
309:     if (pc_ml->gridctx[level].R){MatDestroy(pc_ml->gridctx[level].R);}
310:     if (pc_ml->gridctx[level].x){VecDestroy(pc_ml->gridctx[level].x);}
311:     if (pc_ml->gridctx[level].b){VecDestroy(pc_ml->gridctx[level].b);}
312:     if (pc_ml->gridctx[level+1].r){VecDestroy(pc_ml->gridctx[level+1].r);}
313:   }
314:   PetscFree(pc_ml->gridctx);
315:   PetscFree(pc_ml);
316:   return(0);
317: }
318: /* -------------------------------------------------------------------------- */
319: /*
320:    PCDestroy_ML - Destroys the private context for the ML preconditioner
321:    that was created with PCCreate_ML().

323:    Input Parameter:
324: .  pc - the preconditioner context

326:    Application Interface Routine: PCDestroy()
327: */
330: PetscErrorCode PCDestroy_ML(PC pc)
331: {
332:   PetscErrorCode  ierr;
333:   PC_ML           *pc_ml=PETSC_NULL;
334:   PetscContainer  container;

337:   PetscObjectQuery((PetscObject)pc,"PC_ML",(PetscObject *)&container);
338:   if (container) {
339:     PetscContainerGetPointer(container,(void **)&pc_ml);
340:     pc->ops->destroy = pc_ml->PCDestroy;
341:   } else {
342:     SETERRQ(PETSC_ERR_ARG_NULL,"Container does not exit");
343:   }
344:   PetscContainerDestroy(container);

346:   /* detach pc and PC_ML and dereference container */
347:   PetscObjectCompose((PetscObject)pc,"PC_ML",0);
348:   (*pc->ops->destroy)(pc);
349:   return(0);
350: }

354: PetscErrorCode PCSetFromOptions_ML(PC pc)
355: {
356:   PetscErrorCode  ierr;
357:   PetscInt        indx,m,PrintLevel,MaxNlevels,MaxCoarseSize;
358:   PetscReal       Threshold,DampingFactor;
359:   PetscTruth      flg;
360:   const char      *scheme[] = {"Uncoupled","Coupled","MIS","METIS"};
361:   PC_ML           *pc_ml=PETSC_NULL;
362:   PetscContainer  container;
363:   PCMGType        mgtype;

366:   PetscObjectQuery((PetscObject)pc,"PC_ML",(PetscObject *)&container);
367:   if (container) {
368:     PetscContainerGetPointer(container,(void **)&pc_ml);
369:   } else {
370:     SETERRQ(PETSC_ERR_ARG_NULL,"Container does not exit");
371:   }

373:   /* inherited MG options */
374:   PetscOptionsHead("Multigrid options(inherited)");
375:     PetscOptionsInt("-pc_mg_cycles","1 for V cycle, 2 for W-cycle","MGSetCycles",1,&m,&flg);
376:     PetscOptionsInt("-pc_mg_smoothup","Number of post-smoothing steps","MGSetNumberSmoothUp",1,&m,&flg);
377:     PetscOptionsInt("-pc_mg_smoothdown","Number of pre-smoothing steps","MGSetNumberSmoothDown",1,&m,&flg);
378:     PetscOptionsEnum("-pc_mg_type","Multigrid type","PCMGSetType",PCMGTypes,(PetscEnum)PC_MG_MULTIPLICATIVE,(PetscEnum*)&mgtype,&flg);
379:   PetscOptionsTail();

381:   /* ML options */
382:   PetscOptionsHead("ML options");
383:   /* set defaults */
384:   PrintLevel    = 0;
385:   indx          = 0;
386:   PetscOptionsInt("-pc_ml_PrintLevel","Print level","ML_Set_PrintLevel",PrintLevel,&PrintLevel,PETSC_NULL);
387:   ML_Set_PrintLevel(PrintLevel);
388:   PetscOptionsInt("-pc_ml_maxNlevels","Maximum number of levels","None",pc_ml->MaxNlevels,&pc_ml->MaxNlevels,PETSC_NULL);
389:   PetscOptionsInt("-pc_ml_maxCoarseSize","Maximum coarsest mesh size","ML_Aggregate_Set_MaxCoarseSize",pc_ml->MaxCoarseSize,&pc_ml->MaxCoarseSize,PETSC_NULL);
390:   PetscOptionsEList("-pc_ml_CoarsenScheme","Aggregate Coarsen Scheme","ML_Aggregate_Set_CoarsenScheme_*",scheme,4,scheme[0],&indx,PETSC_NULL); /* ??? */
391:   pc_ml->CoarsenScheme = indx;

393:   PetscOptionsReal("-pc_ml_DampingFactor","P damping factor","ML_Aggregate_Set_DampingFactor",pc_ml->DampingFactor,&pc_ml->DampingFactor,PETSC_NULL);
394: 
395:   PetscOptionsReal("-pc_ml_Threshold","Smoother drop tol","ML_Aggregate_Set_Threshold",pc_ml->Threshold,&pc_ml->Threshold,PETSC_NULL);

397:   PetscOptionsTruth("-pc_ml_SpectralNormScheme_Anorm","Method used for estimating spectral radius","ML_Aggregate_Set_SpectralNormScheme_Anorm",pc_ml->SpectralNormScheme_Anorm,&pc_ml->SpectralNormScheme_Anorm,PETSC_NULL);
398: 
399:   PetscOptionsTail();
400:   return(0);
401: }

403: /* -------------------------------------------------------------------------- */
404: /*
405:    PCCreate_ML - Creates a ML preconditioner context, PC_ML, 
406:    and sets this as the private data within the generic preconditioning 
407:    context, PC, that was created within PCCreate().

409:    Input Parameter:
410: .  pc - the preconditioner context

412:    Application Interface Routine: PCCreate()
413: */

415: /*MC
416:      PCML - Use algebraic multigrid preconditioning. This preconditioner requires you provide 
417:        fine grid discretization matrix. The coarser grid matrices and restriction/interpolation 
418:        operators are computed by ML, with the matrices coverted to PETSc matrices in aij format
419:        and the restriction/interpolation operators wrapped as PETSc shell matrices.

421:    Options Database Key: 
422:    Multigrid options(inherited)
423: +  -pc_mg_cycles <1>: 1 for V cycle, 2 for W-cycle (MGSetCycles)
424: .  -pc_mg_smoothup <1>: Number of post-smoothing steps (MGSetNumberSmoothUp)
425: .  -pc_mg_smoothdown <1>: Number of pre-smoothing steps (MGSetNumberSmoothDown)
426: -  -pc_mg_type <multiplicative>: (one of) additive multiplicative full cascade kascade
427:    
428:    ML options:
429: +  -pc_ml_PrintLevel <0>: Print level (ML_Set_PrintLevel)
430: .  -pc_ml_maxNlevels <10>: Maximum number of levels (None)
431: .  -pc_ml_maxCoarseSize <1>: Maximum coarsest mesh size (ML_Aggregate_Set_MaxCoarseSize)
432: .  -pc_ml_CoarsenScheme <Uncoupled>: (one of) Uncoupled Coupled MIS METIS
433: .  -pc_ml_DampingFactor <1.33333>: P damping factor (ML_Aggregate_Set_DampingFactor)
434: .  -pc_ml_Threshold <0>: Smoother drop tol (ML_Aggregate_Set_Threshold)
435: -  -pc_ml_SpectralNormScheme_Anorm <false>: Method used for estimating spectral radius (ML_Aggregate_Set_SpectralNormScheme_Anorm)

437:    Level: intermediate

439:   Concepts: multigrid
440:  
441: .seealso:  PCCreate(), PCSetType(), PCType (for list of available types), PC, PCMGType, 
442:            PCMGSetLevels(), PCMGGetLevels(), PCMGSetType(), MPSetCycles(), PCMGSetNumberSmoothDown(),
443:            PCMGSetNumberSmoothUp(), PCMGGetCoarseSolve(), PCMGSetResidual(), PCMGSetInterpolation(),
444:            PCMGSetRestriction(), PCMGGetSmoother(), PCMGGetSmootherUp(), PCMGGetSmootherDown(),
445:            PCMGSetCyclesOnLevel(), PCMGSetRhs(), PCMGSetX(), PCMGSetR()      
446: M*/

451: PetscErrorCode  PCCreate_ML(PC pc)
452: {
453:   PetscErrorCode  ierr;
454:   PC_ML           *pc_ml;
455:   PetscContainer  container;

458:   /* PCML is an inherited class of PCMG. Initialize pc as PCMG */
459:   PCSetType(pc,PCMG); /* calls PCCreate_MG() and MGCreate_Private() */

461:   /* create a supporting struct and attach it to pc */
462:   PetscNew(PC_ML,&pc_ml);
463:   PetscLogObjectMemory(pc,sizeof(PC_ML));
464:   PetscContainerCreate(PETSC_COMM_SELF,&container);
465:   PetscContainerSetPointer(container,pc_ml);
466:   PetscContainerSetUserDestroy(container,PetscContainerDestroy_PC_ML);
467:   PetscObjectCompose((PetscObject)pc,"PC_ML",(PetscObject)container);
468: 
469:   pc_ml->ml_object     = 0;
470:   pc_ml->agg_object    = 0;
471:   pc_ml->gridctx       = 0;
472:   pc_ml->PetscMLdata   = 0;
473:   pc_ml->Nlevels       = -1;
474:   pc_ml->MaxNlevels    = 10;
475:   pc_ml->MaxCoarseSize = 1;
476:   pc_ml->CoarsenScheme = 1; /* ??? */
477:   pc_ml->Threshold     = 0.0;
478:   pc_ml->DampingFactor = 4.0/3.0;
479:   pc_ml->SpectralNormScheme_Anorm = PETSC_FALSE;
480:   pc_ml->size          = 0;

482:   pc_ml->PCSetUp   = pc->ops->setup;
483:   pc_ml->PCDestroy = pc->ops->destroy;

485:   /* overwrite the pointers of PCMG by the functions of PCML */
486:   pc->ops->setfromoptions = PCSetFromOptions_ML;
487:   pc->ops->setup          = PCSetUp_ML;
488:   pc->ops->destroy        = PCDestroy_ML;
489:   return(0);
490: }

493: int PetscML_getrow(ML_Operator *ML_data, int N_requested_rows, int requested_rows[],
494:    int allocated_space, int columns[], double values[], int row_lengths[])
495: {
497:   Mat            Aloc;
498:   Mat_SeqAIJ     *a;
499:   PetscInt       m,i,j,k=0,row,*aj;
500:   PetscScalar    *aa;
501:   FineGridCtx    *ml=(FineGridCtx*)ML_Get_MyGetrowData(ML_data);

503:   Aloc = ml->Aloc;
504:   a    = (Mat_SeqAIJ*)Aloc->data;
505:   MatGetSize(Aloc,&m,PETSC_NULL);

507:   for (i = 0; i<N_requested_rows; i++) {
508:     row   = requested_rows[i];
509:     row_lengths[i] = a->ilen[row];
510:     if (allocated_space < k+row_lengths[i]) return(0);
511:     if ( (row >= 0) || (row <= (m-1)) ) {
512:       aj = a->j + a->i[row];
513:       aa = a->a + a->i[row];
514:       for (j=0; j<row_lengths[i]; j++){
515:         columns[k]  = aj[j];
516:         values[k++] = aa[j];
517:       }
518:     }
519:   }
520:   return(1);
521: }

523: int PetscML_matvec(ML_Operator *ML_data,int in_length,double p[],int out_length,double ap[])
524: {
526:   FineGridCtx    *ml=(FineGridCtx*)ML_Get_MyMatvecData(ML_data);
527:   Mat            A=ml->A, Aloc=ml->Aloc;
528:   PetscMPIInt    size;
529:   PetscScalar    *pwork=ml->pwork;
530:   PetscInt       i;

532:   MPI_Comm_size(A->comm,&size);
533:   if (size == 1){
534:     VecPlaceArray(ml->x,p);
535:   } else {
536:     for (i=0; i<in_length; i++) pwork[i] = p[i];
537:     PetscML_comm(pwork,ml);
538:     VecPlaceArray(ml->x,pwork);
539:   }
540:   VecPlaceArray(ml->y,ap);
541:   MatMult(Aloc,ml->x,ml->y);
542:   VecResetArray(ml->x);
543:   VecResetArray(ml->y);
544:   return 0;
545: }

547: int PetscML_comm(double p[],void *ML_data)
548: {
550:   FineGridCtx    *ml=(FineGridCtx*)ML_data;
551:   Mat            A=ml->A;
552:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
553:   PetscMPIInt    size;
554:   PetscInt       i,in_length=A->rmap.n,out_length=ml->Aloc->cmap.n;
555:   PetscScalar    *array;

557:   MPI_Comm_size(A->comm,&size);
558:   if (size == 1) return 0;
559: 
560:   VecPlaceArray(ml->y,p);
561:   VecScatterBegin(a->Mvctx,ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
562:   VecScatterEnd(a->Mvctx,ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
563:   VecResetArray(ml->y);
564:   VecGetArray(a->lvec,&array);
565:   for (i=in_length; i<out_length; i++){
566:     p[i] = array[i-in_length];
567:   }
568:   VecRestoreArray(a->lvec,&array);
569:   return 0;
570: }
573: PetscErrorCode MatMult_ML(Mat A,Vec x,Vec y)
574: {
575:   PetscErrorCode   ierr;
576:   Mat_MLShell      *shell;
577:   PetscScalar      *xarray,*yarray;
578:   PetscInt         x_length,y_length;
579: 
581:   MatShellGetContext(A,(void **)&shell);
582:   VecGetArray(x,&xarray);
583:   VecGetArray(y,&yarray);
584:   x_length = shell->mlmat->invec_leng;
585:   y_length = shell->mlmat->outvec_leng;

587:   ML_Operator_Apply(shell->mlmat,x_length,xarray,y_length,yarray);

589:   VecRestoreArray(x,&xarray);
590:   VecRestoreArray(y,&yarray);
591:   return(0);
592: }
593: /* MatMultAdd_ML -  Compute y = w + A*x */
596: PetscErrorCode MatMultAdd_ML(Mat A,Vec x,Vec w,Vec y)
597: {
598:   PetscErrorCode    ierr;
599:   Mat_MLShell       *shell;
600:   PetscScalar       *xarray,*yarray;
601:   PetscInt          x_length,y_length;
602: 
604:   MatShellGetContext(A,(void **)&shell);
605:   VecGetArray(x,&xarray);
606:   VecGetArray(y,&yarray);

608:   x_length = shell->mlmat->invec_leng;
609:   y_length = shell->mlmat->outvec_leng;

611:   ML_Operator_Apply(shell->mlmat,x_length,xarray,y_length,yarray);

613:   VecRestoreArray(x,&xarray);
614:   VecRestoreArray(y,&yarray);
615:   VecAXPY(y,1.0,w);

617:   return(0);
618: }

620: /* newtype is ignored because "ml" is not listed under Petsc MatType yet */
623: PetscErrorCode MatConvert_MPIAIJ_ML(Mat A,MatType newtype,MatReuse scall,Mat *Aloc)
624: {
625:   PetscErrorCode  ierr;
626:   Mat_MPIAIJ      *mpimat=(Mat_MPIAIJ*)A->data;
627:   Mat_SeqAIJ      *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
628:   PetscInt        *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
629:   PetscScalar     *aa=a->a,*ba=b->a,*ca;
630:   PetscInt        am=A->rmap.n,an=A->cmap.n,i,j,k;
631:   PetscInt        *ci,*cj,ncols;

634:   if (am != an) SETERRQ2(PETSC_ERR_ARG_WRONG,"A must have a square diagonal portion, am: %d != an: %d",am,an);

636:   if (scall == MAT_INITIAL_MATRIX){
637:     PetscMalloc((1+am)*sizeof(PetscInt),&ci);
638:     ci[0] = 0;
639:     for (i=0; i<am; i++){
640:       ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
641:     }
642:     PetscMalloc((1+ci[am])*sizeof(PetscInt),&cj);
643:     PetscMalloc((1+ci[am])*sizeof(PetscScalar),&ca);

645:     k = 0;
646:     for (i=0; i<am; i++){
647:       /* diagonal portion of A */
648:       ncols = ai[i+1] - ai[i];
649:       for (j=0; j<ncols; j++) {
650:         cj[k]   = *aj++;
651:         ca[k++] = *aa++;
652:       }
653:       /* off-diagonal portion of A */
654:       ncols = bi[i+1] - bi[i];
655:       for (j=0; j<ncols; j++) {
656:         cj[k]   = an + (*bj); bj++;
657:         ca[k++] = *ba++;
658:       }
659:     }
660:     if (k != ci[am]) SETERRQ2(PETSC_ERR_ARG_WRONG,"k: %d != ci[am]: %d",k,ci[am]);

662:     /* put together the new matrix */
663:     an = mpimat->A->cmap.n+mpimat->B->cmap.n;
664:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,an,ci,cj,ca,Aloc);

666:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
667:     /* Since these are PETSc arrays, change flags to free them as necessary. */
668:     mat = (Mat_SeqAIJ*)(*Aloc)->data;
669:     mat->free_a       = PETSC_TRUE;
670:     mat->free_ij      = PETSC_TRUE;

672:     mat->nonew    = 0;
673:   } else if (scall == MAT_REUSE_MATRIX){
674:     mat=(Mat_SeqAIJ*)(*Aloc)->data;
675:     ci = mat->i; cj = mat->j; ca = mat->a;
676:     for (i=0; i<am; i++) {
677:       /* diagonal portion of A */
678:       ncols = ai[i+1] - ai[i];
679:       for (j=0; j<ncols; j++) *ca++ = *aa++;
680:       /* off-diagonal portion of A */
681:       ncols = bi[i+1] - bi[i];
682:       for (j=0; j<ncols; j++) *ca++ = *ba++;
683:     }
684:   } else {
685:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
686:   }
687:   return(0);
688: }
692: PetscErrorCode MatDestroy_ML(Mat A)
693: {
695:   Mat_MLShell    *shell;

698:   MatShellGetContext(A,(void **)&shell);
699:   VecDestroy(shell->y);
700:   PetscFree(shell);
701:   MatDestroy_Shell(A);
702:   PetscObjectChangeTypeName((PetscObject)A,0);
703:   return(0);
704: }

708: PetscErrorCode MatWrapML_SeqAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
709: {
710:   struct ML_CSR_MSRdata *matdata = (struct ML_CSR_MSRdata *)mlmat->data;
711:   PetscErrorCode        ierr;
712:   PetscInt              m=mlmat->outvec_leng,n=mlmat->invec_leng,*nnz,nz_max;
713:   PetscInt              *ml_cols=matdata->columns,*aj,i,j,k;
714:   PetscScalar           *ml_vals=matdata->values,*aa;
715: 
717:   if ( mlmat->getrow == NULL) SETERRQ(PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
718:   if (m != n){ /* ML Pmat and Rmat are in CSR format. Pass array pointers into SeqAIJ matrix */
719:     if (reuse){
720:       Mat_SeqAIJ  *aij= (Mat_SeqAIJ*)(*newmat)->data;
721:       aij->i = matdata->rowptr;
722:       aij->j = ml_cols;
723:       aij->a = ml_vals;
724:     } else {
725:       MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,matdata->rowptr,ml_cols,ml_vals,newmat);
726:     }
727:     return(0);
728:   }

730:   /* ML Amat is in MSR format. Copy its data into SeqAIJ matrix */
731:   MatCreate(PETSC_COMM_SELF,newmat);
732:   MatSetSizes(*newmat,m,n,PETSC_DECIDE,PETSC_DECIDE);
733:   MatSetType(*newmat,MATSEQAIJ);

735:   PetscMalloc((m+1)*sizeof(PetscInt),&nnz);
736:   nz_max = 1;
737:   for (i=0; i<m; i++) {
738:     nnz[i] = ml_cols[i+1] - ml_cols[i] + 1;
739:     if (nnz[i] > nz_max) nz_max += nnz[i];
740:   }

742:   MatSeqAIJSetPreallocation(*newmat,0,nnz);
743:   MatSetOption(*newmat,MAT_COLUMNS_SORTED);
744: 
745:   PetscMalloc(nz_max*(sizeof(PetscInt)+sizeof(PetscScalar)),&aj);
746:   aa = (PetscScalar*)(aj + nz_max);
747: 
748:   for (i=0; i<m; i++){
749:     k = 0;
750:     /* diagonal entry */
751:     aj[k] = i; aa[k++] = ml_vals[i];
752:     /* off diagonal entries */
753:     for (j=ml_cols[i]; j<ml_cols[i+1]; j++){
754:       aj[k] = ml_cols[j]; aa[k++] = ml_vals[j];
755:     }
756:     /* sort aj and aa */
757:     PetscSortIntWithScalarArray(nnz[i],aj,aa);
758:     MatSetValues(*newmat,1,&i,nnz[i],aj,aa,INSERT_VALUES);
759:   }
760:   MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
761:   MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);

763:   PetscFree(aj);
764:   PetscFree(nnz);
765:   return(0);
766: }

770: PetscErrorCode MatWrapML_SHELL(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
771: {
773:   PetscInt       m,n;
774:   ML_Comm        *MLcomm;
775:   Mat_MLShell    *shellctx;

778:   m = mlmat->outvec_leng;
779:   n = mlmat->invec_leng;
780:   if (!m || !n){
781:     newmat = PETSC_NULL;
782:     return(0);
783:   }

785:   if (reuse){
786:     MatShellGetContext(*newmat,(void **)&shellctx);
787:     shellctx->mlmat = mlmat;
788:     return(0);
789:   }

791:   MLcomm = mlmat->comm;
792:   PetscNew(Mat_MLShell,&shellctx);
793:   MatCreateShell(MLcomm->USR_comm,m,n,PETSC_DETERMINE,PETSC_DETERMINE,shellctx,newmat);
794:   MatShellSetOperation(*newmat,MATOP_MULT,(void(*)(void))MatMult_ML);
795:   MatShellSetOperation(*newmat,MATOP_MULT_ADD,(void(*)(void))MatMultAdd_ML);
796:   shellctx->A         = *newmat;
797:   shellctx->mlmat     = mlmat;
798:   VecCreate(PETSC_COMM_WORLD,&shellctx->y);
799:   VecSetSizes(shellctx->y,m,PETSC_DECIDE);
800:   VecSetFromOptions(shellctx->y);
801:   (*newmat)->ops->destroy = MatDestroy_ML;
802:   return(0);
803: }

807: PetscErrorCode MatWrapML_MPIAIJ(ML_Operator *mlmat,Mat *newmat)
808: {
809:   struct ML_CSR_MSRdata *matdata = (struct ML_CSR_MSRdata *)mlmat->data;
810:   PetscInt              *ml_cols=matdata->columns,*aj;
811:   PetscScalar           *ml_vals=matdata->values,*aa;
812:   PetscErrorCode        ierr;
813:   PetscInt              i,j,k,*gordering;
814:   PetscInt              m=mlmat->outvec_leng,n,*nnzA,*nnzB,*nnz,nz_max,row;
815:   Mat                   A;

818:   if (mlmat->getrow == NULL) SETERRQ(PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
819:   n = mlmat->invec_leng;
820:   if (m != n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"m %d must equal to n %d",m,n);

822:   MatCreate(mlmat->comm->USR_comm,&A);
823:   MatSetSizes(A,m,n,PETSC_DECIDE,PETSC_DECIDE);
824:   MatSetType(A,MATMPIAIJ);
825:   PetscMalloc3(m,PetscInt,&nnzA,m,PetscInt,&nnzB,m,PetscInt,&nnz);
826: 
827:   nz_max = 0;
828:   for (i=0; i<m; i++){
829:     nnz[i] = ml_cols[i+1] - ml_cols[i] + 1;
830:     if (nz_max < nnz[i]) nz_max = nnz[i];
831:     nnzA[i] = 1; /* diag */
832:     for (j=ml_cols[i]; j<ml_cols[i+1]; j++){
833:       if (ml_cols[j] < m) nnzA[i]++;
834:     }
835:     nnzB[i] = nnz[i] - nnzA[i];
836:   }
837:   MatMPIAIJSetPreallocation(A,0,nnzA,0,nnzB);

839:   /* insert mat values -- remap row and column indices */
840:   nz_max++;
841:   PetscMalloc(nz_max*(sizeof(PetscInt)+sizeof(PetscScalar)),&aj);
842:   aa = (PetscScalar*)(aj + nz_max);
843:   /* create global row numbering for a ML_Operator */
844:   ML_build_global_numbering(mlmat,&gordering,"rows");
845:   for (i=0; i<m; i++){
846:     row = gordering[i];
847:     k = 0;
848:     /* diagonal entry */
849:     aj[k] = row; aa[k++] = ml_vals[i];
850:     /* off diagonal entries */
851:     for (j=ml_cols[i]; j<ml_cols[i+1]; j++){
852:       aj[k] = gordering[ml_cols[j]]; aa[k++] = ml_vals[j];
853:     }
854:     MatSetValues(A,1,&row,nnz[i],aj,aa,INSERT_VALUES);
855:   }
856:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
857:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
858:   *newmat = A;

860:   PetscFree3(nnzA,nnzB,nnz);
861:   PetscFree(aj);
862:   return(0);
863: }