Actual source code: ml.c

petsc-3.11.3 2019-06-26
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  2: /*
  3:    Provides an interface to the ML smoothed Aggregation
  4:    Note: Something non-obvious breaks -pc_mg_type ADDITIVE for parallel runs
  5:                                     Jed Brown, see [PETSC #18321, #18449].
  6: */
  7:  #include <petsc/private/pcimpl.h>
  8:  #include <petsc/private/pcmgimpl.h>
  9:  #include <../src/mat/impls/aij/seq/aij.h>
 10:  #include <../src/mat/impls/aij/mpi/mpiaij.h>
 11: #include <petscdm.h>            /* for DMDestroy(&pc->mg) hack */

 13: EXTERN_C_BEGIN
 14: /* HAVE_CONFIG_H flag is required by ML include files */
 15: #if !defined(HAVE_CONFIG_H)
 16: #define HAVE_CONFIG_H
 17: #endif
 18: #include <ml_include.h>
 19: #include <ml_viz_stats.h>
 20: EXTERN_C_END

 22: typedef enum {PCML_NULLSPACE_AUTO,PCML_NULLSPACE_USER,PCML_NULLSPACE_BLOCK,PCML_NULLSPACE_SCALAR} PCMLNullSpaceType;
 23: static const char *const PCMLNullSpaceTypes[] = {"AUTO","USER","BLOCK","SCALAR","PCMLNullSpaceType","PCML_NULLSPACE_",0};

 25: /* The context (data structure) at each grid level */
 26: typedef struct {
 27:   Vec x,b,r;                  /* global vectors */
 28:   Mat A,P,R;
 29:   KSP ksp;
 30:   Vec coords;                 /* projected by ML, if PCSetCoordinates is called; values packed by node */
 31: } GridCtx;

 33: /* The context used to input PETSc matrix into ML at fine grid */
 34: typedef struct {
 35:   Mat         A;       /* Petsc matrix in aij format */
 36:   Mat         Aloc;    /* local portion of A to be used by ML */
 37:   Vec         x,y;
 38:   ML_Operator *mlmat;
 39:   PetscScalar *pwork;  /* tmp array used by PetscML_comm() */
 40: } FineGridCtx;

 42: /* The context associates a ML matrix with a PETSc shell matrix */
 43: typedef struct {
 44:   Mat         A;        /* PETSc shell matrix associated with mlmat */
 45:   ML_Operator *mlmat;   /* ML matrix assorciated with A */
 46:   Vec         y, work;
 47: } Mat_MLShell;

 49: /* Private context for the ML preconditioner */
 50: typedef struct {
 51:   ML                *ml_object;
 52:   ML_Aggregate      *agg_object;
 53:   GridCtx           *gridctx;
 54:   FineGridCtx       *PetscMLdata;
 55:   PetscInt          Nlevels,MaxNlevels,MaxCoarseSize,CoarsenScheme,EnergyMinimization,MinPerProc,PutOnSingleProc,RepartitionType,ZoltanScheme;
 56:   PetscReal         Threshold,DampingFactor,EnergyMinimizationDropTol,MaxMinRatio,AuxThreshold;
 57:   PetscBool         SpectralNormScheme_Anorm,BlockScaling,EnergyMinimizationCheap,Symmetrize,OldHierarchy,KeepAggInfo,Reusable,Repartition,Aux;
 58:   PetscBool         reuse_interpolation;
 59:   PCMLNullSpaceType nulltype;
 60:   PetscMPIInt       size; /* size of communicator for pc->pmat */
 61:   PetscInt          dim;  /* data from PCSetCoordinates(_ML) */
 62:   PetscInt          nloc;
 63:   PetscReal         *coords; /* ML has a grid object for each level: the finest grid will point into coords */
 64: } PC_ML;

 66: static int PetscML_getrow(ML_Operator *ML_data, int N_requested_rows, int requested_rows[],int allocated_space, int columns[], double values[], int row_lengths[])
 67: {
 69:   PetscInt       m,i,j,k=0,row,*aj;
 70:   PetscScalar    *aa;
 71:   FineGridCtx    *ml=(FineGridCtx*)ML_Get_MyGetrowData(ML_data);
 72:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)ml->Aloc->data;

 74:   MatGetSize(ml->Aloc,&m,NULL); if (ierr) return(0);
 75:   for (i = 0; i<N_requested_rows; i++) {
 76:     row            = requested_rows[i];
 77:     row_lengths[i] = a->ilen[row];
 78:     if (allocated_space < k+row_lengths[i]) return(0);
 79:     if ((row >= 0) || (row <= (m-1))) {
 80:       aj = a->j + a->i[row];
 81:       aa = a->a + a->i[row];
 82:       for (j=0; j<row_lengths[i]; j++) {
 83:         columns[k]  = aj[j];
 84:         values[k++] = aa[j];
 85:       }
 86:     }
 87:   }
 88:   return(1);
 89: }

 91: static PetscErrorCode PetscML_comm(double p[],void *ML_data)
 92: {
 93:   PetscErrorCode    ierr;
 94:   FineGridCtx       *ml = (FineGridCtx*)ML_data;
 95:   Mat               A   = ml->A;
 96:   Mat_MPIAIJ        *a  = (Mat_MPIAIJ*)A->data;
 97:   PetscMPIInt       size;
 98:   PetscInt          i,in_length=A->rmap->n,out_length=ml->Aloc->cmap->n;
 99:   const PetscScalar *array;

102:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
103:   if (size == 1) return 0;

105:   VecPlaceArray(ml->y,p);
106:   VecScatterBegin(a->Mvctx,ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
107:   VecScatterEnd(a->Mvctx,ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
108:   VecResetArray(ml->y);
109:   VecGetArrayRead(a->lvec,&array);
110:   for (i=in_length; i<out_length; i++) p[i] = array[i-in_length];
111:   VecRestoreArrayRead(a->lvec,&array);
112:   return(0);
113: }

115: static int PetscML_matvec(ML_Operator *ML_data,int in_length,double p[],int out_length,double ap[])
116: {
118:   FineGridCtx    *ml = (FineGridCtx*)ML_Get_MyMatvecData(ML_data);
119:   Mat            A   = ml->A, Aloc=ml->Aloc;
120:   PetscMPIInt    size;
121:   PetscScalar    *pwork=ml->pwork;
122:   PetscInt       i;

125:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
126:   if (size == 1) {
127:     VecPlaceArray(ml->x,p);
128:   } else {
129:     for (i=0; i<in_length; i++) pwork[i] = p[i];
130:     PetscML_comm(pwork,ml);
131:     VecPlaceArray(ml->x,pwork);
132:   }
133:   VecPlaceArray(ml->y,ap);
134:   MatMult(Aloc,ml->x,ml->y);
135:   VecResetArray(ml->x);
136:   VecResetArray(ml->y);
137:   return(0);
138: }

140: static PetscErrorCode MatMult_ML(Mat A,Vec x,Vec y)
141: {
142:   PetscErrorCode    ierr;
143:   Mat_MLShell       *shell;
144:   PetscScalar       *yarray;
145:   const PetscScalar *xarray;
146:   PetscInt          x_length,y_length;

149:   MatShellGetContext(A,(void**)&shell);
150:   VecGetArrayRead(x,&xarray);
151:   VecGetArray(y,&yarray);
152:   x_length = shell->mlmat->invec_leng;
153:   y_length = shell->mlmat->outvec_leng;
154:   PetscStackCall("ML_Operator_Apply",ML_Operator_Apply(shell->mlmat,x_length,(PetscScalar*)xarray,y_length,yarray));
155:   VecRestoreArrayRead(x,&xarray);
156:   VecRestoreArray(y,&yarray);
157:   return(0);
158: }

160: /* Computes y = w + A * x
161:    It is possible that w == y, but not x == y
162: */
163: static PetscErrorCode MatMultAdd_ML(Mat A,Vec x,Vec w,Vec y)
164: {
165:   Mat_MLShell       *shell;
166:   PetscScalar       *yarray;
167:   const PetscScalar *xarray;
168:   PetscInt          x_length,y_length;
169:   PetscErrorCode    ierr;

172:   MatShellGetContext(A, (void**) &shell);
173:   if (y == w) {
174:     if (!shell->work) {
175:       VecDuplicate(y, &shell->work);
176:     }
177:     VecGetArrayRead(x,           &xarray);
178:     VecGetArray(shell->work, &yarray);
179:     x_length = shell->mlmat->invec_leng;
180:     y_length = shell->mlmat->outvec_leng;
181:     PetscStackCall("ML_Operator_Apply",ML_Operator_Apply(shell->mlmat, x_length, (PetscScalar*)xarray, y_length, yarray));
182:     VecRestoreArrayRead(x,           &xarray);
183:     VecRestoreArray(shell->work, &yarray);
184:     VecAXPY(y, 1.0, shell->work);
185:   } else {
186:     VecGetArrayRead(x, &xarray);
187:     VecGetArray(y, &yarray);
188:     x_length = shell->mlmat->invec_leng;
189:     y_length = shell->mlmat->outvec_leng;
190:     PetscStackCall("ML_Operator_Apply",ML_Operator_Apply(shell->mlmat, x_length, (PetscScalar *)xarray, y_length, yarray));
191:     VecRestoreArrayRead(x, &xarray);
192:     VecRestoreArray(y, &yarray);
193:     VecAXPY(y, 1.0, w);
194:   }
195:   return(0);
196: }

198: /* newtype is ignored since only handles one case */
199: static PetscErrorCode MatConvert_MPIAIJ_ML(Mat A,MatType newtype,MatReuse scall,Mat *Aloc)
200: {
202:   Mat_MPIAIJ     *mpimat=(Mat_MPIAIJ*)A->data;
203:   Mat_SeqAIJ     *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
204:   PetscInt       *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
205:   PetscScalar    *aa=a->a,*ba=b->a,*ca;
206:   PetscInt       am =A->rmap->n,an=A->cmap->n,i,j,k;
207:   PetscInt       *ci,*cj,ncols;

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

212:   if (scall == MAT_INITIAL_MATRIX) {
213:     PetscMalloc1(1+am,&ci);
214:     ci[0] = 0;
215:     for (i=0; i<am; i++) ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
216:     PetscMalloc1(1+ci[am],&cj);
217:     PetscMalloc1(1+ci[am],&ca);

219:     k = 0;
220:     for (i=0; i<am; i++) {
221:       /* diagonal portion of A */
222:       ncols = ai[i+1] - ai[i];
223:       for (j=0; j<ncols; j++) {
224:         cj[k]   = *aj++;
225:         ca[k++] = *aa++;
226:       }
227:       /* off-diagonal portion of A */
228:       ncols = bi[i+1] - bi[i];
229:       for (j=0; j<ncols; j++) {
230:         cj[k]   = an + (*bj); bj++;
231:         ca[k++] = *ba++;
232:       }
233:     }
234:     if (k != ci[am]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"k: %d != ci[am]: %d",k,ci[am]);

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

240:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
241:     /* Since these are PETSc arrays, change flags to free them as necessary. */
242:     mat          = (Mat_SeqAIJ*)(*Aloc)->data;
243:     mat->free_a  = PETSC_TRUE;
244:     mat->free_ij = PETSC_TRUE;

246:     mat->nonew = 0;
247:   } else if (scall == MAT_REUSE_MATRIX) {
248:     mat=(Mat_SeqAIJ*)(*Aloc)->data;
249:     ci = mat->i; cj = mat->j; ca = mat->a;
250:     for (i=0; i<am; i++) {
251:       /* diagonal portion of A */
252:       ncols = ai[i+1] - ai[i];
253:       for (j=0; j<ncols; j++) *ca++ = *aa++;
254:       /* off-diagonal portion of A */
255:       ncols = bi[i+1] - bi[i];
256:       for (j=0; j<ncols; j++) *ca++ = *ba++;
257:     }
258:   } else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
259:   return(0);
260: }

262: static PetscErrorCode MatDestroy_ML(Mat A)
263: {
265:   Mat_MLShell    *shell;

268:   MatShellGetContext(A,(void**)&shell);
269:   VecDestroy(&shell->y);
270:   if (shell->work) {VecDestroy(&shell->work);}
271:   PetscFree(shell);
272:   return(0);
273: }

275: static PetscErrorCode MatWrapML_SeqAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
276: {
277:   struct ML_CSR_MSRdata *matdata = (struct ML_CSR_MSRdata*)mlmat->data;
278:   PetscErrorCode        ierr;
279:   PetscInt              m       =mlmat->outvec_leng,n=mlmat->invec_leng,*nnz = NULL,nz_max;
280:   PetscInt              *ml_cols=matdata->columns,*ml_rowptr=matdata->rowptr,*aj,i;
281:   PetscScalar           *ml_vals=matdata->values,*aa;

284:   if (!mlmat->getrow) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
285:   if (m != n) { /* ML Pmat and Rmat are in CSR format. Pass array pointers into SeqAIJ matrix */
286:     if (reuse) {
287:       Mat_SeqAIJ *aij= (Mat_SeqAIJ*)(*newmat)->data;
288:       aij->i = ml_rowptr;
289:       aij->j = ml_cols;
290:       aij->a = ml_vals;
291:     } else {
292:       /* sort ml_cols and ml_vals */
293:       PetscMalloc1(m+1,&nnz);
294:       for (i=0; i<m; i++) nnz[i] = ml_rowptr[i+1] - ml_rowptr[i];
295:       aj = ml_cols; aa = ml_vals;
296:       for (i=0; i<m; i++) {
297:         PetscSortIntWithScalarArray(nnz[i],aj,aa);
298:         aj  += nnz[i]; aa += nnz[i];
299:       }
300:       MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,ml_rowptr,ml_cols,ml_vals,newmat);
301:       PetscFree(nnz);
302:     }
303:     return(0);
304:   }

306:   nz_max = PetscMax(1,mlmat->max_nz_per_row);
307:   PetscMalloc2(nz_max,&aa,nz_max,&aj);
308:   if (!reuse) {
309:     MatCreate(PETSC_COMM_SELF,newmat);
310:     MatSetSizes(*newmat,m,n,PETSC_DECIDE,PETSC_DECIDE);
311:     MatSetType(*newmat,MATSEQAIJ);
312:     /* keep track of block size for A matrices */
313:     MatSetBlockSize (*newmat, mlmat->num_PDEs);

315:     PetscMalloc1(m,&nnz);
316:     for (i=0; i<m; i++) {
317:       PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&nnz[i]));
318:     }
319:     MatSeqAIJSetPreallocation(*newmat,0,nnz);
320:   }
321:   for (i=0; i<m; i++) {
322:     PetscInt ncols;

324:     PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&ncols));
325:     MatSetValues(*newmat,1,&i,ncols,aj,aa,INSERT_VALUES);
326:   }
327:   MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
328:   MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);

330:   PetscFree2(aa,aj);
331:   PetscFree(nnz);
332:   return(0);
333: }

335: static PetscErrorCode MatWrapML_SHELL(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
336: {
338:   PetscInt       m,n;
339:   ML_Comm        *MLcomm;
340:   Mat_MLShell    *shellctx;

343:   m = mlmat->outvec_leng;
344:   n = mlmat->invec_leng;

346:   if (reuse) {
347:     MatShellGetContext(*newmat,(void**)&shellctx);
348:     shellctx->mlmat = mlmat;
349:     return(0);
350:   }

352:   MLcomm = mlmat->comm;

354:   PetscNew(&shellctx);
355:   MatCreateShell(MLcomm->USR_comm,m,n,PETSC_DETERMINE,PETSC_DETERMINE,shellctx,newmat);
356:   MatShellSetOperation(*newmat,MATOP_MULT,(void(*)(void))MatMult_ML);
357:   MatShellSetOperation(*newmat,MATOP_MULT_ADD,(void(*)(void))MatMultAdd_ML);
358:   MatShellSetOperation(*newmat,MATOP_DESTROY,(void(*)(void))MatDestroy_ML);

360:   shellctx->A         = *newmat;
361:   shellctx->mlmat     = mlmat;
362:   shellctx->work      = NULL;

364:   VecCreate(MLcomm->USR_comm,&shellctx->y);
365:   VecSetSizes(shellctx->y,m,PETSC_DECIDE);
366:   VecSetType(shellctx->y,VECSTANDARD);
367:   return(0);
368: }

370: static PetscErrorCode MatWrapML_MPIAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
371: {
372:   PetscInt       *aj;
373:   PetscScalar    *aa;
375:   PetscInt       i,j,*gordering;
376:   PetscInt       m=mlmat->outvec_leng,n,nz_max,row;
377:   Mat            A;

380:   if (!mlmat->getrow) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
381:   n = mlmat->invec_leng;
382:   if (m != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"m %d must equal to n %d",m,n);

384:   /* create global row numbering for a ML_Operator */
385:   PetscStackCall("ML_build_global_numbering",ML_build_global_numbering(mlmat,&gordering,"rows"));

387:   nz_max = PetscMax(1,mlmat->max_nz_per_row) + 1;
388:   PetscMalloc2(nz_max,&aa,nz_max,&aj);
389:   if (reuse) {
390:     A = *newmat;
391:   } else {
392:     PetscInt *nnzA,*nnzB,*nnz;
393:     PetscInt rstart;
394:     MatCreate(mlmat->comm->USR_comm,&A);
395:     MatSetSizes(A,m,n,PETSC_DECIDE,PETSC_DECIDE);
396:     MatSetType(A,MATMPIAIJ);
397:     /* keep track of block size for A matrices */
398:     MatSetBlockSize (A,mlmat->num_PDEs);
399:     PetscMalloc3(m,&nnzA,m,&nnzB,m,&nnz);
400:     MPI_Scan(&m,&rstart,1,MPIU_INT,MPI_SUM,mlmat->comm->USR_comm);
401:     rstart -= m;

403:     for (i=0; i<m; i++) {
404:       row = gordering[i] - rstart;
405:       PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&nnz[i]));
406:       nnzA[row] = 0;
407:       for (j=0; j<nnz[i]; j++) {
408:         if (aj[j] < m) nnzA[row]++;
409:       }
410:       nnzB[row] = nnz[i] - nnzA[row];
411:     }
412:     MatMPIAIJSetPreallocation(A,0,nnzA,0,nnzB);
413:     PetscFree3(nnzA,nnzB,nnz);
414:   }
415:   for (i=0; i<m; i++) {
416:     PetscInt ncols;
417:     row = gordering[i];

419:     PetscStackCall(",ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&ncols));
420:     for (j = 0; j < ncols; j++) aj[j] = gordering[aj[j]];
421:     MatSetValues(A,1,&row,ncols,aj,aa,INSERT_VALUES);
422:   }
423:   PetscStackCall("ML_free",ML_free(gordering));
424:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
425:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
426:   *newmat = A;

428:   PetscFree2(aa,aj);
429:   return(0);
430: }

432: /* -------------------------------------------------------------------------- */
433: /*
434:    PCSetCoordinates_ML

436:    Input Parameter:
437:    .  pc - the preconditioner context
438: */
439: static PetscErrorCode PCSetCoordinates_ML(PC pc, PetscInt ndm, PetscInt a_nloc, PetscReal *coords)
440: {
441:   PC_MG          *mg    = (PC_MG*)pc->data;
442:   PC_ML          *pc_ml = (PC_ML*)mg->innerctx;
444:   PetscInt       arrsz,oldarrsz,bs,my0,kk,ii,nloc,Iend,aloc;
445:   Mat            Amat = pc->pmat;

447:   /* this function copied and modified from PCSetCoordinates_GEO -TGI */
450:   MatGetBlockSize(Amat, &bs);

452:   MatGetOwnershipRange(Amat, &my0, &Iend);
453:   aloc = (Iend-my0);
454:   nloc = (Iend-my0)/bs;

456:   if (nloc!=a_nloc && aloc != a_nloc) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Number of local blocks %D must be %D or %D.",a_nloc,nloc,aloc);

458:   oldarrsz    = pc_ml->dim * pc_ml->nloc;
459:   pc_ml->dim  = ndm;
460:   pc_ml->nloc = nloc;
461:   arrsz       = ndm * nloc;

463:   /* create data - syntactic sugar that should be refactored at some point */
464:   if (pc_ml->coords==0 || (oldarrsz != arrsz)) {
465:     PetscFree(pc_ml->coords);
466:     PetscMalloc1(arrsz, &pc_ml->coords);
467:   }
468:   for (kk=0; kk<arrsz; kk++) pc_ml->coords[kk] = -999.;
469:   /* copy data in - column oriented */
470:   if (nloc == a_nloc) {
471:     for (kk = 0; kk < nloc; kk++) {
472:       for (ii = 0; ii < ndm; ii++) {
473:         pc_ml->coords[ii*nloc + kk] =  coords[kk*ndm + ii];
474:       }
475:     }
476:   } else { /* assumes the coordinates are blocked */
477:     for (kk = 0; kk < nloc; kk++) {
478:       for (ii = 0; ii < ndm; ii++) {
479:         pc_ml->coords[ii*nloc + kk] =  coords[bs*kk*ndm + ii];
480:       }
481:     }
482:   }
483:   return(0);
484: }

486: /* -----------------------------------------------------------------------------*/
487: extern PetscErrorCode PCReset_MG(PC);
488: PetscErrorCode PCReset_ML(PC pc)
489: {
491:   PC_MG          *mg    = (PC_MG*)pc->data;
492:   PC_ML          *pc_ml = (PC_ML*)mg->innerctx;
493:   PetscInt       level,fine_level=pc_ml->Nlevels-1,dim=pc_ml->dim;

496:   if (dim) {
497:     for (level=0; level<=fine_level; level++) {
498:       VecDestroy(&pc_ml->gridctx[level].coords);
499:     }
500:     if (pc_ml->ml_object && pc_ml->ml_object->Grid) {
501:       ML_Aggregate_Viz_Stats * grid_info = (ML_Aggregate_Viz_Stats*) pc_ml->ml_object->Grid[0].Grid;
502:       grid_info->x = 0; /* do this so ML doesn't try to free coordinates */
503:       grid_info->y = 0;
504:       grid_info->z = 0;
505:       PetscStackCall("ML_Operator_Getrow",ML_Aggregate_VizAndStats_Clean(pc_ml->ml_object));
506:     }
507:   }
508:   PetscStackCall("ML_Aggregate_Destroy",ML_Aggregate_Destroy(&pc_ml->agg_object));
509:   PetscStackCall("ML_Aggregate_Destroy",ML_Destroy(&pc_ml->ml_object));

511:   if (pc_ml->PetscMLdata) {
512:     PetscFree(pc_ml->PetscMLdata->pwork);
513:     MatDestroy(&pc_ml->PetscMLdata->Aloc);
514:     VecDestroy(&pc_ml->PetscMLdata->x);
515:     VecDestroy(&pc_ml->PetscMLdata->y);
516:   }
517:   PetscFree(pc_ml->PetscMLdata);

519:   if (pc_ml->gridctx) {
520:     for (level=0; level<fine_level; level++) {
521:       if (pc_ml->gridctx[level].A) {MatDestroy(&pc_ml->gridctx[level].A);}
522:       if (pc_ml->gridctx[level].P) {MatDestroy(&pc_ml->gridctx[level].P);}
523:       if (pc_ml->gridctx[level].R) {MatDestroy(&pc_ml->gridctx[level].R);}
524:       if (pc_ml->gridctx[level].x) {VecDestroy(&pc_ml->gridctx[level].x);}
525:       if (pc_ml->gridctx[level].b) {VecDestroy(&pc_ml->gridctx[level].b);}
526:       if (pc_ml->gridctx[level+1].r) {VecDestroy(&pc_ml->gridctx[level+1].r);}
527:     }
528:   }
529:   PetscFree(pc_ml->gridctx);
530:   PetscFree(pc_ml->coords);

532:   pc_ml->dim  = 0;
533:   pc_ml->nloc = 0;
534:   PCReset_MG(pc);
535:   return(0);
536: }
537: /* -------------------------------------------------------------------------- */
538: /*
539:    PCSetUp_ML - Prepares for the use of the ML preconditioner
540:                     by setting data structures and options.

542:    Input Parameter:
543: .  pc - the preconditioner context

545:    Application Interface Routine: PCSetUp()

547:    Notes:
548:    The interface routine PCSetUp() is not usually called directly by
549:    the user, but instead is called by PCApply() if necessary.
550: */
551: extern PetscErrorCode PCSetFromOptions_MG(PetscOptionItems *PetscOptionsObject,PC);
552: extern PetscErrorCode PCReset_MG(PC);

554: PetscErrorCode PCSetUp_ML(PC pc)
555: {
556:   PetscErrorCode   ierr;
557:   PetscMPIInt      size;
558:   FineGridCtx      *PetscMLdata;
559:   ML               *ml_object;
560:   ML_Aggregate     *agg_object;
561:   ML_Operator      *mlmat;
562:   PetscInt         nlocal_allcols,Nlevels,mllevel,level,level1,m,fine_level,bs;
563:   Mat              A,Aloc;
564:   GridCtx          *gridctx;
565:   PC_MG            *mg    = (PC_MG*)pc->data;
566:   PC_ML            *pc_ml = (PC_ML*)mg->innerctx;
567:   PetscBool        isSeq, isMPI;
568:   KSP              smoother;
569:   PC               subpc;
570:   PetscInt         mesh_level, old_mesh_level;
571:   MatInfo          info;
572:   static PetscBool cite = PETSC_FALSE;

575:   PetscCitationsRegister("@TechReport{ml_users_guide,\n  author = {M. Sala and J.J. Hu and R.S. Tuminaro},\n  title = {{ML}3.1 {S}moothed {A}ggregation {U}ser's {G}uide},\n  institution =  {Sandia National Laboratories},\n  number = {SAND2004-4821},\n  year = 2004\n}\n",&cite);
576:   A    = pc->pmat;
577:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);

579:   if (pc->setupcalled) {
580:     if (pc->flag == SAME_NONZERO_PATTERN && pc_ml->reuse_interpolation) {
581:       /*
582:        Reuse interpolaton instead of recomputing aggregates and updating the whole hierarchy. This is less expensive for
583:        multiple solves in which the matrix is not changing too quickly.
584:        */
585:       ml_object             = pc_ml->ml_object;
586:       gridctx               = pc_ml->gridctx;
587:       Nlevels               = pc_ml->Nlevels;
588:       fine_level            = Nlevels - 1;
589:       gridctx[fine_level].A = A;

591:       PetscObjectTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);
592:       PetscObjectTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);
593:       if (isMPI) {
594:         MatConvert_MPIAIJ_ML(A,NULL,MAT_INITIAL_MATRIX,&Aloc);
595:       } else if (isSeq) {
596:         Aloc = A;
597:         PetscObjectReference((PetscObject)Aloc);
598:       } else SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONG, "Matrix type '%s' cannot be used with ML. ML can only handle AIJ matrices.",((PetscObject)A)->type_name);

600:       MatGetSize(Aloc,&m,&nlocal_allcols);
601:       PetscMLdata       = pc_ml->PetscMLdata;
602:       MatDestroy(&PetscMLdata->Aloc);
603:       PetscMLdata->A    = A;
604:       PetscMLdata->Aloc = Aloc;
605:       PetscStackCall("ML_Aggregate_Destroy",ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata));
606:       PetscStackCall("ML_Set_Amatrix_Matvec",ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec));

608:       mesh_level = ml_object->ML_finest_level;
609:       while (ml_object->SingleLevel[mesh_level].Rmat->to) {
610:         old_mesh_level = mesh_level;
611:         mesh_level     = ml_object->SingleLevel[mesh_level].Rmat->to->levelnum;

613:         /* clean and regenerate A */
614:         mlmat = &(ml_object->Amat[mesh_level]);
615:         PetscStackCall("ML_Operator_Clean",ML_Operator_Clean(mlmat));
616:         PetscStackCall("ML_Operator_Init",ML_Operator_Init(mlmat,ml_object->comm));
617:         PetscStackCall("ML_Gen_AmatrixRAP",ML_Gen_AmatrixRAP(ml_object, old_mesh_level, mesh_level));
618:       }

620:       level = fine_level - 1;
621:       if (size == 1) { /* convert ML P, R and A into seqaij format */
622:         for (mllevel=1; mllevel<Nlevels; mllevel++) {
623:           mlmat = &(ml_object->Amat[mllevel]);
624:           MatWrapML_SeqAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);
625:           level--;
626:         }
627:       } else { /* convert ML P and R into shell format, ML A into mpiaij format */
628:         for (mllevel=1; mllevel<Nlevels; mllevel++) {
629:           mlmat  = &(ml_object->Amat[mllevel]);
630:           MatWrapML_MPIAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);
631:           level--;
632:         }
633:       }

635:       for (level=0; level<fine_level; level++) {
636:         if (level > 0) {
637:           PCMGSetResidual(pc,level,PCMGResidualDefault,gridctx[level].A);
638:         }
639:         KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A);
640:       }
641:       PCMGSetResidual(pc,fine_level,PCMGResidualDefault,gridctx[fine_level].A);
642:       KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A);

644:       PCSetUp_MG(pc);
645:       return(0);
646:     } else {
647:       /* since ML can change the size of vectors/matrices at any level we must destroy everything */
648:       PCReset_ML(pc);
649:     }
650:   }

652:   /* setup special features of PCML */
653:   /*--------------------------------*/
654:   /* covert A to Aloc to be used by ML at fine grid */
655:   pc_ml->size = size;
656:   PetscObjectTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);
657:   PetscObjectTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);
658:   if (isMPI) {
659:     MatConvert_MPIAIJ_ML(A,NULL,MAT_INITIAL_MATRIX,&Aloc);
660:   } else if (isSeq) {
661:     Aloc = A;
662:     PetscObjectReference((PetscObject)Aloc);
663:   } else SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONG, "Matrix type '%s' cannot be used with ML. ML can only handle AIJ matrices.",((PetscObject)A)->type_name);

665:   /* create and initialize struct 'PetscMLdata' */
666:   PetscNewLog(pc,&PetscMLdata);
667:   pc_ml->PetscMLdata = PetscMLdata;
668:   PetscMalloc1(Aloc->cmap->n+1,&PetscMLdata->pwork);

670:   VecCreate(PETSC_COMM_SELF,&PetscMLdata->x);
671:   VecSetSizes(PetscMLdata->x,Aloc->cmap->n,Aloc->cmap->n);
672:   VecSetType(PetscMLdata->x,VECSEQ);

674:   VecCreate(PETSC_COMM_SELF,&PetscMLdata->y);
675:   VecSetSizes(PetscMLdata->y,A->rmap->n,PETSC_DECIDE);
676:   VecSetType(PetscMLdata->y,VECSEQ);
677:   PetscMLdata->A    = A;
678:   PetscMLdata->Aloc = Aloc;
679:   if (pc_ml->dim) { /* create vecs around the coordinate data given */
680:     PetscInt  i,j,dim=pc_ml->dim;
681:     PetscInt  nloc = pc_ml->nloc,nlocghost;
682:     PetscReal *ghostedcoords;

684:     MatGetBlockSize(A,&bs);
685:     nlocghost = Aloc->cmap->n / bs;
686:     PetscMalloc1(dim*nlocghost,&ghostedcoords);
687:     for (i = 0; i < dim; i++) {
688:       /* copy coordinate values into first component of pwork */
689:       for (j = 0; j < nloc; j++) {
690:         PetscMLdata->pwork[bs * j] = pc_ml->coords[nloc * i + j];
691:       }
692:       /* get the ghost values */
693:       PetscML_comm(PetscMLdata->pwork,PetscMLdata);
694:       /* write into the vector */
695:       for (j = 0; j < nlocghost; j++) {
696:         ghostedcoords[i * nlocghost + j] = PetscMLdata->pwork[bs * j];
697:       }
698:     }
699:     /* replace the original coords with the ghosted coords, because these are
700:      * what ML needs */
701:     PetscFree(pc_ml->coords);
702:     pc_ml->coords = ghostedcoords;
703:   }

705:   /* create ML discretization matrix at fine grid */
706:   /* ML requires input of fine-grid matrix. It determines nlevels. */
707:   MatGetSize(Aloc,&m,&nlocal_allcols);
708:   MatGetBlockSize(A,&bs);
709:   PetscStackCall("ML_Create",ML_Create(&ml_object,pc_ml->MaxNlevels));
710:   PetscStackCall("ML_Comm_Set_UsrComm",ML_Comm_Set_UsrComm(ml_object->comm,PetscObjectComm((PetscObject)A)));
711:   pc_ml->ml_object = ml_object;
712:   PetscStackCall("ML_Init_Amatrix",ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata));
713:   PetscStackCall("ML_Set_Amatrix_Getrow",ML_Set_Amatrix_Getrow(ml_object,0,PetscML_getrow,PetscML_comm,nlocal_allcols));
714:   PetscStackCall("ML_Set_Amatrix_Matvec",ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec));

716:   PetscStackCall("ML_Set_Symmetrize",ML_Set_Symmetrize(ml_object,pc_ml->Symmetrize ? ML_YES : ML_NO));

718:   /* aggregation */
719:   PetscStackCall("ML_Aggregate_Create",ML_Aggregate_Create(&agg_object));
720:   pc_ml->agg_object = agg_object;

722:   {
723:     MatNullSpace mnull;
724:     MatGetNearNullSpace(A,&mnull);
725:     if (pc_ml->nulltype == PCML_NULLSPACE_AUTO) {
726:       if (mnull) pc_ml->nulltype = PCML_NULLSPACE_USER;
727:       else if (bs > 1) pc_ml->nulltype = PCML_NULLSPACE_BLOCK;
728:       else pc_ml->nulltype = PCML_NULLSPACE_SCALAR;
729:     }
730:     switch (pc_ml->nulltype) {
731:     case PCML_NULLSPACE_USER: {
732:       PetscScalar       *nullvec;
733:       const PetscScalar *v;
734:       PetscBool         has_const;
735:       PetscInt          i,j,mlocal,nvec,M;
736:       const Vec         *vecs;

738:       if (!mnull) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"Must provide explicit null space using MatSetNearNullSpace() to use user-specified null space");
739:       MatGetSize(A,&M,NULL);
740:       MatGetLocalSize(Aloc,&mlocal,NULL);
741:       MatNullSpaceGetVecs(mnull,&has_const,&nvec,&vecs);
742:       PetscMalloc1((nvec+!!has_const)*mlocal,&nullvec);
743:       if (has_const) for (i=0; i<mlocal; i++) nullvec[i] = 1.0/M;
744:       for (i=0; i<nvec; i++) {
745:         VecGetArrayRead(vecs[i],&v);
746:         for (j=0; j<mlocal; j++) nullvec[(i+!!has_const)*mlocal + j] = v[j];
747:         VecRestoreArrayRead(vecs[i],&v);
748:       }
749:       PetscStackCall("ML_Aggregate_Create",ML_Aggregate_Set_NullSpace(agg_object,bs,nvec+!!has_const,nullvec,mlocal);CHKERRQ(ierr));
750:       PetscFree(nullvec);
751:     } break;
752:     case PCML_NULLSPACE_BLOCK:
753:       PetscStackCall("ML_Aggregate_Set_NullSpace",ML_Aggregate_Set_NullSpace(agg_object,bs,bs,0,0);CHKERRQ(ierr));
754:       break;
755:     case PCML_NULLSPACE_SCALAR:
756:       break;
757:     default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_SUP,"Unknown null space type");
758:     }
759:   }
760:   PetscStackCall("ML_Aggregate_Set_MaxCoarseSize",ML_Aggregate_Set_MaxCoarseSize(agg_object,pc_ml->MaxCoarseSize));
761:   /* set options */
762:   switch (pc_ml->CoarsenScheme) {
763:   case 1:
764:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_Coupled",ML_Aggregate_Set_CoarsenScheme_Coupled(agg_object));break;
765:   case 2:
766:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_MIS",ML_Aggregate_Set_CoarsenScheme_MIS(agg_object));break;
767:   case 3:
768:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_METIS",ML_Aggregate_Set_CoarsenScheme_METIS(agg_object));break;
769:   }
770:   PetscStackCall("ML_Aggregate_Set_Threshold",ML_Aggregate_Set_Threshold(agg_object,pc_ml->Threshold));
771:   PetscStackCall("ML_Aggregate_Set_DampingFactor",ML_Aggregate_Set_DampingFactor(agg_object,pc_ml->DampingFactor));
772:   if (pc_ml->SpectralNormScheme_Anorm) {
773:     PetscStackCall("ML_Set_SpectralNormScheme_Anorm",ML_Set_SpectralNormScheme_Anorm(ml_object));
774:   }
775:   agg_object->keep_agg_information      = (int)pc_ml->KeepAggInfo;
776:   agg_object->keep_P_tentative          = (int)pc_ml->Reusable;
777:   agg_object->block_scaled_SA           = (int)pc_ml->BlockScaling;
778:   agg_object->minimizing_energy         = (int)pc_ml->EnergyMinimization;
779:   agg_object->minimizing_energy_droptol = (double)pc_ml->EnergyMinimizationDropTol;
780:   agg_object->cheap_minimizing_energy   = (int)pc_ml->EnergyMinimizationCheap;

782:   if (pc_ml->Aux) {
783:     if (!pc_ml->dim) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"Auxiliary matrix requires coordinates");
784:     ml_object->Amat[0].aux_data->threshold = pc_ml->AuxThreshold;
785:     ml_object->Amat[0].aux_data->enable    = 1;
786:     ml_object->Amat[0].aux_data->max_level = 10;
787:     ml_object->Amat[0].num_PDEs            = bs;
788:   }

790:   MatGetInfo(A,MAT_LOCAL,&info);
791:   ml_object->Amat[0].N_nonzeros = (int) info.nz_used;

793:   if (pc_ml->dim) {
794:     PetscInt               i,dim = pc_ml->dim;
795:     ML_Aggregate_Viz_Stats *grid_info;
796:     PetscInt               nlocghost;

798:     MatGetBlockSize(A,&bs);
799:     nlocghost = Aloc->cmap->n / bs;

801:     PetscStackCall("ML_Aggregate_VizAndStats_Setup(",ML_Aggregate_VizAndStats_Setup(ml_object)); /* create ml info for coords */
802:     grid_info = (ML_Aggregate_Viz_Stats*) ml_object->Grid[0].Grid;
803:     for (i = 0; i < dim; i++) {
804:       /* set the finest level coordinates to point to the column-order array
805:        * in pc_ml */
806:       /* NOTE: must point away before VizAndStats_Clean so ML doesn't free */
807:       switch (i) {
808:       case 0: grid_info->x = pc_ml->coords + nlocghost * i; break;
809:       case 1: grid_info->y = pc_ml->coords + nlocghost * i; break;
810:       case 2: grid_info->z = pc_ml->coords + nlocghost * i; break;
811:       default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_SIZ,"PCML coordinate dimension must be <= 3");
812:       }
813:     }
814:     grid_info->Ndim = dim;
815:   }

817:   /* repartitioning */
818:   if (pc_ml->Repartition) {
819:     PetscStackCall("ML_Repartition_Activate",ML_Repartition_Activate(ml_object));
820:     PetscStackCall("ML_Repartition_Set_LargestMinMaxRatio",ML_Repartition_Set_LargestMinMaxRatio(ml_object,pc_ml->MaxMinRatio));
821:     PetscStackCall("ML_Repartition_Set_MinPerProc",ML_Repartition_Set_MinPerProc(ml_object,pc_ml->MinPerProc));
822:     PetscStackCall("ML_Repartition_Set_PutOnSingleProc",ML_Repartition_Set_PutOnSingleProc(ml_object,pc_ml->PutOnSingleProc));
823: #if 0                           /* Function not yet defined in ml-6.2 */
824:     /* I'm not sure what compatibility issues might crop up if we partitioned
825:      * on the finest level, so to be safe repartition starts on the next
826:      * finest level (reflection default behavior in
827:      * ml_MultiLevelPreconditioner) */
828:     PetscStackCall("ML_Repartition_Set_StartLevel",ML_Repartition_Set_StartLevel(ml_object,1));
829: #endif

831:     if (!pc_ml->RepartitionType) {
832:       PetscInt i;

834:       if (!pc_ml->dim) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"ML Zoltan repartitioning requires coordinates");
835:       PetscStackCall("ML_Repartition_Set_Partitioner",ML_Repartition_Set_Partitioner(ml_object,ML_USEZOLTAN));
836:       PetscStackCall("ML_Aggregate_Set_Dimensions",ML_Aggregate_Set_Dimensions(agg_object, pc_ml->dim));

838:       for (i = 0; i < ml_object->ML_num_levels; i++) {
839:         ML_Aggregate_Viz_Stats *grid_info = (ML_Aggregate_Viz_Stats*)ml_object->Grid[i].Grid;
840:         grid_info->zoltan_type = pc_ml->ZoltanScheme + 1; /* ml numbers options 1, 2, 3 */
841:         /* defaults from ml_agg_info.c */
842:         grid_info->zoltan_estimated_its = 40; /* only relevant to hypergraph / fast hypergraph */
843:         grid_info->zoltan_timers        = 0;
844:         grid_info->smoothing_steps      = 4;  /* only relevant to hypergraph / fast hypergraph */
845:       }
846:     } else {
847:       PetscStackCall("ML_Repartition_Set_Partitioner",ML_Repartition_Set_Partitioner(ml_object,ML_USEPARMETIS));
848:     }
849:   }

851:   if (pc_ml->OldHierarchy) {
852:     PetscStackCall("ML_Gen_MGHierarchy_UsingAggregation",Nlevels = ML_Gen_MGHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object));
853:   } else {
854:     PetscStackCall("ML_Gen_MultiLevelHierarchy_UsingAggregation",Nlevels = ML_Gen_MultiLevelHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object));
855:   }
856:   if (Nlevels<=0) SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_OUTOFRANGE,"Nlevels %d must > 0",Nlevels);
857:   pc_ml->Nlevels = Nlevels;
858:   fine_level     = Nlevels - 1;

860:   PCMGSetLevels(pc,Nlevels,NULL);
861:   /* set default smoothers */
862:   for (level=1; level<=fine_level; level++) {
863:     PCMGGetSmoother(pc,level,&smoother);
864:     KSPSetType(smoother,KSPRICHARDSON);
865:     KSPGetPC(smoother,&subpc);
866:     PCSetType(subpc,PCSOR);
867:   }
868:   PetscObjectOptionsBegin((PetscObject)pc);
869:   PCSetFromOptions_MG(PetscOptionsObject,pc); /* should be called in PCSetFromOptions_ML(), but cannot be called prior to PCMGSetLevels() */
870:   PetscOptionsEnd();

872:   PetscMalloc1(Nlevels,&gridctx);

874:   pc_ml->gridctx = gridctx;

876:   /* wrap ML matrices by PETSc shell matrices at coarsened grids.
877:      Level 0 is the finest grid for ML, but coarsest for PETSc! */
878:   gridctx[fine_level].A = A;

880:   level = fine_level - 1;
881:   if (size == 1) { /* convert ML P, R and A into seqaij format */
882:     for (mllevel=1; mllevel<Nlevels; mllevel++) {
883:       mlmat = &(ml_object->Pmat[mllevel]);
884:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);
885:       mlmat = &(ml_object->Rmat[mllevel-1]);
886:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);

888:       mlmat = &(ml_object->Amat[mllevel]);
889:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
890:       level--;
891:     }
892:   } else { /* convert ML P and R into shell format, ML A into mpiaij format */
893:     for (mllevel=1; mllevel<Nlevels; mllevel++) {
894:       mlmat  = &(ml_object->Pmat[mllevel]);
895:       MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);
896:       mlmat  = &(ml_object->Rmat[mllevel-1]);
897:       MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);

899:       mlmat  = &(ml_object->Amat[mllevel]);
900:       MatWrapML_MPIAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
901:       level--;
902:     }
903:   }

905:   /* create vectors and ksp at all levels */
906:   for (level=0; level<fine_level; level++) {
907:     level1 = level + 1;
908:     VecCreate(((PetscObject)gridctx[level].A)->comm,&gridctx[level].x);
909:     VecSetSizes(gridctx[level].x,gridctx[level].A->cmap->n,PETSC_DECIDE);
910:     VecSetType(gridctx[level].x,VECMPI);
911:     PCMGSetX(pc,level,gridctx[level].x);

913:     VecCreate(((PetscObject)gridctx[level].A)->comm,&gridctx[level].b);
914:     VecSetSizes(gridctx[level].b,gridctx[level].A->rmap->n,PETSC_DECIDE);
915:     VecSetType(gridctx[level].b,VECMPI);
916:     PCMGSetRhs(pc,level,gridctx[level].b);

918:     VecCreate(((PetscObject)gridctx[level1].A)->comm,&gridctx[level1].r);
919:     VecSetSizes(gridctx[level1].r,gridctx[level1].A->rmap->n,PETSC_DECIDE);
920:     VecSetType(gridctx[level1].r,VECMPI);
921:     PCMGSetR(pc,level1,gridctx[level1].r);

923:     if (level == 0) {
924:       PCMGGetCoarseSolve(pc,&gridctx[level].ksp);
925:     } else {
926:       PCMGGetSmoother(pc,level,&gridctx[level].ksp);
927:     }
928:   }
929:   PCMGGetSmoother(pc,fine_level,&gridctx[fine_level].ksp);

931:   /* create coarse level and the interpolation between the levels */
932:   for (level=0; level<fine_level; level++) {
933:     level1 = level + 1;
934:     PCMGSetInterpolation(pc,level1,gridctx[level].P);
935:     PCMGSetRestriction(pc,level1,gridctx[level].R);
936:     if (level > 0) {
937:       PCMGSetResidual(pc,level,PCMGResidualDefault,gridctx[level].A);
938:     }
939:     KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A);
940:   }
941:   PCMGSetResidual(pc,fine_level,PCMGResidualDefault,gridctx[fine_level].A);
942:   KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A);

944:   /* put coordinate info in levels */
945:   if (pc_ml->dim) {
946:     PetscInt  i,j,dim = pc_ml->dim;
947:     PetscInt  bs, nloc;
948:     PC        subpc;
949:     PetscReal *array;

951:     level = fine_level;
952:     for (mllevel = 0; mllevel < Nlevels; mllevel++) {
953:       ML_Aggregate_Viz_Stats *grid_info = (ML_Aggregate_Viz_Stats*)ml_object->Amat[mllevel].to->Grid->Grid;
954:       MPI_Comm               comm       = ((PetscObject)gridctx[level].A)->comm;

956:       MatGetBlockSize (gridctx[level].A, &bs);
957:       MatGetLocalSize (gridctx[level].A, NULL, &nloc);
958:       nloc /= bs; /* number of local nodes */

960:       VecCreate(comm,&gridctx[level].coords);
961:       VecSetSizes(gridctx[level].coords,dim * nloc,PETSC_DECIDE);
962:       VecSetType(gridctx[level].coords,VECMPI);
963:       VecGetArray(gridctx[level].coords,&array);
964:       for (j = 0; j < nloc; j++) {
965:         for (i = 0; i < dim; i++) {
966:           switch (i) {
967:           case 0: array[dim * j + i] = grid_info->x[j]; break;
968:           case 1: array[dim * j + i] = grid_info->y[j]; break;
969:           case 2: array[dim * j + i] = grid_info->z[j]; break;
970:           default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_SIZ,"PCML coordinate dimension must be <= 3");
971:           }
972:         }
973:       }

975:       /* passing coordinates to smoothers/coarse solver, should they need them */
976:       KSPGetPC(gridctx[level].ksp,&subpc);
977:       PCSetCoordinates(subpc,dim,nloc,array);
978:       VecRestoreArray(gridctx[level].coords,&array);
979:       level--;
980:     }
981:   }

983:   /* setupcalled is set to 0 so that MG is setup from scratch */
984:   pc->setupcalled = 0;
985:   PCSetUp_MG(pc);
986:   return(0);
987: }

989: /* -------------------------------------------------------------------------- */
990: /*
991:    PCDestroy_ML - Destroys the private context for the ML preconditioner
992:    that was created with PCCreate_ML().

994:    Input Parameter:
995: .  pc - the preconditioner context

997:    Application Interface Routine: PCDestroy()
998: */
999: PetscErrorCode PCDestroy_ML(PC pc)
1000: {
1002:   PC_MG          *mg   = (PC_MG*)pc->data;
1003:   PC_ML          *pc_ml= (PC_ML*)mg->innerctx;

1006:   PCReset_ML(pc);
1007:   PetscFree(pc_ml);
1008:   PCDestroy_MG(pc);
1009:   PetscObjectComposeFunction((PetscObject)pc,"PCSetCoordinates_C",NULL);
1010:   return(0);
1011: }

1013: PetscErrorCode PCSetFromOptions_ML(PetscOptionItems *PetscOptionsObject,PC pc)
1014: {
1016:   PetscInt       indx,PrintLevel,partindx;
1017:   const char     *scheme[] = {"Uncoupled","Coupled","MIS","METIS"};
1018:   const char     *part[]   = {"Zoltan","ParMETIS"};
1019: #if defined(HAVE_ML_ZOLTAN)
1020:   const char *zscheme[] = {"RCB","hypergraph","fast_hypergraph"};
1021: #endif
1022:   PC_MG       *mg    = (PC_MG*)pc->data;
1023:   PC_ML       *pc_ml = (PC_ML*)mg->innerctx;
1024:   PetscMPIInt size;
1025:   MPI_Comm    comm;

1028:   PetscObjectGetComm((PetscObject)pc,&comm);
1029:   MPI_Comm_size(comm,&size);
1030:   PetscOptionsHead(PetscOptionsObject,"ML options");

1032:   PrintLevel = 0;
1033:   indx       = 0;
1034:   partindx   = 0;

1036:   PetscOptionsInt("-pc_ml_PrintLevel","Print level","ML_Set_PrintLevel",PrintLevel,&PrintLevel,NULL);
1037:   PetscStackCall("ML_Set_PrintLevel",ML_Set_PrintLevel(PrintLevel));
1038:   PetscOptionsInt("-pc_ml_maxNlevels","Maximum number of levels","None",pc_ml->MaxNlevels,&pc_ml->MaxNlevels,NULL);
1039:   PetscOptionsInt("-pc_ml_maxCoarseSize","Maximum coarsest mesh size","ML_Aggregate_Set_MaxCoarseSize",pc_ml->MaxCoarseSize,&pc_ml->MaxCoarseSize,NULL);
1040:   PetscOptionsEList("-pc_ml_CoarsenScheme","Aggregate Coarsen Scheme","ML_Aggregate_Set_CoarsenScheme_*",scheme,4,scheme[0],&indx,NULL);

1042:   pc_ml->CoarsenScheme = indx;

1044:   PetscOptionsReal("-pc_ml_DampingFactor","P damping factor","ML_Aggregate_Set_DampingFactor",pc_ml->DampingFactor,&pc_ml->DampingFactor,NULL);
1045:   PetscOptionsReal("-pc_ml_Threshold","Smoother drop tol","ML_Aggregate_Set_Threshold",pc_ml->Threshold,&pc_ml->Threshold,NULL);
1046:   PetscOptionsBool("-pc_ml_SpectralNormScheme_Anorm","Method used for estimating spectral radius","ML_Set_SpectralNormScheme_Anorm",pc_ml->SpectralNormScheme_Anorm,&pc_ml->SpectralNormScheme_Anorm,NULL);
1047:   PetscOptionsBool("-pc_ml_Symmetrize","Symmetrize aggregation","ML_Set_Symmetrize",pc_ml->Symmetrize,&pc_ml->Symmetrize,NULL);
1048:   PetscOptionsBool("-pc_ml_BlockScaling","Scale all dofs at each node together","None",pc_ml->BlockScaling,&pc_ml->BlockScaling,NULL);
1049:   PetscOptionsEnum("-pc_ml_nullspace","Which type of null space information to use","None",PCMLNullSpaceTypes,(PetscEnum)pc_ml->nulltype,(PetscEnum*)&pc_ml->nulltype,NULL);
1050:   PetscOptionsInt("-pc_ml_EnergyMinimization","Energy minimization norm type (0=no minimization; see ML manual for 1,2,3; -1 and 4 undocumented)","None",pc_ml->EnergyMinimization,&pc_ml->EnergyMinimization,NULL);
1051:   PetscOptionsBool("-pc_ml_reuse_interpolation","Reuse the interpolation operators when possible (cheaper, weaker when matrix entries change a lot)","None",pc_ml->reuse_interpolation,&pc_ml->reuse_interpolation,NULL);
1052:   /*
1053:     The following checks a number of conditions.  If we let this stuff slip by, then ML's error handling will take over.
1054:     This is suboptimal because it amounts to calling exit(1) so we check for the most common conditions.

1056:     We also try to set some sane defaults when energy minimization is activated, otherwise it's hard to find a working
1057:     combination of options and ML's exit(1) explanations don't help matters.
1058:   */
1059:   if (pc_ml->EnergyMinimization < -1 || pc_ml->EnergyMinimization > 4) SETERRQ(comm,PETSC_ERR_ARG_OUTOFRANGE,"EnergyMinimization must be in range -1..4");
1060:   if (pc_ml->EnergyMinimization == 4 && size > 1) SETERRQ(comm,PETSC_ERR_SUP,"Energy minimization type 4 does not work in parallel");
1061:   if (pc_ml->EnergyMinimization == 4) {PetscInfo(pc,"Mandel's energy minimization scheme is experimental and broken in ML-6.2\n");}
1062:   if (pc_ml->EnergyMinimization) {
1063:     PetscOptionsReal("-pc_ml_EnergyMinimizationDropTol","Energy minimization drop tolerance","None",pc_ml->EnergyMinimizationDropTol,&pc_ml->EnergyMinimizationDropTol,NULL);
1064:   }
1065:   if (pc_ml->EnergyMinimization == 2) {
1066:     /* According to ml_MultiLevelPreconditioner.cpp, this option is only meaningful for norm type (2) */
1067:     PetscOptionsBool("-pc_ml_EnergyMinimizationCheap","Use cheaper variant of norm type 2","None",pc_ml->EnergyMinimizationCheap,&pc_ml->EnergyMinimizationCheap,NULL);
1068:   }
1069:   /* energy minimization sometimes breaks if this is turned off, the more classical stuff should be okay without it */
1070:   if (pc_ml->EnergyMinimization) pc_ml->KeepAggInfo = PETSC_TRUE;
1071:   PetscOptionsBool("-pc_ml_KeepAggInfo","Allows the preconditioner to be reused, or auxilliary matrices to be generated","None",pc_ml->KeepAggInfo,&pc_ml->KeepAggInfo,NULL);
1072:   /* Option (-1) doesn't work at all (calls exit(1)) if the tentative restriction operator isn't stored. */
1073:   if (pc_ml->EnergyMinimization == -1) pc_ml->Reusable = PETSC_TRUE;
1074:   PetscOptionsBool("-pc_ml_Reusable","Store intermedaiate data structures so that the multilevel hierarchy is reusable","None",pc_ml->Reusable,&pc_ml->Reusable,NULL);
1075:   /*
1076:     ML's C API is severely underdocumented and lacks significant functionality.  The C++ API calls
1077:     ML_Gen_MultiLevelHierarchy_UsingAggregation() which is a modified copy (!?) of the documented function
1078:     ML_Gen_MGHierarchy_UsingAggregation().  This modification, however, does not provide a strict superset of the
1079:     functionality in the old function, so some users may still want to use it.  Note that many options are ignored in
1080:     this context, but ML doesn't provide a way to find out which ones.
1081:    */
1082:   PetscOptionsBool("-pc_ml_OldHierarchy","Use old routine to generate hierarchy","None",pc_ml->OldHierarchy,&pc_ml->OldHierarchy,NULL);
1083:   PetscOptionsBool("-pc_ml_repartition", "Allow ML to repartition levels of the heirarchy","ML_Repartition_Activate",pc_ml->Repartition,&pc_ml->Repartition,NULL);
1084:   if (pc_ml->Repartition) {
1085:     PetscOptionsReal("-pc_ml_repartitionMaxMinRatio", "Acceptable ratio of repartitioned sizes","ML_Repartition_Set_LargestMinMaxRatio",pc_ml->MaxMinRatio,&pc_ml->MaxMinRatio,NULL);
1086:     PetscOptionsInt("-pc_ml_repartitionMinPerProc", "Smallest repartitioned size","ML_Repartition_Set_MinPerProc",pc_ml->MinPerProc,&pc_ml->MinPerProc,NULL);
1087:     PetscOptionsInt("-pc_ml_repartitionPutOnSingleProc", "Problem size automatically repartitioned to one processor","ML_Repartition_Set_PutOnSingleProc",pc_ml->PutOnSingleProc,&pc_ml->PutOnSingleProc,NULL);
1088: #if defined(HAVE_ML_ZOLTAN)
1089:     partindx = 0;
1090:     PetscOptionsEList("-pc_ml_repartitionType", "Repartitioning library to use","ML_Repartition_Set_Partitioner",part,2,part[0],&partindx,NULL);

1092:     pc_ml->RepartitionType = partindx;
1093:     if (!partindx) {
1094:       PetscInt zindx = 0;

1096:       PetscOptionsEList("-pc_ml_repartitionZoltanScheme", "Repartitioning scheme to use","None",zscheme,3,zscheme[0],&zindx,NULL);

1098:       pc_ml->ZoltanScheme = zindx;
1099:     }
1100: #else
1101:     partindx = 1;
1102:     PetscOptionsEList("-pc_ml_repartitionType", "Repartitioning library to use","ML_Repartition_Set_Partitioner",part,2,part[1],&partindx,NULL);
1103:     pc_ml->RepartitionType = partindx;
1104:     if (!partindx) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_SUP_SYS,"ML not compiled with Zoltan");
1105: #endif
1106:     PetscOptionsBool("-pc_ml_Aux","Aggregate using auxiliary coordinate-based laplacian","None",pc_ml->Aux,&pc_ml->Aux,NULL);
1107:     PetscOptionsReal("-pc_ml_AuxThreshold","Auxiliary smoother drop tol","None",pc_ml->AuxThreshold,&pc_ml->AuxThreshold,NULL);
1108:   }
1109:   PetscOptionsTail();
1110:   return(0);
1111: }

1113: /* -------------------------------------------------------------------------- */
1114: /*
1115:    PCCreate_ML - Creates a ML preconditioner context, PC_ML,
1116:    and sets this as the private data within the generic preconditioning
1117:    context, PC, that was created within PCCreate().

1119:    Input Parameter:
1120: .  pc - the preconditioner context

1122:    Application Interface Routine: PCCreate()
1123: */

1125: /*MC
1126:      PCML - Use algebraic multigrid preconditioning. This preconditioner requires you provide
1127:        fine grid discretization matrix. The coarser grid matrices and restriction/interpolation
1128:        operators are computed by ML, with the matrices coverted to PETSc matrices in aij format
1129:        and the restriction/interpolation operators wrapped as PETSc shell matrices.

1131:    Options Database Key:
1132:    Multigrid options(inherited):
1133: +  -pc_mg_cycles <1>: 1 for V cycle, 2 for W-cycle (MGSetCycles)
1134: .  -pc_mg_distinct_smoothup: Should one configure the up and down smoothers separately (PCMGSetDistinctSmoothUp)
1135: -  -pc_mg_type <multiplicative>: (one of) additive multiplicative full kascade
1136:    ML options:
1137: +  -pc_ml_PrintLevel <0>: Print level (ML_Set_PrintLevel)
1138: .  -pc_ml_maxNlevels <10>: Maximum number of levels (None)
1139: .  -pc_ml_maxCoarseSize <1>: Maximum coarsest mesh size (ML_Aggregate_Set_MaxCoarseSize)
1140: .  -pc_ml_CoarsenScheme <Uncoupled>: (one of) Uncoupled Coupled MIS METIS
1141: .  -pc_ml_DampingFactor <1.33333>: P damping factor (ML_Aggregate_Set_DampingFactor)
1142: .  -pc_ml_Threshold <0>: Smoother drop tol (ML_Aggregate_Set_Threshold)
1143: .  -pc_ml_SpectralNormScheme_Anorm <false>: Method used for estimating spectral radius (ML_Set_SpectralNormScheme_Anorm)
1144: .  -pc_ml_repartition <false>: Allow ML to repartition levels of the heirarchy (ML_Repartition_Activate)
1145: .  -pc_ml_repartitionMaxMinRatio <1.3>: Acceptable ratio of repartitioned sizes (ML_Repartition_Set_LargestMinMaxRatio)
1146: .  -pc_ml_repartitionMinPerProc <512>: Smallest repartitioned size (ML_Repartition_Set_MinPerProc)
1147: .  -pc_ml_repartitionPutOnSingleProc <5000>: Problem size automatically repartitioned to one processor (ML_Repartition_Set_PutOnSingleProc)
1148: .  -pc_ml_repartitionType <Zoltan>: Repartitioning library to use (ML_Repartition_Set_Partitioner)
1149: .  -pc_ml_repartitionZoltanScheme <RCB>: Repartitioning scheme to use (None)
1150: .  -pc_ml_Aux <false>: Aggregate using auxiliary coordinate-based laplacian (None)
1151: -  -pc_ml_AuxThreshold <0.0>: Auxiliary smoother drop tol (None)

1153:    Level: intermediate

1155:   Concepts: multigrid

1157: .seealso:  PCCreate(), PCSetType(), PCType (for list of available types), PC, PCMGType,
1158:            PCMGSetLevels(), PCMGGetLevels(), PCMGSetType(), MPSetCycles(), PCMGSetDistinctSmoothUp(),
1159:            PCMGGetCoarseSolve(), PCMGSetResidual(), PCMGSetInterpolation(),
1160:            PCMGSetRestriction(), PCMGGetSmoother(), PCMGGetSmootherUp(), PCMGGetSmootherDown(),
1161:            PCMGSetCycleTypeOnLevel(), PCMGSetRhs(), PCMGSetX(), PCMGSetR()
1162: M*/

1164: PETSC_EXTERN PetscErrorCode PCCreate_ML(PC pc)
1165: {
1167:   PC_ML          *pc_ml;
1168:   PC_MG          *mg;

1171:   /* PCML is an inherited class of PCMG. Initialize pc as PCMG */
1172:   PCSetType(pc,PCMG); /* calls PCCreate_MG() and MGCreate_Private() */
1173:   PetscObjectChangeTypeName((PetscObject)pc,PCML);
1174:   /* Since PCMG tries to use DM assocated with PC must delete it */
1175:   DMDestroy(&pc->dm);
1176:   PCMGSetGalerkin(pc,PC_MG_GALERKIN_EXTERNAL);
1177:   mg   = (PC_MG*)pc->data;

1179:   /* create a supporting struct and attach it to pc */
1180:   PetscNewLog(pc,&pc_ml);
1181:   mg->innerctx = pc_ml;

1183:   pc_ml->ml_object                = 0;
1184:   pc_ml->agg_object               = 0;
1185:   pc_ml->gridctx                  = 0;
1186:   pc_ml->PetscMLdata              = 0;
1187:   pc_ml->Nlevels                  = -1;
1188:   pc_ml->MaxNlevels               = 10;
1189:   pc_ml->MaxCoarseSize            = 1;
1190:   pc_ml->CoarsenScheme            = 1;
1191:   pc_ml->Threshold                = 0.0;
1192:   pc_ml->DampingFactor            = 4.0/3.0;
1193:   pc_ml->SpectralNormScheme_Anorm = PETSC_FALSE;
1194:   pc_ml->size                     = 0;
1195:   pc_ml->dim                      = 0;
1196:   pc_ml->nloc                     = 0;
1197:   pc_ml->coords                   = 0;
1198:   pc_ml->Repartition              = PETSC_FALSE;
1199:   pc_ml->MaxMinRatio              = 1.3;
1200:   pc_ml->MinPerProc               = 512;
1201:   pc_ml->PutOnSingleProc          = 5000;
1202:   pc_ml->RepartitionType          = 0;
1203:   pc_ml->ZoltanScheme             = 0;
1204:   pc_ml->Aux                      = PETSC_FALSE;
1205:   pc_ml->AuxThreshold             = 0.0;

1207:   /* allow for coordinates to be passed */
1208:   PetscObjectComposeFunction((PetscObject)pc,"PCSetCoordinates_C",PCSetCoordinates_ML);

1210:   /* overwrite the pointers of PCMG by the functions of PCML */
1211:   pc->ops->setfromoptions = PCSetFromOptions_ML;
1212:   pc->ops->setup          = PCSetUp_ML;
1213:   pc->ops->reset          = PCReset_ML;
1214:   pc->ops->destroy        = PCDestroy_ML;
1215:   return(0);
1216: }