Date: 2013-07-02T14:42:57+00:00

Actual source code: mpibaij.c
petsc-3.4.2 2013-07-02

      2: #include <../src/mat/impls/baij/mpi/mpibaij.h>   /*I  "petscmat.h"  I*/
      3: #include <petscblaslapack.h>

      5: extern PetscErrorCode MatSetUpMultiply_MPIBAIJ(Mat);
      6: extern PetscErrorCode MatDisAssemble_MPIBAIJ(Mat);
      7: extern PetscErrorCode MatGetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt [],PetscScalar []);
      8: extern PetscErrorCode MatSetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt [],const PetscScalar [],InsertMode);
      9: extern PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
     10: extern PetscErrorCode MatGetRow_SeqBAIJ(Mat,PetscInt,PetscInt*,PetscInt*[],PetscScalar*[]);
     11: extern PetscErrorCode MatRestoreRow_SeqBAIJ(Mat,PetscInt,PetscInt*,PetscInt*[],PetscScalar*[]);
     12: extern PetscErrorCode MatZeroRows_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscScalar,Vec,Vec);

     16: PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A,Vec v,PetscInt idx[])
     17: {
     18:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
     20:   PetscInt       i,*idxb = 0;
     21:   PetscScalar    *va,*vb;
     22:   Vec            vtmp;

     25:   MatGetRowMaxAbs(a->A,v,idx);
     26:   VecGetArray(v,&va);
     27:   if (idx) {
     28:     for (i=0; i<A->rmap->n; i++) {
     29:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
     30:     }
     31:   }

     33:   VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
     34:   if (idx) {PetscMalloc(A->rmap->n*sizeof(PetscInt),&idxb);}
     35:   MatGetRowMaxAbs(a->B,vtmp,idxb);
     36:   VecGetArray(vtmp,&vb);

     38:   for (i=0; i<A->rmap->n; i++) {
     39:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
     40:       va[i] = vb[i];
     41:       if (idx) idx[i] = A->cmap->bs*a->garray[idxb[i]/A->cmap->bs] + (idxb[i] % A->cmap->bs);
     42:     }
     43:   }

     45:   VecRestoreArray(v,&va);
     46:   VecRestoreArray(vtmp,&vb);
     47:   PetscFree(idxb);
     48:   VecDestroy(&vtmp);
     49:   return(0);
     50: }

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

     60:   MatStoreValues(aij->A);
     61:   MatStoreValues(aij->B);
     62:   return(0);
     63: }

     67: PetscErrorCode  MatRetrieveValues_MPIBAIJ(Mat mat)
     68: {
     69:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)mat->data;

     73:   MatRetrieveValues(aij->A);
     74:   MatRetrieveValues(aij->B);
     75:   return(0);
     76: }

     78: /*
     79:      Local utility routine that creates a mapping from the global column
     80:    number to the local number in the off-diagonal part of the local
     81:    storage of the matrix.  This is done in a non scalable way since the
     82:    length of colmap equals the global matrix length.
     83: */
     86: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
     87: {
     88:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
     89:   Mat_SeqBAIJ    *B    = (Mat_SeqBAIJ*)baij->B->data;
     91:   PetscInt       nbs = B->nbs,i,bs=mat->rmap->bs;

     94: #if defined(PETSC_USE_CTABLE)
     95:   PetscTableCreate(baij->nbs,baij->Nbs+1,&baij->colmap);
     96:   for (i=0; i<nbs; i++) {
     97:     PetscTableAdd(baij->colmap,baij->garray[i]+1,i*bs+1,INSERT_VALUES);
     98:   }
     99: #else
    100:   PetscMalloc((baij->Nbs+1)*sizeof(PetscInt),&baij->colmap);
    101:   PetscLogObjectMemory(mat,baij->Nbs*sizeof(PetscInt));
    102:   PetscMemzero(baij->colmap,baij->Nbs*sizeof(PetscInt));
    103:   for (i=0; i<nbs; i++) baij->colmap[baij->garray[i]] = i*bs+1;
    104: #endif
    105:   return(0);
    106: }

    108: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv) \
    109:   { \
    110:  \
    111:     brow = row/bs;  \
    112:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
    113:     rmax = aimax[brow]; nrow = ailen[brow]; \
    114:     bcol = col/bs; \
    115:     ridx = row % bs; cidx = col % bs; \
    116:     low  = 0; high = nrow; \
    117:     while (high-low > 3) { \
    118:       t = (low+high)/2; \
    119:       if (rp[t] > bcol) high = t; \
    120:       else              low  = t; \
    121:     } \
    122:     for (_i=low; _i<high; _i++) { \
    123:       if (rp[_i] > bcol) break; \
    124:       if (rp[_i] == bcol) { \
    125:         bap = ap +  bs2*_i + bs*cidx + ridx; \
    126:         if (addv == ADD_VALUES) *bap += value;  \
    127:         else                    *bap  = value;  \
    128:         goto a_noinsert; \
    129:       } \
    130:     } \
    131:     if (a->nonew == 1) goto a_noinsert; \
    132:     if (a->nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
    133:     MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,aimax,a->nonew,MatScalar); \
    134:     N = nrow++ - 1;  \
    135:     /* shift up all the later entries in this row */ \
    136:     for (ii=N; ii>=_i; ii--) { \
    137:       rp[ii+1] = rp[ii]; \
    138:       PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
    139:     } \
    140:     if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  \
    141:     rp[_i]                      = bcol;  \
    142:     ap[bs2*_i + bs*cidx + ridx] = value;  \
    143: a_noinsert:; \
    144:     ailen[brow] = nrow; \
    145:   }

    147: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv) \
    148:   { \
    149:     brow = row/bs;  \
    150:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
    151:     rmax = bimax[brow]; nrow = bilen[brow]; \
    152:     bcol = col/bs; \
    153:     ridx = row % bs; cidx = col % bs; \
    154:     low  = 0; high = nrow; \
    155:     while (high-low > 3) { \
    156:       t = (low+high)/2; \
    157:       if (rp[t] > bcol) high = t; \
    158:       else              low  = t; \
    159:     } \
    160:     for (_i=low; _i<high; _i++) { \
    161:       if (rp[_i] > bcol) break; \
    162:       if (rp[_i] == bcol) { \
    163:         bap = ap +  bs2*_i + bs*cidx + ridx; \
    164:         if (addv == ADD_VALUES) *bap += value;  \
    165:         else                    *bap  = value;  \
    166:         goto b_noinsert; \
    167:       } \
    168:     } \
    169:     if (b->nonew == 1) goto b_noinsert; \
    170:     if (b->nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
    171:     MatSeqXAIJReallocateAIJ(B,b->mbs,bs2,nrow,brow,bcol,rmax,ba,bi,bj,rp,ap,bimax,b->nonew,MatScalar); \
    172:     N = nrow++ - 1;  \
    173:     /* shift up all the later entries in this row */ \
    174:     for (ii=N; ii>=_i; ii--) { \
    175:       rp[ii+1] = rp[ii]; \
    176:       PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
    177:     } \
    178:     if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  \
    179:     rp[_i]                      = bcol;  \
    180:     ap[bs2*_i + bs*cidx + ridx] = value;  \
    181: b_noinsert:; \
    182:     bilen[brow] = nrow; \
    183:   }

    187: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
    188: {
    189:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    190:   MatScalar      value;
    191:   PetscBool      roworiented = baij->roworiented;
    193:   PetscInt       i,j,row,col;
    194:   PetscInt       rstart_orig=mat->rmap->rstart;
    195:   PetscInt       rend_orig  =mat->rmap->rend,cstart_orig=mat->cmap->rstart;
    196:   PetscInt       cend_orig  =mat->cmap->rend,bs=mat->rmap->bs;

    198:   /* Some Variables required in the macro */
    199:   Mat         A     = baij->A;
    200:   Mat_SeqBAIJ *a    = (Mat_SeqBAIJ*)(A)->data;
    201:   PetscInt    *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
    202:   MatScalar   *aa   =a->a;

    204:   Mat         B     = baij->B;
    205:   Mat_SeqBAIJ *b    = (Mat_SeqBAIJ*)(B)->data;
    206:   PetscInt    *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
    207:   MatScalar   *ba   =b->a;

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

    215:   for (i=0; i<m; i++) {
    216:     if (im[i] < 0) continue;
    217: #if defined(PETSC_USE_DEBUG)
    218:     if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
    219: #endif
    220:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
    221:       row = im[i] - rstart_orig;
    222:       for (j=0; j<n; j++) {
    223:         if (in[j] >= cstart_orig && in[j] < cend_orig) {
    224:           col = in[j] - cstart_orig;
    225:           if (roworiented) value = v[i*n+j];
    226:           else             value = v[i+j*m];
    227:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv);
    228:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
    229:         } else if (in[j] < 0) continue;
    230: #if defined(PETSC_USE_DEBUG)
    231:         else if (in[j] >= mat->cmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap->N-1);
    232: #endif
    233:         else {
    234:           if (mat->was_assembled) {
    235:             if (!baij->colmap) {
    236:               MatCreateColmap_MPIBAIJ_Private(mat);
    237:             }
    238: #if defined(PETSC_USE_CTABLE)
    239:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
    240:             col  = col - 1;
    241: #else
    242:             col = baij->colmap[in[j]/bs] - 1;
    243: #endif
    244:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->B->data))->nonew) {
    245:               MatDisAssemble_MPIBAIJ(mat);
    246:               col  =  in[j];
    247:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
    248:               B    = baij->B;
    249:               b    = (Mat_SeqBAIJ*)(B)->data;
    250:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
    251:               ba   =b->a;
    252:             } else if (col < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", im[i], in[j]);
    253:             else col += in[j]%bs;
    254:           } else col = in[j];
    255:           if (roworiented) value = v[i*n+j];
    256:           else             value = v[i+j*m];
    257:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv);
    258:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
    259:         }
    260:       }
    261:     } else {
    262:       if (mat->nooffprocentries) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Setting off process row %D even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set",im[i]);
    263:       if (!baij->donotstash) {
    264:         mat->assembled = PETSC_FALSE;
    265:         if (roworiented) {
    266:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,PETSC_FALSE);
    267:         } else {
    268:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,PETSC_FALSE);
    269:         }
    270:       }
    271:     }
    272:   }
    273:   return(0);
    274: }

    278: PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
    279: {
    280:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
    281:   const PetscScalar *value;
    282:   MatScalar         *barray     = baij->barray;
    283:   PetscBool         roworiented = baij->roworiented;
    284:   PetscErrorCode    ierr;
    285:   PetscInt          i,j,ii,jj,row,col,rstart=baij->rstartbs;
    286:   PetscInt          rend=baij->rendbs,cstart=baij->cstartbs,stepval;
    287:   PetscInt          cend=baij->cendbs,bs=mat->rmap->bs,bs2=baij->bs2;

    290:   if (!barray) {
    291:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
    292:     baij->barray = barray;
    293:   }

    295:   if (roworiented) stepval = (n-1)*bs;
    296:   else stepval = (m-1)*bs;

    298:   for (i=0; i<m; i++) {
    299:     if (im[i] < 0) continue;
    300: #if defined(PETSC_USE_DEBUG)
    301:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
    302: #endif
    303:     if (im[i] >= rstart && im[i] < rend) {
    304:       row = im[i] - rstart;
    305:       for (j=0; j<n; j++) {
    306:         /* If NumCol = 1 then a copy is not required */
    307:         if ((roworiented) && (n == 1)) {
    308:           barray = (MatScalar*)v + i*bs2;
    309:         } else if ((!roworiented) && (m == 1)) {
    310:           barray = (MatScalar*)v + j*bs2;
    311:         } else { /* Here a copy is required */
    312:           if (roworiented) {
    313:             value = v + (i*(stepval+bs) + j)*bs;
    314:           } else {
    315:             value = v + (j*(stepval+bs) + i)*bs;
    316:           }
    317:           for (ii=0; ii<bs; ii++,value+=bs+stepval) {
    318:             for (jj=0; jj<bs; jj++) barray[jj] = value[jj];
    319:             barray += bs;
    320:           }
    321:           barray -= bs2;
    322:         }

    324:         if (in[j] >= cstart && in[j] < cend) {
    325:           col  = in[j] - cstart;
    326:           MatSetValuesBlocked_SeqBAIJ(baij->A,1,&row,1,&col,barray,addv);
    327:         } else if (in[j] < 0) continue;
    328: #if defined(PETSC_USE_DEBUG)
    329:         else if (in[j] >= baij->Nbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);
    330: #endif
    331:         else {
    332:           if (mat->was_assembled) {
    333:             if (!baij->colmap) {
    334:               MatCreateColmap_MPIBAIJ_Private(mat);
    335:             }

    337: #if defined(PETSC_USE_DEBUG)
    338: #if defined(PETSC_USE_CTABLE)
    339:             { PetscInt data;
    340:               PetscTableFind(baij->colmap,in[j]+1,&data);
    341:               if ((data - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
    342:             }
    343: #else
    344:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
    345: #endif
    346: #endif
    347: #if defined(PETSC_USE_CTABLE)
    348:             PetscTableFind(baij->colmap,in[j]+1,&col);
    349:             col  = (col - 1)/bs;
    350: #else
    351:             col = (baij->colmap[in[j]] - 1)/bs;
    352: #endif
    353:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->B->data))->nonew) {
    354:               MatDisAssemble_MPIBAIJ(mat);
    355:               col  =  in[j];
    356:             } else if (col < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", bs*im[i], bs*in[j]);
    357:           } else col = in[j];
    358:           MatSetValuesBlocked_SeqBAIJ(baij->B,1,&row,1,&col,barray,addv);
    359:         }
    360:       }
    361:     } else {
    362:       if (mat->nooffprocentries) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Setting off process row %D even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set",im[i]);
    363:       if (!baij->donotstash) {
    364:         if (roworiented) {
    365:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    366:         } else {
    367:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    368:         }
    369:       }
    370:     }
    371:   }
    372:   return(0);
    373: }

    375: #define HASH_KEY 0.6180339887
    376: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(PetscInt)((size)*(tmp-(PetscInt)tmp)))
    377: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
    378: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
    381: PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
    382: {
    383:   Mat_MPIBAIJ    *baij       = (Mat_MPIBAIJ*)mat->data;
    384:   PetscBool      roworiented = baij->roworiented;
    386:   PetscInt       i,j,row,col;
    387:   PetscInt       rstart_orig=mat->rmap->rstart;
    388:   PetscInt       rend_orig  =mat->rmap->rend,Nbs=baij->Nbs;
    389:   PetscInt       h1,key,size=baij->ht_size,bs=mat->rmap->bs,*HT=baij->ht,idx;
    390:   PetscReal      tmp;
    391:   MatScalar      **HD = baij->hd,value;
    392: #if defined(PETSC_USE_DEBUG)
    393:   PetscInt total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
    394: #endif

    398:   for (i=0; i<m; i++) {
    399: #if defined(PETSC_USE_DEBUG)
    400:     if (im[i] < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
    401:     if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
    402: #endif
    403:     row = im[i];
    404:     if (row >= rstart_orig && row < rend_orig) {
    405:       for (j=0; j<n; j++) {
    406:         col = in[j];
    407:         if (roworiented) value = v[i*n+j];
    408:         else             value = v[i+j*m];
    409:         /* Look up PetscInto the Hash Table */
    410:         key = (row/bs)*Nbs+(col/bs)+1;
    411:         h1  = HASH(size,key,tmp);

    414:         idx = h1;
    415: #if defined(PETSC_USE_DEBUG)
    416:         insert_ct++;
    417:         total_ct++;
    418:         if (HT[idx] != key) {
    419:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++) ;
    420:           if (idx == size) {
    421:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++) ;
    422:             if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
    423:           }
    424:         }
    425: #else
    426:         if (HT[idx] != key) {
    427:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++) ;
    428:           if (idx == size) {
    429:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++) ;
    430:             if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
    431:           }
    432:         }
    433: #endif
    434:         /* A HASH table entry is found, so insert the values at the correct address */
    435:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
    436:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
    437:       }
    438:     } else if (!baij->donotstash) {
    439:       if (roworiented) {
    440:         MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,PETSC_FALSE);
    441:       } else {
    442:         MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,PETSC_FALSE);
    443:       }
    444:     }
    445:   }
    446: #if defined(PETSC_USE_DEBUG)
    447:   baij->ht_total_ct  = total_ct;
    448:   baij->ht_insert_ct = insert_ct;
    449: #endif
    450:   return(0);
    451: }

    455: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
    456: {
    457:   Mat_MPIBAIJ       *baij       = (Mat_MPIBAIJ*)mat->data;
    458:   PetscBool         roworiented = baij->roworiented;
    459:   PetscErrorCode    ierr;
    460:   PetscInt          i,j,ii,jj,row,col;
    461:   PetscInt          rstart=baij->rstartbs;
    462:   PetscInt          rend  =mat->rmap->rend,stepval,bs=mat->rmap->bs,bs2=baij->bs2,nbs2=n*bs2;
    463:   PetscInt          h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
    464:   PetscReal         tmp;
    465:   MatScalar         **HD = baij->hd,*baij_a;
    466:   const PetscScalar *v_t,*value;
    467: #if defined(PETSC_USE_DEBUG)
    468:   PetscInt total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
    469: #endif

    472:   if (roworiented) stepval = (n-1)*bs;
    473:   else stepval = (m-1)*bs;

    475:   for (i=0; i<m; i++) {
    476: #if defined(PETSC_USE_DEBUG)
    477:     if (im[i] < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",im[i]);
    478:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],baij->Mbs-1);
    479: #endif
    480:     row = im[i];
    481:     v_t = v + i*nbs2;
    482:     if (row >= rstart && row < rend) {
    483:       for (j=0; j<n; j++) {
    484:         col = in[j];

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

    490:         idx = h1;
    491: #if defined(PETSC_USE_DEBUG)
    492:         total_ct++;
    493:         insert_ct++;
    494:         if (HT[idx] != key) {
    495:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++) ;
    496:           if (idx == size) {
    497:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++) ;
    498:             if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
    499:           }
    500:         }
    501: #else
    502:         if (HT[idx] != key) {
    503:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++) ;
    504:           if (idx == size) {
    505:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++) ;
    506:             if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
    507:           }
    508:         }
    509: #endif
    510:         baij_a = HD[idx];
    511:         if (roworiented) {
    512:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
    513:           /* value = v + (i*(stepval+bs)+j)*bs; */
    514:           value = v_t;
    515:           v_t  += bs;
    516:           if (addv == ADD_VALUES) {
    517:             for (ii=0; ii<bs; ii++,value+=stepval) {
    518:               for (jj=ii; jj<bs2; jj+=bs) {
    519:                 baij_a[jj] += *value++;
    520:               }
    521:             }
    522:           } else {
    523:             for (ii=0; ii<bs; ii++,value+=stepval) {
    524:               for (jj=ii; jj<bs2; jj+=bs) {
    525:                 baij_a[jj] = *value++;
    526:               }
    527:             }
    528:           }
    529:         } else {
    530:           value = v + j*(stepval+bs)*bs + i*bs;
    531:           if (addv == ADD_VALUES) {
    532:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
    533:               for (jj=0; jj<bs; jj++) {
    534:                 baij_a[jj] += *value++;
    535:               }
    536:             }
    537:           } else {
    538:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
    539:               for (jj=0; jj<bs; jj++) {
    540:                 baij_a[jj] = *value++;
    541:               }
    542:             }
    543:           }
    544:         }
    545:       }
    546:     } else {
    547:       if (!baij->donotstash) {
    548:         if (roworiented) {
    549:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    550:         } else {
    551:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    552:         }
    553:       }
    554:     }
    555:   }
    556: #if defined(PETSC_USE_DEBUG)
    557:   baij->ht_total_ct  = total_ct;
    558:   baij->ht_insert_ct = insert_ct;
    559: #endif
    560:   return(0);
    561: }

    565: PetscErrorCode MatGetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
    566: {
    567:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    569:   PetscInt       bs       = mat->rmap->bs,i,j,bsrstart = mat->rmap->rstart,bsrend = mat->rmap->rend;
    570:   PetscInt       bscstart = mat->cmap->rstart,bscend = mat->cmap->rend,row,col,data;

    573:   for (i=0; i<m; i++) {
    574:     if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);*/
    575:     if (idxm[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap->N-1);
    576:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
    577:       row = idxm[i] - bsrstart;
    578:       for (j=0; j<n; j++) {
    579:         if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]); */
    580:         if (idxn[j] >= mat->cmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap->N-1);
    581:         if (idxn[j] >= bscstart && idxn[j] < bscend) {
    582:           col  = idxn[j] - bscstart;
    583:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
    584:         } else {
    585:           if (!baij->colmap) {
    586:             MatCreateColmap_MPIBAIJ_Private(mat);
    587:           }
    588: #if defined(PETSC_USE_CTABLE)
    589:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
    590:           data--;
    591: #else
    592:           data = baij->colmap[idxn[j]/bs]-1;
    593: #endif
    594:           if ((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
    595:           else {
    596:             col  = data + idxn[j]%bs;
    597:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
    598:           }
    599:         }
    600:       }
    601:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
    602:   }
    603:   return(0);
    604: }

    608: PetscErrorCode MatNorm_MPIBAIJ(Mat mat,NormType type,PetscReal *nrm)
    609: {
    610:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    611:   Mat_SeqBAIJ    *amat = (Mat_SeqBAIJ*)baij->A->data,*bmat = (Mat_SeqBAIJ*)baij->B->data;
    613:   PetscInt       i,j,bs2=baij->bs2,bs=baij->A->rmap->bs,nz,row,col;
    614:   PetscReal      sum = 0.0;
    615:   MatScalar      *v;

    618:   if (baij->size == 1) {
    619:      MatNorm(baij->A,type,nrm);
    620:   } else {
    621:     if (type == NORM_FROBENIUS) {
    622:       v  = amat->a;
    623:       nz = amat->nz*bs2;
    624:       for (i=0; i<nz; i++) {
    625:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    626:       }
    627:       v  = bmat->a;
    628:       nz = bmat->nz*bs2;
    629:       for (i=0; i<nz; i++) {
    630:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    631:       }
    632:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
    633:       *nrm = PetscSqrtReal(*nrm);
    634:     } else if (type == NORM_1) { /* max column sum */
    635:       PetscReal *tmp,*tmp2;
    636:       PetscInt  *jj,*garray=baij->garray,cstart=baij->rstartbs;
    637:       PetscMalloc2(mat->cmap->N,PetscReal,&tmp,mat->cmap->N,PetscReal,&tmp2);
    638:       PetscMemzero(tmp,mat->cmap->N*sizeof(PetscReal));
    639:       v    = amat->a; jj = amat->j;
    640:       for (i=0; i<amat->nz; i++) {
    641:         for (j=0; j<bs; j++) {
    642:           col = bs*(cstart + *jj) + j; /* column index */
    643:           for (row=0; row<bs; row++) {
    644:             tmp[col] += PetscAbsScalar(*v);  v++;
    645:           }
    646:         }
    647:         jj++;
    648:       }
    649:       v = bmat->a; jj = bmat->j;
    650:       for (i=0; i<bmat->nz; i++) {
    651:         for (j=0; j<bs; j++) {
    652:           col = bs*garray[*jj] + j;
    653:           for (row=0; row<bs; row++) {
    654:             tmp[col] += PetscAbsScalar(*v); v++;
    655:           }
    656:         }
    657:         jj++;
    658:       }
    659:       MPI_Allreduce(tmp,tmp2,mat->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
    660:       *nrm = 0.0;
    661:       for (j=0; j<mat->cmap->N; j++) {
    662:         if (tmp2[j] > *nrm) *nrm = tmp2[j];
    663:       }
    664:       PetscFree2(tmp,tmp2);
    665:     } else if (type == NORM_INFINITY) { /* max row sum */
    666:       PetscReal *sums;
    667:       PetscMalloc(bs*sizeof(PetscReal),&sums);
    668:       sum  = 0.0;
    669:       for (j=0; j<amat->mbs; j++) {
    670:         for (row=0; row<bs; row++) sums[row] = 0.0;
    671:         v  = amat->a + bs2*amat->i[j];
    672:         nz = amat->i[j+1]-amat->i[j];
    673:         for (i=0; i<nz; i++) {
    674:           for (col=0; col<bs; col++) {
    675:             for (row=0; row<bs; row++) {
    676:               sums[row] += PetscAbsScalar(*v); v++;
    677:             }
    678:           }
    679:         }
    680:         v  = bmat->a + bs2*bmat->i[j];
    681:         nz = bmat->i[j+1]-bmat->i[j];
    682:         for (i=0; i<nz; i++) {
    683:           for (col=0; col<bs; col++) {
    684:             for (row=0; row<bs; row++) {
    685:               sums[row] += PetscAbsScalar(*v); v++;
    686:             }
    687:           }
    688:         }
    689:         for (row=0; row<bs; row++) {
    690:           if (sums[row] > sum) sum = sums[row];
    691:         }
    692:       }
    693:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)mat));
    694:       PetscFree(sums);
    695:     } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No support for this norm yet");
    696:   }
    697:   return(0);
    698: }

    700: /*
    701:   Creates the hash table, and sets the table
    702:   This table is created only once.
    703:   If new entried need to be added to the matrix
    704:   then the hash table has to be destroyed and
    705:   recreated.
    706: */
    709: PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
    710: {
    711:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    712:   Mat            A     = baij->A,B=baij->B;
    713:   Mat_SeqBAIJ    *a    = (Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)B->data;
    714:   PetscInt       i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
    716:   PetscInt       ht_size,bs2=baij->bs2,rstart=baij->rstartbs;
    717:   PetscInt       cstart=baij->cstartbs,*garray=baij->garray,row,col,Nbs=baij->Nbs;
    718:   PetscInt       *HT,key;
    719:   MatScalar      **HD;
    720:   PetscReal      tmp;
    721: #if defined(PETSC_USE_INFO)
    722:   PetscInt ct=0,max=0;
    723: #endif

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

    728:   baij->ht_size = (PetscInt)(factor*nz);
    729:   ht_size       = baij->ht_size;

    731:   /* Allocate Memory for Hash Table */
    732:   PetscMalloc2(ht_size,MatScalar*,&baij->hd,ht_size,PetscInt,&baij->ht);
    733:   PetscMemzero(baij->hd,ht_size*sizeof(MatScalar*));
    734:   PetscMemzero(baij->ht,ht_size*sizeof(PetscInt));
    735:   HD   = baij->hd;
    736:   HT   = baij->ht;

    738:   /* Loop Over A */
    739:   for (i=0; i<a->mbs; i++) {
    740:     for (j=ai[i]; j<ai[i+1]; j++) {
    741:       row = i+rstart;
    742:       col = aj[j]+cstart;

    744:       key = row*Nbs + col + 1;
    745:       h1  = HASH(ht_size,key,tmp);
    746:       for (k=0; k<ht_size; k++) {
    747:         if (!HT[(h1+k)%ht_size]) {
    748:           HT[(h1+k)%ht_size] = key;
    749:           HD[(h1+k)%ht_size] = a->a + j*bs2;
    750:           break;
    751: #if defined(PETSC_USE_INFO)
    752:         } else {
    753:           ct++;
    754: #endif
    755:         }
    756:       }
    757: #if defined(PETSC_USE_INFO)
    758:       if (k> max) max = k;
    759: #endif
    760:     }
    761:   }
    762:   /* Loop Over B */
    763:   for (i=0; i<b->mbs; i++) {
    764:     for (j=bi[i]; j<bi[i+1]; j++) {
    765:       row = i+rstart;
    766:       col = garray[bj[j]];
    767:       key = row*Nbs + col + 1;
    768:       h1  = HASH(ht_size,key,tmp);
    769:       for (k=0; k<ht_size; k++) {
    770:         if (!HT[(h1+k)%ht_size]) {
    771:           HT[(h1+k)%ht_size] = key;
    772:           HD[(h1+k)%ht_size] = b->a + j*bs2;
    773:           break;
    774: #if defined(PETSC_USE_INFO)
    775:         } else {
    776:           ct++;
    777: #endif
    778:         }
    779:       }
    780: #if defined(PETSC_USE_INFO)
    781:       if (k> max) max = k;
    782: #endif
    783:     }
    784:   }

    786:   /* Print Summary */
    787: #if defined(PETSC_USE_INFO)
    788:   for (i=0,j=0; i<ht_size; i++) {
    789:     if (HT[i]) j++;
    790:   }
    791:   PetscInfo2(mat,"Average Search = %5.2f,max search = %D\n",(!j)? 0.0:((PetscReal)(ct+j))/j,max);
    792: #endif
    793:   return(0);
    794: }

    798: PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat,MatAssemblyType mode)
    799: {
    800:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    802:   PetscInt       nstash,reallocs;
    803:   InsertMode     addv;

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

    808:   /* make sure all processors are either in INSERTMODE or ADDMODE */
    809:   MPI_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,PetscObjectComm((PetscObject)mat));
    810:   if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
    811:   mat->insertmode = addv; /* in case this processor had no cache */

    813:   MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
    814:   MatStashScatterBegin_Private(mat,&mat->bstash,baij->rangebs);
    815:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
    816:   PetscInfo2(mat,"Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
    817:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
    818:   PetscInfo2(mat,"Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
    819:   return(0);
    820: }

    824: PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat,MatAssemblyType mode)
    825: {
    826:   Mat_MPIBAIJ    *baij=(Mat_MPIBAIJ*)mat->data;
    827:   Mat_SeqBAIJ    *a   =(Mat_SeqBAIJ*)baij->A->data;
    829:   PetscInt       i,j,rstart,ncols,flg,bs2=baij->bs2;
    830:   PetscInt       *row,*col;
    831:   PetscBool      r1,r2,r3,other_disassembled;
    832:   MatScalar      *val;
    833:   InsertMode     addv = mat->insertmode;
    834:   PetscMPIInt    n;

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

    843:       for (i=0; i<n;) {
    844:         /* Now identify the consecutive vals belonging to the same row */
    845:         for (j=i,rstart=row[j]; j<n; j++) {
    846:           if (row[j] != rstart) break;
    847:         }
    848:         if (j < n) ncols = j-i;
    849:         else       ncols = n-i;
    850:         /* Now assemble all these values with a single function call */
    851:         MatSetValues_MPIBAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
    852:         i    = j;
    853:       }
    854:     }
    855:     MatStashScatterEnd_Private(&mat->stash);
    856:     /* Now process the block-stash. Since the values are stashed column-oriented,
    857:        set the roworiented flag to column oriented, and after MatSetValues()
    858:        restore the original flags */
    859:     r1 = baij->roworiented;
    860:     r2 = a->roworiented;
    861:     r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;

    863:     baij->roworiented = PETSC_FALSE;
    864:     a->roworiented    = PETSC_FALSE;

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

    871:       for (i=0; i<n;) {
    872:         /* Now identify the consecutive vals belonging to the same row */
    873:         for (j=i,rstart=row[j]; j<n; j++) {
    874:           if (row[j] != rstart) break;
    875:         }
    876:         if (j < n) ncols = j-i;
    877:         else       ncols = n-i;
    878:         MatSetValuesBlocked_MPIBAIJ(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
    879:         i    = j;
    880:       }
    881:     }
    882:     MatStashScatterEnd_Private(&mat->bstash);

    884:     baij->roworiented = r1;
    885:     a->roworiented    = r2;

    887:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented = r3; /* b->roworiented */
    888:   }

    890:   MatAssemblyBegin(baij->A,mode);
    891:   MatAssemblyEnd(baij->A,mode);

    893:   /* determine if any processor has disassembled, if so we must
    894:      also disassemble ourselfs, in order that we may reassemble. */
    895:   /*
    896:      if nonzero structure of submatrix B cannot change then we know that
    897:      no processor disassembled thus we can skip this stuff
    898:   */
    899:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew) {
    900:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPIU_BOOL,MPI_PROD,PetscObjectComm((PetscObject)mat));
    901:     if (mat->was_assembled && !other_disassembled) {
    902:       MatDisAssemble_MPIBAIJ(mat);
    903:     }
    904:   }

    906:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
    907:     MatSetUpMultiply_MPIBAIJ(mat);
    908:   }
    909:   MatSetOption(baij->B,MAT_CHECK_COMPRESSED_ROW,PETSC_FALSE);
    910:   MatAssemblyBegin(baij->B,mode);
    911:   MatAssemblyEnd(baij->B,mode);

    913: #if defined(PETSC_USE_INFO)
    914:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
    915:     PetscInfo1(mat,"Average Hash Table Search in MatSetValues = %5.2f\n",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);

    917:     baij->ht_total_ct  = 0;
    918:     baij->ht_insert_ct = 0;
    919:   }
    920: #endif
    921:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
    922:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);

    924:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
    925:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
    926:   }

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

    930:   baij->rowvalues = 0;
    931:   return(0);
    932: }

    934: #include <petscdraw.h>
    937: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
    938: {
    939:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
    940:   PetscErrorCode    ierr;
    941:   PetscMPIInt       size = baij->size,rank = baij->rank;
    942:   PetscInt          bs   = mat->rmap->bs;
    943:   PetscBool         iascii,isdraw;
    944:   PetscViewer       sviewer;
    945:   PetscViewerFormat format;

    948:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    949:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    950:   if (iascii) {
    951:     PetscViewerGetFormat(viewer,&format);
    952:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
    953:       MatInfo info;
    954:       MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
    955:       MatGetInfo(mat,MAT_LOCAL,&info);
    956:       PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);
    957:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",
    958:                                                 rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,mat->rmap->bs,(PetscInt)info.memory);
    959:       MatGetInfo(baij->A,MAT_LOCAL,&info);
    960:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
    961:       MatGetInfo(baij->B,MAT_LOCAL,&info);
    962:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
    963:       PetscViewerFlush(viewer);
    964:       PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);
    965:       PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
    966:       VecScatterView(baij->Mvctx,viewer);
    967:       return(0);
    968:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
    969:       PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
    970:       return(0);
    971:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
    972:       return(0);
    973:     }
    974:   }

    976:   if (isdraw) {
    977:     PetscDraw draw;
    978:     PetscBool isnull;
    979:     PetscViewerDrawGetDraw(viewer,0,&draw);
    980:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
    981:   }

    983:   if (size == 1) {
    984:     PetscObjectSetName((PetscObject)baij->A,((PetscObject)mat)->name);
    985:     MatView(baij->A,viewer);
    986:   } else {
    987:     /* assemble the entire matrix onto first processor. */
    988:     Mat         A;
    989:     Mat_SeqBAIJ *Aloc;
    990:     PetscInt    M = mat->rmap->N,N = mat->cmap->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
    991:     MatScalar   *a;

    993:     /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
    994:     /* Perhaps this should be the type of mat? */
    995:     MatCreate(PetscObjectComm((PetscObject)mat),&A);
    996:     if (!rank) {
    997:       MatSetSizes(A,M,N,M,N);
    998:     } else {
    999:       MatSetSizes(A,0,0,M,N);
    1000:     }
    1001:     MatSetType(A,MATMPIBAIJ);
    1002:     MatMPIBAIJSetPreallocation(A,mat->rmap->bs,0,NULL,0,NULL);
    1003:     MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);
    1004:     PetscLogObjectParent(mat,A);

    1006:     /* copy over the A part */
    1007:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
    1008:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
    1009:     PetscMalloc(bs*sizeof(PetscInt),&rvals);

    1011:     for (i=0; i<mbs; i++) {
    1012:       rvals[0] = bs*(baij->rstartbs + i);
    1013:       for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
    1014:       for (j=ai[i]; j<ai[i+1]; j++) {
    1015:         col = (baij->cstartbs+aj[j])*bs;
    1016:         for (k=0; k<bs; k++) {
    1017:           MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
    1018:           col++; a += bs;
    1019:         }
    1020:       }
    1021:     }
    1022:     /* copy over the B part */
    1023:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
    1024:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
    1025:     for (i=0; i<mbs; i++) {
    1026:       rvals[0] = bs*(baij->rstartbs + i);
    1027:       for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
    1028:       for (j=ai[i]; j<ai[i+1]; j++) {
    1029:         col = baij->garray[aj[j]]*bs;
    1030:         for (k=0; k<bs; k++) {
    1031:           MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
    1032:           col++; a += bs;
    1033:         }
    1034:       }
    1035:     }
    1036:     PetscFree(rvals);
    1037:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    1038:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    1039:     /*
    1040:        Everyone has to call to draw the matrix since the graphics waits are
    1041:        synchronized across all processors that share the PetscDraw object
    1042:     */
    1043:     PetscViewerGetSingleton(viewer,&sviewer);
    1044:     if (!rank) {
    1045:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,((PetscObject)mat)->name);
    1046:       /* Set the type name to MATMPIBAIJ so that the correct type can be printed out by PetscObjectPrintClassNamePrefixType() in MatView_SeqBAIJ_ASCII()*/
    1047:       PetscStrcpy(((PetscObject)((Mat_MPIBAIJ*)(A->data))->A)->type_name,MATMPIBAIJ);
    1048:       MatView(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
    1049:     }
    1050:     PetscViewerRestoreSingleton(viewer,&sviewer);
    1051:     MatDestroy(&A);
    1052:   }
    1053:   return(0);
    1054: }

    1058: static PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat,PetscViewer viewer)
    1059: {
    1060:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)mat->data;
    1061:   Mat_SeqBAIJ    *A = (Mat_SeqBAIJ*)a->A->data;
    1062:   Mat_SeqBAIJ    *B = (Mat_SeqBAIJ*)a->B->data;
    1064:   PetscInt       i,*row_lens,*crow_lens,bs = mat->rmap->bs,j,k,bs2=a->bs2,header[4],nz,rlen;
    1065:   PetscInt       *range=0,nzmax,*column_indices,cnt,col,*garray = a->garray,cstart = mat->cmap->rstart/bs,len,pcnt,l,ll;
    1066:   int            fd;
    1067:   PetscScalar    *column_values;
    1068:   FILE           *file;
    1069:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag;
    1070:   PetscInt       message_count,flowcontrolcount;

    1073:   MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
    1074:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
    1075:   nz   = bs2*(A->nz + B->nz);
    1076:   rlen = mat->rmap->n;
    1077:   if (!rank) {
    1078:     header[0] = MAT_FILE_CLASSID;
    1079:     header[1] = mat->rmap->N;
    1080:     header[2] = mat->cmap->N;

    1082:     MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
    1083:     PetscViewerBinaryGetDescriptor(viewer,&fd);
    1084:     PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
    1085:     /* get largest number of rows any processor has */
    1086:     range = mat->rmap->range;
    1087:     for (i=1; i<size; i++) {
    1088:       rlen = PetscMax(rlen,range[i+1] - range[i]);
    1089:     }
    1090:   } else {
    1091:     MPI_Reduce(&nz,0,1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
    1092:   }

    1094:   PetscMalloc((rlen/bs)*sizeof(PetscInt),&crow_lens);
    1095:   /* compute lengths of each row  */
    1096:   for (i=0; i<a->mbs; i++) {
    1097:     crow_lens[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i];
    1098:   }
    1099:   /* store the row lengths to the file */
    1100:   PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
    1101:   if (!rank) {
    1102:     MPI_Status status;
    1103:     PetscMalloc(rlen*sizeof(PetscInt),&row_lens);
    1104:     rlen = (range[1] - range[0])/bs;
    1105:     for (i=0; i<rlen; i++) {
    1106:       for (j=0; j<bs; j++) {
    1107:         row_lens[i*bs+j] = bs*crow_lens[i];
    1108:       }
    1109:     }
    1110:     PetscBinaryWrite(fd,row_lens,bs*rlen,PETSC_INT,PETSC_TRUE);
    1111:     for (i=1; i<size; i++) {
    1112:       rlen = (range[i+1] - range[i])/bs;
    1113:       PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
    1114:       MPI_Recv(crow_lens,rlen,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
    1115:       for (k=0; k<rlen; k++) {
    1116:         for (j=0; j<bs; j++) {
    1117:           row_lens[k*bs+j] = bs*crow_lens[k];
    1118:         }
    1119:       }
    1120:       PetscBinaryWrite(fd,row_lens,bs*rlen,PETSC_INT,PETSC_TRUE);
    1121:     }
    1122:     PetscViewerFlowControlEndMaster(viewer,&message_count);
    1123:     PetscFree(row_lens);
    1124:   } else {
    1125:     PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
    1126:     MPI_Send(crow_lens,mat->rmap->n/bs,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
    1127:     PetscViewerFlowControlEndWorker(viewer,&message_count);
    1128:   }
    1129:   PetscFree(crow_lens);

    1131:   /* load up the local column indices. Include for all rows not just one for each block row since process 0 does not have the
    1132:      information needed to make it for each row from a block row. This does require more communication but still not more than
    1133:      the communication needed for the nonzero values  */
    1134:   nzmax = nz; /*  space a largest processor needs */
    1135:   MPI_Reduce(&nz,&nzmax,1,MPIU_INT,MPI_MAX,0,PetscObjectComm((PetscObject)mat));
    1136:   PetscMalloc(nzmax*sizeof(PetscInt),&column_indices);
    1137:   cnt   = 0;
    1138:   for (i=0; i<a->mbs; i++) {
    1139:     pcnt = cnt;
    1140:     for (j=B->i[i]; j<B->i[i+1]; j++) {
    1141:       if ((col = garray[B->j[j]]) > cstart) break;
    1142:       for (l=0; l<bs; l++) {
    1143:         column_indices[cnt++] = bs*col+l;
    1144:       }
    1145:     }
    1146:     for (k=A->i[i]; k<A->i[i+1]; k++) {
    1147:       for (l=0; l<bs; l++) {
    1148:         column_indices[cnt++] = bs*(A->j[k] + cstart)+l;
    1149:       }
    1150:     }
    1151:     for (; j<B->i[i+1]; j++) {
    1152:       for (l=0; l<bs; l++) {
    1153:         column_indices[cnt++] = bs*garray[B->j[j]]+l;
    1154:       }
    1155:     }
    1156:     len = cnt - pcnt;
    1157:     for (k=1; k<bs; k++) {
    1158:       PetscMemcpy(&column_indices[cnt],&column_indices[pcnt],len*sizeof(PetscInt));
    1159:       cnt += len;
    1160:     }
    1161:   }
    1162:   if (cnt != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Internal PETSc error: cnt = %D nz = %D",cnt,nz);

    1164:   /* store the columns to the file */
    1165:   PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
    1166:   if (!rank) {
    1167:     MPI_Status status;
    1168:     PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,PETSC_TRUE);
    1169:     for (i=1; i<size; i++) {
    1170:       PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
    1171:       MPI_Recv(&cnt,1,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
    1172:       MPI_Recv(column_indices,cnt,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
    1173:       PetscBinaryWrite(fd,column_indices,cnt,PETSC_INT,PETSC_TRUE);
    1174:     }
    1175:     PetscViewerFlowControlEndMaster(viewer,&message_count);
    1176:   } else {
    1177:     PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
    1178:     MPI_Send(&cnt,1,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
    1179:     MPI_Send(column_indices,cnt,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
    1180:     PetscViewerFlowControlEndWorker(viewer,&message_count);
    1181:   }
    1182:   PetscFree(column_indices);

    1184:   /* load up the numerical values */
    1185:   PetscMalloc(nzmax*sizeof(PetscScalar),&column_values);
    1186:   cnt  = 0;
    1187:   for (i=0; i<a->mbs; i++) {
    1188:     rlen = bs*(B->i[i+1] - B->i[i] + A->i[i+1] - A->i[i]);
    1189:     for (j=B->i[i]; j<B->i[i+1]; j++) {
    1190:       if (garray[B->j[j]] > cstart) break;
    1191:       for (l=0; l<bs; l++) {
    1192:         for (ll=0; ll<bs; ll++) {
    1193:           column_values[cnt + l*rlen + ll] = B->a[bs2*j+l+bs*ll];
    1194:         }
    1195:       }
    1196:       cnt += bs;
    1197:     }
    1198:     for (k=A->i[i]; k<A->i[i+1]; k++) {
    1199:       for (l=0; l<bs; l++) {
    1200:         for (ll=0; ll<bs; ll++) {
    1201:           column_values[cnt + l*rlen + ll] = A->a[bs2*k+l+bs*ll];
    1202:         }
    1203:       }
    1204:       cnt += bs;
    1205:     }
    1206:     for (; j<B->i[i+1]; j++) {
    1207:       for (l=0; l<bs; l++) {
    1208:         for (ll=0; ll<bs; ll++) {
    1209:           column_values[cnt + l*rlen + ll] = B->a[bs2*j+l+bs*ll];
    1210:         }
    1211:       }
    1212:       cnt += bs;
    1213:     }
    1214:     cnt += (bs-1)*rlen;
    1215:   }
    1216:   if (cnt != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Internal PETSc error: cnt = %D nz = %D",cnt,nz);

    1218:   /* store the column values to the file */
    1219:   PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
    1220:   if (!rank) {
    1221:     MPI_Status status;
    1222:     PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,PETSC_TRUE);
    1223:     for (i=1; i<size; i++) {
    1224:       PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
    1225:       MPI_Recv(&cnt,1,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
    1226:       MPI_Recv(column_values,cnt,MPIU_SCALAR,i,tag,PetscObjectComm((PetscObject)mat),&status);
    1227:       PetscBinaryWrite(fd,column_values,cnt,PETSC_SCALAR,PETSC_TRUE);
    1228:     }
    1229:     PetscViewerFlowControlEndMaster(viewer,&message_count);
    1230:   } else {
    1231:     PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
    1232:     MPI_Send(&nz,1,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
    1233:     MPI_Send(column_values,nz,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));
    1234:     PetscViewerFlowControlEndWorker(viewer,&message_count);
    1235:   }
    1236:   PetscFree(column_values);

    1238:   PetscViewerBinaryGetInfoPointer(viewer,&file);
    1239:   if (file) {
    1240:     fprintf(file,"-matload_block_size %d\n",(int)mat->rmap->bs);
    1241:   }
    1242:   return(0);
    1243: }

    1247: PetscErrorCode MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
    1248: {
    1250:   PetscBool      iascii,isdraw,issocket,isbinary;

    1253:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    1254:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    1255:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
    1256:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    1257:   if (iascii || isdraw || issocket) {
    1258:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
    1259:   } else if (isbinary) {
    1260:     MatView_MPIBAIJ_Binary(mat,viewer);
    1261:   }
    1262:   return(0);
    1263: }

    1267: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
    1268: {
    1269:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;

    1273: #if defined(PETSC_USE_LOG)
    1274:   PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap->N,mat->cmap->N);
    1275: #endif
    1276:   MatStashDestroy_Private(&mat->stash);
    1277:   MatStashDestroy_Private(&mat->bstash);
    1278:   MatDestroy(&baij->A);
    1279:   MatDestroy(&baij->B);
    1280: #if defined(PETSC_USE_CTABLE)
    1281:   PetscTableDestroy(&baij->colmap);
    1282: #else
    1283:   PetscFree(baij->colmap);
    1284: #endif
    1285:   PetscFree(baij->garray);
    1286:   VecDestroy(&baij->lvec);
    1287:   VecScatterDestroy(&baij->Mvctx);
    1288:   PetscFree2(baij->rowvalues,baij->rowindices);
    1289:   PetscFree(baij->barray);
    1290:   PetscFree2(baij->hd,baij->ht);
    1291:   PetscFree(baij->rangebs);
    1292:   PetscFree(mat->data);

    1294:   PetscObjectChangeTypeName((PetscObject)mat,0);
    1295:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C",NULL);
    1296:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C",NULL);
    1297:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",NULL);
    1298:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C",NULL);
    1299:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C",NULL);
    1300:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",NULL);
    1301:   PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C",NULL);
    1302:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_mpisbaij_C",NULL);
    1303:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_mpibstrm_C",NULL);
    1304:   return(0);
    1305: }

    1309: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
    1310: {
    1311:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
    1313:   PetscInt       nt;

    1316:   VecGetLocalSize(xx,&nt);
    1317:   if (nt != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
    1318:   VecGetLocalSize(yy,&nt);
    1319:   if (nt != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
    1320:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1321:   (*a->A->ops->mult)(a->A,xx,yy);
    1322:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1323:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
    1324:   return(0);
    1325: }

    1329: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1330: {
    1331:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1335:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1336:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
    1337:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1338:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
    1339:   return(0);
    1340: }

    1344: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
    1345: {
    1346:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
    1348:   PetscBool      merged;

    1351:   VecScatterGetMerged(a->Mvctx,&merged);
    1352:   /* do nondiagonal part */
    1353:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
    1354:   if (!merged) {
    1355:     /* send it on its way */
    1356:     VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
    1357:     /* do local part */
    1358:     (*a->A->ops->multtranspose)(a->A,xx,yy);
    1359:     /* receive remote parts: note this assumes the values are not actually */
    1360:     /* inserted in yy until the next line */
    1361:     VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
    1362:   } else {
    1363:     /* do local part */
    1364:     (*a->A->ops->multtranspose)(a->A,xx,yy);
    1365:     /* send it on its way */
    1366:     VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
    1367:     /* values actually were received in the Begin() but we need to call this nop */
    1368:     VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
    1369:   }
    1370:   return(0);
    1371: }

    1375: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1376: {
    1377:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1381:   /* do nondiagonal part */
    1382:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
    1383:   /* send it on its way */
    1384:   VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
    1385:   /* do local part */
    1386:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
    1387:   /* receive remote parts: note this assumes the values are not actually */
    1388:   /* inserted in yy until the next line, which is true for my implementation*/
    1389:   /* but is not perhaps always true. */
    1390:   VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
    1391:   return(0);
    1392: }

    1394: /*
    1395:   This only works correctly for square matrices where the subblock A->A is the
    1396:    diagonal block
    1397: */
    1400: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
    1401: {
    1402:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1406:   if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
    1407:   MatGetDiagonal(a->A,v);
    1408:   return(0);
    1409: }

    1413: PetscErrorCode MatScale_MPIBAIJ(Mat A,PetscScalar aa)
    1414: {
    1415:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1419:   MatScale(a->A,aa);
    1420:   MatScale(a->B,aa);
    1421:   return(0);
    1422: }

    1426: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    1427: {
    1428:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
    1429:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
    1431:   PetscInt       bs = matin->rmap->bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
    1432:   PetscInt       nztot,nzA,nzB,lrow,brstart = matin->rmap->rstart,brend = matin->rmap->rend;
    1433:   PetscInt       *cmap,*idx_p,cstart = mat->cstartbs;

    1436:   if (row < brstart || row >= brend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local rows");
    1437:   if (mat->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Already active");
    1438:   mat->getrowactive = PETSC_TRUE;

    1440:   if (!mat->rowvalues && (idx || v)) {
    1441:     /*
    1442:         allocate enough space to hold information from the longest row.
    1443:     */
    1444:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
    1445:     PetscInt    max = 1,mbs = mat->mbs,tmp;
    1446:     for (i=0; i<mbs; i++) {
    1447:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
    1448:       if (max < tmp) max = tmp;
    1449:     }
    1450:     PetscMalloc2(max*bs2,PetscScalar,&mat->rowvalues,max*bs2,PetscInt,&mat->rowindices);
    1451:   }
    1452:   lrow = row - brstart;

    1454:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
    1455:   if (!v)   {pvA = 0; pvB = 0;}
    1456:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
    1457:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
    1458:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
    1459:   nztot = nzA + nzB;

    1461:   cmap = mat->garray;
    1462:   if (v  || idx) {
    1463:     if (nztot) {
    1464:       /* Sort by increasing column numbers, assuming A and B already sorted */
    1465:       PetscInt imark = -1;
    1466:       if (v) {
    1467:         *v = v_p = mat->rowvalues;
    1468:         for (i=0; i<nzB; i++) {
    1469:           if (cmap[cworkB[i]/bs] < cstart) v_p[i] = vworkB[i];
    1470:           else break;
    1471:         }
    1472:         imark = i;
    1473:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
    1474:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
    1475:       }
    1476:       if (idx) {
    1477:         *idx = idx_p = mat->rowindices;
    1478:         if (imark > -1) {
    1479:           for (i=0; i<imark; i++) {
    1480:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
    1481:           }
    1482:         } else {
    1483:           for (i=0; i<nzB; i++) {
    1484:             if (cmap[cworkB[i]/bs] < cstart) idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
    1485:             else break;
    1486:           }
    1487:           imark = i;
    1488:         }
    1489:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
    1490:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
    1491:       }
    1492:     } else {
    1493:       if (idx) *idx = 0;
    1494:       if (v)   *v   = 0;
    1495:     }
    1496:   }
    1497:   *nz  = nztot;
    1498:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
    1499:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
    1500:   return(0);
    1501: }

    1505: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    1506: {
    1507:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

    1510:   if (!baij->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
    1511:   baij->getrowactive = PETSC_FALSE;
    1512:   return(0);
    1513: }

    1517: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
    1518: {
    1519:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;

    1523:   MatZeroEntries(l->A);
    1524:   MatZeroEntries(l->B);
    1525:   return(0);
    1526: }

    1530: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
    1531: {
    1532:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)matin->data;
    1533:   Mat            A  = a->A,B = a->B;
    1535:   PetscReal      isend[5],irecv[5];

    1538:   info->block_size = (PetscReal)matin->rmap->bs;

    1540:   MatGetInfo(A,MAT_LOCAL,info);

    1542:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
    1543:   isend[3] = info->memory;  isend[4] = info->mallocs;

    1545:   MatGetInfo(B,MAT_LOCAL,info);

    1547:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
    1548:   isend[3] += info->memory;  isend[4] += info->mallocs;

    1550:   if (flag == MAT_LOCAL) {
    1551:     info->nz_used      = isend[0];
    1552:     info->nz_allocated = isend[1];
    1553:     info->nz_unneeded  = isend[2];
    1554:     info->memory       = isend[3];
    1555:     info->mallocs      = isend[4];
    1556:   } else if (flag == MAT_GLOBAL_MAX) {
    1557:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)matin));

    1559:     info->nz_used      = irecv[0];
    1560:     info->nz_allocated = irecv[1];
    1561:     info->nz_unneeded  = irecv[2];
    1562:     info->memory       = irecv[3];
    1563:     info->mallocs      = irecv[4];
    1564:   } else if (flag == MAT_GLOBAL_SUM) {
    1565:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)matin));

    1567:     info->nz_used      = irecv[0];
    1568:     info->nz_allocated = irecv[1];
    1569:     info->nz_unneeded  = irecv[2];
    1570:     info->memory       = irecv[3];
    1571:     info->mallocs      = irecv[4];
    1572:   } else SETERRQ1(PetscObjectComm((PetscObject)matin),PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
    1573:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
    1574:   info->fill_ratio_needed = 0;
    1575:   info->factor_mallocs    = 0;
    1576:   return(0);
    1577: }

    1581: PetscErrorCode MatSetOption_MPIBAIJ(Mat A,MatOption op,PetscBool flg)
    1582: {
    1583:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1587:   switch (op) {
    1588:   case MAT_NEW_NONZERO_LOCATIONS:
    1589:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
    1590:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
    1591:   case MAT_KEEP_NONZERO_PATTERN:
    1592:   case MAT_NEW_NONZERO_LOCATION_ERR:
    1593:     MatSetOption(a->A,op,flg);
    1594:     MatSetOption(a->B,op,flg);
    1595:     break;
    1596:   case MAT_ROW_ORIENTED:
    1597:     a->roworiented = flg;

    1599:     MatSetOption(a->A,op,flg);
    1600:     MatSetOption(a->B,op,flg);
    1601:     break;
    1602:   case MAT_NEW_DIAGONALS:
    1603:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
    1604:     break;
    1605:   case MAT_IGNORE_OFF_PROC_ENTRIES:
    1606:     a->donotstash = flg;
    1607:     break;
    1608:   case MAT_USE_HASH_TABLE:
    1609:     a->ht_flag = flg;
    1610:     break;
    1611:   case MAT_SYMMETRIC:
    1612:   case MAT_STRUCTURALLY_SYMMETRIC:
    1613:   case MAT_HERMITIAN:
    1614:   case MAT_SYMMETRY_ETERNAL:
    1615:     MatSetOption(a->A,op,flg);
    1616:     break;
    1617:   default:
    1618:     SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"unknown option %d",op);
    1619:   }
    1620:   return(0);
    1621: }

    1625: PetscErrorCode MatTranspose_MPIBAIJ(Mat A,MatReuse reuse,Mat *matout)
    1626: {
    1627:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)A->data;
    1628:   Mat_SeqBAIJ    *Aloc;
    1629:   Mat            B;
    1631:   PetscInt       M =A->rmap->N,N=A->cmap->N,*ai,*aj,i,*rvals,j,k,col;
    1632:   PetscInt       bs=A->rmap->bs,mbs=baij->mbs;
    1633:   MatScalar      *a;

    1636:   if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
    1637:   if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
    1638:     MatCreate(PetscObjectComm((PetscObject)A),&B);
    1639:     MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
    1640:     MatSetType(B,((PetscObject)A)->type_name);
    1641:     /* Do not know preallocation information, but must set block size */
    1642:     MatMPIBAIJSetPreallocation(B,A->rmap->bs,PETSC_DECIDE,NULL,PETSC_DECIDE,NULL);
    1643:   } else {
    1644:     B = *matout;
    1645:   }

    1647:   /* copy over the A part */
    1648:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
    1649:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
    1650:   PetscMalloc(bs*sizeof(PetscInt),&rvals);

    1652:   for (i=0; i<mbs; i++) {
    1653:     rvals[0] = bs*(baij->rstartbs + i);
    1654:     for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
    1655:     for (j=ai[i]; j<ai[i+1]; j++) {
    1656:       col = (baij->cstartbs+aj[j])*bs;
    1657:       for (k=0; k<bs; k++) {
    1658:         MatSetValues_MPIBAIJ(B,1,&col,bs,rvals,a,INSERT_VALUES);

    1660:         col++; a += bs;
    1661:       }
    1662:     }
    1663:   }
    1664:   /* copy over the B part */
    1665:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
    1666:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
    1667:   for (i=0; i<mbs; i++) {
    1668:     rvals[0] = bs*(baij->rstartbs + i);
    1669:     for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
    1670:     for (j=ai[i]; j<ai[i+1]; j++) {
    1671:       col = baij->garray[aj[j]]*bs;
    1672:       for (k=0; k<bs; k++) {
    1673:         MatSetValues_MPIBAIJ(B,1,&col,bs,rvals,a,INSERT_VALUES);
    1674:         col++;
    1675:         a += bs;
    1676:       }
    1677:     }
    1678:   }
    1679:   PetscFree(rvals);
    1680:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    1681:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

    1683:   if (reuse == MAT_INITIAL_MATRIX || *matout != A) *matout = B;
    1684:   else {
    1685:     MatHeaderMerge(A,B);
    1686:   }
    1687:   return(0);
    1688: }

    1692: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
    1693: {
    1694:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
    1695:   Mat            a     = baij->A,b = baij->B;
    1697:   PetscInt       s1,s2,s3;

    1700:   MatGetLocalSize(mat,&s2,&s3);
    1701:   if (rr) {
    1702:     VecGetLocalSize(rr,&s1);
    1703:     if (s1!=s3) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
    1704:     /* Overlap communication with computation. */
    1705:     VecScatterBegin(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1706:   }
    1707:   if (ll) {
    1708:     VecGetLocalSize(ll,&s1);
    1709:     if (s1!=s2) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
    1710:     (*b->ops->diagonalscale)(b,ll,NULL);
    1711:   }
    1712:   /* scale  the diagonal block */
    1713:   (*a->ops->diagonalscale)(a,ll,rr);

    1715:   if (rr) {
    1716:     /* Do a scatter end and then right scale the off-diagonal block */
    1717:     VecScatterEnd(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
    1718:     (*b->ops->diagonalscale)(b,NULL,baij->lvec);
    1719:   }
    1720:   return(0);
    1721: }

    1725: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    1726: {
    1727:   Mat_MPIBAIJ       *l = (Mat_MPIBAIJ*)A->data;
    1728:   PetscErrorCode    ierr;
    1729:   PetscMPIInt       imdex,size = l->size,n,rank = l->rank;
    1730:   PetscInt          i,*owners = A->rmap->range;
    1731:   PetscInt          *nprocs,j,idx,nsends,row;
    1732:   PetscInt          nmax,*svalues,*starts,*owner,nrecvs;
    1733:   PetscInt          *rvalues,tag = ((PetscObject)A)->tag,count,base,slen,*source,lastidx = -1;
    1734:   PetscInt          *lens,*lrows,*values,rstart_bs=A->rmap->rstart;
    1735:   MPI_Comm          comm;
    1736:   MPI_Request       *send_waits,*recv_waits;
    1737:   MPI_Status        recv_status,*send_status;
    1738:   const PetscScalar *xx;
    1739:   PetscScalar       *bb;
    1740: #if defined(PETSC_DEBUG)
    1741:   PetscBool         found = PETSC_FALSE;
    1742: #endif

    1745:   PetscObjectGetComm((PetscObject)A,&comm);
    1746:   /*  first count number of contributors to each processor */
    1747:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
    1748:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
    1749:   PetscMalloc((N+1)*sizeof(PetscInt),&owner);  /* see note*/
    1750:   j    = 0;
    1751:   for (i=0; i<N; i++) {
    1752:     if (lastidx > (idx = rows[i])) j = 0;
    1753:     lastidx = idx;
    1754:     for (; j<size; j++) {
    1755:       if (idx >= owners[j] && idx < owners[j+1]) {
    1756:         nprocs[2*j]++;
    1757:         nprocs[2*j+1] = 1;
    1758:         owner[i]      = j;
    1759: #if defined(PETSC_DEBUG)
    1760:         found = PETSC_TRUE;
    1761: #endif
    1762:         break;
    1763:       }
    1764:     }
    1765: #if defined(PETSC_DEBUG)
    1766:     if (!found) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
    1767:     found = PETSC_FALSE;
    1768: #endif
    1769:   }
    1770:   nsends = 0;  for (i=0; i<size; i++) nsends += nprocs[2*i+1];

    1772:   if (A->nooffproczerorows) {
    1773:     if (nsends > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"You called MatSetOption(,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) but set an off process zero row");
    1774:     nrecvs = nsends;
    1775:     nmax   = N;
    1776:   } else {
    1777:     /* inform other processors of number of messages and max length*/
    1778:     PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
    1779:   }

    1781:   /* post receives:   */
    1782:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
    1783:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
    1784:   for (i=0; i<nrecvs; i++) {
    1785:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
    1786:   }

    1788:   /* do sends:
    1789:      1) starts[i] gives the starting index in svalues for stuff going to
    1790:      the ith processor
    1791:   */
    1792:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
    1793:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
    1794:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
    1795:   starts[0] = 0;
    1796:   for (i=1; i<size; i++) starts[i] = starts[i-1] + nprocs[2*i-2];
    1797:   for (i=0; i<N; i++) {
    1798:     svalues[starts[owner[i]]++] = rows[i];
    1799:   }

    1801:   starts[0] = 0;
    1802:   for (i=1; i<size+1; i++) starts[i] = starts[i-1] + nprocs[2*i-2];
    1803:   count = 0;
    1804:   for (i=0; i<size; i++) {
    1805:     if (nprocs[2*i+1]) {
    1806:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
    1807:     }
    1808:   }
    1809:   PetscFree(starts);

    1811:   base = owners[rank];

    1813:   /*  wait on receives */
    1814:   PetscMalloc2(nrecvs+1,PetscInt,&lens,nrecvs+1,PetscInt,&source);
    1815:   count = nrecvs;
    1816:   slen  = 0;
    1817:   while (count) {
    1818:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
    1819:     /* unpack receives into our local space */
    1820:     MPI_Get_count(&recv_status,MPIU_INT,&n);

    1822:     source[imdex] = recv_status.MPI_SOURCE;
    1823:     lens[imdex]   = n;
    1824:     slen         += n;
    1825:     count--;
    1826:   }
    1827:   PetscFree(recv_waits);

    1829:   /* move the data into the send scatter */
    1830:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
    1831:   count = 0;
    1832:   for (i=0; i<nrecvs; i++) {
    1833:     values = rvalues + i*nmax;
    1834:     for (j=0; j<lens[i]; j++) {
    1835:       lrows[count++] = values[j] - base;
    1836:     }
    1837:   }
    1838:   PetscFree(rvalues);
    1839:   PetscFree2(lens,source);
    1840:   PetscFree(owner);
    1841:   PetscFree(nprocs);

    1843:   /* fix right hand side if needed */
    1844:   if (x && b) {
    1845:     VecGetArrayRead(x,&xx);
    1846:     VecGetArray(b,&bb);
    1847:     for (i=0; i<slen; i++) {
    1848:       bb[lrows[i]] = diag*xx[lrows[i]];
    1849:     }
    1850:     VecRestoreArrayRead(x,&xx);
    1851:     VecRestoreArray(b,&bb);
    1852:   }

    1854:   /* actually zap the local rows */
    1855:   /*
    1856:         Zero the required rows. If the "diagonal block" of the matrix
    1857:      is square and the user wishes to set the diagonal we use separate
    1858:      code so that MatSetValues() is not called for each diagonal allocating
    1859:      new memory, thus calling lots of mallocs and slowing things down.

    1861:   */
    1862:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
    1863:   MatZeroRows_SeqBAIJ(l->B,slen,lrows,0.0,0,0);
    1864:   if ((diag != 0.0) && (l->A->rmap->N == l->A->cmap->N)) {
    1865:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,diag,0,0);
    1866:   } else if (diag != 0.0) {
    1867:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0,0,0);
    1868:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
    1869:        MAT_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
    1870:     for (i=0; i<slen; i++) {
    1871:       row  = lrows[i] + rstart_bs;
    1872:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
    1873:     }
    1874:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    1875:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    1876:   } else {
    1877:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0,0,0);
    1878:   }

    1880:   PetscFree(lrows);

    1882:   /* wait on sends */
    1883:   if (nsends) {
    1884:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
    1885:     MPI_Waitall(nsends,send_waits,send_status);
    1886:     PetscFree(send_status);
    1887:   }
    1888:   PetscFree(send_waits);
    1889:   PetscFree(svalues);
    1890:   return(0);
    1891: }

    1895: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
    1896: {
    1897:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1901:   MatSetUnfactored(a->A);
    1902:   return(0);
    1903: }

    1905: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat*);

    1909: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscBool  *flag)
    1910: {
    1911:   Mat_MPIBAIJ    *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
    1912:   Mat            a,b,c,d;
    1913:   PetscBool      flg;

    1917:   a = matA->A; b = matA->B;
    1918:   c = matB->A; d = matB->B;

    1920:   MatEqual(a,c,&flg);
    1921:   if (flg) {
    1922:     MatEqual(b,d,&flg);
    1923:   }
    1924:   MPI_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
    1925:   return(0);
    1926: }

    1930: PetscErrorCode MatCopy_MPIBAIJ(Mat A,Mat B,MatStructure str)
    1931: {
    1933:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
    1934:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)B->data;

    1937:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
    1938:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
    1939:     MatCopy_Basic(A,B,str);
    1940:   } else {
    1941:     MatCopy(a->A,b->A,str);
    1942:     MatCopy(a->B,b->B,str);
    1943:   }
    1944:   return(0);
    1945: }

    1949: PetscErrorCode MatSetUp_MPIBAIJ(Mat A)
    1950: {

    1954:    MatMPIBAIJSetPreallocation(A,A->rmap->bs,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
    1955:   return(0);
    1956: }

    1960: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
    1961: {
    1963:   Mat_MPIBAIJ    *xx=(Mat_MPIBAIJ*)X->data,*yy=(Mat_MPIBAIJ*)Y->data;
    1964:   PetscBLASInt   bnz,one=1;
    1965:   Mat_SeqBAIJ    *x,*y;

    1968:   if (str == SAME_NONZERO_PATTERN) {
    1969:     PetscScalar alpha = a;
    1970:     x    = (Mat_SeqBAIJ*)xx->A->data;
    1971:     y    = (Mat_SeqBAIJ*)yy->A->data;
    1972:     PetscBLASIntCast(x->nz,&bnz);
    1973:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
    1974:     x    = (Mat_SeqBAIJ*)xx->B->data;
    1975:     y    = (Mat_SeqBAIJ*)yy->B->data;
    1976:     PetscBLASIntCast(x->nz,&bnz);
    1977:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
    1978:   } else {
    1979:     MatAXPY_Basic(Y,a,X,str);
    1980:   }
    1981:   return(0);
    1982: }

    1986: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
    1987: {
    1988:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    1992:   MatRealPart(a->A);
    1993:   MatRealPart(a->B);
    1994:   return(0);
    1995: }

    1999: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
    2000: {
    2001:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

    2005:   MatImaginaryPart(a->A);
    2006:   MatImaginaryPart(a->B);
    2007:   return(0);
    2008: }

    2012: PetscErrorCode MatGetSubMatrix_MPIBAIJ(Mat mat,IS isrow,IS iscol,MatReuse call,Mat *newmat)
    2013: {
    2015:   IS             iscol_local;
    2016:   PetscInt       csize;

    2019:   ISGetLocalSize(iscol,&csize);
    2020:   if (call == MAT_REUSE_MATRIX) {
    2021:     PetscObjectQuery((PetscObject)*newmat,"ISAllGather",(PetscObject*)&iscol_local);
    2022:     if (!iscol_local) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
    2023:   } else {
    2024:     ISAllGather(iscol,&iscol_local);
    2025:   }
    2026:   MatGetSubMatrix_MPIBAIJ_Private(mat,isrow,iscol_local,csize,call,newmat);
    2027:   if (call == MAT_INITIAL_MATRIX) {
    2028:     PetscObjectCompose((PetscObject)*newmat,"ISAllGather",(PetscObject)iscol_local);
    2029:     ISDestroy(&iscol_local);
    2030:   }
    2031:   return(0);
    2032: }
    2033: extern PetscErrorCode MatGetSubMatrices_MPIBAIJ_local(Mat,PetscInt,const IS[],const IS[],MatReuse,PetscBool*,PetscBool*,Mat*);
    2036: /*
    2037:   Not great since it makes two copies of the submatrix, first an SeqBAIJ
    2038:   in local and then by concatenating the local matrices the end result.
    2039:   Writing it directly would be much like MatGetSubMatrices_MPIBAIJ()
    2040: */
    2041: PetscErrorCode MatGetSubMatrix_MPIBAIJ_Private(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
    2042: {
    2044:   PetscMPIInt    rank,size;
    2045:   PetscInt       i,m,n,rstart,row,rend,nz,*cwork,j,bs;
    2046:   PetscInt       *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal,ncol,nrow;
    2047:   Mat            M,Mreuse;
    2048:   MatScalar      *vwork,*aa;
    2049:   MPI_Comm       comm;
    2050:   IS             isrow_new, iscol_new;
    2051:   PetscBool      idflag,allrows, allcols;
    2052:   Mat_SeqBAIJ    *aij;

    2055:   PetscObjectGetComm((PetscObject)mat,&comm);
    2056:   MPI_Comm_rank(comm,&rank);
    2057:   MPI_Comm_size(comm,&size);
    2058:   /* The compression and expansion should be avoided. Doesn't point
    2059:      out errors, might change the indices, hence buggey */
    2060:   ISCompressIndicesGeneral(mat->rmap->N,mat->rmap->n,mat->rmap->bs,1,&isrow,&isrow_new);
    2061:   ISCompressIndicesGeneral(mat->cmap->N,mat->cmap->n,mat->cmap->bs,1,&iscol,&iscol_new);

    2063:   /* Check for special case: each processor gets entire matrix columns */
    2064:   ISIdentity(iscol,&idflag);
    2065:   ISGetLocalSize(iscol,&ncol);
    2066:   if (idflag && ncol == mat->cmap->N) allcols = PETSC_TRUE;
    2067:   else allcols = PETSC_FALSE;

    2069:   ISIdentity(isrow,&idflag);
    2070:   ISGetLocalSize(isrow,&nrow);
    2071:   if (idflag && nrow == mat->rmap->N) allrows = PETSC_TRUE;
    2072:   else allrows = PETSC_FALSE;

    2074:   if (call ==  MAT_REUSE_MATRIX) {
    2075:     PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject*)&Mreuse);
    2076:     if (!Mreuse) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
    2077:     MatGetSubMatrices_MPIBAIJ_local(mat,1,&isrow_new,&iscol_new,MAT_REUSE_MATRIX,&allrows,&allcols,&Mreuse);
    2078:   } else {
    2079:     MatGetSubMatrices_MPIBAIJ_local(mat,1,&isrow_new,&iscol_new,MAT_INITIAL_MATRIX,&allrows,&allcols,&Mreuse);
    2080:   }
    2081:   ISDestroy(&isrow_new);
    2082:   ISDestroy(&iscol_new);
    2083:   /*
    2084:       m - number of local rows
    2085:       n - number of columns (same on all processors)
    2086:       rstart - first row in new global matrix generated
    2087:   */
    2088:   MatGetBlockSize(mat,&bs);
    2089:   MatGetSize(Mreuse,&m,&n);
    2090:   m    = m/bs;
    2091:   n    = n/bs;

    2093:   if (call == MAT_INITIAL_MATRIX) {
    2094:     aij = (Mat_SeqBAIJ*)(Mreuse)->data;
    2095:     ii  = aij->i;
    2096:     jj  = aij->j;

    2098:     /*
    2099:         Determine the number of non-zeros in the diagonal and off-diagonal
    2100:         portions of the matrix in order to do correct preallocation
    2101:     */

    2103:     /* first get start and end of "diagonal" columns */
    2104:     if (csize == PETSC_DECIDE) {
    2105:       ISGetSize(isrow,&mglobal);
    2106:       if (mglobal == n*bs) { /* square matrix */
    2107:         nlocal = m;
    2108:       } else {
    2109:         nlocal = n/size + ((n % size) > rank);
    2110:       }
    2111:     } else {
    2112:       nlocal = csize/bs;
    2113:     }
    2114:     MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
    2115:     rstart = rend - nlocal;
    2116:     if (rank == size - 1 && rend != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);

    2118:     /* next, compute all the lengths */
    2119:     PetscMalloc((2*m+1)*sizeof(PetscInt),&dlens);
    2120:     olens = dlens + m;
    2121:     for (i=0; i<m; i++) {
    2122:       jend = ii[i+1] - ii[i];
    2123:       olen = 0;
    2124:       dlen = 0;
    2125:       for (j=0; j<jend; j++) {
    2126:         if (*jj < rstart || *jj >= rend) olen++;
    2127:         else dlen++;
    2128:         jj++;
    2129:       }
    2130:       olens[i] = olen;
    2131:       dlens[i] = dlen;
    2132:     }
    2133:     MatCreate(comm,&M);
    2134:     MatSetSizes(M,bs*m,bs*nlocal,PETSC_DECIDE,bs*n);
    2135:     MatSetType(M,((PetscObject)mat)->type_name);
    2136:     MatMPIBAIJSetPreallocation(M,bs,0,dlens,0,olens);
    2137:     PetscFree(dlens);
    2138:   } else {
    2139:     PetscInt ml,nl;

    2141:     M    = *newmat;
    2142:     MatGetLocalSize(M,&ml,&nl);
    2143:     if (ml != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
    2144:     MatZeroEntries(M);
    2145:     /*
    2146:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
    2147:        rather than the slower MatSetValues().
    2148:     */
    2149:     M->was_assembled = PETSC_TRUE;
    2150:     M->assembled     = PETSC_FALSE;
    2151:   }
    2152:   MatSetOption(M,MAT_ROW_ORIENTED,PETSC_FALSE);
    2153:   MatGetOwnershipRange(M,&rstart,&rend);
    2154:   aij  = (Mat_SeqBAIJ*)(Mreuse)->data;
    2155:   ii   = aij->i;
    2156:   jj   = aij->j;
    2157:   aa   = aij->a;
    2158:   for (i=0; i<m; i++) {
    2159:     row   = rstart/bs + i;
    2160:     nz    = ii[i+1] - ii[i];
    2161:     cwork = jj;     jj += nz;
    2162:     vwork = aa;     aa += nz*bs*bs;
    2163:     MatSetValuesBlocked_MPIBAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
    2164:   }

    2166:   MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
    2167:   MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
    2168:   *newmat = M;

    2170:   /* save submatrix used in processor for next request */
    2171:   if (call ==  MAT_INITIAL_MATRIX) {
    2172:     PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
    2173:     PetscObjectDereference((PetscObject)Mreuse);
    2174:   }
    2175:   return(0);
    2176: }

    2180: PetscErrorCode MatPermute_MPIBAIJ(Mat A,IS rowp,IS colp,Mat *B)
    2181: {
    2182:   MPI_Comm       comm,pcomm;
    2183:   PetscInt       first,rlocal_size,clocal_size,nrows;
    2184:   const PetscInt *rows;
    2185:   PetscMPIInt    size;
    2186:   IS             crowp,growp,irowp,lrowp,lcolp;

    2190:   PetscObjectGetComm((PetscObject)A,&comm);
    2191:   /* make a collective version of 'rowp' */
    2192:   PetscObjectGetComm((PetscObject)rowp,&pcomm);
    2193:   if (pcomm==comm) {
    2194:     crowp = rowp;
    2195:   } else {
    2196:     ISGetSize(rowp,&nrows);
    2197:     ISGetIndices(rowp,&rows);
    2198:     ISCreateGeneral(comm,nrows,rows,PETSC_COPY_VALUES,&crowp);
    2199:     ISRestoreIndices(rowp,&rows);
    2200:   }
    2201:   /* collect the global row permutation and invert it */
    2202:   ISAllGather(crowp,&growp);
    2203:   ISSetPermutation(growp);
    2204:   if (pcomm!=comm) {
    2205:     ISDestroy(&crowp);
    2206:   }
    2207:   ISInvertPermutation(growp,PETSC_DECIDE,&irowp);
    2208:   ISDestroy(&growp);
    2209:   /* get the local target indices */
    2210:   MatGetOwnershipRange(A,&first,NULL);
    2211:   MatGetLocalSize(A,&rlocal_size,&clocal_size);
    2212:   ISGetIndices(irowp,&rows);
    2213:   ISCreateGeneral(MPI_COMM_SELF,rlocal_size,rows+first,PETSC_COPY_VALUES,&lrowp);
    2214:   ISRestoreIndices(irowp,&rows);
    2215:   ISDestroy(&irowp);
    2216:   /* the column permutation is so much easier;
    2217:      make a local version of 'colp' and invert it */
    2218:   PetscObjectGetComm((PetscObject)colp,&pcomm);
    2219:   MPI_Comm_size(pcomm,&size);
    2220:   if (size==1) {
    2221:     lcolp = colp;
    2222:   } else {
    2223:     ISAllGather(colp,&lcolp);
    2224:   }
    2225:   ISSetPermutation(lcolp);
    2226:   /* now we just get the submatrix */
    2227:   MatGetSubMatrix_MPIBAIJ_Private(A,lrowp,lcolp,clocal_size,MAT_INITIAL_MATRIX,B);
    2228:   if (size>1) {
    2229:     ISDestroy(&lcolp);
    2230:   }
    2231:   /* clean up */
    2232:   ISDestroy(&lrowp);
    2233:   return(0);
    2234: }

    2238: PetscErrorCode  MatGetGhosts_MPIBAIJ(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[])
    2239: {
    2240:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*) mat->data;
    2241:   Mat_SeqBAIJ *B    = (Mat_SeqBAIJ*)baij->B->data;

    2244:   if (nghosts) *nghosts = B->nbs;
    2245:   if (ghosts) *ghosts = baij->garray;
    2246:   return(0);
    2247: }

    2249: extern PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat);

    2253: /*
    2254:     This routine is almost identical to MatFDColoringCreate_MPIBAIJ()!
    2255: */
    2256: PetscErrorCode MatFDColoringCreate_MPIBAIJ(Mat mat,ISColoring iscoloring,MatFDColoring c)
    2257: {
    2258:   Mat_MPIBAIJ            *baij = (Mat_MPIBAIJ*)mat->data;
    2259:   PetscErrorCode         ierr;
    2260:   PetscMPIInt            size,*ncolsonproc,*disp,nn;
    2261:   PetscInt               bs,i,n,nrows,j,k,m,ncols,col;
    2262:   const PetscInt         *is,*rows = 0,*A_ci,*A_cj,*B_ci,*B_cj,*ltog;
    2263:   PetscInt               nis = iscoloring->n,nctot,*cols;
    2264:   PetscInt               *rowhit,M,cstart,cend,colb;
    2265:   PetscInt               *columnsforrow,l;
    2266:   IS                     *isa;
    2267:   PetscBool              done,flg;
    2268:   ISLocalToGlobalMapping map = mat->cmap->bmapping;
    2269:   PetscInt               ctype=c->ctype;

    2272:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix must be assembled first; MatAssemblyBegin/End();");
    2273:   if (ctype == IS_COLORING_GHOSTED && !map) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_INCOMP,"When using ghosted differencing matrix must have local to global mapping provided with MatSetLocalToGlobalMappingBlock");

    2275:   if (map) {ISLocalToGlobalMappingGetIndices(map,&ltog);}
    2276:   else     ltog = NULL;
    2277:   ISColoringGetIS(iscoloring,PETSC_IGNORE,&isa);
    2278:   MatGetBlockSize(mat,&bs);

    2280:   M         = mat->rmap->n/bs;
    2281:   cstart    = mat->cmap->rstart/bs;
    2282:   cend      = mat->cmap->rend/bs;
    2283:   c->M      = mat->rmap->N/bs;         /* set the global rows and columns and local rows */
    2284:   c->N      = mat->cmap->N/bs;
    2285:   c->m      = mat->rmap->n/bs;
    2286:   c->rstart = mat->rmap->rstart/bs;

    2288:   c->ncolors = nis;
    2289:   PetscMalloc(nis*sizeof(PetscInt),&c->ncolumns);
    2290:   PetscMalloc(nis*sizeof(PetscInt*),&c->columns);
    2291:   PetscMalloc(nis*sizeof(PetscInt),&c->nrows);
    2292:   PetscMalloc(nis*sizeof(PetscInt*),&c->rows);
    2293:   PetscMalloc(nis*sizeof(PetscInt*),&c->columnsforrow);
    2294:   PetscLogObjectMemory(c,5*nis*sizeof(PetscInt));

    2296:   /* Allow access to data structures of local part of matrix */
    2297:   if (!baij->colmap) {
    2298:     MatCreateColmap_MPIBAIJ_Private(mat);
    2299:   }
    2300:   MatGetColumnIJ(baij->A,0,PETSC_FALSE,PETSC_FALSE,&ncols,&A_ci,&A_cj,&done);
    2301:   MatGetColumnIJ(baij->B,0,PETSC_FALSE,PETSC_FALSE,&ncols,&B_ci,&B_cj,&done);

    2303:   PetscMalloc((M+1)*sizeof(PetscInt),&rowhit);
    2304:   PetscMalloc((M+1)*sizeof(PetscInt),&columnsforrow);

    2306:   for (i=0; i<nis; i++) {
    2307:     ISGetLocalSize(isa[i],&n);
    2308:     ISGetIndices(isa[i],&is);

    2310:     c->ncolumns[i] = n;
    2311:     if (n) {
    2312:       PetscMalloc(n*sizeof(PetscInt),&c->columns[i]);
    2313:       PetscLogObjectMemory(c,n*sizeof(PetscInt));
    2314:       PetscMemcpy(c->columns[i],is,n*sizeof(PetscInt));
    2315:     } else {
    2316:       c->columns[i] = 0;
    2317:     }

    2319:     if (ctype == IS_COLORING_GLOBAL) {
    2320:       /* Determine the total (parallel) number of columns of this color */
    2321:       MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
    2322:       PetscMalloc2(size,PetscMPIInt,&ncolsonproc,size,PetscMPIInt,&disp);

    2324:       PetscMPIIntCast(n,&nn);
    2325:       MPI_Allgather(&nn,1,MPI_INT,ncolsonproc,1,MPI_INT,PetscObjectComm((PetscObject)mat));
    2326:       nctot = 0; for (j=0; j<size; j++) nctot += ncolsonproc[j];
    2327:       if (!nctot) {
    2328:         PetscInfo(mat,"Coloring of matrix has some unneeded colors with no corresponding rows\n");
    2329:       }

    2331:       disp[0] = 0;
    2332:       for (j=1; j<size; j++) {
    2333:         disp[j] = disp[j-1] + ncolsonproc[j-1];
    2334:       }

    2336:       /* Get complete list of columns for color on each processor */
    2337:       PetscMalloc((nctot+1)*sizeof(PetscInt),&cols);
    2338:       MPI_Allgatherv((void*)is,n,MPIU_INT,cols,ncolsonproc,disp,MPIU_INT,PetscObjectComm((PetscObject)mat));
    2339:       PetscFree2(ncolsonproc,disp);
    2340:     } else if (ctype == IS_COLORING_GHOSTED) {
    2341:       /* Determine local number of columns of this color on this process, including ghost points */
    2342:       nctot = n;
    2343:       PetscMalloc((nctot+1)*sizeof(PetscInt),&cols);
    2344:       PetscMemcpy(cols,is,n*sizeof(PetscInt));
    2345:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not provided for this MatFDColoring type");

    2347:     /*
    2348:        Mark all rows affect by these columns
    2349:     */
    2350:     /* Temporary option to allow for debugging/testing */
    2351:     flg  = PETSC_FALSE;
    2352:     PetscOptionsGetBool(NULL,"-matfdcoloring_slow",&flg,NULL);
    2353:     if (!flg) { /*-----------------------------------------------------------------------------*/
    2354:       /* crude, fast version */
    2355:       PetscMemzero(rowhit,M*sizeof(PetscInt));
    2356:       /* loop over columns*/
    2357:       for (j=0; j<nctot; j++) {
    2358:         if (ctype == IS_COLORING_GHOSTED) {
    2359:           col = ltog[cols[j]];
    2360:         } else {
    2361:           col = cols[j];
    2362:         }
    2363:         if (col >= cstart && col < cend) {
    2364:           /* column is in diagonal block of matrix */
    2365:           rows = A_cj + A_ci[col-cstart];
    2366:           m    = A_ci[col-cstart+1] - A_ci[col-cstart];
    2367:         } else {
    2368: #if defined(PETSC_USE_CTABLE)
    2369:           PetscTableFind(baij->colmap,col+1,&colb);
    2370:           colb--;
    2371: #else
    2372:           colb = baij->colmap[col] - 1;
    2373: #endif
    2374:           if (colb == -1) {
    2375:             m = 0;
    2376:           } else {
    2377:             colb = colb/bs;
    2378:             rows = B_cj + B_ci[colb];
    2379:             m    = B_ci[colb+1] - B_ci[colb];
    2380:           }
    2381:         }
    2382:         /* loop over columns marking them in rowhit */
    2383:         for (k=0; k<m; k++) {
    2384:           rowhit[*rows++] = col + 1;
    2385:         }
    2386:       }

    2388:       /* count the number of hits */
    2389:       nrows = 0;
    2390:       for (j=0; j<M; j++) {
    2391:         if (rowhit[j]) nrows++;
    2392:       }
    2393:       c->nrows[i] = nrows;
    2394:       PetscMalloc((nrows+1)*sizeof(PetscInt),&c->rows[i]);
    2395:       PetscMalloc((nrows+1)*sizeof(PetscInt),&c->columnsforrow[i]);
    2396:       PetscLogObjectMemory(c,2*(nrows+1)*sizeof(PetscInt));
    2397:       nrows       = 0;
    2398:       for (j=0; j<M; j++) {
    2399:         if (rowhit[j]) {
    2400:           c->rows[i][nrows]          = j;
    2401:           c->columnsforrow[i][nrows] = rowhit[j] - 1;
    2402:           nrows++;
    2403:         }
    2404:       }
    2405:     } else { /*-------------------------------------------------------------------------------*/
    2406:       /* slow version, using rowhit as a linked list */
    2407:       PetscInt currentcol,fm,mfm;
    2408:       rowhit[M] = M;
    2409:       nrows     = 0;
    2410:       /* loop over columns*/
    2411:       for (j=0; j<nctot; j++) {
    2412:         if (ctype == IS_COLORING_GHOSTED) {
    2413:           col = ltog[cols[j]];
    2414:         } else {
    2415:           col = cols[j];
    2416:         }
    2417:         if (col >= cstart && col < cend) {
    2418:           /* column is in diagonal block of matrix */
    2419:           rows = A_cj + A_ci[col-cstart];
    2420:           m    = A_ci[col-cstart+1] - A_ci[col-cstart];
    2421:         } else {
    2422: #if defined(PETSC_USE_CTABLE)
    2423:           PetscTableFind(baij->colmap,col+1,&colb);
    2424:           colb--;
    2425: #else
    2426:           colb = baij->colmap[col] - 1;
    2427: #endif
    2428:           if (colb == -1) {
    2429:             m = 0;
    2430:           } else {
    2431:             colb = colb/bs;
    2432:             rows = B_cj + B_ci[colb];
    2433:             m    = B_ci[colb+1] - B_ci[colb];
    2434:           }
    2435:         }

    2437:         /* loop over columns marking them in rowhit */
    2438:         fm = M;    /* fm points to first entry in linked list */
    2439:         for (k=0; k<m; k++) {
    2440:           currentcol = *rows++;
    2441:           /* is it already in the list? */
    2442:           do {
    2443:             mfm = fm;
    2444:             fm  = rowhit[fm];
    2445:           } while (fm < currentcol);
    2446:           /* not in list so add it */
    2447:           if (fm != currentcol) {
    2448:             nrows++;
    2449:             columnsforrow[currentcol] = col;
    2450:             /* next three lines insert new entry into linked list */
    2451:             rowhit[mfm]        = currentcol;
    2452:             rowhit[currentcol] = fm;
    2453:             fm                 = currentcol;
    2454:             /* fm points to present position in list since we know the columns are sorted */
    2455:           } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Invalid coloring of matrix detected");
    2456:         }
    2457:       }
    2458:       c->nrows[i] = nrows;
    2459:       PetscMalloc((nrows+1)*sizeof(PetscInt),&c->rows[i]);
    2460:       PetscMalloc((nrows+1)*sizeof(PetscInt),&c->columnsforrow[i]);
    2461:       PetscLogObjectMemory(c,(nrows+1)*sizeof(PetscInt));
    2462:       /* now store the linked list of rows into c->rows[i] */
    2463:       nrows = 0;
    2464:       fm    = rowhit[M];
    2465:       do {
    2466:         c->rows[i][nrows]            = fm;
    2467:         c->columnsforrow[i][nrows++] = columnsforrow[fm];
    2468:         fm                           = rowhit[fm];
    2469:       } while (fm < M);
    2470:     } /* ---------------------------------------------------------------------------------------*/
    2471:     PetscFree(cols);
    2472:   }

    2474:   /* Optimize by adding the vscale, and scaleforrow[][] fields */
    2475:   /*
    2476:        vscale will contain the "diagonal" on processor scalings followed by the off processor
    2477:   */
    2478:   if (ctype == IS_COLORING_GLOBAL) {
    2479:     PetscInt *garray;
    2480:     PetscMalloc(baij->B->cmap->n*sizeof(PetscInt),&garray);
    2481:     for (i=0; i<baij->B->cmap->n/bs; i++) {
    2482:       for (j=0; j<bs; j++) {
    2483:         garray[i*bs+j] = bs*baij->garray[i]+j;
    2484:       }
    2485:     }
    2486:     VecCreateGhost(PetscObjectComm((PetscObject)mat),baij->A->rmap->n,PETSC_DETERMINE,baij->B->cmap->n,garray,&c->vscale);
    2487:     PetscFree(garray);
    2488:     PetscMalloc(c->ncolors*sizeof(PetscInt*),&c->vscaleforrow);
    2489:     for (k=0; k<c->ncolors; k++) {
    2490:       PetscMalloc((c->nrows[k]+1)*sizeof(PetscInt),&c->vscaleforrow[k]);
    2491:       for (l=0; l<c->nrows[k]; l++) {
    2492:         col = c->columnsforrow[k][l];
    2493:         if (col >= cstart && col < cend) {
    2494:           /* column is in diagonal block of matrix */
    2495:           colb = col - cstart;
    2496:         } else {
    2497:           /* column  is in "off-processor" part */
    2498: #if defined(PETSC_USE_CTABLE)
    2499:           PetscTableFind(baij->colmap,col+1,&colb);
    2500:           colb--;
    2501: #else
    2502:           colb = baij->colmap[col] - 1;
    2503: #endif
    2504:           colb  = colb/bs;
    2505:           colb += cend - cstart;
    2506:         }
    2507:         c->vscaleforrow[k][l] = colb;
    2508:       }
    2509:     }
    2510:   } else if (ctype == IS_COLORING_GHOSTED) {
    2511:     /* Get gtol mapping */
    2512:     PetscInt N = mat->cmap->N,nlocal,*gtol;
    2513:     PetscMalloc((N+1)*sizeof(PetscInt),&gtol);
    2514:     for (i=0; i<N; i++) gtol[i] = -1;
    2515:     ISLocalToGlobalMappingGetSize(map,&nlocal);
    2516:     for (i=0; i<nlocal; i++) gtol[ltog[i]] = i;

    2518:     c->vscale = 0; /* will be created in MatFDColoringApply() */
    2519:     PetscMalloc(c->ncolors*sizeof(PetscInt*),&c->vscaleforrow);
    2520:     for (k=0; k<c->ncolors; k++) {
    2521:       PetscMalloc((c->nrows[k]+1)*sizeof(PetscInt),&c->vscaleforrow[k]);
    2522:       for (l=0; l<c->nrows[k]; l++) {
    2523:         col = c->columnsforrow[k][l];      /* global column index */

    2525:         c->vscaleforrow[k][l] = gtol[col]; /* local column index */
    2526:       }
    2527:     }
    2528:     PetscFree(gtol);
    2529:   }
    2530:   ISColoringRestoreIS(iscoloring,&isa);

    2532:   PetscFree(rowhit);
    2533:   PetscFree(columnsforrow);
    2534:   MatRestoreColumnIJ(baij->A,0,PETSC_FALSE,PETSC_FALSE,&ncols,&A_ci,&A_cj,&done);
    2535:   MatRestoreColumnIJ(baij->B,0,PETSC_FALSE,PETSC_FALSE,&ncols,&B_ci,&B_cj,&done);
    2536:   if (map) {ISLocalToGlobalMappingRestoreIndices(map,&ltog);}
    2537:   return(0);
    2538: }

    2542: PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A,Mat *newmat)
    2543: {
    2544:   Mat            B;
    2545:   Mat_MPIBAIJ    *a  = (Mat_MPIBAIJ*)A->data;
    2546:   Mat_SeqBAIJ    *ad = (Mat_SeqBAIJ*)a->A->data,*bd = (Mat_SeqBAIJ*)a->B->data;
    2547:   Mat_SeqAIJ     *b;
    2549:   PetscMPIInt    size,rank,*recvcounts = 0,*displs = 0;
    2550:   PetscInt       sendcount,i,*rstarts = A->rmap->range,n,cnt,j,bs = A->rmap->bs;
    2551:   PetscInt       m,*garray = a->garray,*lens,*jsendbuf,*a_jsendbuf,*b_jsendbuf;

    2554:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
    2555:   MPI_Comm_rank(PetscObjectComm((PetscObject)A),&rank);

    2557:   /* ----------------------------------------------------------------
    2558:      Tell every processor the number of nonzeros per row
    2559:   */
    2560:   PetscMalloc((A->rmap->N/bs)*sizeof(PetscInt),&lens);
    2561:   for (i=A->rmap->rstart/bs; i<A->rmap->rend/bs; i++) {
    2562:     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];
    2563:   }
    2564:   sendcount = A->rmap->rend/bs - A->rmap->rstart/bs;
    2565:   PetscMalloc(2*size*sizeof(PetscMPIInt),&recvcounts);
    2566:   displs    = recvcounts + size;
    2567:   for (i=0; i<size; i++) {
    2568:     recvcounts[i] = A->rmap->range[i+1]/bs - A->rmap->range[i]/bs;
    2569:     displs[i]     = A->rmap->range[i]/bs;
    2570:   }
    2571: #if defined(PETSC_HAVE_MPI_IN_PLACE)
    2572:   MPI_Allgatherv(MPI_IN_PLACE,0,MPI_DATATYPE_NULL,lens,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
    2573: #else
    2574:   MPI_Allgatherv(lens+A->rmap->rstart/bs,sendcount,MPIU_INT,lens,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
    2575: #endif
    2576:   /* ---------------------------------------------------------------
    2577:      Create the sequential matrix of the same type as the local block diagonal
    2578:   */
    2579:   MatCreate(PETSC_COMM_SELF,&B);
    2580:   MatSetSizes(B,A->rmap->N/bs,A->cmap->N/bs,PETSC_DETERMINE,PETSC_DETERMINE);
    2581:   MatSetType(B,MATSEQAIJ);
    2582:   MatSeqAIJSetPreallocation(B,0,lens);
    2583:   b    = (Mat_SeqAIJ*)B->data;

    2585:   /*--------------------------------------------------------------------
    2586:     Copy my part of matrix column indices over
    2587:   */
    2588:   sendcount  = ad->nz + bd->nz;
    2589:   jsendbuf   = b->j + b->i[rstarts[rank]/bs];
    2590:   a_jsendbuf = ad->j;
    2591:   b_jsendbuf = bd->j;
    2592:   n          = A->rmap->rend/bs - A->rmap->rstart/bs;
    2593:   cnt        = 0;
    2594:   for (i=0; i<n; i++) {

    2596:     /* put in lower diagonal portion */
    2597:     m = bd->i[i+1] - bd->i[i];
    2598:     while (m > 0) {
    2599:       /* is it above diagonal (in bd (compressed) numbering) */
    2600:       if (garray[*b_jsendbuf] > A->rmap->rstart/bs + i) break;
    2601:       jsendbuf[cnt++] = garray[*b_jsendbuf++];
    2602:       m--;
    2603:     }

    2605:     /* put in diagonal portion */
    2606:     for (j=ad->i[i]; j<ad->i[i+1]; j++) {
    2607:       jsendbuf[cnt++] = A->rmap->rstart/bs + *a_jsendbuf++;
    2608:     }

    2610:     /* put in upper diagonal portion */
    2611:     while (m-- > 0) {
    2612:       jsendbuf[cnt++] = garray[*b_jsendbuf++];
    2613:     }
    2614:   }
    2615:   if (cnt != sendcount) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Corrupted PETSc matrix: nz given %D actual nz %D",sendcount,cnt);

    2617:   /*--------------------------------------------------------------------
    2618:     Gather all column indices to all processors
    2619:   */
    2620:   for (i=0; i<size; i++) {
    2621:     recvcounts[i] = 0;
    2622:     for (j=A->rmap->range[i]/bs; j<A->rmap->range[i+1]/bs; j++) {
    2623:       recvcounts[i] += lens[j];
    2624:     }
    2625:   }
    2626:   displs[0] = 0;
    2627:   for (i=1; i<size; i++) {
    2628:     displs[i] = displs[i-1] + recvcounts[i-1];
    2629:   }
    2630: #if defined(PETSC_HAVE_MPI_IN_PLACE)
    2631:   MPI_Allgatherv(MPI_IN_PLACE,0,MPI_DATATYPE_NULL,b->j,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
    2632: #else
    2633:   MPI_Allgatherv(jsendbuf,sendcount,MPIU_INT,b->j,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
    2634: #endif
    2635:   /*--------------------------------------------------------------------
    2636:     Assemble the matrix into useable form (note numerical values not yet set)
    2637:   */
    2638:   /* set the b->ilen (length of each row) values */
    2639:   PetscMemcpy(b->ilen,lens,(A->rmap->N/bs)*sizeof(PetscInt));
    2640:   /* set the b->i indices */
    2641:   b->i[0] = 0;
    2642:   for (i=1; i<=A->rmap->N/bs; i++) {
    2643:     b->i[i] = b->i[i-1] + lens[i-1];
    2644:   }
    2645:   PetscFree(lens);
    2646:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    2647:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    2648:   PetscFree(recvcounts);

    2650:   if (A->symmetric) {
    2651:     MatSetOption(B,MAT_SYMMETRIC,PETSC_TRUE);
    2652:   } else if (A->hermitian) {
    2653:     MatSetOption(B,MAT_HERMITIAN,PETSC_TRUE);
    2654:   } else if (A->structurally_symmetric) {
    2655:     MatSetOption(B,MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
    2656:   }
    2657:   *newmat = B;
    2658:   return(0);
    2659: }

    2663: PetscErrorCode MatSOR_MPIBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
    2664: {
    2665:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
    2667:   Vec            bb1 = 0;

    2670:   if (flag == SOR_APPLY_UPPER) {
    2671:     (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
    2672:     return(0);
    2673:   }

    2675:   if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) {
    2676:     VecDuplicate(bb,&bb1);
    2677:   }

    2679:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
    2680:     if (flag & SOR_ZERO_INITIAL_GUESS) {
    2681:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
    2682:       its--;
    2683:     }

    2685:     while (its--) {
    2686:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
    2687:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

    2689:       /* update rhs: bb1 = bb - B*x */
    2690:       VecScale(mat->lvec,-1.0);
    2691:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

    2693:       /* local sweep */
    2694:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,1,xx);
    2695:     }
    2696:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
    2697:     if (flag & SOR_ZERO_INITIAL_GUESS) {
    2698:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
    2699:       its--;
    2700:     }
    2701:     while (its--) {
    2702:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
    2703:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

    2705:       /* update rhs: bb1 = bb - B*x */
    2706:       VecScale(mat->lvec,-1.0);
    2707:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

    2709:       /* local sweep */
    2710:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,1,xx);
    2711:     }
    2712:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2713:     if (flag & SOR_ZERO_INITIAL_GUESS) {
    2714:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
    2715:       its--;
    2716:     }
    2717:     while (its--) {
    2718:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
    2719:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

    2721:       /* update rhs: bb1 = bb - B*x */
    2722:       VecScale(mat->lvec,-1.0);
    2723:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

    2725:       /* local sweep */
    2726:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,1,xx);
    2727:     }
    2728:   } else SETERRQ(PetscObjectComm((PetscObject)matin),PETSC_ERR_SUP,"Parallel version of SOR requested not supported");

    2730:   VecDestroy(&bb1);
    2731:   return(0);
    2732: }

    2734: extern PetscErrorCode  MatFDColoringApply_BAIJ(Mat,MatFDColoring,Vec,MatStructure*,void*);

    2738: PetscErrorCode  MatInvertBlockDiagonal_MPIBAIJ(Mat A,const PetscScalar **values)
    2739: {
    2740:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*) A->data;

    2744:   MatInvertBlockDiagonal(a->A,values);
    2745:   return(0);
    2746: }

    2749: /* -------------------------------------------------------------------*/
    2750: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
    2751:                                        MatGetRow_MPIBAIJ,
    2752:                                        MatRestoreRow_MPIBAIJ,
    2753:                                        MatMult_MPIBAIJ,
    2754:                                 /* 4*/ MatMultAdd_MPIBAIJ,
    2755:                                        MatMultTranspose_MPIBAIJ,
    2756:                                        MatMultTransposeAdd_MPIBAIJ,
    2757:                                        0,
    2758:                                        0,
    2759:                                        0,
    2760:                                 /*10*/ 0,
    2761:                                        0,
    2762:                                        0,
    2763:                                        MatSOR_MPIBAIJ,
    2764:                                        MatTranspose_MPIBAIJ,
    2765:                                 /*15*/ MatGetInfo_MPIBAIJ,
    2766:                                        MatEqual_MPIBAIJ,
    2767:                                        MatGetDiagonal_MPIBAIJ,
    2768:                                        MatDiagonalScale_MPIBAIJ,
    2769:                                        MatNorm_MPIBAIJ,
    2770:                                 /*20*/ MatAssemblyBegin_MPIBAIJ,
    2771:                                        MatAssemblyEnd_MPIBAIJ,
    2772:                                        MatSetOption_MPIBAIJ,
    2773:                                        MatZeroEntries_MPIBAIJ,
    2774:                                 /*24*/ MatZeroRows_MPIBAIJ,
    2775:                                        0,
    2776:                                        0,
    2777:                                        0,
    2778:                                        0,
    2779:                                 /*29*/ MatSetUp_MPIBAIJ,
    2780:                                        0,
    2781:                                        0,
    2782:                                        0,
    2783:                                        0,
    2784:                                 /*34*/ MatDuplicate_MPIBAIJ,
    2785:                                        0,
    2786:                                        0,
    2787:                                        0,
    2788:                                        0,
    2789:                                 /*39*/ MatAXPY_MPIBAIJ,
    2790:                                        MatGetSubMatrices_MPIBAIJ,
    2791:                                        MatIncreaseOverlap_MPIBAIJ,
    2792:                                        MatGetValues_MPIBAIJ,
    2793:                                        MatCopy_MPIBAIJ,
    2794:                                 /*44*/ 0,
    2795:                                        MatScale_MPIBAIJ,
    2796:                                        0,
    2797:                                        0,
    2798:                                        0,
    2799:                                 /*49*/ 0,
    2800:                                        0,
    2801:                                        0,
    2802:                                        0,
    2803:                                        0,
    2804:                                 /*54*/ MatFDColoringCreate_MPIBAIJ,
    2805:                                        0,
    2806:                                        MatSetUnfactored_MPIBAIJ,
    2807:                                        MatPermute_MPIBAIJ,
    2808:                                        MatSetValuesBlocked_MPIBAIJ,
    2809:                                 /*59*/ MatGetSubMatrix_MPIBAIJ,
    2810:                                        MatDestroy_MPIBAIJ,
    2811:                                        MatView_MPIBAIJ,
    2812:                                        0,
    2813:                                        0,
    2814:                                 /*64*/ 0,
    2815:                                        0,
    2816:                                        0,
    2817:                                        0,
    2818:                                        0,
    2819:                                 /*69*/ MatGetRowMaxAbs_MPIBAIJ,
    2820:                                        0,
    2821:                                        0,
    2822:                                        0,
    2823:                                        0,
    2824:                                 /*74*/ 0,
    2825:                                        MatFDColoringApply_BAIJ,
    2826:                                        0,
    2827:                                        0,
    2828:                                        0,
    2829:                                 /*79*/ 0,
    2830:                                        0,
    2831:                                        0,
    2832:                                        0,
    2833:                                        MatLoad_MPIBAIJ,
    2834:                                 /*84*/ 0,
    2835:                                        0,
    2836:                                        0,
    2837:                                        0,
    2838:                                        0,
    2839:                                 /*89*/ 0,
    2840:                                        0,
    2841:                                        0,
    2842:                                        0,
    2843:                                        0,
    2844:                                 /*94*/ 0,
    2845:                                        0,
    2846:                                        0,
    2847:                                        0,
    2848:                                        0,
    2849:                                 /*99*/ 0,
    2850:                                        0,
    2851:                                        0,
    2852:                                        0,
    2853:                                        0,
    2854:                                 /*104*/0,
    2855:                                        MatRealPart_MPIBAIJ,
    2856:                                        MatImaginaryPart_MPIBAIJ,
    2857:                                        0,
    2858:                                        0,
    2859:                                 /*109*/0,
    2860:                                        0,
    2861:                                        0,
    2862:                                        0,
    2863:                                        0,
    2864:                                 /*114*/MatGetSeqNonzeroStructure_MPIBAIJ,
    2865:                                        0,
    2866:                                        MatGetGhosts_MPIBAIJ,
    2867:                                        0,
    2868:                                        0,
    2869:                                 /*119*/0,
    2870:                                        0,
    2871:                                        0,
    2872:                                        0,
    2873:                                        0,
    2874:                                 /*124*/0,
    2875:                                        0,
    2876:                                        MatInvertBlockDiagonal_MPIBAIJ,
    2877:                                        0,
    2878:                                        0,
    2879:                                /*129*/ 0,
    2880:                                        0,
    2881:                                        0,
    2882:                                        0,
    2883:                                        0,
    2884:                                /*134*/ 0,
    2885:                                        0,
    2886:                                        0,
    2887:                                        0,
    2888:                                        0,
    2889:                                /*139*/ 0,
    2890:                                        0
    2891: };

    2895: PetscErrorCode  MatGetDiagonalBlock_MPIBAIJ(Mat A,Mat *a)
    2896: {
    2898:   *a = ((Mat_MPIBAIJ*)A->data)->A;
    2899:   return(0);
    2900: }

    2902: PETSC_EXTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat, MatType,MatReuse,Mat*);

    2906: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt ii[],const PetscInt jj[],const PetscScalar V[])
    2907: {
    2908:   PetscInt       m,rstart,cstart,cend;
    2909:   PetscInt       i,j,d,nz,nz_max=0,*d_nnz=0,*o_nnz=0;
    2910:   const PetscInt *JJ    =0;
    2911:   PetscScalar    *values=0;

    2915:   PetscLayoutSetBlockSize(B->rmap,bs);
    2916:   PetscLayoutSetBlockSize(B->cmap,bs);
    2917:   PetscLayoutSetUp(B->rmap);
    2918:   PetscLayoutSetUp(B->cmap);
    2919:   PetscLayoutGetBlockSize(B->rmap,&bs);
    2920:   m      = B->rmap->n/bs;
    2921:   rstart = B->rmap->rstart/bs;
    2922:   cstart = B->cmap->rstart/bs;
    2923:   cend   = B->cmap->rend/bs;

    2925:   if (ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"ii[0] must be 0 but it is %D",ii[0]);
    2926:   PetscMalloc2(m,PetscInt,&d_nnz,m,PetscInt,&o_nnz);
    2927:   for (i=0; i<m; i++) {
    2928:     nz = ii[i+1] - ii[i];
    2929:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative number of columns %D",i,nz);
    2930:     nz_max = PetscMax(nz_max,nz);
    2931:     JJ     = jj + ii[i];
    2932:     for (j=0; j<nz; j++) {
    2933:       if (*JJ >= cstart) break;
    2934:       JJ++;
    2935:     }
    2936:     d = 0;
    2937:     for (; j<nz; j++) {
    2938:       if (*JJ++ >= cend) break;
    2939:       d++;
    2940:     }
    2941:     d_nnz[i] = d;
    2942:     o_nnz[i] = nz - d;
    2943:   }
    2944:   MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
    2945:   PetscFree2(d_nnz,o_nnz);

    2947:   values = (PetscScalar*)V;
    2948:   if (!values) {
    2949:     PetscMalloc(bs*bs*nz_max*sizeof(PetscScalar),&values);
    2950:     PetscMemzero(values,bs*bs*nz_max*sizeof(PetscScalar));
    2951:   }
    2952:   for (i=0; i<m; i++) {
    2953:     PetscInt          row    = i + rstart;
    2954:     PetscInt          ncols  = ii[i+1] - ii[i];
    2955:     const PetscInt    *icols = jj + ii[i];
    2956:     const PetscScalar *svals = values + (V ? (bs*bs*ii[i]) : 0);
    2957:     MatSetValuesBlocked_MPIBAIJ(B,1,&row,ncols,icols,svals,INSERT_VALUES);
    2958:   }

    2960:   if (!V) { PetscFree(values); }
    2961:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    2962:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    2963:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
    2964:   return(0);
    2965: }

    2969: /*@C
    2970:    MatMPIBAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in BAIJ format
    2971:    (the default parallel PETSc format).

    2973:    Collective on MPI_Comm

    2975:    Input Parameters:
    2976: +  A - the matrix
    2977: .  bs - the block size
    2978: .  i - the indices into j for the start of each local row (starts with zero)
    2979: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
    2980: -  v - optional values in the matrix

    2982:    Level: developer

    2984: .keywords: matrix, aij, compressed row, sparse, parallel

    2986: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateAIJ(), MPIAIJ
    2987: @*/
    2988: PetscErrorCode  MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
    2989: {

    2996:   PetscTryMethod(B,"MatMPIBAIJSetPreallocationCSR_C",(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,bs,i,j,v));
    2997:   return(0);
    2998: }

    3002: PetscErrorCode  MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt *d_nnz,PetscInt o_nz,const PetscInt *o_nnz)
    3003: {
    3004:   Mat_MPIBAIJ    *b;
    3006:   PetscInt       i;

    3009:   PetscLayoutSetBlockSize(B->rmap,bs);
    3010:   PetscLayoutSetBlockSize(B->cmap,bs);
    3011:   PetscLayoutSetUp(B->rmap);
    3012:   PetscLayoutSetUp(B->cmap);
    3013:   PetscLayoutGetBlockSize(B->rmap,&bs);

    3015:   if (d_nnz) {
    3016:     for (i=0; i<B->rmap->n/bs; i++) {
    3017:       if (d_nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than -1: local row %D value %D",i,d_nnz[i]);
    3018:     }
    3019:   }
    3020:   if (o_nnz) {
    3021:     for (i=0; i<B->rmap->n/bs; i++) {
    3022:       if (o_nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than -1: local row %D value %D",i,o_nnz[i]);
    3023:     }
    3024:   }

    3026:   b      = (Mat_MPIBAIJ*)B->data;
    3027:   b->bs2 = bs*bs;
    3028:   b->mbs = B->rmap->n/bs;
    3029:   b->nbs = B->cmap->n/bs;
    3030:   b->Mbs = B->rmap->N/bs;
    3031:   b->Nbs = B->cmap->N/bs;

    3033:   for (i=0; i<=b->size; i++) {
    3034:     b->rangebs[i] = B->rmap->range[i]/bs;
    3035:   }
    3036:   b->rstartbs = B->rmap->rstart/bs;
    3037:   b->rendbs   = B->rmap->rend/bs;
    3038:   b->cstartbs = B->cmap->rstart/bs;
    3039:   b->cendbs   = B->cmap->rend/bs;

    3041:   if (!B->preallocated) {
    3042:     MatCreate(PETSC_COMM_SELF,&b->A);
    3043:     MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
    3044:     MatSetType(b->A,MATSEQBAIJ);
    3045:     PetscLogObjectParent(B,b->A);
    3046:     MatCreate(PETSC_COMM_SELF,&b->B);
    3047:     MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
    3048:     MatSetType(b->B,MATSEQBAIJ);
    3049:     PetscLogObjectParent(B,b->B);
    3050:     MatStashCreate_Private(PetscObjectComm((PetscObject)B),bs,&B->bstash);
    3051:   }

    3053:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
    3054:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
    3055:   B->preallocated = PETSC_TRUE;
    3056:   return(0);
    3057: }

    3059: extern PetscErrorCode  MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
    3060: extern PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);

    3064: PETSC_EXTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype,MatReuse reuse,Mat *adj)
    3065: {
    3066:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)B->data;
    3068:   Mat_SeqBAIJ    *d  = (Mat_SeqBAIJ*) b->A->data,*o = (Mat_SeqBAIJ*) b->B->data;
    3069:   PetscInt       M   = B->rmap->n/B->rmap->bs,i,*ii,*jj,cnt,j,k,rstart = B->rmap->rstart/B->rmap->bs;
    3070:   const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;

    3073:   PetscMalloc((M+1)*sizeof(PetscInt),&ii);
    3074:   ii[0] = 0;
    3075:   for (i=0; i<M; i++) {
    3076:     if ((id[i+1] - id[i]) < 0) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Indices wrong %D %D %D",i,id[i],id[i+1]);
    3077:     if ((io[i+1] - io[i]) < 0) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Indices wrong %D %D %D",i,io[i],io[i+1]);
    3078:     ii[i+1] = ii[i] + id[i+1] - id[i] + io[i+1] - io[i];
    3079:     /* remove one from count of matrix has diagonal */
    3080:     for (j=id[i]; j<id[i+1]; j++) {
    3081:       if (jd[j] == i) {ii[i+1]--;break;}
    3082:     }
    3083:   }
    3084:   PetscMalloc(ii[M]*sizeof(PetscInt),&jj);
    3085:   cnt  = 0;
    3086:   for (i=0; i<M; i++) {
    3087:     for (j=io[i]; j<io[i+1]; j++) {
    3088:       if (garray[jo[j]] > rstart) break;
    3089:       jj[cnt++] = garray[jo[j]];
    3090:     }
    3091:     for (k=id[i]; k<id[i+1]; k++) {
    3092:       if (jd[k] != i) {
    3093:         jj[cnt++] = rstart + jd[k];
    3094:       }
    3095:     }
    3096:     for (; j<io[i+1]; j++) {
    3097:       jj[cnt++] = garray[jo[j]];
    3098:     }
    3099:   }
    3100:   MatCreateMPIAdj(PetscObjectComm((PetscObject)B),M,B->cmap->N/B->rmap->bs,ii,jj,NULL,adj);
    3101:   return(0);
    3102: }

    3104: #include <../src/mat/impls/aij/mpi/mpiaij.h>

    3106: PETSC_EXTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat,MatType,MatReuse,Mat*);

    3110: PETSC_EXTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A,MatType newtype,MatReuse reuse,Mat *newmat)
    3111: {
    3113:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
    3114:   Mat            B;
    3115:   Mat_MPIAIJ     *b;

    3118:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix must be assembled");

    3120:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    3121:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    3122:   MatSetType(B,MATMPIAIJ);
    3123:   MatSeqAIJSetPreallocation(B,0,NULL);
    3124:   MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);
    3125:   b    = (Mat_MPIAIJ*) B->data;

    3127:   MatDestroy(&b->A);
    3128:   MatDestroy(&b->B);
    3129:   MatDisAssemble_MPIBAIJ(A);
    3130:   MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A);
    3131:   MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B);
    3132:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    3133:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    3134:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    3135:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    3136:   if (reuse == MAT_REUSE_MATRIX) {
    3137:     MatHeaderReplace(A,B);
    3138:   } else {
    3139:    *newmat = B;
    3140:   }
    3141:   return(0);
    3142: }

    3144: #if defined(PETSC_HAVE_MUMPS)
    3145: PETSC_EXTERN PetscErrorCode MatGetFactor_baij_mumps(Mat,MatFactorType,Mat*);
    3146: #endif

    3148: /*MC
    3149:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

    3151:    Options Database Keys:
    3152: + -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()
    3153: . -mat_block_size <bs> - set the blocksize used to store the matrix
    3154: - -mat_use_hash_table <fact>

    3156:   Level: beginner

    3158: .seealso: MatCreateMPIBAIJ
    3159: M*/

    3161: PETSC_EXTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat,MatType,MatReuse,Mat*);

    3165: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
    3166: {
    3167:   Mat_MPIBAIJ    *b;
    3169:   PetscBool      flg;

    3172:   PetscNewLog(B,Mat_MPIBAIJ,&b);
    3173:   B->data = (void*)b;

    3175:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
    3176:   B->assembled = PETSC_FALSE;

    3178:   B->insertmode = NOT_SET_VALUES;
    3179:   MPI_Comm_rank(PetscObjectComm((PetscObject)B),&b->rank);
    3180:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&b->size);

    3182:   /* build local table of row and column ownerships */
    3183:   PetscMalloc((b->size+1)*sizeof(PetscInt),&b->rangebs);

    3185:   /* build cache for off array entries formed */
    3186:   MatStashCreate_Private(PetscObjectComm((PetscObject)B),1,&B->stash);

    3188:   b->donotstash  = PETSC_FALSE;
    3189:   b->colmap      = NULL;
    3190:   b->garray      = NULL;
    3191:   b->roworiented = PETSC_TRUE;

    3193:   /* stuff used in block assembly */
    3194:   b->barray = 0;

    3196:   /* stuff used for matrix vector multiply */
    3197:   b->lvec  = 0;
    3198:   b->Mvctx = 0;

    3200:   /* stuff for MatGetRow() */
    3201:   b->rowindices   = 0;
    3202:   b->rowvalues    = 0;
    3203:   b->getrowactive = PETSC_FALSE;

    3205:   /* hash table stuff */
    3206:   b->ht           = 0;
    3207:   b->hd           = 0;
    3208:   b->ht_size      = 0;
    3209:   b->ht_flag      = PETSC_FALSE;
    3210:   b->ht_fact      = 0;
    3211:   b->ht_total_ct  = 0;
    3212:   b->ht_insert_ct = 0;

    3214:   /* stuff for MatGetSubMatrices_MPIBAIJ_local() */
    3215:   b->ijonly = PETSC_FALSE;

    3217:   PetscOptionsBegin(PetscObjectComm((PetscObject)B),NULL,"Options for loading MPIBAIJ matrix 1","Mat");
    3218:   PetscOptionsBool("-mat_use_hash_table","Use hash table to save memory in constructing matrix","MatSetOption",PETSC_FALSE,&flg,NULL);
    3219:   if (flg) {
    3220:     PetscReal fact = 1.39;
    3221:     MatSetOption(B,MAT_USE_HASH_TABLE,PETSC_TRUE);
    3222:     PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,NULL);
    3223:     if (fact <= 1.0) fact = 1.39;
    3224:     MatMPIBAIJSetHashTableFactor(B,fact);
    3225:     PetscInfo1(B,"Hash table Factor used %5.2f\n",fact);
    3226:   }
    3227:   PetscOptionsEnd();

    3229: #if defined(PETSC_HAVE_MUMPS)
    3230:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_mumps_C",MatGetFactor_baij_mumps);
    3231: #endif
    3232:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpiadj_C",MatConvert_MPIBAIJ_MPIAdj);
    3233:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpiaij_C",MatConvert_MPIBAIJ_MPIAIJ);
    3234:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpisbaij_C",MatConvert_MPIBAIJ_MPISBAIJ);
    3235:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_MPIBAIJ);
    3236:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_MPIBAIJ);
    3237:   PetscObjectComposeFunction((PetscObject)B,"MatGetDiagonalBlock_C",MatGetDiagonalBlock_MPIBAIJ);
    3238:   PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",MatMPIBAIJSetPreallocation_MPIBAIJ);
    3239:   PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
    3240:   PetscObjectComposeFunction((PetscObject)B,"MatDiagonalScaleLocal_C",MatDiagonalScaleLocal_MPIBAIJ);
    3241:   PetscObjectComposeFunction((PetscObject)B,"MatSetHashTableFactor_C",MatSetHashTableFactor_MPIBAIJ);
    3242:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpibstrm_C",MatConvert_MPIBAIJ_MPIBSTRM);
    3243:   PetscObjectChangeTypeName((PetscObject)B,MATMPIBAIJ);
    3244:   return(0);
    3245: }

    3247: /*MC
    3248:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

    3250:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
    3251:    and MATMPIBAIJ otherwise.

    3253:    Options Database Keys:
    3254: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

    3256:   Level: beginner

    3258: .seealso: MatCreateBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
    3259: M*/

    3263: /*@C
    3264:    MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
    3265:    (block compressed row).  For good matrix assembly performance
    3266:    the user should preallocate the matrix storage by setting the parameters
    3267:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
    3268:    performance can be increased by more than a factor of 50.

    3270:    Collective on Mat

    3272:    Input Parameters:
    3273: +  A - the matrix
    3274: .  bs   - size of blockk
    3275: .  d_nz  - number of block nonzeros per block row in diagonal portion of local
    3276:            submatrix  (same for all local rows)
    3277: .  d_nnz - array containing the number of block nonzeros in the various block rows
    3278:            of the in diagonal portion of the local (possibly different for each block
    3279:            row) or NULL.  If you plan to factor the matrix you must leave room for the diagonal entry and
    3280:            set it even if it is zero.
    3281: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
    3282:            submatrix (same for all local rows).
    3283: -  o_nnz - array containing the number of nonzeros in the various block rows of the
    3284:            off-diagonal portion of the local submatrix (possibly different for
    3285:            each block row) or NULL.

    3287:    If the *_nnz parameter is given then the *_nz parameter is ignored

    3289:    Options Database Keys:
    3290: +   -mat_block_size - size of the blocks to use
    3291: -   -mat_use_hash_table <fact>

    3293:    Notes:
    3294:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
    3295:    than it must be used on all processors that share the object for that argument.

    3297:    Storage Information:
    3298:    For a square global matrix we define each processor's diagonal portion
    3299:    to be its local rows and the corresponding columns (a square submatrix);
    3300:    each processor's off-diagonal portion encompasses the remainder of the
    3301:    local matrix (a rectangular submatrix).

    3303:    The user can specify preallocated storage for the diagonal part of
    3304:    the local submatrix with either d_nz or d_nnz (not both).  Set
    3305:    d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
    3306:    memory allocation.  Likewise, specify preallocated storage for the
    3307:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

    3309:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
    3310:    the figure below we depict these three local rows and all columns (0-11).

    3312: .vb
    3313:            0 1 2 3 4 5 6 7 8 9 10 11
    3314:           --------------------------
    3315:    row 3  |o o o d d d o o o o  o  o
    3316:    row 4  |o o o d d d o o o o  o  o
    3317:    row 5  |o o o d d d o o o o  o  o
    3318:           --------------------------
    3319: .ve

    3321:    Thus, any entries in the d locations are stored in the d (diagonal)
    3322:    submatrix, and any entries in the o locations are stored in the
    3323:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
    3324:    stored simply in the MATSEQBAIJ format for compressed row storage.

    3326:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
    3327:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
    3328:    In general, for PDE problems in which most nonzeros are near the diagonal,
    3329:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
    3330:    or you will get TERRIBLE performance; see the users' manual chapter on
    3331:    matrices.

    3333:    You can call MatGetInfo() to get information on how effective the preallocation was;
    3334:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
    3335:    You can also run with the option -info and look for messages with the string
    3336:    malloc in them to see if additional memory allocation was needed.

    3338:    Level: intermediate

    3340: .keywords: matrix, block, aij, compressed row, sparse, parallel

    3342: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateBAIJ(), MatMPIBAIJSetPreallocationCSR(), PetscSplitOwnership()
    3343: @*/
    3344: PetscErrorCode  MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
    3345: {

    3352:   PetscTryMethod(B,"MatMPIBAIJSetPreallocation_C",(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]),(B,bs,d_nz,d_nnz,o_nz,o_nnz));
    3353:   return(0);
    3354: }

    3358: /*@C
    3359:    MatCreateBAIJ - Creates a sparse parallel matrix in block AIJ format
    3360:    (block compressed row).  For good matrix assembly performance
    3361:    the user should preallocate the matrix storage by setting the parameters
    3362:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
    3363:    performance can be increased by more than a factor of 50.

    3365:    Collective on MPI_Comm

    3367:    Input Parameters:
    3368: +  comm - MPI communicator
    3369: .  bs   - size of blockk
    3370: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
    3371:            This value should be the same as the local size used in creating the
    3372:            y vector for the matrix-vector product y = Ax.
    3373: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
    3374:            This value should be the same as the local size used in creating the
    3375:            x vector for the matrix-vector product y = Ax.
    3376: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
    3377: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
    3378: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local
    3379:            submatrix  (same for all local rows)
    3380: .  d_nnz - array containing the number of nonzero blocks in the various block rows
    3381:            of the in diagonal portion of the local (possibly different for each block
    3382:            row) or NULL.  If you plan to factor the matrix you must leave room for the diagonal entry
    3383:            and set it even if it is zero.
    3384: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
    3385:            submatrix (same for all local rows).
    3386: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
    3387:            off-diagonal portion of the local submatrix (possibly different for
    3388:            each block row) or NULL.

    3390:    Output Parameter:
    3391: .  A - the matrix

    3393:    Options Database Keys:
    3394: +   -mat_block_size - size of the blocks to use
    3395: -   -mat_use_hash_table <fact>

    3397:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
    3398:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
    3399:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

    3401:    Notes:
    3402:    If the *_nnz parameter is given then the *_nz parameter is ignored

    3404:    A nonzero block is any block that as 1 or more nonzeros in it

    3406:    The user MUST specify either the local or global matrix dimensions
    3407:    (possibly both).

    3409:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
    3410:    than it must be used on all processors that share the object for that argument.

    3412:    Storage Information:
    3413:    For a square global matrix we define each processor's diagonal portion
    3414:    to be its local rows and the corresponding columns (a square submatrix);
    3415:    each processor's off-diagonal portion encompasses the remainder of the
    3416:    local matrix (a rectangular submatrix).

    3418:    The user can specify preallocated storage for the diagonal part of
    3419:    the local submatrix with either d_nz or d_nnz (not both).  Set
    3420:    d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
    3421:    memory allocation.  Likewise, specify preallocated storage for the
    3422:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

    3424:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
    3425:    the figure below we depict these three local rows and all columns (0-11).

    3427: .vb
    3428:            0 1 2 3 4 5 6 7 8 9 10 11
    3429:           --------------------------
    3430:    row 3  |o o o d d d o o o o  o  o
    3431:    row 4  |o o o d d d o o o o  o  o
    3432:    row 5  |o o o d d d o o o o  o  o
    3433:           --------------------------
    3434: .ve

    3436:    Thus, any entries in the d locations are stored in the d (diagonal)
    3437:    submatrix, and any entries in the o locations are stored in the
    3438:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
    3439:    stored simply in the MATSEQBAIJ format for compressed row storage.

    3441:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
    3442:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
    3443:    In general, for PDE problems in which most nonzeros are near the diagonal,
    3444:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
    3445:    or you will get TERRIBLE performance; see the users' manual chapter on
    3446:    matrices.

    3448:    Level: intermediate

    3450: .keywords: matrix, block, aij, compressed row, sparse, parallel

    3452: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
    3453: @*/
    3454: 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)
    3455: {
    3457:   PetscMPIInt    size;

    3460:   MatCreate(comm,A);
    3461:   MatSetSizes(*A,m,n,M,N);
    3462:   MPI_Comm_size(comm,&size);
    3463:   if (size > 1) {
    3464:     MatSetType(*A,MATMPIBAIJ);
    3465:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
    3466:   } else {
    3467:     MatSetType(*A,MATSEQBAIJ);
    3468:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
    3469:   }
    3470:   return(0);
    3471: }

    3475: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
    3476: {
    3477:   Mat            mat;
    3478:   Mat_MPIBAIJ    *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
    3480:   PetscInt       len=0;

    3483:   *newmat = 0;
    3484:   MatCreate(PetscObjectComm((PetscObject)matin),&mat);
    3485:   MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
    3486:   MatSetType(mat,((PetscObject)matin)->type_name);
    3487:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));

    3489:   mat->factortype   = matin->factortype;
    3490:   mat->preallocated = PETSC_TRUE;
    3491:   mat->assembled    = PETSC_TRUE;
    3492:   mat->insertmode   = NOT_SET_VALUES;

    3494:   a             = (Mat_MPIBAIJ*)mat->data;
    3495:   mat->rmap->bs = matin->rmap->bs;
    3496:   a->bs2        = oldmat->bs2;
    3497:   a->mbs        = oldmat->mbs;
    3498:   a->nbs        = oldmat->nbs;
    3499:   a->Mbs        = oldmat->Mbs;
    3500:   a->Nbs        = oldmat->Nbs;

    3502:   PetscLayoutReference(matin->rmap,&mat->rmap);
    3503:   PetscLayoutReference(matin->cmap,&mat->cmap);

    3505:   a->size         = oldmat->size;
    3506:   a->rank         = oldmat->rank;
    3507:   a->donotstash   = oldmat->donotstash;
    3508:   a->roworiented  = oldmat->roworiented;
    3509:   a->rowindices   = 0;
    3510:   a->rowvalues    = 0;
    3511:   a->getrowactive = PETSC_FALSE;
    3512:   a->barray       = 0;
    3513:   a->rstartbs     = oldmat->rstartbs;
    3514:   a->rendbs       = oldmat->rendbs;
    3515:   a->cstartbs     = oldmat->cstartbs;
    3516:   a->cendbs       = oldmat->cendbs;

    3518:   /* hash table stuff */
    3519:   a->ht           = 0;
    3520:   a->hd           = 0;
    3521:   a->ht_size      = 0;
    3522:   a->ht_flag      = oldmat->ht_flag;
    3523:   a->ht_fact      = oldmat->ht_fact;
    3524:   a->ht_total_ct  = 0;
    3525:   a->ht_insert_ct = 0;

    3527:   PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+1)*sizeof(PetscInt));
    3528:   if (oldmat->colmap) {
    3529: #if defined(PETSC_USE_CTABLE)
    3530:     PetscTableCreateCopy(oldmat->colmap,&a->colmap);
    3531: #else
    3532:     PetscMalloc((a->Nbs)*sizeof(PetscInt),&a->colmap);
    3533:     PetscLogObjectMemory(mat,(a->Nbs)*sizeof(PetscInt));
    3534:     PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
    3535: #endif
    3536:   } else a->colmap = 0;

    3538:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
    3539:     PetscMalloc(len*sizeof(PetscInt),&a->garray);
    3540:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
    3541:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
    3542:   } else a->garray = 0;

    3544:   MatStashCreate_Private(PetscObjectComm((PetscObject)matin),matin->rmap->bs,&mat->bstash);
    3545:   VecDuplicate(oldmat->lvec,&a->lvec);
    3546:   PetscLogObjectParent(mat,a->lvec);
    3547:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
    3548:   PetscLogObjectParent(mat,a->Mvctx);

    3550:   MatDuplicate(oldmat->A,cpvalues,&a->A);
    3551:   PetscLogObjectParent(mat,a->A);
    3552:   MatDuplicate(oldmat->B,cpvalues,&a->B);
    3553:   PetscLogObjectParent(mat,a->B);
    3554:   PetscFunctionListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
    3555:   *newmat = mat;
    3556:   return(0);
    3557: }

    3561: PetscErrorCode MatLoad_MPIBAIJ(Mat newmat,PetscViewer viewer)
    3562: {
    3564:   int            fd;
    3565:   PetscInt       i,nz,j,rstart,rend;
    3566:   PetscScalar    *vals,*buf;
    3567:   MPI_Comm       comm;
    3568:   MPI_Status     status;
    3569:   PetscMPIInt    rank,size,maxnz;
    3570:   PetscInt       header[4],*rowlengths = 0,M,N,m,*rowners,*cols;
    3571:   PetscInt       *locrowlens = NULL,*procsnz = NULL,*browners = NULL;
    3572:   PetscInt       jj,*mycols,*ibuf,bs=1,Mbs,mbs,extra_rows,mmax;
    3573:   PetscMPIInt    tag    = ((PetscObject)viewer)->tag;
    3574:   PetscInt       *dlens = NULL,*odlens = NULL,*mask = NULL,*masked1 = NULL,*masked2 = NULL,rowcount,odcount;
    3575:   PetscInt       dcount,kmax,k,nzcount,tmp,mend,sizesset=1,grows,gcols;

    3578:   PetscObjectGetComm((PetscObject)viewer,&comm);
    3579:   PetscOptionsBegin(comm,NULL,"Options for loading MPIBAIJ matrix 2","Mat");
    3580:   PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
    3581:   PetscOptionsEnd();

    3583:   MPI_Comm_size(comm,&size);
    3584:   MPI_Comm_rank(comm,&rank);
    3585:   if (!rank) {
    3586:     PetscViewerBinaryGetDescriptor(viewer,&fd);
    3587:     PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
    3588:     if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
    3589:   }

    3591:   if (newmat->rmap->n < 0 && newmat->rmap->N < 0 && newmat->cmap->n < 0 && newmat->cmap->N < 0) sizesset = 0;

    3593:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
    3594:   M    = header[1]; N = header[2];

    3596:   /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */
    3597:   if (sizesset && newmat->rmap->N < 0) newmat->rmap->N = M;
    3598:   if (sizesset && newmat->cmap->N < 0) newmat->cmap->N = N;

    3600:   /* If global sizes are set, check if they are consistent with that given in the file */
    3601:   if (sizesset) {
    3602:     MatGetSize(newmat,&grows,&gcols);
    3603:   }
    3604:   if (sizesset && newmat->rmap->N != grows) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows:Matrix in file has (%d) and input matrix has (%d)",M,grows);
    3605:   if (sizesset && newmat->cmap->N != gcols) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of cols:Matrix in file has (%d) and input matrix has (%d)",N,gcols);

    3607:   if (M != N) SETERRQ(PetscObjectComm((PetscObject)viewer),PETSC_ERR_SUP,"Can only do square matrices");

    3609:   /*
    3610:      This code adds extra rows to make sure the number of rows is
    3611:      divisible by the blocksize
    3612:   */
    3613:   Mbs        = M/bs;
    3614:   extra_rows = bs - M + bs*Mbs;
    3615:   if (extra_rows == bs) extra_rows = 0;
    3616:   else                  Mbs++;
    3617:   if (extra_rows && !rank) {
    3618:     PetscInfo(viewer,"Padding loaded matrix to match blocksize\n");
    3619:   }

    3621:   /* determine ownership of all rows */
    3622:   if (newmat->rmap->n < 0) { /* PETSC_DECIDE */
    3623:     mbs = Mbs/size + ((Mbs % size) > rank);
    3624:     m   = mbs*bs;
    3625:   } else { /* User set */
    3626:     m   = newmat->rmap->n;
    3627:     mbs = m/bs;
    3628:   }
    3629:   PetscMalloc2(size+1,PetscInt,&rowners,size+1,PetscInt,&browners);
    3630:   MPI_Allgather(&mbs,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);

    3632:   /* process 0 needs enough room for process with most rows */
    3633:   if (!rank) {
    3634:     mmax = rowners[1];
    3635:     for (i=2; i<=size; i++) {
    3636:       mmax = PetscMax(mmax,rowners[i]);
    3637:     }
    3638:     mmax*=bs;
    3639:   } else mmax = -1;             /* unused, but compiler warns anyway */

    3641:   rowners[0] = 0;
    3642:   for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
    3643:   for (i=0; i<=size; i++) browners[i] = rowners[i]*bs;
    3644:   rstart = rowners[rank];
    3645:   rend   = rowners[rank+1];

    3647:   /* distribute row lengths to all processors */
    3648:   PetscMalloc(m*sizeof(PetscInt),&locrowlens);
    3649:   if (!rank) {
    3650:     mend = m;
    3651:     if (size == 1) mend = mend - extra_rows;
    3652:     PetscBinaryRead(fd,locrowlens,mend,PETSC_INT);
    3653:     for (j=mend; j<m; j++) locrowlens[j] = 1;
    3654:     PetscMalloc(mmax*sizeof(PetscInt),&rowlengths);
    3655:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
    3656:     PetscMemzero(procsnz,size*sizeof(PetscInt));
    3657:     for (j=0; j<m; j++) {
    3658:       procsnz[0] += locrowlens[j];
    3659:     }
    3660:     for (i=1; i<size; i++) {
    3661:       mend = browners[i+1] - browners[i];
    3662:       if (i == size-1) mend = mend - extra_rows;
    3663:       PetscBinaryRead(fd,rowlengths,mend,PETSC_INT);
    3664:       for (j=mend; j<browners[i+1] - browners[i]; j++) rowlengths[j] = 1;
    3665:       /* calculate the number of nonzeros on each processor */
    3666:       for (j=0; j<browners[i+1]-browners[i]; j++) {
    3667:         procsnz[i] += rowlengths[j];
    3668:       }
    3669:       MPI_Send(rowlengths,browners[i+1]-browners[i],MPIU_INT,i,tag,comm);
    3670:     }
    3671:     PetscFree(rowlengths);
    3672:   } else {
    3673:     MPI_Recv(locrowlens,m,MPIU_INT,0,tag,comm,&status);
    3674:   }

    3676:   if (!rank) {
    3677:     /* determine max buffer needed and allocate it */
    3678:     maxnz = procsnz[0];
    3679:     for (i=1; i<size; i++) {
    3680:       maxnz = PetscMax(maxnz,procsnz[i]);
    3681:     }
    3682:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

    3684:     /* read in my part of the matrix column indices  */
    3685:     nz     = procsnz[0];
    3686:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
    3687:     mycols = ibuf;
    3688:     if (size == 1) nz -= extra_rows;
    3689:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
    3690:     if (size == 1) {
    3691:       for (i=0; i< extra_rows; i++) mycols[nz+i] = M+i;
    3692:     }

    3694:     /* read in every ones (except the last) and ship off */
    3695:     for (i=1; i<size-1; i++) {
    3696:       nz   = procsnz[i];
    3697:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
    3698:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
    3699:     }
    3700:     /* read in the stuff for the last proc */
    3701:     if (size != 1) {
    3702:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
    3703:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
    3704:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
    3705:       MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
    3706:     }
    3707:     PetscFree(cols);
    3708:   } else {
    3709:     /* determine buffer space needed for message */
    3710:     nz = 0;
    3711:     for (i=0; i<m; i++) {
    3712:       nz += locrowlens[i];
    3713:     }
    3714:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
    3715:     mycols = ibuf;
    3716:     /* receive message of column indices*/
    3717:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
    3718:     MPI_Get_count(&status,MPIU_INT,&maxnz);
    3719:     if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
    3720:   }

    3722:   /* loop over local rows, determining number of off diagonal entries */
    3723:   PetscMalloc2(rend-rstart,PetscInt,&dlens,rend-rstart,PetscInt,&odlens);
    3724:   PetscMalloc3(Mbs,PetscInt,&mask,Mbs,PetscInt,&masked1,Mbs,PetscInt,&masked2);
    3725:   PetscMemzero(mask,Mbs*sizeof(PetscInt));
    3726:   PetscMemzero(masked1,Mbs*sizeof(PetscInt));
    3727:   PetscMemzero(masked2,Mbs*sizeof(PetscInt));
    3728:   rowcount = 0; nzcount = 0;
    3729:   for (i=0; i<mbs; i++) {
    3730:     dcount  = 0;
    3731:     odcount = 0;
    3732:     for (j=0; j<bs; j++) {
    3733:       kmax = locrowlens[rowcount];
    3734:       for (k=0; k<kmax; k++) {
    3735:         tmp = mycols[nzcount++]/bs;
    3736:         if (!mask[tmp]) {
    3737:           mask[tmp] = 1;
    3738:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
    3739:           else masked1[dcount++] = tmp;
    3740:         }
    3741:       }
    3742:       rowcount++;
    3743:     }

    3745:     dlens[i]  = dcount;
    3746:     odlens[i] = odcount;

    3748:     /* zero out the mask elements we set */
    3749:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
    3750:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
    3751:   }

    3754:   if (!sizesset) {
    3755:     MatSetSizes(newmat,m,m,M+extra_rows,N+extra_rows);
    3756:   }
    3757:   MatMPIBAIJSetPreallocation(newmat,bs,0,dlens,0,odlens);

    3759:   if (!rank) {
    3760:     PetscMalloc((maxnz+1)*sizeof(PetscScalar),&buf);
    3761:     /* read in my part of the matrix numerical values  */
    3762:     nz     = procsnz[0];
    3763:     vals   = buf;
    3764:     mycols = ibuf;
    3765:     if (size == 1) nz -= extra_rows;
    3766:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
    3767:     if (size == 1) {
    3768:       for (i=0; i< extra_rows; i++) vals[nz+i] = 1.0;
    3769:     }

    3771:     /* insert into matrix */
    3772:     jj = rstart*bs;
    3773:     for (i=0; i<m; i++) {
    3774:       MatSetValues_MPIBAIJ(newmat,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
    3775:       mycols += locrowlens[i];
    3776:       vals   += locrowlens[i];
    3777:       jj++;
    3778:     }
    3779:     /* read in other processors (except the last one) and ship out */
    3780:     for (i=1; i<size-1; i++) {
    3781:       nz   = procsnz[i];
    3782:       vals = buf;
    3783:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
    3784:       MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);
    3785:     }
    3786:     /* the last proc */
    3787:     if (size != 1) {
    3788:       nz   = procsnz[i] - extra_rows;
    3789:       vals = buf;
    3790:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
    3791:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
    3792:       MPIULong_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,((PetscObject)newmat)->tag,comm);
    3793:     }
    3794:     PetscFree(procsnz);
    3795:   } else {
    3796:     /* receive numeric values */
    3797:     PetscMalloc((nz+1)*sizeof(PetscScalar),&buf);

    3799:     /* receive message of values*/
    3800:     vals   = buf;
    3801:     mycols = ibuf;
    3802:     MPIULong_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm);

    3804:     /* insert into matrix */
    3805:     jj = rstart*bs;
    3806:     for (i=0; i<m; i++) {
    3807:       MatSetValues_MPIBAIJ(newmat,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
    3808:       mycols += locrowlens[i];
    3809:       vals   += locrowlens[i];
    3810:       jj++;
    3811:     }
    3812:   }
    3813:   PetscFree(locrowlens);
    3814:   PetscFree(buf);
    3815:   PetscFree(ibuf);
    3816:   PetscFree2(rowners,browners);
    3817:   PetscFree2(dlens,odlens);
    3818:   PetscFree3(mask,masked1,masked2);
    3819:   MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
    3820:   MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
    3821:   return(0);
    3822: }

    3826: /*@
    3827:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

    3829:    Input Parameters:
    3830: .  mat  - the matrix
    3831: .  fact - factor

    3833:    Not Collective, each process can use a different factor

    3835:    Level: advanced

    3837:   Notes:
    3838:    This can also be set by the command line option: -mat_use_hash_table <fact>

    3840: .keywords: matrix, hashtable, factor, HT

    3842: .seealso: MatSetOption()
    3843: @*/
    3844: PetscErrorCode  MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
    3845: {

    3849:   PetscTryMethod(mat,"MatSetHashTableFactor_C",(Mat,PetscReal),(mat,fact));
    3850:   return(0);
    3851: }

    3855: PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
    3856: {
    3857:   Mat_MPIBAIJ *baij;

    3860:   baij          = (Mat_MPIBAIJ*)mat->data;
    3861:   baij->ht_fact = fact;
    3862:   return(0);
    3863: }

    3867: PetscErrorCode  MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,const PetscInt *colmap[])
    3868: {
    3869:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;

    3872:   *Ad     = a->A;
    3873:   *Ao     = a->B;
    3874:   *colmap = a->garray;
    3875:   return(0);
    3876: }

    3878: /*
    3879:     Special version for direct calls from Fortran (to eliminate two function call overheads
    3880: */
    3881: #if defined(PETSC_HAVE_FORTRAN_CAPS)
    3882: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
    3883: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
    3884: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
    3885: #endif

    3889: /*@C
    3890:   MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to MatSetValuesBlocked()

    3892:   Collective on Mat

    3894:   Input Parameters:
    3895: + mat - the matrix
    3896: . min - number of input rows
    3897: . im - input rows
    3898: . nin - number of input columns
    3899: . in - input columns
    3900: . v - numerical values input
    3901: - addvin - INSERT_VALUES or ADD_VALUES

    3903:   Notes: This has a complete copy of MatSetValuesBlocked_MPIBAIJ() which is terrible code un-reuse.

    3905:   Level: advanced

    3907: .seealso:   MatSetValuesBlocked()
    3908: @*/
    3909: PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin,PetscInt *min,const PetscInt im[],PetscInt *nin,const PetscInt in[],const MatScalar v[],InsertMode *addvin)
    3910: {
    3911:   /* convert input arguments to C version */
    3912:   Mat        mat  = *matin;
    3913:   PetscInt   m    = *min, n = *nin;
    3914:   InsertMode addv = *addvin;

    3916:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
    3917:   const MatScalar *value;
    3918:   MatScalar       *barray     = baij->barray;
    3919:   PetscBool       roworiented = baij->roworiented;
    3920:   PetscErrorCode  ierr;
    3921:   PetscInt        i,j,ii,jj,row,col,rstart=baij->rstartbs;
    3922:   PetscInt        rend=baij->rendbs,cstart=baij->cstartbs,stepval;
    3923:   PetscInt        cend=baij->cendbs,bs=mat->rmap->bs,bs2=baij->bs2;

    3926:   /* tasks normally handled by MatSetValuesBlocked() */
    3927:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
    3928: #if defined(PETSC_USE_DEBUG)
    3929:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
    3930:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3931: #endif
    3932:   if (mat->assembled) {
    3933:     mat->was_assembled = PETSC_TRUE;
    3934:     mat->assembled     = PETSC_FALSE;
    3935:   }
    3936:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);

    3939:   if (!barray) {
    3940:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
    3941:     baij->barray = barray;
    3942:   }

    3944:   if (roworiented) stepval = (n-1)*bs;
    3945:   else stepval = (m-1)*bs;

    3947:   for (i=0; i<m; i++) {
    3948:     if (im[i] < 0) continue;
    3949: #if defined(PETSC_USE_DEBUG)
    3950:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
    3951: #endif
    3952:     if (im[i] >= rstart && im[i] < rend) {
    3953:       row = im[i] - rstart;
    3954:       for (j=0; j<n; j++) {
    3955:         /* If NumCol = 1 then a copy is not required */
    3956:         if ((roworiented) && (n == 1)) {
    3957:           barray = (MatScalar*)v + i*bs2;
    3958:         } else if ((!roworiented) && (m == 1)) {
    3959:           barray = (MatScalar*)v + j*bs2;
    3960:         } else { /* Here a copy is required */
    3961:           if (roworiented) {
    3962:             value = v + i*(stepval+bs)*bs + j*bs;
    3963:           } else {
    3964:             value = v + j*(stepval+bs)*bs + i*bs;
    3965:           }
    3966:           for (ii=0; ii<bs; ii++,value+=stepval) {
    3967:             for (jj=0; jj<bs; jj++) {
    3968:               *barray++ = *value++;
    3969:             }
    3970:           }
    3971:           barray -=bs2;
    3972:         }

    3974:         if (in[j] >= cstart && in[j] < cend) {
    3975:           col  = in[j] - cstart;
    3976:           MatSetValuesBlocked_SeqBAIJ(baij->A,1,&row,1,&col,barray,addv);
    3977:         } else if (in[j] < 0) continue;
    3978: #if defined(PETSC_USE_DEBUG)
    3979:         else if (in[j] >= baij->Nbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);
    3980: #endif
    3981:         else {
    3982:           if (mat->was_assembled) {
    3983:             if (!baij->colmap) {
    3984:               MatCreateColmap_MPIBAIJ_Private(mat);
    3985:             }

    3987: #if defined(PETSC_USE_DEBUG)
    3988: #if defined(PETSC_USE_CTABLE)
    3989:             { PetscInt data;
    3990:               PetscTableFind(baij->colmap,in[j]+1,&data);
    3991:               if ((data - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
    3992:             }
    3993: #else
    3994:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
    3995: #endif
    3996: #endif
    3997: #if defined(PETSC_USE_CTABLE)
    3998:             PetscTableFind(baij->colmap,in[j]+1,&col);
    3999:             col  = (col - 1)/bs;
    4000: #else
    4001:             col = (baij->colmap[in[j]] - 1)/bs;
    4002: #endif
    4003:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
    4004:               MatDisAssemble_MPIBAIJ(mat);
    4005:               col  =  in[j];
    4006:             }
    4007:           } else col = in[j];
    4008:           MatSetValuesBlocked_SeqBAIJ(baij->B,1,&row,1,&col,barray,addv);
    4009:         }
    4010:       }
    4011:     } else {
    4012:       if (!baij->donotstash) {
    4013:         if (roworiented) {
    4014:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    4015:         } else {
    4016:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
    4017:         }
    4018:       }
    4019:     }
    4020:   }

    4022:   /* task normally handled by MatSetValuesBlocked() */
    4023:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
    4024:   return(0);
    4025: }

    4029: /*@
    4030:      MatCreateMPIBAIJWithArrays - creates a MPI BAIJ matrix using arrays that contain in standard
    4031:          CSR format the local rows.

    4033:    Collective on MPI_Comm

    4035:    Input Parameters:
    4036: +  comm - MPI communicator
    4037: .  bs - the block size, only a block size of 1 is supported
    4038: .  m - number of local rows (Cannot be PETSC_DECIDE)
    4039: .  n - This value should be the same as the local size used in creating the
    4040:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
    4041:        calculated if N is given) For square matrices n is almost always m.
    4042: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
    4043: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
    4044: .   i - row indices
    4045: .   j - column indices
    4046: -   a - matrix values

    4048:    Output Parameter:
    4049: .   mat - the matrix

    4051:    Level: intermediate

    4053:    Notes:
    4054:        The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
    4055:      thus you CANNOT change the matrix entries by changing the values of a[] after you have
    4056:      called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.

    4058:        The i and j indices are 0 based, and i indices are indices corresponding to the local j array.

    4060: .keywords: matrix, aij, compressed row, sparse, parallel

    4062: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
    4063:           MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithSplitArrays()
    4064: @*/
    4065: 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)
    4066: {

    4070:   if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
    4071:   if (m < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
    4072:   MatCreate(comm,mat);
    4073:   MatSetSizes(*mat,m,n,M,N);
    4074:   MatSetType(*mat,MATMPISBAIJ);
    4075:   MatMPIBAIJSetPreallocationCSR(*mat,bs,i,j,a);
    4076:   return(0);
    4077: }