Date: 2022-03-31T04:46:53+00:00

Actual source code: mpibaij.c

      1: #include <../src/mat/impls/baij/mpi/mpibaij.h>

      3: #include <petsc/private/hashseti.h>
      4: #include <petscblaslapack.h>
      5: #include <petscsf.h>

      7: #if defined(PETSC_HAVE_HYPRE)
      8: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat,MatType,MatReuse,Mat*);
      9: #endif

     11: PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A,Vec v,PetscInt idx[])
     12: {
     13:   Mat_MPIBAIJ       *a = (Mat_MPIBAIJ*)A->data;
     14:   PetscInt          i,*idxb = NULL,m = A->rmap->n,bs = A->cmap->bs;
     15:   PetscScalar       *va,*vv;
     16:   Vec               vB,vA;
     17:   const PetscScalar *vb;

     19:   VecCreateSeq(PETSC_COMM_SELF,m,&vA);
     20:   MatGetRowMaxAbs(a->A,vA,idx);

     22:   VecGetArrayWrite(vA,&va);
     23:   if (idx) {
     24:     for (i=0; i<m; i++) {
     25:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
     26:     }
     27:   }

     29:   VecCreateSeq(PETSC_COMM_SELF,m,&vB);
     30:   PetscMalloc1(m,&idxb);
     31:   MatGetRowMaxAbs(a->B,vB,idxb);

     33:   VecGetArrayWrite(v,&vv);
     34:   VecGetArrayRead(vB,&vb);
     35:   for (i=0; i<m; i++) {
     36:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
     37:       vv[i] = vb[i];
     38:       if (idx) idx[i] = bs*a->garray[idxb[i]/bs] + (idxb[i] % bs);
     39:     } else {
     40:       vv[i] = va[i];
     41:       if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > bs*a->garray[idxb[i]/bs] + (idxb[i] % bs))
     42:         idx[i] = bs*a->garray[idxb[i]/bs] + (idxb[i] % bs);
     43:     }
     44:   }
     45:   VecRestoreArrayWrite(vA,&vv);
     46:   VecRestoreArrayWrite(vA,&va);
     47:   VecRestoreArrayRead(vB,&vb);
     48:   PetscFree(idxb);
     49:   VecDestroy(&vA);
     50:   VecDestroy(&vB);
     51:   return 0;
     52: }

     54: PetscErrorCode  MatStoreValues_MPIBAIJ(Mat mat)
     55: {
     56:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)mat->data;

     58:   MatStoreValues(aij->A);
     59:   MatStoreValues(aij->B);
     60:   return 0;
     61: }

     63: PetscErrorCode  MatRetrieveValues_MPIBAIJ(Mat mat)
     64: {
     65:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)mat->data;

     67:   MatRetrieveValues(aij->A);
     68:   MatRetrieveValues(aij->B);
     69:   return 0;
     70: }

     72: /*
     73:      Local utility routine that creates a mapping from the global column
     74:    number to the local number in the off-diagonal part of the local
     75:    storage of the matrix.  This is done in a non scalable way since the
     76:    length of colmap equals the global matrix length.
     77: */
     78: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
     79: {
     80:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
     81:   Mat_SeqBAIJ    *B    = (Mat_SeqBAIJ*)baij->B->data;
     82:   PetscInt       nbs = B->nbs,i,bs=mat->rmap->bs;

     84: #if defined(PETSC_USE_CTABLE)
     85:   PetscTableCreate(baij->nbs,baij->Nbs+1,&baij->colmap);
     86:   for (i=0; i<nbs; i++) {
     87:     PetscTableAdd(baij->colmap,baij->garray[i]+1,i*bs+1,INSERT_VALUES);
     88:   }
     89: #else
     90:   PetscCalloc1(baij->Nbs+1,&baij->colmap);
     91:   PetscLogObjectMemory((PetscObject)mat,baij->Nbs*sizeof(PetscInt));
     92:   for (i=0; i<nbs; i++) baij->colmap[baij->garray[i]] = i*bs+1;
     93: #endif
     94:   return 0;
     95: }

     97: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv,orow,ocol)       \
     98:   { \
     99:     brow = row/bs;  \
    100:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
    101:     rmax = aimax[brow]; nrow = ailen[brow]; \
    102:     bcol = col/bs; \
    103:     ridx = row % bs; cidx = col % bs; \
    104:     low  = 0; high = nrow; \
    105:     while (high-low > 3) { \
    106:       t = (low+high)/2; \
    107:       if (rp[t] > bcol) high = t; \
    108:       else              low  = t; \
    109:     } \
    110:     for (_i=low; _i<high; _i++) { \
    111:       if (rp[_i] > bcol) break; \
    112:       if (rp[_i] == bcol) { \
    113:         bap = ap +  bs2*_i + bs*cidx + ridx; \
    114:         if (addv == ADD_VALUES) *bap += value;  \
    115:         else                    *bap  = value;  \
    116:         goto a_noinsert; \
    117:       } \
    118:     } \
    119:     if (a->nonew == 1) goto a_noinsert; \
    121:     MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,aimax,a->nonew,MatScalar); \
    122:     N = nrow++ - 1;  \
    123:     /* shift up all the later entries in this row */ \
    124:     PetscArraymove(rp+_i+1,rp+_i,N-_i+1);\
    125:     PetscArraymove(ap+bs2*(_i+1),ap+bs2*_i,bs2*(N-_i+1)); \
    126:     PetscArrayzero(ap+bs2*_i,bs2);  \
    127:     rp[_i]                      = bcol;  \
    128:     ap[bs2*_i + bs*cidx + ridx] = value;  \
    129: a_noinsert:; \
    130:     ailen[brow] = nrow; \
    131:   }

    133: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv,orow,ocol)       \
    134:   { \
    135:     brow = row/bs;  \
    136:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
    137:     rmax = bimax[brow]; nrow = bilen[brow]; \
    138:     bcol = col/bs; \
    139:     ridx = row % bs; cidx = col % bs; \
    140:     low  = 0; high = nrow; \
    141:     while (high-low > 3) { \
    142:       t = (low+high)/2; \
    143:       if (rp[t] > bcol) high = t; \
    144:       else              low  = t; \
    145:     } \
    146:     for (_i=low; _i<high; _i++) { \
    147:       if (rp[_i] > bcol) break; \
    148:       if (rp[_i] == bcol) { \
    149:         bap = ap +  bs2*_i + bs*cidx + ridx; \
    150:         if (addv == ADD_VALUES) *bap += value;  \
    151:         else                    *bap  = value;  \
    152:         goto b_noinsert; \
    153:       } \
    154:     } \
    155:     if (b->nonew == 1) goto b_noinsert; \
    157:     MatSeqXAIJReallocateAIJ(B,b->mbs,bs2,nrow,brow,bcol,rmax,ba,bi,bj,rp,ap,bimax,b->nonew,MatScalar); \
    158:     N = nrow++ - 1;  \
    159:     /* shift up all the later entries in this row */ \
    160:     PetscArraymove(rp+_i+1,rp+_i,N-_i+1);\
    161:     PetscArraymove(ap+bs2*(_i+1),ap+bs2*_i,bs2*(N-_i+1));\
    162:     PetscArrayzero(ap+bs2*_i,bs2);  \
    163:     rp[_i]                      = bcol;  \
    164:     ap[bs2*_i + bs*cidx + ridx] = value;  \
    165: b_noinsert:; \
    166:     bilen[brow] = nrow; \
    167:   }

    169: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
    170: {
    171:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    172:   MatScalar      value;
    173:   PetscBool      roworiented = baij->roworiented;
    174:   PetscInt       i,j,row,col;
    175:   PetscInt       rstart_orig=mat->rmap->rstart;
    176:   PetscInt       rend_orig  =mat->rmap->rend,cstart_orig=mat->cmap->rstart;
    177:   PetscInt       cend_orig  =mat->cmap->rend,bs=mat->rmap->bs;

    179:   /* Some Variables required in the macro */
    180:   Mat         A     = baij->A;
    181:   Mat_SeqBAIJ *a    = (Mat_SeqBAIJ*)(A)->data;
    182:   PetscInt    *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
    183:   MatScalar   *aa   =a->a;

    185:   Mat         B     = baij->B;
    186:   Mat_SeqBAIJ *b    = (Mat_SeqBAIJ*)(B)->data;
    187:   PetscInt    *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
    188:   MatScalar   *ba   =b->a;

    190:   PetscInt  *rp,ii,nrow,_i,rmax,N,brow,bcol;
    191:   PetscInt  low,high,t,ridx,cidx,bs2=a->bs2;
    192:   MatScalar *ap,*bap;

    194:   for (i=0; i<m; i++) {
    195:     if (im[i] < 0) continue;
    197:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
    198:       row = im[i] - rstart_orig;
    199:       for (j=0; j<n; j++) {
    200:         if (in[j] >= cstart_orig && in[j] < cend_orig) {
    201:           col = in[j] - cstart_orig;
    202:           if (roworiented) value = v[i*n+j];
    203:           else             value = v[i+j*m];
    204:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv,im[i],in[j]);
    205:         } else if (in[j] < 0) continue;
    207:         else {
    208:           if (mat->was_assembled) {
    209:             if (!baij->colmap) {
    210:               MatCreateColmap_MPIBAIJ_Private(mat);
    211:             }
    212: #if defined(PETSC_USE_CTABLE)
    213:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
    214:             col  = col - 1;
    215: #else
    216:             col = baij->colmap[in[j]/bs] - 1;
    217: #endif
    218:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->B->data))->nonew) {
    219:               MatDisAssemble_MPIBAIJ(mat);
    220:               col  =  in[j];
    221:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
    222:               B    = baij->B;
    223:               b    = (Mat_SeqBAIJ*)(B)->data;
    224:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
    225:               ba   =b->a;
    227:             else col += in[j]%bs;
    228:           } else col = in[j];
    229:           if (roworiented) value = v[i*n+j];
    230:           else             value = v[i+j*m];
    231:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv,im[i],in[j]);
    232:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
    233:         }
    234:       }
    235:     } else {
    237:       if (!baij->donotstash) {
    238:         mat->assembled = PETSC_FALSE;
    239:         if (roworiented) {
    240:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,PETSC_FALSE);
    241:         } else {
    242:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,PETSC_FALSE);
    243:         }
    244:       }
    245:     }
    246:   }
    247:   return 0;
    248: }

    250: static inline PetscErrorCode MatSetValuesBlocked_SeqBAIJ_Inlined(Mat A,PetscInt row,PetscInt col,const PetscScalar v[],InsertMode is,PetscInt orow,PetscInt ocol)
    251: {
    252:   Mat_SeqBAIJ       *a = (Mat_SeqBAIJ*)A->data;
    253:   PetscInt          *rp,low,high,t,ii,jj,nrow,i,rmax,N;
    254:   PetscInt          *imax=a->imax,*ai=a->i,*ailen=a->ilen;
    255:   PetscInt          *aj        =a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap->bs;
    256:   PetscBool         roworiented=a->roworiented;
    257:   const PetscScalar *value     = v;
    258:   MatScalar         *ap,*aa = a->a,*bap;

    260:   rp   = aj + ai[row];
    261:   ap   = aa + bs2*ai[row];
    262:   rmax = imax[row];
    263:   nrow = ailen[row];
    264:   value = v;
    265:   low = 0;
    266:   high = nrow;
    267:   while (high-low > 7) {
    268:     t = (low+high)/2;
    269:     if (rp[t] > col) high = t;
    270:     else             low  = t;
    271:   }
    272:   for (i=low; i<high; i++) {
    273:     if (rp[i] > col) break;
    274:     if (rp[i] == col) {
    275:       bap = ap +  bs2*i;
    276:       if (roworiented) {
    277:         if (is == ADD_VALUES) {
    278:           for (ii=0; ii<bs; ii++) {
    279:             for (jj=ii; jj<bs2; jj+=bs) {
    280:               bap[jj] += *value++;
    281:             }
    282:           }
    283:         } else {
    284:           for (ii=0; ii<bs; ii++) {
    285:             for (jj=ii; jj<bs2; jj+=bs) {
    286:               bap[jj] = *value++;
    287:             }
    288:           }
    289:         }
    290:       } else {
    291:         if (is == ADD_VALUES) {
    292:           for (ii=0; ii<bs; ii++,value+=bs) {
    293:             for (jj=0; jj<bs; jj++) {
    294:               bap[jj] += value[jj];
    295:             }
    296:             bap += bs;
    297:           }
    298:         } else {
    299:           for (ii=0; ii<bs; ii++,value+=bs) {
    300:             for (jj=0; jj<bs; jj++) {
    301:               bap[jj]  = value[jj];
    302:             }
    303:             bap += bs;
    304:           }
    305:         }
    306:       }
    307:       goto noinsert2;
    308:     }
    309:   }
    310:   if (nonew == 1) goto noinsert2;
    312:   MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    313:   N = nrow++ - 1; high++;
    314:   /* shift up all the later entries in this row */
    315:   PetscArraymove(rp+i+1,rp+i,N-i+1);
    316:   PetscArraymove(ap+bs2*(i+1),ap+bs2*i,bs2*(N-i+1));
    317:   rp[i] = col;
    318:   bap   = ap +  bs2*i;
    319:   if (roworiented) {
    320:     for (ii=0; ii<bs; ii++) {
    321:       for (jj=ii; jj<bs2; jj+=bs) {
    322:         bap[jj] = *value++;
    323:       }
    324:     }
    325:   } else {
    326:     for (ii=0; ii<bs; ii++) {
    327:       for (jj=0; jj<bs; jj++) {
    328:         *bap++ = *value++;
    329:       }
    330:     }
    331:   }
    332:   noinsert2:;
    333:   ailen[row] = nrow;
    334:   return 0;
    335: }

    337: /*
    338:     This routine should be optimized so that the block copy at ** Here a copy is required ** below is not needed
    339:     by passing additional stride information into the MatSetValuesBlocked_SeqBAIJ_Inlined() routine
    340: */
    341: PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
    342: {
    343:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
    344:   const PetscScalar *value;
    345:   MatScalar         *barray     = baij->barray;
    346:   PetscBool         roworiented = baij->roworiented;
    347:   PetscInt          i,j,ii,jj,row,col,rstart=baij->rstartbs;
    348:   PetscInt          rend=baij->rendbs,cstart=baij->cstartbs,stepval;
    349:   PetscInt          cend=baij->cendbs,bs=mat->rmap->bs,bs2=baij->bs2;

    351:   if (!barray) {
    352:     PetscMalloc1(bs2,&barray);
    353:     baij->barray = barray;
    354:   }

    356:   if (roworiented) stepval = (n-1)*bs;
    357:   else stepval = (m-1)*bs;

    359:   for (i=0; i<m; i++) {
    360:     if (im[i] < 0) continue;
    362:     if (im[i] >= rstart && im[i] < rend) {
    363:       row = im[i] - rstart;
    364:       for (j=0; j<n; j++) {
    365:         /* If NumCol = 1 then a copy is not required */
    366:         if ((roworiented) && (n == 1)) {
    367:           barray = (MatScalar*)v + i*bs2;
    368:         } else if ((!roworiented) && (m == 1)) {
    369:           barray = (MatScalar*)v + j*bs2;
    370:         } else { /* Here a copy is required */
    371:           if (roworiented) {
    372:             value = v + (i*(stepval+bs) + j)*bs;
    373:           } else {
    374:             value = v + (j*(stepval+bs) + i)*bs;
    375:           }
    376:           for (ii=0; ii<bs; ii++,value+=bs+stepval) {
    377:             for (jj=0; jj<bs; jj++) barray[jj] = value[jj];
    378:             barray += bs;
    379:           }
    380:           barray -= bs2;
    381:         }

    383:         if (in[j] >= cstart && in[j] < cend) {
    384:           col  = in[j] - cstart;
    385:           MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A,row,col,barray,addv,im[i],in[j]);
    386:         } else if (in[j] < 0) continue;
    388:         else {
    389:           if (mat->was_assembled) {
    390:             if (!baij->colmap) {
    391:               MatCreateColmap_MPIBAIJ_Private(mat);
    392:             }

    394: #if defined(PETSC_USE_DEBUG)
    395: #if defined(PETSC_USE_CTABLE)
    396:             { PetscInt data;
    397:               PetscTableFind(baij->colmap,in[j]+1,&data);
    399:             }
    400: #else
    402: #endif
    403: #endif
    404: #if defined(PETSC_USE_CTABLE)
    405:             PetscTableFind(baij->colmap,in[j]+1,&col);
    406:             col  = (col - 1)/bs;
    407: #else
    408:             col = (baij->colmap[in[j]] - 1)/bs;
    409: #endif
    410:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->B->data))->nonew) {
    411:               MatDisAssemble_MPIBAIJ(mat);
    412:               col  =  in[j];
    414:           } else col = in[j];
    415:           MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B,row,col,barray,addv,im[i],in[j]);
    416:         }
    417:       }
    418:     } else {
    420:       if (!baij->donotstash) {
    421:         if (roworiented) {
    422:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    423:         } else {
    424:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    425:         }
    426:       }
    427:     }
    428:   }
    429:   return 0;
    430: }

    432: #define HASH_KEY 0.6180339887
    433: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(PetscInt)((size)*(tmp-(PetscInt)tmp)))
    434: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
    435: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
    436: PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
    437: {
    438:   Mat_MPIBAIJ    *baij       = (Mat_MPIBAIJ*)mat->data;
    439:   PetscBool      roworiented = baij->roworiented;
    440:   PetscInt       i,j,row,col;
    441:   PetscInt       rstart_orig=mat->rmap->rstart;
    442:   PetscInt       rend_orig  =mat->rmap->rend,Nbs=baij->Nbs;
    443:   PetscInt       h1,key,size=baij->ht_size,bs=mat->rmap->bs,*HT=baij->ht,idx;
    444:   PetscReal      tmp;
    445:   MatScalar      **HD = baij->hd,value;
    446:   PetscInt       total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;

    448:   for (i=0; i<m; i++) {
    449:     if (PetscDefined(USE_DEBUG)) {
    452:     }
    453:     row = im[i];
    454:     if (row >= rstart_orig && row < rend_orig) {
    455:       for (j=0; j<n; j++) {
    456:         col = in[j];
    457:         if (roworiented) value = v[i*n+j];
    458:         else             value = v[i+j*m];
    459:         /* Look up PetscInto the Hash Table */
    460:         key = (row/bs)*Nbs+(col/bs)+1;
    461:         h1  = HASH(size,key,tmp);

    463:         idx = h1;
    464:         if (PetscDefined(USE_DEBUG)) {
    465:           insert_ct++;
    466:           total_ct++;
    467:           if (HT[idx] != key) {
    468:             for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++) ;
    469:             if (idx == size) {
    470:               for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++) ;
    472:             }
    473:           }
    474:         } else if (HT[idx] != key) {
    475:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++) ;
    476:           if (idx == size) {
    477:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++) ;
    479:           }
    480:         }
    481:         /* A HASH table entry is found, so insert the values at the correct address */
    482:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
    483:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
    484:       }
    485:     } else if (!baij->donotstash) {
    486:       if (roworiented) {
    487:         MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,PETSC_FALSE);
    488:       } else {
    489:         MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,PETSC_FALSE);
    490:       }
    491:     }
    492:   }
    493:   if (PetscDefined(USE_DEBUG)) {
    494:     baij->ht_total_ct  += total_ct;
    495:     baij->ht_insert_ct += insert_ct;
    496:   }
    497:   return 0;
    498: }

    500: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
    501: {
    502:   Mat_MPIBAIJ       *baij       = (Mat_MPIBAIJ*)mat->data;
    503:   PetscBool         roworiented = baij->roworiented;
    504:   PetscInt          i,j,ii,jj,row,col;
    505:   PetscInt          rstart=baij->rstartbs;
    506:   PetscInt          rend  =mat->rmap->rend,stepval,bs=mat->rmap->bs,bs2=baij->bs2,nbs2=n*bs2;
    507:   PetscInt          h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
    508:   PetscReal         tmp;
    509:   MatScalar         **HD = baij->hd,*baij_a;
    510:   const PetscScalar *v_t,*value;
    511:   PetscInt          total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;

    513:   if (roworiented) stepval = (n-1)*bs;
    514:   else stepval = (m-1)*bs;

    516:   for (i=0; i<m; i++) {
    517:     if (PetscDefined(USE_DEBUG)) {
    520:     }
    521:     row = im[i];
    522:     v_t = v + i*nbs2;
    523:     if (row >= rstart && row < rend) {
    524:       for (j=0; j<n; j++) {
    525:         col = in[j];

    527:         /* Look up into the Hash Table */
    528:         key = row*Nbs+col+1;
    529:         h1  = HASH(size,key,tmp);

    531:         idx = h1;
    532:         if (PetscDefined(USE_DEBUG)) {
    533:           total_ct++;
    534:           insert_ct++;
    535:           if (HT[idx] != key) {
    536:             for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++) ;
    537:             if (idx == size) {
    538:               for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++) ;
    540:             }
    541:           }
    542:         } else if (HT[idx] != key) {
    543:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++) ;
    544:           if (idx == size) {
    545:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++) ;
    547:           }
    548:         }
    549:         baij_a = HD[idx];
    550:         if (roworiented) {
    551:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
    552:           /* value = v + (i*(stepval+bs)+j)*bs; */
    553:           value = v_t;
    554:           v_t  += bs;
    555:           if (addv == ADD_VALUES) {
    556:             for (ii=0; ii<bs; ii++,value+=stepval) {
    557:               for (jj=ii; jj<bs2; jj+=bs) {
    558:                 baij_a[jj] += *value++;
    559:               }
    560:             }
    561:           } else {
    562:             for (ii=0; ii<bs; ii++,value+=stepval) {
    563:               for (jj=ii; jj<bs2; jj+=bs) {
    564:                 baij_a[jj] = *value++;
    565:               }
    566:             }
    567:           }
    568:         } else {
    569:           value = v + j*(stepval+bs)*bs + i*bs;
    570:           if (addv == ADD_VALUES) {
    571:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
    572:               for (jj=0; jj<bs; jj++) {
    573:                 baij_a[jj] += *value++;
    574:               }
    575:             }
    576:           } else {
    577:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
    578:               for (jj=0; jj<bs; jj++) {
    579:                 baij_a[jj] = *value++;
    580:               }
    581:             }
    582:           }
    583:         }
    584:       }
    585:     } else {
    586:       if (!baij->donotstash) {
    587:         if (roworiented) {
    588:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    589:         } else {
    590:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    591:         }
    592:       }
    593:     }
    594:   }
    595:   if (PetscDefined(USE_DEBUG)) {
    596:     baij->ht_total_ct  += total_ct;
    597:     baij->ht_insert_ct += insert_ct;
    598:   }
    599:   return 0;
    600: }

    602: PetscErrorCode MatGetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
    603: {
    604:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    605:   PetscInt       bs       = mat->rmap->bs,i,j,bsrstart = mat->rmap->rstart,bsrend = mat->rmap->rend;
    606:   PetscInt       bscstart = mat->cmap->rstart,bscend = mat->cmap->rend,row,col,data;

    608:   for (i=0; i<m; i++) {
    609:     if (idxm[i] < 0) continue; /* negative row */
    611:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
    612:       row = idxm[i] - bsrstart;
    613:       for (j=0; j<n; j++) {
    614:         if (idxn[j] < 0) continue; /* negative column */
    616:         if (idxn[j] >= bscstart && idxn[j] < bscend) {
    617:           col  = idxn[j] - bscstart;
    618:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
    619:         } else {
    620:           if (!baij->colmap) {
    621:             MatCreateColmap_MPIBAIJ_Private(mat);
    622:           }
    623: #if defined(PETSC_USE_CTABLE)
    624:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
    625:           data--;
    626: #else
    627:           data = baij->colmap[idxn[j]/bs]-1;
    628: #endif
    629:           if ((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
    630:           else {
    631:             col  = data + idxn[j]%bs;
    632:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
    633:           }
    634:         }
    635:       }
    636:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
    637:   }
    638:   return 0;
    639: }

    641: PetscErrorCode MatNorm_MPIBAIJ(Mat mat,NormType type,PetscReal *nrm)
    642: {
    643:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    644:   Mat_SeqBAIJ    *amat = (Mat_SeqBAIJ*)baij->A->data,*bmat = (Mat_SeqBAIJ*)baij->B->data;
    645:   PetscInt       i,j,bs2=baij->bs2,bs=baij->A->rmap->bs,nz,row,col;
    646:   PetscReal      sum = 0.0;
    647:   MatScalar      *v;

    649:   if (baij->size == 1) {
    650:     MatNorm(baij->A,type,nrm);
    651:   } else {
    652:     if (type == NORM_FROBENIUS) {
    653:       v  = amat->a;
    654:       nz = amat->nz*bs2;
    655:       for (i=0; i<nz; i++) {
    656:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    657:       }
    658:       v  = bmat->a;
    659:       nz = bmat->nz*bs2;
    660:       for (i=0; i<nz; i++) {
    661:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    662:       }
    663:       MPIU_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
    664:       *nrm = PetscSqrtReal(*nrm);
    665:     } else if (type == NORM_1) { /* max column sum */
    666:       PetscReal *tmp,*tmp2;
    667:       PetscInt  *jj,*garray=baij->garray,cstart=baij->rstartbs;
    668:       PetscCalloc1(mat->cmap->N,&tmp);
    669:       PetscMalloc1(mat->cmap->N,&tmp2);
    670:       v    = amat->a; jj = amat->j;
    671:       for (i=0; i<amat->nz; i++) {
    672:         for (j=0; j<bs; j++) {
    673:           col = bs*(cstart + *jj) + j; /* column index */
    674:           for (row=0; row<bs; row++) {
    675:             tmp[col] += PetscAbsScalar(*v);  v++;
    676:           }
    677:         }
    678:         jj++;
    679:       }
    680:       v = bmat->a; jj = bmat->j;
    681:       for (i=0; i<bmat->nz; i++) {
    682:         for (j=0; j<bs; j++) {
    683:           col = bs*garray[*jj] + j;
    684:           for (row=0; row<bs; row++) {
    685:             tmp[col] += PetscAbsScalar(*v); v++;
    686:           }
    687:         }
    688:         jj++;
    689:       }
    690:       MPIU_Allreduce(tmp,tmp2,mat->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
    691:       *nrm = 0.0;
    692:       for (j=0; j<mat->cmap->N; j++) {
    693:         if (tmp2[j] > *nrm) *nrm = tmp2[j];
    694:       }
    695:       PetscFree(tmp);
    696:       PetscFree(tmp2);
    697:     } else if (type == NORM_INFINITY) { /* max row sum */
    698:       PetscReal *sums;
    699:       PetscMalloc1(bs,&sums);
    700:       sum  = 0.0;
    701:       for (j=0; j<amat->mbs; j++) {
    702:         for (row=0; row<bs; row++) sums[row] = 0.0;
    703:         v  = amat->a + bs2*amat->i[j];
    704:         nz = amat->i[j+1]-amat->i[j];
    705:         for (i=0; i<nz; i++) {
    706:           for (col=0; col<bs; col++) {
    707:             for (row=0; row<bs; row++) {
    708:               sums[row] += PetscAbsScalar(*v); v++;
    709:             }
    710:           }
    711:         }
    712:         v  = bmat->a + bs2*bmat->i[j];
    713:         nz = bmat->i[j+1]-bmat->i[j];
    714:         for (i=0; i<nz; i++) {
    715:           for (col=0; col<bs; col++) {
    716:             for (row=0; row<bs; row++) {
    717:               sums[row] += PetscAbsScalar(*v); v++;
    718:             }
    719:           }
    720:         }
    721:         for (row=0; row<bs; row++) {
    722:           if (sums[row] > sum) sum = sums[row];
    723:         }
    724:       }
    725:       MPIU_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)mat));
    726:       PetscFree(sums);
    727:     } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No support for this norm yet");
    728:   }
    729:   return 0;
    730: }

    732: /*
    733:   Creates the hash table, and sets the table
    734:   This table is created only once.
    735:   If new entried need to be added to the matrix
    736:   then the hash table has to be destroyed and
    737:   recreated.
    738: */
    739: PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
    740: {
    741:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    742:   Mat            A     = baij->A,B=baij->B;
    743:   Mat_SeqBAIJ    *a    = (Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)B->data;
    744:   PetscInt       i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
    745:   PetscInt       ht_size,bs2=baij->bs2,rstart=baij->rstartbs;
    746:   PetscInt       cstart=baij->cstartbs,*garray=baij->garray,row,col,Nbs=baij->Nbs;
    747:   PetscInt       *HT,key;
    748:   MatScalar      **HD;
    749:   PetscReal      tmp;
    750: #if defined(PETSC_USE_INFO)
    751:   PetscInt ct=0,max=0;
    752: #endif

    754:   if (baij->ht) return 0;

    756:   baij->ht_size = (PetscInt)(factor*nz);
    757:   ht_size       = baij->ht_size;

    759:   /* Allocate Memory for Hash Table */
    760:   PetscCalloc2(ht_size,&baij->hd,ht_size,&baij->ht);
    761:   HD   = baij->hd;
    762:   HT   = baij->ht;

    764:   /* Loop Over A */
    765:   for (i=0; i<a->mbs; i++) {
    766:     for (j=ai[i]; j<ai[i+1]; j++) {
    767:       row = i+rstart;
    768:       col = aj[j]+cstart;

    770:       key = row*Nbs + col + 1;
    771:       h1  = HASH(ht_size,key,tmp);
    772:       for (k=0; k<ht_size; k++) {
    773:         if (!HT[(h1+k)%ht_size]) {
    774:           HT[(h1+k)%ht_size] = key;
    775:           HD[(h1+k)%ht_size] = a->a + j*bs2;
    776:           break;
    777: #if defined(PETSC_USE_INFO)
    778:         } else {
    779:           ct++;
    780: #endif
    781:         }
    782:       }
    783: #if defined(PETSC_USE_INFO)
    784:       if (k> max) max = k;
    785: #endif
    786:     }
    787:   }
    788:   /* Loop Over B */
    789:   for (i=0; i<b->mbs; i++) {
    790:     for (j=bi[i]; j<bi[i+1]; j++) {
    791:       row = i+rstart;
    792:       col = garray[bj[j]];
    793:       key = row*Nbs + col + 1;
    794:       h1  = HASH(ht_size,key,tmp);
    795:       for (k=0; k<ht_size; k++) {
    796:         if (!HT[(h1+k)%ht_size]) {
    797:           HT[(h1+k)%ht_size] = key;
    798:           HD[(h1+k)%ht_size] = b->a + j*bs2;
    799:           break;
    800: #if defined(PETSC_USE_INFO)
    801:         } else {
    802:           ct++;
    803: #endif
    804:         }
    805:       }
    806: #if defined(PETSC_USE_INFO)
    807:       if (k> max) max = k;
    808: #endif
    809:     }
    810:   }

    812:   /* Print Summary */
    813: #if defined(PETSC_USE_INFO)
    814:   for (i=0,j=0; i<ht_size; i++) {
    815:     if (HT[i]) j++;
    816:   }
    817:   PetscInfo(mat,"Average Search = %5.2g,max search = %" PetscInt_FMT "\n",(!j) ? (double)0.0:(double)(((PetscReal)(ct+j))/(double)j),max);
    818: #endif
    819:   return 0;
    820: }

    822: PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat,MatAssemblyType mode)
    823: {
    824:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    825:   PetscInt       nstash,reallocs;

    827:   if (baij->donotstash || mat->nooffprocentries) return 0;

    829:   MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
    830:   MatStashScatterBegin_Private(mat,&mat->bstash,baij->rangebs);
    831:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
    832:   PetscInfo(mat,"Stash has %" PetscInt_FMT " entries,uses %" PetscInt_FMT " mallocs.\n",nstash,reallocs);
    833:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
    834:   PetscInfo(mat,"Block-Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n",nstash,reallocs);
    835:   return 0;
    836: }

    838: PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat,MatAssemblyType mode)
    839: {
    840:   Mat_MPIBAIJ    *baij=(Mat_MPIBAIJ*)mat->data;
    841:   Mat_SeqBAIJ    *a   =(Mat_SeqBAIJ*)baij->A->data;
    842:   PetscInt       i,j,rstart,ncols,flg,bs2=baij->bs2;
    843:   PetscInt       *row,*col;
    844:   PetscBool      r1,r2,r3,other_disassembled;
    845:   MatScalar      *val;
    846:   PetscMPIInt    n;

    848:   /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
    849:   if (!baij->donotstash && !mat->nooffprocentries) {
    850:     while (1) {
    851:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
    852:       if (!flg) break;

    854:       for (i=0; i<n;) {
    855:         /* Now identify the consecutive vals belonging to the same row */
    856:         for (j=i,rstart=row[j]; j<n; j++) {
    857:           if (row[j] != rstart) break;
    858:         }
    859:         if (j < n) ncols = j-i;
    860:         else       ncols = n-i;
    861:         /* Now assemble all these values with a single function call */
    862:         MatSetValues_MPIBAIJ(mat,1,row+i,ncols,col+i,val+i,mat->insertmode);
    863:         i    = j;
    864:       }
    865:     }
    866:     MatStashScatterEnd_Private(&mat->stash);
    867:     /* Now process the block-stash. Since the values are stashed column-oriented,
    868:        set the roworiented flag to column oriented, and after MatSetValues()
    869:        restore the original flags */
    870:     r1 = baij->roworiented;
    871:     r2 = a->roworiented;
    872:     r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;

    874:     baij->roworiented = PETSC_FALSE;
    875:     a->roworiented    = PETSC_FALSE;

    877:     (((Mat_SeqBAIJ*)baij->B->data))->roworiented = PETSC_FALSE; /* b->roworiented */
    878:     while (1) {
    879:       MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
    880:       if (!flg) break;

    882:       for (i=0; i<n;) {
    883:         /* Now identify the consecutive vals belonging to the same row */
    884:         for (j=i,rstart=row[j]; j<n; j++) {
    885:           if (row[j] != rstart) break;
    886:         }
    887:         if (j < n) ncols = j-i;
    888:         else       ncols = n-i;
    889:         MatSetValuesBlocked_MPIBAIJ(mat,1,row+i,ncols,col+i,val+i*bs2,mat->insertmode);
    890:         i    = j;
    891:       }
    892:     }
    893:     MatStashScatterEnd_Private(&mat->bstash);

    895:     baij->roworiented = r1;
    896:     a->roworiented    = r2;

    898:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented = r3; /* b->roworiented */
    899:   }

    901:   MatAssemblyBegin(baij->A,mode);
    902:   MatAssemblyEnd(baij->A,mode);

    904:   /* determine if any processor has disassembled, if so we must
    905:      also disassemble ourselfs, in order that we may reassemble. */
    906:   /*
    907:      if nonzero structure of submatrix B cannot change then we know that
    908:      no processor disassembled thus we can skip this stuff
    909:   */
    910:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew) {
    911:     MPIU_Allreduce(&mat->was_assembled,&other_disassembled,1,MPIU_BOOL,MPI_PROD,PetscObjectComm((PetscObject)mat));
    912:     if (mat->was_assembled && !other_disassembled) {
    913:       MatDisAssemble_MPIBAIJ(mat);
    914:     }
    915:   }

    917:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
    918:     MatSetUpMultiply_MPIBAIJ(mat);
    919:   }
    920:   MatAssemblyBegin(baij->B,mode);
    921:   MatAssemblyEnd(baij->B,mode);

    923: #if defined(PETSC_USE_INFO)
    924:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
    925:     PetscInfo(mat,"Average Hash Table Search in MatSetValues = %5.2f\n",(double)((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);

    927:     baij->ht_total_ct  = 0;
    928:     baij->ht_insert_ct = 0;
    929:   }
    930: #endif
    931:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
    932:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);

    934:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
    935:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
    936:   }

    938:   PetscFree2(baij->rowvalues,baij->rowindices);

    940:   baij->rowvalues = NULL;

    942:   /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
    943:   if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
    944:     PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
    945:     MPIU_Allreduce(&state,&mat->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)mat));
    946:   }
    947:   return 0;
    948: }

    950: extern PetscErrorCode MatView_SeqBAIJ(Mat,PetscViewer);
    951: #include <petscdraw.h>
    952: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
    953: {
    954:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
    955:   PetscMPIInt       rank = baij->rank;
    956:   PetscInt          bs   = mat->rmap->bs;
    957:   PetscBool         iascii,isdraw;
    958:   PetscViewer       sviewer;
    959:   PetscViewerFormat format;

    961:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    962:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    963:   if (iascii) {
    964:     PetscViewerGetFormat(viewer,&format);
    965:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
    966:       MatInfo info;
    967:       MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
    968:       MatGetInfo(mat,MAT_LOCAL,&info);
    969:       PetscViewerASCIIPushSynchronized(viewer);
    970:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " bs %" PetscInt_FMT " mem %g\n",
    971:                                                  rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,mat->rmap->bs,(double)info.memory));
    972:       MatGetInfo(baij->A,MAT_LOCAL,&info);
    973:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %" PetscInt_FMT " \n",rank,(PetscInt)info.nz_used);
    974:       MatGetInfo(baij->B,MAT_LOCAL,&info);
    975:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %" PetscInt_FMT " \n",rank,(PetscInt)info.nz_used);
    976:       PetscViewerFlush(viewer);
    977:       PetscViewerASCIIPopSynchronized(viewer);
    978:       PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
    979:       VecScatterView(baij->Mvctx,viewer);
    980:       return 0;
    981:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
    982:       PetscViewerASCIIPrintf(viewer,"  block size is %" PetscInt_FMT "\n",bs);
    983:       return 0;
    984:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
    985:       return 0;
    986:     }
    987:   }

    989:   if (isdraw) {
    990:     PetscDraw draw;
    991:     PetscBool isnull;
    992:     PetscViewerDrawGetDraw(viewer,0,&draw);
    993:     PetscDrawIsNull(draw,&isnull);
    994:     if (isnull) return 0;
    995:   }

    997:   {
    998:     /* assemble the entire matrix onto first processor. */
    999:     Mat         A;
    1000:     Mat_SeqBAIJ *Aloc;
    1001:     PetscInt    M = mat->rmap->N,N = mat->cmap->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
    1002:     MatScalar   *a;
    1003:     const char  *matname;

    1005:     /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
    1006:     /* Perhaps this should be the type of mat? */
    1007:     MatCreate(PetscObjectComm((PetscObject)mat),&A);
    1008:     if (rank == 0) {
    1009:       MatSetSizes(A,M,N,M,N);
    1010:     } else {
    1011:       MatSetSizes(A,0,0,M,N);
    1012:     }
    1013:     MatSetType(A,MATMPIBAIJ);
    1014:     MatMPIBAIJSetPreallocation(A,mat->rmap->bs,0,NULL,0,NULL);
    1015:     MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);
    1016:     PetscLogObjectParent((PetscObject)mat,(PetscObject)A);

    1018:     /* copy over the A part */
    1019:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
    1020:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
    1021:     PetscMalloc1(bs,&rvals);

    1023:     for (i=0; i<mbs; i++) {
    1024:       rvals[0] = bs*(baij->rstartbs + i);
    1025:       for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
    1026:       for (j=ai[i]; j<ai[i+1]; j++) {
    1027:         col = (baij->cstartbs+aj[j])*bs;
    1028:         for (k=0; k<bs; k++) {
    1029:           MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
    1030:           col++; a += bs;
    1031:         }
    1032:       }
    1033:     }
    1034:     /* copy over the B part */
    1035:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
    1036:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
    1037:     for (i=0; i<mbs; i++) {
    1038:       rvals[0] = bs*(baij->rstartbs + i);
    1039:       for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
    1040:       for (j=ai[i]; j<ai[i+1]; j++) {
    1041:         col = baij->garray[aj[j]]*bs;
    1042:         for (k=0; k<bs; k++) {
    1043:           MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
    1044:           col++; a += bs;
    1045:         }
    1046:       }
    1047:     }
    1048:     PetscFree(rvals);
    1049:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    1050:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    1051:     /*
    1052:        Everyone has to call to draw the matrix since the graphics waits are
    1053:        synchronized across all processors that share the PetscDraw object
    1054:     */
    1055:     PetscViewerGetSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
    1056:     PetscObjectGetName((PetscObject)mat,&matname);
    1057:     if (rank == 0) {
    1058:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,matname);
    1059:       MatView_SeqBAIJ(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
    1060:     }
    1061:     PetscViewerRestoreSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
    1062:     PetscViewerFlush(viewer);
    1063:     MatDestroy(&A);
    1064:   }
    1065:   return 0;
    1066: }

    1068: /* Used for both MPIBAIJ and MPISBAIJ matrices */
    1069: PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat,PetscViewer viewer)
    1070: {
    1071:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)mat->data;
    1072:   Mat_SeqBAIJ    *A   = (Mat_SeqBAIJ*)aij->A->data;
    1073:   Mat_SeqBAIJ    *B   = (Mat_SeqBAIJ*)aij->B->data;
    1074:   const PetscInt *garray = aij->garray;
    1075:   PetscInt       header[4],M,N,m,rs,cs,bs,nz,cnt,i,j,ja,jb,k,l;
    1076:   PetscInt       *rowlens,*colidxs;
    1077:   PetscScalar    *matvals;

    1079:   PetscViewerSetUp(viewer);

    1081:   M  = mat->rmap->N;
    1082:   N  = mat->cmap->N;
    1083:   m  = mat->rmap->n;
    1084:   rs = mat->rmap->rstart;
    1085:   cs = mat->cmap->rstart;
    1086:   bs = mat->rmap->bs;
    1087:   nz = bs*bs*(A->nz + B->nz);

    1089:   /* write matrix header */
    1090:   header[0] = MAT_FILE_CLASSID;
    1091:   header[1] = M; header[2] = N; header[3] = nz;
    1092:   MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
    1093:   PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);

    1095:   /* fill in and store row lengths */
    1096:   PetscMalloc1(m,&rowlens);
    1097:   for (cnt=0, i=0; i<A->mbs; i++)
    1098:     for (j=0; j<bs; j++)
    1099:       rowlens[cnt++] = bs*(A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i]);
    1100:   PetscViewerBinaryWriteAll(viewer,rowlens,m,rs,M,PETSC_INT);
    1101:   PetscFree(rowlens);

    1103:   /* fill in and store column indices */
    1104:   PetscMalloc1(nz,&colidxs);
    1105:   for (cnt=0, i=0; i<A->mbs; i++) {
    1106:     for (k=0; k<bs; k++) {
    1107:       for (jb=B->i[i]; jb<B->i[i+1]; jb++) {
    1108:         if (garray[B->j[jb]] > cs/bs) break;
    1109:         for (l=0; l<bs; l++)
    1110:           colidxs[cnt++] = bs*garray[B->j[jb]] + l;
    1111:       }
    1112:       for (ja=A->i[i]; ja<A->i[i+1]; ja++)
    1113:         for (l=0; l<bs; l++)
    1114:           colidxs[cnt++] = bs*A->j[ja] + l + cs;
    1115:       for (; jb<B->i[i+1]; jb++)
    1116:         for (l=0; l<bs; l++)
    1117:           colidxs[cnt++] = bs*garray[B->j[jb]] + l;
    1118:     }
    1119:   }
    1121:   PetscViewerBinaryWriteAll(viewer,colidxs,nz,PETSC_DECIDE,PETSC_DECIDE,PETSC_INT);
    1122:   PetscFree(colidxs);

    1124:   /* fill in and store nonzero values */
    1125:   PetscMalloc1(nz,&matvals);
    1126:   for (cnt=0, i=0; i<A->mbs; i++) {
    1127:     for (k=0; k<bs; k++) {
    1128:       for (jb=B->i[i]; jb<B->i[i+1]; jb++) {
    1129:         if (garray[B->j[jb]] > cs/bs) break;
    1130:         for (l=0; l<bs; l++)
    1131:           matvals[cnt++] = B->a[bs*(bs*jb + l) + k];
    1132:       }
    1133:       for (ja=A->i[i]; ja<A->i[i+1]; ja++)
    1134:         for (l=0; l<bs; l++)
    1135:           matvals[cnt++] = A->a[bs*(bs*ja + l) + k];
    1136:       for (; jb<B->i[i+1]; jb++)
    1137:         for (l=0; l<bs; l++)
    1138:           matvals[cnt++] = B->a[bs*(bs*jb + l) + k];
    1139:     }
    1140:   }
    1141:   PetscViewerBinaryWriteAll(viewer,matvals,nz,PETSC_DECIDE,PETSC_DECIDE,PETSC_SCALAR);
    1142:   PetscFree(matvals);

    1144:   /* write block size option to the viewer's .info file */
    1145:   MatView_Binary_BlockSizes(mat,viewer);
    1146:   return 0;
    1147: }

    1149: PetscErrorCode MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
    1150: {
    1151:   PetscBool      iascii,isdraw,issocket,isbinary;

    1153:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    1154:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    1155:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
    1156:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    1157:   if (iascii || isdraw || issocket) {
    1158:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
    1159:   } else if (isbinary) {
    1160:     MatView_MPIBAIJ_Binary(mat,viewer);
    1161:   }
    1162:   return 0;
    1163: }

    1165: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
    1166: {
    1167:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;

    1169: #if defined(PETSC_USE_LOG)
    1170:   PetscLogObjectState((PetscObject)mat,"Rows=%" PetscInt_FMT ",Cols=%" PetscInt_FMT,mat->rmap->N,mat->cmap->N);
    1171: #endif
    1172:   MatStashDestroy_Private(&mat->stash);
    1173:   MatStashDestroy_Private(&mat->bstash);
    1174:   MatDestroy(&baij->A);
    1175:   MatDestroy(&baij->B);
    1176: #if defined(PETSC_USE_CTABLE)
    1177:   PetscTableDestroy(&baij->colmap);
    1178: #else
    1179:   PetscFree(baij->colmap);
    1180: #endif
    1181:   PetscFree(baij->garray);
    1182:   VecDestroy(&baij->lvec);
    1183:   VecScatterDestroy(&baij->Mvctx);
    1184:   PetscFree2(baij->rowvalues,baij->rowindices);
    1185:   PetscFree(baij->barray);
    1186:   PetscFree2(baij->hd,baij->ht);
    1187:   PetscFree(baij->rangebs);
    1188:   PetscFree(mat->data);

    1190:   PetscObjectChangeTypeName((PetscObject)mat,NULL);
    1191:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C",NULL);
    1192:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C",NULL);
    1193:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C",NULL);
    1194:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C",NULL);
    1195:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",NULL);
    1196:   PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C",NULL);
    1197:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_mpisbaij_C",NULL);
    1198:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_mpibstrm_C",NULL);
    1199: #if defined(PETSC_HAVE_HYPRE)
    1200:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_hypre_C",NULL);
    1201: #endif
    1202:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_is_C",NULL);
    1203:   return 0;
    1204: }

    1206: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
    1207: {
    1208:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
    1209:   PetscInt       nt;

    1211:   VecGetLocalSize(xx,&nt);
    1213:   VecGetLocalSize(yy,&nt);
    1215:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1216:   (*a->A->ops->mult)(a->A,xx,yy);
    1217:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1218:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
    1219:   return 0;
    1220: }

    1222: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1223: {
    1224:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1226:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1227:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
    1228:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1229:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
    1230:   return 0;
    1231: }

    1233: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
    1234: {
    1235:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1237:   /* do nondiagonal part */
    1238:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
    1239:   /* do local part */
    1240:   (*a->A->ops->multtranspose)(a->A,xx,yy);
    1241:   /* add partial results together */
    1242:   VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
    1243:   VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
    1244:   return 0;
    1245: }

    1247: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1248: {
    1249:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1251:   /* do nondiagonal part */
    1252:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
    1253:   /* do local part */
    1254:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
    1255:   /* add partial results together */
    1256:   VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
    1257:   VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
    1258:   return 0;
    1259: }

    1261: /*
    1262:   This only works correctly for square matrices where the subblock A->A is the
    1263:    diagonal block
    1264: */
    1265: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
    1266: {
    1268:   MatGetDiagonal(((Mat_MPIBAIJ*)A->data)->A,v);
    1269:   return 0;
    1270: }

    1272: PetscErrorCode MatScale_MPIBAIJ(Mat A,PetscScalar aa)
    1273: {
    1274:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1276:   MatScale(a->A,aa);
    1277:   MatScale(a->B,aa);
    1278:   return 0;
    1279: }

    1281: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    1282: {
    1283:   Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)matin->data;
    1284:   PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
    1285:   PetscInt    bs = matin->rmap->bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
    1286:   PetscInt    nztot,nzA,nzB,lrow,brstart = matin->rmap->rstart,brend = matin->rmap->rend;
    1287:   PetscInt    *cmap,*idx_p,cstart = mat->cstartbs;

    1291:   mat->getrowactive = PETSC_TRUE;

    1293:   if (!mat->rowvalues && (idx || v)) {
    1294:     /*
    1295:         allocate enough space to hold information from the longest row.
    1296:     */
    1297:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
    1298:     PetscInt    max = 1,mbs = mat->mbs,tmp;
    1299:     for (i=0; i<mbs; i++) {
    1300:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
    1301:       if (max < tmp) max = tmp;
    1302:     }
    1303:     PetscMalloc2(max*bs2,&mat->rowvalues,max*bs2,&mat->rowindices);
    1304:   }
    1305:   lrow = row - brstart;

    1307:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
    1308:   if (!v)   {pvA = NULL; pvB = NULL;}
    1309:   if (!idx) {pcA = NULL; if (!v) pcB = NULL;}
    1310:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
    1311:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
    1312:   nztot = nzA + nzB;

    1314:   cmap = mat->garray;
    1315:   if (v  || idx) {
    1316:     if (nztot) {
    1317:       /* Sort by increasing column numbers, assuming A and B already sorted */
    1318:       PetscInt imark = -1;
    1319:       if (v) {
    1320:         *v = v_p = mat->rowvalues;
    1321:         for (i=0; i<nzB; i++) {
    1322:           if (cmap[cworkB[i]/bs] < cstart) v_p[i] = vworkB[i];
    1323:           else break;
    1324:         }
    1325:         imark = i;
    1326:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
    1327:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
    1328:       }
    1329:       if (idx) {
    1330:         *idx = idx_p = mat->rowindices;
    1331:         if (imark > -1) {
    1332:           for (i=0; i<imark; i++) {
    1333:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
    1334:           }
    1335:         } else {
    1336:           for (i=0; i<nzB; i++) {
    1337:             if (cmap[cworkB[i]/bs] < cstart) idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
    1338:             else break;
    1339:           }
    1340:           imark = i;
    1341:         }
    1342:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
    1343:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
    1344:       }
    1345:     } else {
    1346:       if (idx) *idx = NULL;
    1347:       if (v)   *v   = NULL;
    1348:     }
    1349:   }
    1350:   *nz  = nztot;
    1351:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
    1352:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
    1353:   return 0;
    1354: }

    1356: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    1357: {
    1358:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

    1361:   baij->getrowactive = PETSC_FALSE;
    1362:   return 0;
    1363: }

    1365: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
    1366: {
    1367:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;

    1369:   MatZeroEntries(l->A);
    1370:   MatZeroEntries(l->B);
    1371:   return 0;
    1372: }

    1374: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
    1375: {
    1376:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)matin->data;
    1377:   Mat            A  = a->A,B = a->B;
    1378:   PetscLogDouble isend[5],irecv[5];

    1380:   info->block_size = (PetscReal)matin->rmap->bs;

    1382:   MatGetInfo(A,MAT_LOCAL,info);

    1384:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
    1385:   isend[3] = info->memory;  isend[4] = info->mallocs;

    1387:   MatGetInfo(B,MAT_LOCAL,info);

    1389:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
    1390:   isend[3] += info->memory;  isend[4] += info->mallocs;

    1392:   if (flag == MAT_LOCAL) {
    1393:     info->nz_used      = isend[0];
    1394:     info->nz_allocated = isend[1];
    1395:     info->nz_unneeded  = isend[2];
    1396:     info->memory       = isend[3];
    1397:     info->mallocs      = isend[4];
    1398:   } else if (flag == MAT_GLOBAL_MAX) {
    1399:     MPIU_Allreduce(isend,irecv,5,MPIU_PETSCLOGDOUBLE,MPI_MAX,PetscObjectComm((PetscObject)matin));

    1401:     info->nz_used      = irecv[0];
    1402:     info->nz_allocated = irecv[1];
    1403:     info->nz_unneeded  = irecv[2];
    1404:     info->memory       = irecv[3];
    1405:     info->mallocs      = irecv[4];
    1406:   } else if (flag == MAT_GLOBAL_SUM) {
    1407:     MPIU_Allreduce(isend,irecv,5,MPIU_PETSCLOGDOUBLE,MPI_SUM,PetscObjectComm((PetscObject)matin));

    1409:     info->nz_used      = irecv[0];
    1410:     info->nz_allocated = irecv[1];
    1411:     info->nz_unneeded  = irecv[2];
    1412:     info->memory       = irecv[3];
    1413:     info->mallocs      = irecv[4];
    1414:   } else SETERRQ(PetscObjectComm((PetscObject)matin),PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
    1415:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
    1416:   info->fill_ratio_needed = 0;
    1417:   info->factor_mallocs    = 0;
    1418:   return 0;
    1419: }

    1421: PetscErrorCode MatSetOption_MPIBAIJ(Mat A,MatOption op,PetscBool flg)
    1422: {
    1423:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1425:   switch (op) {
    1426:   case MAT_NEW_NONZERO_LOCATIONS:
    1427:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
    1428:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
    1429:   case MAT_KEEP_NONZERO_PATTERN:
    1430:   case MAT_NEW_NONZERO_LOCATION_ERR:
    1431:     MatCheckPreallocated(A,1);
    1432:     MatSetOption(a->A,op,flg);
    1433:     MatSetOption(a->B,op,flg);
    1434:     break;
    1435:   case MAT_ROW_ORIENTED:
    1436:     MatCheckPreallocated(A,1);
    1437:     a->roworiented = flg;

    1439:     MatSetOption(a->A,op,flg);
    1440:     MatSetOption(a->B,op,flg);
    1441:     break;
    1442:   case MAT_FORCE_DIAGONAL_ENTRIES:
    1443:   case MAT_SORTED_FULL:
    1444:     PetscInfo(A,"Option %s ignored\n",MatOptions[op]);
    1445:     break;
    1446:   case MAT_IGNORE_OFF_PROC_ENTRIES:
    1447:     a->donotstash = flg;
    1448:     break;
    1449:   case MAT_USE_HASH_TABLE:
    1450:     a->ht_flag = flg;
    1451:     a->ht_fact = 1.39;
    1452:     break;
    1453:   case MAT_SYMMETRIC:
    1454:   case MAT_STRUCTURALLY_SYMMETRIC:
    1455:   case MAT_HERMITIAN:
    1456:   case MAT_SUBMAT_SINGLEIS:
    1457:   case MAT_SYMMETRY_ETERNAL:
    1458:     MatCheckPreallocated(A,1);
    1459:     MatSetOption(a->A,op,flg);
    1460:     break;
    1461:   default:
    1462:     SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"unknown option %d",op);
    1463:   }
    1464:   return 0;
    1465: }

    1467: PetscErrorCode MatTranspose_MPIBAIJ(Mat A,MatReuse reuse,Mat *matout)
    1468: {
    1469:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)A->data;
    1470:   Mat_SeqBAIJ    *Aloc;
    1471:   Mat            B;
    1472:   PetscInt       M =A->rmap->N,N=A->cmap->N,*ai,*aj,i,*rvals,j,k,col;
    1473:   PetscInt       bs=A->rmap->bs,mbs=baij->mbs;
    1474:   MatScalar      *a;

    1476:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
    1477:     MatCreate(PetscObjectComm((PetscObject)A),&B);
    1478:     MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
    1479:     MatSetType(B,((PetscObject)A)->type_name);
    1480:     /* Do not know preallocation information, but must set block size */
    1481:     MatMPIBAIJSetPreallocation(B,A->rmap->bs,PETSC_DECIDE,NULL,PETSC_DECIDE,NULL);
    1482:   } else {
    1483:     B = *matout;
    1484:   }

    1486:   /* copy over the A part */
    1487:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
    1488:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
    1489:   PetscMalloc1(bs,&rvals);

    1491:   for (i=0; i<mbs; i++) {
    1492:     rvals[0] = bs*(baij->rstartbs + i);
    1493:     for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
    1494:     for (j=ai[i]; j<ai[i+1]; j++) {
    1495:       col = (baij->cstartbs+aj[j])*bs;
    1496:       for (k=0; k<bs; k++) {
    1497:         MatSetValues_MPIBAIJ(B,1,&col,bs,rvals,a,INSERT_VALUES);

    1499:         col++; a += bs;
    1500:       }
    1501:     }
    1502:   }
    1503:   /* copy over the B part */
    1504:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
    1505:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
    1506:   for (i=0; i<mbs; i++) {
    1507:     rvals[0] = bs*(baij->rstartbs + i);
    1508:     for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
    1509:     for (j=ai[i]; j<ai[i+1]; j++) {
    1510:       col = baij->garray[aj[j]]*bs;
    1511:       for (k=0; k<bs; k++) {
    1512:         MatSetValues_MPIBAIJ(B,1,&col,bs,rvals,a,INSERT_VALUES);
    1513:         col++;
    1514:         a += bs;
    1515:       }
    1516:     }
    1517:   }
    1518:   PetscFree(rvals);
    1519:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    1520:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

    1522:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) *matout = B;
    1523:   else {
    1524:     MatHeaderMerge(A,&B);
    1525:   }
    1526:   return 0;
    1527: }

    1529: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
    1530: {
    1531:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    1532:   Mat            a     = baij->A,b = baij->B;
    1533:   PetscInt       s1,s2,s3;

    1535:   MatGetLocalSize(mat,&s2,&s3);
    1536:   if (rr) {
    1537:     VecGetLocalSize(rr,&s1);
    1539:     /* Overlap communication with computation. */
    1540:     VecScatterBegin(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1541:   }
    1542:   if (ll) {
    1543:     VecGetLocalSize(ll,&s1);
    1545:     (*b->ops->diagonalscale)(b,ll,NULL);
    1546:   }
    1547:   /* scale  the diagonal block */
    1548:   (*a->ops->diagonalscale)(a,ll,rr);

    1550:   if (rr) {
    1551:     /* Do a scatter end and then right scale the off-diagonal block */
    1552:     VecScatterEnd(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1553:     (*b->ops->diagonalscale)(b,NULL,baij->lvec);
    1554:   }
    1555:   return 0;
    1556: }

    1558: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    1559: {
    1560:   Mat_MPIBAIJ   *l      = (Mat_MPIBAIJ *) A->data;
    1561:   PetscInt      *lrows;
    1562:   PetscInt       r, len;
    1563:   PetscBool      cong;

    1565:   /* get locally owned rows */
    1566:   MatZeroRowsMapLocal_Private(A,N,rows,&len,&lrows);
    1567:   /* fix right hand side if needed */
    1568:   if (x && b) {
    1569:     const PetscScalar *xx;
    1570:     PetscScalar       *bb;

    1572:     VecGetArrayRead(x,&xx);
    1573:     VecGetArray(b,&bb);
    1574:     for (r = 0; r < len; ++r) bb[lrows[r]] = diag*xx[lrows[r]];
    1575:     VecRestoreArrayRead(x,&xx);
    1576:     VecRestoreArray(b,&bb);
    1577:   }

    1579:   /* actually zap the local rows */
    1580:   /*
    1581:         Zero the required rows. If the "diagonal block" of the matrix
    1582:      is square and the user wishes to set the diagonal we use separate
    1583:      code so that MatSetValues() is not called for each diagonal allocating
    1584:      new memory, thus calling lots of mallocs and slowing things down.

    1586:   */
    1587:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
    1588:   MatZeroRows_SeqBAIJ(l->B,len,lrows,0.0,NULL,NULL);
    1589:   MatHasCongruentLayouts(A,&cong);
    1590:   if ((diag != 0.0) && cong) {
    1591:     MatZeroRows_SeqBAIJ(l->A,len,lrows,diag,NULL,NULL);
    1592:   } else if (diag != 0.0) {
    1593:     MatZeroRows_SeqBAIJ(l->A,len,lrows,0.0,NULL,NULL);
    1595:        MAT_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
    1596:     for (r = 0; r < len; ++r) {
    1597:       const PetscInt row = lrows[r] + A->rmap->rstart;
    1598:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
    1599:     }
    1600:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    1601:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    1602:   } else {
    1603:     MatZeroRows_SeqBAIJ(l->A,len,lrows,0.0,NULL,NULL);
    1604:   }
    1605:   PetscFree(lrows);

    1607:   /* only change matrix nonzero state if pattern was allowed to be changed */
    1608:   if (!((Mat_SeqBAIJ*)(l->A->data))->keepnonzeropattern) {
    1609:     PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
    1610:     MPIU_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
    1611:   }
    1612:   return 0;
    1613: }

    1615: PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    1616: {
    1617:   Mat_MPIBAIJ       *l = (Mat_MPIBAIJ*)A->data;
    1618:   PetscMPIInt       n = A->rmap->n,p = 0;
    1619:   PetscInt          i,j,k,r,len = 0,row,col,count;
    1620:   PetscInt          *lrows,*owners = A->rmap->range;
    1621:   PetscSFNode       *rrows;
    1622:   PetscSF           sf;
    1623:   const PetscScalar *xx;
    1624:   PetscScalar       *bb,*mask;
    1625:   Vec               xmask,lmask;
    1626:   Mat_SeqBAIJ       *baij = (Mat_SeqBAIJ*)l->B->data;
    1627:   PetscInt           bs = A->rmap->bs, bs2 = baij->bs2;
    1628:   PetscScalar       *aa;

    1630:   /* Create SF where leaves are input rows and roots are owned rows */
    1631:   PetscMalloc1(n, &lrows);
    1632:   for (r = 0; r < n; ++r) lrows[r] = -1;
    1633:   PetscMalloc1(N, &rrows);
    1634:   for (r = 0; r < N; ++r) {
    1635:     const PetscInt idx   = rows[r];
    1637:     if (idx < owners[p] || owners[p+1] <= idx) { /* short-circuit the search if the last p owns this row too */
    1638:       PetscLayoutFindOwner(A->rmap,idx,&p);
    1639:     }
    1640:     rrows[r].rank  = p;
    1641:     rrows[r].index = rows[r] - owners[p];
    1642:   }
    1643:   PetscSFCreate(PetscObjectComm((PetscObject) A), &sf);
    1644:   PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER);
    1645:   /* Collect flags for rows to be zeroed */
    1646:   PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
    1647:   PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
    1648:   PetscSFDestroy(&sf);
    1649:   /* Compress and put in row numbers */
    1650:   for (r = 0; r < n; ++r) if (lrows[r] >= 0) lrows[len++] = r;
    1651:   /* zero diagonal part of matrix */
    1652:   MatZeroRowsColumns(l->A,len,lrows,diag,x,b);
    1653:   /* handle off diagonal part of matrix */
    1654:   MatCreateVecs(A,&xmask,NULL);
    1655:   VecDuplicate(l->lvec,&lmask);
    1656:   VecGetArray(xmask,&bb);
    1657:   for (i=0; i<len; i++) bb[lrows[i]] = 1;
    1658:   VecRestoreArray(xmask,&bb);
    1659:   VecScatterBegin(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
    1660:   VecScatterEnd(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
    1661:   VecDestroy(&xmask);
    1662:   if (x) {
    1663:     VecScatterBegin(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1664:     VecScatterEnd(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1665:     VecGetArrayRead(l->lvec,&xx);
    1666:     VecGetArray(b,&bb);
    1667:   }
    1668:   VecGetArray(lmask,&mask);
    1669:   /* remove zeroed rows of off diagonal matrix */
    1670:   for (i = 0; i < len; ++i) {
    1671:     row   = lrows[i];
    1672:     count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
    1673:     aa    = ((MatScalar*)(baij->a)) + baij->i[row/bs]*bs2 + (row%bs);
    1674:     for (k = 0; k < count; ++k) {
    1675:       aa[0] = 0.0;
    1676:       aa   += bs;
    1677:     }
    1678:   }
    1679:   /* loop over all elements of off process part of matrix zeroing removed columns*/
    1680:   for (i = 0; i < l->B->rmap->N; ++i) {
    1681:     row = i/bs;
    1682:     for (j = baij->i[row]; j < baij->i[row+1]; ++j) {
    1683:       for (k = 0; k < bs; ++k) {
    1684:         col = bs*baij->j[j] + k;
    1685:         if (PetscAbsScalar(mask[col])) {
    1686:           aa = ((MatScalar*)(baij->a)) + j*bs2 + (i%bs) + bs*k;
    1687:           if (x) bb[i] -= aa[0]*xx[col];
    1688:           aa[0] = 0.0;
    1689:         }
    1690:       }
    1691:     }
    1692:   }
    1693:   if (x) {
    1694:     VecRestoreArray(b,&bb);
    1695:     VecRestoreArrayRead(l->lvec,&xx);
    1696:   }
    1697:   VecRestoreArray(lmask,&mask);
    1698:   VecDestroy(&lmask);
    1699:   PetscFree(lrows);

    1701:   /* only change matrix nonzero state if pattern was allowed to be changed */
    1702:   if (!((Mat_SeqBAIJ*)(l->A->data))->keepnonzeropattern) {
    1703:     PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
    1704:     MPIU_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
    1705:   }
    1706:   return 0;
    1707: }

    1709: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
    1710: {
    1711:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1713:   MatSetUnfactored(a->A);
    1714:   return 0;
    1715: }

    1717: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat*);

    1719: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscBool  *flag)
    1720: {
    1721:   Mat_MPIBAIJ    *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
    1722:   Mat            a,b,c,d;
    1723:   PetscBool      flg;

    1725:   a = matA->A; b = matA->B;
    1726:   c = matB->A; d = matB->B;

    1728:   MatEqual(a,c,&flg);
    1729:   if (flg) {
    1730:     MatEqual(b,d,&flg);
    1731:   }
    1732:   MPIU_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
    1733:   return 0;
    1734: }

    1736: PetscErrorCode MatCopy_MPIBAIJ(Mat A,Mat B,MatStructure str)
    1737: {
    1738:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
    1739:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)B->data;

    1741:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
    1742:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
    1743:     MatCopy_Basic(A,B,str);
    1744:   } else {
    1745:     MatCopy(a->A,b->A,str);
    1746:     MatCopy(a->B,b->B,str);
    1747:   }
    1748:   PetscObjectStateIncrease((PetscObject)B);
    1749:   return 0;
    1750: }

    1752: PetscErrorCode MatSetUp_MPIBAIJ(Mat A)
    1753: {
    1754:   MatMPIBAIJSetPreallocation(A,A->rmap->bs,PETSC_DEFAULT,NULL,PETSC_DEFAULT,NULL);
    1755:   return 0;
    1756: }

    1758: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y,const PetscInt *yltog,Mat X,const PetscInt *xltog,PetscInt *nnz)
    1759: {
    1760:   PetscInt       bs = Y->rmap->bs,m = Y->rmap->N/bs;
    1761:   Mat_SeqBAIJ    *x = (Mat_SeqBAIJ*)X->data;
    1762:   Mat_SeqBAIJ    *y = (Mat_SeqBAIJ*)Y->data;

    1764:   MatAXPYGetPreallocation_MPIX_private(m,x->i,x->j,xltog,y->i,y->j,yltog,nnz);
    1765:   return 0;
    1766: }

    1768: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
    1769: {
    1770:   Mat_MPIBAIJ    *xx=(Mat_MPIBAIJ*)X->data,*yy=(Mat_MPIBAIJ*)Y->data;
    1771:   PetscBLASInt   bnz,one=1;
    1772:   Mat_SeqBAIJ    *x,*y;
    1773:   PetscInt       bs2 = Y->rmap->bs*Y->rmap->bs;

    1775:   if (str == SAME_NONZERO_PATTERN) {
    1776:     PetscScalar alpha = a;
    1777:     x    = (Mat_SeqBAIJ*)xx->A->data;
    1778:     y    = (Mat_SeqBAIJ*)yy->A->data;
    1779:     PetscBLASIntCast(x->nz*bs2,&bnz);
    1780:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
    1781:     x    = (Mat_SeqBAIJ*)xx->B->data;
    1782:     y    = (Mat_SeqBAIJ*)yy->B->data;
    1783:     PetscBLASIntCast(x->nz*bs2,&bnz);
    1784:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
    1785:     PetscObjectStateIncrease((PetscObject)Y);
    1786:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
    1787:     MatAXPY_Basic(Y,a,X,str);
    1788:   } else {
    1789:     Mat      B;
    1790:     PetscInt *nnz_d,*nnz_o,bs=Y->rmap->bs;
    1791:     PetscMalloc1(yy->A->rmap->N,&nnz_d);
    1792:     PetscMalloc1(yy->B->rmap->N,&nnz_o);
    1793:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
    1794:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
    1795:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
    1796:     MatSetBlockSizesFromMats(B,Y,Y);
    1797:     MatSetType(B,MATMPIBAIJ);
    1798:     MatAXPYGetPreallocation_SeqBAIJ(yy->A,xx->A,nnz_d);
    1799:     MatAXPYGetPreallocation_MPIBAIJ(yy->B,yy->garray,xx->B,xx->garray,nnz_o);
    1800:     MatMPIBAIJSetPreallocation(B,bs,0,nnz_d,0,nnz_o);
    1801:     /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
    1802:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
    1803:     MatHeaderMerge(Y,&B);
    1804:     PetscFree(nnz_d);
    1805:     PetscFree(nnz_o);
    1806:   }
    1807:   return 0;
    1808: }

    1810: PetscErrorCode MatConjugate_MPIBAIJ(Mat mat)
    1811: {
    1812:   if (PetscDefined(USE_COMPLEX)) {
    1813:     Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)mat->data;

    1815:     MatConjugate_SeqBAIJ(a->A);
    1816:     MatConjugate_SeqBAIJ(a->B);
    1817:   }
    1818:   return 0;
    1819: }

    1821: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
    1822: {
    1823:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1825:   MatRealPart(a->A);
    1826:   MatRealPart(a->B);
    1827:   return 0;
    1828: }

    1830: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
    1831: {
    1832:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1834:   MatImaginaryPart(a->A);
    1835:   MatImaginaryPart(a->B);
    1836:   return 0;
    1837: }

    1839: PetscErrorCode MatCreateSubMatrix_MPIBAIJ(Mat mat,IS isrow,IS iscol,MatReuse call,Mat *newmat)
    1840: {
    1841:   IS             iscol_local;
    1842:   PetscInt       csize;

    1844:   ISGetLocalSize(iscol,&csize);
    1845:   if (call == MAT_REUSE_MATRIX) {
    1846:     PetscObjectQuery((PetscObject)*newmat,"ISAllGather",(PetscObject*)&iscol_local);
    1848:   } else {
    1849:     ISAllGather(iscol,&iscol_local);
    1850:   }
    1851:   MatCreateSubMatrix_MPIBAIJ_Private(mat,isrow,iscol_local,csize,call,newmat);
    1852:   if (call == MAT_INITIAL_MATRIX) {
    1853:     PetscObjectCompose((PetscObject)*newmat,"ISAllGather",(PetscObject)iscol_local);
    1854:     ISDestroy(&iscol_local);
    1855:   }
    1856:   return 0;
    1857: }

    1859: /*
    1860:   Not great since it makes two copies of the submatrix, first an SeqBAIJ
    1861:   in local and then by concatenating the local matrices the end result.
    1862:   Writing it directly would be much like MatCreateSubMatrices_MPIBAIJ().
    1863:   This routine is used for BAIJ and SBAIJ matrices (unfortunate dependency).
    1864: */
    1865: PetscErrorCode MatCreateSubMatrix_MPIBAIJ_Private(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
    1866: {
    1867:   PetscMPIInt    rank,size;
    1868:   PetscInt       i,m,n,rstart,row,rend,nz,*cwork,j,bs;
    1869:   PetscInt       *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal;
    1870:   Mat            M,Mreuse;
    1871:   MatScalar      *vwork,*aa;
    1872:   MPI_Comm       comm;
    1873:   IS             isrow_new, iscol_new;
    1874:   Mat_SeqBAIJ    *aij;

    1876:   PetscObjectGetComm((PetscObject)mat,&comm);
    1877:   MPI_Comm_rank(comm,&rank);
    1878:   MPI_Comm_size(comm,&size);
    1879:   /* The compression and expansion should be avoided. Doesn't point
    1880:      out errors, might change the indices, hence buggey */
    1881:   ISCompressIndicesGeneral(mat->rmap->N,mat->rmap->n,mat->rmap->bs,1,&isrow,&isrow_new);
    1882:   ISCompressIndicesGeneral(mat->cmap->N,mat->cmap->n,mat->cmap->bs,1,&iscol,&iscol_new);

    1884:   if (call ==  MAT_REUSE_MATRIX) {
    1885:     PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject*)&Mreuse);
    1887:     MatCreateSubMatrices_MPIBAIJ_local(mat,1,&isrow_new,&iscol_new,MAT_REUSE_MATRIX,&Mreuse);
    1888:   } else {
    1889:     MatCreateSubMatrices_MPIBAIJ_local(mat,1,&isrow_new,&iscol_new,MAT_INITIAL_MATRIX,&Mreuse);
    1890:   }
    1891:   ISDestroy(&isrow_new);
    1892:   ISDestroy(&iscol_new);
    1893:   /*
    1894:       m - number of local rows
    1895:       n - number of columns (same on all processors)
    1896:       rstart - first row in new global matrix generated
    1897:   */
    1898:   MatGetBlockSize(mat,&bs);
    1899:   MatGetSize(Mreuse,&m,&n);
    1900:   m    = m/bs;
    1901:   n    = n/bs;

    1903:   if (call == MAT_INITIAL_MATRIX) {
    1904:     aij = (Mat_SeqBAIJ*)(Mreuse)->data;
    1905:     ii  = aij->i;
    1906:     jj  = aij->j;

    1908:     /*
    1909:         Determine the number of non-zeros in the diagonal and off-diagonal
    1910:         portions of the matrix in order to do correct preallocation
    1911:     */

    1913:     /* first get start and end of "diagonal" columns */
    1914:     if (csize == PETSC_DECIDE) {
    1915:       ISGetSize(isrow,&mglobal);
    1916:       if (mglobal == n*bs) { /* square matrix */
    1917:         nlocal = m;
    1918:       } else {
    1919:         nlocal = n/size + ((n % size) > rank);
    1920:       }
    1921:     } else {
    1922:       nlocal = csize/bs;
    1923:     }
    1924:     MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
    1925:     rstart = rend - nlocal;

    1928:     /* next, compute all the lengths */
    1929:     PetscMalloc2(m+1,&dlens,m+1,&olens);
    1930:     for (i=0; i<m; i++) {
    1931:       jend = ii[i+1] - ii[i];
    1932:       olen = 0;
    1933:       dlen = 0;
    1934:       for (j=0; j<jend; j++) {
    1935:         if (*jj < rstart || *jj >= rend) olen++;
    1936:         else dlen++;
    1937:         jj++;
    1938:       }
    1939:       olens[i] = olen;
    1940:       dlens[i] = dlen;
    1941:     }
    1942:     MatCreate(comm,&M);
    1943:     MatSetSizes(M,bs*m,bs*nlocal,PETSC_DECIDE,bs*n);
    1944:     MatSetType(M,((PetscObject)mat)->type_name);
    1945:     MatMPIBAIJSetPreallocation(M,bs,0,dlens,0,olens);
    1946:     MatMPISBAIJSetPreallocation(M,bs,0,dlens,0,olens);
    1947:     PetscFree2(dlens,olens);
    1948:   } else {
    1949:     PetscInt ml,nl;

    1951:     M    = *newmat;
    1952:     MatGetLocalSize(M,&ml,&nl);
    1954:     MatZeroEntries(M);
    1955:     /*
    1956:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
    1957:        rather than the slower MatSetValues().
    1958:     */
    1959:     M->was_assembled = PETSC_TRUE;
    1960:     M->assembled     = PETSC_FALSE;
    1961:   }
    1962:   MatSetOption(M,MAT_ROW_ORIENTED,PETSC_FALSE);
    1963:   MatGetOwnershipRange(M,&rstart,&rend);
    1964:   aij  = (Mat_SeqBAIJ*)(Mreuse)->data;
    1965:   ii   = aij->i;
    1966:   jj   = aij->j;
    1967:   aa   = aij->a;
    1968:   for (i=0; i<m; i++) {
    1969:     row   = rstart/bs + i;
    1970:     nz    = ii[i+1] - ii[i];
    1971:     cwork = jj;     jj += nz;
    1972:     vwork = aa;     aa += nz*bs*bs;
    1973:     MatSetValuesBlocked_MPIBAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
    1974:   }

    1976:   MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
    1977:   MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
    1978:   *newmat = M;

    1980:   /* save submatrix used in processor for next request */
    1981:   if (call ==  MAT_INITIAL_MATRIX) {
    1982:     PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
    1983:     PetscObjectDereference((PetscObject)Mreuse);
    1984:   }
    1985:   return 0;
    1986: }

    1988: PetscErrorCode MatPermute_MPIBAIJ(Mat A,IS rowp,IS colp,Mat *B)
    1989: {
    1990:   MPI_Comm       comm,pcomm;
    1991:   PetscInt       clocal_size,nrows;
    1992:   const PetscInt *rows;
    1993:   PetscMPIInt    size;
    1994:   IS             crowp,lcolp;

    1996:   PetscObjectGetComm((PetscObject)A,&comm);
    1997:   /* make a collective version of 'rowp' */
    1998:   PetscObjectGetComm((PetscObject)rowp,&pcomm);
    1999:   if (pcomm==comm) {
    2000:     crowp = rowp;
    2001:   } else {
    2002:     ISGetSize(rowp,&nrows);
    2003:     ISGetIndices(rowp,&rows);
    2004:     ISCreateGeneral(comm,nrows,rows,PETSC_COPY_VALUES,&crowp);
    2005:     ISRestoreIndices(rowp,&rows);
    2006:   }
    2007:   ISSetPermutation(crowp);
    2008:   /* make a local version of 'colp' */
    2009:   PetscObjectGetComm((PetscObject)colp,&pcomm);
    2010:   MPI_Comm_size(pcomm,&size);
    2011:   if (size==1) {
    2012:     lcolp = colp;
    2013:   } else {
    2014:     ISAllGather(colp,&lcolp);
    2015:   }
    2016:   ISSetPermutation(lcolp);
    2017:   /* now we just get the submatrix */
    2018:   MatGetLocalSize(A,NULL,&clocal_size);
    2019:   MatCreateSubMatrix_MPIBAIJ_Private(A,crowp,lcolp,clocal_size,MAT_INITIAL_MATRIX,B);
    2020:   /* clean up */
    2021:   if (pcomm!=comm) {
    2022:     ISDestroy(&crowp);
    2023:   }
    2024:   if (size>1) {
    2025:     ISDestroy(&lcolp);
    2026:   }
    2027:   return 0;
    2028: }

    2030: PetscErrorCode  MatGetGhosts_MPIBAIJ(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[])
    2031: {
    2032:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*) mat->data;
    2033:   Mat_SeqBAIJ *B    = (Mat_SeqBAIJ*)baij->B->data;

    2035:   if (nghosts) *nghosts = B->nbs;
    2036:   if (ghosts) *ghosts = baij->garray;
    2037:   return 0;
    2038: }

    2040: PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A,Mat *newmat)
    2041: {
    2042:   Mat            B;
    2043:   Mat_MPIBAIJ    *a  = (Mat_MPIBAIJ*)A->data;
    2044:   Mat_SeqBAIJ    *ad = (Mat_SeqBAIJ*)a->A->data,*bd = (Mat_SeqBAIJ*)a->B->data;
    2045:   Mat_SeqAIJ     *b;
    2046:   PetscMPIInt    size,rank,*recvcounts = NULL,*displs = NULL;
    2047:   PetscInt       sendcount,i,*rstarts = A->rmap->range,n,cnt,j,bs = A->rmap->bs;
    2048:   PetscInt       m,*garray = a->garray,*lens,*jsendbuf,*a_jsendbuf,*b_jsendbuf;

    2050:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
    2051:   MPI_Comm_rank(PetscObjectComm((PetscObject)A),&rank);

    2053:   /* ----------------------------------------------------------------
    2054:      Tell every processor the number of nonzeros per row
    2055:   */
    2056:   PetscMalloc1(A->rmap->N/bs,&lens);
    2057:   for (i=A->rmap->rstart/bs; i<A->rmap->rend/bs; i++) {
    2058:     lens[i] = ad->i[i-A->rmap->rstart/bs+1] - ad->i[i-A->rmap->rstart/bs] + bd->i[i-A->rmap->rstart/bs+1] - bd->i[i-A->rmap->rstart/bs];
    2059:   }
    2060:   PetscMalloc1(2*size,&recvcounts);
    2061:   displs    = recvcounts + size;
    2062:   for (i=0; i<size; i++) {
    2063:     recvcounts[i] = A->rmap->range[i+1]/bs - A->rmap->range[i]/bs;
    2064:     displs[i]     = A->rmap->range[i]/bs;
    2065:   }
    2066:   MPI_Allgatherv(MPI_IN_PLACE,0,MPI_DATATYPE_NULL,lens,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
    2067:   /* ---------------------------------------------------------------
    2068:      Create the sequential matrix of the same type as the local block diagonal
    2069:   */
    2070:   MatCreate(PETSC_COMM_SELF,&B);
    2071:   MatSetSizes(B,A->rmap->N/bs,A->cmap->N/bs,PETSC_DETERMINE,PETSC_DETERMINE);
    2072:   MatSetType(B,MATSEQAIJ);
    2073:   MatSeqAIJSetPreallocation(B,0,lens);
    2074:   b    = (Mat_SeqAIJ*)B->data;

    2076:   /*--------------------------------------------------------------------
    2077:     Copy my part of matrix column indices over
    2078:   */
    2079:   sendcount  = ad->nz + bd->nz;
    2080:   jsendbuf   = b->j + b->i[rstarts[rank]/bs];
    2081:   a_jsendbuf = ad->j;
    2082:   b_jsendbuf = bd->j;
    2083:   n          = A->rmap->rend/bs - A->rmap->rstart/bs;
    2084:   cnt        = 0;
    2085:   for (i=0; i<n; i++) {

    2087:     /* put in lower diagonal portion */
    2088:     m = bd->i[i+1] - bd->i[i];
    2089:     while (m > 0) {
    2090:       /* is it above diagonal (in bd (compressed) numbering) */
    2091:       if (garray[*b_jsendbuf] > A->rmap->rstart/bs + i) break;
    2092:       jsendbuf[cnt++] = garray[*b_jsendbuf++];
    2093:       m--;
    2094:     }

    2096:     /* put in diagonal portion */
    2097:     for (j=ad->i[i]; j<ad->i[i+1]; j++) {
    2098:       jsendbuf[cnt++] = A->rmap->rstart/bs + *a_jsendbuf++;
    2099:     }

    2101:     /* put in upper diagonal portion */
    2102:     while (m-- > 0) {
    2103:       jsendbuf[cnt++] = garray[*b_jsendbuf++];
    2104:     }
    2105:   }

    2108:   /*--------------------------------------------------------------------
    2109:     Gather all column indices to all processors
    2110:   */
    2111:   for (i=0; i<size; i++) {
    2112:     recvcounts[i] = 0;
    2113:     for (j=A->rmap->range[i]/bs; j<A->rmap->range[i+1]/bs; j++) {
    2114:       recvcounts[i] += lens[j];
    2115:     }
    2116:   }
    2117:   displs[0] = 0;
    2118:   for (i=1; i<size; i++) {
    2119:     displs[i] = displs[i-1] + recvcounts[i-1];
    2120:   }
    2121:   MPI_Allgatherv(MPI_IN_PLACE,0,MPI_DATATYPE_NULL,b->j,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
    2122:   /*--------------------------------------------------------------------
    2123:     Assemble the matrix into useable form (note numerical values not yet set)
    2124:   */
    2125:   /* set the b->ilen (length of each row) values */
    2126:   PetscArraycpy(b->ilen,lens,A->rmap->N/bs);
    2127:   /* set the b->i indices */
    2128:   b->i[0] = 0;
    2129:   for (i=1; i<=A->rmap->N/bs; i++) {
    2130:     b->i[i] = b->i[i-1] + lens[i-1];
    2131:   }
    2132:   PetscFree(lens);
    2133:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    2134:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    2135:   PetscFree(recvcounts);

    2137:   if (A->symmetric) {
    2138:     MatSetOption(B,MAT_SYMMETRIC,PETSC_TRUE);
    2139:   } else if (A->hermitian) {
    2140:     MatSetOption(B,MAT_HERMITIAN,PETSC_TRUE);
    2141:   } else if (A->structurally_symmetric) {
    2142:     MatSetOption(B,MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
    2143:   }
    2144:   *newmat = B;
    2145:   return 0;
    2146: }

    2148: PetscErrorCode MatSOR_MPIBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
    2149: {
    2150:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
    2151:   Vec            bb1 = NULL;

    2153:   if (flag == SOR_APPLY_UPPER) {
    2154:     (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
    2155:     return 0;
    2156:   }

    2158:   if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) {
    2159:     VecDuplicate(bb,&bb1);
    2160:   }

    2162:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
    2163:     if (flag & SOR_ZERO_INITIAL_GUESS) {
    2164:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
    2165:       its--;
    2166:     }

    2168:     while (its--) {
    2169:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
    2170:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

    2172:       /* update rhs: bb1 = bb - B*x */
    2173:       VecScale(mat->lvec,-1.0);
    2174:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

    2176:       /* local sweep */
    2177:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,1,xx);
    2178:     }
    2179:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
    2180:     if (flag & SOR_ZERO_INITIAL_GUESS) {
    2181:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
    2182:       its--;
    2183:     }
    2184:     while (its--) {
    2185:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
    2186:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

    2188:       /* update rhs: bb1 = bb - B*x */
    2189:       VecScale(mat->lvec,-1.0);
    2190:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

    2192:       /* local sweep */
    2193:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,1,xx);
    2194:     }
    2195:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2196:     if (flag & SOR_ZERO_INITIAL_GUESS) {
    2197:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
    2198:       its--;
    2199:     }
    2200:     while (its--) {
    2201:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
    2202:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

    2204:       /* update rhs: bb1 = bb - B*x */
    2205:       VecScale(mat->lvec,-1.0);
    2206:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

    2208:       /* local sweep */
    2209:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,1,xx);
    2210:     }
    2211:   } else SETERRQ(PetscObjectComm((PetscObject)matin),PETSC_ERR_SUP,"Parallel version of SOR requested not supported");

    2213:   VecDestroy(&bb1);
    2214:   return 0;
    2215: }

    2217: PetscErrorCode MatGetColumnReductions_MPIBAIJ(Mat A,PetscInt type,PetscReal *reductions)
    2218: {
    2219:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)A->data;
    2220:   PetscInt       m,N,i,*garray = aij->garray;
    2221:   PetscInt       ib,jb,bs = A->rmap->bs;
    2222:   Mat_SeqBAIJ    *a_aij = (Mat_SeqBAIJ*) aij->A->data;
    2223:   MatScalar      *a_val = a_aij->a;
    2224:   Mat_SeqBAIJ    *b_aij = (Mat_SeqBAIJ*) aij->B->data;
    2225:   MatScalar      *b_val = b_aij->a;
    2226:   PetscReal      *work;

    2228:   MatGetSize(A,&m,&N);
    2229:   PetscCalloc1(N,&work);
    2230:   if (type == NORM_2) {
    2231:     for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
    2232:       for (jb=0; jb<bs; jb++) {
    2233:         for (ib=0; ib<bs; ib++) {
    2234:           work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
    2235:           a_val++;
    2236:         }
    2237:       }
    2238:     }
    2239:     for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
    2240:       for (jb=0; jb<bs; jb++) {
    2241:         for (ib=0; ib<bs; ib++) {
    2242:           work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
    2243:           b_val++;
    2244:         }
    2245:       }
    2246:     }
    2247:   } else if (type == NORM_1) {
    2248:     for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
    2249:       for (jb=0; jb<bs; jb++) {
    2250:         for (ib=0; ib<bs; ib++) {
    2251:           work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
    2252:           a_val++;
    2253:         }
    2254:       }
    2255:     }
    2256:     for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
    2257:       for (jb=0; jb<bs; jb++) {
    2258:        for (ib=0; ib<bs; ib++) {
    2259:           work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
    2260:           b_val++;
    2261:         }
    2262:       }
    2263:     }
    2264:   } else if (type == NORM_INFINITY) {
    2265:     for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
    2266:       for (jb=0; jb<bs; jb++) {
    2267:         for (ib=0; ib<bs; ib++) {
    2268:           int col = A->cmap->rstart + a_aij->j[i] * bs + jb;
    2269:           work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
    2270:           a_val++;
    2271:         }
    2272:       }
    2273:     }
    2274:     for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
    2275:       for (jb=0; jb<bs; jb++) {
    2276:         for (ib=0; ib<bs; ib++) {
    2277:           int col = garray[b_aij->j[i]] * bs + jb;
    2278:           work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
    2279:           b_val++;
    2280:         }
    2281:       }
    2282:     }
    2283:   } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
    2284:     for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
    2285:       for (jb=0; jb<bs; jb++) {
    2286:         for (ib=0; ib<bs; ib++) {
    2287:           work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
    2288:           a_val++;
    2289:         }
    2290:       }
    2291:     }
    2292:     for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
    2293:       for (jb=0; jb<bs; jb++) {
    2294:        for (ib=0; ib<bs; ib++) {
    2295:           work[garray[b_aij->j[i]] * bs + jb] += PetscRealPart(*b_val);
    2296:           b_val++;
    2297:         }
    2298:       }
    2299:     }
    2300:   } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
    2301:     for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
    2302:       for (jb=0; jb<bs; jb++) {
    2303:         for (ib=0; ib<bs; ib++) {
    2304:           work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
    2305:           a_val++;
    2306:         }
    2307:       }
    2308:     }
    2309:     for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
    2310:       for (jb=0; jb<bs; jb++) {
    2311:        for (ib=0; ib<bs; ib++) {
    2312:           work[garray[b_aij->j[i]] * bs + jb] += PetscImaginaryPart(*b_val);
    2313:           b_val++;
    2314:         }
    2315:       }
    2316:     }
    2317:   } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Unknown reduction type");
    2318:   if (type == NORM_INFINITY) {
    2319:     MPIU_Allreduce(work,reductions,N,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
    2320:   } else {
    2321:     MPIU_Allreduce(work,reductions,N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
    2322:   }
    2323:   PetscFree(work);
    2324:   if (type == NORM_2) {
    2325:     for (i=0; i<N; i++) reductions[i] = PetscSqrtReal(reductions[i]);
    2326:   } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
    2327:     for (i=0; i<N; i++) reductions[i] /= m;
    2328:   }
    2329:   return 0;
    2330: }

    2332: PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A,const PetscScalar **values)
    2333: {
    2334:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*) A->data;

    2336:   MatInvertBlockDiagonal(a->A,values);
    2337:   A->factorerrortype             = a->A->factorerrortype;
    2338:   A->factorerror_zeropivot_value = a->A->factorerror_zeropivot_value;
    2339:   A->factorerror_zeropivot_row   = a->A->factorerror_zeropivot_row;
    2340:   return 0;
    2341: }

    2343: PetscErrorCode MatShift_MPIBAIJ(Mat Y,PetscScalar a)
    2344: {
    2345:   Mat_MPIBAIJ    *maij = (Mat_MPIBAIJ*)Y->data;
    2346:   Mat_SeqBAIJ    *aij = (Mat_SeqBAIJ*)maij->A->data;

    2348:   if (!Y->preallocated) {
    2349:     MatMPIBAIJSetPreallocation(Y,Y->rmap->bs,1,NULL,0,NULL);
    2350:   } else if (!aij->nz) {
    2351:     PetscInt nonew = aij->nonew;
    2352:     MatSeqBAIJSetPreallocation(maij->A,Y->rmap->bs,1,NULL);
    2353:     aij->nonew = nonew;
    2354:   }
    2355:   MatShift_Basic(Y,a);
    2356:   return 0;
    2357: }

    2359: PetscErrorCode MatMissingDiagonal_MPIBAIJ(Mat A,PetscBool  *missing,PetscInt *d)
    2360: {
    2361:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    2364:   MatMissingDiagonal(a->A,missing,d);
    2365:   if (d) {
    2366:     PetscInt rstart;
    2367:     MatGetOwnershipRange(A,&rstart,NULL);
    2368:     *d += rstart/A->rmap->bs;

    2370:   }
    2371:   return 0;
    2372: }

    2374: PetscErrorCode  MatGetDiagonalBlock_MPIBAIJ(Mat A,Mat *a)
    2375: {
    2376:   *a = ((Mat_MPIBAIJ*)A->data)->A;
    2377:   return 0;
    2378: }

    2380: /* -------------------------------------------------------------------*/
    2381: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
    2382:                                        MatGetRow_MPIBAIJ,
    2383:                                        MatRestoreRow_MPIBAIJ,
    2384:                                        MatMult_MPIBAIJ,
    2385:                                 /* 4*/ MatMultAdd_MPIBAIJ,
    2386:                                        MatMultTranspose_MPIBAIJ,
    2387:                                        MatMultTransposeAdd_MPIBAIJ,
    2388:                                        NULL,
    2389:                                        NULL,
    2390:                                        NULL,
    2391:                                 /*10*/ NULL,
    2392:                                        NULL,
    2393:                                        NULL,
    2394:                                        MatSOR_MPIBAIJ,
    2395:                                        MatTranspose_MPIBAIJ,
    2396:                                 /*15*/ MatGetInfo_MPIBAIJ,
    2397:                                        MatEqual_MPIBAIJ,
    2398:                                        MatGetDiagonal_MPIBAIJ,
    2399:                                        MatDiagonalScale_MPIBAIJ,
    2400:                                        MatNorm_MPIBAIJ,
    2401:                                 /*20*/ MatAssemblyBegin_MPIBAIJ,
    2402:                                        MatAssemblyEnd_MPIBAIJ,
    2403:                                        MatSetOption_MPIBAIJ,
    2404:                                        MatZeroEntries_MPIBAIJ,
    2405:                                 /*24*/ MatZeroRows_MPIBAIJ,
    2406:                                        NULL,
    2407:                                        NULL,
    2408:                                        NULL,
    2409:                                        NULL,
    2410:                                 /*29*/ MatSetUp_MPIBAIJ,
    2411:                                        NULL,
    2412:                                        NULL,
    2413:                                        MatGetDiagonalBlock_MPIBAIJ,
    2414:                                        NULL,
    2415:                                 /*34*/ MatDuplicate_MPIBAIJ,
    2416:                                        NULL,
    2417:                                        NULL,
    2418:                                        NULL,
    2419:                                        NULL,
    2420:                                 /*39*/ MatAXPY_MPIBAIJ,
    2421:                                        MatCreateSubMatrices_MPIBAIJ,
    2422:                                        MatIncreaseOverlap_MPIBAIJ,
    2423:                                        MatGetValues_MPIBAIJ,
    2424:                                        MatCopy_MPIBAIJ,
    2425:                                 /*44*/ NULL,
    2426:                                        MatScale_MPIBAIJ,
    2427:                                        MatShift_MPIBAIJ,
    2428:                                        NULL,
    2429:                                        MatZeroRowsColumns_MPIBAIJ,
    2430:                                 /*49*/ NULL,
    2431:                                        NULL,
    2432:                                        NULL,
    2433:                                        NULL,
    2434:                                        NULL,
    2435:                                 /*54*/ MatFDColoringCreate_MPIXAIJ,
    2436:                                        NULL,
    2437:                                        MatSetUnfactored_MPIBAIJ,
    2438:                                        MatPermute_MPIBAIJ,
    2439:                                        MatSetValuesBlocked_MPIBAIJ,
    2440:                                 /*59*/ MatCreateSubMatrix_MPIBAIJ,
    2441:                                        MatDestroy_MPIBAIJ,
    2442:                                        MatView_MPIBAIJ,
    2443:                                        NULL,
    2444:                                        NULL,
    2445:                                 /*64*/ NULL,
    2446:                                        NULL,
    2447:                                        NULL,
    2448:                                        NULL,
    2449:                                        NULL,
    2450:                                 /*69*/ MatGetRowMaxAbs_MPIBAIJ,
    2451:                                        NULL,
    2452:                                        NULL,
    2453:                                        NULL,
    2454:                                        NULL,
    2455:                                 /*74*/ NULL,
    2456:                                        MatFDColoringApply_BAIJ,
    2457:                                        NULL,
    2458:                                        NULL,
    2459:                                        NULL,
    2460:                                 /*79*/ NULL,
    2461:                                        NULL,
    2462:                                        NULL,
    2463:                                        NULL,
    2464:                                        MatLoad_MPIBAIJ,
    2465:                                 /*84*/ NULL,
    2466:                                        NULL,
    2467:                                        NULL,
    2468:                                        NULL,
    2469:                                        NULL,
    2470:                                 /*89*/ NULL,
    2471:                                        NULL,
    2472:                                        NULL,
    2473:                                        NULL,
    2474:                                        NULL,
    2475:                                 /*94*/ NULL,
    2476:                                        NULL,
    2477:                                        NULL,
    2478:                                        NULL,
    2479:                                        NULL,
    2480:                                 /*99*/ NULL,
    2481:                                        NULL,
    2482:                                        NULL,
    2483:                                        MatConjugate_MPIBAIJ,
    2484:                                        NULL,
    2485:                                 /*104*/NULL,
    2486:                                        MatRealPart_MPIBAIJ,
    2487:                                        MatImaginaryPart_MPIBAIJ,
    2488:                                        NULL,
    2489:                                        NULL,
    2490:                                 /*109*/NULL,
    2491:                                        NULL,
    2492:                                        NULL,
    2493:                                        NULL,
    2494:                                        MatMissingDiagonal_MPIBAIJ,
    2495:                                 /*114*/MatGetSeqNonzeroStructure_MPIBAIJ,
    2496:                                        NULL,
    2497:                                        MatGetGhosts_MPIBAIJ,
    2498:                                        NULL,
    2499:                                        NULL,
    2500:                                 /*119*/NULL,
    2501:                                        NULL,
    2502:                                        NULL,
    2503:                                        NULL,
    2504:                                        MatGetMultiProcBlock_MPIBAIJ,
    2505:                                 /*124*/NULL,
    2506:                                        MatGetColumnReductions_MPIBAIJ,
    2507:                                        MatInvertBlockDiagonal_MPIBAIJ,
    2508:                                        NULL,
    2509:                                        NULL,
    2510:                                /*129*/ NULL,
    2511:                                        NULL,
    2512:                                        NULL,
    2513:                                        NULL,
    2514:                                        NULL,
    2515:                                /*134*/ NULL,
    2516:                                        NULL,
    2517:                                        NULL,
    2518:                                        NULL,
    2519:                                        NULL,
    2520:                                /*139*/ MatSetBlockSizes_Default,
    2521:                                        NULL,
    2522:                                        NULL,
    2523:                                        MatFDColoringSetUp_MPIXAIJ,
    2524:                                        NULL,
    2525:                                 /*144*/MatCreateMPIMatConcatenateSeqMat_MPIBAIJ,
    2526:                                        NULL,
    2527:                                        NULL,
    2528:                                        NULL
    2529: };

    2531: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat,MatType,MatReuse,Mat*);
    2532: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);

    2534: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt ii[],const PetscInt jj[],const PetscScalar V[])
    2535: {
    2536:   PetscInt       m,rstart,cstart,cend;
    2537:   PetscInt       i,j,dlen,olen,nz,nz_max=0,*d_nnz=NULL,*o_nnz=NULL;
    2538:   const PetscInt *JJ    =NULL;
    2539:   PetscScalar    *values=NULL;
    2540:   PetscBool      roworiented = ((Mat_MPIBAIJ*)B->data)->roworiented;
    2541:   PetscBool      nooffprocentries;

    2543:   PetscLayoutSetBlockSize(B->rmap,bs);
    2544:   PetscLayoutSetBlockSize(B->cmap,bs);
    2545:   PetscLayoutSetUp(B->rmap);
    2546:   PetscLayoutSetUp(B->cmap);
    2547:   PetscLayoutGetBlockSize(B->rmap,&bs);
    2548:   m      = B->rmap->n/bs;
    2549:   rstart = B->rmap->rstart/bs;
    2550:   cstart = B->cmap->rstart/bs;
    2551:   cend   = B->cmap->rend/bs;

    2554:   PetscMalloc2(m,&d_nnz,m,&o_nnz);
    2555:   for (i=0; i<m; i++) {
    2556:     nz = ii[i+1] - ii[i];
    2558:     nz_max = PetscMax(nz_max,nz);
    2559:     dlen   = 0;
    2560:     olen   = 0;
    2561:     JJ     = jj + ii[i];
    2562:     for (j=0; j<nz; j++) {
    2563:       if (*JJ < cstart || *JJ >= cend) olen++;
    2564:       else dlen++;
    2565:       JJ++;
    2566:     }
    2567:     d_nnz[i] = dlen;
    2568:     o_nnz[i] = olen;
    2569:   }
    2570:   MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
    2571:   PetscFree2(d_nnz,o_nnz);

    2573:   values = (PetscScalar*)V;
    2574:   if (!values) {
    2575:     PetscCalloc1(bs*bs*nz_max,&values);
    2576:   }
    2577:   for (i=0; i<m; i++) {
    2578:     PetscInt          row    = i + rstart;
    2579:     PetscInt          ncols  = ii[i+1] - ii[i];
    2580:     const PetscInt    *icols = jj + ii[i];
    2581:     if (bs == 1 || !roworiented) {         /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
    2582:       const PetscScalar *svals = values + (V ? (bs*bs*ii[i]) : 0);
    2583:       MatSetValuesBlocked_MPIBAIJ(B,1,&row,ncols,icols,svals,INSERT_VALUES);
    2584:     } else {                    /* block ordering does not match so we can only insert one block at a time. */
    2585:       PetscInt j;
    2586:       for (j=0; j<ncols; j++) {
    2587:         const PetscScalar *svals = values + (V ? (bs*bs*(ii[i]+j)) : 0);
    2588:         MatSetValuesBlocked_MPIBAIJ(B,1,&row,1,&icols[j],svals,INSERT_VALUES);
    2589:       }
    2590:     }
    2591:   }

    2593:   if (!V) PetscFree(values);
    2594:   nooffprocentries    = B->nooffprocentries;
    2595:   B->nooffprocentries = PETSC_TRUE;
    2596:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    2597:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    2598:   B->nooffprocentries = nooffprocentries;

    2600:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
    2601:   return 0;
    2602: }

    2604: /*@C
    2605:    MatMPIBAIJSetPreallocationCSR - Creates a sparse parallel matrix in BAIJ format using the given nonzero structure and (optional) numerical values

    2607:    Collective

    2609:    Input Parameters:
    2610: +  B - the matrix
    2611: .  bs - the block size
    2612: .  i - the indices into j for the start of each local row (starts with zero)
    2613: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
    2614: -  v - optional values in the matrix

    2616:    Level: advanced

    2618:    Notes:
    2619:     The order of the entries in values is specified by the MatOption MAT_ROW_ORIENTED.  For example, C programs
    2620:    may want to use the default MAT_ROW_ORIENTED=PETSC_TRUE and use an array v[nnz][bs][bs] where the second index is
    2621:    over rows within a block and the last index is over columns within a block row.  Fortran programs will likely set
    2622:    MAT_ROW_ORIENTED=PETSC_FALSE and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a
    2623:    block column and the second index is over columns within a block.

    2625:    Though this routine has Preallocation() in the name it also sets the exact nonzero locations of the matrix entries and usually the numerical values as well

    2627: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateAIJ(), MPIAIJ, MatCreateMPIBAIJWithArrays(), MPIBAIJ
    2628: @*/
    2629: PetscErrorCode  MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
    2630: {
    2634:   PetscTryMethod(B,"MatMPIBAIJSetPreallocationCSR_C",(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,bs,i,j,v));
    2635:   return 0;
    2636: }

    2638: PetscErrorCode  MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt *d_nnz,PetscInt o_nz,const PetscInt *o_nnz)
    2639: {
    2640:   Mat_MPIBAIJ    *b;
    2641:   PetscInt       i;
    2642:   PetscMPIInt    size;

    2644:   MatSetBlockSize(B,PetscAbs(bs));
    2645:   PetscLayoutSetUp(B->rmap);
    2646:   PetscLayoutSetUp(B->cmap);
    2647:   PetscLayoutGetBlockSize(B->rmap,&bs);

    2649:   if (d_nnz) {
    2650:     for (i=0; i<B->rmap->n/bs; i++) {
    2652:     }
    2653:   }
    2654:   if (o_nnz) {
    2655:     for (i=0; i<B->rmap->n/bs; i++) {
    2657:     }
    2658:   }

    2660:   b      = (Mat_MPIBAIJ*)B->data;
    2661:   b->bs2 = bs*bs;
    2662:   b->mbs = B->rmap->n/bs;
    2663:   b->nbs = B->cmap->n/bs;
    2664:   b->Mbs = B->rmap->N/bs;
    2665:   b->Nbs = B->cmap->N/bs;

    2667:   for (i=0; i<=b->size; i++) {
    2668:     b->rangebs[i] = B->rmap->range[i]/bs;
    2669:   }
    2670:   b->rstartbs = B->rmap->rstart/bs;
    2671:   b->rendbs   = B->rmap->rend/bs;
    2672:   b->cstartbs = B->cmap->rstart/bs;
    2673:   b->cendbs   = B->cmap->rend/bs;

    2675: #if defined(PETSC_USE_CTABLE)
    2676:   PetscTableDestroy(&b->colmap);
    2677: #else
    2678:   PetscFree(b->colmap);
    2679: #endif
    2680:   PetscFree(b->garray);
    2681:   VecDestroy(&b->lvec);
    2682:   VecScatterDestroy(&b->Mvctx);

    2684:   /* Because the B will have been resized we simply destroy it and create a new one each time */
    2685:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
    2686:   MatDestroy(&b->B);
    2687:   MatCreate(PETSC_COMM_SELF,&b->B);
    2688:   MatSetSizes(b->B,B->rmap->n,size > 1 ? B->cmap->N : 0,B->rmap->n,size > 1 ? B->cmap->N : 0);
    2689:   MatSetType(b->B,MATSEQBAIJ);
    2690:   PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);

    2692:   if (!B->preallocated) {
    2693:     MatCreate(PETSC_COMM_SELF,&b->A);
    2694:     MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
    2695:     MatSetType(b->A,MATSEQBAIJ);
    2696:     PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
    2697:     MatStashCreate_Private(PetscObjectComm((PetscObject)B),bs,&B->bstash);
    2698:   }

    2700:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
    2701:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
    2702:   B->preallocated  = PETSC_TRUE;
    2703:   B->was_assembled = PETSC_FALSE;
    2704:   B->assembled     = PETSC_FALSE;
    2705:   return 0;
    2706: }

    2708: extern PetscErrorCode  MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
    2709: extern PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);

    2711: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype,MatReuse reuse,Mat *adj)
    2712: {
    2713:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)B->data;
    2714:   Mat_SeqBAIJ    *d  = (Mat_SeqBAIJ*) b->A->data,*o = (Mat_SeqBAIJ*) b->B->data;
    2715:   PetscInt       M   = B->rmap->n/B->rmap->bs,i,*ii,*jj,cnt,j,k,rstart = B->rmap->rstart/B->rmap->bs;
    2716:   const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;

    2718:   PetscMalloc1(M+1,&ii);
    2719:   ii[0] = 0;
    2720:   for (i=0; i<M; i++) {
    2723:     ii[i+1] = ii[i] + id[i+1] - id[i] + io[i+1] - io[i];
    2724:     /* remove one from count of matrix has diagonal */
    2725:     for (j=id[i]; j<id[i+1]; j++) {
    2726:       if (jd[j] == i) {ii[i+1]--;break;}
    2727:     }
    2728:   }
    2729:   PetscMalloc1(ii[M],&jj);
    2730:   cnt  = 0;
    2731:   for (i=0; i<M; i++) {
    2732:     for (j=io[i]; j<io[i+1]; j++) {
    2733:       if (garray[jo[j]] > rstart) break;
    2734:       jj[cnt++] = garray[jo[j]];
    2735:     }
    2736:     for (k=id[i]; k<id[i+1]; k++) {
    2737:       if (jd[k] != i) {
    2738:         jj[cnt++] = rstart + jd[k];
    2739:       }
    2740:     }
    2741:     for (; j<io[i+1]; j++) {
    2742:       jj[cnt++] = garray[jo[j]];
    2743:     }
    2744:   }
    2745:   MatCreateMPIAdj(PetscObjectComm((PetscObject)B),M,B->cmap->N/B->rmap->bs,ii,jj,NULL,adj);
    2746:   return 0;
    2747: }

    2749: #include <../src/mat/impls/aij/mpi/mpiaij.h>

    2751: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat,MatType,MatReuse,Mat*);

    2753: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A,MatType newtype,MatReuse reuse,Mat *newmat)
    2754: {
    2755:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
    2756:   Mat_MPIAIJ  *b;
    2757:   Mat         B;

    2761:   if (reuse == MAT_REUSE_MATRIX) {
    2762:     B = *newmat;
    2763:   } else {
    2764:     MatCreate(PetscObjectComm((PetscObject)A),&B);
    2765:     MatSetType(B,MATMPIAIJ);
    2766:     MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    2767:     MatSetBlockSizes(B,A->rmap->bs,A->cmap->bs);
    2768:     MatSeqAIJSetPreallocation(B,0,NULL);
    2769:     MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);
    2770:   }
    2771:   b = (Mat_MPIAIJ*) B->data;

    2773:   if (reuse == MAT_REUSE_MATRIX) {
    2774:     MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A);
    2775:     MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B);
    2776:   } else {
    2777:     MatDestroy(&b->A);
    2778:     MatDestroy(&b->B);
    2779:     MatDisAssemble_MPIBAIJ(A);
    2780:     MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A);
    2781:     MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B);
    2782:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    2783:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    2784:   }
    2785:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    2786:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

    2788:   if (reuse == MAT_INPLACE_MATRIX) {
    2789:     MatHeaderReplace(A,&B);
    2790:   } else {
    2791:    *newmat = B;
    2792:   }
    2793:   return 0;
    2794: }

    2796: /*MC
    2797:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

    2799:    Options Database Keys:
    2800: + -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()
    2801: . -mat_block_size <bs> - set the blocksize used to store the matrix
    2802: . -mat_baij_mult_version version - indicate the version of the matrix-vector product to use  (0 often indicates using BLAS)
    2803: - -mat_use_hash_table <fact> - set hash table factor

    2805:    Level: beginner

    2807:    Notes:
    2808:     MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
    2809:     space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored

    2811: .seealso: MatCreateBAIJ
    2812: M*/

    2814: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat,MatType,MatReuse,Mat*);

    2816: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
    2817: {
    2818:   Mat_MPIBAIJ    *b;
    2820:   PetscBool      flg = PETSC_FALSE;

    2822:   PetscNewLog(B,&b);
    2823:   B->data = (void*)b;

    2825:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
    2826:   B->assembled = PETSC_FALSE;

    2828:   B->insertmode = NOT_SET_VALUES;
    2829:   MPI_Comm_rank(PetscObjectComm((PetscObject)B),&b->rank);
    2830:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&b->size);

    2832:   /* build local table of row and column ownerships */
    2833:   PetscMalloc1(b->size+1,&b->rangebs);

    2835:   /* build cache for off array entries formed */
    2836:   MatStashCreate_Private(PetscObjectComm((PetscObject)B),1,&B->stash);

    2838:   b->donotstash  = PETSC_FALSE;
    2839:   b->colmap      = NULL;
    2840:   b->garray      = NULL;
    2841:   b->roworiented = PETSC_TRUE;

    2843:   /* stuff used in block assembly */
    2844:   b->barray = NULL;

    2846:   /* stuff used for matrix vector multiply */
    2847:   b->lvec  = NULL;
    2848:   b->Mvctx = NULL;

    2850:   /* stuff for MatGetRow() */
    2851:   b->rowindices   = NULL;
    2852:   b->rowvalues    = NULL;
    2853:   b->getrowactive = PETSC_FALSE;

    2855:   /* hash table stuff */
    2856:   b->ht           = NULL;
    2857:   b->hd           = NULL;
    2858:   b->ht_size      = 0;
    2859:   b->ht_flag      = PETSC_FALSE;
    2860:   b->ht_fact      = 0;
    2861:   b->ht_total_ct  = 0;
    2862:   b->ht_insert_ct = 0;

    2864:   /* stuff for MatCreateSubMatrices_MPIBAIJ_local() */
    2865:   b->ijonly = PETSC_FALSE;

    2867:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpiadj_C",MatConvert_MPIBAIJ_MPIAdj);
    2868:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpiaij_C",MatConvert_MPIBAIJ_MPIAIJ);
    2869:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpisbaij_C",MatConvert_MPIBAIJ_MPISBAIJ);
    2870: #if defined(PETSC_HAVE_HYPRE)
    2871:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_hypre_C",MatConvert_AIJ_HYPRE);
    2872: #endif
    2873:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_MPIBAIJ);
    2874:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_MPIBAIJ);
    2875:   PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",MatMPIBAIJSetPreallocation_MPIBAIJ);
    2876:   PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
    2877:   PetscObjectComposeFunction((PetscObject)B,"MatDiagonalScaleLocal_C",MatDiagonalScaleLocal_MPIBAIJ);
    2878:   PetscObjectComposeFunction((PetscObject)B,"MatSetHashTableFactor_C",MatSetHashTableFactor_MPIBAIJ);
    2879:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_is_C",MatConvert_XAIJ_IS);
    2880:   PetscObjectChangeTypeName((PetscObject)B,MATMPIBAIJ);

    2882:   PetscOptionsBegin(PetscObjectComm((PetscObject)B),NULL,"Options for loading MPIBAIJ matrix 1","Mat");
    2883:   PetscOptionsName("-mat_use_hash_table","Use hash table to save time in constructing matrix","MatSetOption",&flg);
    2884:   if (flg) {
    2885:     PetscReal fact = 1.39;
    2886:     MatSetOption(B,MAT_USE_HASH_TABLE,PETSC_TRUE);
    2887:     PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,NULL);
    2888:     if (fact <= 1.0) fact = 1.39;
    2889:     MatMPIBAIJSetHashTableFactor(B,fact);
    2890:     PetscInfo(B,"Hash table Factor used %5.2g\n",(double)fact);
    2891:   }
    2892:   PetscOptionsEnd();
    2893:   return 0;
    2894: }

    2896: /*MC
    2897:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

    2899:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
    2900:    and MATMPIBAIJ otherwise.

    2902:    Options Database Keys:
    2903: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

    2905:   Level: beginner

    2907: .seealso: MatCreateBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
    2908: M*/

    2910: /*@C
    2911:    MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
    2912:    (block compressed row).  For good matrix assembly performance
    2913:    the user should preallocate the matrix storage by setting the parameters
    2914:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
    2915:    performance can be increased by more than a factor of 50.

    2917:    Collective on Mat

    2919:    Input Parameters:
    2920: +  B - the matrix
    2921: .  bs   - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row
    2922:           blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs()
    2923: .  d_nz  - number of block nonzeros per block row in diagonal portion of local
    2924:            submatrix  (same for all local rows)
    2925: .  d_nnz - array containing the number of block nonzeros in the various block rows
    2926:            of the in diagonal portion of the local (possibly different for each block
    2927:            row) or NULL.  If you plan to factor the matrix you must leave room for the diagonal entry and
    2928:            set it even if it is zero.
    2929: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
    2930:            submatrix (same for all local rows).
    2931: -  o_nnz - array containing the number of nonzeros in the various block rows of the
    2932:            off-diagonal portion of the local submatrix (possibly different for
    2933:            each block row) or NULL.

    2935:    If the *_nnz parameter is given then the *_nz parameter is ignored

    2937:    Options Database Keys:
    2938: +   -mat_block_size - size of the blocks to use
    2939: -   -mat_use_hash_table <fact> - set hash table factor

    2941:    Notes:
    2942:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
    2943:    than it must be used on all processors that share the object for that argument.

    2945:    Storage Information:
    2946:    For a square global matrix we define each processor's diagonal portion
    2947:    to be its local rows and the corresponding columns (a square submatrix);
    2948:    each processor's off-diagonal portion encompasses the remainder of the
    2949:    local matrix (a rectangular submatrix).

    2951:    The user can specify preallocated storage for the diagonal part of
    2952:    the local submatrix with either d_nz or d_nnz (not both).  Set
    2953:    d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
    2954:    memory allocation.  Likewise, specify preallocated storage for the
    2955:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

    2957:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
    2958:    the figure below we depict these three local rows and all columns (0-11).

    2960: .vb
    2961:            0 1 2 3 4 5 6 7 8 9 10 11
    2962:           --------------------------
    2963:    row 3  |o o o d d d o o o o  o  o
    2964:    row 4  |o o o d d d o o o o  o  o
    2965:    row 5  |o o o d d d o o o o  o  o
    2966:           --------------------------
    2967: .ve

    2969:    Thus, any entries in the d locations are stored in the d (diagonal)
    2970:    submatrix, and any entries in the o locations are stored in the
    2971:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
    2972:    stored simply in the MATSEQBAIJ format for compressed row storage.

    2974:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
    2975:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
    2976:    In general, for PDE problems in which most nonzeros are near the diagonal,
    2977:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
    2978:    or you will get TERRIBLE performance; see the users' manual chapter on
    2979:    matrices.

    2981:    You can call MatGetInfo() to get information on how effective the preallocation was;
    2982:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
    2983:    You can also run with the option -info and look for messages with the string
    2984:    malloc in them to see if additional memory allocation was needed.

    2986:    Level: intermediate

    2988: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateBAIJ(), MatMPIBAIJSetPreallocationCSR(), PetscSplitOwnership()
    2989: @*/
    2990: PetscErrorCode  MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
    2991: {
    2995:   PetscTryMethod(B,"MatMPIBAIJSetPreallocation_C",(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]),(B,bs,d_nz,d_nnz,o_nz,o_nnz));
    2996:   return 0;
    2997: }

    2999: /*@C
    3000:    MatCreateBAIJ - Creates a sparse parallel matrix in block AIJ format
    3001:    (block compressed row).  For good matrix assembly performance
    3002:    the user should preallocate the matrix storage by setting the parameters
    3003:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
    3004:    performance can be increased by more than a factor of 50.

    3006:    Collective

    3008:    Input Parameters:
    3009: +  comm - MPI communicator
    3010: .  bs   - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row
    3011:           blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs()
    3012: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
    3013:            This value should be the same as the local size used in creating the
    3014:            y vector for the matrix-vector product y = Ax.
    3015: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
    3016:            This value should be the same as the local size used in creating the
    3017:            x vector for the matrix-vector product y = Ax.
    3018: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
    3019: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
    3020: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local
    3021:            submatrix  (same for all local rows)
    3022: .  d_nnz - array containing the number of nonzero blocks in the various block rows
    3023:            of the in diagonal portion of the local (possibly different for each block
    3024:            row) or NULL.  If you plan to factor the matrix you must leave room for the diagonal entry
    3025:            and set it even if it is zero.
    3026: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
    3027:            submatrix (same for all local rows).
    3028: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
    3029:            off-diagonal portion of the local submatrix (possibly different for
    3030:            each block row) or NULL.

    3032:    Output Parameter:
    3033: .  A - the matrix

    3035:    Options Database Keys:
    3036: +   -mat_block_size - size of the blocks to use
    3037: -   -mat_use_hash_table <fact> - set hash table factor

    3039:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
    3040:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
    3041:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

    3043:    Notes:
    3044:    If the *_nnz parameter is given then the *_nz parameter is ignored

    3046:    A nonzero block is any block that as 1 or more nonzeros in it

    3048:    The user MUST specify either the local or global matrix dimensions
    3049:    (possibly both).

    3051:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
    3052:    than it must be used on all processors that share the object for that argument.

    3054:    Storage Information:
    3055:    For a square global matrix we define each processor's diagonal portion
    3056:    to be its local rows and the corresponding columns (a square submatrix);
    3057:    each processor's off-diagonal portion encompasses the remainder of the
    3058:    local matrix (a rectangular submatrix).

    3060:    The user can specify preallocated storage for the diagonal part of
    3061:    the local submatrix with either d_nz or d_nnz (not both).  Set
    3062:    d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
    3063:    memory allocation.  Likewise, specify preallocated storage for the
    3064:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

    3066:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
    3067:    the figure below we depict these three local rows and all columns (0-11).

    3069: .vb
    3070:            0 1 2 3 4 5 6 7 8 9 10 11
    3071:           --------------------------
    3072:    row 3  |o o o d d d o o o o  o  o
    3073:    row 4  |o o o d d d o o o o  o  o
    3074:    row 5  |o o o d d d o o o o  o  o
    3075:           --------------------------
    3076: .ve

    3078:    Thus, any entries in the d locations are stored in the d (diagonal)
    3079:    submatrix, and any entries in the o locations are stored in the
    3080:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
    3081:    stored simply in the MATSEQBAIJ format for compressed row storage.

    3083:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
    3084:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
    3085:    In general, for PDE problems in which most nonzeros are near the diagonal,
    3086:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
    3087:    or you will get TERRIBLE performance; see the users' manual chapter on
    3088:    matrices.

    3090:    Level: intermediate

    3092: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
    3093: @*/
    3094: PetscErrorCode  MatCreateBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
    3095: {
    3096:   PetscMPIInt    size;

    3098:   MatCreate(comm,A);
    3099:   MatSetSizes(*A,m,n,M,N);
    3100:   MPI_Comm_size(comm,&size);
    3101:   if (size > 1) {
    3102:     MatSetType(*A,MATMPIBAIJ);
    3103:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
    3104:   } else {
    3105:     MatSetType(*A,MATSEQBAIJ);
    3106:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
    3107:   }
    3108:   return 0;
    3109: }

    3111: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
    3112: {
    3113:   Mat            mat;
    3114:   Mat_MPIBAIJ    *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
    3115:   PetscInt       len=0;

    3117:   *newmat = NULL;
    3118:   MatCreate(PetscObjectComm((PetscObject)matin),&mat);
    3119:   MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
    3120:   MatSetType(mat,((PetscObject)matin)->type_name);

    3122:   mat->factortype   = matin->factortype;
    3123:   mat->preallocated = PETSC_TRUE;
    3124:   mat->assembled    = PETSC_TRUE;
    3125:   mat->insertmode   = NOT_SET_VALUES;

    3127:   a             = (Mat_MPIBAIJ*)mat->data;
    3128:   mat->rmap->bs = matin->rmap->bs;
    3129:   a->bs2        = oldmat->bs2;
    3130:   a->mbs        = oldmat->mbs;
    3131:   a->nbs        = oldmat->nbs;
    3132:   a->Mbs        = oldmat->Mbs;
    3133:   a->Nbs        = oldmat->Nbs;

    3135:   PetscLayoutReference(matin->rmap,&mat->rmap);
    3136:   PetscLayoutReference(matin->cmap,&mat->cmap);

    3138:   a->size         = oldmat->size;
    3139:   a->rank         = oldmat->rank;
    3140:   a->donotstash   = oldmat->donotstash;
    3141:   a->roworiented  = oldmat->roworiented;
    3142:   a->rowindices   = NULL;
    3143:   a->rowvalues    = NULL;
    3144:   a->getrowactive = PETSC_FALSE;
    3145:   a->barray       = NULL;
    3146:   a->rstartbs     = oldmat->rstartbs;
    3147:   a->rendbs       = oldmat->rendbs;
    3148:   a->cstartbs     = oldmat->cstartbs;
    3149:   a->cendbs       = oldmat->cendbs;

    3151:   /* hash table stuff */
    3152:   a->ht           = NULL;
    3153:   a->hd           = NULL;
    3154:   a->ht_size      = 0;
    3155:   a->ht_flag      = oldmat->ht_flag;
    3156:   a->ht_fact      = oldmat->ht_fact;
    3157:   a->ht_total_ct  = 0;
    3158:   a->ht_insert_ct = 0;

    3160:   PetscArraycpy(a->rangebs,oldmat->rangebs,a->size+1);
    3161:   if (oldmat->colmap) {
    3162: #if defined(PETSC_USE_CTABLE)
    3163:     PetscTableCreateCopy(oldmat->colmap,&a->colmap);
    3164: #else
    3165:     PetscMalloc1(a->Nbs,&a->colmap);
    3166:     PetscLogObjectMemory((PetscObject)mat,(a->Nbs)*sizeof(PetscInt));
    3167:     PetscArraycpy(a->colmap,oldmat->colmap,a->Nbs);
    3168: #endif
    3169:   } else a->colmap = NULL;

    3171:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
    3172:     PetscMalloc1(len,&a->garray);
    3173:     PetscLogObjectMemory((PetscObject)mat,len*sizeof(PetscInt));
    3174:     PetscArraycpy(a->garray,oldmat->garray,len);
    3175:   } else a->garray = NULL;

    3177:   MatStashCreate_Private(PetscObjectComm((PetscObject)matin),matin->rmap->bs,&mat->bstash);
    3178:   VecDuplicate(oldmat->lvec,&a->lvec);
    3179:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->lvec);
    3180:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
    3181:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->Mvctx);

    3183:   MatDuplicate(oldmat->A,cpvalues,&a->A);
    3184:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
    3185:   MatDuplicate(oldmat->B,cpvalues,&a->B);
    3186:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->B);
    3187:   PetscFunctionListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
    3188:   *newmat = mat;
    3189:   return 0;
    3190: }

    3192: /* Used for both MPIBAIJ and MPISBAIJ matrices */
    3193: PetscErrorCode MatLoad_MPIBAIJ_Binary(Mat mat,PetscViewer viewer)
    3194: {
    3195:   PetscInt       header[4],M,N,nz,bs,m,n,mbs,nbs,rows,cols,sum,i,j,k;
    3196:   PetscInt       *rowidxs,*colidxs,rs,cs,ce;
    3197:   PetscScalar    *matvals;

    3199:   PetscViewerSetUp(viewer);

    3201:   /* read in matrix header */
    3202:   PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
    3204:   M  = header[1]; N = header[2]; nz = header[3];

    3209:   /* set block sizes from the viewer's .info file */
    3210:   MatLoad_Binary_BlockSizes(mat,viewer);
    3211:   /* set local sizes if not set already */
    3212:   if (mat->rmap->n < 0 && M == N) mat->rmap->n = mat->cmap->n;
    3213:   if (mat->cmap->n < 0 && M == N) mat->cmap->n = mat->rmap->n;
    3214:   /* set global sizes if not set already */
    3215:   if (mat->rmap->N < 0) mat->rmap->N = M;
    3216:   if (mat->cmap->N < 0) mat->cmap->N = N;
    3217:   PetscLayoutSetUp(mat->rmap);
    3218:   PetscLayoutSetUp(mat->cmap);

    3220:   /* check if the matrix sizes are correct */
    3221:   MatGetSize(mat,&rows,&cols);
    3223:   MatGetBlockSize(mat,&bs);
    3224:   MatGetLocalSize(mat,&m,&n);
    3225:   PetscLayoutGetRange(mat->rmap,&rs,NULL);
    3226:   PetscLayoutGetRange(mat->cmap,&cs,&ce);
    3227:   mbs = m/bs; nbs = n/bs;

    3229:   /* read in row lengths and build row indices */
    3230:   PetscMalloc1(m+1,&rowidxs);
    3231:   PetscViewerBinaryReadAll(viewer,rowidxs+1,m,PETSC_DECIDE,M,PETSC_INT);
    3232:   rowidxs[0] = 0; for (i=0; i<m; i++) rowidxs[i+1] += rowidxs[i];
    3233:   MPIU_Allreduce(&rowidxs[m],&sum,1,MPIU_INT,MPI_SUM,PetscObjectComm((PetscObject)viewer));

    3236:   /* read in column indices and matrix values */
    3237:   PetscMalloc2(rowidxs[m],&colidxs,rowidxs[m],&matvals);
    3238:   PetscViewerBinaryReadAll(viewer,colidxs,rowidxs[m],PETSC_DETERMINE,PETSC_DETERMINE,PETSC_INT);
    3239:   PetscViewerBinaryReadAll(viewer,matvals,rowidxs[m],PETSC_DETERMINE,PETSC_DETERMINE,PETSC_SCALAR);

    3241:   { /* preallocate matrix storage */
    3242:     PetscBT    bt; /* helper bit set to count diagonal nonzeros */
    3243:     PetscHSetI ht; /* helper hash set to count off-diagonal nonzeros */
    3244:     PetscBool  sbaij,done;
    3245:     PetscInt   *d_nnz,*o_nnz;

    3247:     PetscBTCreate(nbs,&bt);
    3248:     PetscHSetICreate(&ht);
    3249:     PetscCalloc2(mbs,&d_nnz,mbs,&o_nnz);
    3250:     PetscObjectTypeCompare((PetscObject)mat,MATMPISBAIJ,&sbaij);
    3251:     for (i=0; i<mbs; i++) {
    3252:       PetscBTMemzero(nbs,bt);
    3253:       PetscHSetIClear(ht);
    3254:       for (k=0; k<bs; k++) {
    3255:         PetscInt row = bs*i + k;
    3256:         for (j=rowidxs[row]; j<rowidxs[row+1]; j++) {
    3257:           PetscInt col = colidxs[j];
    3258:           if (!sbaij || col >= row) {
    3259:             if (col >= cs && col < ce) {
    3260:               if (!PetscBTLookupSet(bt,(col-cs)/bs)) d_nnz[i]++;
    3261:             } else {
    3262:               PetscHSetIQueryAdd(ht,col/bs,&done);
    3263:               if (done) o_nnz[i]++;
    3264:             }
    3265:           }
    3266:         }
    3267:       }
    3268:     }
    3269:     PetscBTDestroy(&bt);
    3270:     PetscHSetIDestroy(&ht);
    3271:     MatMPIBAIJSetPreallocation(mat,bs,0,d_nnz,0,o_nnz);
    3272:     MatMPISBAIJSetPreallocation(mat,bs,0,d_nnz,0,o_nnz);
    3273:     PetscFree2(d_nnz,o_nnz);
    3274:   }

    3276:   /* store matrix values */
    3277:   for (i=0; i<m; i++) {
    3278:     PetscInt row = rs + i, s = rowidxs[i], e = rowidxs[i+1];
    3279:     (*mat->ops->setvalues)(mat,1,&row,e-s,colidxs+s,matvals+s,INSERT_VALUES);
    3280:   }

    3282:   PetscFree(rowidxs);
    3283:   PetscFree2(colidxs,matvals);
    3284:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
    3285:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
    3286:   return 0;
    3287: }

    3289: PetscErrorCode MatLoad_MPIBAIJ(Mat mat,PetscViewer viewer)
    3290: {
    3291:   PetscBool isbinary;

    3293:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    3295:   MatLoad_MPIBAIJ_Binary(mat,viewer);
    3296:   return 0;
    3297: }

    3299: /*@
    3300:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

    3302:    Input Parameters:
    3303: +  mat  - the matrix
    3304: -  fact - factor

    3306:    Not Collective, each process can use a different factor

    3308:    Level: advanced

    3310:   Notes:
    3311:    This can also be set by the command line option: -mat_use_hash_table <fact>

    3313: .seealso: MatSetOption()
    3314: @*/
    3315: PetscErrorCode  MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
    3316: {
    3317:   PetscTryMethod(mat,"MatSetHashTableFactor_C",(Mat,PetscReal),(mat,fact));
    3318:   return 0;
    3319: }

    3321: PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
    3322: {
    3323:   Mat_MPIBAIJ *baij;

    3325:   baij          = (Mat_MPIBAIJ*)mat->data;
    3326:   baij->ht_fact = fact;
    3327:   return 0;
    3328: }

    3330: PetscErrorCode  MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,const PetscInt *colmap[])
    3331: {
    3332:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
    3333:   PetscBool    flg;

    3335:   PetscObjectTypeCompare((PetscObject)A,MATMPIBAIJ,&flg);
    3337:   if (Ad)     *Ad     = a->A;
    3338:   if (Ao)     *Ao     = a->B;
    3339:   if (colmap) *colmap = a->garray;
    3340:   return 0;
    3341: }

    3343: /*
    3344:     Special version for direct calls from Fortran (to eliminate two function call overheads
    3345: */
    3346: #if defined(PETSC_HAVE_FORTRAN_CAPS)
    3347: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
    3348: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
    3349: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
    3350: #endif

    3352: /*@C
    3353:   MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to MatSetValuesBlocked()

    3355:   Collective on Mat

    3357:   Input Parameters:
    3358: + mat - the matrix
    3359: . min - number of input rows
    3360: . im - input rows
    3361: . nin - number of input columns
    3362: . in - input columns
    3363: . v - numerical values input
    3364: - addvin - INSERT_VALUES or ADD_VALUES

    3366:   Notes:
    3367:     This has a complete copy of MatSetValuesBlocked_MPIBAIJ() which is terrible code un-reuse.

    3369:   Level: advanced

    3371: .seealso:   MatSetValuesBlocked()
    3372: @*/
    3373: PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin,PetscInt *min,const PetscInt im[],PetscInt *nin,const PetscInt in[],const MatScalar v[],InsertMode *addvin)
    3374: {
    3375:   /* convert input arguments to C version */
    3376:   Mat        mat  = *matin;
    3377:   PetscInt   m    = *min, n = *nin;
    3378:   InsertMode addv = *addvin;

    3380:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
    3381:   const MatScalar *value;
    3382:   MatScalar       *barray     = baij->barray;
    3383:   PetscBool       roworiented = baij->roworiented;
    3384:   PetscInt        i,j,ii,jj,row,col,rstart=baij->rstartbs;
    3385:   PetscInt        rend=baij->rendbs,cstart=baij->cstartbs,stepval;
    3386:   PetscInt        cend=baij->cendbs,bs=mat->rmap->bs,bs2=baij->bs2;

    3388:   /* tasks normally handled by MatSetValuesBlocked() */
    3389:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
    3392:   if (mat->assembled) {
    3393:     mat->was_assembled = PETSC_TRUE;
    3394:     mat->assembled     = PETSC_FALSE;
    3395:   }
    3396:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);

    3398:   if (!barray) {
    3399:     PetscMalloc1(bs2,&barray);
    3400:     baij->barray = barray;
    3401:   }

    3403:   if (roworiented) stepval = (n-1)*bs;
    3404:   else stepval = (m-1)*bs;

    3406:   for (i=0; i<m; i++) {
    3407:     if (im[i] < 0) continue;
    3409:     if (im[i] >= rstart && im[i] < rend) {
    3410:       row = im[i] - rstart;
    3411:       for (j=0; j<n; j++) {
    3412:         /* If NumCol = 1 then a copy is not required */
    3413:         if ((roworiented) && (n == 1)) {
    3414:           barray = (MatScalar*)v + i*bs2;
    3415:         } else if ((!roworiented) && (m == 1)) {
    3416:           barray = (MatScalar*)v + j*bs2;
    3417:         } else { /* Here a copy is required */
    3418:           if (roworiented) {
    3419:             value = v + i*(stepval+bs)*bs + j*bs;
    3420:           } else {
    3421:             value = v + j*(stepval+bs)*bs + i*bs;
    3422:           }
    3423:           for (ii=0; ii<bs; ii++,value+=stepval) {
    3424:             for (jj=0; jj<bs; jj++) {
    3425:               *barray++ = *value++;
    3426:             }
    3427:           }
    3428:           barray -=bs2;
    3429:         }

    3431:         if (in[j] >= cstart && in[j] < cend) {
    3432:           col  = in[j] - cstart;
    3433:           MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A,row,col,barray,addv,im[i],in[j]);
    3434:         } else if (in[j] < 0) continue;
    3436:         else {
    3437:           if (mat->was_assembled) {
    3438:             if (!baij->colmap) {
    3439:               MatCreateColmap_MPIBAIJ_Private(mat);
    3440:             }

    3442: #if defined(PETSC_USE_DEBUG)
    3443: #if defined(PETSC_USE_CTABLE)
    3444:             { PetscInt data;
    3445:               PetscTableFind(baij->colmap,in[j]+1,&data);
    3447:             }
    3448: #else
    3450: #endif
    3451: #endif
    3452: #if defined(PETSC_USE_CTABLE)
    3453:             PetscTableFind(baij->colmap,in[j]+1,&col);
    3454:             col  = (col - 1)/bs;
    3455: #else
    3456:             col = (baij->colmap[in[j]] - 1)/bs;
    3457: #endif
    3458:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
    3459:               MatDisAssemble_MPIBAIJ(mat);
    3460:               col  =  in[j];
    3461:             }
    3462:           } else col = in[j];
    3463:           MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B,row,col,barray,addv,im[i],in[j]);
    3464:         }
    3465:       }
    3466:     } else {
    3467:       if (!baij->donotstash) {
    3468:         if (roworiented) {
    3469:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    3470:         } else {
    3471:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    3472:         }
    3473:       }
    3474:     }
    3475:   }

    3477:   /* task normally handled by MatSetValuesBlocked() */
    3478:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
    3479:   return 0;
    3480: }

    3482: /*@
    3483:      MatCreateMPIBAIJWithArrays - creates a MPI BAIJ matrix using arrays that contain in standard block
    3484:          CSR format the local rows.

    3486:    Collective

    3488:    Input Parameters:
    3489: +  comm - MPI communicator
    3490: .  bs - the block size, only a block size of 1 is supported
    3491: .  m - number of local rows (Cannot be PETSC_DECIDE)
    3492: .  n - This value should be the same as the local size used in creating the
    3493:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
    3494:        calculated if N is given) For square matrices n is almost always m.
    3495: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
    3496: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
    3497: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of block elements in that rowth block row of the matrix
    3498: .   j - column indices
    3499: -   a - matrix values

    3501:    Output Parameter:
    3502: .   mat - the matrix

    3504:    Level: intermediate

    3506:    Notes:
    3507:        The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
    3508:      thus you CANNOT change the matrix entries by changing the values of a[] after you have
    3509:      called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.

    3511:      The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
    3512:      the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
    3513:      block, followed by the second column of the first block etc etc.  That is, the blocks are contiguous in memory
    3514:      with column-major ordering within blocks.

    3516:        The i and j indices are 0 based, and i indices are indices corresponding to the local j array.

    3518: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
    3519:           MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithSplitArrays()
    3520: @*/
    3521: PetscErrorCode  MatCreateMPIBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
    3522: {
    3525:   MatCreate(comm,mat);
    3526:   MatSetSizes(*mat,m,n,M,N);
    3527:   MatSetType(*mat,MATMPIBAIJ);
    3528:   MatSetBlockSize(*mat,bs);
    3529:   MatSetUp(*mat);
    3530:   MatSetOption(*mat,MAT_ROW_ORIENTED,PETSC_FALSE);
    3531:   MatMPIBAIJSetPreallocationCSR(*mat,bs,i,j,a);
    3532:   MatSetOption(*mat,MAT_ROW_ORIENTED,PETSC_TRUE);
    3533:   return 0;
    3534: }

    3536: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
    3537: {
    3539:   PetscInt       m,N,i,rstart,nnz,Ii,bs,cbs;
    3540:   PetscInt       *indx;
    3541:   PetscScalar    *values;

    3543:   MatGetSize(inmat,&m,&N);
    3544:   if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
    3545:     Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)inmat->data;
    3546:     PetscInt       *dnz,*onz,mbs,Nbs,nbs;
    3547:     PetscInt       *bindx,rmax=a->rmax,j;
    3548:     PetscMPIInt    rank,size;

    3550:     MatGetBlockSizes(inmat,&bs,&cbs);
    3551:     mbs = m/bs; Nbs = N/cbs;
    3552:     if (n == PETSC_DECIDE) {
    3553:       PetscSplitOwnershipBlock(comm,cbs,&n,&N);
    3554:     }
    3555:     nbs = n/cbs;

    3557:     PetscMalloc1(rmax,&bindx);
    3558:     MatPreallocateInitialize(comm,mbs,nbs,dnz,onz); /* inline function, output __end and __rstart are used below */

    3560:     MPI_Comm_rank(comm,&rank);
    3561:     MPI_Comm_rank(comm,&size);
    3562:     if (rank == size-1) {
    3563:       /* Check sum(nbs) = Nbs */
    3565:     }

    3567:     rstart = __rstart; /* block rstart of *outmat; see inline function MatPreallocateInitialize */
    3568:     for (i=0; i<mbs; i++) {
    3569:       MatGetRow_SeqBAIJ(inmat,i*bs,&nnz,&indx,NULL); /* non-blocked nnz and indx */
    3570:       nnz = nnz/bs;
    3571:       for (j=0; j<nnz; j++) bindx[j] = indx[j*bs]/bs;
    3572:       MatPreallocateSet(i+rstart,nnz,bindx,dnz,onz);
    3573:       MatRestoreRow_SeqBAIJ(inmat,i*bs,&nnz,&indx,NULL);
    3574:     }
    3575:     PetscFree(bindx);

    3577:     MatCreate(comm,outmat);
    3578:     MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
    3579:     MatSetBlockSizes(*outmat,bs,cbs);
    3580:     MatSetType(*outmat,MATBAIJ);
    3581:     MatSeqBAIJSetPreallocation(*outmat,bs,0,dnz);
    3582:     MatMPIBAIJSetPreallocation(*outmat,bs,0,dnz,0,onz);
    3583:     MatPreallocateFinalize(dnz,onz);
    3584:     MatSetOption(*outmat,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE);
    3585:   }

    3587:   /* numeric phase */
    3588:   MatGetBlockSizes(inmat,&bs,&cbs);
    3589:   MatGetOwnershipRange(*outmat,&rstart,NULL);

    3591:   for (i=0; i<m; i++) {
    3592:     MatGetRow_SeqBAIJ(inmat,i,&nnz,&indx,&values);
    3593:     Ii   = i + rstart;
    3594:     MatSetValues(*outmat,1,&Ii,nnz,indx,values,INSERT_VALUES);
    3595:     MatRestoreRow_SeqBAIJ(inmat,i,&nnz,&indx,&values);
    3596:   }
    3597:   MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
    3598:   MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);
    3599:   return 0;
    3600: }