Actual source code: ml.c

petsc-master 2019-12-10
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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: } Mat_MLShell;

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

 65: 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[])
 66: {
 68:   PetscInt       m,i,j,k=0,row,*aj;
 69:   PetscScalar    *aa;
 70:   FineGridCtx    *ml=(FineGridCtx*)ML_Get_MyGetrowData(ML_data);
 71:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)ml->Aloc->data;

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

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

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

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

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

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

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

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

159: /* newtype is ignored since only handles one case */
160: static PetscErrorCode MatConvert_MPIAIJ_ML(Mat A,MatType newtype,MatReuse scall,Mat *Aloc)
161: {
163:   Mat_MPIAIJ     *mpimat=(Mat_MPIAIJ*)A->data;
164:   Mat_SeqAIJ     *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
165:   PetscInt       *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
166:   PetscScalar    *aa,*ba,*ca;
167:   PetscInt       am =A->rmap->n,an=A->cmap->n,i,j,k;
168:   PetscInt       *ci,*cj,ncols;

171:   if (am != an) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"A must have a square diagonal portion, am: %d != an: %d",am,an);
172:   MatSeqAIJGetArrayRead(mpimat->A,(const PetscScalar**)&aa);
173:   MatSeqAIJGetArrayRead(mpimat->B,(const PetscScalar**)&ba);
174:   if (scall == MAT_INITIAL_MATRIX) {
175:     PetscMalloc1(1+am,&ci);
176:     ci[0] = 0;
177:     for (i=0; i<am; i++) ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
178:     PetscMalloc1(1+ci[am],&cj);
179:     PetscMalloc1(1+ci[am],&ca);

181:     k = 0;
182:     for (i=0; i<am; i++) {
183:       /* diagonal portion of A */
184:       ncols = ai[i+1] - ai[i];
185:       for (j=0; j<ncols; j++) {
186:         cj[k]   = *aj++;
187:         ca[k++] = *aa++;
188:       }
189:       /* off-diagonal portion of A */
190:       ncols = bi[i+1] - bi[i];
191:       for (j=0; j<ncols; j++) {
192:         cj[k]   = an + (*bj); bj++;
193:         ca[k++] = *ba++;
194:       }
195:     }
196:     if (k != ci[am]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"k: %d != ci[am]: %d",k,ci[am]);

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

202:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
203:     /* Since these are PETSc arrays, change flags to free them as necessary. */
204:     mat          = (Mat_SeqAIJ*)(*Aloc)->data;
205:     mat->free_a  = PETSC_TRUE;
206:     mat->free_ij = PETSC_TRUE;

208:     mat->nonew = 0;
209:   } else if (scall == MAT_REUSE_MATRIX) {
210:     mat=(Mat_SeqAIJ*)(*Aloc)->data;
211:     ci = mat->i; cj = mat->j; ca = mat->a;
212:     for (i=0; i<am; i++) {
213:       /* diagonal portion of A */
214:       ncols = ai[i+1] - ai[i];
215:       for (j=0; j<ncols; j++) *ca++ = *aa++;
216:       /* off-diagonal portion of A */
217:       ncols = bi[i+1] - bi[i];
218:       for (j=0; j<ncols; j++) *ca++ = *ba++;
219:     }
220:   } else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
221:   MatSeqAIJRestoreArrayRead(mpimat->A,(const PetscScalar**)&aa);
222:   MatSeqAIJRestoreArrayRead(mpimat->B,(const PetscScalar**)&ba);
223:   return(0);
224: }

226: static PetscErrorCode MatDestroy_ML(Mat A)
227: {
229:   Mat_MLShell    *shell;

232:   MatShellGetContext(A,(void**)&shell);
233:   PetscFree(shell);
234:   return(0);
235: }

237: static PetscErrorCode MatWrapML_SeqAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
238: {
239:   struct ML_CSR_MSRdata *matdata = (struct ML_CSR_MSRdata*)mlmat->data;
240:   PetscErrorCode        ierr;
241:   PetscInt              m       =mlmat->outvec_leng,n=mlmat->invec_leng,*nnz = NULL,nz_max;
242:   PetscInt              *ml_cols=matdata->columns,*ml_rowptr=matdata->rowptr,*aj,i;
243:   PetscScalar           *ml_vals=matdata->values,*aa;

246:   if (!mlmat->getrow) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
247:   if (m != n) { /* ML Pmat and Rmat are in CSR format. Pass array pointers into SeqAIJ matrix */
248:     if (reuse) {
249:       Mat_SeqAIJ *aij= (Mat_SeqAIJ*)(*newmat)->data;
250:       aij->i = ml_rowptr;
251:       aij->j = ml_cols;
252:       aij->a = ml_vals;
253:     } else {
254:       /* sort ml_cols and ml_vals */
255:       PetscMalloc1(m+1,&nnz);
256:       for (i=0; i<m; i++) nnz[i] = ml_rowptr[i+1] - ml_rowptr[i];
257:       aj = ml_cols; aa = ml_vals;
258:       for (i=0; i<m; i++) {
259:         PetscSortIntWithScalarArray(nnz[i],aj,aa);
260:         aj  += nnz[i]; aa += nnz[i];
261:       }
262:       MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,ml_rowptr,ml_cols,ml_vals,newmat);
263:       PetscFree(nnz);
264:     }
265:     MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
266:     MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);
267:     return(0);
268:   }

270:   nz_max = PetscMax(1,mlmat->max_nz_per_row);
271:   PetscMalloc2(nz_max,&aa,nz_max,&aj);
272:   if (!reuse) {
273:     MatCreate(PETSC_COMM_SELF,newmat);
274:     MatSetSizes(*newmat,m,n,PETSC_DECIDE,PETSC_DECIDE);
275:     MatSetType(*newmat,MATSEQAIJ);
276:     /* keep track of block size for A matrices */
277:     MatSetBlockSize (*newmat, mlmat->num_PDEs);

279:     PetscMalloc1(m,&nnz);
280:     for (i=0; i<m; i++) {
281:       PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&nnz[i]));
282:     }
283:     MatSeqAIJSetPreallocation(*newmat,0,nnz);
284:   }
285:   for (i=0; i<m; i++) {
286:     PetscInt ncols;

288:     PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&ncols));
289:     MatSetValues(*newmat,1,&i,ncols,aj,aa,INSERT_VALUES);
290:   }
291:   MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
292:   MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);

294:   PetscFree2(aa,aj);
295:   PetscFree(nnz);
296:   return(0);
297: }

299: static PetscErrorCode MatWrapML_SHELL(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
300: {
302:   PetscInt       m,n;
303:   ML_Comm        *MLcomm;
304:   Mat_MLShell    *shellctx;

307:   m = mlmat->outvec_leng;
308:   n = mlmat->invec_leng;

310:   if (reuse) {
311:     MatShellGetContext(*newmat,(void**)&shellctx);
312:     shellctx->mlmat = mlmat;
313:     return(0);
314:   }

316:   MLcomm = mlmat->comm;

318:   PetscNew(&shellctx);
319:   MatCreateShell(MLcomm->USR_comm,m,n,PETSC_DETERMINE,PETSC_DETERMINE,shellctx,newmat);
320:   MatShellSetOperation(*newmat,MATOP_MULT,(void(*)(void))MatMult_ML);
321:   MatShellSetOperation(*newmat,MATOP_DESTROY,(void(*)(void))MatDestroy_ML);

323:   shellctx->A         = *newmat;
324:   shellctx->mlmat     = mlmat;
325:   return(0);
326: }

328: static PetscErrorCode MatWrapML_MPIAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
329: {
330:   PetscInt       *aj;
331:   PetscScalar    *aa;
333:   PetscInt       i,j,*gordering;
334:   PetscInt       m=mlmat->outvec_leng,n,nz_max,row;
335:   Mat            A;

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

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

345:   nz_max = PetscMax(1,mlmat->max_nz_per_row) + 1;
346:   PetscMalloc2(nz_max,&aa,nz_max,&aj);
347:   if (reuse) {
348:     A = *newmat;
349:   } else {
350:     PetscInt *nnzA,*nnzB,*nnz;
351:     PetscInt rstart;
352:     MatCreate(mlmat->comm->USR_comm,&A);
353:     MatSetSizes(A,m,n,PETSC_DECIDE,PETSC_DECIDE);
354:     MatSetType(A,MATMPIAIJ);
355:     /* keep track of block size for A matrices */
356:     MatSetBlockSize (A,mlmat->num_PDEs);
357:     PetscMalloc3(m,&nnzA,m,&nnzB,m,&nnz);
358:     MPI_Scan(&m,&rstart,1,MPIU_INT,MPI_SUM,mlmat->comm->USR_comm);
359:     rstart -= m;

361:     for (i=0; i<m; i++) {
362:       row = gordering[i] - rstart;
363:       PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&nnz[i]));
364:       nnzA[row] = 0;
365:       for (j=0; j<nnz[i]; j++) {
366:         if (aj[j] < m) nnzA[row]++;
367:       }
368:       nnzB[row] = nnz[i] - nnzA[row];
369:     }
370:     MatMPIAIJSetPreallocation(A,0,nnzA,0,nnzB);
371:     PetscFree3(nnzA,nnzB,nnz);
372:   }
373:   for (i=0; i<m; i++) {
374:     PetscInt ncols;
375:     row = gordering[i];

377:     PetscStackCall(",ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&ncols));
378:     for (j = 0; j < ncols; j++) aj[j] = gordering[aj[j]];
379:     MatSetValues(A,1,&row,ncols,aj,aa,INSERT_VALUES);
380:   }
381:   PetscStackCall("ML_free",ML_free(gordering));
382:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
383:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
384:   *newmat = A;

386:   PetscFree2(aa,aj);
387:   return(0);
388: }

390: /* -------------------------------------------------------------------------- */
391: /*
392:    PCSetCoordinates_ML

394:    Input Parameter:
395:    .  pc - the preconditioner context
396: */
397: static PetscErrorCode PCSetCoordinates_ML(PC pc, PetscInt ndm, PetscInt a_nloc, PetscReal *coords)
398: {
399:   PC_MG          *mg    = (PC_MG*)pc->data;
400:   PC_ML          *pc_ml = (PC_ML*)mg->innerctx;
402:   PetscInt       arrsz,oldarrsz,bs,my0,kk,ii,nloc,Iend,aloc;
403:   Mat            Amat = pc->pmat;

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

410:   MatGetOwnershipRange(Amat, &my0, &Iend);
411:   aloc = (Iend-my0);
412:   nloc = (Iend-my0)/bs;

414:   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);

416:   oldarrsz    = pc_ml->dim * pc_ml->nloc;
417:   pc_ml->dim  = ndm;
418:   pc_ml->nloc = nloc;
419:   arrsz       = ndm * nloc;

421:   /* create data - syntactic sugar that should be refactored at some point */
422:   if (pc_ml->coords==0 || (oldarrsz != arrsz)) {
423:     PetscFree(pc_ml->coords);
424:     PetscMalloc1(arrsz, &pc_ml->coords);
425:   }
426:   for (kk=0; kk<arrsz; kk++) pc_ml->coords[kk] = -999.;
427:   /* copy data in - column oriented */
428:   if (nloc == a_nloc) {
429:     for (kk = 0; kk < nloc; kk++) {
430:       for (ii = 0; ii < ndm; ii++) {
431:         pc_ml->coords[ii*nloc + kk] =  coords[kk*ndm + ii];
432:       }
433:     }
434:   } else { /* assumes the coordinates are blocked */
435:     for (kk = 0; kk < nloc; kk++) {
436:       for (ii = 0; ii < ndm; ii++) {
437:         pc_ml->coords[ii*nloc + kk] =  coords[bs*kk*ndm + ii];
438:       }
439:     }
440:   }
441:   return(0);
442: }

444: /* -----------------------------------------------------------------------------*/
445: extern PetscErrorCode PCReset_MG(PC);
446: PetscErrorCode PCReset_ML(PC pc)
447: {
449:   PC_MG          *mg    = (PC_MG*)pc->data;
450:   PC_ML          *pc_ml = (PC_ML*)mg->innerctx;
451:   PetscInt       level,fine_level=pc_ml->Nlevels-1,dim=pc_ml->dim;

454:   if (dim) {
455:     for (level=0; level<=fine_level; level++) {
456:       VecDestroy(&pc_ml->gridctx[level].coords);
457:     }
458:     if (pc_ml->ml_object && pc_ml->ml_object->Grid) {
459:       ML_Aggregate_Viz_Stats * grid_info = (ML_Aggregate_Viz_Stats*) pc_ml->ml_object->Grid[0].Grid;
460:       grid_info->x = 0; /* do this so ML doesn't try to free coordinates */
461:       grid_info->y = 0;
462:       grid_info->z = 0;
463:       PetscStackCall("ML_Operator_Getrow",ML_Aggregate_VizAndStats_Clean(pc_ml->ml_object));
464:     }
465:   }
466:   PetscStackCall("ML_Aggregate_Destroy",ML_Aggregate_Destroy(&pc_ml->agg_object));
467:   PetscStackCall("ML_Aggregate_Destroy",ML_Destroy(&pc_ml->ml_object));

469:   if (pc_ml->PetscMLdata) {
470:     PetscFree(pc_ml->PetscMLdata->pwork);
471:     MatDestroy(&pc_ml->PetscMLdata->Aloc);
472:     VecDestroy(&pc_ml->PetscMLdata->x);
473:     VecDestroy(&pc_ml->PetscMLdata->y);
474:   }
475:   PetscFree(pc_ml->PetscMLdata);

477:   if (pc_ml->gridctx) {
478:     for (level=0; level<fine_level; level++) {
479:       if (pc_ml->gridctx[level].A) {MatDestroy(&pc_ml->gridctx[level].A);}
480:       if (pc_ml->gridctx[level].P) {MatDestroy(&pc_ml->gridctx[level].P);}
481:       if (pc_ml->gridctx[level].R) {MatDestroy(&pc_ml->gridctx[level].R);}
482:       if (pc_ml->gridctx[level].x) {VecDestroy(&pc_ml->gridctx[level].x);}
483:       if (pc_ml->gridctx[level].b) {VecDestroy(&pc_ml->gridctx[level].b);}
484:       if (pc_ml->gridctx[level+1].r) {VecDestroy(&pc_ml->gridctx[level+1].r);}
485:     }
486:   }
487:   PetscFree(pc_ml->gridctx);
488:   PetscFree(pc_ml->coords);

490:   pc_ml->dim  = 0;
491:   pc_ml->nloc = 0;
492:   PCReset_MG(pc);
493:   return(0);
494: }
495: /* -------------------------------------------------------------------------- */
496: /*
497:    PCSetUp_ML - Prepares for the use of the ML preconditioner
498:                     by setting data structures and options.

500:    Input Parameter:
501: .  pc - the preconditioner context

503:    Application Interface Routine: PCSetUp()

505:    Notes:
506:    The interface routine PCSetUp() is not usually called directly by
507:    the user, but instead is called by PCApply() if necessary.
508: */
509: extern PetscErrorCode PCSetFromOptions_MG(PetscOptionItems *PetscOptionsObject,PC);
510: extern PetscErrorCode PCReset_MG(PC);

512: PetscErrorCode PCSetUp_ML(PC pc)
513: {
514:   PetscErrorCode   ierr;
515:   PetscMPIInt      size;
516:   FineGridCtx      *PetscMLdata;
517:   ML               *ml_object;
518:   ML_Aggregate     *agg_object;
519:   ML_Operator      *mlmat;
520:   PetscInt         nlocal_allcols,Nlevels,mllevel,level,level1,m,fine_level,bs;
521:   Mat              A,Aloc;
522:   GridCtx          *gridctx;
523:   PC_MG            *mg    = (PC_MG*)pc->data;
524:   PC_ML            *pc_ml = (PC_ML*)mg->innerctx;
525:   PetscBool        isSeq, isMPI;
526:   KSP              smoother;
527:   PC               subpc;
528:   PetscInt         mesh_level, old_mesh_level;
529:   MatInfo          info;
530:   static PetscBool cite = PETSC_FALSE;

533:   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);
534:   A    = pc->pmat;
535:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);

537:   if (pc->setupcalled) {
538:     if (pc->flag == SAME_NONZERO_PATTERN && pc_ml->reuse_interpolation) {
539:       /*
540:        Reuse interpolaton instead of recomputing aggregates and updating the whole hierarchy. This is less expensive for
541:        multiple solves in which the matrix is not changing too quickly.
542:        */
543:       ml_object             = pc_ml->ml_object;
544:       gridctx               = pc_ml->gridctx;
545:       Nlevels               = pc_ml->Nlevels;
546:       fine_level            = Nlevels - 1;
547:       gridctx[fine_level].A = A;

549:       PetscObjectBaseTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);
550:       PetscObjectBaseTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);
551:       if (isMPI) {
552:         MatConvert_MPIAIJ_ML(A,NULL,MAT_INITIAL_MATRIX,&Aloc);
553:       } else if (isSeq) {
554:         Aloc = A;
555:         PetscObjectReference((PetscObject)Aloc);
556:       } 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);

558:       MatGetSize(Aloc,&m,&nlocal_allcols);
559:       PetscMLdata       = pc_ml->PetscMLdata;
560:       MatDestroy(&PetscMLdata->Aloc);
561:       PetscMLdata->A    = A;
562:       PetscMLdata->Aloc = Aloc;
563:       PetscStackCall("ML_Aggregate_Destroy",ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata));
564:       PetscStackCall("ML_Set_Amatrix_Matvec",ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec));

566:       mesh_level = ml_object->ML_finest_level;
567:       while (ml_object->SingleLevel[mesh_level].Rmat->to) {
568:         old_mesh_level = mesh_level;
569:         mesh_level     = ml_object->SingleLevel[mesh_level].Rmat->to->levelnum;

571:         /* clean and regenerate A */
572:         mlmat = &(ml_object->Amat[mesh_level]);
573:         PetscStackCall("ML_Operator_Clean",ML_Operator_Clean(mlmat));
574:         PetscStackCall("ML_Operator_Init",ML_Operator_Init(mlmat,ml_object->comm));
575:         PetscStackCall("ML_Gen_AmatrixRAP",ML_Gen_AmatrixRAP(ml_object, old_mesh_level, mesh_level));
576:       }

578:       level = fine_level - 1;
579:       if (size == 1) { /* convert ML P, R and A into seqaij format */
580:         for (mllevel=1; mllevel<Nlevels; mllevel++) {
581:           mlmat = &(ml_object->Amat[mllevel]);
582:           MatWrapML_SeqAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);
583:           level--;
584:         }
585:       } else { /* convert ML P and R into shell format, ML A into mpiaij format */
586:         for (mllevel=1; mllevel<Nlevels; mllevel++) {
587:           mlmat  = &(ml_object->Amat[mllevel]);
588:           MatWrapML_MPIAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);
589:           level--;
590:         }
591:       }

593:       for (level=0; level<fine_level; level++) {
594:         if (level > 0) {
595:           PCMGSetResidual(pc,level,PCMGResidualDefault,gridctx[level].A);
596:         }
597:         KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A);
598:       }
599:       PCMGSetResidual(pc,fine_level,PCMGResidualDefault,gridctx[fine_level].A);
600:       KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A);

602:       PCSetUp_MG(pc);
603:       return(0);
604:     } else {
605:       /* since ML can change the size of vectors/matrices at any level we must destroy everything */
606:       PCReset_ML(pc);
607:     }
608:   }

610:   /* setup special features of PCML */
611:   /*--------------------------------*/
612:   /* covert A to Aloc to be used by ML at fine grid */
613:   pc_ml->size = size;
614:   PetscObjectBaseTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);
615:   PetscObjectBaseTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);
616:   if (isMPI) {
617:     MatConvert_MPIAIJ_ML(A,NULL,MAT_INITIAL_MATRIX,&Aloc);
618:   } else if (isSeq) {
619:     Aloc = A;
620:     PetscObjectReference((PetscObject)Aloc);
621:   } 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);

623:   /* create and initialize struct 'PetscMLdata' */
624:   PetscNewLog(pc,&PetscMLdata);
625:   pc_ml->PetscMLdata = PetscMLdata;
626:   PetscMalloc1(Aloc->cmap->n+1,&PetscMLdata->pwork);

628:   MatCreateVecs(Aloc,&PetscMLdata->x,&PetscMLdata->y);

630:   PetscMLdata->A    = A;
631:   PetscMLdata->Aloc = Aloc;
632:   if (pc_ml->dim) { /* create vecs around the coordinate data given */
633:     PetscInt  i,j,dim=pc_ml->dim;
634:     PetscInt  nloc = pc_ml->nloc,nlocghost;
635:     PetscReal *ghostedcoords;

637:     MatGetBlockSize(A,&bs);
638:     nlocghost = Aloc->cmap->n / bs;
639:     PetscMalloc1(dim*nlocghost,&ghostedcoords);
640:     for (i = 0; i < dim; i++) {
641:       /* copy coordinate values into first component of pwork */
642:       for (j = 0; j < nloc; j++) {
643:         PetscMLdata->pwork[bs * j] = pc_ml->coords[nloc * i + j];
644:       }
645:       /* get the ghost values */
646:       PetscML_comm(PetscMLdata->pwork,PetscMLdata);
647:       /* write into the vector */
648:       for (j = 0; j < nlocghost; j++) {
649:         ghostedcoords[i * nlocghost + j] = PetscMLdata->pwork[bs * j];
650:       }
651:     }
652:     /* replace the original coords with the ghosted coords, because these are
653:      * what ML needs */
654:     PetscFree(pc_ml->coords);
655:     pc_ml->coords = ghostedcoords;
656:   }

658:   /* create ML discretization matrix at fine grid */
659:   /* ML requires input of fine-grid matrix. It determines nlevels. */
660:   MatGetSize(Aloc,&m,&nlocal_allcols);
661:   MatGetBlockSize(A,&bs);
662:   PetscStackCall("ML_Create",ML_Create(&ml_object,pc_ml->MaxNlevels));
663:   PetscStackCall("ML_Comm_Set_UsrComm",ML_Comm_Set_UsrComm(ml_object->comm,PetscObjectComm((PetscObject)A)));
664:   pc_ml->ml_object = ml_object;
665:   PetscStackCall("ML_Init_Amatrix",ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata));
666:   PetscStackCall("ML_Set_Amatrix_Getrow",ML_Set_Amatrix_Getrow(ml_object,0,PetscML_getrow,PetscML_comm,nlocal_allcols));
667:   PetscStackCall("ML_Set_Amatrix_Matvec",ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec));

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

671:   /* aggregation */
672:   PetscStackCall("ML_Aggregate_Create",ML_Aggregate_Create(&agg_object));
673:   pc_ml->agg_object = agg_object;

675:   {
676:     MatNullSpace mnull;
677:     MatGetNearNullSpace(A,&mnull);
678:     if (pc_ml->nulltype == PCML_NULLSPACE_AUTO) {
679:       if (mnull) pc_ml->nulltype = PCML_NULLSPACE_USER;
680:       else if (bs > 1) pc_ml->nulltype = PCML_NULLSPACE_BLOCK;
681:       else pc_ml->nulltype = PCML_NULLSPACE_SCALAR;
682:     }
683:     switch (pc_ml->nulltype) {
684:     case PCML_NULLSPACE_USER: {
685:       PetscScalar       *nullvec;
686:       const PetscScalar *v;
687:       PetscBool         has_const;
688:       PetscInt          i,j,mlocal,nvec,M;
689:       const Vec         *vecs;

691:       if (!mnull) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"Must provide explicit null space using MatSetNearNullSpace() to use user-specified null space");
692:       MatGetSize(A,&M,NULL);
693:       MatGetLocalSize(Aloc,&mlocal,NULL);
694:       MatNullSpaceGetVecs(mnull,&has_const,&nvec,&vecs);
695:       PetscMalloc1((nvec+!!has_const)*mlocal,&nullvec);
696:       if (has_const) for (i=0; i<mlocal; i++) nullvec[i] = 1.0/M;
697:       for (i=0; i<nvec; i++) {
698:         VecGetArrayRead(vecs[i],&v);
699:         for (j=0; j<mlocal; j++) nullvec[(i+!!has_const)*mlocal + j] = v[j];
700:         VecRestoreArrayRead(vecs[i],&v);
701:       }
702:       PetscStackCall("ML_Aggregate_Create",ML_Aggregate_Set_NullSpace(agg_object,bs,nvec+!!has_const,nullvec,mlocal);CHKERRQ(ierr));
703:       PetscFree(nullvec);
704:     } break;
705:     case PCML_NULLSPACE_BLOCK:
706:       PetscStackCall("ML_Aggregate_Set_NullSpace",ML_Aggregate_Set_NullSpace(agg_object,bs,bs,0,0);CHKERRQ(ierr));
707:       break;
708:     case PCML_NULLSPACE_SCALAR:
709:       break;
710:     default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_SUP,"Unknown null space type");
711:     }
712:   }
713:   PetscStackCall("ML_Aggregate_Set_MaxCoarseSize",ML_Aggregate_Set_MaxCoarseSize(agg_object,pc_ml->MaxCoarseSize));
714:   /* set options */
715:   switch (pc_ml->CoarsenScheme) {
716:   case 1:
717:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_Coupled",ML_Aggregate_Set_CoarsenScheme_Coupled(agg_object));break;
718:   case 2:
719:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_MIS",ML_Aggregate_Set_CoarsenScheme_MIS(agg_object));break;
720:   case 3:
721:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_METIS",ML_Aggregate_Set_CoarsenScheme_METIS(agg_object));break;
722:   }
723:   PetscStackCall("ML_Aggregate_Set_Threshold",ML_Aggregate_Set_Threshold(agg_object,pc_ml->Threshold));
724:   PetscStackCall("ML_Aggregate_Set_DampingFactor",ML_Aggregate_Set_DampingFactor(agg_object,pc_ml->DampingFactor));
725:   if (pc_ml->SpectralNormScheme_Anorm) {
726:     PetscStackCall("ML_Set_SpectralNormScheme_Anorm",ML_Set_SpectralNormScheme_Anorm(ml_object));
727:   }
728:   agg_object->keep_agg_information      = (int)pc_ml->KeepAggInfo;
729:   agg_object->keep_P_tentative          = (int)pc_ml->Reusable;
730:   agg_object->block_scaled_SA           = (int)pc_ml->BlockScaling;
731:   agg_object->minimizing_energy         = (int)pc_ml->EnergyMinimization;
732:   agg_object->minimizing_energy_droptol = (double)pc_ml->EnergyMinimizationDropTol;
733:   agg_object->cheap_minimizing_energy   = (int)pc_ml->EnergyMinimizationCheap;

735:   if (pc_ml->Aux) {
736:     if (!pc_ml->dim) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"Auxiliary matrix requires coordinates");
737:     ml_object->Amat[0].aux_data->threshold = pc_ml->AuxThreshold;
738:     ml_object->Amat[0].aux_data->enable    = 1;
739:     ml_object->Amat[0].aux_data->max_level = 10;
740:     ml_object->Amat[0].num_PDEs            = bs;
741:   }

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

746:   if (pc_ml->dim) {
747:     PetscInt               i,dim = pc_ml->dim;
748:     ML_Aggregate_Viz_Stats *grid_info;
749:     PetscInt               nlocghost;

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

754:     PetscStackCall("ML_Aggregate_VizAndStats_Setup(",ML_Aggregate_VizAndStats_Setup(ml_object)); /* create ml info for coords */
755:     grid_info = (ML_Aggregate_Viz_Stats*) ml_object->Grid[0].Grid;
756:     for (i = 0; i < dim; i++) {
757:       /* set the finest level coordinates to point to the column-order array
758:        * in pc_ml */
759:       /* NOTE: must point away before VizAndStats_Clean so ML doesn't free */
760:       switch (i) {
761:       case 0: grid_info->x = pc_ml->coords + nlocghost * i; break;
762:       case 1: grid_info->y = pc_ml->coords + nlocghost * i; break;
763:       case 2: grid_info->z = pc_ml->coords + nlocghost * i; break;
764:       default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_SIZ,"PCML coordinate dimension must be <= 3");
765:       }
766:     }
767:     grid_info->Ndim = dim;
768:   }

770:   /* repartitioning */
771:   if (pc_ml->Repartition) {
772:     PetscStackCall("ML_Repartition_Activate",ML_Repartition_Activate(ml_object));
773:     PetscStackCall("ML_Repartition_Set_LargestMinMaxRatio",ML_Repartition_Set_LargestMinMaxRatio(ml_object,pc_ml->MaxMinRatio));
774:     PetscStackCall("ML_Repartition_Set_MinPerProc",ML_Repartition_Set_MinPerProc(ml_object,pc_ml->MinPerProc));
775:     PetscStackCall("ML_Repartition_Set_PutOnSingleProc",ML_Repartition_Set_PutOnSingleProc(ml_object,pc_ml->PutOnSingleProc));
776: #if 0                           /* Function not yet defined in ml-6.2 */
777:     /* I'm not sure what compatibility issues might crop up if we partitioned
778:      * on the finest level, so to be safe repartition starts on the next
779:      * finest level (reflection default behavior in
780:      * ml_MultiLevelPreconditioner) */
781:     PetscStackCall("ML_Repartition_Set_StartLevel",ML_Repartition_Set_StartLevel(ml_object,1));
782: #endif

784:     if (!pc_ml->RepartitionType) {
785:       PetscInt i;

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

791:       for (i = 0; i < ml_object->ML_num_levels; i++) {
792:         ML_Aggregate_Viz_Stats *grid_info = (ML_Aggregate_Viz_Stats*)ml_object->Grid[i].Grid;
793:         grid_info->zoltan_type = pc_ml->ZoltanScheme + 1; /* ml numbers options 1, 2, 3 */
794:         /* defaults from ml_agg_info.c */
795:         grid_info->zoltan_estimated_its = 40; /* only relevant to hypergraph / fast hypergraph */
796:         grid_info->zoltan_timers        = 0;
797:         grid_info->smoothing_steps      = 4;  /* only relevant to hypergraph / fast hypergraph */
798:       }
799:     } else {
800:       PetscStackCall("ML_Repartition_Set_Partitioner",ML_Repartition_Set_Partitioner(ml_object,ML_USEPARMETIS));
801:     }
802:   }

804:   if (pc_ml->OldHierarchy) {
805:     PetscStackCall("ML_Gen_MGHierarchy_UsingAggregation",Nlevels = ML_Gen_MGHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object));
806:   } else {
807:     PetscStackCall("ML_Gen_MultiLevelHierarchy_UsingAggregation",Nlevels = ML_Gen_MultiLevelHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object));
808:   }
809:   if (Nlevels<=0) SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_OUTOFRANGE,"Nlevels %d must > 0",Nlevels);
810:   pc_ml->Nlevels = Nlevels;
811:   fine_level     = Nlevels - 1;

813:   PCMGSetLevels(pc,Nlevels,NULL);
814:   /* set default smoothers */
815:   for (level=1; level<=fine_level; level++) {
816:     PCMGGetSmoother(pc,level,&smoother);
817:     KSPSetType(smoother,KSPRICHARDSON);
818:     KSPGetPC(smoother,&subpc);
819:     PCSetType(subpc,PCSOR);
820:   }
821:   PetscObjectOptionsBegin((PetscObject)pc);
822:   PCSetFromOptions_MG(PetscOptionsObject,pc); /* should be called in PCSetFromOptions_ML(), but cannot be called prior to PCMGSetLevels() */
823:   PetscOptionsEnd();

825:   PetscMalloc1(Nlevels,&gridctx);

827:   pc_ml->gridctx = gridctx;

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

833:   level = fine_level - 1;
834:   if (size == 1) { /* convert ML P, R and A into seqaij format */
835:     for (mllevel=1; mllevel<Nlevels; mllevel++) {
836:       mlmat = &(ml_object->Pmat[mllevel]);
837:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);
838:       mlmat = &(ml_object->Rmat[mllevel-1]);
839:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);

841:       mlmat = &(ml_object->Amat[mllevel]);
842:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
843:       level--;
844:     }
845:   } else { /* convert ML P and R into shell format, ML A into mpiaij format */
846:     for (mllevel=1; mllevel<Nlevels; mllevel++) {
847:       mlmat  = &(ml_object->Pmat[mllevel]);
848:       MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);
849:       mlmat  = &(ml_object->Rmat[mllevel-1]);
850:       MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);

852:       mlmat  = &(ml_object->Amat[mllevel]);
853:       MatWrapML_MPIAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
854:       level--;
855:     }
856:   }

858:   /* create vectors and ksp at all levels */
859:   for (level=0; level<fine_level; level++) {
860:     level1 = level + 1;

862:     MatCreateVecs(gridctx[level].A,&gridctx[level].x,&gridctx[level].b);
863:     MatCreateVecs(gridctx[level1].A,NULL,&gridctx[level1].r);
864:     PCMGSetX(pc,level,gridctx[level].x);
865:     PCMGSetRhs(pc,level,gridctx[level].b);
866:     PCMGSetR(pc,level1,gridctx[level1].r);

868:     if (level == 0) {
869:       PCMGGetCoarseSolve(pc,&gridctx[level].ksp);
870:     } else {
871:       PCMGGetSmoother(pc,level,&gridctx[level].ksp);
872:     }
873:   }
874:   PCMGGetSmoother(pc,fine_level,&gridctx[fine_level].ksp);

876:   /* create coarse level and the interpolation between the levels */
877:   for (level=0; level<fine_level; level++) {
878:     level1 = level + 1;

880:     PCMGSetInterpolation(pc,level1,gridctx[level].P);
881:     PCMGSetRestriction(pc,level1,gridctx[level].R);
882:     if (level > 0) {
883:       PCMGSetResidual(pc,level,PCMGResidualDefault,gridctx[level].A);
884:     }
885:     KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A);
886:   }
887:   PCMGSetResidual(pc,fine_level,PCMGResidualDefault,gridctx[fine_level].A);
888:   KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A);

890:   /* put coordinate info in levels */
891:   if (pc_ml->dim) {
892:     PetscInt  i,j,dim = pc_ml->dim;
893:     PetscInt  bs, nloc;
894:     PC        subpc;
895:     PetscReal *array;

897:     level = fine_level;
898:     for (mllevel = 0; mllevel < Nlevels; mllevel++) {
899:       ML_Aggregate_Viz_Stats *grid_info = (ML_Aggregate_Viz_Stats*)ml_object->Amat[mllevel].to->Grid->Grid;
900:       MPI_Comm               comm       = ((PetscObject)gridctx[level].A)->comm;

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

906:       VecCreate(comm,&gridctx[level].coords);
907:       VecSetSizes(gridctx[level].coords,dim * nloc,PETSC_DECIDE);
908:       VecSetType(gridctx[level].coords,VECMPI);
909:       VecGetArray(gridctx[level].coords,&array);
910:       for (j = 0; j < nloc; j++) {
911:         for (i = 0; i < dim; i++) {
912:           switch (i) {
913:           case 0: array[dim * j + i] = grid_info->x[j]; break;
914:           case 1: array[dim * j + i] = grid_info->y[j]; break;
915:           case 2: array[dim * j + i] = grid_info->z[j]; break;
916:           default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_SIZ,"PCML coordinate dimension must be <= 3");
917:           }
918:         }
919:       }

921:       /* passing coordinates to smoothers/coarse solver, should they need them */
922:       KSPGetPC(gridctx[level].ksp,&subpc);
923:       PCSetCoordinates(subpc,dim,nloc,array);
924:       VecRestoreArray(gridctx[level].coords,&array);
925:       level--;
926:     }
927:   }

929:   /* setupcalled is set to 0 so that MG is setup from scratch */
930:   pc->setupcalled = 0;
931:   PCSetUp_MG(pc);
932:   return(0);
933: }

935: /* -------------------------------------------------------------------------- */
936: /*
937:    PCDestroy_ML - Destroys the private context for the ML preconditioner
938:    that was created with PCCreate_ML().

940:    Input Parameter:
941: .  pc - the preconditioner context

943:    Application Interface Routine: PCDestroy()
944: */
945: PetscErrorCode PCDestroy_ML(PC pc)
946: {
948:   PC_MG          *mg   = (PC_MG*)pc->data;
949:   PC_ML          *pc_ml= (PC_ML*)mg->innerctx;

952:   PCReset_ML(pc);
953:   PetscFree(pc_ml);
954:   PCDestroy_MG(pc);
955:   PetscObjectComposeFunction((PetscObject)pc,"PCSetCoordinates_C",NULL);
956:   return(0);
957: }

959: PetscErrorCode PCSetFromOptions_ML(PetscOptionItems *PetscOptionsObject,PC pc)
960: {
962:   PetscInt       indx,PrintLevel,partindx;
963:   const char     *scheme[] = {"Uncoupled","Coupled","MIS","METIS"};
964:   const char     *part[]   = {"Zoltan","ParMETIS"};
965: #if defined(HAVE_ML_ZOLTAN)
966:   const char *zscheme[] = {"RCB","hypergraph","fast_hypergraph"};
967: #endif
968:   PC_MG       *mg    = (PC_MG*)pc->data;
969:   PC_ML       *pc_ml = (PC_ML*)mg->innerctx;
970:   PetscMPIInt size;
971:   MPI_Comm    comm;

974:   PetscObjectGetComm((PetscObject)pc,&comm);
975:   MPI_Comm_size(comm,&size);
976:   PetscOptionsHead(PetscOptionsObject,"ML options");

978:   PrintLevel = 0;
979:   indx       = 0;
980:   partindx   = 0;

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

988:   pc_ml->CoarsenScheme = indx;

990:   PetscOptionsReal("-pc_ml_DampingFactor","P damping factor","ML_Aggregate_Set_DampingFactor",pc_ml->DampingFactor,&pc_ml->DampingFactor,NULL);
991:   PetscOptionsReal("-pc_ml_Threshold","Smoother drop tol","ML_Aggregate_Set_Threshold",pc_ml->Threshold,&pc_ml->Threshold,NULL);
992:   PetscOptionsBool("-pc_ml_SpectralNormScheme_Anorm","Method used for estimating spectral radius","ML_Set_SpectralNormScheme_Anorm",pc_ml->SpectralNormScheme_Anorm,&pc_ml->SpectralNormScheme_Anorm,NULL);
993:   PetscOptionsBool("-pc_ml_Symmetrize","Symmetrize aggregation","ML_Set_Symmetrize",pc_ml->Symmetrize,&pc_ml->Symmetrize,NULL);
994:   PetscOptionsBool("-pc_ml_BlockScaling","Scale all dofs at each node together","None",pc_ml->BlockScaling,&pc_ml->BlockScaling,NULL);
995:   PetscOptionsEnum("-pc_ml_nullspace","Which type of null space information to use","None",PCMLNullSpaceTypes,(PetscEnum)pc_ml->nulltype,(PetscEnum*)&pc_ml->nulltype,NULL);
996:   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);
997:   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);
998:   /*
999:     The following checks a number of conditions.  If we let this stuff slip by, then ML's error handling will take over.
1000:     This is suboptimal because it amounts to calling exit(1) so we check for the most common conditions.

1002:     We also try to set some sane defaults when energy minimization is activated, otherwise it's hard to find a working
1003:     combination of options and ML's exit(1) explanations don't help matters.
1004:   */
1005:   if (pc_ml->EnergyMinimization < -1 || pc_ml->EnergyMinimization > 4) SETERRQ(comm,PETSC_ERR_ARG_OUTOFRANGE,"EnergyMinimization must be in range -1..4");
1006:   if (pc_ml->EnergyMinimization == 4 && size > 1) SETERRQ(comm,PETSC_ERR_SUP,"Energy minimization type 4 does not work in parallel");
1007:   if (pc_ml->EnergyMinimization == 4) {PetscInfo(pc,"Mandel's energy minimization scheme is experimental and broken in ML-6.2\n");}
1008:   if (pc_ml->EnergyMinimization) {
1009:     PetscOptionsReal("-pc_ml_EnergyMinimizationDropTol","Energy minimization drop tolerance","None",pc_ml->EnergyMinimizationDropTol,&pc_ml->EnergyMinimizationDropTol,NULL);
1010:   }
1011:   if (pc_ml->EnergyMinimization == 2) {
1012:     /* According to ml_MultiLevelPreconditioner.cpp, this option is only meaningful for norm type (2) */
1013:     PetscOptionsBool("-pc_ml_EnergyMinimizationCheap","Use cheaper variant of norm type 2","None",pc_ml->EnergyMinimizationCheap,&pc_ml->EnergyMinimizationCheap,NULL);
1014:   }
1015:   /* energy minimization sometimes breaks if this is turned off, the more classical stuff should be okay without it */
1016:   if (pc_ml->EnergyMinimization) pc_ml->KeepAggInfo = PETSC_TRUE;
1017:   PetscOptionsBool("-pc_ml_KeepAggInfo","Allows the preconditioner to be reused, or auxilliary matrices to be generated","None",pc_ml->KeepAggInfo,&pc_ml->KeepAggInfo,NULL);
1018:   /* Option (-1) doesn't work at all (calls exit(1)) if the tentative restriction operator isn't stored. */
1019:   if (pc_ml->EnergyMinimization == -1) pc_ml->Reusable = PETSC_TRUE;
1020:   PetscOptionsBool("-pc_ml_Reusable","Store intermedaiate data structures so that the multilevel hierarchy is reusable","None",pc_ml->Reusable,&pc_ml->Reusable,NULL);
1021:   /*
1022:     ML's C API is severely underdocumented and lacks significant functionality.  The C++ API calls
1023:     ML_Gen_MultiLevelHierarchy_UsingAggregation() which is a modified copy (!?) of the documented function
1024:     ML_Gen_MGHierarchy_UsingAggregation().  This modification, however, does not provide a strict superset of the
1025:     functionality in the old function, so some users may still want to use it.  Note that many options are ignored in
1026:     this context, but ML doesn't provide a way to find out which ones.
1027:    */
1028:   PetscOptionsBool("-pc_ml_OldHierarchy","Use old routine to generate hierarchy","None",pc_ml->OldHierarchy,&pc_ml->OldHierarchy,NULL);
1029:   PetscOptionsBool("-pc_ml_repartition", "Allow ML to repartition levels of the heirarchy","ML_Repartition_Activate",pc_ml->Repartition,&pc_ml->Repartition,NULL);
1030:   if (pc_ml->Repartition) {
1031:     PetscOptionsReal("-pc_ml_repartitionMaxMinRatio", "Acceptable ratio of repartitioned sizes","ML_Repartition_Set_LargestMinMaxRatio",pc_ml->MaxMinRatio,&pc_ml->MaxMinRatio,NULL);
1032:     PetscOptionsInt("-pc_ml_repartitionMinPerProc", "Smallest repartitioned size","ML_Repartition_Set_MinPerProc",pc_ml->MinPerProc,&pc_ml->MinPerProc,NULL);
1033:     PetscOptionsInt("-pc_ml_repartitionPutOnSingleProc", "Problem size automatically repartitioned to one processor","ML_Repartition_Set_PutOnSingleProc",pc_ml->PutOnSingleProc,&pc_ml->PutOnSingleProc,NULL);
1034: #if defined(HAVE_ML_ZOLTAN)
1035:     partindx = 0;
1036:     PetscOptionsEList("-pc_ml_repartitionType", "Repartitioning library to use","ML_Repartition_Set_Partitioner",part,2,part[0],&partindx,NULL);

1038:     pc_ml->RepartitionType = partindx;
1039:     if (!partindx) {
1040:       PetscInt zindx = 0;

1042:       PetscOptionsEList("-pc_ml_repartitionZoltanScheme", "Repartitioning scheme to use","None",zscheme,3,zscheme[0],&zindx,NULL);

1044:       pc_ml->ZoltanScheme = zindx;
1045:     }
1046: #else
1047:     partindx = 1;
1048:     PetscOptionsEList("-pc_ml_repartitionType", "Repartitioning library to use","ML_Repartition_Set_Partitioner",part,2,part[1],&partindx,NULL);
1049:     pc_ml->RepartitionType = partindx;
1050:     if (!partindx) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_SUP_SYS,"ML not compiled with Zoltan");
1051: #endif
1052:     PetscOptionsBool("-pc_ml_Aux","Aggregate using auxiliary coordinate-based laplacian","None",pc_ml->Aux,&pc_ml->Aux,NULL);
1053:     PetscOptionsReal("-pc_ml_AuxThreshold","Auxiliary smoother drop tol","None",pc_ml->AuxThreshold,&pc_ml->AuxThreshold,NULL);
1054:   }
1055:   PetscOptionsTail();
1056:   return(0);
1057: }

1059: /* -------------------------------------------------------------------------- */
1060: /*
1061:    PCCreate_ML - Creates a ML preconditioner context, PC_ML,
1062:    and sets this as the private data within the generic preconditioning
1063:    context, PC, that was created within PCCreate().

1065:    Input Parameter:
1066: .  pc - the preconditioner context

1068:    Application Interface Routine: PCCreate()
1069: */

1071: /*MC
1072:      PCML - Use algebraic multigrid preconditioning. This preconditioner requires you provide
1073:        fine grid discretization matrix. The coarser grid matrices and restriction/interpolation
1074:        operators are computed by ML, with the matrices coverted to PETSc matrices in aij format
1075:        and the restriction/interpolation operators wrapped as PETSc shell matrices.

1077:    Options Database Key:
1078:    Multigrid options(inherited):
1079: +  -pc_mg_cycles <1> - 1 for V cycle, 2 for W-cycle (MGSetCycles)
1080: .  -pc_mg_distinct_smoothup - Should one configure the up and down smoothers separately (PCMGSetDistinctSmoothUp)
1081: -  -pc_mg_type <multiplicative> - (one of) additive multiplicative full kascade

1083:    ML options:
1084: +  -pc_ml_PrintLevel <0> - Print level (ML_Set_PrintLevel)
1085: .  -pc_ml_maxNlevels <10> - Maximum number of levels (None)
1086: .  -pc_ml_maxCoarseSize <1> - Maximum coarsest mesh size (ML_Aggregate_Set_MaxCoarseSize)
1087: .  -pc_ml_CoarsenScheme <Uncoupled> - (one of) Uncoupled Coupled MIS METIS
1088: .  -pc_ml_DampingFactor <1.33333> - P damping factor (ML_Aggregate_Set_DampingFactor)
1089: .  -pc_ml_Threshold <0> - Smoother drop tol (ML_Aggregate_Set_Threshold)
1090: .  -pc_ml_SpectralNormScheme_Anorm <false> - Method used for estimating spectral radius (ML_Set_SpectralNormScheme_Anorm)
1091: .  -pc_ml_repartition <false> - Allow ML to repartition levels of the heirarchy (ML_Repartition_Activate)
1092: .  -pc_ml_repartitionMaxMinRatio <1.3> - Acceptable ratio of repartitioned sizes (ML_Repartition_Set_LargestMinMaxRatio)
1093: .  -pc_ml_repartitionMinPerProc <512>: Smallest repartitioned size (ML_Repartition_Set_MinPerProc)
1094: .  -pc_ml_repartitionPutOnSingleProc <5000> - Problem size automatically repartitioned to one processor (ML_Repartition_Set_PutOnSingleProc)
1095: .  -pc_ml_repartitionType <Zoltan> - Repartitioning library to use (ML_Repartition_Set_Partitioner)
1096: .  -pc_ml_repartitionZoltanScheme <RCB> - Repartitioning scheme to use (None)
1097: .  -pc_ml_Aux <false> - Aggregate using auxiliary coordinate-based laplacian (None)
1098: -  -pc_ml_AuxThreshold <0.0> - Auxiliary smoother drop tol (None)

1100:    Level: intermediate

1102: .seealso:  PCCreate(), PCSetType(), PCType (for list of available types), PC, PCMGType,
1103:            PCMGSetLevels(), PCMGGetLevels(), PCMGSetType(), MPSetCycles(), PCMGSetDistinctSmoothUp(),
1104:            PCMGGetCoarseSolve(), PCMGSetResidual(), PCMGSetInterpolation(),
1105:            PCMGSetRestriction(), PCMGGetSmoother(), PCMGGetSmootherUp(), PCMGGetSmootherDown(),
1106:            PCMGSetCycleTypeOnLevel(), PCMGSetRhs(), PCMGSetX(), PCMGSetR()
1107: M*/

1109: PETSC_EXTERN PetscErrorCode PCCreate_ML(PC pc)
1110: {
1112:   PC_ML          *pc_ml;
1113:   PC_MG          *mg;

1116:   /* PCML is an inherited class of PCMG. Initialize pc as PCMG */
1117:   PCSetType(pc,PCMG); /* calls PCCreate_MG() and MGCreate_Private() */
1118:   PetscObjectChangeTypeName((PetscObject)pc,PCML);
1119:   /* Since PCMG tries to use DM assocated with PC must delete it */
1120:   DMDestroy(&pc->dm);
1121:   PCMGSetGalerkin(pc,PC_MG_GALERKIN_EXTERNAL);
1122:   mg   = (PC_MG*)pc->data;

1124:   /* create a supporting struct and attach it to pc */
1125:   PetscNewLog(pc,&pc_ml);
1126:   mg->innerctx = pc_ml;

1128:   pc_ml->ml_object                = 0;
1129:   pc_ml->agg_object               = 0;
1130:   pc_ml->gridctx                  = 0;
1131:   pc_ml->PetscMLdata              = 0;
1132:   pc_ml->Nlevels                  = -1;
1133:   pc_ml->MaxNlevels               = 10;
1134:   pc_ml->MaxCoarseSize            = 1;
1135:   pc_ml->CoarsenScheme            = 1;
1136:   pc_ml->Threshold                = 0.0;
1137:   pc_ml->DampingFactor            = 4.0/3.0;
1138:   pc_ml->SpectralNormScheme_Anorm = PETSC_FALSE;
1139:   pc_ml->size                     = 0;
1140:   pc_ml->dim                      = 0;
1141:   pc_ml->nloc                     = 0;
1142:   pc_ml->coords                   = 0;
1143:   pc_ml->Repartition              = PETSC_FALSE;
1144:   pc_ml->MaxMinRatio              = 1.3;
1145:   pc_ml->MinPerProc               = 512;
1146:   pc_ml->PutOnSingleProc          = 5000;
1147:   pc_ml->RepartitionType          = 0;
1148:   pc_ml->ZoltanScheme             = 0;
1149:   pc_ml->Aux                      = PETSC_FALSE;
1150:   pc_ml->AuxThreshold             = 0.0;

1152:   /* allow for coordinates to be passed */
1153:   PetscObjectComposeFunction((PetscObject)pc,"PCSetCoordinates_C",PCSetCoordinates_ML);

1155:   /* overwrite the pointers of PCMG by the functions of PCML */
1156:   pc->ops->setfromoptions = PCSetFromOptions_ML;
1157:   pc->ops->setup          = PCSetUp_ML;
1158:   pc->ops->reset          = PCReset_ML;
1159:   pc->ops->destroy        = PCDestroy_ML;
1160:   return(0);
1161: }