Actual source code: mpidense.c

petsc-3.11.3 2019-06-26
Report Typos and Errors

  2: /*
  3:    Basic functions for basic parallel dense matrices.
  4: */


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

 11: /*@

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

 16:     Input Parameter:
 17: .      A - the Seq or MPI dense matrix

 19:     Output Parameter:
 20: .      B - the inner matrix

 22:     Level: intermediate

 24: @*/
 25: PetscErrorCode MatDenseGetLocalMatrix(Mat A,Mat *B)
 26: {
 27:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
 29:   PetscBool      flg;

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

 38: PetscErrorCode MatGetRow_MPIDense(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
 39: {
 40:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
 42:   PetscInt       lrow,rstart = A->rmap->rstart,rend = A->rmap->rend;

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

 51: PetscErrorCode MatRestoreRow_MPIDense(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
 52: {

 56:   if (idx) {PetscFree(*idx);}
 57:   if (v) {PetscFree(*v);}
 58:   return(0);
 59: }

 61: PetscErrorCode  MatGetDiagonalBlock_MPIDense(Mat A,Mat *a)
 62: {
 63:   Mat_MPIDense   *mdn = (Mat_MPIDense*)A->data;
 65:   PetscInt       m = A->rmap->n,rstart = A->rmap->rstart;
 66:   PetscScalar    *array;
 67:   MPI_Comm       comm;
 68:   Mat            B;

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

 73:   PetscObjectQuery((PetscObject)A,"DiagonalBlock",(PetscObject*)&B);
 74:   if (!B) {
 75:     PetscObjectGetComm((PetscObject)(mdn->A),&comm);
 76:     MatCreate(comm,&B);
 77:     MatSetSizes(B,m,m,m,m);
 78:     MatSetType(B,((PetscObject)mdn->A)->type_name);
 79:     MatDenseGetArray(mdn->A,&array);
 80:     MatSeqDenseSetPreallocation(B,array+m*rstart);
 81:     MatDenseRestoreArray(mdn->A,&array);
 82:     MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
 83:     MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
 84:     PetscObjectCompose((PetscObject)A,"DiagonalBlock",(PetscObject)B);
 85:     *a   = B;
 86:     MatDestroy(&B);
 87:   } else *a = B;
 88:   return(0);
 89: }

 91: PetscErrorCode MatSetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
 92: {
 93:   Mat_MPIDense   *A = (Mat_MPIDense*)mat->data;
 95:   PetscInt       i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row;
 96:   PetscBool      roworiented = A->roworiented;

 99:   for (i=0; i<m; i++) {
100:     if (idxm[i] < 0) continue;
101:     if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
102:     if (idxm[i] >= rstart && idxm[i] < rend) {
103:       row = idxm[i] - rstart;
104:       if (roworiented) {
105:         MatSetValues(A->A,1,&row,n,idxn,v+i*n,addv);
106:       } else {
107:         for (j=0; j<n; j++) {
108:           if (idxn[j] < 0) continue;
109:           if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
110:           MatSetValues(A->A,1,&row,1,&idxn[j],v+i+j*m,addv);
111:         }
112:       }
113:     } else if (!A->donotstash) {
114:       mat->assembled = PETSC_FALSE;
115:       if (roworiented) {
116:         MatStashValuesRow_Private(&mat->stash,idxm[i],n,idxn,v+i*n,PETSC_FALSE);
117:       } else {
118:         MatStashValuesCol_Private(&mat->stash,idxm[i],n,idxn,v+i,m,PETSC_FALSE);
119:       }
120:     }
121:   }
122:   return(0);
123: }

125: PetscErrorCode MatGetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
126: {
127:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;
129:   PetscInt       i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row;

132:   for (i=0; i<m; i++) {
133:     if (idxm[i] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */
134:     if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
135:     if (idxm[i] >= rstart && idxm[i] < rend) {
136:       row = idxm[i] - rstart;
137:       for (j=0; j<n; j++) {
138:         if (idxn[j] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */
139:         if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
140:         MatGetValues(mdn->A,1,&row,1,&idxn[j],v+i*n+j);
141:       }
142:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
143:   }
144:   return(0);
145: }

147: static PetscErrorCode MatDenseGetArray_MPIDense(Mat A,PetscScalar *array[])
148: {
149:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

153:   MatDenseGetArray(a->A,array);
154:   return(0);
155: }

157: static PetscErrorCode MatDenseGetArrayRead_MPIDense(Mat A,const PetscScalar *array[])
158: {
159:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

163:   MatDenseGetArrayRead(a->A,array);
164:   return(0);
165: }

167: static PetscErrorCode MatDensePlaceArray_MPIDense(Mat A,const PetscScalar array[])
168: {
169:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

173:   MatDensePlaceArray(a->A,array);
174:   return(0);
175: }

177: static PetscErrorCode MatDenseResetArray_MPIDense(Mat A)
178: {
179:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

183:   MatDenseResetArray(a->A);
184:   return(0);
185: }

187: static PetscErrorCode MatCreateSubMatrix_MPIDense(Mat A,IS isrow,IS iscol,MatReuse scall,Mat *B)
188: {
189:   Mat_MPIDense   *mat  = (Mat_MPIDense*)A->data,*newmatd;
190:   Mat_SeqDense   *lmat = (Mat_SeqDense*)mat->A->data;
192:   PetscInt       i,j,rstart,rend,nrows,ncols,Ncols,nlrows,nlcols;
193:   const PetscInt *irow,*icol;
194:   PetscScalar    *av,*bv,*v = lmat->v;
195:   Mat            newmat;
196:   IS             iscol_local;
197:   MPI_Comm       comm_is,comm_mat;

200:   PetscObjectGetComm((PetscObject)A,&comm_mat);
201:   PetscObjectGetComm((PetscObject)iscol,&comm_is);
202:   if (comm_mat != comm_is) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NOTSAMECOMM,"IS communicator must match matrix communicator");

204:   ISAllGather(iscol,&iscol_local);
205:   ISGetIndices(isrow,&irow);
206:   ISGetIndices(iscol_local,&icol);
207:   ISGetLocalSize(isrow,&nrows);
208:   ISGetLocalSize(iscol,&ncols);
209:   ISGetSize(iscol,&Ncols); /* global number of columns, size of iscol_local */

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

215:   MatGetLocalSize(A,&nlrows,&nlcols);
216:   MatGetOwnershipRange(A,&rstart,&rend);

218:   /* Check submatrix call */
219:   if (scall == MAT_REUSE_MATRIX) {
220:     /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); */
221:     /* Really need to test rows and column sizes! */
222:     newmat = *B;
223:   } else {
224:     /* Create and fill new matrix */
225:     MatCreate(PetscObjectComm((PetscObject)A),&newmat);
226:     MatSetSizes(newmat,nrows,ncols,PETSC_DECIDE,Ncols);
227:     MatSetType(newmat,((PetscObject)A)->type_name);
228:     MatMPIDenseSetPreallocation(newmat,NULL);
229:   }

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

235:   for (i=0; i<Ncols; i++) {
236:     av = v + ((Mat_SeqDense*)mat->A->data)->lda*icol[i];
237:     for (j=0; j<nrows; j++) {
238:       *bv++ = av[irow[j] - rstart];
239:     }
240:   }

242:   /* Assemble the matrices so that the correct flags are set */
243:   MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
244:   MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);

246:   /* Free work space */
247:   ISRestoreIndices(isrow,&irow);
248:   ISRestoreIndices(iscol_local,&icol);
249:   ISDestroy(&iscol_local);
250:   *B   = newmat;
251:   return(0);
252: }

254: PetscErrorCode MatDenseRestoreArray_MPIDense(Mat A,PetscScalar *array[])
255: {
256:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

260:   MatDenseRestoreArray(a->A,array);
261:   return(0);
262: }

264: PetscErrorCode MatDenseRestoreArrayRead_MPIDense(Mat A,const PetscScalar *array[])
265: {
266:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

270:   MatDenseRestoreArrayRead(a->A,array);
271:   return(0);
272: }

274: PetscErrorCode MatAssemblyBegin_MPIDense(Mat mat,MatAssemblyType mode)
275: {
276:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;
277:   MPI_Comm       comm;
279:   PetscInt       nstash,reallocs;
280:   InsertMode     addv;

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

289:   MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
290:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
291:   PetscInfo2(mdn->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
292:   return(0);
293: }

295: PetscErrorCode MatAssemblyEnd_MPIDense(Mat mat,MatAssemblyType mode)
296: {
297:   Mat_MPIDense   *mdn=(Mat_MPIDense*)mat->data;
299:   PetscInt       i,*row,*col,flg,j,rstart,ncols;
300:   PetscMPIInt    n;
301:   PetscScalar    *val;

304:   /*  wait on receives */
305:   while (1) {
306:     MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
307:     if (!flg) break;

309:     for (i=0; i<n;) {
310:       /* Now identify the consecutive vals belonging to the same row */
311:       for (j=i,rstart=row[j]; j<n; j++) {
312:         if (row[j] != rstart) break;
313:       }
314:       if (j < n) ncols = j-i;
315:       else       ncols = n-i;
316:       /* Now assemble all these values with a single function call */
317:       MatSetValues_MPIDense(mat,1,row+i,ncols,col+i,val+i,mat->insertmode);
318:       i    = j;
319:     }
320:   }
321:   MatStashScatterEnd_Private(&mat->stash);

323:   MatAssemblyBegin(mdn->A,mode);
324:   MatAssemblyEnd(mdn->A,mode);

326:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
327:     MatSetUpMultiply_MPIDense(mat);
328:   }
329:   return(0);
330: }

332: PetscErrorCode MatZeroEntries_MPIDense(Mat A)
333: {
335:   Mat_MPIDense   *l = (Mat_MPIDense*)A->data;

338:   MatZeroEntries(l->A);
339:   return(0);
340: }

342: /* the code does not do the diagonal entries correctly unless the
343:    matrix is square and the column and row owerships are identical.
344:    This is a BUG. The only way to fix it seems to be to access
345:    mdn->A and mdn->B directly and not through the MatZeroRows()
346:    routine.
347: */
348: PetscErrorCode MatZeroRows_MPIDense(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
349: {
350:   Mat_MPIDense      *l = (Mat_MPIDense*)A->data;
351:   PetscErrorCode    ierr;
352:   PetscInt          i,*owners = A->rmap->range;
353:   PetscInt          *sizes,j,idx,nsends;
354:   PetscInt          nmax,*svalues,*starts,*owner,nrecvs;
355:   PetscInt          *rvalues,tag = ((PetscObject)A)->tag,count,base,slen,*source;
356:   PetscInt          *lens,*lrows,*values;
357:   PetscMPIInt       n,imdex,rank = l->rank,size = l->size;
358:   MPI_Comm          comm;
359:   MPI_Request       *send_waits,*recv_waits;
360:   MPI_Status        recv_status,*send_status;
361:   PetscBool         found;
362:   const PetscScalar *xx;
363:   PetscScalar       *bb;

366:   PetscObjectGetComm((PetscObject)A,&comm);
367:   if (A->rmap->N != A->cmap->N) SETERRQ(comm,PETSC_ERR_SUP,"Only handles square matrices");
368:   if (A->rmap->n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only handles matrices with identical column and row ownership");
369:   /*  first count number of contributors to each processor */
370:   PetscCalloc1(2*size,&sizes);
371:   PetscMalloc1(N+1,&owner);  /* see note*/
372:   for (i=0; i<N; i++) {
373:     idx   = rows[i];
374:     found = PETSC_FALSE;
375:     for (j=0; j<size; j++) {
376:       if (idx >= owners[j] && idx < owners[j+1]) {
377:         sizes[2*j]++; sizes[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break;
378:       }
379:     }
380:     if (!found) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
381:   }
382:   nsends = 0;
383:   for (i=0; i<size; i++) nsends += sizes[2*i+1];

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

388:   /* post receives:   */
389:   PetscMalloc1((nrecvs+1)*(nmax+1),&rvalues);
390:   PetscMalloc1(nrecvs+1,&recv_waits);
391:   for (i=0; i<nrecvs; i++) {
392:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
393:   }

395:   /* do sends:
396:       1) starts[i] gives the starting index in svalues for stuff going to
397:          the ith processor
398:   */
399:   PetscMalloc1(N+1,&svalues);
400:   PetscMalloc1(nsends+1,&send_waits);
401:   PetscMalloc1(size+1,&starts);

403:   starts[0] = 0;
404:   for (i=1; i<size; i++) starts[i] = starts[i-1] + sizes[2*i-2];
405:   for (i=0; i<N; i++) svalues[starts[owner[i]]++] = rows[i];

407:   starts[0] = 0;
408:   for (i=1; i<size+1; i++) starts[i] = starts[i-1] + sizes[2*i-2];
409:   count = 0;
410:   for (i=0; i<size; i++) {
411:     if (sizes[2*i+1]) {
412:       MPI_Isend(svalues+starts[i],sizes[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
413:     }
414:   }
415:   PetscFree(starts);

417:   base = owners[rank];

419:   /*  wait on receives */
420:   PetscMalloc2(nrecvs,&lens,nrecvs,&source);
421:   count = nrecvs;
422:   slen  = 0;
423:   while (count) {
424:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
425:     /* unpack receives into our local space */
426:     MPI_Get_count(&recv_status,MPIU_INT,&n);

428:     source[imdex] = recv_status.MPI_SOURCE;
429:     lens[imdex]   = n;
430:     slen += n;
431:     count--;
432:   }
433:   PetscFree(recv_waits);

435:   /* move the data into the send scatter */
436:   PetscMalloc1(slen+1,&lrows);
437:   count = 0;
438:   for (i=0; i<nrecvs; i++) {
439:     values = rvalues + i*nmax;
440:     for (j=0; j<lens[i]; j++) {
441:       lrows[count++] = values[j] - base;
442:     }
443:   }
444:   PetscFree(rvalues);
445:   PetscFree2(lens,source);
446:   PetscFree(owner);
447:   PetscFree(sizes);

449:   /* fix right hand side if needed */
450:   if (x && b) {
451:     VecGetArrayRead(x,&xx);
452:     VecGetArray(b,&bb);
453:     for (i=0; i<slen; i++) {
454:       bb[lrows[i]] = diag*xx[lrows[i]];
455:     }
456:     VecRestoreArrayRead(x,&xx);
457:     VecRestoreArray(b,&bb);
458:   }

460:   /* actually zap the local rows */
461:   MatZeroRows(l->A,slen,lrows,0.0,0,0);
462:   if (diag != 0.0) {
463:     Mat_SeqDense *ll = (Mat_SeqDense*)l->A->data;
464:     PetscInt     m   = ll->lda, i;

466:     for (i=0; i<slen; i++) {
467:       ll->v[lrows[i] + m*(A->cmap->rstart + lrows[i])] = diag;
468:     }
469:   }
470:   PetscFree(lrows);

472:   /* wait on sends */
473:   if (nsends) {
474:     PetscMalloc1(nsends,&send_status);
475:     MPI_Waitall(nsends,send_waits,send_status);
476:     PetscFree(send_status);
477:   }
478:   PetscFree(send_waits);
479:   PetscFree(svalues);
480:   return(0);
481: }

483: PETSC_INTERN PetscErrorCode MatMult_SeqDense(Mat,Vec,Vec);
484: PETSC_INTERN PetscErrorCode MatMultAdd_SeqDense(Mat,Vec,Vec,Vec);
485: PETSC_INTERN PetscErrorCode MatMultTranspose_SeqDense(Mat,Vec,Vec);
486: PETSC_INTERN PetscErrorCode MatMultTransposeAdd_SeqDense(Mat,Vec,Vec,Vec);

488: PetscErrorCode MatMult_MPIDense(Mat mat,Vec xx,Vec yy)
489: {
490:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;

494:   VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
495:   VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
496:   MatMult_SeqDense(mdn->A,mdn->lvec,yy);
497:   return(0);
498: }

500: PetscErrorCode MatMultAdd_MPIDense(Mat mat,Vec xx,Vec yy,Vec zz)
501: {
502:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;

506:   VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
507:   VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
508:   MatMultAdd_SeqDense(mdn->A,mdn->lvec,yy,zz);
509:   return(0);
510: }

512: PetscErrorCode MatMultTranspose_MPIDense(Mat A,Vec xx,Vec yy)
513: {
514:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;
516:   PetscScalar    zero = 0.0;

519:   VecSet(yy,zero);
520:   MatMultTranspose_SeqDense(a->A,xx,a->lvec);
521:   VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
522:   VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
523:   return(0);
524: }

526: PetscErrorCode MatMultTransposeAdd_MPIDense(Mat A,Vec xx,Vec yy,Vec zz)
527: {
528:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

532:   VecCopy(yy,zz);
533:   MatMultTranspose_SeqDense(a->A,xx,a->lvec);
534:   VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
535:   VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
536:   return(0);
537: }

539: PetscErrorCode MatGetDiagonal_MPIDense(Mat A,Vec v)
540: {
541:   Mat_MPIDense   *a    = (Mat_MPIDense*)A->data;
542:   Mat_SeqDense   *aloc = (Mat_SeqDense*)a->A->data;
544:   PetscInt       len,i,n,m = A->rmap->n,radd;
545:   PetscScalar    *x,zero = 0.0;

548:   VecSet(v,zero);
549:   VecGetArray(v,&x);
550:   VecGetSize(v,&n);
551:   if (n != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming mat and vec");
552:   len  = PetscMin(a->A->rmap->n,a->A->cmap->n);
553:   radd = A->rmap->rstart*m;
554:   for (i=0; i<len; i++) {
555:     x[i] = aloc->v[radd + i*m + i];
556:   }
557:   VecRestoreArray(v,&x);
558:   return(0);
559: }

561: PetscErrorCode MatDestroy_MPIDense(Mat mat)
562: {
563:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;

567: #if defined(PETSC_USE_LOG)
568:   PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N);
569: #endif
570:   MatStashDestroy_Private(&mat->stash);
571:   MatDestroy(&mdn->A);
572:   VecDestroy(&mdn->lvec);
573:   VecScatterDestroy(&mdn->Mvctx);

575:   PetscFree(mat->data);
576:   PetscObjectChangeTypeName((PetscObject)mat,0);

578:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",NULL);
579:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",NULL);
580:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArrayRead_C",NULL);
581:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArrayRead_C",NULL);
582:   PetscObjectComposeFunction((PetscObject)mat,"MatDensePlaceArray_C",NULL);
583:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseResetArray_C",NULL);
584: #if defined(PETSC_HAVE_ELEMENTAL)
585:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpidense_elemental_C",NULL);
586: #endif
587:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",NULL);
588:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",NULL);
589:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",NULL);
590:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",NULL);
591:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMult_mpiaij_mpidense_C",NULL);
592:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultSymbolic_mpiaij_mpidense_C",NULL);
593:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultNumeric_mpiaij_mpidense_C",NULL);
594:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetColumn_C",NULL);
595:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreColumn_C",NULL);
596:   return(0);
597: }

599: static PetscErrorCode MatView_MPIDense_Binary(Mat mat,PetscViewer viewer)
600: {
601:   Mat_MPIDense      *mdn = (Mat_MPIDense*)mat->data;
602:   PetscErrorCode    ierr;
603:   PetscViewerFormat format;
604:   int               fd;
605:   PetscInt          header[4],mmax,N = mat->cmap->N,i,j,m,k;
606:   PetscMPIInt       rank,tag  = ((PetscObject)viewer)->tag,size;
607:   PetscScalar       *work,*v,*vv;
608:   Mat_SeqDense      *a = (Mat_SeqDense*)mdn->A->data;

611:   if (mdn->size == 1) {
612:     MatView(mdn->A,viewer);
613:   } else {
614:     PetscViewerBinaryGetDescriptor(viewer,&fd);
615:     MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
616:     MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);

618:     PetscViewerGetFormat(viewer,&format);
619:     if (format == PETSC_VIEWER_NATIVE) {

621:       if (!rank) {
622:         /* store the matrix as a dense matrix */
623:         header[0] = MAT_FILE_CLASSID;
624:         header[1] = mat->rmap->N;
625:         header[2] = N;
626:         header[3] = MATRIX_BINARY_FORMAT_DENSE;
627:         PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);

629:         /* get largest work array needed for transposing array */
630:         mmax = mat->rmap->n;
631:         for (i=1; i<size; i++) {
632:           mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]);
633:         }
634:         PetscMalloc1(mmax*N,&work);

636:         /* write out local array, by rows */
637:         m = mat->rmap->n;
638:         v = a->v;
639:         for (j=0; j<N; j++) {
640:           for (i=0; i<m; i++) {
641:             work[j + i*N] = *v++;
642:           }
643:         }
644:         PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
645:         /* get largest work array to receive messages from other processes, excludes process zero */
646:         mmax = 0;
647:         for (i=1; i<size; i++) {
648:           mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]);
649:         }
650:         PetscMalloc1(mmax*N,&vv);
651:         for (k = 1; k < size; k++) {
652:           v    = vv;
653:           m    = mat->rmap->range[k+1] - mat->rmap->range[k];
654:           MPIULong_Recv(v,m*N,MPIU_SCALAR,k,tag,PetscObjectComm((PetscObject)mat));

656:           for (j = 0; j < N; j++) {
657:             for (i = 0; i < m; i++) {
658:               work[j + i*N] = *v++;
659:             }
660:           }
661:           PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
662:         }
663:         PetscFree(work);
664:         PetscFree(vv);
665:       } else {
666:         MPIULong_Send(a->v,mat->rmap->n*mat->cmap->N,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));
667:       }
668:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"To store a parallel dense matrix you must first call PetscViewerPushFormat(viewer,PETSC_VIEWER_NATIVE)");
669:   }
670:   return(0);
671: }

673: extern PetscErrorCode MatView_SeqDense(Mat,PetscViewer);
674:  #include <petscdraw.h>
675: static PetscErrorCode MatView_MPIDense_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
676: {
677:   Mat_MPIDense      *mdn = (Mat_MPIDense*)mat->data;
678:   PetscErrorCode    ierr;
679:   PetscMPIInt       rank = mdn->rank;
680:   PetscViewerType   vtype;
681:   PetscBool         iascii,isdraw;
682:   PetscViewer       sviewer;
683:   PetscViewerFormat format;

686:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
687:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
688:   if (iascii) {
689:     PetscViewerGetType(viewer,&vtype);
690:     PetscViewerGetFormat(viewer,&format);
691:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
692:       MatInfo info;
693:       MatGetInfo(mat,MAT_LOCAL,&info);
694:       PetscViewerASCIIPushSynchronized(viewer);
695:       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);
696:       PetscViewerFlush(viewer);
697:       PetscViewerASCIIPopSynchronized(viewer);
698:       VecScatterView(mdn->Mvctx,viewer);
699:       return(0);
700:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
701:       return(0);
702:     }
703:   } else if (isdraw) {
704:     PetscDraw draw;
705:     PetscBool isnull;

707:     PetscViewerDrawGetDraw(viewer,0,&draw);
708:     PetscDrawIsNull(draw,&isnull);
709:     if (isnull) return(0);
710:   }

712:   {
713:     /* assemble the entire matrix onto first processor. */
714:     Mat         A;
715:     PetscInt    M = mat->rmap->N,N = mat->cmap->N,m,row,i,nz;
716:     PetscInt    *cols;
717:     PetscScalar *vals;

719:     MatCreate(PetscObjectComm((PetscObject)mat),&A);
720:     if (!rank) {
721:       MatSetSizes(A,M,N,M,N);
722:     } else {
723:       MatSetSizes(A,0,0,M,N);
724:     }
725:     /* Since this is a temporary matrix, MATMPIDENSE instead of ((PetscObject)A)->type_name here is probably acceptable. */
726:     MatSetType(A,MATMPIDENSE);
727:     MatMPIDenseSetPreallocation(A,NULL);
728:     PetscLogObjectParent((PetscObject)mat,(PetscObject)A);

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

734:     row = mat->rmap->rstart;
735:     m   = mdn->A->rmap->n;
736:     for (i=0; i<m; i++) {
737:       MatGetRow_MPIDense(mat,row,&nz,&cols,&vals);
738:       MatSetValues_MPIDense(A,1,&row,nz,cols,vals,INSERT_VALUES);
739:       MatRestoreRow_MPIDense(mat,row,&nz,&cols,&vals);
740:       row++;
741:     }

743:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
744:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
745:     PetscViewerGetSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
746:     if (!rank) {
747:       PetscObjectSetName((PetscObject)((Mat_MPIDense*)(A->data))->A,((PetscObject)mat)->name);
748:       MatView_SeqDense(((Mat_MPIDense*)(A->data))->A,sviewer);
749:     }
750:     PetscViewerRestoreSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
751:     PetscViewerFlush(viewer);
752:     MatDestroy(&A);
753:   }
754:   return(0);
755: }

757: PetscErrorCode MatView_MPIDense(Mat mat,PetscViewer viewer)
758: {
760:   PetscBool      iascii,isbinary,isdraw,issocket;

763:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
764:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
765:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
766:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);

768:   if (iascii || issocket || isdraw) {
769:     MatView_MPIDense_ASCIIorDraworSocket(mat,viewer);
770:   } else if (isbinary) {
771:     MatView_MPIDense_Binary(mat,viewer);
772:   }
773:   return(0);
774: }

776: PetscErrorCode MatGetInfo_MPIDense(Mat A,MatInfoType flag,MatInfo *info)
777: {
778:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
779:   Mat            mdn  = mat->A;
781:   PetscReal      isend[5],irecv[5];

784:   info->block_size = 1.0;

786:   MatGetInfo(mdn,MAT_LOCAL,info);

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

799:     info->nz_used      = irecv[0];
800:     info->nz_allocated = irecv[1];
801:     info->nz_unneeded  = irecv[2];
802:     info->memory       = irecv[3];
803:     info->mallocs      = irecv[4];
804:   } else if (flag == MAT_GLOBAL_SUM) {
805:     MPIU_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));

807:     info->nz_used      = irecv[0];
808:     info->nz_allocated = irecv[1];
809:     info->nz_unneeded  = irecv[2];
810:     info->memory       = irecv[3];
811:     info->mallocs      = irecv[4];
812:   }
813:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
814:   info->fill_ratio_needed = 0;
815:   info->factor_mallocs    = 0;
816:   return(0);
817: }

819: PetscErrorCode MatSetOption_MPIDense(Mat A,MatOption op,PetscBool flg)
820: {
821:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

825:   switch (op) {
826:   case MAT_NEW_NONZERO_LOCATIONS:
827:   case MAT_NEW_NONZERO_LOCATION_ERR:
828:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
829:     MatCheckPreallocated(A,1);
830:     MatSetOption(a->A,op,flg);
831:     break;
832:   case MAT_ROW_ORIENTED:
833:     MatCheckPreallocated(A,1);
834:     a->roworiented = flg;
835:     MatSetOption(a->A,op,flg);
836:     break;
837:   case MAT_NEW_DIAGONALS:
838:   case MAT_KEEP_NONZERO_PATTERN:
839:   case MAT_USE_HASH_TABLE:
840:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
841:     break;
842:   case MAT_IGNORE_OFF_PROC_ENTRIES:
843:     a->donotstash = flg;
844:     break;
845:   case MAT_SYMMETRIC:
846:   case MAT_STRUCTURALLY_SYMMETRIC:
847:   case MAT_HERMITIAN:
848:   case MAT_SYMMETRY_ETERNAL:
849:   case MAT_IGNORE_LOWER_TRIANGULAR:
850:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
851:     break;
852:   default:
853:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %s",MatOptions[op]);
854:   }
855:   return(0);
856: }


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

869:   MatGetLocalSize(A,&s2,&s3);
870:   if (ll) {
871:     VecGetLocalSize(ll,&s2a);
872:     if (s2a != s2) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector non-conforming local size, %d != %d.", s2a, s2);
873:     VecGetArrayRead(ll,&l);
874:     for (i=0; i<m; i++) {
875:       x = l[i];
876:       v = mat->v + i;
877:       for (j=0; j<n; j++) { (*v) *= x; v+= m;}
878:     }
879:     VecRestoreArrayRead(ll,&l);
880:     PetscLogFlops(n*m);
881:   }
882:   if (rr) {
883:     VecGetLocalSize(rr,&s3a);
884:     if (s3a != s3) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vec non-conforming local size, %d != %d.", s3a, s3);
885:     VecScatterBegin(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
886:     VecScatterEnd(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
887:     VecGetArrayRead(mdn->lvec,&r);
888:     for (i=0; i<n; i++) {
889:       x = r[i];
890:       v = mat->v + i*m;
891:       for (j=0; j<m; j++) (*v++) *= x;
892:     }
893:     VecRestoreArrayRead(mdn->lvec,&r);
894:     PetscLogFlops(n*m);
895:   }
896:   return(0);
897: }

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: }

946: PetscErrorCode MatTranspose_MPIDense(Mat A,MatReuse reuse,Mat *matout)
947: {
948:   Mat_MPIDense   *a    = (Mat_MPIDense*)A->data;
949:   Mat_SeqDense   *Aloc = (Mat_SeqDense*)a->A->data;
950:   Mat            B;
951:   PetscInt       M = A->rmap->N,N = A->cmap->N,m,n,*rwork,rstart = A->rmap->rstart;
953:   PetscInt       j,i;
954:   PetscScalar    *v;

957:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
958:     MatCreate(PetscObjectComm((PetscObject)A),&B);
959:     MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
960:     MatSetType(B,((PetscObject)A)->type_name);
961:     MatMPIDenseSetPreallocation(B,NULL);
962:   } else {
963:     B = *matout;
964:   }

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


985: static PetscErrorCode MatDuplicate_MPIDense(Mat,MatDuplicateOption,Mat*);
986: extern PetscErrorCode MatScale_MPIDense(Mat,PetscScalar);

988: PetscErrorCode MatSetUp_MPIDense(Mat A)
989: {

993:    MatMPIDenseSetPreallocation(A,0);
994:   return(0);
995: }

997: PetscErrorCode MatAXPY_MPIDense(Mat Y,PetscScalar alpha,Mat X,MatStructure str)
998: {
1000:   Mat_MPIDense   *A = (Mat_MPIDense*)Y->data, *B = (Mat_MPIDense*)X->data;

1003:   MatAXPY(A->A,alpha,B->A,str);
1004:   PetscObjectStateIncrease((PetscObject)Y);
1005:   return(0);
1006: }

1008: PetscErrorCode  MatConjugate_MPIDense(Mat mat)
1009: {
1010:   Mat_MPIDense   *a = (Mat_MPIDense*)mat->data;

1014:   MatConjugate(a->A);
1015:   return(0);
1016: }

1018: PetscErrorCode MatRealPart_MPIDense(Mat A)
1019: {
1020:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

1024:   MatRealPart(a->A);
1025:   return(0);
1026: }

1028: PetscErrorCode MatImaginaryPart_MPIDense(Mat A)
1029: {
1030:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

1034:   MatImaginaryPart(a->A);
1035:   return(0);
1036: }

1038: extern PetscErrorCode MatGetColumnNorms_SeqDense(Mat,NormType,PetscReal*);
1039: PetscErrorCode MatGetColumnNorms_MPIDense(Mat A,NormType type,PetscReal *norms)
1040: {
1042:   PetscInt       i,n;
1043:   Mat_MPIDense   *a = (Mat_MPIDense*) A->data;
1044:   PetscReal      *work;

1047:   MatGetSize(A,NULL,&n);
1048:   PetscMalloc1(n,&work);
1049:   MatGetColumnNorms_SeqDense(a->A,type,work);
1050:   if (type == NORM_2) {
1051:     for (i=0; i<n; i++) work[i] *= work[i];
1052:   }
1053:   if (type == NORM_INFINITY) {
1054:     MPIU_Allreduce(work,norms,n,MPIU_REAL,MPIU_MAX,A->hdr.comm);
1055:   } else {
1056:     MPIU_Allreduce(work,norms,n,MPIU_REAL,MPIU_SUM,A->hdr.comm);
1057:   }
1058:   PetscFree(work);
1059:   if (type == NORM_2) {
1060:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
1061:   }
1062:   return(0);
1063: }

1065: static PetscErrorCode  MatSetRandom_MPIDense(Mat x,PetscRandom rctx)
1066: {
1067:   Mat_MPIDense   *d = (Mat_MPIDense*)x->data;
1069:   PetscScalar    *a;
1070:   PetscInt       m,n,i;

1073:   MatGetSize(d->A,&m,&n);
1074:   MatDenseGetArray(d->A,&a);
1075:   for (i=0; i<m*n; i++) {
1076:     PetscRandomGetValue(rctx,a+i);
1077:   }
1078:   MatDenseRestoreArray(d->A,&a);
1079:   return(0);
1080: }

1082: extern PetscErrorCode MatMatMultNumeric_MPIDense(Mat A,Mat,Mat);

1084: static PetscErrorCode MatMissingDiagonal_MPIDense(Mat A,PetscBool  *missing,PetscInt *d)
1085: {
1087:   *missing = PETSC_FALSE;
1088:   return(0);
1089: }

1091: static PetscErrorCode MatMatTransposeMult_MPIDense_MPIDense(Mat,Mat,MatReuse,PetscReal,Mat*);
1092: static PetscErrorCode MatMatTransposeMultSymbolic_MPIDense_MPIDense(Mat,Mat,PetscReal,Mat*);
1093: static PetscErrorCode MatMatTransposeMultNumeric_MPIDense_MPIDense(Mat,Mat,Mat);

1095: /* -------------------------------------------------------------------*/
1096: static struct _MatOps MatOps_Values = { MatSetValues_MPIDense,
1097:                                         MatGetRow_MPIDense,
1098:                                         MatRestoreRow_MPIDense,
1099:                                         MatMult_MPIDense,
1100:                                 /*  4*/ MatMultAdd_MPIDense,
1101:                                         MatMultTranspose_MPIDense,
1102:                                         MatMultTransposeAdd_MPIDense,
1103:                                         0,
1104:                                         0,
1105:                                         0,
1106:                                 /* 10*/ 0,
1107:                                         0,
1108:                                         0,
1109:                                         0,
1110:                                         MatTranspose_MPIDense,
1111:                                 /* 15*/ MatGetInfo_MPIDense,
1112:                                         MatEqual_MPIDense,
1113:                                         MatGetDiagonal_MPIDense,
1114:                                         MatDiagonalScale_MPIDense,
1115:                                         MatNorm_MPIDense,
1116:                                 /* 20*/ MatAssemblyBegin_MPIDense,
1117:                                         MatAssemblyEnd_MPIDense,
1118:                                         MatSetOption_MPIDense,
1119:                                         MatZeroEntries_MPIDense,
1120:                                 /* 24*/ MatZeroRows_MPIDense,
1121:                                         0,
1122:                                         0,
1123:                                         0,
1124:                                         0,
1125:                                 /* 29*/ MatSetUp_MPIDense,
1126:                                         0,
1127:                                         0,
1128:                                         MatGetDiagonalBlock_MPIDense,
1129:                                         0,
1130:                                 /* 34*/ MatDuplicate_MPIDense,
1131:                                         0,
1132:                                         0,
1133:                                         0,
1134:                                         0,
1135:                                 /* 39*/ MatAXPY_MPIDense,
1136:                                         MatCreateSubMatrices_MPIDense,
1137:                                         0,
1138:                                         MatGetValues_MPIDense,
1139:                                         0,
1140:                                 /* 44*/ 0,
1141:                                         MatScale_MPIDense,
1142:                                         MatShift_Basic,
1143:                                         0,
1144:                                         0,
1145:                                 /* 49*/ MatSetRandom_MPIDense,
1146:                                         0,
1147:                                         0,
1148:                                         0,
1149:                                         0,
1150:                                 /* 54*/ 0,
1151:                                         0,
1152:                                         0,
1153:                                         0,
1154:                                         0,
1155:                                 /* 59*/ MatCreateSubMatrix_MPIDense,
1156:                                         MatDestroy_MPIDense,
1157:                                         MatView_MPIDense,
1158:                                         0,
1159:                                         0,
1160:                                 /* 64*/ 0,
1161:                                         0,
1162:                                         0,
1163:                                         0,
1164:                                         0,
1165:                                 /* 69*/ 0,
1166:                                         0,
1167:                                         0,
1168:                                         0,
1169:                                         0,
1170:                                 /* 74*/ 0,
1171:                                         0,
1172:                                         0,
1173:                                         0,
1174:                                         0,
1175:                                 /* 79*/ 0,
1176:                                         0,
1177:                                         0,
1178:                                         0,
1179:                                 /* 83*/ MatLoad_MPIDense,
1180:                                         0,
1181:                                         0,
1182:                                         0,
1183:                                         0,
1184:                                         0,
1185: #if defined(PETSC_HAVE_ELEMENTAL)
1186:                                 /* 89*/ MatMatMult_MPIDense_MPIDense,
1187:                                         MatMatMultSymbolic_MPIDense_MPIDense,
1188: #else
1189:                                 /* 89*/ 0,
1190:                                         0,
1191: #endif
1192:                                         MatMatMultNumeric_MPIDense,
1193:                                         0,
1194:                                         0,
1195:                                 /* 94*/ 0,
1196:                                         MatMatTransposeMult_MPIDense_MPIDense,
1197:                                         MatMatTransposeMultSymbolic_MPIDense_MPIDense,
1198:                                         MatMatTransposeMultNumeric_MPIDense_MPIDense,
1199:                                         0,
1200:                                 /* 99*/ 0,
1201:                                         0,
1202:                                         0,
1203:                                         MatConjugate_MPIDense,
1204:                                         0,
1205:                                 /*104*/ 0,
1206:                                         MatRealPart_MPIDense,
1207:                                         MatImaginaryPart_MPIDense,
1208:                                         0,
1209:                                         0,
1210:                                 /*109*/ 0,
1211:                                         0,
1212:                                         0,
1213:                                         0,
1214:                                         MatMissingDiagonal_MPIDense,
1215:                                 /*114*/ 0,
1216:                                         0,
1217:                                         0,
1218:                                         0,
1219:                                         0,
1220:                                 /*119*/ 0,
1221:                                         0,
1222:                                         0,
1223:                                         0,
1224:                                         0,
1225:                                 /*124*/ 0,
1226:                                         MatGetColumnNorms_MPIDense,
1227:                                         0,
1228:                                         0,
1229:                                         0,
1230:                                 /*129*/ 0,
1231:                                         MatTransposeMatMult_MPIDense_MPIDense,
1232:                                         MatTransposeMatMultSymbolic_MPIDense_MPIDense,
1233:                                         MatTransposeMatMultNumeric_MPIDense_MPIDense,
1234:                                         0,
1235:                                 /*134*/ 0,
1236:                                         0,
1237:                                         0,
1238:                                         0,
1239:                                         0,
1240:                                 /*139*/ 0,
1241:                                         0,
1242:                                         0,
1243:                                         0,
1244:                                         0,
1245:                                 /*144*/ MatCreateMPIMatConcatenateSeqMat_MPIDense
1246: };

1248: PetscErrorCode  MatMPIDenseSetPreallocation_MPIDense(Mat mat,PetscScalar *data)
1249: {
1250:   Mat_MPIDense   *a;

1254:   if (mat->preallocated) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix has already been preallocated");
1255:   mat->preallocated = PETSC_TRUE;
1256:   /* Note:  For now, when data is specified above, this assumes the user correctly
1257:    allocates the local dense storage space.  We should add error checking. */

1259:   a       = (Mat_MPIDense*)mat->data;
1260:   PetscLayoutSetUp(mat->rmap);
1261:   PetscLayoutSetUp(mat->cmap);
1262:   a->nvec = mat->cmap->n;

1264:   MatCreate(PETSC_COMM_SELF,&a->A);
1265:   MatSetSizes(a->A,mat->rmap->n,mat->cmap->N,mat->rmap->n,mat->cmap->N);
1266:   MatSetType(a->A,MATSEQDENSE);
1267:   MatSeqDenseSetPreallocation(a->A,data);
1268:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
1269:   return(0);
1270: }

1272: #if defined(PETSC_HAVE_ELEMENTAL)
1273: PETSC_INTERN PetscErrorCode MatConvert_MPIDense_Elemental(Mat A, MatType newtype,MatReuse reuse,Mat *newmat)
1274: {
1275:   Mat            mat_elemental;
1277:   PetscScalar    *v;
1278:   PetscInt       m=A->rmap->n,N=A->cmap->N,rstart=A->rmap->rstart,i,*rows,*cols;
1279: 
1281:   if (reuse == MAT_REUSE_MATRIX) {
1282:     mat_elemental = *newmat;
1283:     MatZeroEntries(*newmat);
1284:   } else {
1285:     MatCreate(PetscObjectComm((PetscObject)A), &mat_elemental);
1286:     MatSetSizes(mat_elemental,PETSC_DECIDE,PETSC_DECIDE,A->rmap->N,A->cmap->N);
1287:     MatSetType(mat_elemental,MATELEMENTAL);
1288:     MatSetUp(mat_elemental);
1289:     MatSetOption(mat_elemental,MAT_ROW_ORIENTED,PETSC_FALSE);
1290:   }

1292:   PetscMalloc2(m,&rows,N,&cols);
1293:   for (i=0; i<N; i++) cols[i] = i;
1294:   for (i=0; i<m; i++) rows[i] = rstart + i;
1295: 
1296:   /* PETSc-Elemental interaface uses axpy for setting off-processor entries, only ADD_VALUES is allowed */
1297:   MatDenseGetArray(A,&v);
1298:   MatSetValues(mat_elemental,m,rows,N,cols,v,ADD_VALUES);
1299:   MatAssemblyBegin(mat_elemental, MAT_FINAL_ASSEMBLY);
1300:   MatAssemblyEnd(mat_elemental, MAT_FINAL_ASSEMBLY);
1301:   MatDenseRestoreArray(A,&v);
1302:   PetscFree2(rows,cols);

1304:   if (reuse == MAT_INPLACE_MATRIX) {
1305:     MatHeaderReplace(A,&mat_elemental);
1306:   } else {
1307:     *newmat = mat_elemental;
1308:   }
1309:   return(0);
1310: }
1311: #endif

1313: static PetscErrorCode MatDenseGetColumn_MPIDense(Mat A,PetscInt col,PetscScalar **vals)
1314: {
1315:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;

1319:   MatDenseGetColumn(mat->A,col,vals);
1320:   return(0);
1321: }

1323: static PetscErrorCode MatDenseRestoreColumn_MPIDense(Mat A,PetscScalar **vals)
1324: {
1325:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;

1329:   MatDenseRestoreColumn(mat->A,vals);
1330:   return(0);
1331: }

1333: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIDense(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
1334: {
1336:   Mat_MPIDense   *mat;
1337:   PetscInt       m,nloc,N;

1340:   MatGetSize(inmat,&m,&N);
1341:   MatGetLocalSize(inmat,NULL,&nloc);
1342:   if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
1343:     PetscInt sum;

1345:     if (n == PETSC_DECIDE) {
1346:       PetscSplitOwnership(comm,&n,&N);
1347:     }
1348:     /* Check sum(n) = N */
1349:     MPIU_Allreduce(&n,&sum,1,MPIU_INT,MPI_SUM,comm);
1350:     if (sum != N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Sum of local columns %D != global columns %D",sum,N);

1352:     MatCreateDense(comm,m,n,PETSC_DETERMINE,N,NULL,outmat);
1353:   }

1355:   /* numeric phase */
1356:   mat = (Mat_MPIDense*)(*outmat)->data;
1357:   MatCopy(inmat,mat->A,SAME_NONZERO_PATTERN);
1358:   MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
1359:   MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);
1360:   return(0);
1361: }

1363: PETSC_EXTERN PetscErrorCode MatCreate_MPIDense(Mat mat)
1364: {
1365:   Mat_MPIDense   *a;

1369:   PetscNewLog(mat,&a);
1370:   mat->data = (void*)a;
1371:   PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));

1373:   mat->insertmode = NOT_SET_VALUES;
1374:   MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&a->rank);
1375:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&a->size);

1377:   /* build cache for off array entries formed */
1378:   a->donotstash = PETSC_FALSE;

1380:   MatStashCreate_Private(PetscObjectComm((PetscObject)mat),1,&mat->stash);

1382:   /* stuff used for matrix vector multiply */
1383:   a->lvec        = 0;
1384:   a->Mvctx       = 0;
1385:   a->roworiented = PETSC_TRUE;

1387:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",MatDenseGetArray_MPIDense);
1388:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",MatDenseRestoreArray_MPIDense);
1389:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArrayRead_C",MatDenseGetArrayRead_MPIDense);
1390:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArrayRead_C",MatDenseRestoreArrayRead_MPIDense);
1391:   PetscObjectComposeFunction((PetscObject)mat,"MatDensePlaceArray_C",MatDensePlaceArray_MPIDense);
1392:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseResetArray_C",MatDenseResetArray_MPIDense);
1393: #if defined(PETSC_HAVE_ELEMENTAL)
1394:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpidense_elemental_C",MatConvert_MPIDense_Elemental);
1395: #endif
1396:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",MatMPIDenseSetPreallocation_MPIDense);
1397:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",MatMatMult_MPIAIJ_MPIDense);
1398:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",MatMatMultSymbolic_MPIAIJ_MPIDense);
1399:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",MatMatMultNumeric_MPIAIJ_MPIDense);

1401:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMult_mpiaij_mpidense_C",MatTransposeMatMult_MPIAIJ_MPIDense);
1402:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultSymbolic_mpiaij_mpidense_C",MatTransposeMatMultSymbolic_MPIAIJ_MPIDense);
1403:   PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultNumeric_mpiaij_mpidense_C",MatTransposeMatMultNumeric_MPIAIJ_MPIDense);
1404:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetColumn_C",MatDenseGetColumn_MPIDense);
1405:   PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreColumn_C",MatDenseRestoreColumn_MPIDense);
1406:   PetscObjectChangeTypeName((PetscObject)mat,MATMPIDENSE);
1407:   return(0);
1408: }

1410: /*MC
1411:    MATDENSE - MATDENSE = "dense" - A matrix type to be used for dense matrices.

1413:    This matrix type is identical to MATSEQDENSE when constructed with a single process communicator,
1414:    and MATMPIDENSE otherwise.

1416:    Options Database Keys:
1417: . -mat_type dense - sets the matrix type to "dense" during a call to MatSetFromOptions()

1419:   Level: beginner


1422: .seealso: MatCreateMPIDense,MATSEQDENSE,MATMPIDENSE
1423: M*/

1425: /*@C
1426:    MatMPIDenseSetPreallocation - Sets the array used to store the matrix entries

1428:    Not collective

1430:    Input Parameters:
1431: .  B - the matrix
1432: -  data - optional location of matrix data.  Set data=NULL for PETSc
1433:    to control all matrix memory allocation.

1435:    Notes:
1436:    The dense format is fully compatible with standard Fortran 77
1437:    storage by columns.

1439:    The data input variable is intended primarily for Fortran programmers
1440:    who wish to allocate their own matrix memory space.  Most users should
1441:    set data=NULL.

1443:    Level: intermediate

1445: .keywords: matrix,dense, parallel

1447: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
1448: @*/
1449: PetscErrorCode  MatMPIDenseSetPreallocation(Mat B,PetscScalar *data)
1450: {

1454:   PetscTryMethod(B,"MatMPIDenseSetPreallocation_C",(Mat,PetscScalar*),(B,data));
1455:   return(0);
1456: }

1458: /*@
1459:    MatDensePlaceArray - Allows one to replace the array in a dense array with an
1460:    array provided by the user. This is useful to avoid copying an array
1461:    into a matrix

1463:    Not Collective

1465:    Input Parameters:
1466: +  mat - the matrix
1467: -  array - the array in column major order

1469:    Notes:
1470:    You can return to the original array with a call to MatDenseResetArray(). The user is responsible for freeing this array; it will not be
1471:    freed when the matrix is destroyed.

1473:    Level: developer

1475: .seealso: MatDenseGetArray(), MatDenseResetArray(), VecPlaceArray(), VecGetArray(), VecRestoreArray(), VecReplaceArray(), VecResetArray()

1477: @*/
1478: PetscErrorCode  MatDensePlaceArray(Mat mat,const PetscScalar array[])
1479: {
1482:   PetscUseMethod(mat,"MatDensePlaceArray_C",(Mat,const PetscScalar*),(mat,array));
1483:   PetscObjectStateIncrease((PetscObject)mat);
1484:   return(0);
1485: }

1487: /*@
1488:    MatDenseResetArray - Resets the matrix array to that it previously had before the call to MatDensePlaceArray()

1490:    Not Collective

1492:    Input Parameters:
1493: .  mat - the matrix

1495:    Notes:
1496:    You can only call this after a call to MatDensePlaceArray()

1498:    Level: developer

1500: .seealso: MatDenseGetArray(), MatDensePlaceArray(), VecPlaceArray(), VecGetArray(), VecRestoreArray(), VecReplaceArray(), VecResetArray()

1502: @*/
1503: PetscErrorCode  MatDenseResetArray(Mat mat)
1504: {
1507:   PetscUseMethod(mat,"MatDenseResetArray_C",(Mat),(mat));
1508:   PetscObjectStateIncrease((PetscObject)mat);
1509:   return(0);
1510: }

1512: /*@C
1513:    MatCreateDense - Creates a parallel matrix in dense format.

1515:    Collective on MPI_Comm

1517:    Input Parameters:
1518: +  comm - MPI communicator
1519: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1520: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1521: .  M - number of global rows (or PETSC_DECIDE to have calculated if m is given)
1522: .  N - number of global columns (or PETSC_DECIDE to have calculated if n is given)
1523: -  data - optional location of matrix data.  Set data=NULL (PETSC_NULL_SCALAR for Fortran users) for PETSc
1524:    to control all matrix memory allocation.

1526:    Output Parameter:
1527: .  A - the matrix

1529:    Notes:
1530:    The dense format is fully compatible with standard Fortran 77
1531:    storage by columns.

1533:    The data input variable is intended primarily for Fortran programmers
1534:    who wish to allocate their own matrix memory space.  Most users should
1535:    set data=NULL (PETSC_NULL_SCALAR for Fortran users).

1537:    The user MUST specify either the local or global matrix dimensions
1538:    (possibly both).

1540:    Level: intermediate

1542: .keywords: matrix,dense, parallel

1544: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
1545: @*/
1546: PetscErrorCode  MatCreateDense(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscScalar *data,Mat *A)
1547: {
1549:   PetscMPIInt    size;

1552:   MatCreate(comm,A);
1553:   MatSetSizes(*A,m,n,M,N);
1554:   MPI_Comm_size(comm,&size);
1555:   if (size > 1) {
1556:     MatSetType(*A,MATMPIDENSE);
1557:     MatMPIDenseSetPreallocation(*A,data);
1558:     if (data) {  /* user provided data array, so no need to assemble */
1559:       MatSetUpMultiply_MPIDense(*A);
1560:       (*A)->assembled = PETSC_TRUE;
1561:     }
1562:   } else {
1563:     MatSetType(*A,MATSEQDENSE);
1564:     MatSeqDenseSetPreallocation(*A,data);
1565:   }
1566:   return(0);
1567: }

1569: static PetscErrorCode MatDuplicate_MPIDense(Mat A,MatDuplicateOption cpvalues,Mat *newmat)
1570: {
1571:   Mat            mat;
1572:   Mat_MPIDense   *a,*oldmat = (Mat_MPIDense*)A->data;

1576:   *newmat = 0;
1577:   MatCreate(PetscObjectComm((PetscObject)A),&mat);
1578:   MatSetSizes(mat,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1579:   MatSetType(mat,((PetscObject)A)->type_name);
1580:   a       = (Mat_MPIDense*)mat->data;

1582:   mat->factortype   = A->factortype;
1583:   mat->assembled    = PETSC_TRUE;
1584:   mat->preallocated = PETSC_TRUE;

1586:   a->size         = oldmat->size;
1587:   a->rank         = oldmat->rank;
1588:   mat->insertmode = NOT_SET_VALUES;
1589:   a->nvec         = oldmat->nvec;
1590:   a->donotstash   = oldmat->donotstash;

1592:   PetscLayoutReference(A->rmap,&mat->rmap);
1593:   PetscLayoutReference(A->cmap,&mat->cmap);

1595:   MatSetUpMultiply_MPIDense(mat);
1596:   MatDuplicate(oldmat->A,cpvalues,&a->A);
1597:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);

1599:   *newmat = mat;
1600:   return(0);
1601: }

1603: PetscErrorCode MatLoad_MPIDense_DenseInFile(MPI_Comm comm,PetscInt fd,PetscInt M,PetscInt N,Mat newmat)
1604: {
1606:   PetscMPIInt    rank,size;
1607:   const PetscInt *rowners;
1608:   PetscInt       i,m,n,nz,j,mMax;
1609:   PetscScalar    *array,*vals,*vals_ptr;
1610:   Mat_MPIDense   *a = (Mat_MPIDense*)newmat->data;

1613:   MPI_Comm_rank(comm,&rank);
1614:   MPI_Comm_size(comm,&size);

1616:   /* determine ownership of rows and columns */
1617:   m = (newmat->rmap->n < 0) ? PETSC_DECIDE : newmat->rmap->n;
1618:   n = (newmat->cmap->n < 0) ? PETSC_DECIDE : newmat->cmap->n;

1620:   MatSetSizes(newmat,m,n,M,N);
1621:   if (!a->A) {
1622:     MatMPIDenseSetPreallocation(newmat,NULL);
1623:   }
1624:   MatDenseGetArray(newmat,&array);
1625:   MatGetLocalSize(newmat,&m,NULL);
1626:   MatGetOwnershipRanges(newmat,&rowners);
1627:   MPI_Reduce(&m,&mMax,1,MPIU_INT,MPI_MAX,0,comm);
1628:   if (!rank) {
1629:     PetscMalloc1(mMax*N,&vals);

1631:     /* read in my part of the matrix numerical values  */
1632:     PetscBinaryRead(fd,vals,m*N,PETSC_SCALAR);

1634:     /* insert into matrix-by row (this is why cannot directly read into array */
1635:     vals_ptr = vals;
1636:     for (i=0; i<m; i++) {
1637:       for (j=0; j<N; j++) {
1638:         array[i + j*m] = *vals_ptr++;
1639:       }
1640:     }

1642:     /* read in other processors and ship out */
1643:     for (i=1; i<size; i++) {
1644:       nz   = (rowners[i+1] - rowners[i])*N;
1645:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1646:       MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)(newmat))->tag,comm);
1647:     }
1648:   } else {
1649:     /* receive numeric values */
1650:     PetscMalloc1(m*N,&vals);

1652:     /* receive message of values*/
1653:     MPIULong_Recv(vals,m*N,MPIU_SCALAR,0,((PetscObject)(newmat))->tag,comm);

1655:     /* insert into matrix-by row (this is why cannot directly read into array */
1656:     vals_ptr = vals;
1657:     for (i=0; i<m; i++) {
1658:       for (j=0; j<N; j++) {
1659:         array[i + j*m] = *vals_ptr++;
1660:       }
1661:     }
1662:   }
1663:   MatDenseRestoreArray(newmat,&array);
1664:   PetscFree(vals);
1665:   MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
1666:   MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
1667:   return(0);
1668: }

1670: PetscErrorCode MatLoad_MPIDense(Mat newmat,PetscViewer viewer)
1671: {
1672:   Mat_MPIDense   *a;
1673:   PetscScalar    *vals,*svals;
1674:   MPI_Comm       comm;
1675:   MPI_Status     status;
1676:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag,*rowners,*sndcounts,m,n,maxnz;
1677:   PetscInt       header[4],*rowlengths = 0,M,N,*cols;
1678:   PetscInt       *ourlens,*procsnz = 0,jj,*mycols,*smycols;
1679:   PetscInt       i,nz,j,rstart,rend;
1680:   int            fd;
1681:   PetscBool      isbinary;

1685:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1686:   if (!isbinary) SETERRQ2(PetscObjectComm((PetscObject)newmat),PETSC_ERR_SUP,"Viewer type %s not yet supported for reading %s matrices",((PetscObject)viewer)->type_name,((PetscObject)newmat)->type_name);

1688:   /* force binary viewer to load .info file if it has not yet done so */
1689:   PetscViewerSetUp(viewer);
1690:   PetscObjectGetComm((PetscObject)viewer,&comm);
1691:   MPI_Comm_size(comm,&size);
1692:   MPI_Comm_rank(comm,&rank);
1693:   PetscViewerBinaryGetDescriptor(viewer,&fd);
1694:   if (!rank) {
1695:     PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
1696:     if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
1697:   }
1698:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
1699:   M    = header[1]; N = header[2]; nz = header[3];

1701:   /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */
1702:   if (newmat->rmap->N < 0) newmat->rmap->N = M;
1703:   if (newmat->cmap->N < 0) newmat->cmap->N = N;

1705:   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);
1706:   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);

1708:   /*
1709:        Handle case where matrix is stored on disk as a dense matrix
1710:   */
1711:   if (nz == MATRIX_BINARY_FORMAT_DENSE) {
1712:     MatLoad_MPIDense_DenseInFile(comm,fd,M,N,newmat);
1713:     return(0);
1714:   }

1716:   /* determine ownership of all rows */
1717:   if (newmat->rmap->n < 0) {
1718:     PetscMPIIntCast(M/size + ((M % size) > rank),&m);
1719:   } else {
1720:     PetscMPIIntCast(newmat->rmap->n,&m);
1721:   }
1722:   if (newmat->cmap->n < 0) {
1723:     n = PETSC_DECIDE;
1724:   } else {
1725:     PetscMPIIntCast(newmat->cmap->n,&n);
1726:   }

1728:   PetscMalloc1(size+2,&rowners);
1729:   MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);
1730:   rowners[0] = 0;
1731:   for (i=2; i<=size; i++) {
1732:     rowners[i] += rowners[i-1];
1733:   }
1734:   rstart = rowners[rank];
1735:   rend   = rowners[rank+1];

1737:   /* distribute row lengths to all processors */
1738:   PetscMalloc1(rend-rstart,&ourlens);
1739:   if (!rank) {
1740:     PetscMalloc1(M,&rowlengths);
1741:     PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
1742:     PetscMalloc1(size,&sndcounts);
1743:     for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i];
1744:     MPI_Scatterv(rowlengths,sndcounts,rowners,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
1745:     PetscFree(sndcounts);
1746:   } else {
1747:     MPI_Scatterv(0,0,0,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
1748:   }

1750:   if (!rank) {
1751:     /* calculate the number of nonzeros on each processor */
1752:     PetscMalloc1(size,&procsnz);
1753:     PetscMemzero(procsnz,size*sizeof(PetscInt));
1754:     for (i=0; i<size; i++) {
1755:       for (j=rowners[i]; j< rowners[i+1]; j++) {
1756:         procsnz[i] += rowlengths[j];
1757:       }
1758:     }
1759:     PetscFree(rowlengths);

1761:     /* determine max buffer needed and allocate it */
1762:     maxnz = 0;
1763:     for (i=0; i<size; i++) {
1764:       maxnz = PetscMax(maxnz,procsnz[i]);
1765:     }
1766:     PetscMalloc1(maxnz,&cols);

1768:     /* read in my part of the matrix column indices  */
1769:     nz   = procsnz[0];
1770:     PetscMalloc1(nz,&mycols);
1771:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);

1773:     /* read in every one elses and ship off */
1774:     for (i=1; i<size; i++) {
1775:       nz   = procsnz[i];
1776:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
1777:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
1778:     }
1779:     PetscFree(cols);
1780:   } else {
1781:     /* determine buffer space needed for message */
1782:     nz = 0;
1783:     for (i=0; i<m; i++) {
1784:       nz += ourlens[i];
1785:     }
1786:     PetscMalloc1(nz+1,&mycols);

1788:     /* receive message of column indices*/
1789:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
1790:     MPI_Get_count(&status,MPIU_INT,&maxnz);
1791:     if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
1792:   }

1794:   MatSetSizes(newmat,m,n,M,N);
1795:   a = (Mat_MPIDense*)newmat->data;
1796:   if (!a->A) {
1797:     MatMPIDenseSetPreallocation(newmat,NULL);
1798:   }

1800:   if (!rank) {
1801:     PetscMalloc1(maxnz,&vals);

1803:     /* read in my part of the matrix numerical values  */
1804:     nz   = procsnz[0];
1805:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);

1807:     /* insert into matrix */
1808:     jj      = rstart;
1809:     smycols = mycols;
1810:     svals   = vals;
1811:     for (i=0; i<m; i++) {
1812:       MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
1813:       smycols += ourlens[i];
1814:       svals   += ourlens[i];
1815:       jj++;
1816:     }

1818:     /* read in other processors and ship out */
1819:     for (i=1; i<size; i++) {
1820:       nz   = procsnz[i];
1821:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1822:       MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);
1823:     }
1824:     PetscFree(procsnz);
1825:   } else {
1826:     /* receive numeric values */
1827:     PetscMalloc1(nz+1,&vals);

1829:     /* receive message of values*/
1830:     MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm,&status);
1831:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
1832:     if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

1834:     /* insert into matrix */
1835:     jj      = rstart;
1836:     smycols = mycols;
1837:     svals   = vals;
1838:     for (i=0; i<m; i++) {
1839:       MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
1840:       smycols += ourlens[i];
1841:       svals   += ourlens[i];
1842:       jj++;
1843:     }
1844:   }
1845:   PetscFree(ourlens);
1846:   PetscFree(vals);
1847:   PetscFree(mycols);
1848:   PetscFree(rowners);

1850:   MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
1851:   MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
1852:   return(0);
1853: }

1855: PetscErrorCode MatEqual_MPIDense(Mat A,Mat B,PetscBool  *flag)
1856: {
1857:   Mat_MPIDense   *matB = (Mat_MPIDense*)B->data,*matA = (Mat_MPIDense*)A->data;
1858:   Mat            a,b;
1859:   PetscBool      flg;

1863:   a    = matA->A;
1864:   b    = matB->A;
1865:   MatEqual(a,b,&flg);
1866:   MPIU_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
1867:   return(0);
1868: }

1870: PetscErrorCode MatDestroy_MatTransMatMult_MPIDense_MPIDense(Mat A)
1871: {
1872:   PetscErrorCode        ierr;
1873:   Mat_MPIDense          *a = (Mat_MPIDense*)A->data;
1874:   Mat_TransMatMultDense *atb = a->atbdense;

1877:   PetscFree3(atb->sendbuf,atb->atbarray,atb->recvcounts);
1878:   (atb->destroy)(A);
1879:   PetscFree(atb);
1880:   return(0);
1881: }

1883: PetscErrorCode MatDestroy_MatMatTransMult_MPIDense_MPIDense(Mat A)
1884: {
1885:   PetscErrorCode        ierr;
1886:   Mat_MPIDense          *a = (Mat_MPIDense*)A->data;
1887:   Mat_MatTransMultDense *abt = a->abtdense;

1890:   PetscFree2(abt->buf[0],abt->buf[1]);
1891:   PetscFree2(abt->recvcounts,abt->recvdispls);
1892:   (abt->destroy)(A);
1893:   PetscFree(abt);
1894:   return(0);
1895: }

1897: PetscErrorCode MatTransposeMatMultNumeric_MPIDense_MPIDense(Mat A,Mat B,Mat C)
1898: {
1899:   Mat_MPIDense   *a=(Mat_MPIDense*)A->data, *b=(Mat_MPIDense*)B->data, *c=(Mat_MPIDense*)C->data;
1900:   Mat_SeqDense   *aseq=(Mat_SeqDense*)(a->A)->data, *bseq=(Mat_SeqDense*)(b->A)->data;
1901:   Mat_TransMatMultDense *atb = c->atbdense;
1903:   MPI_Comm       comm;
1904:   PetscMPIInt    rank,size,*recvcounts=atb->recvcounts;
1905:   PetscScalar    *carray,*atbarray=atb->atbarray,*sendbuf=atb->sendbuf;
1906:   PetscInt       i,cN=C->cmap->N,cM=C->rmap->N,proc,k,j;
1907:   PetscScalar    _DOne=1.0,_DZero=0.0;
1908:   PetscBLASInt   am,an,bn,aN;
1909:   const PetscInt *ranges;

1912:   PetscObjectGetComm((PetscObject)A,&comm);
1913:   MPI_Comm_rank(comm,&rank);
1914:   MPI_Comm_size(comm,&size);

1916:   /* compute atbarray = aseq^T * bseq */
1917:   PetscBLASIntCast(a->A->cmap->n,&an);
1918:   PetscBLASIntCast(b->A->cmap->n,&bn);
1919:   PetscBLASIntCast(a->A->rmap->n,&am);
1920:   PetscBLASIntCast(A->cmap->N,&aN);
1921:   PetscStackCallBLAS("BLASgemm",BLASgemm_("T","N",&an,&bn,&am,&_DOne,aseq->v,&aseq->lda,bseq->v,&bseq->lda,&_DZero,atbarray,&aN));

1923:   MatGetOwnershipRanges(C,&ranges);
1924:   for (i=0; i<size; i++) recvcounts[i] = (ranges[i+1] - ranges[i])*cN;

1926:   /* arrange atbarray into sendbuf */
1927:   k = 0;
1928:   for (proc=0; proc<size; proc++) {
1929:     for (j=0; j<cN; j++) {
1930:       for (i=ranges[proc]; i<ranges[proc+1]; i++) sendbuf[k++] = atbarray[i+j*cM];
1931:     }
1932:   }
1933:   /* sum all atbarray to local values of C */
1934:   MatDenseGetArray(c->A,&carray);
1935:   MPI_Reduce_scatter(sendbuf,carray,recvcounts,MPIU_SCALAR,MPIU_SUM,comm);
1936:   MatDenseRestoreArray(c->A,&carray);
1937:   return(0);
1938: }

1940: PetscErrorCode MatTransposeMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C)
1941: {
1942:   PetscErrorCode        ierr;
1943:   Mat                   Cdense;
1944:   MPI_Comm              comm;
1945:   PetscMPIInt           size;
1946:   PetscInt              cm=A->cmap->n,cM,cN=B->cmap->N;
1947:   Mat_MPIDense          *c;
1948:   Mat_TransMatMultDense *atb;

1951:   PetscObjectGetComm((PetscObject)A,&comm);
1952:   if (A->rmap->rstart != B->rmap->rstart || A->rmap->rend != B->rmap->rend) {
1953:     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);
1954:   }

1956:   /* create matrix product Cdense */
1957:   MatCreate(comm,&Cdense);
1958:   MatSetSizes(Cdense,cm,B->cmap->n,PETSC_DECIDE,PETSC_DECIDE);
1959:   MatSetType(Cdense,MATMPIDENSE);
1960:   MatMPIDenseSetPreallocation(Cdense,NULL);
1961:   MatAssemblyBegin(Cdense,MAT_FINAL_ASSEMBLY);
1962:   MatAssemblyEnd(Cdense,MAT_FINAL_ASSEMBLY);
1963:   *C   = Cdense;

1965:   /* create data structure for reuse Cdense */
1966:   MPI_Comm_size(comm,&size);
1967:   PetscNew(&atb);
1968:   cM = Cdense->rmap->N;
1969:   PetscMalloc3(cM*cN,&atb->sendbuf,cM*cN,&atb->atbarray,size,&atb->recvcounts);
1970: 
1971:   c                    = (Mat_MPIDense*)Cdense->data;
1972:   c->atbdense          = atb;
1973:   atb->destroy         = Cdense->ops->destroy;
1974:   Cdense->ops->destroy = MatDestroy_MatTransMatMult_MPIDense_MPIDense;
1975:   return(0);
1976: }

1978: PetscErrorCode MatTransposeMatMult_MPIDense_MPIDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
1979: {

1983:   if (scall == MAT_INITIAL_MATRIX) {
1984:     MatTransposeMatMultSymbolic_MPIDense_MPIDense(A,B,fill,C);
1985:   }
1986:   MatTransposeMatMultNumeric_MPIDense_MPIDense(A,B,*C);
1987:   return(0);
1988: }

1990: static PetscErrorCode MatMatTransposeMultSymbolic_MPIDense_MPIDense(Mat A, Mat B, PetscReal fill, Mat *C)
1991: {
1992:   PetscErrorCode        ierr;
1993:   Mat                   Cdense;
1994:   MPI_Comm              comm;
1995:   PetscMPIInt           i, size;
1996:   PetscInt              maxRows, bufsiz;
1997:   Mat_MPIDense          *c;
1998:   PetscMPIInt           tag;
1999:   const char            *algTypes[2] = {"allgatherv","cyclic"};
2000:   PetscInt              alg, nalg = 2;
2001:   Mat_MatTransMultDense *abt;

2004:   PetscObjectGetComm((PetscObject)A,&comm);
2005:   alg = 0; /* default is allgatherv */
2006:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MatMatTransposeMult","Mat");
2007:   PetscOptionsEList("-matmattransmult_mpidense_mpidense_via","Algorithmic approach","MatMatTransposeMult",algTypes,nalg,algTypes[0],&alg,NULL);
2008:   PetscOptionsEnd();
2009:   if (A->cmap->N != B->cmap->N) {
2010:     SETERRQ2(comm,PETSC_ERR_ARG_SIZ,"Matrix global column dimensions are incompatible, A (%D) != B (%D)",A->cmap->N,B->cmap->N);
2011:   }

2013:   /* create matrix product Cdense */
2014:   MatCreate(comm,&Cdense);
2015:   MatSetSizes(Cdense,A->rmap->n,B->rmap->n,A->rmap->N,B->rmap->N);
2016:   MatSetType(Cdense,MATMPIDENSE);
2017:   MatMPIDenseSetPreallocation(Cdense,NULL);
2018:   PetscObjectGetNewTag((PetscObject)Cdense, &tag);
2019:   MatAssemblyBegin(Cdense,MAT_FINAL_ASSEMBLY);
2020:   MatAssemblyEnd(Cdense,MAT_FINAL_ASSEMBLY);
2021:   *C   = Cdense;

2023:   /* create data structure for reuse Cdense */
2024:   MPI_Comm_size(comm,&size);
2025:   PetscNew(&abt);
2026:   abt->tag = tag;
2027:   abt->alg = alg;
2028:   switch (alg) {
2029:   case 1:
2030:     for (maxRows = 0, i = 0; i < size; i++) maxRows = PetscMax(maxRows, (B->rmap->range[i + 1] - B->rmap->range[i]));
2031:     bufsiz = A->cmap->N * maxRows;
2032:     PetscMalloc2(bufsiz,&(abt->buf[0]),bufsiz,&(abt->buf[1]));
2033:     break;
2034:   default:
2035:     PetscMalloc2(B->rmap->n * B->cmap->N, &(abt->buf[0]), B->rmap->N * B->cmap->N, &(abt->buf[1]));
2036:     PetscMalloc2(size,&(abt->recvcounts),size+1,&(abt->recvdispls));
2037:     for (i = 0; i <= size; i++) abt->recvdispls[i] = B->rmap->range[i] * A->cmap->N;
2038:     for (i = 0; i < size; i++) abt->recvcounts[i] = abt->recvdispls[i + 1] - abt->recvdispls[i];
2039:     break;
2040:   }

2042:   c                    = (Mat_MPIDense*)Cdense->data;
2043:   c->abtdense          = abt;
2044:   abt->destroy         = Cdense->ops->destroy;
2045:   Cdense->ops->destroy = MatDestroy_MatMatTransMult_MPIDense_MPIDense;
2046:   return(0);
2047: }

2049: static PetscErrorCode MatMatTransposeMultNumeric_MPIDense_MPIDense_Cyclic(Mat A, Mat B, Mat C)
2050: {
2051:   Mat_MPIDense   *a=(Mat_MPIDense*)A->data, *b=(Mat_MPIDense*)B->data, *c=(Mat_MPIDense*)C->data;
2052:   Mat_SeqDense   *aseq=(Mat_SeqDense*)(a->A)->data, *bseq=(Mat_SeqDense*)(b->A)->data;
2053:   Mat_SeqDense   *cseq=(Mat_SeqDense*)(c->A)->data;
2054:   Mat_MatTransMultDense *abt = c->abtdense;
2056:   MPI_Comm       comm;
2057:   PetscMPIInt    rank,size, sendsiz, recvsiz, sendto, recvfrom, recvisfrom;
2058:   PetscScalar    *sendbuf, *recvbuf=0, *carray;
2059:   PetscInt       i,cK=A->cmap->N,k,j,bn;
2060:   PetscScalar    _DOne=1.0,_DZero=0.0;
2061:   PetscBLASInt   cm, cn, ck;
2062:   MPI_Request    reqs[2];
2063:   const PetscInt *ranges;

2066:   PetscObjectGetComm((PetscObject)A,&comm);
2067:   MPI_Comm_rank(comm,&rank);
2068:   MPI_Comm_size(comm,&size);

2070:   MatGetOwnershipRanges(B,&ranges);
2071:   bn = B->rmap->n;
2072:   if (bseq->lda == bn) {
2073:     sendbuf = bseq->v;
2074:   } else {
2075:     sendbuf = abt->buf[0];
2076:     for (k = 0, i = 0; i < cK; i++) {
2077:       for (j = 0; j < bn; j++, k++) {
2078:         sendbuf[k] = bseq->v[i * bseq->lda + j];
2079:       }
2080:     }
2081:   }
2082:   if (size > 1) {
2083:     sendto = (rank + size - 1) % size;
2084:     recvfrom = (rank + size + 1) % size;
2085:   } else {
2086:     sendto = recvfrom = 0;
2087:   }
2088:   PetscBLASIntCast(cK,&ck);
2089:   PetscBLASIntCast(c->A->rmap->n,&cm);
2090:   recvisfrom = rank;
2091:   for (i = 0; i < size; i++) {
2092:     /* we have finished receiving in sending, bufs can be read/modified */
2093:     PetscInt nextrecvisfrom = (recvisfrom + 1) % size; /* which process the next recvbuf will originate on */
2094:     PetscInt nextbn = ranges[nextrecvisfrom + 1] - ranges[nextrecvisfrom];

2096:     if (nextrecvisfrom != rank) {
2097:       /* start the cyclic sends from sendbuf, to recvbuf (which will switch to sendbuf) */
2098:       sendsiz = cK * bn;
2099:       recvsiz = cK * nextbn;
2100:       recvbuf = (i & 1) ? abt->buf[0] : abt->buf[1];
2101:       MPI_Isend(sendbuf, sendsiz, MPIU_SCALAR, sendto, abt->tag, comm, &reqs[0]);
2102:       MPI_Irecv(recvbuf, recvsiz, MPIU_SCALAR, recvfrom, abt->tag, comm, &reqs[1]);
2103:     }

2105:     /* local aseq * sendbuf^T */
2106:     PetscBLASIntCast(ranges[recvisfrom + 1] - ranges[recvisfrom], &cn);
2107:     carray = &cseq->v[cseq->lda * ranges[recvisfrom]];
2108:     PetscStackCallBLAS("BLASgemm",BLASgemm_("N","T",&cm,&cn,&ck,&_DOne,aseq->v,&aseq->lda,sendbuf,&cn,&_DZero,carray,&cseq->lda));

2110:     if (nextrecvisfrom != rank) {
2111:       /* wait for the sends and receives to complete, swap sendbuf and recvbuf */
2112:       MPI_Waitall(2, reqs, MPI_STATUSES_IGNORE);
2113:     }
2114:     bn = nextbn;
2115:     recvisfrom = nextrecvisfrom;
2116:     sendbuf = recvbuf;
2117:   }
2118:   return(0);
2119: }

2121: static PetscErrorCode MatMatTransposeMultNumeric_MPIDense_MPIDense_Allgatherv(Mat A, Mat B, Mat C)
2122: {
2123:   Mat_MPIDense   *a=(Mat_MPIDense*)A->data, *b=(Mat_MPIDense*)B->data, *c=(Mat_MPIDense*)C->data;
2124:   Mat_SeqDense   *aseq=(Mat_SeqDense*)(a->A)->data, *bseq=(Mat_SeqDense*)(b->A)->data;
2125:   Mat_SeqDense   *cseq=(Mat_SeqDense*)(c->A)->data;
2126:   Mat_MatTransMultDense *abt = c->abtdense;
2128:   MPI_Comm       comm;
2129:   PetscMPIInt    rank,size;
2130:   PetscScalar    *sendbuf, *recvbuf;
2131:   PetscInt       i,cK=A->cmap->N,k,j,bn;
2132:   PetscScalar    _DOne=1.0,_DZero=0.0;
2133:   PetscBLASInt   cm, cn, ck;

2136:   PetscObjectGetComm((PetscObject)A,&comm);
2137:   MPI_Comm_rank(comm,&rank);
2138:   MPI_Comm_size(comm,&size);

2140:   /* copy transpose of B into buf[0] */
2141:   bn      = B->rmap->n;
2142:   sendbuf = abt->buf[0];
2143:   recvbuf = abt->buf[1];
2144:   for (k = 0, j = 0; j < bn; j++) {
2145:     for (i = 0; i < cK; i++, k++) {
2146:       sendbuf[k] = bseq->v[i * bseq->lda + j];
2147:     }
2148:   }
2149:   MPI_Allgatherv(sendbuf, bn * cK, MPIU_SCALAR, recvbuf, abt->recvcounts, abt->recvdispls, MPIU_SCALAR, comm);
2150:   PetscBLASIntCast(cK,&ck);
2151:   PetscBLASIntCast(c->A->rmap->n,&cm);
2152:   PetscBLASIntCast(c->A->cmap->n,&cn);
2153:   PetscStackCallBLAS("BLASgemm",BLASgemm_("N","N",&cm,&cn,&ck,&_DOne,aseq->v,&aseq->lda,recvbuf,&ck,&_DZero,cseq->v,&cseq->lda));
2154:   return(0);
2155: }

2157: static PetscErrorCode MatMatTransposeMultNumeric_MPIDense_MPIDense(Mat A, Mat B, Mat C)
2158: {
2159:   Mat_MPIDense   *c=(Mat_MPIDense*)C->data;
2160:   Mat_MatTransMultDense *abt = c->abtdense;

2164:   switch (abt->alg) {
2165:   case 1:
2166:     MatMatTransposeMultNumeric_MPIDense_MPIDense_Cyclic(A, B, C);
2167:     break;
2168:   default:
2169:     MatMatTransposeMultNumeric_MPIDense_MPIDense_Allgatherv(A, B, C);
2170:     break;
2171:   }
2172:   return(0);
2173: }

2175: static PetscErrorCode MatMatTransposeMult_MPIDense_MPIDense(Mat A,Mat B, MatReuse scall, PetscReal fill, Mat *C)
2176: {

2180:   if (scall == MAT_INITIAL_MATRIX) {
2181:     MatMatTransposeMultSymbolic_MPIDense_MPIDense(A,B,fill,C);
2182:   }
2183:   MatMatTransposeMultNumeric_MPIDense_MPIDense(A,B,*C);
2184:   return(0);
2185: }

2187: PetscErrorCode MatDestroy_MatMatMult_MPIDense_MPIDense(Mat A)
2188: {
2189:   PetscErrorCode   ierr;
2190:   Mat_MPIDense     *a = (Mat_MPIDense*)A->data;
2191:   Mat_MatMultDense *ab = a->abdense;

2194:   MatDestroy(&ab->Ce);
2195:   MatDestroy(&ab->Ae);
2196:   MatDestroy(&ab->Be);

2198:   (ab->destroy)(A);
2199:   PetscFree(ab);
2200:   return(0);
2201: }

2203: #if defined(PETSC_HAVE_ELEMENTAL)
2204: PetscErrorCode MatMatMultNumeric_MPIDense_MPIDense(Mat A,Mat B,Mat C)
2205: {
2206:   PetscErrorCode   ierr;
2207:   Mat_MPIDense     *c=(Mat_MPIDense*)C->data;
2208:   Mat_MatMultDense *ab=c->abdense;

2211:   MatConvert_MPIDense_Elemental(A,MATELEMENTAL,MAT_REUSE_MATRIX, &ab->Ae);
2212:   MatConvert_MPIDense_Elemental(B,MATELEMENTAL,MAT_REUSE_MATRIX, &ab->Be);
2213:   MatMatMultNumeric(ab->Ae,ab->Be,ab->Ce);
2214:   MatConvert(ab->Ce,MATMPIDENSE,MAT_REUSE_MATRIX,&C);
2215:   return(0);
2216: }

2218: PetscErrorCode MatMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C)
2219: {
2220:   PetscErrorCode   ierr;
2221:   Mat              Ae,Be,Ce;
2222:   Mat_MPIDense     *c;
2223:   Mat_MatMultDense *ab;

2226:   if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) {
2227:     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);
2228:   }

2230:   /* convert A and B to Elemental matrices Ae and Be */
2231:   MatConvert(A,MATELEMENTAL,MAT_INITIAL_MATRIX, &Ae);
2232:   MatConvert(B,MATELEMENTAL,MAT_INITIAL_MATRIX, &Be);

2234:   /* Ce = Ae*Be */
2235:   MatMatMultSymbolic(Ae,Be,fill,&Ce);
2236:   MatMatMultNumeric(Ae,Be,Ce);
2237: 
2238:   /* convert Ce to C */
2239:   MatConvert(Ce,MATMPIDENSE,MAT_INITIAL_MATRIX,C);

2241:   /* create data structure for reuse Cdense */
2242:   PetscNew(&ab);
2243:   c                  = (Mat_MPIDense*)(*C)->data;
2244:   c->abdense         = ab;

2246:   ab->Ae             = Ae;
2247:   ab->Be             = Be;
2248:   ab->Ce             = Ce;
2249:   ab->destroy        = (*C)->ops->destroy;
2250:   (*C)->ops->destroy        = MatDestroy_MatMatMult_MPIDense_MPIDense;
2251:   (*C)->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIDense;
2252:   return(0);
2253: }

2255: PETSC_INTERN PetscErrorCode MatMatMult_MPIDense_MPIDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
2256: {

2260:   if (scall == MAT_INITIAL_MATRIX) { /* simbolic product includes numeric product */
2261:     PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
2262:     MatMatMultSymbolic_MPIDense_MPIDense(A,B,fill,C);
2263:     PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
2264:   } else {
2265:     PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
2266:     MatMatMultNumeric_MPIDense_MPIDense(A,B,*C);
2267:     PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
2268:   }
2269:   return(0);
2270: }
2271: #endif