Actual source code: ml.c

  1: #define PETSCKSP_DLL

  3: /* 
  4:    Provides an interface to the ML 3.0 smoothed Aggregation 
  5: */
 6:  #include src/ksp/pc/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

 12: #include <math.h> 
 13: #include "ml_include.h"

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

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

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

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

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

 64: /* -------------------------------------------------------------------------- */
 65: /*
 66:    PCSetUp_ML - Prepares for the use of the ML preconditioner
 67:                     by setting data structures and options.   

 69:    Input Parameter:
 70: .  pc - the preconditioner context

 72:    Application Interface Routine: PCSetUp()

 74:    Notes:
 75:    The interface routine PCSetUp() is not usually called directly by
 76:    the user, but instead is called by PCApply() if necessary.
 77: */
 81: PetscErrorCode PCSetUp_ML(PC pc)
 82: {
 83:   PetscErrorCode       ierr;
 84:   PetscMPIInt          size;
 85:   FineGridCtx          *PetscMLdata;
 86:   ML                   *ml_object;
 87:   ML_Aggregate         *agg_object;
 88:   ML_Operator          *mlmat;
 89:   PetscInt             nlocal_allcols,Nlevels,mllevel,level,level1,m,fine_level;
 90:   Mat                  A,Aloc;
 91:   GridCtx              *gridctx;
 92:   PC_ML                *pc_ml=PETSC_NULL;
 93:   PetscObjectContainer container;

 96:   PetscObjectQuery((PetscObject)pc,"PC_ML",(PetscObject *)&container);
 97:   if (container) {
 98:     PetscObjectContainerGetPointer(container,(void **)&pc_ml);
 99:   } else {
100:     SETERRQ(PETSC_ERR_ARG_NULL,"Container does not exit");
101:   }
102: 
103:   /* setup special features of PCML */
104:   /*--------------------------------*/
105:   /* covert A to Aloc to be used by ML at fine grid */
106:   A = pc->pmat;
107:   MPI_Comm_size(A->comm,&size);
108:   pc_ml->size = size;
109:   if (size > 1){
110:     MatConvert_MPIAIJ_ML(A,PETSC_NULL,MAT_INITIAL_MATRIX,&Aloc);
111:   } else {
112:     Aloc = A;
113:   }

115:   /* create and initialize struct 'PetscMLdata' */
116:   PetscNew(FineGridCtx,&PetscMLdata);
117:   PetscMLdata->A    = A;
118:   PetscMLdata->Aloc = Aloc;
119:   PetscMalloc((Aloc->n+1)*sizeof(PetscScalar),&PetscMLdata->pwork);
120:   pc_ml->PetscMLdata = PetscMLdata;

122:   VecCreate(PETSC_COMM_SELF,&PetscMLdata->x);
123:   if (size == 1){
124:     VecSetSizes(PetscMLdata->x,A->n,A->n);
125:   } else {
126:     VecSetSizes(PetscMLdata->x,Aloc->n,Aloc->n);
127:   }
128:   VecSetType(PetscMLdata->x,VECSEQ);

130:   VecCreate(PETSC_COMM_SELF,&PetscMLdata->y);
131:   VecSetSizes(PetscMLdata->y,A->m,PETSC_DECIDE);
132:   VecSetType(PetscMLdata->y,VECSEQ);
133: 
134:   /* create ML discretization matrix at fine grid */
135:   MatGetSize(Aloc,&m,&nlocal_allcols);
136:   ML_Create(&ml_object,pc_ml->MaxNlevels);
137:   ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata);
138:   ML_Set_Amatrix_Getrow(ml_object,0,PetscML_getrow,PetscML_comm,nlocal_allcols);
139:   ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec);

141:   /* aggregation */
142:   ML_Aggregate_Create(&agg_object);
143:   ML_Aggregate_Set_MaxCoarseSize(agg_object,pc_ml->MaxCoarseSize);
144:   /* set options */
145:   switch (pc_ml->CoarsenScheme) {
146:   case 1:
147:     ML_Aggregate_Set_CoarsenScheme_Coupled(agg_object);break;
148:   case 2:
149:     ML_Aggregate_Set_CoarsenScheme_MIS(agg_object);break;
150:   case 3:
151:     ML_Aggregate_Set_CoarsenScheme_METIS(agg_object);break;
152:   }
153:   ML_Aggregate_Set_Threshold(agg_object,pc_ml->Threshold);
154:   ML_Aggregate_Set_DampingFactor(agg_object,pc_ml->DampingFactor);
155:   if (pc_ml->SpectralNormScheme_Anorm){
156:     ML_Aggregate_Set_SpectralNormScheme_Anorm(agg_object);
157:   }
158: 
159:   Nlevels = ML_Gen_MGHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object);
160:   if (Nlevels<=0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Nlevels %d must > 0",Nlevels);
161:   PCMGSetLevels(pc,Nlevels,PETSC_NULL);
162:   PCSetFromOptions_MG(pc); /* should be called in PCSetFromOptions_ML(), but cannot be called prior to PCMGSetLevels() */
163:   pc_ml->ml_object  = ml_object;
164:   pc_ml->agg_object = agg_object;

166:   PetscMalloc(Nlevels*sizeof(GridCtx),&gridctx);
167:   fine_level = Nlevels - 1;
168:   pc_ml->gridctx = gridctx;
169:   pc_ml->fine_level = fine_level;

171:   /* wrap ML matrices by PETSc shell matrices at coarsened grids. 
172:      Level 0 is the finest grid for ML, but coarsest for PETSc! */
173:   gridctx[fine_level].A = A;
174:   level = fine_level - 1;
175:   if (size == 1){ /* convert ML P, R and A into seqaij format */
176:     for (mllevel=1; mllevel<Nlevels; mllevel++){
177:       mlmat  = &(ml_object->Pmat[mllevel]);
178:       MatWrapML_SeqAIJ(mlmat,&gridctx[level].P);
179:       mlmat  = &(ml_object->Amat[mllevel]);
180:       MatWrapML_SeqAIJ(mlmat,&gridctx[level].A);
181:       mlmat  = &(ml_object->Rmat[mllevel-1]);
182:       MatWrapML_SeqAIJ(mlmat,&gridctx[level].R);
183:       level--;
184:     }
185:   } else { /* convert ML P and R into shell format, ML A into mpiaij format */
186:     for (mllevel=1; mllevel<Nlevels; mllevel++){
187:       mlmat  = &(ml_object->Pmat[mllevel]);
188:       MatWrapML_SHELL(mlmat,&gridctx[level].P);
189:       mlmat  = &(ml_object->Rmat[mllevel-1]);
190:       MatWrapML_SHELL(mlmat,&gridctx[level].R);
191:       mlmat  = &(ml_object->Amat[mllevel]);
192:       MatWrapML_MPIAIJ(mlmat,&gridctx[level].A);
193:       level--;
194:     }
195:   }

197:   /* create coarse level and the interpolation between the levels */
198:   for (level=0; level<fine_level; level++){
199:     VecCreate(gridctx[level].A->comm,&gridctx[level].x);
200:     VecSetSizes(gridctx[level].x,gridctx[level].A->n,PETSC_DECIDE);
201:     VecSetType(gridctx[level].x,VECMPI);
202:     PCMGSetX(pc,level,gridctx[level].x);
203: 
204:     VecCreate(gridctx[level].A->comm,&gridctx[level].b);
205:     VecSetSizes(gridctx[level].b,gridctx[level].A->m,PETSC_DECIDE);
206:     VecSetType(gridctx[level].b,VECMPI);
207:     PCMGSetRhs(pc,level,gridctx[level].b);
208: 
209:     level1 = level + 1;
210:     VecCreate(gridctx[level1].A->comm,&gridctx[level1].r);
211:     VecSetSizes(gridctx[level1].r,gridctx[level1].A->m,PETSC_DECIDE);
212:     VecSetType(gridctx[level1].r,VECMPI);
213:     PCMGSetR(pc,level1,gridctx[level1].r);

215:     PCMGSetInterpolate(pc,level1,gridctx[level].P);
216:     PCMGSetRestriction(pc,level1,gridctx[level].R);

218:     if (level == 0){
219:       PCMGGetCoarseSolve(pc,&gridctx[level].ksp);
220:     } else {
221:       PCMGGetSmoother(pc,level,&gridctx[level].ksp);
222:       PCMGSetResidual(pc,level,PCMGDefaultResidual,gridctx[level].A);
223:     }
224:     KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A,DIFFERENT_NONZERO_PATTERN);
225:   }
226:   PCMGGetSmoother(pc,fine_level,&gridctx[fine_level].ksp);
227:   PCMGSetResidual(pc,fine_level,PCMGDefaultResidual,gridctx[fine_level].A);
228:   KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A,DIFFERENT_NONZERO_PATTERN);
229:   KSPSetOptionsPrefix(gridctx[fine_level].ksp,"mg_fine_");
230: 
231:   /* now call PCSetUp_MG()         */
232:   /*--------------------------------*/
233:   (*pc_ml->PCSetUp)(pc);
234:   return(0);
235: }

239: PetscErrorCode PetscObjectContainerDestroy_PC_ML(void *ptr)
240: {
241:   PetscErrorCode       ierr;
242:   PC_ML                *pc_ml = (PC_ML*)ptr;
243:   PetscInt             level;

246:   if (pc_ml->size > 1){MatDestroy(pc_ml->PetscMLdata->Aloc);}
247:   ML_Aggregate_Destroy(&pc_ml->agg_object);
248:   ML_Destroy(&pc_ml->ml_object);

250:   PetscFree(pc_ml->PetscMLdata->pwork);
251:   if (pc_ml->PetscMLdata->x){VecDestroy(pc_ml->PetscMLdata->x);}
252:   if (pc_ml->PetscMLdata->y){VecDestroy(pc_ml->PetscMLdata->y);}
253:   PetscFree(pc_ml->PetscMLdata);

255:   for (level=0; level<pc_ml->fine_level; level++){
256:     if (pc_ml->gridctx[level].A){MatDestroy(pc_ml->gridctx[level].A);}
257:     if (pc_ml->gridctx[level].P){MatDestroy(pc_ml->gridctx[level].P);}
258:     if (pc_ml->gridctx[level].R){MatDestroy(pc_ml->gridctx[level].R);}
259:     if (pc_ml->gridctx[level].x){VecDestroy(pc_ml->gridctx[level].x);}
260:     if (pc_ml->gridctx[level].b){VecDestroy(pc_ml->gridctx[level].b);}
261:     if (pc_ml->gridctx[level+1].r){VecDestroy(pc_ml->gridctx[level+1].r);}
262:   }
263:   PetscFree(pc_ml->gridctx);
264:   PetscFree(pc_ml);
265:   return(0);
266: }
267: /* -------------------------------------------------------------------------- */
268: /*
269:    PCDestroy_ML - Destroys the private context for the ML preconditioner
270:    that was created with PCCreate_ML().

272:    Input Parameter:
273: .  pc - the preconditioner context

275:    Application Interface Routine: PCDestroy()
276: */
279: PetscErrorCode PCDestroy_ML(PC pc)
280: {
281:   PetscErrorCode       ierr;
282:   PC_ML                *pc_ml=PETSC_NULL;
283:   PetscObjectContainer container;

286:   PetscObjectQuery((PetscObject)pc,"PC_ML",(PetscObject *)&container);
287:   if (container) {
288:     PetscObjectContainerGetPointer(container,(void **)&pc_ml);
289:     pc->ops->destroy = pc_ml->PCDestroy;
290:   } else {
291:     SETERRQ(PETSC_ERR_ARG_NULL,"Container does not exit");
292:   }
293:   /* detach pc and PC_ML and dereference container */
294:   PetscObjectCompose((PetscObject)pc,"PC_ML",0);
295:   (*pc->ops->destroy)(pc);

297:   PetscObjectContainerDestroy(container);
298:   return(0);
299: }

303: PetscErrorCode PCSetFromOptions_ML(PC pc)
304: {
305:   PetscErrorCode       ierr;
306:   PetscInt             indx,m,PrintLevel,MaxNlevels,MaxCoarseSize;
307:   PetscReal            Threshold,DampingFactor;
308:   PetscTruth           flg;
309:   const char           *scheme[] = {"Uncoupled","Coupled","MIS","METIS"};
310:   PC_ML                *pc_ml=PETSC_NULL;
311:   PetscObjectContainer container;
312:   PCMGType             mgtype;

315:   PetscObjectQuery((PetscObject)pc,"PC_ML",(PetscObject *)&container);
316:   if (container) {
317:     PetscObjectContainerGetPointer(container,(void **)&pc_ml);
318:   } else {
319:     SETERRQ(PETSC_ERR_ARG_NULL,"Container does not exit");
320:   }

322:   /* inherited MG options */
323:   PetscOptionsHead("Multigrid options(inherited)");
324:     PetscOptionsInt("-pc_mg_cycles","1 for V cycle, 2 for W-cycle","MGSetCycles",1,&m,&flg);
325:     PetscOptionsInt("-pc_mg_smoothup","Number of post-smoothing steps","MGSetNumberSmoothUp",1,&m,&flg);
326:     PetscOptionsInt("-pc_mg_smoothdown","Number of pre-smoothing steps","MGSetNumberSmoothDown",1,&m,&flg);
327:     PetscOptionsEnum("-pc_mg_type","Multigrid type","PCMGSetType",PCMGTypes,(PetscEnum)PC_MG_MULTIPLICATIVE,(PetscEnum*)&mgtype,&flg);
328:   PetscOptionsTail();

330:   /* ML options */
331:   PetscOptionsHead("ML options");
332:   /* set defaults */
333:   PrintLevel    = 0;
334:   MaxNlevels    = 10;
335:   MaxCoarseSize = 1;
336:   indx          = 0;
337:   Threshold     = 0.0;
338:   DampingFactor = 4.0/3.0;
339: 
340:   PetscOptionsInt("-pc_ml_PrintLevel","Print level","ML_Set_PrintLevel",PrintLevel,&PrintLevel,PETSC_NULL);
341:   ML_Set_PrintLevel(PrintLevel);

343:   PetscOptionsInt("-pc_ml_maxNlevels","Maximum number of levels","None",MaxNlevels,&MaxNlevels,PETSC_NULL);
344:   pc_ml->MaxNlevels = MaxNlevels;

346:   PetscOptionsInt("-pc_ml_maxCoarseSize","Maximum coarsest mesh size","ML_Aggregate_Set_MaxCoarseSize",MaxCoarseSize,&MaxCoarseSize,PETSC_NULL);
347:   pc_ml->MaxCoarseSize = MaxCoarseSize;

349:   PetscOptionsEList("-pc_ml_CoarsenScheme","Aggregate Coarsen Scheme","ML_Aggregate_Set_CoarsenScheme_*",scheme,4,scheme[0],&indx,PETSC_NULL);
350:   pc_ml->CoarsenScheme = indx;

352:   PetscOptionsReal("-pc_ml_DampingFactor","P damping factor","ML_Aggregate_Set_DampingFactor",DampingFactor,&DampingFactor,PETSC_NULL);
353:   pc_ml->DampingFactor = DampingFactor;
354: 
355:   PetscOptionsReal("-pc_ml_Threshold","Smoother drop tol","ML_Aggregate_Set_Threshold",Threshold,&Threshold,PETSC_NULL);
356:   pc_ml->Threshold = Threshold;

358:   PetscOptionsTruth("-pc_ml_SpectralNormScheme_Anorm","Method used for estimating spectral radius","ML_Aggregate_Set_SpectralNormScheme_Anorm",PETSC_FALSE,&pc_ml->SpectralNormScheme_Anorm,PETSC_FALSE);
359: 
360:   PetscOptionsTail();
361:   return(0);
362: }

364: /* -------------------------------------------------------------------------- */
365: /*
366:    PCCreate_ML - Creates a ML preconditioner context, PC_ML, 
367:    and sets this as the private data within the generic preconditioning 
368:    context, PC, that was created within PCCreate().

370:    Input Parameter:
371: .  pc - the preconditioner context

373:    Application Interface Routine: PCCreate()
374: */

376: /*MC
377:      PCML - Use geometric multigrid preconditioning. This preconditioner requires you provide 
378:        fine grid discretization matrix. The coarser grid matrices and restriction/interpolation 
379:        operators are computed by ML, with the matrices coverted to PETSc matrices in aij format
380:        and the restriction/interpolation operators wrapped as PETSc shell matrices.

382:    Options Database Key: 
383:    Multigrid options(inherited)
384: +  -pc_mg_cycles <1>: 1 for V cycle, 2 for W-cycle (MGSetCycles)
385: .  -pc_mg_smoothup <1>: Number of post-smoothing steps (MGSetNumberSmoothUp)
386: .  -pc_mg_smoothdown <1>: Number of pre-smoothing steps (MGSetNumberSmoothDown)
387: -  -pc_mg_type <multiplicative> (one of) additive multiplicative full cascade kascade
388:    
389:    ML options
390: +  -pc_ml_PrintLevel <0>: Print level (ML_Set_PrintLevel)
391: .  -pc_ml_maxNlevels <10>: Maximum number of levels (None)
392: .  -pc_ml_maxCoarseSize <1>: Maximum coarsest mesh size (ML_Aggregate_Set_MaxCoarseSize)
393: .  -pc_ml_CoarsenScheme <Uncoupled> (one of) Uncoupled Coupled MIS METIS
394: .  -pc_ml_DampingFactor <1.33333>: P damping factor (ML_Aggregate_Set_DampingFactor)
395: .  -pc_ml_Threshold <0>: Smoother drop tol (ML_Aggregate_Set_Threshold)
396: -  -pc_ml_SpectralNormScheme_Anorm: <false> Method used for estimating spectral radius (ML_Aggregate_Set_SpectralNormScheme_Anorm)

398:    Level: intermediate

400:   Concepts: multigrid
401:  
402: .seealso:  PCCreate(), PCSetType(), PCType (for list of available types), PC, PCMGType, 
403:            PCMGSetLevels(), PCMGGetLevels(), PCMGSetType(), MPSetCycles(), PCMGSetNumberSmoothDown(),
404:            PCMGSetNumberSmoothUp(), PCMGGetCoarseSolve(), PCMGSetResidual(), PCMGSetInterpolation(),
405:            PCMGSetRestriction(), PCMGGetSmoother(), PCMGGetSmootherUp(), PCMGGetSmootherDown(),
406:            PCMGSetCyclesOnLevel(), PCMGSetRhs(), PCMGSetX(), PCMGSetR()      
407: M*/

412: PetscErrorCode PETSCKSP_DLLEXPORT PCCreate_ML(PC pc)
413: {
414:   PetscErrorCode       ierr;
415:   PC_ML                *pc_ml;
416:   PetscObjectContainer container;

419:   /* initialize pc as PCMG */
420:   PCSetType(pc,PCMG); /* calls PCCreate_MG() and MGCreate_Private() */

422:   /* create a supporting struct and attach it to pc */
423:   PetscNew(PC_ML,&pc_ml);
424:   PetscObjectContainerCreate(PETSC_COMM_SELF,&container);
425:   PetscObjectContainerSetPointer(container,pc_ml);
426:   PetscObjectContainerSetUserDestroy(container,PetscObjectContainerDestroy_PC_ML);
427:   PetscObjectCompose((PetscObject)pc,"PC_ML",(PetscObject)container);
428: 
429:   pc_ml->PCSetUp   = pc->ops->setup;
430:   pc_ml->PCDestroy = pc->ops->destroy;

432:   /* overwrite the pointers of PCMG by the functions of PCML */
433:   pc->ops->setfromoptions = PCSetFromOptions_ML;
434:   pc->ops->setup          = PCSetUp_ML;
435:   pc->ops->destroy        = PCDestroy_ML;
436:   return(0);
437: }

440: int PetscML_getrow(ML_Operator *ML_data, int N_requested_rows, int requested_rows[],
441:    int allocated_space, int columns[], double values[], int row_lengths[])
442: {
444:   Mat            Aloc;
445:   Mat_SeqAIJ     *a;
446:   PetscInt       m,i,j,k=0,row,*aj;
447:   PetscScalar    *aa;
448:   FineGridCtx    *ml=(FineGridCtx*)ML_Get_MyGetrowData(ML_data);

450:   Aloc = ml->Aloc;
451:   a    = (Mat_SeqAIJ*)Aloc->data;
452:   MatGetSize(Aloc,&m,PETSC_NULL);

454:   for (i = 0; i<N_requested_rows; i++) {
455:     row   = requested_rows[i];
456:     row_lengths[i] = a->ilen[row];
457:     if (allocated_space < k+row_lengths[i]) return(0);
458:     if ( (row >= 0) || (row <= (m-1)) ) {
459:       aj = a->j + a->i[row];
460:       aa = a->a + a->i[row];
461:       for (j=0; j<row_lengths[i]; j++){
462:         columns[k]  = aj[j];
463:         values[k++] = aa[j];
464:       }
465:     }
466:   }
467:   return(1);
468: }

470: int PetscML_matvec(ML_Operator *ML_data,int in_length,double p[],int out_length,double ap[])
471: {
473:   FineGridCtx    *ml=(FineGridCtx*)ML_Get_MyMatvecData(ML_data);
474:   Mat            A=ml->A, Aloc=ml->Aloc;
475:   PetscMPIInt    size;
476:   PetscScalar    *pwork=ml->pwork;
477:   PetscInt       i;

479:   MPI_Comm_size(A->comm,&size);
480:   if (size == 1){
481:     VecPlaceArray(ml->x,p);
482:   } else {
483:     for (i=0; i<in_length; i++) pwork[i] = p[i];
484:     PetscML_comm(pwork,ml);
485:     VecPlaceArray(ml->x,pwork);
486:   }
487:   VecPlaceArray(ml->y,ap);
488:   MatMult(Aloc,ml->x,ml->y);
489:   return 0;
490: }

492: int PetscML_comm(double p[],void *ML_data)
493: {
495:   FineGridCtx    *ml=(FineGridCtx*)ML_data;
496:   Mat            A=ml->A;
497:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
498:   PetscMPIInt    size;
499:   PetscInt       i,in_length=A->m,out_length=ml->Aloc->n;
500:   PetscScalar    *array;

502:   MPI_Comm_size(A->comm,&size);
503:   if (size == 1) return 0;
504: 
505:   VecPlaceArray(ml->y,p);
506:   VecScatterBegin(ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
507:   VecScatterEnd(ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
508:   VecGetArray(a->lvec,&array);
509:   for (i=in_length; i<out_length; i++){
510:     p[i] = array[i-in_length];
511:   }
512:   return 0;
513: }
516: PetscErrorCode MatMult_ML(Mat A,Vec x,Vec y)
517: {
518:   PetscErrorCode   ierr;
519:   Mat_MLShell      *shell;
520:   PetscScalar      *xarray,*yarray;
521:   PetscInt         x_length,y_length;
522: 
524:   MatShellGetContext(A,(void *)&shell);
525:   VecGetArray(x,&xarray);
526:   VecGetArray(y,&yarray);
527:   x_length = shell->mlmat->invec_leng;
528:   y_length = shell->mlmat->outvec_leng;

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

532:   VecRestoreArray(x,&xarray);
533:   VecRestoreArray(y,&yarray);
534:   return(0);
535: }
536: /* MatMultAdd_ML -  Compute y = w + A*x */
539: PetscErrorCode MatMultAdd_ML(Mat A,Vec x,Vec w,Vec y)
540: {
541:   PetscErrorCode    ierr;
542:   Mat_MLShell       *shell;
543:   PetscScalar       *xarray,*yarray;
544:   const PetscScalar one=1.0;
545:   PetscInt          x_length,y_length;
546: 
548:   MatShellGetContext(A,(void *)&shell);
549:   VecGetArray(x,&xarray);
550:   VecGetArray(y,&yarray);

552:   x_length = shell->mlmat->invec_leng;
553:   y_length = shell->mlmat->outvec_leng;

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

557:   VecRestoreArray(x,&xarray);
558:   VecRestoreArray(y,&yarray);
559:   VecAXPY(y,one,w);

561:   return(0);
562: }

564: /* newtype is ignored because "ml" is not listed under Petsc MatType yet */
567: PetscErrorCode MatConvert_MPIAIJ_ML(Mat A,MatType newtype,MatReuse scall,Mat *Aloc)
568: {
569:   PetscErrorCode  ierr;
570:   Mat_MPIAIJ      *mpimat=(Mat_MPIAIJ*)A->data;
571:   Mat_SeqAIJ      *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
572:   PetscInt        *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
573:   PetscScalar     *aa=a->a,*ba=b->a,*ca;
574:   PetscInt        am=A->m,an=A->n,i,j,k;
575:   PetscInt        *ci,*cj,ncols;

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

580:   if (scall == MAT_INITIAL_MATRIX){
581:     PetscMalloc((1+am)*sizeof(PetscInt),&ci);
582:     ci[0] = 0;
583:     for (i=0; i<am; i++){
584:       ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
585:     }
586:     PetscMalloc((1+ci[am])*sizeof(PetscInt),&cj);
587:     PetscMalloc((1+ci[am])*sizeof(PetscScalar),&ca);

589:     k = 0;
590:     for (i=0; i<am; i++){
591:       /* diagonal portion of A */
592:       ncols = ai[i+1] - ai[i];
593:       for (j=0; j<ncols; j++) {
594:         cj[k]   = *aj++;
595:         ca[k++] = *aa++;
596:       }
597:       /* off-diagonal portion of A */
598:       ncols = bi[i+1] - bi[i];
599:       for (j=0; j<ncols; j++) {
600:         cj[k]   = an + (*bj); bj++;
601:         ca[k++] = *ba++;
602:       }
603:     }
604:     if (k != ci[am]) SETERRQ2(PETSC_ERR_ARG_WRONG,"k: %d != ci[am]: %d",k,ci[am]);

606:     /* put together the new matrix */
607:     an = mpimat->A->n+mpimat->B->n;
608:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,an,ci,cj,ca,Aloc);

610:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
611:     /* Since these are PETSc arrays, change flags to free them as necessary. */
612:     mat = (Mat_SeqAIJ*)(*Aloc)->data;
613:     mat->freedata = PETSC_TRUE;
614:     mat->nonew    = 0;
615:   } else if (scall == MAT_REUSE_MATRIX){
616:     mat=(Mat_SeqAIJ*)(*Aloc)->data;
617:     ci = mat->i; cj = mat->j; ca = mat->a;
618:     for (i=0; i<am; i++) {
619:       /* diagonal portion of A */
620:       ncols = ai[i+1] - ai[i];
621:       for (j=0; j<ncols; j++) *ca++ = *aa++;
622:       /* off-diagonal portion of A */
623:       ncols = bi[i+1] - bi[i];
624:       for (j=0; j<ncols; j++) *ca++ = *ba++;
625:     }
626:   } else {
627:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
628:   }
629:   return(0);
630: }
634: PetscErrorCode MatDestroy_ML(Mat A)
635: {
637:   Mat_MLShell    *shell;

640:   MatShellGetContext(A,(void *)&shell);
641:   VecDestroy(shell->y);
642:   PetscFree(shell);
643:   MatDestroy_Shell(A);
644:   return(0);
645: }

649: PetscErrorCode MatWrapML_SeqAIJ(ML_Operator *mlmat,Mat *newmat)
650: {
651:   struct ML_CSR_MSRdata *matdata = (struct ML_CSR_MSRdata *)mlmat->data;
652:   PetscErrorCode        ierr;
653:   PetscInt              m=mlmat->outvec_leng,n=mlmat->invec_leng,*nnz,nz_max;
654:   PetscInt              *ml_cols=matdata->columns,*aj,i,j,k;
655:   PetscScalar           *ml_vals=matdata->values,*aa;
656: 
658:   if ( mlmat->getrow == NULL) SETERRQ(PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
659:   if (m != n){ /* ML Pmat and Rmat are in CSR format. Pass array pointers into SeqAIJ matrix */
660:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,matdata->rowptr,ml_cols,ml_vals,newmat);
661:     return(0);
662:   }

664:   /* ML Amat is in MSR format. Copy its data into SeqAIJ matrix */
665:   MatCreate(PETSC_COMM_SELF,newmat);
666:   MatSetSizes(*newmat,m,n,PETSC_DECIDE,PETSC_DECIDE);
667:   MatSetType(*newmat,MATSEQAIJ);
668:   PetscMalloc((m+1)*sizeof(PetscInt),&nnz);

670:   nz_max = 0;
671:   for (i=0; i<m; i++) {
672:     nnz[i] = ml_cols[i+1] - ml_cols[i] + 1;
673:     if (nnz[i] > nz_max) nz_max = nnz[i];
674:   }
675:   MatSeqAIJSetPreallocation(*newmat,0,nnz);
676:   MatSetOption(*newmat,MAT_COLUMNS_SORTED); /* check! */

678:   nz_max++;
679:   PetscMalloc(nz_max*(sizeof(PetscInt)+sizeof(PetscScalar)),&aj);
680:   aa = (PetscScalar*)(aj + nz_max);

682:   for (i=0; i<m; i++){
683:     k = 0;
684:     /* diagonal entry */
685:     aj[k] = i; aa[k++] = ml_vals[i];
686:     /* off diagonal entries */
687:     for (j=ml_cols[i]; j<ml_cols[i+1]; j++){
688:       aj[k] = ml_cols[j]; aa[k++] = ml_vals[j];
689:     }
690:     /* sort aj and aa */
691:     PetscSortIntWithScalarArray(nnz[i],aj,aa);
692:     MatSetValues(*newmat,1,&i,nnz[i],aj,aa,INSERT_VALUES);
693:   }
694:   MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
695:   MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);
696:   PetscFree(aj);
697:   PetscFree(nnz);
698:   return(0);
699: }

703: PetscErrorCode MatWrapML_SHELL(ML_Operator *mlmat,Mat *newmat)
704: {
706:   PetscInt       m,n;
707:   ML_Comm        *MLcomm;
708:   Mat_MLShell    *shellctx;

711:   m = mlmat->outvec_leng;
712:   n = mlmat->invec_leng;
713:   if (!m || !n){
714:     newmat = PETSC_NULL;
715:   } else {
716:     MLcomm = mlmat->comm;
717:     PetscNew(Mat_MLShell,&shellctx);
718:     MatCreateShell(MLcomm->USR_comm,m,n,PETSC_DETERMINE,PETSC_DETERMINE,shellctx,newmat);
719:     MatShellSetOperation(*newmat,MATOP_MULT,(void(*)(void))MatMult_ML);
720:     MatShellSetOperation(*newmat,MATOP_MULT_ADD,(void(*)(void))MatMultAdd_ML);
721:     shellctx->A         = *newmat;
722:     shellctx->mlmat     = mlmat;
723:     VecCreate(PETSC_COMM_WORLD,&shellctx->y);
724:     VecSetSizes(shellctx->y,m,PETSC_DECIDE);
725:     VecSetFromOptions(shellctx->y);
726:     (*newmat)->ops->destroy = MatDestroy_ML;
727:   }
728:   return(0);
729: }

733: PetscErrorCode MatWrapML_MPIAIJ(ML_Operator *mlmat,Mat *newmat)
734: {
735:   struct ML_CSR_MSRdata *matdata = (struct ML_CSR_MSRdata *)mlmat->data;
736:   PetscInt              *ml_cols=matdata->columns,*aj;
737:   PetscScalar           *ml_vals=matdata->values,*aa;
738:   PetscErrorCode        ierr;
739:   PetscInt              i,j,k,*gordering;
740:   PetscInt              m=mlmat->outvec_leng,n,*nnzA,*nnzB,*nnz,nz_max,row;
741:   Mat                   A;

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

748:   MatCreate(mlmat->comm->USR_comm,&A);
749:   MatSetSizes(A,m,n,PETSC_DECIDE,PETSC_DECIDE);
750:   MatSetType(A,MATMPIAIJ);
751:   PetscMalloc3(m,PetscInt,&nnzA,m,PetscInt,&nnzB,m,PetscInt,&nnz);
752: 
753:   nz_max = 0;
754:   for (i=0; i<m; i++){
755:     nnz[i] = ml_cols[i+1] - ml_cols[i] + 1;
756:     if (nz_max < nnz[i]) nz_max = nnz[i];
757:     nnzA[i] = 1; /* diag */
758:     for (j=ml_cols[i]; j<ml_cols[i+1]; j++){
759:       if (ml_cols[j] < m) nnzA[i]++;
760:     }
761:     nnzB[i] = nnz[i] - nnzA[i];
762:   }
763:   MatMPIAIJSetPreallocation(A,0,nnzA,0,nnzB);

765:   /* insert mat values -- remap row and column indices */
766:   nz_max++;
767:   PetscMalloc(nz_max*(sizeof(PetscInt)+sizeof(PetscScalar)),&aj);
768:   aa = (PetscScalar*)(aj + nz_max);
769:   ML_build_global_numbering(mlmat,mlmat->comm,&gordering);
770:   for (i=0; i<m; i++){
771:     row = gordering[i];
772:     k = 0;
773:     /* diagonal entry */
774:     aj[k] = row; aa[k++] = ml_vals[i];
775:     /* off diagonal entries */
776:     for (j=ml_cols[i]; j<ml_cols[i+1]; j++){
777:       aj[k] = gordering[ml_cols[j]]; aa[k++] = ml_vals[j];
778:     }
779:     MatSetValues(A,1,&row,nnz[i],aj,aa,INSERT_VALUES);
780:   }
781:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
782:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
783:   *newmat = A;

785:   PetscFree3(nnzA,nnzB,nnz);
786:   PetscFree(aj);
787:   return(0);
788: }