Actual source code: mpidense.c
petsc-3.7.1 2016-05-15
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      2: /*
      3:    Basic functions for basic parallel dense matrices.
      4: */

      7: #include <../src/mat/impls/dense/mpi/mpidense.h>    /*I   "petscmat.h"  I*/
      8: #include <../src/mat/impls/aij/mpi/mpiaij.h>
      9: #include <petscblaslapack.h>

     13: /*@

     15:       MatDenseGetLocalMatrix - For a MATMPIDENSE or MATSEQDENSE matrix returns the sequential
     16:               matrix that represents the operator. For sequential matrices it returns itself.

     18:     Input Parameter:
     19: .      A - the Seq or MPI dense matrix

     21:     Output Parameter:
     22: .      B - the inner matrix

     24:     Level: intermediate

     26: @*/
     27: PetscErrorCode MatDenseGetLocalMatrix(Mat A,Mat *B)
     28: {
     29:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
     31:   PetscBool      flg;

     34:   PetscObjectTypeCompare((PetscObject)A,MATMPIDENSE,&flg);
     35:   if (flg) *B = mat->A;
     36:   else *B = A;
     37:   return(0);
     38: }

     42: PetscErrorCode MatGetRow_MPIDense(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
     43: {
     44:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
     46:   PetscInt       lrow,rstart = A->rmap->rstart,rend = A->rmap->rend;

     49:   if (row < rstart || row >= rend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"only local rows");
     50:   lrow = row - rstart;
     51:   MatGetRow(mat->A,lrow,nz,(const PetscInt**)idx,(const PetscScalar**)v);
     52:   return(0);
     53: }

     57: PetscErrorCode MatRestoreRow_MPIDense(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
     58: {

     62:   if (idx) {PetscFree(*idx);}
     63:   if (v) {PetscFree(*v);}
     64:   return(0);
     65: }

     69: PetscErrorCode  MatGetDiagonalBlock_MPIDense(Mat A,Mat *a)
     70: {
     71:   Mat_MPIDense   *mdn = (Mat_MPIDense*)A->data;
     73:   PetscInt       m = A->rmap->n,rstart = A->rmap->rstart;
     74:   PetscScalar    *array;
     75:   MPI_Comm       comm;
     76:   Mat            B;

     79:   if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only square matrices supported.");

     81:   PetscObjectQuery((PetscObject)A,"DiagonalBlock",(PetscObject*)&B);
     82:   if (!B) {
     83:     PetscObjectGetComm((PetscObject)(mdn->A),&comm);
     84:     MatCreate(comm,&B);
     85:     MatSetSizes(B,m,m,m,m);
     86:     MatSetType(B,((PetscObject)mdn->A)->type_name);
     87:     MatDenseGetArray(mdn->A,&array);
     88:     MatSeqDenseSetPreallocation(B,array+m*rstart);
     89:     MatDenseRestoreArray(mdn->A,&array);
     90:     MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
     91:     MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
     92:     PetscObjectCompose((PetscObject)A,"DiagonalBlock",(PetscObject)B);
     93:     *a   = B;
     94:     MatDestroy(&B);
     95:   } else *a = B;
     96:   return(0);
     97: }

    101: PetscErrorCode MatSetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
    102: {
    103:   Mat_MPIDense   *A = (Mat_MPIDense*)mat->data;
    105:   PetscInt       i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row;
    106:   PetscBool      roworiented = A->roworiented;

    109:   for (i=0; i<m; i++) {
    110:     if (idxm[i] < 0) continue;
    111:     if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
    112:     if (idxm[i] >= rstart && idxm[i] < rend) {
    113:       row = idxm[i] - rstart;
    114:       if (roworiented) {
    115:         MatSetValues(A->A,1,&row,n,idxn,v+i*n,addv);
    116:       } else {
    117:         for (j=0; j<n; j++) {
    118:           if (idxn[j] < 0) continue;
    119:           if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
    120:           MatSetValues(A->A,1,&row,1,&idxn[j],v+i+j*m,addv);
    121:         }
    122:       }
    123:     } else if (!A->donotstash) {
    124:       mat->assembled = PETSC_FALSE;
    125:       if (roworiented) {
    126:         MatStashValuesRow_Private(&mat->stash,idxm[i],n,idxn,v+i*n,PETSC_FALSE);
    127:       } else {
    128:         MatStashValuesCol_Private(&mat->stash,idxm[i],n,idxn,v+i,m,PETSC_FALSE);
    129:       }
    130:     }
    131:   }
    132:   return(0);
    133: }

    137: PetscErrorCode MatGetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
    138: {
    139:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;
    141:   PetscInt       i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row;

    144:   for (i=0; i<m; i++) {
    145:     if (idxm[i] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */
    146:     if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
    147:     if (idxm[i] >= rstart && idxm[i] < rend) {
    148:       row = idxm[i] - rstart;
    149:       for (j=0; j<n; j++) {
    150:         if (idxn[j] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */
    151:         if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
    152:         MatGetValues(mdn->A,1,&row,1,&idxn[j],v+i*n+j);
    153:       }
    154:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
    155:   }
    156:   return(0);
    157: }

    161: PetscErrorCode MatDenseGetArray_MPIDense(Mat A,PetscScalar *array[])
    162: {
    163:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

    167:   MatDenseGetArray(a->A,array);
    168:   return(0);
    169: }

    173: static PetscErrorCode MatGetSubMatrix_MPIDense(Mat A,IS isrow,IS iscol,MatReuse scall,Mat *B)
    174: {
    175:   Mat_MPIDense   *mat  = (Mat_MPIDense*)A->data,*newmatd;
    176:   Mat_SeqDense   *lmat = (Mat_SeqDense*)mat->A->data;
    178:   PetscInt       i,j,rstart,rend,nrows,ncols,Ncols,nlrows,nlcols;
    179:   const PetscInt *irow,*icol;
    180:   PetscScalar    *av,*bv,*v = lmat->v;
    181:   Mat            newmat;
    182:   IS             iscol_local;

    185:   ISAllGather(iscol,&iscol_local);
    186:   ISGetIndices(isrow,&irow);
    187:   ISGetIndices(iscol_local,&icol);
    188:   ISGetLocalSize(isrow,&nrows);
    189:   ISGetLocalSize(iscol,&ncols);
    190:   ISGetSize(iscol,&Ncols); /* global number of columns, size of iscol_local */

    192:   /* No parallel redistribution currently supported! Should really check each index set
    193:      to comfirm that it is OK.  ... Currently supports only submatrix same partitioning as
    194:      original matrix! */

    196:   MatGetLocalSize(A,&nlrows,&nlcols);
    197:   MatGetOwnershipRange(A,&rstart,&rend);

    199:   /* Check submatrix call */
    200:   if (scall == MAT_REUSE_MATRIX) {
    201:     /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); */
    202:     /* Really need to test rows and column sizes! */
    203:     newmat = *B;
    204:   } else {
    205:     /* Create and fill new matrix */
    206:     MatCreate(PetscObjectComm((PetscObject)A),&newmat);
    207:     MatSetSizes(newmat,nrows,ncols,PETSC_DECIDE,Ncols);
    208:     MatSetType(newmat,((PetscObject)A)->type_name);
    209:     MatMPIDenseSetPreallocation(newmat,NULL);
    210:   }

    212:   /* Now extract the data pointers and do the copy, column at a time */
    213:   newmatd = (Mat_MPIDense*)newmat->data;
    214:   bv      = ((Mat_SeqDense*)newmatd->A->data)->v;

    216:   for (i=0; i<Ncols; i++) {
    217:     av = v + ((Mat_SeqDense*)mat->A->data)->lda*icol[i];
    218:     for (j=0; j<nrows; j++) {
    219:       *bv++ = av[irow[j] - rstart];
    220:     }
    221:   }

    223:   /* Assemble the matrices so that the correct flags are set */
    224:   MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
    225:   MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);

    227:   /* Free work space */
    228:   ISRestoreIndices(isrow,&irow);
    229:   ISRestoreIndices(iscol_local,&icol);
    230:   ISDestroy(&iscol_local);
    231:   *B   = newmat;
    232:   return(0);
    233: }

    237: PetscErrorCode MatDenseRestoreArray_MPIDense(Mat A,PetscScalar *array[])
    238: {
    239:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

    243:   MatDenseRestoreArray(a->A,array);
    244:   return(0);
    245: }

    249: PetscErrorCode MatAssemblyBegin_MPIDense(Mat mat,MatAssemblyType mode)
    250: {
    251:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;
    252:   MPI_Comm       comm;
    254:   PetscInt       nstash,reallocs;
    255:   InsertMode     addv;

    258:   PetscObjectGetComm((PetscObject)mat,&comm);
    259:   /* make sure all processors are either in INSERTMODE or ADDMODE */
    260:   MPIU_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,comm);
    261:   if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot mix adds/inserts on different procs");
    262:   mat->insertmode = addv; /* in case this processor had no cache */

    264:   MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
    265:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
    266:   PetscInfo2(mdn->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
    267:   return(0);
    268: }

    272: PetscErrorCode MatAssemblyEnd_MPIDense(Mat mat,MatAssemblyType mode)
    273: {
    274:   Mat_MPIDense   *mdn=(Mat_MPIDense*)mat->data;
    276:   PetscInt       i,*row,*col,flg,j,rstart,ncols;
    277:   PetscMPIInt    n;
    278:   PetscScalar    *val;

    281:   /*  wait on receives */
    282:   while (1) {
    283:     MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
    284:     if (!flg) break;

    286:     for (i=0; i<n;) {
    287:       /* Now identify the consecutive vals belonging to the same row */
    288:       for (j=i,rstart=row[j]; j<n; j++) {
    289:         if (row[j] != rstart) break;
    290:       }
    291:       if (j < n) ncols = j-i;
    292:       else       ncols = n-i;
    293:       /* Now assemble all these values with a single function call */
    294:       MatSetValues_MPIDense(mat,1,row+i,ncols,col+i,val+i,mat->insertmode);
    295:       i    = j;
    296:     }
    297:   }
    298:   MatStashScatterEnd_Private(&mat->stash);

    300:   MatAssemblyBegin(mdn->A,mode);
    301:   MatAssemblyEnd(mdn->A,mode);

    303:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
    304:     MatSetUpMultiply_MPIDense(mat);
    305:   }
    306:   return(0);
    307: }

    311: PetscErrorCode MatZeroEntries_MPIDense(Mat A)
    312: {
    314:   Mat_MPIDense   *l = (Mat_MPIDense*)A->data;

    317:   MatZeroEntries(l->A);
    318:   return(0);
    319: }

    321: /* the code does not do the diagonal entries correctly unless the
    322:    matrix is square and the column and row owerships are identical.
    323:    This is a BUG. The only way to fix it seems to be to access
    324:    mdn->A and mdn->B directly and not through the MatZeroRows()
    325:    routine.
    326: */
    329: PetscErrorCode MatZeroRows_MPIDense(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    330: {
    331:   Mat_MPIDense      *l = (Mat_MPIDense*)A->data;
    332:   PetscErrorCode    ierr;
    333:   PetscInt          i,*owners = A->rmap->range;
    334:   PetscInt          *sizes,j,idx,nsends;
    335:   PetscInt          nmax,*svalues,*starts,*owner,nrecvs;
    336:   PetscInt          *rvalues,tag = ((PetscObject)A)->tag,count,base,slen,*source;
    337:   PetscInt          *lens,*lrows,*values;
    338:   PetscMPIInt       n,imdex,rank = l->rank,size = l->size;
    339:   MPI_Comm          comm;
    340:   MPI_Request       *send_waits,*recv_waits;
    341:   MPI_Status        recv_status,*send_status;
    342:   PetscBool         found;
    343:   const PetscScalar *xx;
    344:   PetscScalar       *bb;

    347:   PetscObjectGetComm((PetscObject)A,&comm);
    348:   if (A->rmap->N != A->cmap->N) SETERRQ(comm,PETSC_ERR_SUP,"Only handles square matrices");
    349:   if (A->rmap->n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only handles matrices with identical column and row ownership");
    350:   /*  first count number of contributors to each processor */
    351:   PetscCalloc1(2*size,&sizes);
    352:   PetscMalloc1(N+1,&owner);  /* see note*/
    353:   for (i=0; i<N; i++) {
    354:     idx   = rows[i];
    355:     found = PETSC_FALSE;
    356:     for (j=0; j<size; j++) {
    357:       if (idx >= owners[j] && idx < owners[j+1]) {
    358:         sizes[2*j]++; sizes[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break;
    359:       }
    360:     }
    361:     if (!found) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
    362:   }
    363:   nsends = 0;
    364:   for (i=0; i<size; i++) nsends += sizes[2*i+1];

    366:   /* inform other processors of number of messages and max length*/
    367:   PetscMaxSum(comm,sizes,&nmax,&nrecvs);

    369:   /* post receives:   */
    370:   PetscMalloc1((nrecvs+1)*(nmax+1),&rvalues);
    371:   PetscMalloc1(nrecvs+1,&recv_waits);
    372:   for (i=0; i<nrecvs; i++) {
    373:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
    374:   }

    376:   /* do sends:
    377:       1) starts[i] gives the starting index in svalues for stuff going to
    378:          the ith processor
    379:   */
    380:   PetscMalloc1(N+1,&svalues);
    381:   PetscMalloc1(nsends+1,&send_waits);
    382:   PetscMalloc1(size+1,&starts);

    384:   starts[0] = 0;
    385:   for (i=1; i<size; i++) starts[i] = starts[i-1] + sizes[2*i-2];
    386:   for (i=0; i<N; i++) svalues[starts[owner[i]]++] = rows[i];

    388:   starts[0] = 0;
    389:   for (i=1; i<size+1; i++) starts[i] = starts[i-1] + sizes[2*i-2];
    390:   count = 0;
    391:   for (i=0; i<size; i++) {
    392:     if (sizes[2*i+1]) {
    393:       MPI_Isend(svalues+starts[i],sizes[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
    394:     }
    395:   }
    396:   PetscFree(starts);

    398:   base = owners[rank];

    400:   /*  wait on receives */
    401:   PetscMalloc2(nrecvs,&lens,nrecvs,&source);
    402:   count = nrecvs;
    403:   slen  = 0;
    404:   while (count) {
    405:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
    406:     /* unpack receives into our local space */
    407:     MPI_Get_count(&recv_status,MPIU_INT,&n);

    409:     source[imdex] = recv_status.MPI_SOURCE;
    410:     lens[imdex]   = n;
    411:     slen += n;
    412:     count--;
    413:   }
    414:   PetscFree(recv_waits);

    416:   /* move the data into the send scatter */
    417:   PetscMalloc1(slen+1,&lrows);
    418:   count = 0;
    419:   for (i=0; i<nrecvs; i++) {
    420:     values = rvalues + i*nmax;
    421:     for (j=0; j<lens[i]; j++) {
    422:       lrows[count++] = values[j] - base;
    423:     }
    424:   }
    425:   PetscFree(rvalues);
    426:   PetscFree2(lens,source);
    427:   PetscFree(owner);
    428:   PetscFree(sizes);

    430:   /* fix right hand side if needed */
    431:   if (x && b) {
    432:     VecGetArrayRead(x,&xx);
    433:     VecGetArray(b,&bb);
    434:     for (i=0; i<slen; i++) {
    435:       bb[lrows[i]] = diag*xx[lrows[i]];
    436:     }
    437:     VecRestoreArrayRead(x,&xx);
    438:     VecRestoreArray(b,&bb);
    439:   }

    441:   /* actually zap the local rows */
    442:   MatZeroRows(l->A,slen,lrows,0.0,0,0);
    443:   if (diag != 0.0) {
    444:     Mat_SeqDense *ll = (Mat_SeqDense*)l->A->data;
    445:     PetscInt     m   = ll->lda, i;

    447:     for (i=0; i<slen; i++) {
    448:       ll->v[lrows[i] + m*(A->cmap->rstart + lrows[i])] = diag;
    449:     }
    450:   }
    451:   PetscFree(lrows);

    453:   /* wait on sends */
    454:   if (nsends) {
    455:     PetscMalloc1(nsends,&send_status);
    456:     MPI_Waitall(nsends,send_waits,send_status);
    457:     PetscFree(send_status);
    458:   }
    459:   PetscFree(send_waits);
    460:   PetscFree(svalues);
    461:   return(0);
    462: }

    464: PETSC_INTERN PetscErrorCode MatMult_SeqDense(Mat,Vec,Vec);
    465: PETSC_INTERN PetscErrorCode MatMultAdd_SeqDense(Mat,Vec,Vec,Vec);
    466: PETSC_INTERN PetscErrorCode MatMultTranspose_SeqDense(Mat,Vec,Vec);
    467: PETSC_INTERN PetscErrorCode MatMultTransposeAdd_SeqDense(Mat,Vec,Vec,Vec);

    471: PetscErrorCode MatMult_MPIDense(Mat mat,Vec xx,Vec yy)
    472: {
    473:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;

    477:   VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
    478:   VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
    479:   MatMult_SeqDense(mdn->A,mdn->lvec,yy);
    480:   return(0);
    481: }

    485: PetscErrorCode MatMultAdd_MPIDense(Mat mat,Vec xx,Vec yy,Vec zz)
    486: {
    487:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;

    491:   VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
    492:   VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
    493:   MatMultAdd_SeqDense(mdn->A,mdn->lvec,yy,zz);
    494:   return(0);
    495: }

    499: PetscErrorCode MatMultTranspose_MPIDense(Mat A,Vec xx,Vec yy)
    500: {
    501:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;
    503:   PetscScalar    zero = 0.0;

    506:   VecSet(yy,zero);
    507:   MatMultTranspose_SeqDense(a->A,xx,a->lvec);
    508:   VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
    509:   VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
    510:   return(0);
    511: }

    515: PetscErrorCode MatMultTransposeAdd_MPIDense(Mat A,Vec xx,Vec yy,Vec zz)
    516: {
    517:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

    521:   VecCopy(yy,zz);
    522:   MatMultTranspose_SeqDense(a->A,xx,a->lvec);
    523:   VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
    524:   VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
    525:   return(0);
    526: }

    530: PetscErrorCode MatGetDiagonal_MPIDense(Mat A,Vec v)
    531: {
    532:   Mat_MPIDense   *a    = (Mat_MPIDense*)A->data;
    533:   Mat_SeqDense   *aloc = (Mat_SeqDense*)a->A->data;
    535:   PetscInt       len,i,n,m = A->rmap->n,radd;
    536:   PetscScalar    *x,zero = 0.0;

    539:   VecSet(v,zero);
    540:   VecGetArray(v,&x);
    541:   VecGetSize(v,&n);
    542:   if (n != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming mat and vec");
    543:   len  = PetscMin(a->A->rmap->n,a->A->cmap->n);
    544:   radd = A->rmap->rstart*m;
    545:   for (i=0; i<len; i++) {
    546:     x[i] = aloc->v[radd + i*m + i];
    547:   }
    548:   VecRestoreArray(v,&x);
    549:   return(0);
    550: }

    554: PetscErrorCode MatDestroy_MPIDense(Mat mat)
    555: {
    556:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;

    560: #if defined(PETSC_USE_LOG)
    561:   PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N);
    562: #endif
    563:   MatStashDestroy_Private(&mat->stash);
    564:   MatDestroy(&mdn->A);
    565:   VecDestroy(&mdn->lvec);
    566:   VecScatterDestroy(&mdn->Mvctx);

    568:   PetscFree(mat->data);
    569:   PetscObjectChangeTypeName((PetscObject)mat,0);

    571:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",NULL);
    572:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",NULL);
    573: #if defined(PETSC_HAVE_ELEMENTAL)
    574:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpidense_elemental_C",NULL);
    575: #endif
    576:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",NULL);
    577:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",NULL);
    578:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",NULL);
    579:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",NULL);
    580:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",NULL);
    581:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMult_mpiaij_mpidense_C",NULL);
    582:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultSymbolic_mpiaij_mpidense_C",NULL);
    583:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultNumeric_mpiaij_mpidense_C",NULL);
    584:   return(0);
    585: }

    589: static PetscErrorCode MatView_MPIDense_Binary(Mat mat,PetscViewer viewer)
    590: {
    591:   Mat_MPIDense      *mdn = (Mat_MPIDense*)mat->data;
    592:   PetscErrorCode    ierr;
    593:   PetscViewerFormat format;
    594:   int               fd;
    595:   PetscInt          header[4],mmax,N = mat->cmap->N,i,j,m,k;
    596:   PetscMPIInt       rank,tag  = ((PetscObject)viewer)->tag,size;
    597:   PetscScalar       *work,*v,*vv;
    598:   Mat_SeqDense      *a = (Mat_SeqDense*)mdn->A->data;

    601:   if (mdn->size == 1) {
    602:     MatView(mdn->A,viewer);
    603:   } else {
    604:     PetscViewerBinaryGetDescriptor(viewer,&fd);
    605:     MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
    606:     MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);

    608:     PetscViewerGetFormat(viewer,&format);
    609:     if (format == PETSC_VIEWER_NATIVE) {

    611:       if (!rank) {
    612:         /* store the matrix as a dense matrix */
    613:         header[0] = MAT_FILE_CLASSID;
    614:         header[1] = mat->rmap->N;
    615:         header[2] = N;
    616:         header[3] = MATRIX_BINARY_FORMAT_DENSE;
    617:         PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);

    619:         /* get largest work array needed for transposing array */
    620:         mmax = mat->rmap->n;
    621:         for (i=1; i<size; i++) {
    622:           mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]);
    623:         }
    624:         PetscMalloc1(mmax*N,&work);

    626:         /* write out local array, by rows */
    627:         m = mat->rmap->n;
    628:         v = a->v;
    629:         for (j=0; j<N; j++) {
    630:           for (i=0; i<m; i++) {
    631:             work[j + i*N] = *v++;
    632:           }
    633:         }
    634:         PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
    635:         /* get largest work array to receive messages from other processes, excludes process zero */
    636:         mmax = 0;
    637:         for (i=1; i<size; i++) {
    638:           mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]);
    639:         }
    640:         PetscMalloc1(mmax*N,&vv);
    641:         for (k = 1; k < size; k++) {
    642:           v    = vv;
    643:           m    = mat->rmap->range[k+1] - mat->rmap->range[k];
    644:           MPIULong_Recv(v,m*N,MPIU_SCALAR,k,tag,PetscObjectComm((PetscObject)mat));

    646:           for (j = 0; j < N; j++) {
    647:             for (i = 0; i < m; i++) {
    648:               work[j + i*N] = *v++;
    649:             }
    650:           }
    651:           PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
    652:         }
    653:         PetscFree(work);
    654:         PetscFree(vv);
    655:       } else {
    656:         MPIULong_Send(a->v,mat->rmap->n*mat->cmap->N,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));
    657:       }
    658:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"To store a parallel dense matrix you must first call PetscViewerPushFormat(viewer,PETSC_VIEWER_NATIVE)");
    659:   }
    660:   return(0);
    661: }

    663: extern PetscErrorCode MatView_SeqDense(Mat,PetscViewer);
    664: #include <petscdraw.h>
    667: static PetscErrorCode MatView_MPIDense_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
    668: {
    669:   Mat_MPIDense      *mdn = (Mat_MPIDense*)mat->data;
    670:   PetscErrorCode    ierr;
    671:   PetscMPIInt       rank = mdn->rank;
    672:   PetscViewerType   vtype;
    673:   PetscBool         iascii,isdraw;
    674:   PetscViewer       sviewer;
    675:   PetscViewerFormat format;

    678:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    679:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    680:   if (iascii) {
    681:     PetscViewerGetType(viewer,&vtype);
    682:     PetscViewerGetFormat(viewer,&format);
    683:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
    684:       MatInfo info;
    685:       MatGetInfo(mat,MAT_LOCAL,&info);
    686:       PetscViewerASCIIPushSynchronized(viewer);
    687:       PetscViewerASCIISynchronizedPrintf(viewer,"  [%d] local rows %D nz %D nz alloced %D mem %D \n",rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
    688:       PetscViewerFlush(viewer);
    689:       PetscViewerASCIIPopSynchronized(viewer);
    690:       VecScatterView(mdn->Mvctx,viewer);
    691:       return(0);
    692:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
    693:       return(0);
    694:     }
    695:   } else if (isdraw) {
    696:     PetscDraw draw;
    697:     PetscBool isnull;

    699:     PetscViewerDrawGetDraw(viewer,0,&draw);
    700:     PetscDrawIsNull(draw,&isnull);
    701:     if (isnull) return(0);
    702:   }

    704:   {
    705:     /* assemble the entire matrix onto first processor. */
    706:     Mat         A;
    707:     PetscInt    M = mat->rmap->N,N = mat->cmap->N,m,row,i,nz;
    708:     PetscInt    *cols;
    709:     PetscScalar *vals;

    711:     MatCreate(PetscObjectComm((PetscObject)mat),&A);
    712:     if (!rank) {
    713:       MatSetSizes(A,M,N,M,N);
    714:     } else {
    715:       MatSetSizes(A,0,0,M,N);
    716:     }
    717:     /* Since this is a temporary matrix, MATMPIDENSE instead of ((PetscObject)A)->type_name here is probably acceptable. */
    718:     MatSetType(A,MATMPIDENSE);
    719:     MatMPIDenseSetPreallocation(A,NULL);
    720:     PetscLogObjectParent((PetscObject)mat,(PetscObject)A);

    722:     /* Copy the matrix ... This isn't the most efficient means,
    723:        but it's quick for now */
    724:     A->insertmode = INSERT_VALUES;

    726:     row = mat->rmap->rstart;
    727:     m   = mdn->A->rmap->n;
    728:     for (i=0; i<m; i++) {
    729:       MatGetRow_MPIDense(mat,row,&nz,&cols,&vals);
    730:       MatSetValues_MPIDense(A,1,&row,nz,cols,vals,INSERT_VALUES);
    731:       MatRestoreRow_MPIDense(mat,row,&nz,&cols,&vals);
    732:       row++;
    733:     }

    735:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    736:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    737:     PetscViewerGetSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
    738:     if (!rank) {
    739:         MatView_SeqDense(((Mat_MPIDense*)(A->data))->A,sviewer);
    740:     }
    741:     PetscViewerRestoreSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
    742:     PetscViewerFlush(viewer);
    743:     MatDestroy(&A);
    744:   }
    745:   return(0);
    746: }

    750: PetscErrorCode MatView_MPIDense(Mat mat,PetscViewer viewer)
    751: {
    753:   PetscBool      iascii,isbinary,isdraw,issocket;

    756:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    757:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    758:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
    759:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);

    761:   if (iascii || issocket || isdraw) {
    762:     MatView_MPIDense_ASCIIorDraworSocket(mat,viewer);
    763:   } else if (isbinary) {
    764:     MatView_MPIDense_Binary(mat,viewer);
    765:   }
    766:   return(0);
    767: }

    771: PetscErrorCode MatGetInfo_MPIDense(Mat A,MatInfoType flag,MatInfo *info)
    772: {
    773:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
    774:   Mat            mdn  = mat->A;
    776:   PetscReal      isend[5],irecv[5];

    779:   info->block_size = 1.0;

    781:   MatGetInfo(mdn,MAT_LOCAL,info);

    783:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
    784:   isend[3] = info->memory;  isend[4] = info->mallocs;
    785:   if (flag == MAT_LOCAL) {
    786:     info->nz_used      = isend[0];
    787:     info->nz_allocated = isend[1];
    788:     info->nz_unneeded  = isend[2];
    789:     info->memory       = isend[3];
    790:     info->mallocs      = isend[4];
    791:   } else if (flag == MAT_GLOBAL_MAX) {
    792:     MPIU_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));

    794:     info->nz_used      = irecv[0];
    795:     info->nz_allocated = irecv[1];
    796:     info->nz_unneeded  = irecv[2];
    797:     info->memory       = irecv[3];
    798:     info->mallocs      = irecv[4];
    799:   } else if (flag == MAT_GLOBAL_SUM) {
    800:     MPIU_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));

    802:     info->nz_used      = irecv[0];
    803:     info->nz_allocated = irecv[1];
    804:     info->nz_unneeded  = irecv[2];
    805:     info->memory       = irecv[3];
    806:     info->mallocs      = irecv[4];
    807:   }
    808:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
    809:   info->fill_ratio_needed = 0;
    810:   info->factor_mallocs    = 0;
    811:   return(0);
    812: }

    816: PetscErrorCode MatSetOption_MPIDense(Mat A,MatOption op,PetscBool flg)
    817: {
    818:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

    822:   switch (op) {
    823:   case MAT_NEW_NONZERO_LOCATIONS:
    824:   case MAT_NEW_NONZERO_LOCATION_ERR:
    825:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
    826:     MatCheckPreallocated(A,1);
    827:     MatSetOption(a->A,op,flg);
    828:     break;
    829:   case MAT_ROW_ORIENTED:
    830:     MatCheckPreallocated(A,1);
    831:     a->roworiented = flg;
    832:     MatSetOption(a->A,op,flg);
    833:     break;
    834:   case MAT_NEW_DIAGONALS:
    835:   case MAT_KEEP_NONZERO_PATTERN:
    836:   case MAT_USE_HASH_TABLE:
    837:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
    838:     break;
    839:   case MAT_IGNORE_OFF_PROC_ENTRIES:
    840:     a->donotstash = flg;
    841:     break;
    842:   case MAT_SYMMETRIC:
    843:   case MAT_STRUCTURALLY_SYMMETRIC:
    844:   case MAT_HERMITIAN:
    845:   case MAT_SYMMETRY_ETERNAL:
    846:   case MAT_IGNORE_LOWER_TRIANGULAR:
    847:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
    848:     break;
    849:   default:
    850:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %s",MatOptions[op]);
    851:   }
    852:   return(0);
    853: }

    858: PetscErrorCode MatDiagonalScale_MPIDense(Mat A,Vec ll,Vec rr)
    859: {
    860:   Mat_MPIDense   *mdn = (Mat_MPIDense*)A->data;
    861:   Mat_SeqDense   *mat = (Mat_SeqDense*)mdn->A->data;
    862:   PetscScalar    *l,*r,x,*v;
    864:   PetscInt       i,j,s2a,s3a,s2,s3,m=mdn->A->rmap->n,n=mdn->A->cmap->n;

    867:   MatGetLocalSize(A,&s2,&s3);
    868:   if (ll) {
    869:     VecGetLocalSize(ll,&s2a);
    870:     if (s2a != s2) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector non-conforming local size, %d != %d.", s2a, s2);
    871:     VecGetArray(ll,&l);
    872:     for (i=0; i<m; i++) {
    873:       x = l[i];
    874:       v = mat->v + i;
    875:       for (j=0; j<n; j++) { (*v) *= x; v+= m;}
    876:     }
    877:     VecRestoreArray(ll,&l);
    878:     PetscLogFlops(n*m);
    879:   }
    880:   if (rr) {
    881:     VecGetLocalSize(rr,&s3a);
    882:     if (s3a != s3) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vec non-conforming local size, %d != %d.", s3a, s3);
    883:     VecScatterBegin(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
    884:     VecScatterEnd(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
    885:     VecGetArray(mdn->lvec,&r);
    886:     for (i=0; i<n; i++) {
    887:       x = r[i];
    888:       v = mat->v + i*m;
    889:       for (j=0; j<m; j++) (*v++) *= x;
    890:     }
    891:     VecRestoreArray(mdn->lvec,&r);
    892:     PetscLogFlops(n*m);
    893:   }
    894:   return(0);
    895: }

    899: PetscErrorCode MatNorm_MPIDense(Mat A,NormType type,PetscReal *nrm)
    900: {
    901:   Mat_MPIDense   *mdn = (Mat_MPIDense*)A->data;
    902:   Mat_SeqDense   *mat = (Mat_SeqDense*)mdn->A->data;
    904:   PetscInt       i,j;
    905:   PetscReal      sum = 0.0;
    906:   PetscScalar    *v  = mat->v;

    909:   if (mdn->size == 1) {
    910:      MatNorm(mdn->A,type,nrm);
    911:   } else {
    912:     if (type == NORM_FROBENIUS) {
    913:       for (i=0; i<mdn->A->cmap->n*mdn->A->rmap->n; i++) {
    914:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    915:       }
    916:       MPIU_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
    917:       *nrm = PetscSqrtReal(*nrm);
    918:       PetscLogFlops(2.0*mdn->A->cmap->n*mdn->A->rmap->n);
    919:     } else if (type == NORM_1) {
    920:       PetscReal *tmp,*tmp2;
    921:       PetscMalloc2(A->cmap->N,&tmp,A->cmap->N,&tmp2);
    922:       PetscMemzero(tmp,A->cmap->N*sizeof(PetscReal));
    923:       PetscMemzero(tmp2,A->cmap->N*sizeof(PetscReal));
    924:       *nrm = 0.0;
    925:       v    = mat->v;
    926:       for (j=0; j<mdn->A->cmap->n; j++) {
    927:         for (i=0; i<mdn->A->rmap->n; i++) {
    928:           tmp[j] += PetscAbsScalar(*v);  v++;
    929:         }
    930:       }
    931:       MPIU_Allreduce(tmp,tmp2,A->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
    932:       for (j=0; j<A->cmap->N; j++) {
    933:         if (tmp2[j] > *nrm) *nrm = tmp2[j];
    934:       }
    935:       PetscFree2(tmp,tmp2);
    936:       PetscLogFlops(A->cmap->n*A->rmap->n);
    937:     } else if (type == NORM_INFINITY) { /* max row norm */
    938:       PetscReal ntemp;
    939:       MatNorm(mdn->A,type,&ntemp);
    940:       MPIU_Allreduce(&ntemp,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
    941:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for two norm");
    942:   }
    943:   return(0);
    944: }

    948: PetscErrorCode MatTranspose_MPIDense(Mat A,MatReuse reuse,Mat *matout)
    949: {
    950:   Mat_MPIDense   *a    = (Mat_MPIDense*)A->data;
    951:   Mat_SeqDense   *Aloc = (Mat_SeqDense*)a->A->data;
    952:   Mat            B;
    953:   PetscInt       M = A->rmap->N,N = A->cmap->N,m,n,*rwork,rstart = A->rmap->rstart;
    955:   PetscInt       j,i;
    956:   PetscScalar    *v;

    959:   if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Supports square matrix only in-place");
    960:   if (reuse == MAT_INITIAL_MATRIX || A == *matout) {
    961:     MatCreate(PetscObjectComm((PetscObject)A),&B);
    962:     MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
    963:     MatSetType(B,((PetscObject)A)->type_name);
    964:     MatMPIDenseSetPreallocation(B,NULL);
    965:   } else {
    966:     B = *matout;
    967:   }

    969:   m    = a->A->rmap->n; n = a->A->cmap->n; v = Aloc->v;
    970:   PetscMalloc1(m,&rwork);
    971:   for (i=0; i<m; i++) rwork[i] = rstart + i;
    972:   for (j=0; j<n; j++) {
    973:     MatSetValues(B,1,&j,m,rwork,v,INSERT_VALUES);
    974:     v   += m;
    975:   }
    976:   PetscFree(rwork);
    977:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    978:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    979:   if (reuse == MAT_INITIAL_MATRIX || *matout != A) {
    980:     *matout = B;
    981:   } else {
    982:     MatHeaderMerge(A,&B);
    983:   }
    984:   return(0);
    985: }

    988: static PetscErrorCode MatDuplicate_MPIDense(Mat,MatDuplicateOption,Mat*);
    989: extern PetscErrorCode MatScale_MPIDense(Mat,PetscScalar);

    993: PetscErrorCode MatSetUp_MPIDense(Mat A)
    994: {

    998:    MatMPIDenseSetPreallocation(A,0);
    999:   return(0);
    1000: }

    1004: PetscErrorCode MatAXPY_MPIDense(Mat Y,PetscScalar alpha,Mat X,MatStructure str)
    1005: {
    1007:   Mat_MPIDense   *A = (Mat_MPIDense*)Y->data, *B = (Mat_MPIDense*)X->data;

    1010:   MatAXPY(A->A,alpha,B->A,str);
    1011:   PetscObjectStateIncrease((PetscObject)Y);
    1012:   return(0);
    1013: }

    1017: PetscErrorCode  MatConjugate_MPIDense(Mat mat)
    1018: {
    1019:   Mat_MPIDense   *a = (Mat_MPIDense*)mat->data;

    1023:   MatConjugate(a->A);
    1024:   return(0);
    1025: }

    1029: PetscErrorCode MatRealPart_MPIDense(Mat A)
    1030: {
    1031:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

    1035:   MatRealPart(a->A);
    1036:   return(0);
    1037: }

    1041: PetscErrorCode MatImaginaryPart_MPIDense(Mat A)
    1042: {
    1043:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

    1047:   MatImaginaryPart(a->A);
    1048:   return(0);
    1049: }

    1051: extern PetscErrorCode MatGetColumnNorms_SeqDense(Mat,NormType,PetscReal*);
    1054: PetscErrorCode MatGetColumnNorms_MPIDense(Mat A,NormType type,PetscReal *norms)
    1055: {
    1057:   PetscInt       i,n;
    1058:   Mat_MPIDense   *a = (Mat_MPIDense*) A->data;
    1059:   PetscReal      *work;

    1062:   MatGetSize(A,NULL,&n);
    1063:   PetscMalloc1(n,&work);
    1064:   MatGetColumnNorms_SeqDense(a->A,type,work);
    1065:   if (type == NORM_2) {
    1066:     for (i=0; i<n; i++) work[i] *= work[i];
    1067:   }
    1068:   if (type == NORM_INFINITY) {
    1069:     MPIU_Allreduce(work,norms,n,MPIU_REAL,MPIU_MAX,A->hdr.comm);
    1070:   } else {
    1071:     MPIU_Allreduce(work,norms,n,MPIU_REAL,MPIU_SUM,A->hdr.comm);
    1072:   }
    1073:   PetscFree(work);
    1074:   if (type == NORM_2) {
    1075:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
    1076:   }
    1077:   return(0);
    1078: }

    1082: static PetscErrorCode  MatSetRandom_MPIDense(Mat x,PetscRandom rctx)
    1083: {
    1084:   Mat_MPIDense   *d = (Mat_MPIDense*)x->data;
    1086:   PetscScalar    *a;
    1087:   PetscInt       m,n,i;

    1090:   MatGetSize(d->A,&m,&n);
    1091:   MatDenseGetArray(d->A,&a);
    1092:   for (i=0; i<m*n; i++) {
    1093:     PetscRandomGetValue(rctx,a+i);
    1094:   }
    1095:   MatDenseRestoreArray(d->A,&a);
    1096:   return(0);
    1097: }

    1099: extern PetscErrorCode MatMatMultNumeric_MPIDense(Mat A,Mat,Mat);

    1103: static PetscErrorCode MatMissingDiagonal_MPIDense(Mat A,PetscBool  *missing,PetscInt *d)
    1104: {
    1106:   *missing = PETSC_FALSE;
    1107:   return(0);
    1108: }

    1110: /* -------------------------------------------------------------------*/
    1111: static struct _MatOps MatOps_Values = { MatSetValues_MPIDense,
    1112:                                         MatGetRow_MPIDense,
    1113:                                         MatRestoreRow_MPIDense,
    1114:                                         MatMult_MPIDense,
    1115:                                 /*  4*/ MatMultAdd_MPIDense,
    1116:                                         MatMultTranspose_MPIDense,
    1117:                                         MatMultTransposeAdd_MPIDense,
    1118:                                         0,
    1119:                                         0,
    1120:                                         0,
    1121:                                 /* 10*/ 0,
    1122:                                         0,
    1123:                                         0,
    1124:                                         0,
    1125:                                         MatTranspose_MPIDense,
    1126:                                 /* 15*/ MatGetInfo_MPIDense,
    1127:                                         MatEqual_MPIDense,
    1128:                                         MatGetDiagonal_MPIDense,
    1129:                                         MatDiagonalScale_MPIDense,
    1130:                                         MatNorm_MPIDense,
    1131:                                 /* 20*/ MatAssemblyBegin_MPIDense,
    1132:                                         MatAssemblyEnd_MPIDense,
    1133:                                         MatSetOption_MPIDense,
    1134:                                         MatZeroEntries_MPIDense,
    1135:                                 /* 24*/ MatZeroRows_MPIDense,
    1136:                                         0,
    1137:                                         0,
    1138:                                         0,
    1139:                                         0,
    1140:                                 /* 29*/ MatSetUp_MPIDense,
    1141:                                         0,
    1142:                                         0,
    1143:                                         0,
    1144:                                         0,
    1145:                                 /* 34*/ MatDuplicate_MPIDense,
    1146:                                         0,
    1147:                                         0,
    1148:                                         0,
    1149:                                         0,
    1150:                                 /* 39*/ MatAXPY_MPIDense,
    1151:                                         MatGetSubMatrices_MPIDense,
    1152:                                         0,
    1153:                                         MatGetValues_MPIDense,
    1154:                                         0,
    1155:                                 /* 44*/ 0,
    1156:                                         MatScale_MPIDense,
    1157:                                         MatShift_Basic,
    1158:                                         0,
    1159:                                         0,
    1160:                                 /* 49*/ MatSetRandom_MPIDense,
    1161:                                         0,
    1162:                                         0,
    1163:                                         0,
    1164:                                         0,
    1165:                                 /* 54*/ 0,
    1166:                                         0,
    1167:                                         0,
    1168:                                         0,
    1169:                                         0,
    1170:                                 /* 59*/ MatGetSubMatrix_MPIDense,
    1171:                                         MatDestroy_MPIDense,
    1172:                                         MatView_MPIDense,
    1173:                                         0,
    1174:                                         0,
    1175:                                 /* 64*/ 0,
    1176:                                         0,
    1177:                                         0,
    1178:                                         0,
    1179:                                         0,
    1180:                                 /* 69*/ 0,
    1181:                                         0,
    1182:                                         0,
    1183:                                         0,
    1184:                                         0,
    1185:                                 /* 74*/ 0,
    1186:                                         0,
    1187:                                         0,
    1188:                                         0,
    1189:                                         0,
    1190:                                 /* 79*/ 0,
    1191:                                         0,
    1192:                                         0,
    1193:                                         0,
    1194:                                 /* 83*/ MatLoad_MPIDense,
    1195:                                         0,
    1196:                                         0,
    1197:                                         0,
    1198:                                         0,
    1199:                                         0,
    1200: #if defined(PETSC_HAVE_ELEMENTAL)
    1201:                                 /* 89*/ MatMatMult_MPIDense_MPIDense,
    1202:                                         MatMatMultSymbolic_MPIDense_MPIDense,
    1203: #else
    1204:                                 /* 89*/ 0,
    1205:                                         0,
    1206: #endif
    1207:                                         MatMatMultNumeric_MPIDense,
    1208:                                         0,
    1209:                                         0,
    1210:                                 /* 94*/ 0,
    1211:                                         0,
    1212:                                         0,
    1213:                                         0,
    1214:                                         0,
    1215:                                 /* 99*/ 0,
    1216:                                         0,
    1217:                                         0,
    1218:                                         MatConjugate_MPIDense,
    1219:                                         0,
    1220:                                 /*104*/ 0,
    1221:                                         MatRealPart_MPIDense,
    1222:                                         MatImaginaryPart_MPIDense,
    1223:                                         0,
    1224:                                         0,
    1225:                                 /*109*/ 0,
    1226:                                         0,
    1227:                                         0,
    1228:                                         0,
    1229:                                         MatMissingDiagonal_MPIDense,
    1230:                                 /*114*/ 0,
    1231:                                         0,
    1232:                                         0,
    1233:                                         0,
    1234:                                         0,
    1235:                                 /*119*/ 0,
    1236:                                         0,
    1237:                                         0,
    1238:                                         0,
    1239:                                         0,
    1240:                                 /*124*/ 0,
    1241:                                         MatGetColumnNorms_MPIDense,
    1242:                                         0,
    1243:                                         0,
    1244:                                         0,
    1245:                                 /*129*/ 0,
    1246:                                         MatTransposeMatMult_MPIDense_MPIDense,
    1247:                                         MatTransposeMatMultSymbolic_MPIDense_MPIDense,
    1248:                                         MatTransposeMatMultNumeric_MPIDense_MPIDense,
    1249:                                         0,
    1250:                                 /*134*/ 0,
    1251:                                         0,
    1252:                                         0,
    1253:                                         0,
    1254:                                         0,
    1255:                                 /*139*/ 0,
    1256:                                         0,
    1257:                                         0
    1258: };

    1262: PetscErrorCode  MatMPIDenseSetPreallocation_MPIDense(Mat mat,PetscScalar *data)
    1263: {
    1264:   Mat_MPIDense   *a;

    1268:   mat->preallocated = PETSC_TRUE;
    1269:   /* Note:  For now, when data is specified above, this assumes the user correctly
    1270:    allocates the local dense storage space.  We should add error checking. */

    1272:   a       = (Mat_MPIDense*)mat->data;
    1273:   PetscLayoutSetUp(mat->rmap);
    1274:   PetscLayoutSetUp(mat->cmap);
    1275:   a->nvec = mat->cmap->n;

    1277:   MatCreate(PETSC_COMM_SELF,&a->A);
    1278:   MatSetSizes(a->A,mat->rmap->n,mat->cmap->N,mat->rmap->n,mat->cmap->N);
    1279:   MatSetType(a->A,MATSEQDENSE);
    1280:   MatSeqDenseSetPreallocation(a->A,data);
    1281:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
    1282:   return(0);
    1283: }

    1285: #if defined(PETSC_HAVE_ELEMENTAL)
    1288: PETSC_INTERN PetscErrorCode MatConvert_MPIDense_Elemental(Mat A, MatType newtype,MatReuse reuse,Mat *newmat)
    1289: {
    1290:   Mat            mat_elemental;
    1292:   PetscScalar    *v;
    1293:   PetscInt       m=A->rmap->n,N=A->cmap->N,rstart=A->rmap->rstart,i,*rows,*cols;
    1294:
    1296:   if (reuse == MAT_REUSE_MATRIX) {
    1297:     mat_elemental = *newmat;
    1298:     MatZeroEntries(*newmat);
    1299:   } else {
    1300:     MatCreate(PetscObjectComm((PetscObject)A), &mat_elemental);
    1301:     MatSetSizes(mat_elemental,PETSC_DECIDE,PETSC_DECIDE,A->rmap->N,A->cmap->N);
    1302:     MatSetType(mat_elemental,MATELEMENTAL);
    1303:     MatSetUp(mat_elemental);
    1304:     MatSetOption(mat_elemental,MAT_ROW_ORIENTED,PETSC_FALSE);
    1305:   }

    1307:   PetscMalloc2(m,&rows,N,&cols);
    1308:   for (i=0; i<N; i++) cols[i] = i;
    1309:   for (i=0; i<m; i++) rows[i] = rstart + i;
    1310:
    1311:   /* PETSc-Elemental interaface uses axpy for setting off-processor entries, only ADD_VALUES is allowed */
    1312:   MatDenseGetArray(A,&v);
    1313:   MatSetValues(mat_elemental,m,rows,N,cols,v,ADD_VALUES);
    1314:   MatAssemblyBegin(mat_elemental, MAT_FINAL_ASSEMBLY);
    1315:   MatAssemblyEnd(mat_elemental, MAT_FINAL_ASSEMBLY);
    1316:   MatDenseRestoreArray(A,&v);
    1317:   PetscFree2(rows,cols);

    1319:   if (reuse == MAT_INPLACE_MATRIX) {
    1320:     MatHeaderReplace(A,&mat_elemental);
    1321:   } else {
    1322:     *newmat = mat_elemental;
    1323:   }
    1324:   return(0);
    1325: }
    1326: #endif

    1330: PETSC_EXTERN PetscErrorCode MatCreate_MPIDense(Mat mat)
    1331: {
    1332:   Mat_MPIDense   *a;

    1336:   PetscNewLog(mat,&a);
    1337:   mat->data = (void*)a;
    1338:   PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));

    1340:   mat->insertmode = NOT_SET_VALUES;
    1341:   MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&a->rank);
    1342:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&a->size);

    1344:   /* build cache for off array entries formed */
    1345:   a->donotstash = PETSC_FALSE;

    1347:   MatStashCreate_Private(PetscObjectComm((PetscObject)mat),1,&mat->stash);

    1349:   /* stuff used for matrix vector multiply */
    1350:   a->lvec        = 0;
    1351:   a->Mvctx       = 0;
    1352:   a->roworiented = PETSC_TRUE;

    1354:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",MatDenseGetArray_MPIDense);
    1355:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",MatDenseRestoreArray_MPIDense);
    1356: #if defined(PETSC_HAVE_ELEMENTAL)
    1357:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpidense_elemental_C",MatConvert_MPIDense_Elemental);
    1358: #endif
    1359:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",MatGetDiagonalBlock_MPIDense);
    1360:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",MatMPIDenseSetPreallocation_MPIDense);
    1361:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",MatMatMult_MPIAIJ_MPIDense);
    1362:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",MatMatMultSymbolic_MPIAIJ_MPIDense);
    1363:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",MatMatMultNumeric_MPIAIJ_MPIDense);

    1365:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMult_mpiaij_mpidense_C",MatTransposeMatMult_MPIAIJ_MPIDense);
    1366:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultSymbolic_mpiaij_mpidense_C",MatTransposeMatMultSymbolic_MPIAIJ_MPIDense);
    1367:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultNumeric_mpiaij_mpidense_C",MatTransposeMatMultNumeric_MPIAIJ_MPIDense);
    1368:   PetscObjectChangeTypeName((PetscObject)mat,MATMPIDENSE);
    1369:   return(0);
    1370: }

    1372: /*MC
    1373:    MATDENSE - MATDENSE = "dense" - A matrix type to be used for dense matrices.

    1375:    This matrix type is identical to MATSEQDENSE when constructed with a single process communicator,
    1376:    and MATMPIDENSE otherwise.

    1378:    Options Database Keys:
    1379: . -mat_type dense - sets the matrix type to "dense" during a call to MatSetFromOptions()

    1381:   Level: beginner

    1384: .seealso: MatCreateMPIDense,MATSEQDENSE,MATMPIDENSE
    1385: M*/

    1389: /*@C
    1390:    MatMPIDenseSetPreallocation - Sets the array used to store the matrix entries

    1392:    Not collective

    1394:    Input Parameters:
    1395: .  B - the matrix
    1396: -  data - optional location of matrix data.  Set data=NULL for PETSc
    1397:    to control all matrix memory allocation.

    1399:    Notes:
    1400:    The dense format is fully compatible with standard Fortran 77
    1401:    storage by columns.

    1403:    The data input variable is intended primarily for Fortran programmers
    1404:    who wish to allocate their own matrix memory space.  Most users should
    1405:    set data=NULL.

    1407:    Level: intermediate

    1409: .keywords: matrix,dense, parallel

    1411: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
    1412: @*/
    1413: PetscErrorCode  MatMPIDenseSetPreallocation(Mat B,PetscScalar *data)
    1414: {

    1418:   PetscTryMethod(B,"MatMPIDenseSetPreallocation_C",(Mat,PetscScalar*),(B,data));
    1419:   return(0);
    1420: }

    1424: /*@C
    1425:    MatCreateDense - Creates a parallel matrix in dense format.

    1427:    Collective on MPI_Comm

    1429:    Input Parameters:
    1430: +  comm - MPI communicator
    1431: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
    1432: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
    1433: .  M - number of global rows (or PETSC_DECIDE to have calculated if m is given)
    1434: .  N - number of global columns (or PETSC_DECIDE to have calculated if n is given)
    1435: -  data - optional location of matrix data.  Set data=NULL (PETSC_NULL_SCALAR for Fortran users) for PETSc
    1436:    to control all matrix memory allocation.

    1438:    Output Parameter:
    1439: .  A - the matrix

    1441:    Notes:
    1442:    The dense format is fully compatible with standard Fortran 77
    1443:    storage by columns.

    1445:    The data input variable is intended primarily for Fortran programmers
    1446:    who wish to allocate their own matrix memory space.  Most users should
    1447:    set data=NULL (PETSC_NULL_SCALAR for Fortran users).

    1449:    The user MUST specify either the local or global matrix dimensions
    1450:    (possibly both).

    1452:    Level: intermediate

    1454: .keywords: matrix,dense, parallel

    1456: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
    1457: @*/
    1458: PetscErrorCode  MatCreateDense(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscScalar *data,Mat *A)
    1459: {
    1461:   PetscMPIInt    size;

    1464:   MatCreate(comm,A);
    1465:   MatSetSizes(*A,m,n,M,N);
    1466:   MPI_Comm_size(comm,&size);
    1467:   if (size > 1) {
    1468:     MatSetType(*A,MATMPIDENSE);
    1469:     MatMPIDenseSetPreallocation(*A,data);
    1470:     if (data) {  /* user provided data array, so no need to assemble */
    1471:       MatSetUpMultiply_MPIDense(*A);
    1472:       (*A)->assembled = PETSC_TRUE;
    1473:     }
    1474:   } else {
    1475:     MatSetType(*A,MATSEQDENSE);
    1476:     MatSeqDenseSetPreallocation(*A,data);
    1477:   }
    1478:   return(0);
    1479: }

    1483: static PetscErrorCode MatDuplicate_MPIDense(Mat A,MatDuplicateOption cpvalues,Mat *newmat)
    1484: {
    1485:   Mat            mat;
    1486:   Mat_MPIDense   *a,*oldmat = (Mat_MPIDense*)A->data;

    1490:   *newmat = 0;
    1491:   MatCreate(PetscObjectComm((PetscObject)A),&mat);
    1492:   MatSetSizes(mat,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    1493:   MatSetType(mat,((PetscObject)A)->type_name);
    1494:   a       = (Mat_MPIDense*)mat->data;
    1495:   PetscMemcpy(mat->ops,A->ops,sizeof(struct _MatOps));

    1497:   mat->factortype   = A->factortype;
    1498:   mat->assembled    = PETSC_TRUE;
    1499:   mat->preallocated = PETSC_TRUE;

    1501:   a->size         = oldmat->size;
    1502:   a->rank         = oldmat->rank;
    1503:   mat->insertmode = NOT_SET_VALUES;
    1504:   a->nvec         = oldmat->nvec;
    1505:   a->donotstash   = oldmat->donotstash;

    1507:   PetscLayoutReference(A->rmap,&mat->rmap);
    1508:   PetscLayoutReference(A->cmap,&mat->cmap);

    1510:   MatSetUpMultiply_MPIDense(mat);
    1511:   MatDuplicate(oldmat->A,cpvalues,&a->A);
    1512:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);

    1514:   *newmat = mat;
    1515:   return(0);
    1516: }

    1520: PetscErrorCode MatLoad_MPIDense_DenseInFile(MPI_Comm comm,PetscInt fd,PetscInt M,PetscInt N,Mat newmat)
    1521: {
    1523:   PetscMPIInt    rank,size;
    1524:   const PetscInt *rowners;
    1525:   PetscInt       i,m,n,nz,j,mMax;
    1526:   PetscScalar    *array,*vals,*vals_ptr;
    1527:   Mat_MPIDense   *a = (Mat_MPIDense*)newmat->data;

    1530:   MPI_Comm_rank(comm,&rank);
    1531:   MPI_Comm_size(comm,&size);

    1533:   /* determine ownership of rows and columns */
    1534:   m = (newmat->rmap->n < 0) ? PETSC_DECIDE : newmat->rmap->n;
    1535:   n = (newmat->cmap->n < 0) ? PETSC_DECIDE : newmat->cmap->n;

    1537:   MatSetSizes(newmat,m,n,M,N);
    1538:   if (!a->A || !((Mat_SeqDense*)(a->A->data))->user_alloc) {
    1539:     MatMPIDenseSetPreallocation(newmat,NULL);
    1540:   }
    1541:   MatDenseGetArray(newmat,&array);
    1542:   MatGetLocalSize(newmat,&m,NULL);
    1543:   MatGetOwnershipRanges(newmat,&rowners);
    1544:   MPI_Reduce(&m,&mMax,1,MPIU_INT,MPI_MAX,0,comm);
    1545:   if (!rank) {
    1546:     PetscMalloc1(mMax*N,&vals);

    1548:     /* read in my part of the matrix numerical values  */
    1549:     PetscBinaryRead(fd,vals,m*N,PETSC_SCALAR);

    1551:     /* insert into matrix-by row (this is why cannot directly read into array */
    1552:     vals_ptr = vals;
    1553:     for (i=0; i<m; i++) {
    1554:       for (j=0; j<N; j++) {
    1555:         array[i + j*m] = *vals_ptr++;
    1556:       }
    1557:     }

    1559:     /* read in other processors and ship out */
    1560:     for (i=1; i<size; i++) {
    1561:       nz   = (rowners[i+1] - rowners[i])*N;
    1562:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
    1563:       MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)(newmat))->tag,comm);
    1564:     }
    1565:   } else {
    1566:     /* receive numeric values */
    1567:     PetscMalloc1(m*N,&vals);

    1569:     /* receive message of values*/
    1570:     MPIULong_Recv(vals,m*N,MPIU_SCALAR,0,((PetscObject)(newmat))->tag,comm);

    1572:     /* insert into matrix-by row (this is why cannot directly read into array */
    1573:     vals_ptr = vals;
    1574:     for (i=0; i<m; i++) {
    1575:       for (j=0; j<N; j++) {
    1576:         array[i + j*m] = *vals_ptr++;
    1577:       }
    1578:     }
    1579:   }
    1580:   MatDenseRestoreArray(newmat,&array);
    1581:   PetscFree(vals);
    1582:   MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
    1583:   MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
    1584:   return(0);
    1585: }

    1589: PetscErrorCode MatLoad_MPIDense(Mat newmat,PetscViewer viewer)
    1590: {
    1591:   Mat_MPIDense   *a;
    1592:   PetscScalar    *vals,*svals;
    1593:   MPI_Comm       comm;
    1594:   MPI_Status     status;
    1595:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag,*rowners,*sndcounts,m,n,maxnz;
    1596:   PetscInt       header[4],*rowlengths = 0,M,N,*cols;
    1597:   PetscInt       *ourlens,*procsnz = 0,jj,*mycols,*smycols;
    1598:   PetscInt       i,nz,j,rstart,rend;
    1599:   int            fd;

    1603:   /* force binary viewer to load .info file if it has not yet done so */
    1604:   PetscViewerSetUp(viewer);
    1605:   PetscObjectGetComm((PetscObject)viewer,&comm);
    1606:   MPI_Comm_size(comm,&size);
    1607:   MPI_Comm_rank(comm,&rank);
    1608:   PetscViewerBinaryGetDescriptor(viewer,&fd);
    1609:   if (!rank) {
    1610:     PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
    1611:     if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
    1612:   }
    1613:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
    1614:   M    = header[1]; N = header[2]; nz = header[3];

    1616:   /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */
    1617:   if (newmat->rmap->N < 0) newmat->rmap->N = M;
    1618:   if (newmat->cmap->N < 0) newmat->cmap->N = N;

    1620:   if (newmat->rmap->N != M) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows:Matrix in file has (%D) and input matrix has (%D)",M,newmat->rmap->N);
    1621:   if (newmat->cmap->N != N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of cols:Matrix in file has (%D) and input matrix has (%D)",N,newmat->cmap->N);

    1623:   /*
    1624:        Handle case where matrix is stored on disk as a dense matrix
    1625:   */
    1626:   if (nz == MATRIX_BINARY_FORMAT_DENSE) {
    1627:     MatLoad_MPIDense_DenseInFile(comm,fd,M,N,newmat);
    1628:     return(0);
    1629:   }

    1631:   /* determine ownership of all rows */
    1632:   if (newmat->rmap->n < 0) {
    1633:     PetscMPIIntCast(M/size + ((M % size) > rank),&m);
    1634:   } else {
    1635:     PetscMPIIntCast(newmat->rmap->n,&m);
    1636:   }
    1637:   if (newmat->cmap->n < 0) {
    1638:     n = PETSC_DECIDE;
    1639:   } else {
    1640:     PetscMPIIntCast(newmat->cmap->n,&n);
    1641:   }

    1643:   PetscMalloc1(size+2,&rowners);
    1644:   MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);
    1645:   rowners[0] = 0;
    1646:   for (i=2; i<=size; i++) {
    1647:     rowners[i] += rowners[i-1];
    1648:   }
    1649:   rstart = rowners[rank];
    1650:   rend   = rowners[rank+1];

    1652:   /* distribute row lengths to all processors */
    1653:   PetscMalloc1(rend-rstart,&ourlens);
    1654:   if (!rank) {
    1655:     PetscMalloc1(M,&rowlengths);
    1656:     PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
    1657:     PetscMalloc1(size,&sndcounts);
    1658:     for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i];
    1659:     MPI_Scatterv(rowlengths,sndcounts,rowners,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
    1660:     PetscFree(sndcounts);
    1661:   } else {
    1662:     MPI_Scatterv(0,0,0,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
    1663:   }

    1665:   if (!rank) {
    1666:     /* calculate the number of nonzeros on each processor */
    1667:     PetscMalloc1(size,&procsnz);
    1668:     PetscMemzero(procsnz,size*sizeof(PetscInt));
    1669:     for (i=0; i<size; i++) {
    1670:       for (j=rowners[i]; j< rowners[i+1]; j++) {
    1671:         procsnz[i] += rowlengths[j];
    1672:       }
    1673:     }
    1674:     PetscFree(rowlengths);

    1676:     /* determine max buffer needed and allocate it */
    1677:     maxnz = 0;
    1678:     for (i=0; i<size; i++) {
    1679:       maxnz = PetscMax(maxnz,procsnz[i]);
    1680:     }
    1681:     PetscMalloc1(maxnz,&cols);

    1683:     /* read in my part of the matrix column indices  */
    1684:     nz   = procsnz[0];
    1685:     PetscMalloc1(nz,&mycols);
    1686:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);

    1688:     /* read in every one elses and ship off */
    1689:     for (i=1; i<size; i++) {
    1690:       nz   = procsnz[i];
    1691:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
    1692:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
    1693:     }
    1694:     PetscFree(cols);
    1695:   } else {
    1696:     /* determine buffer space needed for message */
    1697:     nz = 0;
    1698:     for (i=0; i<m; i++) {
    1699:       nz += ourlens[i];
    1700:     }
    1701:     PetscMalloc1(nz+1,&mycols);

    1703:     /* receive message of column indices*/
    1704:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
    1705:     MPI_Get_count(&status,MPIU_INT,&maxnz);
    1706:     if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
    1707:   }

    1709:   MatSetSizes(newmat,m,n,M,N);
    1710:   a = (Mat_MPIDense*)newmat->data;
    1711:   if (!a->A || !((Mat_SeqDense*)(a->A->data))->user_alloc) {
    1712:     MatMPIDenseSetPreallocation(newmat,NULL);
    1713:   }

    1715:   if (!rank) {
    1716:     PetscMalloc1(maxnz,&vals);

    1718:     /* read in my part of the matrix numerical values  */
    1719:     nz   = procsnz[0];
    1720:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);

    1722:     /* insert into matrix */
    1723:     jj      = rstart;
    1724:     smycols = mycols;
    1725:     svals   = vals;
    1726:     for (i=0; i<m; i++) {
    1727:       MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
    1728:       smycols += ourlens[i];
    1729:       svals   += ourlens[i];
    1730:       jj++;
    1731:     }

    1733:     /* read in other processors and ship out */
    1734:     for (i=1; i<size; i++) {
    1735:       nz   = procsnz[i];
    1736:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
    1737:       MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);
    1738:     }
    1739:     PetscFree(procsnz);
    1740:   } else {
    1741:     /* receive numeric values */
    1742:     PetscMalloc1(nz+1,&vals);

    1744:     /* receive message of values*/
    1745:     MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm,&status);
    1746:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
    1747:     if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

    1749:     /* insert into matrix */
    1750:     jj      = rstart;
    1751:     smycols = mycols;
    1752:     svals   = vals;
    1753:     for (i=0; i<m; i++) {
    1754:       MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
    1755:       smycols += ourlens[i];
    1756:       svals   += ourlens[i];
    1757:       jj++;
    1758:     }
    1759:   }
    1760:   PetscFree(ourlens);
    1761:   PetscFree(vals);
    1762:   PetscFree(mycols);
    1763:   PetscFree(rowners);

    1765:   MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
    1766:   MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
    1767:   return(0);
    1768: }

    1772: PetscErrorCode MatEqual_MPIDense(Mat A,Mat B,PetscBool  *flag)
    1773: {
    1774:   Mat_MPIDense   *matB = (Mat_MPIDense*)B->data,*matA = (Mat_MPIDense*)A->data;
    1775:   Mat            a,b;
    1776:   PetscBool      flg;

    1780:   a    = matA->A;
    1781:   b    = matB->A;
    1782:   MatEqual(a,b,&flg);
    1783:   MPIU_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
    1784:   return(0);
    1785: }

    1789: PetscErrorCode MatDestroy_MatTransMatMult_MPIDense_MPIDense(Mat A)
    1790: {
    1791:   PetscErrorCode        ierr;
    1792:   Mat_MPIDense          *a = (Mat_MPIDense*)A->data;
    1793:   Mat_TransMatMultDense *atb = a->atbdense;

    1796:   PetscFree3(atb->sendbuf,atb->atbarray,atb->recvcounts);
    1797:   (atb->destroy)(A);
    1798:   PetscFree(atb);
    1799:   return(0);
    1800: }

    1804: PetscErrorCode MatTransposeMatMultNumeric_MPIDense_MPIDense(Mat A,Mat B,Mat C)
    1805: {
    1806:   Mat_MPIDense   *a=(Mat_MPIDense*)A->data, *b=(Mat_MPIDense*)B->data, *c=(Mat_MPIDense*)C->data;
    1807:   Mat_SeqDense   *aseq=(Mat_SeqDense*)(a->A)->data, *bseq=(Mat_SeqDense*)(b->A)->data;
    1808:   Mat_TransMatMultDense *atb = c->atbdense;
    1810:   MPI_Comm       comm;
    1811:   PetscMPIInt    rank,size,*recvcounts=atb->recvcounts;
    1812:   PetscScalar    *carray,*atbarray=atb->atbarray,*sendbuf=atb->sendbuf;
    1813:   PetscInt       i,cN=C->cmap->N,cM=C->rmap->N,proc,k,j;
    1814:   PetscScalar    _DOne=1.0,_DZero=0.0;
    1815:   PetscBLASInt   am,an,bn,aN;
    1816:   const PetscInt *ranges;

    1819:   PetscObjectGetComm((PetscObject)A,&comm);
    1820:   MPI_Comm_rank(comm,&rank);
    1821:   MPI_Comm_size(comm,&size);

    1823:   /* compute atbarray = aseq^T * bseq */
    1824:   PetscBLASIntCast(a->A->cmap->n,&an);
    1825:   PetscBLASIntCast(b->A->cmap->n,&bn);
    1826:   PetscBLASIntCast(a->A->rmap->n,&am);
    1827:   PetscBLASIntCast(A->cmap->N,&aN);
    1828:   PetscStackCallBLAS("BLASgemm",BLASgemm_("T","N",&an,&bn,&am,&_DOne,aseq->v,&aseq->lda,bseq->v,&bseq->lda,&_DZero,atbarray,&aN));
    1829:
    1830:   MatGetOwnershipRanges(C,&ranges);
    1831:   for (i=0; i<size; i++) recvcounts[i] = (ranges[i+1] - ranges[i])*cN;
    1832:
    1833:   /* arrange atbarray into sendbuf */
    1834:   k = 0;
    1835:   for (proc=0; proc<size; proc++) {
    1836:     for (j=0; j<cN; j++) {
    1837:       for (i=ranges[proc]; i<ranges[proc+1]; i++) sendbuf[k++] = atbarray[i+j*cM];
    1838:     }
    1839:   }
    1840:   /* sum all atbarray to local values of C */
    1841:   MatDenseGetArray(c->A,&carray);
    1842:   MPI_Reduce_scatter(sendbuf,carray,recvcounts,MPIU_SCALAR,MPIU_SUM,comm);
    1843:   MatDenseRestoreArray(c->A,&carray);
    1844:   return(0);
    1845: }

    1849: PetscErrorCode MatTransposeMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C)
    1850: {
    1851:   PetscErrorCode        ierr;
    1852:   Mat                   Cdense;
    1853:   MPI_Comm              comm;
    1854:   PetscMPIInt           size;
    1855:   PetscInt              cm=A->cmap->n,cM,cN=B->cmap->N;
    1856:   Mat_MPIDense          *c;
    1857:   Mat_TransMatMultDense *atb;

    1860:   PetscObjectGetComm((PetscObject)A,&comm);
    1861:   if (A->rmap->rstart != B->rmap->rstart || A->rmap->rend != B->rmap->rend) {
    1862:     SETERRQ4(comm,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A (%D, %D) != B (%D,%D)",A->rmap->rstart,A->rmap->rend,B->rmap->rstart,B->rmap->rend);
    1863:   }

    1865:   /* create matrix product Cdense */
    1866:   MatCreate(comm,&Cdense);
    1867:   MatSetSizes(Cdense,cm,B->cmap->n,PETSC_DECIDE,PETSC_DECIDE);
    1868:   MatSetType(Cdense,MATMPIDENSE);
    1869:   MatMPIDenseSetPreallocation(Cdense,NULL);
    1870:   MatAssemblyBegin(Cdense,MAT_FINAL_ASSEMBLY);
    1871:   MatAssemblyEnd(Cdense,MAT_FINAL_ASSEMBLY);
    1872:   *C   = Cdense;

    1874:   /* create data structure for reuse Cdense */
    1875:   MPI_Comm_size(comm,&size);
    1876:   PetscNew(&atb);
    1877:   cM = Cdense->rmap->N;
    1878:   PetscMalloc3(cM*cN,&atb->sendbuf,cM*cN,&atb->atbarray,size,&atb->recvcounts);
    1879:
    1880:   c                    = (Mat_MPIDense*)Cdense->data;
    1881:   c->atbdense          = atb;
    1882:   atb->destroy         = Cdense->ops->destroy;
    1883:   Cdense->ops->destroy = MatDestroy_MatTransMatMult_MPIDense_MPIDense;
    1884:   return(0);
    1885: }

    1889: PetscErrorCode MatTransposeMatMult_MPIDense_MPIDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
    1890: {

    1894:   if (scall == MAT_INITIAL_MATRIX) {
    1895:     MatTransposeMatMultSymbolic_MPIDense_MPIDense(A,B,fill,C);
    1896:   }
    1897:   MatTransposeMatMultNumeric_MPIDense_MPIDense(A,B,*C);
    1898:   return(0);
    1899: }

    1903: PetscErrorCode MatDestroy_MatMatMult_MPIDense_MPIDense(Mat A)
    1904: {
    1905:   PetscErrorCode   ierr;
    1906:   Mat_MPIDense     *a = (Mat_MPIDense*)A->data;
    1907:   Mat_MatMultDense *ab = a->abdense;

    1910:   MatDestroy(&ab->Ce);
    1911:   MatDestroy(&ab->Ae);
    1912:   MatDestroy(&ab->Be);

    1914:   (ab->destroy)(A);
    1915:   PetscFree(ab);
    1916:   return(0);
    1917: }

    1919: #if defined(PETSC_HAVE_ELEMENTAL)
    1922: PetscErrorCode MatMatMultNumeric_MPIDense_MPIDense(Mat A,Mat B,Mat C)
    1923: {
    1924:   PetscErrorCode   ierr;
    1925:   Mat_MPIDense     *c=(Mat_MPIDense*)C->data;
    1926:   Mat_MatMultDense *ab=c->abdense;

    1929:   MatConvert_MPIDense_Elemental(A,MATELEMENTAL,MAT_REUSE_MATRIX, &ab->Ae);
    1930:   MatConvert_MPIDense_Elemental(B,MATELEMENTAL,MAT_REUSE_MATRIX, &ab->Be);
    1931:   MatMatMultNumeric(ab->Ae,ab->Be,ab->Ce);
    1932:   MatConvert(ab->Ce,MATMPIDENSE,MAT_REUSE_MATRIX,&C);
    1933:   return(0);
    1934: }

    1938: PetscErrorCode MatMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C)
    1939: {
    1940:   PetscErrorCode   ierr;
    1941:   Mat              Ae,Be,Ce;
    1942:   Mat_MPIDense     *c;
    1943:   Mat_MatMultDense *ab;

    1946:   if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) {
    1947:     SETERRQ4(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A (%D, %D) != B (%D,%D)",A->rmap->rstart,A->rmap->rend,B->rmap->rstart,B->rmap->rend);
    1948:   }

    1950:   /* convert A and B to Elemental matrices Ae and Be */
    1951:   MatConvert(A,MATELEMENTAL,MAT_INITIAL_MATRIX, &Ae);
    1952:   MatConvert(B,MATELEMENTAL,MAT_INITIAL_MATRIX, &Be);

    1954:   /* Ce = Ae*Be */
    1955:   MatMatMultSymbolic(Ae,Be,fill,&Ce);
    1956:   MatMatMultNumeric(Ae,Be,Ce);
    1957:
    1958:   /* convert Ce to C */
    1959:   MatConvert(Ce,MATMPIDENSE,MAT_INITIAL_MATRIX,C);

    1961:   /* create data structure for reuse Cdense */
    1962:   PetscNew(&ab);
    1963:   c                  = (Mat_MPIDense*)(*C)->data;
    1964:   c->abdense         = ab;

    1966:   ab->Ae             = Ae;
    1967:   ab->Be             = Be;
    1968:   ab->Ce             = Ce;
    1969:   ab->destroy        = (*C)->ops->destroy;
    1970:   (*C)->ops->destroy        = MatDestroy_MatMatMult_MPIDense_MPIDense;
    1971:   (*C)->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIDense;
    1972:   return(0);
    1973: }

    1977: PETSC_INTERN PetscErrorCode MatMatMult_MPIDense_MPIDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
    1978: {

    1982:   if (scall == MAT_INITIAL_MATRIX) { /* simbolic product includes numeric product */
    1983:     PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
    1984:     MatMatMultSymbolic_MPIDense_MPIDense(A,B,fill,C);
    1985:     PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
    1986:   } else {
    1987:     PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
    1988:     MatMatMultNumeric_MPIDense_MPIDense(A,B,*C);
    1989:     PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
    1990:   }
    1991:   return(0);
    1992: }
    1993: #endif