Actual source code: classical.c

      1: #include <../src/ksp/pc/impls/gamg/gamg.h>
      2: #include <petscsf.h>

      4: PetscFunctionList PCGAMGClassicalProlongatorList    = NULL;
      5: PetscBool         PCGAMGClassicalPackageInitialized = PETSC_FALSE;

      7: typedef struct {
      8:   PetscReal interp_threshold; /* interpolation threshold */
      9:   char      prolongtype[256];
     10:   PetscInt  nsmooths;         /* number of jacobi smoothings on the prolongator */
     11: } PC_GAMG_Classical;

     13: /*@C
     14:    PCGAMGClassicalSetType - Sets the type of classical interpolation to use

     16:    Collective on PC

     18:    Input Parameters:
     19: .  pc - the preconditioner context

     21:    Options Database Key:
     22: .  -pc_gamg_classical_type

     24:    Level: intermediate

     26: .seealso: ()
     27: @*/
     28: PetscErrorCode PCGAMGClassicalSetType(PC pc, PCGAMGClassicalType type)
     29: {

     34:   PetscTryMethod(pc,"PCGAMGClassicalSetType_C",(PC,PCGAMGClassicalType),(pc,type));
     35:   return(0);
     36: }

     38: /*@C
     39:    PCGAMGClassicalGetType - Gets the type of classical interpolation to use

     41:    Collective on PC

     43:    Input Parameter:
     44: .  pc - the preconditioner context

     46:    Output Parameter:
     47: .  type - the type used

     49:    Level: intermediate

     51: .seealso: ()
     52: @*/
     53: PetscErrorCode PCGAMGClassicalGetType(PC pc, PCGAMGClassicalType *type)
     54: {

     59:   PetscUseMethod(pc,"PCGAMGClassicalGetType_C",(PC,PCGAMGClassicalType*),(pc,type));
     60:   return(0);
     61: }

     63: static PetscErrorCode PCGAMGClassicalSetType_GAMG(PC pc, PCGAMGClassicalType type)
     64: {
     65:   PetscErrorCode    ierr;
     66:   PC_MG             *mg          = (PC_MG*)pc->data;
     67:   PC_GAMG           *pc_gamg     = (PC_GAMG*)mg->innerctx;
     68:   PC_GAMG_Classical *cls         = (PC_GAMG_Classical*)pc_gamg->subctx;

     71:   PetscStrcpy(cls->prolongtype,type);
     72:   return(0);
     73: }

     75: static PetscErrorCode PCGAMGClassicalGetType_GAMG(PC pc, PCGAMGClassicalType *type)
     76: {
     77:   PC_MG             *mg          = (PC_MG*)pc->data;
     78:   PC_GAMG           *pc_gamg     = (PC_GAMG*)mg->innerctx;
     79:   PC_GAMG_Classical *cls         = (PC_GAMG_Classical*)pc_gamg->subctx;

     82:   *type = cls->prolongtype;
     83:   return(0);
     84: }

     86: PetscErrorCode PCGAMGGraph_Classical(PC pc,Mat A,Mat *G)
     87: {
     88:   PetscInt          s,f,n,idx,lidx,gidx;
     89:   PetscInt          r,c,ncols;
     90:   const PetscInt    *rcol;
     91:   const PetscScalar *rval;
     92:   PetscInt          *gcol;
     93:   PetscScalar       *gval;
     94:   PetscReal         rmax;
     95:   PetscInt          cmax = 0;
     96:   PC_MG             *mg = (PC_MG *)pc->data;
     97:   PC_GAMG           *gamg = (PC_GAMG *)mg->innerctx;
     98:   PetscErrorCode    ierr;
     99:   PetscInt          *gsparse,*lsparse;
    100:   PetscScalar       *Amax;
    101:   MatType           mtype;

    104:   MatGetOwnershipRange(A,&s,&f);
    105:   n=f-s;
    106:   PetscMalloc3(n,&lsparse,n,&gsparse,n,&Amax);

    108:   for (r = 0;r < n;r++) {
    109:     lsparse[r] = 0;
    110:     gsparse[r] = 0;
    111:   }

    113:   for (r = s;r < f;r++) {
    114:     /* determine the maximum off-diagonal in each row */
    115:     rmax = 0.;
    116:     MatGetRow(A,r,&ncols,&rcol,&rval);
    117:     for (c = 0; c < ncols; c++) {
    118:       if (PetscRealPart(-rval[c]) > rmax && rcol[c] != r) {
    119:         rmax = PetscRealPart(-rval[c]);
    120:       }
    121:     }
    122:     Amax[r-s] = rmax;
    123:     if (ncols > cmax) cmax = ncols;
    124:     lidx = 0;
    125:     gidx = 0;
    126:     /* create the local and global sparsity patterns */
    127:     for (c = 0; c < ncols; c++) {
    128:       if (PetscRealPart(-rval[c]) > gamg->threshold[0]*PetscRealPart(Amax[r-s]) || rcol[c] == r) {
    129:         if (rcol[c] < f && rcol[c] >= s) {
    130:           lidx++;
    131:         } else {
    132:           gidx++;
    133:         }
    134:       }
    135:     }
    136:     MatRestoreRow(A,r,&ncols,&rcol,&rval);
    137:     lsparse[r-s] = lidx;
    138:     gsparse[r-s] = gidx;
    139:   }
    140:   PetscMalloc2(cmax,&gval,cmax,&gcol);

    142:   MatCreate(PetscObjectComm((PetscObject)A),G);
    143:   MatGetType(A,&mtype);
    144:   MatSetType(*G,mtype);
    145:   MatSetSizes(*G,n,n,PETSC_DETERMINE,PETSC_DETERMINE);
    146:   MatMPIAIJSetPreallocation(*G,0,lsparse,0,gsparse);
    147:   MatSeqAIJSetPreallocation(*G,0,lsparse);
    148:   for (r = s;r < f;r++) {
    149:     MatGetRow(A,r,&ncols,&rcol,&rval);
    150:     idx = 0;
    151:     for (c = 0; c < ncols; c++) {
    152:       /* classical strength of connection */
    153:       if (PetscRealPart(-rval[c]) > gamg->threshold[0]*PetscRealPart(Amax[r-s]) || rcol[c] == r) {
    154:         gcol[idx] = rcol[c];
    155:         gval[idx] = rval[c];
    156:         idx++;
    157:       }
    158:     }
    159:     MatSetValues(*G,1,&r,idx,gcol,gval,INSERT_VALUES);
    160:     MatRestoreRow(A,r,&ncols,&rcol,&rval);
    161:   }
    162:   MatAssemblyBegin(*G, MAT_FINAL_ASSEMBLY);
    163:   MatAssemblyEnd(*G, MAT_FINAL_ASSEMBLY);

    165:   PetscFree2(gval,gcol);
    166:   PetscFree3(lsparse,gsparse,Amax);
    167:   return(0);
    168: }

    171: PetscErrorCode PCGAMGCoarsen_Classical(PC pc,Mat *G,PetscCoarsenData **agg_lists)
    172: {
    173:   PetscErrorCode   ierr;
    174:   MatCoarsen       crs;
    175:   MPI_Comm         fcomm = ((PetscObject)pc)->comm;

    178:   if (!G) SETERRQ(fcomm,PETSC_ERR_ARG_WRONGSTATE,"Must set Graph in PC in PCGAMG before coarsening");

    180:   MatCoarsenCreate(fcomm,&crs);
    181:   MatCoarsenSetFromOptions(crs);
    182:   MatCoarsenSetAdjacency(crs,*G);
    183:   MatCoarsenSetStrictAggs(crs,PETSC_TRUE);
    184:   MatCoarsenApply(crs);
    185:   MatCoarsenGetData(crs,agg_lists);
    186:   MatCoarsenDestroy(&crs);
    187:   return(0);
    188: }

    190: PetscErrorCode PCGAMGProlongator_Classical_Direct(PC pc, Mat A, Mat G, PetscCoarsenData *agg_lists,Mat *P)
    191: {
    192:   PetscErrorCode    ierr;
    193:   PC_MG             *mg          = (PC_MG*)pc->data;
    194:   PC_GAMG           *gamg        = (PC_GAMG*)mg->innerctx;
    195:   PetscBool         iscoarse,isMPIAIJ,isSEQAIJ;
    196:   PetscInt          fn,cn,fs,fe,cs,ce,i,j,ncols,col,row_f,row_c,cmax=0,idx,noff;
    197:   PetscInt          *lcid,*gcid,*lsparse,*gsparse,*colmap,*pcols;
    198:   const PetscInt    *rcol;
    199:   PetscReal         *Amax_pos,*Amax_neg;
    200:   PetscScalar       g_pos,g_neg,a_pos,a_neg,diag,invdiag,alpha,beta,pij;
    201:   PetscScalar       *pvals;
    202:   const PetscScalar *rval;
    203:   Mat               lA,gA=NULL;
    204:   MatType           mtype;
    205:   Vec               C,lvec;
    206:   PetscLayout       clayout;
    207:   PetscSF           sf;
    208:   Mat_MPIAIJ        *mpiaij;

    211:   MatGetOwnershipRange(A,&fs,&fe);
    212:   fn = fe-fs;
    213:   PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);
    214:   PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSEQAIJ);
    215:   if (!isMPIAIJ && !isSEQAIJ) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Classical AMG requires MPIAIJ matrix");
    216:   if (isMPIAIJ) {
    217:     mpiaij = (Mat_MPIAIJ*)A->data;
    218:     lA = mpiaij->A;
    219:     gA = mpiaij->B;
    220:     lvec = mpiaij->lvec;
    221:     VecGetSize(lvec,&noff);
    222:     colmap = mpiaij->garray;
    223:     MatGetLayouts(A,NULL,&clayout);
    224:     PetscSFCreate(PetscObjectComm((PetscObject)A),&sf);
    225:     PetscSFSetGraphLayout(sf,clayout,noff,NULL,PETSC_COPY_VALUES,colmap);
    226:     PetscMalloc1(noff,&gcid);
    227:   } else {
    228:     lA = A;
    229:   }
    230:   PetscMalloc5(fn,&lsparse,fn,&gsparse,fn,&lcid,fn,&Amax_pos,fn,&Amax_neg);

    232:   /* count the number of coarse unknowns */
    233:   cn = 0;
    234:   for (i=0;i<fn;i++) {
    235:     /* filter out singletons */
    236:     PetscCDEmptyAt(agg_lists,i,&iscoarse);
    237:     lcid[i] = -1;
    238:     if (!iscoarse) {
    239:       cn++;
    240:     }
    241:   }

    243:    /* create the coarse vector */
    244:   VecCreateMPI(PetscObjectComm((PetscObject)A),cn,PETSC_DECIDE,&C);
    245:   VecGetOwnershipRange(C,&cs,&ce);

    247:   cn = 0;
    248:   for (i=0;i<fn;i++) {
    249:     PetscCDEmptyAt(agg_lists,i,&iscoarse);
    250:     if (!iscoarse) {
    251:       lcid[i] = cs+cn;
    252:       cn++;
    253:     } else {
    254:       lcid[i] = -1;
    255:     }
    256:   }

    258:   if (gA) {
    259:     PetscSFBcastBegin(sf,MPIU_INT,lcid,gcid,MPI_REPLACE);
    260:     PetscSFBcastEnd(sf,MPIU_INT,lcid,gcid,MPI_REPLACE);
    261:   }

    263:   /* determine the largest off-diagonal entries in each row */
    264:   for (i=fs;i<fe;i++) {
    265:     Amax_pos[i-fs] = 0.;
    266:     Amax_neg[i-fs] = 0.;
    267:     MatGetRow(A,i,&ncols,&rcol,&rval);
    268:     for (j=0;j<ncols;j++){
    269:       if ((PetscRealPart(-rval[j]) > Amax_neg[i-fs]) && i != rcol[j]) Amax_neg[i-fs] = PetscAbsScalar(rval[j]);
    270:       if ((PetscRealPart(rval[j])  > Amax_pos[i-fs]) && i != rcol[j]) Amax_pos[i-fs] = PetscAbsScalar(rval[j]);
    271:     }
    272:     if (ncols > cmax) cmax = ncols;
    273:     MatRestoreRow(A,i,&ncols,&rcol,&rval);
    274:   }
    275:   PetscMalloc2(cmax,&pcols,cmax,&pvals);
    276:   VecDestroy(&C);

    278:   /* count the on and off processor sparsity patterns for the prolongator */
    279:   for (i=0;i<fn;i++) {
    280:     /* on */
    281:     lsparse[i] = 0;
    282:     gsparse[i] = 0;
    283:     if (lcid[i] >= 0) {
    284:       lsparse[i] = 1;
    285:       gsparse[i] = 0;
    286:     } else {
    287:       MatGetRow(lA,i,&ncols,&rcol,&rval);
    288:       for (j = 0;j < ncols;j++) {
    289:         col = rcol[j];
    290:         if (lcid[col] >= 0 && (PetscRealPart(rval[j]) > gamg->threshold[0]*Amax_pos[i] || PetscRealPart(-rval[j]) > gamg->threshold[0]*Amax_neg[i])) {
    291:           lsparse[i] += 1;
    292:         }
    293:       }
    294:       MatRestoreRow(lA,i,&ncols,&rcol,&rval);
    295:       /* off */
    296:       if (gA) {
    297:         MatGetRow(gA,i,&ncols,&rcol,&rval);
    298:         for (j = 0; j < ncols; j++) {
    299:           col = rcol[j];
    300:           if (gcid[col] >= 0 && (PetscRealPart(rval[j]) > gamg->threshold[0]*Amax_pos[i] || PetscRealPart(-rval[j]) > gamg->threshold[0]*Amax_neg[i])) {
    301:             gsparse[i] += 1;
    302:           }
    303:         }
    304:         MatRestoreRow(gA,i,&ncols,&rcol,&rval);
    305:       }
    306:     }
    307:   }

    309:   /* preallocate and create the prolongator */
    310:   MatCreate(PetscObjectComm((PetscObject)A),P);
    311:   MatGetType(G,&mtype);
    312:   MatSetType(*P,mtype);
    313:   MatSetSizes(*P,fn,cn,PETSC_DETERMINE,PETSC_DETERMINE);
    314:   MatMPIAIJSetPreallocation(*P,0,lsparse,0,gsparse);
    315:   MatSeqAIJSetPreallocation(*P,0,lsparse);

    317:   /* loop over local fine nodes -- get the diagonal, the sum of positive and negative strong and weak weights, and set up the row */
    318:   for (i = 0;i < fn;i++) {
    319:     /* determine on or off */
    320:     row_f = i + fs;
    321:     row_c = lcid[i];
    322:     if (row_c >= 0) {
    323:       pij = 1.;
    324:       MatSetValues(*P,1,&row_f,1,&row_c,&pij,INSERT_VALUES);
    325:     } else {
    326:       g_pos = 0.;
    327:       g_neg = 0.;
    328:       a_pos = 0.;
    329:       a_neg = 0.;
    330:       diag  = 0.;

    332:       /* local connections */
    333:       MatGetRow(lA,i,&ncols,&rcol,&rval);
    334:       for (j = 0; j < ncols; j++) {
    335:         col = rcol[j];
    336:         if (lcid[col] >= 0 && (PetscRealPart(rval[j]) > gamg->threshold[0]*Amax_pos[i] || PetscRealPart(-rval[j]) > gamg->threshold[0]*Amax_neg[i])) {
    337:           if (PetscRealPart(rval[j]) > 0.) {
    338:             g_pos += rval[j];
    339:           } else {
    340:             g_neg += rval[j];
    341:           }
    342:         }
    343:         if (col != i) {
    344:           if (PetscRealPart(rval[j]) > 0.) {
    345:             a_pos += rval[j];
    346:           } else {
    347:             a_neg += rval[j];
    348:           }
    349:         } else {
    350:           diag = rval[j];
    351:         }
    352:       }
    353:       MatRestoreRow(lA,i,&ncols,&rcol,&rval);

    355:       /* ghosted connections */
    356:       if (gA) {
    357:         MatGetRow(gA,i,&ncols,&rcol,&rval);
    358:         for (j = 0; j < ncols; j++) {
    359:           col = rcol[j];
    360:           if (gcid[col] >= 0 && (PetscRealPart(rval[j]) > gamg->threshold[0]*Amax_pos[i] || PetscRealPart(-rval[j]) > gamg->threshold[0]*Amax_neg[i])) {
    361:             if (PetscRealPart(rval[j]) > 0.) {
    362:               g_pos += rval[j];
    363:             } else {
    364:               g_neg += rval[j];
    365:             }
    366:           }
    367:           if (PetscRealPart(rval[j]) > 0.) {
    368:             a_pos += rval[j];
    369:           } else {
    370:             a_neg += rval[j];
    371:           }
    372:         }
    373:         MatRestoreRow(gA,i,&ncols,&rcol,&rval);
    374:       }

    376:       if (g_neg == 0.) {
    377:         alpha = 0.;
    378:       } else {
    379:         alpha = -a_neg/g_neg;
    380:       }

    382:       if (g_pos == 0.) {
    383:         diag += a_pos;
    384:         beta = 0.;
    385:       } else {
    386:         beta = -a_pos/g_pos;
    387:       }
    388:       if (diag == 0.) {
    389:         invdiag = 0.;
    390:       } else invdiag = 1. / diag;
    391:       /* on */
    392:       MatGetRow(lA,i,&ncols,&rcol,&rval);
    393:       idx = 0;
    394:       for (j = 0;j < ncols;j++) {
    395:         col = rcol[j];
    396:         if (lcid[col] >= 0 && (PetscRealPart(rval[j]) > gamg->threshold[0]*Amax_pos[i] || PetscRealPart(-rval[j]) > gamg->threshold[0]*Amax_neg[i])) {
    397:           row_f = i + fs;
    398:           row_c = lcid[col];
    399:           /* set the values for on-processor ones */
    400:           if (PetscRealPart(rval[j]) < 0.) {
    401:             pij = rval[j]*alpha*invdiag;
    402:           } else {
    403:             pij = rval[j]*beta*invdiag;
    404:           }
    405:           if (PetscAbsScalar(pij) != 0.) {
    406:             pvals[idx] = pij;
    407:             pcols[idx] = row_c;
    408:             idx++;
    409:           }
    410:         }
    411:       }
    412:       MatRestoreRow(lA,i,&ncols,&rcol,&rval);
    413:       /* off */
    414:       if (gA) {
    415:         MatGetRow(gA,i,&ncols,&rcol,&rval);
    416:         for (j = 0; j < ncols; j++) {
    417:           col = rcol[j];
    418:           if (gcid[col] >= 0 && (PetscRealPart(rval[j]) > gamg->threshold[0]*Amax_pos[i] || PetscRealPart(-rval[j]) > gamg->threshold[0]*Amax_neg[i])) {
    419:             row_f = i + fs;
    420:             row_c = gcid[col];
    421:             /* set the values for on-processor ones */
    422:             if (PetscRealPart(rval[j]) < 0.) {
    423:               pij = rval[j]*alpha*invdiag;
    424:             } else {
    425:               pij = rval[j]*beta*invdiag;
    426:             }
    427:             if (PetscAbsScalar(pij) != 0.) {
    428:               pvals[idx] = pij;
    429:               pcols[idx] = row_c;
    430:               idx++;
    431:             }
    432:           }
    433:         }
    434:         MatRestoreRow(gA,i,&ncols,&rcol,&rval);
    435:       }
    436:       MatSetValues(*P,1,&row_f,idx,pcols,pvals,INSERT_VALUES);
    437:     }
    438:   }

    440:   MatAssemblyBegin(*P, MAT_FINAL_ASSEMBLY);
    441:   MatAssemblyEnd(*P, MAT_FINAL_ASSEMBLY);

    443:   PetscFree5(lsparse,gsparse,lcid,Amax_pos,Amax_neg);

    445:   PetscFree2(pcols,pvals);
    446:   if (gA) {
    447:     PetscSFDestroy(&sf);
    448:     PetscFree(gcid);
    449:   }
    450:   return(0);
    451: }

    453: PetscErrorCode PCGAMGTruncateProlongator_Private(PC pc,Mat *P)
    454: {
    455:   PetscInt          j,i,ps,pf,pn,pcs,pcf,pcn,idx,cmax;
    456:   PetscErrorCode    ierr;
    457:   const PetscScalar *pval;
    458:   const PetscInt    *pcol;
    459:   PetscScalar       *pnval;
    460:   PetscInt          *pncol;
    461:   PetscInt          ncols;
    462:   Mat               Pnew;
    463:   PetscInt          *lsparse,*gsparse;
    464:   PetscReal         pmax_pos,pmax_neg,ptot_pos,ptot_neg,pthresh_pos,pthresh_neg;
    465:   PC_MG             *mg          = (PC_MG*)pc->data;
    466:   PC_GAMG           *pc_gamg     = (PC_GAMG*)mg->innerctx;
    467:   PC_GAMG_Classical *cls         = (PC_GAMG_Classical*)pc_gamg->subctx;
    468:   MatType           mtype;

    471:   /* trim and rescale with reallocation */
    472:   MatGetOwnershipRange(*P,&ps,&pf);
    473:   MatGetOwnershipRangeColumn(*P,&pcs,&pcf);
    474:   pn = pf-ps;
    475:   pcn = pcf-pcs;
    476:   PetscMalloc2(pn,&lsparse,pn,&gsparse);
    477:   /* allocate */
    478:   cmax = 0;
    479:   for (i=ps;i<pf;i++) {
    480:     lsparse[i-ps] = 0;
    481:     gsparse[i-ps] = 0;
    482:     MatGetRow(*P,i,&ncols,&pcol,&pval);
    483:     if (ncols > cmax) {
    484:       cmax = ncols;
    485:     }
    486:     pmax_pos = 0.;
    487:     pmax_neg = 0.;
    488:     for (j=0;j<ncols;j++) {
    489:       if (PetscRealPart(pval[j]) > pmax_pos) {
    490:         pmax_pos = PetscRealPart(pval[j]);
    491:       } else if (PetscRealPart(pval[j]) < pmax_neg) {
    492:         pmax_neg = PetscRealPart(pval[j]);
    493:       }
    494:     }
    495:     for (j=0;j<ncols;j++) {
    496:       if (PetscRealPart(pval[j]) >= pmax_pos*cls->interp_threshold || PetscRealPart(pval[j]) <= pmax_neg*cls->interp_threshold) {
    497:         if (pcol[j] >= pcs && pcol[j] < pcf) {
    498:           lsparse[i-ps]++;
    499:         } else {
    500:           gsparse[i-ps]++;
    501:         }
    502:       }
    503:     }
    504:     MatRestoreRow(*P,i,&ncols,&pcol,&pval);
    505:   }

    507:   PetscMalloc2(cmax,&pnval,cmax,&pncol);

    509:   MatGetType(*P,&mtype);
    510:   MatCreate(PetscObjectComm((PetscObject)*P),&Pnew);
    511:   MatSetType(Pnew, mtype);
    512:   MatSetSizes(Pnew,pn,pcn,PETSC_DETERMINE,PETSC_DETERMINE);
    513:   MatSeqAIJSetPreallocation(Pnew,0,lsparse);
    514:   MatMPIAIJSetPreallocation(Pnew,0,lsparse,0,gsparse);

    516:   for (i=ps;i<pf;i++) {
    517:     MatGetRow(*P,i,&ncols,&pcol,&pval);
    518:     pmax_pos = 0.;
    519:     pmax_neg = 0.;
    520:     for (j=0;j<ncols;j++) {
    521:       if (PetscRealPart(pval[j]) > pmax_pos) {
    522:         pmax_pos = PetscRealPart(pval[j]);
    523:       } else if (PetscRealPart(pval[j]) < pmax_neg) {
    524:         pmax_neg = PetscRealPart(pval[j]);
    525:       }
    526:     }
    527:     pthresh_pos = 0.;
    528:     pthresh_neg = 0.;
    529:     ptot_pos = 0.;
    530:     ptot_neg = 0.;
    531:     for (j=0;j<ncols;j++) {
    532:       if (PetscRealPart(pval[j]) >= cls->interp_threshold*pmax_pos) {
    533:         pthresh_pos += PetscRealPart(pval[j]);
    534:       } else if (PetscRealPart(pval[j]) <= cls->interp_threshold*pmax_neg) {
    535:         pthresh_neg += PetscRealPart(pval[j]);
    536:       }
    537:       if (PetscRealPart(pval[j]) > 0.) {
    538:         ptot_pos += PetscRealPart(pval[j]);
    539:       } else {
    540:         ptot_neg += PetscRealPart(pval[j]);
    541:       }
    542:     }
    543:     if (PetscAbsReal(pthresh_pos) > 0.) ptot_pos /= pthresh_pos;
    544:     if (PetscAbsReal(pthresh_neg) > 0.) ptot_neg /= pthresh_neg;
    545:     idx=0;
    546:     for (j=0;j<ncols;j++) {
    547:       if (PetscRealPart(pval[j]) >= pmax_pos*cls->interp_threshold) {
    548:         pnval[idx] = ptot_pos*pval[j];
    549:         pncol[idx] = pcol[j];
    550:         idx++;
    551:       } else if (PetscRealPart(pval[j]) <= pmax_neg*cls->interp_threshold) {
    552:         pnval[idx] = ptot_neg*pval[j];
    553:         pncol[idx] = pcol[j];
    554:         idx++;
    555:       }
    556:     }
    557:     MatRestoreRow(*P,i,&ncols,&pcol,&pval);
    558:     MatSetValues(Pnew,1,&i,idx,pncol,pnval,INSERT_VALUES);
    559:   }

    561:   MatAssemblyBegin(Pnew, MAT_FINAL_ASSEMBLY);
    562:   MatAssemblyEnd(Pnew, MAT_FINAL_ASSEMBLY);
    563:   MatDestroy(P);

    565:   *P = Pnew;
    566:   PetscFree2(lsparse,gsparse);
    567:   PetscFree2(pnval,pncol);
    568:   return(0);
    569: }

    571: PetscErrorCode PCGAMGProlongator_Classical_Standard(PC pc, Mat A, Mat G, PetscCoarsenData *agg_lists,Mat *P)
    572: {
    573:   PetscErrorCode    ierr;
    574:   Mat               lA,*lAs;
    575:   MatType           mtype;
    576:   Vec               cv;
    577:   PetscInt          *gcid,*lcid,*lsparse,*gsparse,*picol;
    578:   PetscInt          fs,fe,cs,ce,nl,i,j,k,li,lni,ci,ncols,maxcols,fn,cn,cid;
    579:   PetscMPIInt       size;
    580:   const PetscInt    *lidx,*icol,*gidx;
    581:   PetscBool         iscoarse;
    582:   PetscScalar       vi,pentry,pjentry;
    583:   PetscScalar       *pcontrib,*pvcol;
    584:   const PetscScalar *vcol;
    585:   PetscReal         diag,jdiag,jwttotal;
    586:   PetscInt          pncols;
    587:   PetscSF           sf;
    588:   PetscLayout       clayout;
    589:   IS                lis;

    592:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
    593:   MatGetOwnershipRange(A,&fs,&fe);
    594:   fn = fe-fs;
    595:   ISCreateStride(PETSC_COMM_SELF,fe-fs,fs,1,&lis);
    596:   if (size > 1) {
    597:     MatGetLayouts(A,NULL,&clayout);
    598:     /* increase the overlap by two to get neighbors of neighbors */
    599:     MatIncreaseOverlap(A,1,&lis,2);
    600:     ISSort(lis);
    601:     /* get the local part of A */
    602:     MatCreateSubMatrices(A,1,&lis,&lis,MAT_INITIAL_MATRIX,&lAs);
    603:     lA = lAs[0];
    604:     /* build an SF out of it */
    605:     ISGetLocalSize(lis,&nl);
    606:     PetscSFCreate(PetscObjectComm((PetscObject)A),&sf);
    607:     ISGetIndices(lis,&lidx);
    608:     PetscSFSetGraphLayout(sf,clayout,nl,NULL,PETSC_COPY_VALUES,lidx);
    609:     ISRestoreIndices(lis,&lidx);
    610:   } else {
    611:     lA = A;
    612:     nl = fn;
    613:   }
    614:   /* create a communication structure for the overlapped portion and transmit coarse indices */
    615:   PetscMalloc3(fn,&lsparse,fn,&gsparse,nl,&pcontrib);
    616:   /* create coarse vector */
    617:   cn = 0;
    618:   for (i=0;i<fn;i++) {
    619:     PetscCDEmptyAt(agg_lists,i,&iscoarse);
    620:     if (!iscoarse) {
    621:       cn++;
    622:     }
    623:   }
    624:   PetscMalloc1(fn,&gcid);
    625:   VecCreateMPI(PetscObjectComm((PetscObject)A),cn,PETSC_DECIDE,&cv);
    626:   VecGetOwnershipRange(cv,&cs,&ce);
    627:   cn = 0;
    628:   for (i=0;i<fn;i++) {
    629:     PetscCDEmptyAt(agg_lists,i,&iscoarse);
    630:     if (!iscoarse) {
    631:       gcid[i] = cs+cn;
    632:       cn++;
    633:     } else {
    634:       gcid[i] = -1;
    635:     }
    636:   }
    637:   if (size > 1) {
    638:     PetscMalloc1(nl,&lcid);
    639:     PetscSFBcastBegin(sf,MPIU_INT,gcid,lcid,MPI_REPLACE);
    640:     PetscSFBcastEnd(sf,MPIU_INT,gcid,lcid,MPI_REPLACE);
    641:   } else {
    642:     lcid = gcid;
    643:   }
    644:   /* count to preallocate the prolongator */
    645:   ISGetIndices(lis,&gidx);
    646:   maxcols = 0;
    647:   /* count the number of unique contributing coarse cells for each fine */
    648:   for (i=0;i<nl;i++) {
    649:     pcontrib[i] = 0.;
    650:     MatGetRow(lA,i,&ncols,&icol,NULL);
    651:     if (gidx[i] >= fs && gidx[i] < fe) {
    652:       li = gidx[i] - fs;
    653:       lsparse[li] = 0;
    654:       gsparse[li] = 0;
    655:       cid = lcid[i];
    656:       if (cid >= 0) {
    657:         lsparse[li] = 1;
    658:       } else {
    659:         for (j=0;j<ncols;j++) {
    660:           if (lcid[icol[j]] >= 0) {
    661:             pcontrib[icol[j]] = 1.;
    662:           } else {
    663:             ci = icol[j];
    664:             MatRestoreRow(lA,i,&ncols,&icol,NULL);
    665:             MatGetRow(lA,ci,&ncols,&icol,NULL);
    666:             for (k=0;k<ncols;k++) {
    667:               if (lcid[icol[k]] >= 0) {
    668:                 pcontrib[icol[k]] = 1.;
    669:               }
    670:             }
    671:             MatRestoreRow(lA,ci,&ncols,&icol,NULL);
    672:             MatGetRow(lA,i,&ncols,&icol,NULL);
    673:           }
    674:         }
    675:         for (j=0;j<ncols;j++) {
    676:           if (lcid[icol[j]] >= 0 && pcontrib[icol[j]] != 0.) {
    677:             lni = lcid[icol[j]];
    678:             if (lni >= cs && lni < ce) {
    679:               lsparse[li]++;
    680:             } else {
    681:               gsparse[li]++;
    682:             }
    683:             pcontrib[icol[j]] = 0.;
    684:           } else {
    685:             ci = icol[j];
    686:             MatRestoreRow(lA,i,&ncols,&icol,NULL);
    687:             MatGetRow(lA,ci,&ncols,&icol,NULL);
    688:             for (k=0;k<ncols;k++) {
    689:               if (lcid[icol[k]] >= 0 && pcontrib[icol[k]] != 0.) {
    690:                 lni = lcid[icol[k]];
    691:                 if (lni >= cs && lni < ce) {
    692:                   lsparse[li]++;
    693:                 } else {
    694:                   gsparse[li]++;
    695:                 }
    696:                 pcontrib[icol[k]] = 0.;
    697:               }
    698:             }
    699:             MatRestoreRow(lA,ci,&ncols,&icol,NULL);
    700:             MatGetRow(lA,i,&ncols,&icol,NULL);
    701:           }
    702:         }
    703:       }
    704:       if (lsparse[li] + gsparse[li] > maxcols) maxcols = lsparse[li]+gsparse[li];
    705:     }
    706:     MatRestoreRow(lA,i,&ncols,&icol,&vcol);
    707:   }
    708:   PetscMalloc2(maxcols,&picol,maxcols,&pvcol);
    709:   MatCreate(PetscObjectComm((PetscObject)A),P);
    710:   MatGetType(A,&mtype);
    711:   MatSetType(*P,mtype);
    712:   MatSetSizes(*P,fn,cn,PETSC_DETERMINE,PETSC_DETERMINE);
    713:   MatMPIAIJSetPreallocation(*P,0,lsparse,0,gsparse);
    714:   MatSeqAIJSetPreallocation(*P,0,lsparse);
    715:   for (i=0;i<nl;i++) {
    716:     diag = 0.;
    717:     if (gidx[i] >= fs && gidx[i] < fe) {
    718:       pncols=0;
    719:       cid = lcid[i];
    720:       if (cid >= 0) {
    721:         pncols = 1;
    722:         picol[0] = cid;
    723:         pvcol[0] = 1.;
    724:       } else {
    725:         MatGetRow(lA,i,&ncols,&icol,&vcol);
    726:         for (j=0;j<ncols;j++) {
    727:           pentry = vcol[j];
    728:           if (lcid[icol[j]] >= 0) {
    729:             /* coarse neighbor */
    730:             pcontrib[icol[j]] += pentry;
    731:           } else if (icol[j] != i) {
    732:             /* the neighbor is a strongly connected fine node */
    733:             ci = icol[j];
    734:             vi = vcol[j];
    735:             MatRestoreRow(lA,i,&ncols,&icol,&vcol);
    736:             MatGetRow(lA,ci,&ncols,&icol,&vcol);
    737:             jwttotal=0.;
    738:             jdiag = 0.;
    739:             for (k=0;k<ncols;k++) {
    740:               if (ci == icol[k]) {
    741:                 jdiag = PetscRealPart(vcol[k]);
    742:               }
    743:             }
    744:             for (k=0;k<ncols;k++) {
    745:               if (lcid[icol[k]] >= 0 && jdiag*PetscRealPart(vcol[k]) < 0.) {
    746:                 pjentry = vcol[k];
    747:                 jwttotal += PetscRealPart(pjentry);
    748:               }
    749:             }
    750:             if (jwttotal != 0.) {
    751:               jwttotal = PetscRealPart(vi)/jwttotal;
    752:               for (k=0;k<ncols;k++) {
    753:                 if (lcid[icol[k]] >= 0 && jdiag*PetscRealPart(vcol[k]) < 0.) {
    754:                   pjentry = vcol[k]*jwttotal;
    755:                   pcontrib[icol[k]] += pjentry;
    756:                 }
    757:               }
    758:             } else {
    759:               diag += PetscRealPart(vi);
    760:             }
    761:             MatRestoreRow(lA,ci,&ncols,&icol,&vcol);
    762:             MatGetRow(lA,i,&ncols,&icol,&vcol);
    763:           } else {
    764:             diag += PetscRealPart(vcol[j]);
    765:           }
    766:         }
    767:         if (diag != 0.) {
    768:           diag = 1./diag;
    769:           for (j=0;j<ncols;j++) {
    770:             if (lcid[icol[j]] >= 0 && pcontrib[icol[j]] != 0.) {
    771:               /* the neighbor is a coarse node */
    772:               if (PetscAbsScalar(pcontrib[icol[j]]) > 0.0) {
    773:                 lni = lcid[icol[j]];
    774:                 pvcol[pncols] = -pcontrib[icol[j]]*diag;
    775:                 picol[pncols] = lni;
    776:                 pncols++;
    777:               }
    778:               pcontrib[icol[j]] = 0.;
    779:             } else {
    780:               /* the neighbor is a strongly connected fine node */
    781:               ci = icol[j];
    782:               MatRestoreRow(lA,i,&ncols,&icol,&vcol);
    783:               MatGetRow(lA,ci,&ncols,&icol,&vcol);
    784:               for (k=0;k<ncols;k++) {
    785:                 if (lcid[icol[k]] >= 0 && pcontrib[icol[k]] != 0.) {
    786:                   if (PetscAbsScalar(pcontrib[icol[k]]) > 0.0) {
    787:                     lni = lcid[icol[k]];
    788:                     pvcol[pncols] = -pcontrib[icol[k]]*diag;
    789:                     picol[pncols] = lni;
    790:                     pncols++;
    791:                   }
    792:                   pcontrib[icol[k]] = 0.;
    793:                 }
    794:               }
    795:               MatRestoreRow(lA,ci,&ncols,&icol,&vcol);
    796:               MatGetRow(lA,i,&ncols,&icol,&vcol);
    797:             }
    798:             pcontrib[icol[j]] = 0.;
    799:           }
    800:           MatRestoreRow(lA,i,&ncols,&icol,&vcol);
    801:         }
    802:       }
    803:       ci = gidx[i];
    804:       if (pncols > 0) {
    805:         MatSetValues(*P,1,&ci,pncols,picol,pvcol,INSERT_VALUES);
    806:       }
    807:     }
    808:   }
    809:   ISRestoreIndices(lis,&gidx);
    810:   PetscFree2(picol,pvcol);
    811:   PetscFree3(lsparse,gsparse,pcontrib);
    812:   ISDestroy(&lis);
    813:   PetscFree(gcid);
    814:   if (size > 1) {
    815:     PetscFree(lcid);
    816:     MatDestroyMatrices(1,&lAs);
    817:     PetscSFDestroy(&sf);
    818:   }
    819:   VecDestroy(&cv);
    820:   MatAssemblyBegin(*P, MAT_FINAL_ASSEMBLY);
    821:   MatAssemblyEnd(*P, MAT_FINAL_ASSEMBLY);
    822:   return(0);
    823: }

    825: PetscErrorCode PCGAMGOptProlongator_Classical_Jacobi(PC pc,Mat A,Mat *P)
    826: {

    828:   PetscErrorCode    ierr;
    829:   PetscInt          f,s,n,cf,cs,i,idx;
    830:   PetscInt          *coarserows;
    831:   PetscInt          ncols;
    832:   const PetscInt    *pcols;
    833:   const PetscScalar *pvals;
    834:   Mat               Pnew;
    835:   Vec               diag;
    836:   PC_MG             *mg          = (PC_MG*)pc->data;
    837:   PC_GAMG           *pc_gamg     = (PC_GAMG*)mg->innerctx;
    838:   PC_GAMG_Classical *cls         = (PC_GAMG_Classical*)pc_gamg->subctx;

    841:   if (cls->nsmooths == 0) {
    842:     PCGAMGTruncateProlongator_Private(pc,P);
    843:     return(0);
    844:   }
    845:   MatGetOwnershipRange(*P,&s,&f);
    846:   n = f-s;
    847:   MatGetOwnershipRangeColumn(*P,&cs,&cf);
    848:   PetscMalloc1(n,&coarserows);
    849:   /* identify the rows corresponding to coarse unknowns */
    850:   idx = 0;
    851:   for (i=s;i<f;i++) {
    852:     MatGetRow(*P,i,&ncols,&pcols,&pvals);
    853:     /* assume, for now, that it's a coarse unknown if it has a single unit entry */
    854:     if (ncols == 1) {
    855:       if (pvals[0] == 1.) {
    856:         coarserows[idx] = i;
    857:         idx++;
    858:       }
    859:     }
    860:     MatRestoreRow(*P,i,&ncols,&pcols,&pvals);
    861:   }
    862:   MatCreateVecs(A,&diag,NULL);
    863:   MatGetDiagonal(A,diag);
    864:   VecReciprocal(diag);
    865:   for (i=0;i<cls->nsmooths;i++) {
    866:     MatMatMult(A,*P,MAT_INITIAL_MATRIX,PETSC_DEFAULT,&Pnew);
    867:     MatZeroRows(Pnew,idx,coarserows,0.,NULL,NULL);
    868:     MatDiagonalScale(Pnew,diag,NULL);
    869:     MatAYPX(Pnew,-1.0,*P,DIFFERENT_NONZERO_PATTERN);
    870:     MatDestroy(P);
    871:     *P  = Pnew;
    872:     Pnew = NULL;
    873:   }
    874:   VecDestroy(&diag);
    875:   PetscFree(coarserows);
    876:   PCGAMGTruncateProlongator_Private(pc,P);
    877:   return(0);
    878: }

    880: PetscErrorCode PCGAMGProlongator_Classical(PC pc, Mat A, Mat G, PetscCoarsenData *agg_lists,Mat *P)
    881: {
    882:   PetscErrorCode    ierr;
    883:   PetscErrorCode    (*f)(PC,Mat,Mat,PetscCoarsenData*,Mat*);
    884:   PC_MG             *mg          = (PC_MG*)pc->data;
    885:   PC_GAMG           *pc_gamg     = (PC_GAMG*)mg->innerctx;
    886:   PC_GAMG_Classical *cls         = (PC_GAMG_Classical*)pc_gamg->subctx;

    889:   PetscFunctionListFind(PCGAMGClassicalProlongatorList,cls->prolongtype,&f);
    890:   if (!f)SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Cannot find PCGAMG Classical prolongator type");
    891:   (*f)(pc,A,G,agg_lists,P);
    892:   return(0);
    893: }

    895: PetscErrorCode PCGAMGDestroy_Classical(PC pc)
    896: {
    898:   PC_MG          *mg          = (PC_MG*)pc->data;
    899:   PC_GAMG        *pc_gamg     = (PC_GAMG*)mg->innerctx;

    902:   PetscFree(pc_gamg->subctx);
    903:   PetscObjectComposeFunction((PetscObject)pc,"PCGAMGClassicalSetType_C",NULL);
    904:   PetscObjectComposeFunction((PetscObject)pc,"PCGAMGClassicalGetType_C",NULL);
    905:   return(0);
    906: }

    908: PetscErrorCode PCGAMGSetFromOptions_Classical(PetscOptionItems *PetscOptionsObject,PC pc)
    909: {
    910:   PC_MG             *mg          = (PC_MG*)pc->data;
    911:   PC_GAMG           *pc_gamg     = (PC_GAMG*)mg->innerctx;
    912:   PC_GAMG_Classical *cls         = (PC_GAMG_Classical*)pc_gamg->subctx;
    913:   char              tname[256];
    914:   PetscErrorCode    ierr;
    915:   PetscBool         flg;

    918:   PetscOptionsHead(PetscOptionsObject,"GAMG-Classical options");
    919:   PetscOptionsFList("-pc_gamg_classical_type","Type of Classical AMG prolongation","PCGAMGClassicalSetType",PCGAMGClassicalProlongatorList,cls->prolongtype, tname, sizeof(tname), &flg);
    920:   if (flg) {
    921:     PCGAMGClassicalSetType(pc,tname);
    922:   }
    923:   PetscOptionsReal("-pc_gamg_classical_interp_threshold","Threshold for classical interpolator entries","",cls->interp_threshold,&cls->interp_threshold,NULL);
    924:   PetscOptionsInt("-pc_gamg_classical_nsmooths","Threshold for classical interpolator entries","",cls->nsmooths,&cls->nsmooths,NULL);
    925:   PetscOptionsTail();
    926:   return(0);
    927: }

    929: PetscErrorCode PCGAMGSetData_Classical(PC pc, Mat A)
    930: {
    931:   PC_MG          *mg      = (PC_MG*)pc->data;
    932:   PC_GAMG        *pc_gamg = (PC_GAMG*)mg->innerctx;

    935:   /* no data for classical AMG */
    936:   pc_gamg->data           = NULL;
    937:   pc_gamg->data_cell_cols = 0;
    938:   pc_gamg->data_cell_rows = 0;
    939:   pc_gamg->data_sz        = 0;
    940:   return(0);
    941: }

    944: PetscErrorCode PCGAMGClassicalFinalizePackage(void)
    945: {

    949:   PCGAMGClassicalPackageInitialized = PETSC_FALSE;
    950:   PetscFunctionListDestroy(&PCGAMGClassicalProlongatorList);
    951:   return(0);
    952: }

    954: PetscErrorCode PCGAMGClassicalInitializePackage(void)
    955: {

    959:   if (PCGAMGClassicalPackageInitialized) return(0);
    960:   PetscFunctionListAdd(&PCGAMGClassicalProlongatorList,PCGAMGCLASSICALDIRECT,PCGAMGProlongator_Classical_Direct);
    961:   PetscFunctionListAdd(&PCGAMGClassicalProlongatorList,PCGAMGCLASSICALSTANDARD,PCGAMGProlongator_Classical_Standard);
    962:   PetscRegisterFinalize(PCGAMGClassicalFinalizePackage);
    963:   return(0);
    964: }

    966: /* -------------------------------------------------------------------------- */
    967: /*
    968:    PCCreateGAMG_Classical

    970: */
    971: PetscErrorCode  PCCreateGAMG_Classical(PC pc)
    972: {
    974:   PC_MG             *mg      = (PC_MG*)pc->data;
    975:   PC_GAMG           *pc_gamg = (PC_GAMG*)mg->innerctx;
    976:   PC_GAMG_Classical *pc_gamg_classical;

    979:   PCGAMGClassicalInitializePackage();
    980:   if (pc_gamg->subctx) {
    981:     /* call base class */
    982:     PCDestroy_GAMG(pc);
    983:   }

    985:   /* create sub context for SA */
    986:   PetscNewLog(pc,&pc_gamg_classical);
    987:   pc_gamg->subctx = pc_gamg_classical;
    988:   pc->ops->setfromoptions = PCGAMGSetFromOptions_Classical;
    989:   /* reset does not do anything; setup not virtual */

    991:   /* set internal function pointers */
    992:   pc_gamg->ops->destroy        = PCGAMGDestroy_Classical;
    993:   pc_gamg->ops->graph          = PCGAMGGraph_Classical;
    994:   pc_gamg->ops->coarsen        = PCGAMGCoarsen_Classical;
    995:   pc_gamg->ops->prolongator    = PCGAMGProlongator_Classical;
    996:   pc_gamg->ops->optprolongator = PCGAMGOptProlongator_Classical_Jacobi;
    997:   pc_gamg->ops->setfromoptions = PCGAMGSetFromOptions_Classical;

    999:   pc_gamg->ops->createdefaultdata = PCGAMGSetData_Classical;
    1000:   pc_gamg_classical->interp_threshold = 0.2;
    1001:   pc_gamg_classical->nsmooths         = 0;
    1002:   PetscObjectComposeFunction((PetscObject)pc,"PCGAMGClassicalSetType_C",PCGAMGClassicalSetType_GAMG);
    1003:   PetscObjectComposeFunction((PetscObject)pc,"PCGAMGClassicalGetType_C",PCGAMGClassicalGetType_GAMG);
    1004:   PCGAMGClassicalSetType(pc,PCGAMGCLASSICALSTANDARD);
    1005:   return(0);
    1006: }