Actual source code: greedy.c
petsc-3.10.5 2019-03-28
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      1:  #include <petsc/private/matimpl.h>
      2:  #include <../src/mat/impls/aij/seq/aij.h>
      3:  #include <../src/mat/impls/aij/mpi/mpiaij.h>
      4:  #include <petscsf.h>

      6: typedef struct {
      7:   PetscBool symmetric;
      8: } MC_Greedy;

     10: static PetscErrorCode MatColoringDestroy_Greedy(MatColoring mc)
     11: {

     15:   PetscFree(mc->data);
     16:   return(0);
     17: }

     19: static PetscErrorCode GreedyColoringLocalDistanceOne_Private(MatColoring mc,PetscReal *wts,PetscInt *lperm,ISColoringValue *colors)
     20: {
     21:   PetscInt        i,j,k,s,e,n,no,nd,nd_global,n_global,idx,ncols,maxcolors,masksize,ccol,*mask;
     22:   PetscErrorCode  ierr;
     23:   Mat             m=mc->mat;
     24:   Mat_MPIAIJ      *aij = (Mat_MPIAIJ*)m->data;
     25:   Mat             md=NULL,mo=NULL;
     26:   const PetscInt  *md_i,*mo_i,*md_j,*mo_j;
     27:   PetscBool       isMPIAIJ,isSEQAIJ;
     28:   ISColoringValue pcol;
     29:   const PetscInt  *cidx;
     30:   PetscInt        *lcolors,*ocolors;
     31:   PetscReal       *owts=NULL;
     32:   PetscSF         sf;
     33:   PetscLayout     layout;

     36:   MatGetSize(m,&n_global,NULL);
     37:   MatGetOwnershipRange(m,&s,&e);
     38:   n=e-s;
     39:   masksize=20;
     40:   nd_global = 0;
     41:   /* get the matrix communication structures */
     42:   PetscObjectTypeCompare((PetscObject)m, MATMPIAIJ, &isMPIAIJ);
     43:   PetscObjectBaseTypeCompare((PetscObject)m, MATSEQAIJ, &isSEQAIJ);
     44:   if (isMPIAIJ) {
     45:     /* get the CSR data for on and off diagonal portions of m */
     46:     Mat_SeqAIJ *dseq;
     47:     Mat_SeqAIJ *oseq;
     48:     md=aij->A;
     49:     dseq = (Mat_SeqAIJ*)md->data;
     50:     mo=aij->B;
     51:     oseq = (Mat_SeqAIJ*)mo->data;
     52:     md_i = dseq->i;
     53:     md_j = dseq->j;
     54:     mo_i = oseq->i;
     55:     mo_j = oseq->j;
     56:   } else if (isSEQAIJ) {
     57:     /* get the CSR data for m */
     58:     Mat_SeqAIJ *dseq;
     59:     /* no off-processor nodes */
     60:     md=m;
     61:     dseq = (Mat_SeqAIJ*)md->data;
     62:     mo=NULL;
     63:     no=0;
     64:     md_i = dseq->i;
     65:     md_j = dseq->j;
     66:     mo_i = NULL;
     67:     mo_j = NULL;
     68:   } else SETERRQ(PetscObjectComm((PetscObject)mc),PETSC_ERR_ARG_WRONG,"Matrix must be AIJ for greedy coloring");
     69:   MatColoringGetMaxColors(mc,&maxcolors);
     70:   if (mo) {
     71:     VecGetSize(aij->lvec,&no);
     72:     PetscMalloc2(no,&ocolors,no,&owts);
     73:     for(i=0;i<no;i++) {
     74:       ocolors[i]=maxcolors;
     75:     }
     76:   }

     78:   PetscMalloc1(masksize,&mask);
     79:   PetscMalloc1(n,&lcolors);
     80:   for(i=0;i<n;i++) {
     81:     lcolors[i]=maxcolors;
     82:   }
     83:   for (i=0;i<masksize;i++) {
     84:     mask[i]=-1;
     85:   }
     86:   if (mo) {
     87:     /* transfer neighbor weights */
     88:     PetscSFCreate(PetscObjectComm((PetscObject)m),&sf);
     89:     MatGetLayouts(m,&layout,NULL);
     90:     PetscSFSetGraphLayout(sf,layout,no,NULL,PETSC_COPY_VALUES,aij->garray);
     91:     PetscSFBcastBegin(sf,MPIU_REAL,wts,owts);
     92:     PetscSFBcastEnd(sf,MPIU_REAL,wts,owts);
     93:   }
     94:   while (nd_global < n_global) {
     95:     nd=n;
     96:     /* assign lowest possible color to each local vertex */
     97:     PetscLogEventBegin(MATCOLORING_Local,mc,0,0,0);
     98:     for (i=0;i<n;i++) {
     99:       idx=lperm[i];
    100:       if (lcolors[idx] == maxcolors) {
    101:         ncols = md_i[idx+1]-md_i[idx];
    102:         cidx = &(md_j[md_i[idx]]);
    103:         for (j=0;j<ncols;j++) {
    104:           if (lcolors[cidx[j]] != maxcolors) {
    105:             ccol=lcolors[cidx[j]];
    106:             if (ccol>=masksize) {
    107:               PetscInt *newmask;
    108:               PetscMalloc1(masksize*2,&newmask);
    109:               for(k=0;k<2*masksize;k++) {
    110:                 newmask[k]=-1;
    111:               }
    112:               for(k=0;k<masksize;k++) {
    113:                 newmask[k]=mask[k];
    114:               }
    115:               PetscFree(mask);
    116:               mask=newmask;
    117:               masksize*=2;
    118:             }
    119:             mask[ccol]=idx;
    120:           }
    121:         }
    122:         if (mo) {
    123:           ncols = mo_i[idx+1]-mo_i[idx];
    124:           cidx = &(mo_j[mo_i[idx]]);
    125:           for (j=0;j<ncols;j++) {
    126:             if (ocolors[cidx[j]] != maxcolors) {
    127:               ccol=ocolors[cidx[j]];
    128:               if (ccol>=masksize) {
    129:                 PetscInt *newmask;
    130:                 PetscMalloc1(masksize*2,&newmask);
    131:                 for(k=0;k<2*masksize;k++) {
    132:                   newmask[k]=-1;
    133:                 }
    134:                 for(k=0;k<masksize;k++) {
    135:                   newmask[k]=mask[k];
    136:                 }
    137:                 PetscFree(mask);
    138:                 mask=newmask;
    139:                 masksize*=2;
    140:               }
    141:               mask[ccol]=idx;
    142:             }
    143:           }
    144:         }
    145:         for (j=0;j<masksize;j++) {
    146:           if (mask[j]!=idx) {
    147:             break;
    148:           }
    149:         }
    150:         pcol=j;
    151:         if (pcol>maxcolors)pcol=maxcolors;
    152:         lcolors[idx]=pcol;
    153:       }
    154:     }
    155:     PetscLogEventEnd(MATCOLORING_Local,mc,0,0,0);
    156:     if (mo) {
    157:       /* transfer neighbor colors */
    158:       PetscLogEventBegin(MATCOLORING_Comm,mc,0,0,0);
    159:       PetscSFBcastBegin(sf,MPIU_INT,lcolors,ocolors);
    160:       PetscSFBcastEnd(sf,MPIU_INT,lcolors,ocolors);
    161:       /* check for conflicts -- this is merely checking if any adjacent off-processor rows have the same color and marking the ones that are lower weight locally for changing */
    162:       for (i=0;i<n;i++) {
    163:         ncols = mo_i[i+1]-mo_i[i];
    164:         cidx = &(mo_j[mo_i[i]]);
    165:         for (j=0;j<ncols;j++) {
    166:           /* in the case of conflicts, the highest weight one stays and the others go */
    167:           if ((ocolors[cidx[j]] == lcolors[i]) && (owts[cidx[j]] > wts[i]) && lcolors[i] < maxcolors) {
    168:             lcolors[i]=maxcolors;
    169:             nd--;
    170:           }
    171:         }
    172:       }
    173:       nd_global=0;
    174:     }
    175:     MPIU_Allreduce(&nd,&nd_global,1,MPIU_INT,MPI_SUM,PetscObjectComm((PetscObject)mc));
    176:   }
    177:   for (i=0;i<n;i++) {
    178:     colors[i] = (ISColoringValue)lcolors[i];
    179:   }
    180:   PetscFree(mask);
    181:   PetscFree(lcolors);
    182:   if (mo) {
    183:     PetscFree2(ocolors,owts);
    184:     PetscSFDestroy(&sf);
    185:   }
    186:   return(0);
    187: }

    189: static PetscErrorCode GreedyColoringLocalDistanceTwo_Private(MatColoring mc,PetscReal *wts,PetscInt *lperm,ISColoringValue *colors)
    190: {
    191:   MC_Greedy       *gr = (MC_Greedy *) mc->data;
    192:   PetscInt        i,j,k,l,s,e,n,nd,nd_global,n_global,idx,ncols,maxcolors,mcol,mcol_global,nd1cols,*mask,masksize,*d1cols,*bad,*badnext,nbad,badsize,ccol,no,cbad;
    193:   Mat             m = mc->mat, mt;
    194:   Mat_MPIAIJ      *aij = (Mat_MPIAIJ*)m->data;
    195:   Mat             md=NULL,mo=NULL;
    196:   const PetscInt  *md_i,*mo_i,*md_j,*mo_j;
    197:   const PetscInt  *rmd_i,*rmo_i,*rmd_j,*rmo_j;
    198:   PetscBool       isMPIAIJ,isSEQAIJ;
    199:   PetscInt        pcol,*dcolors,*ocolors;
    200:   ISColoringValue *badidx;
    201:   const PetscInt  *cidx;
    202:   PetscReal       *owts,*colorweights;
    203:   PetscInt        *oconf,*conf;
    204:   PetscSF         sf;
    205:   PetscLayout     layout;
    206:   PetscErrorCode  ierr;

    209:   MatGetSize(m,&n_global,NULL);
    210:   MatGetOwnershipRange(m,&s,&e);
    211:   n=e-s;
    212:   nd_global = 0;
    213:   /* get the matrix communication structures */
    214:   PetscObjectBaseTypeCompare((PetscObject)m, MATMPIAIJ, &isMPIAIJ);
    215:   PetscObjectBaseTypeCompare((PetscObject)m, MATSEQAIJ, &isSEQAIJ);
    216:   if (isMPIAIJ) {
    217:     Mat_SeqAIJ *dseq;
    218:     Mat_SeqAIJ *oseq;
    219:     md=aij->A;
    220:     dseq = (Mat_SeqAIJ*)md->data;
    221:     mo=aij->B;
    222:     oseq = (Mat_SeqAIJ*)mo->data;
    223:     md_i = dseq->i;
    224:     md_j = dseq->j;
    225:     mo_i = oseq->i;
    226:     mo_j = oseq->j;
    227:     rmd_i = dseq->i;
    228:     rmd_j = dseq->j;
    229:     rmo_i = oseq->i;
    230:     rmo_j = oseq->j;
    231:   } else if (isSEQAIJ) {
    232:     Mat_SeqAIJ *dseq;
    233:     /* no off-processor nodes */
    234:     md=m;
    235:     dseq = (Mat_SeqAIJ*)md->data;
    236:     md_i = dseq->i;
    237:     md_j = dseq->j;
    238:     mo_i = NULL;
    239:     mo_j = NULL;
    240:     rmd_i = dseq->i;
    241:     rmd_j = dseq->j;
    242:     rmo_i = NULL;
    243:     rmo_j = NULL;
    244:   } else SETERRQ(PetscObjectComm((PetscObject)mc),PETSC_ERR_ARG_WRONG,"Matrix must be AIJ for greedy coloring");
    245:   if (!gr->symmetric) {
    246:     MatTranspose(m, MAT_INITIAL_MATRIX, &mt);
    247:     if (isSEQAIJ) {
    248:       Mat_SeqAIJ *dseq = (Mat_SeqAIJ*) mt->data;
    249:       rmd_i = dseq->i;
    250:       rmd_j = dseq->j;
    251:       rmo_i = NULL;
    252:       rmo_j = NULL;
    253:     } else SETERRQ(PetscObjectComm((PetscObject) mc), PETSC_ERR_SUP, "Nonsymmetric greedy coloring only works in serial");
    254:   }
    255:   /* create the vectors and communication structures if necessary */
    256:   no=0;
    257:   if (mo) {
    258:     VecGetLocalSize(aij->lvec,&no);
    259:     PetscSFCreate(PetscObjectComm((PetscObject)m),&sf);
    260:     MatGetLayouts(m,&layout,NULL);
    261:     PetscSFSetGraphLayout(sf,layout,no,NULL,PETSC_COPY_VALUES,aij->garray);
    262:   }
    263:   MatColoringGetMaxColors(mc,&maxcolors);
    264:   masksize=n;
    265:   nbad=0;
    266:   badsize=n;
    267:   PetscMalloc1(masksize,&mask);
    268:   PetscMalloc4(n,&d1cols,n,&dcolors,n,&conf,n,&bad);
    269:   PetscMalloc2(badsize,&badidx,badsize,&badnext);
    270:   for(i=0;i<masksize;i++) {
    271:     mask[i]=-1;
    272:   }
    273:   for (i=0;i<n;i++) {
    274:     dcolors[i]=maxcolors;
    275:     bad[i]=-1;
    276:   }
    277:   for (i=0;i<badsize;i++) {
    278:     badnext[i]=-1;
    279:   }
    280:   if (mo) {
    281:     PetscMalloc3(no,&owts,no,&oconf,no,&ocolors);
    282:     PetscSFBcastBegin(sf,MPIU_REAL,wts,owts);
    283:     PetscSFBcastEnd(sf,MPIU_REAL,wts,owts);
    284:     for (i=0;i<no;i++) {
    285:       ocolors[i]=maxcolors;
    286:     }
    287:   } else {                      /* Appease overzealous -Wmaybe-initialized */
    288:     owts = NULL;
    289:     oconf = NULL;
    290:     ocolors = NULL;
    291:   }
    292:   mcol=0;
    293:   while (nd_global < n_global) {
    294:     nd=n;
    295:     /* assign lowest possible color to each local vertex */
    296:     mcol_global=0;
    297:     PetscLogEventBegin(MATCOLORING_Local,mc,0,0,0);
    298:     for (i=0;i<n;i++) {
    299:       idx=lperm[i];
    300:       if (dcolors[idx] == maxcolors) {
    301:         /* entries in bad */
    302:         cbad=bad[idx];
    303:         while (cbad>=0) {
    304:           ccol=badidx[cbad];
    305:           if (ccol>=masksize) {
    306:             PetscInt *newmask;
    307:             PetscMalloc1(masksize*2,&newmask);
    308:             for(k=0;k<2*masksize;k++) {
    309:               newmask[k]=-1;
    310:             }
    311:             for(k=0;k<masksize;k++) {
    312:               newmask[k]=mask[k];
    313:             }
    314:             PetscFree(mask);
    315:             mask=newmask;
    316:             masksize*=2;
    317:           }
    318:           mask[ccol]=idx;
    319:           cbad=badnext[cbad];
    320:         }
    321:         /* diagonal distance-one rows */
    322:         nd1cols=0;
    323:         ncols = rmd_i[idx+1]-rmd_i[idx];
    324:         cidx = &(rmd_j[rmd_i[idx]]);
    325:         for (j=0;j<ncols;j++) {
    326:           d1cols[nd1cols] = cidx[j];
    327:           nd1cols++;
    328:           ccol=dcolors[cidx[j]];
    329:           if (ccol != maxcolors) {
    330:             if (ccol>=masksize) {
    331:               PetscInt *newmask;
    332:               PetscMalloc1(masksize*2,&newmask);
    333:               for(k=0;k<2*masksize;k++) {
    334:                 newmask[k]=-1;
    335:               }
    336:               for(k=0;k<masksize;k++) {
    337:                 newmask[k]=mask[k];
    338:               }
    339:               PetscFree(mask);
    340:               mask=newmask;
    341:               masksize*=2;
    342:             }
    343:             mask[ccol]=idx;
    344:           }
    345:         }
    346:         /* off-diagonal distance-one rows */
    347:         if (mo) {
    348:           ncols = rmo_i[idx+1]-rmo_i[idx];
    349:           cidx = &(rmo_j[rmo_i[idx]]);
    350:           for (j=0;j<ncols;j++) {
    351:             ccol=ocolors[cidx[j]];
    352:             if (ccol != maxcolors) {
    353:               if (ccol>=masksize) {
    354:                 PetscInt *newmask;
    355:                 PetscMalloc1(masksize*2,&newmask);
    356:                 for(k=0;k<2*masksize;k++) {
    357:                   newmask[k]=-1;
    358:                 }
    359:                 for(k=0;k<masksize;k++) {
    360:                   newmask[k]=mask[k];
    361:                 }
    362:                 PetscFree(mask);
    363:                 mask=newmask;
    364:                 masksize*=2;
    365:               }
    366:               mask[ccol]=idx;
    367:             }
    368:           }
    369:         }
    370:         /* diagonal distance-two rows */
    371:         for (j=0;j<nd1cols;j++) {
    372:           ncols = md_i[d1cols[j]+1]-md_i[d1cols[j]];
    373:           cidx = &(md_j[md_i[d1cols[j]]]);
    374:           for (l=0;l<ncols;l++) {
    375:             ccol=dcolors[cidx[l]];
    376:             if (ccol != maxcolors) {
    377:               if (ccol>=masksize) {
    378:                 PetscInt *newmask;
    379:                 PetscMalloc1(masksize*2,&newmask);
    380:                 for(k=0;k<2*masksize;k++) {
    381:                   newmask[k]=-1;
    382:                 }
    383:                 for(k=0;k<masksize;k++) {
    384:                   newmask[k]=mask[k];
    385:                 }
    386:                 PetscFree(mask);
    387:                 mask=newmask;
    388:                 masksize*=2;
    389:               }
    390:               mask[ccol]=idx;
    391:             }
    392:           }
    393:         }
    394:         /* off-diagonal distance-two rows */
    395:         if (mo) {
    396:           for (j=0;j<nd1cols;j++) {
    397:             ncols = mo_i[d1cols[j]+1]-mo_i[d1cols[j]];
    398:             cidx = &(mo_j[mo_i[d1cols[j]]]);
    399:             for (l=0;l<ncols;l++) {
    400:               ccol=ocolors[cidx[l]];
    401:               if (ccol != maxcolors) {
    402:                 if (ccol>=masksize) {
    403:                   PetscInt *newmask;
    404:                   PetscMalloc1(masksize*2,&newmask);
    405:                   for(k=0;k<2*masksize;k++) {
    406:                     newmask[k]=-1;
    407:                   }
    408:                   for(k=0;k<masksize;k++) {
    409:                     newmask[k]=mask[k];
    410:                   }
    411:                   PetscFree(mask);
    412:                   mask=newmask;
    413:                   masksize*=2;
    414:                 }
    415:                 mask[ccol]=idx;
    416:               }
    417:             }
    418:           }
    419:         }
    420:         /* assign this one the lowest color possible by seeing if there's a gap in the sequence of sorted neighbor colors */
    421:         for (j=0;j<masksize;j++) {
    422:           if (mask[j]!=idx) {
    423:             break;
    424:           }
    425:         }
    426:         pcol=j;
    427:         if (pcol>maxcolors) pcol=maxcolors;
    428:         dcolors[idx]=pcol;
    429:         if (pcol>mcol) mcol=pcol;
    430:       }
    431:     }
    432:     PetscLogEventEnd(MATCOLORING_Local,mc,0,0,0);
    433:     if (mo) {
    434:       /* transfer neighbor colors */
    435:       PetscSFBcastBegin(sf,MPIU_INT,dcolors,ocolors);
    436:       PetscSFBcastEnd(sf,MPIU_INT,dcolors,ocolors);
    437:       /* find the maximum color assigned locally and allocate a mask */
    438:       MPIU_Allreduce(&mcol,&mcol_global,1,MPIU_INT,MPI_MAX,PetscObjectComm((PetscObject)mc));
    439:       PetscMalloc1(mcol_global+1,&colorweights);
    440:       /* check for conflicts */
    441:       for (i=0;i<n;i++) {
    442:         conf[i]=PETSC_FALSE;
    443:       }
    444:       for (i=0;i<no;i++) {
    445:         oconf[i]=PETSC_FALSE;
    446:       }
    447:       for (i=0;i<n;i++) {
    448:         ncols = mo_i[i+1]-mo_i[i];
    449:         cidx = &(mo_j[mo_i[i]]);
    450:         if (ncols > 0) {
    451:           /* fill in the mask */
    452:           for (j=0;j<mcol_global+1;j++) {
    453:             colorweights[j]=0;
    454:           }
    455:           colorweights[dcolors[i]]=wts[i];
    456:           /* fill in the off-diagonal part of the mask */
    457:           for (j=0;j<ncols;j++) {
    458:             ccol=ocolors[cidx[j]];
    459:             if (ccol < maxcolors) {
    460:               if (colorweights[ccol] < owts[cidx[j]]) {
    461:                 colorweights[ccol] = owts[cidx[j]];
    462:               }
    463:             }
    464:           }
    465:           /* fill in the on-diagonal part of the mask */
    466:           ncols = md_i[i+1]-md_i[i];
    467:           cidx = &(md_j[md_i[i]]);
    468:           for (j=0;j<ncols;j++) {
    469:             ccol=dcolors[cidx[j]];
    470:             if (ccol < maxcolors) {
    471:               if (colorweights[ccol] < wts[cidx[j]]) {
    472:                 colorweights[ccol] = wts[cidx[j]];
    473:               }
    474:             }
    475:           }
    476:           /* go back through and set up on and off-diagonal conflict vectors */
    477:           ncols = md_i[i+1]-md_i[i];
    478:           cidx = &(md_j[md_i[i]]);
    479:           for (j=0;j<ncols;j++) {
    480:             ccol=dcolors[cidx[j]];
    481:             if (ccol < maxcolors) {
    482:               if (colorweights[ccol] > wts[cidx[j]]) {
    483:                 conf[cidx[j]]=PETSC_TRUE;
    484:               }
    485:             }
    486:           }
    487:           ncols = mo_i[i+1]-mo_i[i];
    488:           cidx = &(mo_j[mo_i[i]]);
    489:           for (j=0;j<ncols;j++) {
    490:             ccol=ocolors[cidx[j]];
    491:             if (ccol < maxcolors) {
    492:               if (colorweights[ccol] > owts[cidx[j]]) {
    493:                 oconf[cidx[j]]=PETSC_TRUE;
    494:               }
    495:             }
    496:           }
    497:         }
    498:       }
    499:       nd_global=0;
    500:       PetscFree(colorweights);
    501:       PetscLogEventBegin(MATCOLORING_Comm,mc,0,0,0);
    502:       PetscSFReduceBegin(sf,MPIU_INT,oconf,conf,MPIU_SUM);
    503:       PetscSFReduceEnd(sf,MPIU_INT,oconf,conf,MPIU_SUM);
    504:       PetscLogEventEnd(MATCOLORING_Comm,mc,0,0,0);
    505:       /* go through and unset local colors that have conflicts */
    506:       for (i=0;i<n;i++) {
    507:         if (conf[i]>0) {
    508:           /* push this color onto the bad stack */
    509:           badidx[nbad]=dcolors[i];
    510:           badnext[nbad]=bad[i];
    511:           bad[i]=nbad;
    512:           nbad++;
    513:           if (nbad>=badsize) {
    514:             PetscInt *newbadnext;
    515:             ISColoringValue *newbadidx;
    516:             PetscMalloc2(badsize*2,&newbadidx,badsize*2,&newbadnext);
    517:             for(k=0;k<2*badsize;k++) {
    518:               newbadnext[k]=-1;
    519:             }
    520:             for(k=0;k<badsize;k++) {
    521:               newbadidx[k]=badidx[k];
    522:               newbadnext[k]=badnext[k];
    523:             }
    524:             PetscFree2(badidx,badnext);
    525:             badidx=newbadidx;
    526:             badnext=newbadnext;
    527:             badsize*=2;
    528:           }
    529:           dcolors[i] = maxcolors;
    530:           nd--;
    531:         }
    532:       }
    533:     }
    534:     MPIU_Allreduce(&nd,&nd_global,1,MPIU_INT,MPI_SUM,PetscObjectComm((PetscObject)mc));
    535:   }
    536:   if (mo) {
    537:     PetscSFDestroy(&sf);
    538:     PetscFree3(owts,oconf,ocolors);
    539:   }
    540:   for (i=0;i<n;i++) {
    541:     colors[i]=dcolors[i];
    542:   }
    543:   PetscFree(mask);
    544:   PetscFree4(d1cols,dcolors,conf,bad);
    545:   PetscFree2(badidx,badnext);
    546:   if (!gr->symmetric) {MatDestroy(&mt);}
    547:   return(0);
    548: }

    550: static PetscErrorCode MatColoringApply_Greedy(MatColoring mc,ISColoring *iscoloring)
    551: {
    552:   PetscErrorCode  ierr;
    553:   PetscInt        finalcolor,finalcolor_global;
    554:   ISColoringValue *colors;
    555:   PetscInt        ncolstotal,ncols;
    556:   PetscReal       *wts;
    557:   PetscInt        i,*lperm;

    560:   MatGetSize(mc->mat,NULL,&ncolstotal);
    561:   MatGetLocalSize(mc->mat,NULL,&ncols);
    562:   if (!mc->user_weights) {
    563:     MatColoringCreateWeights(mc,&wts,&lperm);
    564:   } else {
    565:     wts = mc->user_weights;
    566:     lperm = mc->user_lperm;
    567:   }
    568:   PetscMalloc1(ncols,&colors);
    569:   if (mc->dist == 1) {
    570:     GreedyColoringLocalDistanceOne_Private(mc,wts,lperm,colors);
    571:   } else if (mc->dist == 2) {
    572:     GreedyColoringLocalDistanceTwo_Private(mc,wts,lperm,colors);
    573:   } else SETERRQ(PetscObjectComm((PetscObject)mc),PETSC_ERR_ARG_OUTOFRANGE,"Only distance 1 and distance 2 supported by MatColoringGreedy");
    574:   finalcolor=0;
    575:   for (i=0;i<ncols;i++) {
    576:     if (colors[i] > finalcolor) finalcolor=colors[i];
    577:   }
    578:   finalcolor_global=0;
    579:   MPIU_Allreduce(&finalcolor,&finalcolor_global,1,MPIU_INT,MPI_MAX,PetscObjectComm((PetscObject)mc));
    580:   PetscLogEventBegin(MATCOLORING_ISCreate,mc,0,0,0);
    581:   ISColoringCreate(PetscObjectComm((PetscObject)mc),finalcolor_global+1,ncols,colors,PETSC_OWN_POINTER,iscoloring);
    582:   PetscLogEventEnd(MATCOLORING_ISCreate,mc,0,0,0);
    583:   if (!mc->user_weights) {
    584:     PetscFree(wts);
    585:     PetscFree(lperm);
    586:   }
    587:   return(0);
    588: }

    590: static PetscErrorCode MatColoringSetFromOptions_Greedy(PetscOptionItems *PetscOptionsObject, MatColoring mc)
    591: {
    592:   MC_Greedy     *gr = (MC_Greedy *) mc->data;

    596:   PetscOptionsHead(PetscOptionsObject, "Greedy options");
    597:   PetscOptionsBool("-mat_coloring_greedy_symmetric", "Flag for assuming a symmetric matrix", "", gr->symmetric, &gr->symmetric, NULL);
    598:   PetscOptionsTail();
    599:   return(0);
    600: }

    602: /*MC
    603:   MATCOLORINGGREEDY - Greedy-with-conflict correction based Matrix Coloring for distance 1 and 2.

    605:    Level: beginner

    607:    Notes:

    609:    These algorithms proceed in two phases -- local coloring and conflict resolution.  The local coloring
    610:    tentatively colors all vertices at the distance required given what's known of the global coloring.  Then,
    611:    the updated colors are transferred to different processors at distance one.  In the distance one case, each
    612:    vertex with nonlocal neighbors is then checked to see if it conforms, with the vertex being
    613:    marked for recoloring if its lower weight than its same colored neighbor.  In the distance two case,
    614:    each boundary vertex's immediate star is checked for validity of the coloring.  Lower-weight conflict
    615:    vertices are marked, and then the conflicts are gathered back on owning processors.  In both cases
    616:    this is done until each column has received a valid color.

    618:    References:
    619: .  1. - Bozdag et al. "A Parallel Distance 2 Graph Coloring Algorithm for Distributed Memory Computers"
    620:    HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications

    622: .seealso: MatColoringCreate(), MatColoring, MatColoringSetType(), MatColoringType
    623: M*/
    624: PETSC_EXTERN PetscErrorCode MatColoringCreate_Greedy(MatColoring mc)
    625: {
    626:   MC_Greedy      *gr;

    630:   PetscNewLog(mc,&gr);
    631:   mc->data                = gr;
    632:   mc->ops->apply          = MatColoringApply_Greedy;
    633:   mc->ops->view           = NULL;
    634:   mc->ops->destroy        = MatColoringDestroy_Greedy;
    635:   mc->ops->setfromoptions = MatColoringSetFromOptions_Greedy;

    637:   gr->symmetric = PETSC_TRUE;
    638:   return(0);
    639: }