Actual source code: mpidense.c

  1: #define PETSCMAT_DLL

  3: /*
  4:    Basic functions for basic parallel dense matrices.
  5: */
  6: 
 7:  #include src/mat/impls/dense/mpi/mpidense.h

 11: PetscErrorCode MatLUFactorSymbolic_MPIDense(Mat A,IS row,IS col,MatFactorInfo *info,Mat *fact)
 12: {

 16:   MatDuplicate(A,MAT_DO_NOT_COPY_VALUES,fact);
 17:   return(0);
 18: }

 22: PetscErrorCode MatCholeskyFactorSymbolic_MPIDense(Mat A,IS perm,MatFactorInfo *info,Mat *fact)
 23: {

 27:   MatDuplicate(A,MAT_DO_NOT_COPY_VALUES,fact);
 28:   return(0);
 29: }

 33: /*@

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

 38:     Input Parameter:
 39: .      A - the Seq or MPI dense matrix

 41:     Output Parameter:
 42: .      B - the inner matrix

 44:     Level: intermediate

 46: @*/
 47: PetscErrorCode MatDenseGetLocalMatrix(Mat A,Mat *B)
 48: {
 49:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
 51:   PetscTruth     flg;
 52:   PetscMPIInt    size;

 55:   PetscTypeCompare((PetscObject)A,MATMPIDENSE,&flg);
 56:   if (!flg) {  /* this check sucks! */
 57:     PetscTypeCompare((PetscObject)A,MATDENSE,&flg);
 58:     if (flg) {
 59:       MPI_Comm_size(((PetscObject)A)->comm,&size);
 60:       if (size == 1) flg = PETSC_FALSE;
 61:     }
 62:   }
 63:   if (flg) {
 64:     *B = mat->A;
 65:   } else {
 66:     *B = A;
 67:   }
 68:   return(0);
 69: }

 73: PetscErrorCode MatGetRow_MPIDense(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
 74: {
 75:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
 77:   PetscInt       lrow,rstart = A->rmap.rstart,rend = A->rmap.rend;

 80:   if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_SUP,"only local rows")
 81:   lrow = row - rstart;
 82:   MatGetRow(mat->A,lrow,nz,(const PetscInt **)idx,(const PetscScalar **)v);
 83:   return(0);
 84: }

 88: PetscErrorCode MatRestoreRow_MPIDense(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
 89: {

 93:   if (idx) {PetscFree(*idx);}
 94:   if (v) {PetscFree(*v);}
 95:   return(0);
 96: }

101: PetscErrorCode  MatGetDiagonalBlock_MPIDense(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *B)
102: {
103:   Mat_MPIDense   *mdn = (Mat_MPIDense*)A->data;
105:   PetscInt       m = A->rmap.n,rstart = A->rmap.rstart;
106:   PetscScalar    *array;
107:   MPI_Comm       comm;

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

112:   /* The reuse aspect is not implemented efficiently */
113:   if (reuse) { MatDestroy(*B);}

115:   PetscObjectGetComm((PetscObject)(mdn->A),&comm);
116:   MatGetArray(mdn->A,&array);
117:   MatCreate(comm,B);
118:   MatSetSizes(*B,m,m,m,m);
119:   MatSetType(*B,((PetscObject)mdn->A)->type_name);
120:   MatSeqDenseSetPreallocation(*B,array+m*rstart);
121:   MatRestoreArray(mdn->A,&array);
122:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
123:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
124: 
125:   *iscopy = PETSC_TRUE;
126:   return(0);
127: }

132: PetscErrorCode MatSetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
133: {
134:   Mat_MPIDense   *A = (Mat_MPIDense*)mat->data;
136:   PetscInt       i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend,row;
137:   PetscTruth     roworiented = A->roworiented;

140:   for (i=0; i<m; i++) {
141:     if (idxm[i] < 0) continue;
142:     if (idxm[i] >= mat->rmap.N) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
143:     if (idxm[i] >= rstart && idxm[i] < rend) {
144:       row = idxm[i] - rstart;
145:       if (roworiented) {
146:         MatSetValues(A->A,1,&row,n,idxn,v+i*n,addv);
147:       } else {
148:         for (j=0; j<n; j++) {
149:           if (idxn[j] < 0) continue;
150:           if (idxn[j] >= mat->cmap.N) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
151:           MatSetValues(A->A,1,&row,1,&idxn[j],v+i+j*m,addv);
152:         }
153:       }
154:     } else {
155:       if (!A->donotstash) {
156:         if (roworiented) {
157:           MatStashValuesRow_Private(&mat->stash,idxm[i],n,idxn,v+i*n);
158:         } else {
159:           MatStashValuesCol_Private(&mat->stash,idxm[i],n,idxn,v+i,m);
160:         }
161:       }
162:     }
163:   }
164:   return(0);
165: }

169: PetscErrorCode MatGetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
170: {
171:   Mat_MPIDense   *mdn = (Mat_MPIDense*)mat->data;
173:   PetscInt       i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend,row;

176:   for (i=0; i<m; i++) {
177:     if (idxm[i] < 0) continue; /* SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */
178:     if (idxm[i] >= mat->rmap.N) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
179:     if (idxm[i] >= rstart && idxm[i] < rend) {
180:       row = idxm[i] - rstart;
181:       for (j=0; j<n; j++) {
182:         if (idxn[j] < 0) continue; /* SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */
183:         if (idxn[j] >= mat->cmap.N) {
184:           SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
185:         }
186:         MatGetValues(mdn->A,1,&row,1,&idxn[j],v+i*n+j);
187:       }
188:     } else {
189:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
190:     }
191:   }
192:   return(0);
193: }

197: PetscErrorCode MatGetArray_MPIDense(Mat A,PetscScalar *array[])
198: {
199:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

203:   MatGetArray(a->A,array);
204:   return(0);
205: }

209: static PetscErrorCode MatGetSubMatrix_MPIDense(Mat A,IS isrow,IS iscol,PetscInt cs,MatReuse scall,Mat *B)
210: {
211:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data,*newmatd;
212:   Mat_SeqDense   *lmat = (Mat_SeqDense*)mat->A->data;
214:   PetscInt       i,j,*irow,*icol,rstart,rend,nrows,ncols,nlrows,nlcols;
215:   PetscScalar    *av,*bv,*v = lmat->v;
216:   Mat            newmat;

219:   ISGetIndices(isrow,&irow);
220:   ISGetIndices(iscol,&icol);
221:   ISGetLocalSize(isrow,&nrows);
222:   ISGetLocalSize(iscol,&ncols);

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

228:   MatGetLocalSize(A,&nlrows,&nlcols);
229:   MatGetOwnershipRange(A,&rstart,&rend);
230: 
231:   /* Check submatrix call */
232:   if (scall == MAT_REUSE_MATRIX) {
233:     /* SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); */
234:     /* Really need to test rows and column sizes! */
235:     newmat = *B;
236:   } else {
237:     /* Create and fill new matrix */
238:     MatCreate(((PetscObject)A)->comm,&newmat);
239:     MatSetSizes(newmat,nrows,cs,PETSC_DECIDE,ncols);
240:     MatSetType(newmat,((PetscObject)A)->type_name);
241:     MatMPIDenseSetPreallocation(newmat,PETSC_NULL);
242:   }

244:   /* Now extract the data pointers and do the copy, column at a time */
245:   newmatd = (Mat_MPIDense*)newmat->data;
246:   bv      = ((Mat_SeqDense *)newmatd->A->data)->v;
247: 
248:   for (i=0; i<ncols; i++) {
249:     av = v + nlrows*icol[i];
250:     for (j=0; j<nrows; j++) {
251:       *bv++ = av[irow[j] - rstart];
252:     }
253:   }

255:   /* Assemble the matrices so that the correct flags are set */
256:   MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
257:   MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);

259:   /* Free work space */
260:   ISRestoreIndices(isrow,&irow);
261:   ISRestoreIndices(iscol,&icol);
262:   *B = newmat;
263:   return(0);
264: }

268: PetscErrorCode MatRestoreArray_MPIDense(Mat A,PetscScalar *array[])
269: {
271:   return(0);
272: }

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

285:   /* make sure all processors are either in INSERTMODE or ADDMODE */
286:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,comm);
287:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
288:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix adds/inserts on different procs");
289:   }
290:   mat->insertmode = addv; /* in case this processor had no cache */

292:   MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap.range);
293:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
294:   PetscInfo2(mdn->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
295:   return(0);
296: }

300: PetscErrorCode MatAssemblyEnd_MPIDense(Mat mat,MatAssemblyType mode)
301: {
302:   Mat_MPIDense    *mdn=(Mat_MPIDense*)mat->data;
303:   PetscErrorCode  ierr;
304:   PetscInt        i,*row,*col,flg,j,rstart,ncols;
305:   PetscMPIInt     n;
306:   PetscScalar     *val;
307:   InsertMode      addv=mat->insertmode;

310:   /*  wait on receives */
311:   while (1) {
312:     MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
313:     if (!flg) break;
314: 
315:     for (i=0; i<n;) {
316:       /* Now identify the consecutive vals belonging to the same row */
317:       for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
318:       if (j < n) ncols = j-i;
319:       else       ncols = n-i;
320:       /* Now assemble all these values with a single function call */
321:       MatSetValues_MPIDense(mat,1,row+i,ncols,col+i,val+i,addv);
322:       i = j;
323:     }
324:   }
325:   MatStashScatterEnd_Private(&mat->stash);
326: 
327:   MatAssemblyBegin(mdn->A,mode);
328:   MatAssemblyEnd(mdn->A,mode);

330:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
331:     MatSetUpMultiply_MPIDense(mat);
332:   }
333:   return(0);
334: }

338: PetscErrorCode MatZeroEntries_MPIDense(Mat A)
339: {
341:   Mat_MPIDense   *l = (Mat_MPIDense*)A->data;

344:   MatZeroEntries(l->A);
345:   return(0);
346: }

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

372:   /*  first count number of contributors to each processor */
373:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
374:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
375:   PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
376:   for (i=0; i<N; i++) {
377:     idx = rows[i];
378:     found = PETSC_FALSE;
379:     for (j=0; j<size; j++) {
380:       if (idx >= owners[j] && idx < owners[j+1]) {
381:         nprocs[2*j]++; nprocs[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break;
382:       }
383:     }
384:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
385:   }
386:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}

388:   /* inform other processors of number of messages and max length*/
389:   PetscMaxSum(comm,nprocs,&nmax,&nrecvs);

391:   /* post receives:   */
392:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
393:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
394:   for (i=0; i<nrecvs; i++) {
395:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
396:   }

398:   /* do sends:
399:       1) starts[i] gives the starting index in svalues for stuff going to 
400:          the ith processor
401:   */
402:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
403:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
404:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
405:   starts[0]  = 0;
406:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
407:   for (i=0; i<N; i++) {
408:     svalues[starts[owner[i]]++] = rows[i];
409:   }

411:   starts[0] = 0;
412:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
413:   count = 0;
414:   for (i=0; i<size; i++) {
415:     if (nprocs[2*i+1]) {
416:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
417:     }
418:   }
419:   PetscFree(starts);

421:   base = owners[rank];

423:   /*  wait on receives */
424:   PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
425:   source = lens + nrecvs;
426:   count  = nrecvs; slen = 0;
427:   while (count) {
428:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
429:     /* unpack receives into our local space */
430:     MPI_Get_count(&recv_status,MPIU_INT,&n);
431:     source[imdex]  = recv_status.MPI_SOURCE;
432:     lens[imdex]    = n;
433:     slen += n;
434:     count--;
435:   }
436:   PetscFree(recv_waits);
437: 
438:   /* move the data into the send scatter */
439:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
440:   count = 0;
441:   for (i=0; i<nrecvs; i++) {
442:     values = rvalues + i*nmax;
443:     for (j=0; j<lens[i]; j++) {
444:       lrows[count++] = values[j] - base;
445:     }
446:   }
447:   PetscFree(rvalues);
448:   PetscFree(lens);
449:   PetscFree(owner);
450:   PetscFree(nprocs);
451: 
452:   /* actually zap the local rows */
453:   MatZeroRows(l->A,slen,lrows,diag);
454:   PetscFree(lrows);

456:   /* wait on sends */
457:   if (nsends) {
458:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
459:     MPI_Waitall(nsends,send_waits,send_status);
460:     PetscFree(send_status);
461:   }
462:   PetscFree(send_waits);
463:   PetscFree(svalues);

465:   return(0);
466: }

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

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

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

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

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

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

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

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

529: PetscErrorCode MatGetDiagonal_MPIDense(Mat A,Vec v)
530: {
531:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;
532:   Mat_SeqDense   *aloc = (Mat_SeqDense*)a->A->data;
534:   PetscInt       len,i,n,m = A->rmap.n,radd;
535:   PetscScalar    *x,zero = 0.0;
536: 
538:   VecSet(v,zero);
539:   VecGetArray(v,&x);
540:   VecGetSize(v,&n);
541:   if (n != A->rmap.N) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming mat and vec");
542:   len  = PetscMin(a->A->rmap.n,a->A->cmap.n);
543:   radd = A->rmap.rstart*m;
544:   for (i=0; i<len; i++) {
545:     x[i] = aloc->v[radd + i*m + i];
546:   }
547:   VecRestoreArray(v,&x);
548:   return(0);
549: }

553: PetscErrorCode MatDestroy_MPIDense(Mat mat)
554: {
555:   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:   if (mdn->lvec)   {VecDestroy(mdn->lvec);}
566:   if (mdn->Mvctx)  {VecScatterDestroy(mdn->Mvctx);}
567:   if (mdn->factor) {
568:     PetscFree(mdn->factor->temp);
569:     PetscFree(mdn->factor->tag);
570:     PetscFree(mdn->factor->pivots);
571:     PetscFree(mdn->factor);
572:   }
573:   PetscFree(mdn);
574:   PetscObjectChangeTypeName((PetscObject)mat,0);
575:   PetscObjectComposeFunctionDynamic((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
576:   PetscObjectComposeFunctionDynamic((PetscObject)mat,"MatMPIDenseSetPreallocation_C","",PETSC_NULL);
577:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C","",PETSC_NULL);
578:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C","",PETSC_NULL);
579:   PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C","",PETSC_NULL);
580:   return(0);
581: }

585: static PetscErrorCode MatView_MPIDense_Binary(Mat mat,PetscViewer viewer)
586: {
587:   Mat_MPIDense      *mdn = (Mat_MPIDense*)mat->data;
588:   PetscErrorCode    ierr;
589:   PetscViewerFormat format;
590:   int               fd;
591:   PetscInt          header[4],mmax,N = mat->cmap.N,i,j,m,k;
592:   PetscMPIInt       rank,tag  = ((PetscObject)viewer)->tag,size;
593:   PetscScalar       *work,*v,*vv;
594:   Mat_SeqDense      *a = (Mat_SeqDense*)mdn->A->data;
595:   MPI_Status        status;

598:   if (mdn->size == 1) {
599:     MatView(mdn->A,viewer);
600:   } else {
601:     PetscViewerBinaryGetDescriptor(viewer,&fd);
602:     MPI_Comm_rank(((PetscObject)mat)->comm,&rank);
603:     MPI_Comm_size(((PetscObject)mat)->comm,&size);

605:     PetscViewerGetFormat(viewer,&format);
606:     if (format == PETSC_VIEWER_BINARY_NATIVE) {

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

616:         /* get largest work array needed for transposing array */
617:         mmax = mat->rmap.n;
618:         for (i=1; i<size; i++) {
619:           mmax = PetscMax(mmax,mat->rmap.range[i+1] - mat->rmap.range[i]);
620:         }
621:         PetscMalloc(mmax*N*sizeof(PetscScalar),&work);

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

643:           for (j=0; j<N; j++) {
644:             for (i=0; i<m; i++) {
645:               work[j + i*N] = *v++;
646:             }
647:           }
648:           PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
649:         }
650:         PetscFree(work);
651:         PetscFree(vv);
652:       } else {
653:         MPI_Send(a->v,mat->rmap.n*mat->cmap.N,MPIU_SCALAR,0,tag,((PetscObject)mat)->comm);
654:       }
655:     }
656:   }
657:   return(0);
658: }

662: static PetscErrorCode MatView_MPIDense_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
663: {
664:   Mat_MPIDense      *mdn = (Mat_MPIDense*)mat->data;
665:   PetscErrorCode    ierr;
666:   PetscMPIInt       size = mdn->size,rank = mdn->rank;
667:   PetscViewerType   vtype;
668:   PetscTruth        iascii,isdraw;
669:   PetscViewer       sviewer;
670:   PetscViewerFormat format;

673:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
674:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
675:   if (iascii) {
676:     PetscViewerGetType(viewer,&vtype);
677:     PetscViewerGetFormat(viewer,&format);
678:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
679:       MatInfo info;
680:       MatGetInfo(mat,MAT_LOCAL,&info);
681:       PetscViewerASCIISynchronizedPrintf(viewer,"  [%d] local rows %D nz %D nz alloced %D mem %D \n",rank,mat->rmap.n,
682:                    (PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
683:       PetscViewerFlush(viewer);
684:       VecScatterView(mdn->Mvctx,viewer);
685:       return(0);
686:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
687:       return(0);
688:     }
689:   } else if (isdraw) {
690:     PetscDraw  draw;
691:     PetscTruth isnull;

693:     PetscViewerDrawGetDraw(viewer,0,&draw);
694:     PetscDrawIsNull(draw,&isnull);
695:     if (isnull) return(0);
696:   }

698:   if (size == 1) {
699:     MatView(mdn->A,viewer);
700:   } else {
701:     /* assemble the entire matrix onto first processor. */
702:     Mat         A;
703:     PetscInt    M = mat->rmap.N,N = mat->cmap.N,m,row,i,nz;
704:     PetscInt    *cols;
705:     PetscScalar *vals;

707:     MatCreate(((PetscObject)mat)->comm,&A);
708:     if (!rank) {
709:       MatSetSizes(A,M,N,M,N);
710:     } else {
711:       MatSetSizes(A,0,0,M,N);
712:     }
713:     /* Since this is a temporary matrix, MATMPIDENSE instead of ((PetscObject)A)->type_name here is probably acceptable. */
714:     MatSetType(A,MATMPIDENSE);
715:     MatMPIDenseSetPreallocation(A,PETSC_NULL);
716:     PetscLogObjectParent(mat,A);

718:     /* Copy the matrix ... This isn't the most efficient means,
719:        but it's quick for now */
720:     A->insertmode = INSERT_VALUES;
721:     row = mat->rmap.rstart; m = mdn->A->rmap.n;
722:     for (i=0; i<m; i++) {
723:       MatGetRow_MPIDense(mat,row,&nz,&cols,&vals);
724:       MatSetValues_MPIDense(A,1,&row,nz,cols,vals,INSERT_VALUES);
725:       MatRestoreRow_MPIDense(mat,row,&nz,&cols,&vals);
726:       row++;
727:     }

729:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
730:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
731:     PetscViewerGetSingleton(viewer,&sviewer);
732:     if (!rank) {
733:       MatView(((Mat_MPIDense*)(A->data))->A,sviewer);
734:     }
735:     PetscViewerRestoreSingleton(viewer,&sviewer);
736:     PetscViewerFlush(viewer);
737:     MatDestroy(A);
738:   }
739:   return(0);
740: }

744: PetscErrorCode MatView_MPIDense(Mat mat,PetscViewer viewer)
745: {
747:   PetscTruth     iascii,isbinary,isdraw,issocket;
748: 
750: 
751:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
752:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
753:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
754:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);

756:   if (iascii || issocket || isdraw) {
757:     MatView_MPIDense_ASCIIorDraworSocket(mat,viewer);
758:   } else if (isbinary) {
759:     MatView_MPIDense_Binary(mat,viewer);
760:   } else {
761:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPI dense matrix",((PetscObject)viewer)->type_name);
762:   }
763:   return(0);
764: }

768: PetscErrorCode MatGetInfo_MPIDense(Mat A,MatInfoType flag,MatInfo *info)
769: {
770:   Mat_MPIDense   *mat = (Mat_MPIDense*)A->data;
771:   Mat            mdn = mat->A;
773:   PetscReal      isend[5],irecv[5];

776:   info->rows_global    = (double)A->rmap.N;
777:   info->columns_global = (double)A->cmap.N;
778:   info->rows_local     = (double)A->rmap.n;
779:   info->columns_local  = (double)A->cmap.N;
780:   info->block_size     = 1.0;
781:   MatGetInfo(mdn,MAT_LOCAL,info);
782:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
783:   isend[3] = info->memory;  isend[4] = info->mallocs;
784:   if (flag == MAT_LOCAL) {
785:     info->nz_used      = isend[0];
786:     info->nz_allocated = isend[1];
787:     info->nz_unneeded  = isend[2];
788:     info->memory       = isend[3];
789:     info->mallocs      = isend[4];
790:   } else if (flag == MAT_GLOBAL_MAX) {
791:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,((PetscObject)A)->comm);
792:     info->nz_used      = irecv[0];
793:     info->nz_allocated = irecv[1];
794:     info->nz_unneeded  = irecv[2];
795:     info->memory       = irecv[3];
796:     info->mallocs      = irecv[4];
797:   } else if (flag == MAT_GLOBAL_SUM) {
798:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,((PetscObject)A)->comm);
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:   }
805:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
806:   info->fill_ratio_needed = 0;
807:   info->factor_mallocs    = 0;
808:   return(0);
809: }

813: PetscErrorCode MatSetOption_MPIDense(Mat A,MatOption op,PetscTruth flg)
814: {
815:   Mat_MPIDense   *a = (Mat_MPIDense*)A->data;

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


851: PetscErrorCode MatDiagonalScale_MPIDense(Mat A,Vec ll,Vec rr)
852: {
853:   Mat_MPIDense   *mdn = (Mat_MPIDense*)A->data;
854:   Mat_SeqDense   *mat = (Mat_SeqDense*)mdn->A->data;
855:   PetscScalar    *l,*r,x,*v;
857:   PetscInt       i,j,s2a,s3a,s2,s3,m=mdn->A->rmap.n,n=mdn->A->cmap.n;

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

892: PetscErrorCode MatNorm_MPIDense(Mat A,NormType type,PetscReal *nrm)
893: {
894:   Mat_MPIDense   *mdn = (Mat_MPIDense*)A->data;
895:   Mat_SeqDense   *mat = (Mat_SeqDense*)mdn->A->data;
897:   PetscInt       i,j;
898:   PetscReal      sum = 0.0;
899:   PetscScalar    *v = mat->v;

902:   if (mdn->size == 1) {
903:      MatNorm(mdn->A,type,nrm);
904:   } else {
905:     if (type == NORM_FROBENIUS) {
906:       for (i=0; i<mdn->A->cmap.n*mdn->A->rmap.n; i++) {
907: #if defined(PETSC_USE_COMPLEX)
908:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
909: #else
910:         sum += (*v)*(*v); v++;
911: #endif
912:       }
913:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPI_SUM,((PetscObject)A)->comm);
914:       *nrm = sqrt(*nrm);
915:       PetscLogFlops(2*mdn->A->cmap.n*mdn->A->rmap.n);
916:     } else if (type == NORM_1) {
917:       PetscReal *tmp,*tmp2;
918:       PetscMalloc(2*A->cmap.N*sizeof(PetscReal),&tmp);
919:       tmp2 = tmp + A->cmap.N;
920:       PetscMemzero(tmp,2*A->cmap.N*sizeof(PetscReal));
921:       *nrm = 0.0;
922:       v = mat->v;
923:       for (j=0; j<mdn->A->cmap.n; j++) {
924:         for (i=0; i<mdn->A->rmap.n; i++) {
925:           tmp[j] += PetscAbsScalar(*v);  v++;
926:         }
927:       }
928:       MPI_Allreduce(tmp,tmp2,A->cmap.N,MPIU_REAL,MPI_SUM,((PetscObject)A)->comm);
929:       for (j=0; j<A->cmap.N; j++) {
930:         if (tmp2[j] > *nrm) *nrm = tmp2[j];
931:       }
932:       PetscFree(tmp);
933:       PetscLogFlops(A->cmap.n*A->rmap.n);
934:     } else if (type == NORM_INFINITY) { /* max row norm */
935:       PetscReal ntemp;
936:       MatNorm(mdn->A,type,&ntemp);
937:       MPI_Allreduce(&ntemp,nrm,1,MPIU_REAL,MPI_MAX,((PetscObject)A)->comm);
938:     } else {
939:       SETERRQ(PETSC_ERR_SUP,"No support for two norm");
940:     }
941:   }
942:   return(0);
943: }

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

958:   if (!matout && M != N) {
959:     SETERRQ(PETSC_ERR_SUP,"Supports square matrix only in-place");
960:   }
961:   MatCreate(((PetscObject)A)->comm,&B);
962:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,N,M);
963:   MatSetType(B,((PetscObject)A)->type_name);
964:   MatMPIDenseSetPreallocation(B,PETSC_NULL);

966:   m = a->A->rmap.n; n = a->A->cmap.n; v = Aloc->v;
967:   PetscMalloc(m*sizeof(PetscInt),&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 (matout) {
977:     *matout = B;
978:   } else {
979:     MatHeaderCopy(A,B);
980:   }
981:   return(0);
982: }

984:  #include petscblaslapack.h
987: PetscErrorCode MatScale_MPIDense(Mat inA,PetscScalar alpha)
988: {
989:   Mat_MPIDense   *A = (Mat_MPIDense*)inA->data;
990:   Mat_SeqDense   *a = (Mat_SeqDense*)A->A->data;
991:   PetscScalar    oalpha = alpha;
992:   PetscBLASInt   one = 1,nz = (PetscBLASInt)inA->rmap.n*inA->cmap.N;

996:   BLASscal_(&nz,&oalpha,a->v,&one);
997:   PetscLogFlops(nz);
998:   return(0);
999: }

1001: static PetscErrorCode MatDuplicate_MPIDense(Mat,MatDuplicateOption,Mat *);

1005: PetscErrorCode MatSetUpPreallocation_MPIDense(Mat A)
1006: {

1010:    MatMPIDenseSetPreallocation(A,0);
1011:   return(0);
1012: }

1016: PetscErrorCode MatMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C)
1017: {
1019:   PetscInt       m=A->rmap.n,n=B->cmap.n;
1020:   Mat            Cmat;

1023:   if (A->cmap.n != B->rmap.n) SETERRQ2(PETSC_ERR_ARG_SIZ,"A->cmap.n %d != B->rmap.n %d\n",A->cmap.n,B->rmap.n);
1024:   MatCreate(((PetscObject)B)->comm,&Cmat);
1025:   MatSetSizes(Cmat,m,n,A->rmap.N,B->cmap.N);
1026:   MatSetType(Cmat,MATMPIDENSE);
1027:   MatMPIDenseSetPreallocation(Cmat,PETSC_NULL);
1028:   MatAssemblyBegin(Cmat,MAT_FINAL_ASSEMBLY);
1029:   MatAssemblyEnd(Cmat,MAT_FINAL_ASSEMBLY);
1030:   *C = Cmat;
1031:   return(0);
1032: }

1034: /* -------------------------------------------------------------------*/
1035: static struct _MatOps MatOps_Values = {MatSetValues_MPIDense,
1036:        MatGetRow_MPIDense,
1037:        MatRestoreRow_MPIDense,
1038:        MatMult_MPIDense,
1039: /* 4*/ MatMultAdd_MPIDense,
1040:        MatMultTranspose_MPIDense,
1041:        MatMultTransposeAdd_MPIDense,
1042:        0,
1043:        0,
1044:        0,
1045: /*10*/ 0,
1046:        0,
1047:        0,
1048:        0,
1049:        MatTranspose_MPIDense,
1050: /*15*/ MatGetInfo_MPIDense,
1051:        MatEqual_MPIDense,
1052:        MatGetDiagonal_MPIDense,
1053:        MatDiagonalScale_MPIDense,
1054:        MatNorm_MPIDense,
1055: /*20*/ MatAssemblyBegin_MPIDense,
1056:        MatAssemblyEnd_MPIDense,
1057:        0,
1058:        MatSetOption_MPIDense,
1059:        MatZeroEntries_MPIDense,
1060: /*25*/ MatZeroRows_MPIDense,
1061:        MatLUFactorSymbolic_MPIDense,
1062:        0,
1063:        MatCholeskyFactorSymbolic_MPIDense,
1064:        0,
1065: /*30*/ MatSetUpPreallocation_MPIDense,
1066:        0,
1067:        0,
1068:        MatGetArray_MPIDense,
1069:        MatRestoreArray_MPIDense,
1070: /*35*/ MatDuplicate_MPIDense,
1071:        0,
1072:        0,
1073:        0,
1074:        0,
1075: /*40*/ 0,
1076:        MatGetSubMatrices_MPIDense,
1077:        0,
1078:        MatGetValues_MPIDense,
1079:        0,
1080: /*45*/ 0,
1081:        MatScale_MPIDense,
1082:        0,
1083:        0,
1084:        0,
1085: /*50*/ 0,
1086:        0,
1087:        0,
1088:        0,
1089:        0,
1090: /*55*/ 0,
1091:        0,
1092:        0,
1093:        0,
1094:        0,
1095: /*60*/ MatGetSubMatrix_MPIDense,
1096:        MatDestroy_MPIDense,
1097:        MatView_MPIDense,
1098:        0,
1099:        0,
1100: /*65*/ 0,
1101:        0,
1102:        0,
1103:        0,
1104:        0,
1105: /*70*/ 0,
1106:        0,
1107:        0,
1108:        0,
1109:        0,
1110: /*75*/ 0,
1111:        0,
1112:        0,
1113:        0,
1114:        0,
1115: /*80*/ 0,
1116:        0,
1117:        0,
1118:        0,
1119: /*84*/ MatLoad_MPIDense,
1120:        0,
1121:        0,
1122:        0,
1123:        0,
1124:        0,
1125: /*90*/ 0,
1126:        MatMatMultSymbolic_MPIDense_MPIDense,
1127:        0,
1128:        0,
1129:        0,
1130: /*95*/ 0,
1131:        0,
1132:        0,
1133:        0};

1138: PetscErrorCode  MatMPIDenseSetPreallocation_MPIDense(Mat mat,PetscScalar *data)
1139: {
1140:   Mat_MPIDense   *a;

1144:   mat->preallocated = PETSC_TRUE;
1145:   /* Note:  For now, when data is specified above, this assumes the user correctly
1146:    allocates the local dense storage space.  We should add error checking. */

1148:   a    = (Mat_MPIDense*)mat->data;
1149:   MatCreate(PETSC_COMM_SELF,&a->A);
1150:   MatSetSizes(a->A,mat->rmap.n,mat->cmap.N,mat->rmap.n,mat->cmap.N);
1151:   MatSetType(a->A,MATSEQDENSE);
1152:   MatSeqDenseSetPreallocation(a->A,data);
1153:   PetscLogObjectParent(mat,a->A);
1154:   return(0);
1155: }

1158: /*MC
1159:    MATMPIDENSE - MATMPIDENSE = "mpidense" - A matrix type to be used for distributed dense matrices.

1161:    Options Database Keys:
1162: . -mat_type mpidense - sets the matrix type to "mpidense" during a call to MatSetFromOptions()

1164:   Level: beginner

1166: .seealso: MatCreateMPIDense
1167: M*/

1172: PetscErrorCode  MatCreate_MPIDense(Mat mat)
1173: {
1174:   Mat_MPIDense   *a;

1178:   PetscNewLog(mat,Mat_MPIDense,&a);
1179:   mat->data         = (void*)a;
1180:   PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));
1181:   mat->factor       = 0;
1182:   mat->mapping      = 0;

1184:   a->factor       = 0;
1185:   mat->insertmode = NOT_SET_VALUES;
1186:   MPI_Comm_rank(((PetscObject)mat)->comm,&a->rank);
1187:   MPI_Comm_size(((PetscObject)mat)->comm,&a->size);

1189:   mat->rmap.bs = mat->cmap.bs = 1;
1190:   PetscMapSetUp(&mat->rmap);
1191:   PetscMapSetUp(&mat->cmap);
1192:   a->nvec = mat->cmap.n;

1194:   /* build cache for off array entries formed */
1195:   a->donotstash = PETSC_FALSE;
1196:   MatStashCreate_Private(((PetscObject)mat)->comm,1,&mat->stash);

1198:   /* stuff used for matrix vector multiply */
1199:   a->lvec        = 0;
1200:   a->Mvctx       = 0;
1201:   a->roworiented = PETSC_TRUE;

1203:   PetscObjectComposeFunctionDynamic((PetscObject)mat,"MatGetDiagonalBlock_C",
1204:                                      "MatGetDiagonalBlock_MPIDense",
1205:                                      MatGetDiagonalBlock_MPIDense);
1206:   PetscObjectComposeFunctionDynamic((PetscObject)mat,"MatMPIDenseSetPreallocation_C",
1207:                                      "MatMPIDenseSetPreallocation_MPIDense",
1208:                                      MatMPIDenseSetPreallocation_MPIDense);
1209:   PetscObjectComposeFunctionDynamic((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",
1210:                                      "MatMatMult_MPIAIJ_MPIDense",
1211:                                       MatMatMult_MPIAIJ_MPIDense);
1212:   PetscObjectComposeFunctionDynamic((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",
1213:                                      "MatMatMultSymbolic_MPIAIJ_MPIDense",
1214:                                       MatMatMultSymbolic_MPIAIJ_MPIDense);
1215:   PetscObjectComposeFunctionDynamic((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",
1216:                                      "MatMatMultNumeric_MPIAIJ_MPIDense",
1217:                                       MatMatMultNumeric_MPIAIJ_MPIDense);
1218:   PetscObjectChangeTypeName((PetscObject)mat,MATMPIDENSE);
1219:   return(0);
1220: }

1223: /*MC
1224:    MATDENSE - MATDENSE = "dense" - A matrix type to be used for dense matrices.

1226:    This matrix type is identical to MATSEQDENSE when constructed with a single process communicator,
1227:    and MATMPIDENSE otherwise.

1229:    Options Database Keys:
1230: . -mat_type dense - sets the matrix type to "dense" during a call to MatSetFromOptions()

1232:   Level: beginner

1234: .seealso: MatCreateMPIDense,MATSEQDENSE,MATMPIDENSE
1235: M*/

1240: PetscErrorCode  MatCreate_Dense(Mat A)
1241: {
1243:   PetscMPIInt    size;

1246:   MPI_Comm_size(((PetscObject)A)->comm,&size);
1247:   if (size == 1) {
1248:     MatSetType(A,MATSEQDENSE);
1249:   } else {
1250:     MatSetType(A,MATMPIDENSE);
1251:   }
1252:   return(0);
1253: }

1258: /*@C
1259:    MatMPIDenseSetPreallocation - Sets the array used to store the matrix entries

1261:    Not collective

1263:    Input Parameters:
1264: .  A - the matrix
1265: -  data - optional location of matrix data.  Set data=PETSC_NULL for PETSc
1266:    to control all matrix memory allocation.

1268:    Notes:
1269:    The dense format is fully compatible with standard Fortran 77
1270:    storage by columns.

1272:    The data input variable is intended primarily for Fortran programmers
1273:    who wish to allocate their own matrix memory space.  Most users should
1274:    set data=PETSC_NULL.

1276:    Level: intermediate

1278: .keywords: matrix,dense, parallel

1280: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
1281: @*/
1282: PetscErrorCode  MatMPIDenseSetPreallocation(Mat mat,PetscScalar *data)
1283: {
1284:   PetscErrorCode ierr,(*f)(Mat,PetscScalar *);

1287:   PetscObjectQueryFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",(void (**)(void))&f);
1288:   if (f) {
1289:     (*f)(mat,data);
1290:   }
1291:   return(0);
1292: }

1296: /*@C
1297:    MatCreateMPIDense - Creates a sparse parallel matrix in dense format.

1299:    Collective on MPI_Comm

1301:    Input Parameters:
1302: +  comm - MPI communicator
1303: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1304: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1305: .  M - number of global rows (or PETSC_DECIDE to have calculated if m is given)
1306: .  N - number of global columns (or PETSC_DECIDE to have calculated if n is given)
1307: -  data - optional location of matrix data.  Set data=PETSC_NULL (PETSC_NULL_SCALAR for Fortran users) for PETSc
1308:    to control all matrix memory allocation.

1310:    Output Parameter:
1311: .  A - the matrix

1313:    Notes:
1314:    The dense format is fully compatible with standard Fortran 77
1315:    storage by columns.

1317:    The data input variable is intended primarily for Fortran programmers
1318:    who wish to allocate their own matrix memory space.  Most users should
1319:    set data=PETSC_NULL (PETSC_NULL_SCALAR for Fortran users).

1321:    The user MUST specify either the local or global matrix dimensions
1322:    (possibly both).

1324:    Level: intermediate

1326: .keywords: matrix,dense, parallel

1328: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
1329: @*/
1330: PetscErrorCode  MatCreateMPIDense(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscScalar *data,Mat *A)
1331: {
1333:   PetscMPIInt    size;

1336:   MatCreate(comm,A);
1337:   MatSetSizes(*A,m,n,M,N);
1338:   MPI_Comm_size(comm,&size);
1339:   if (size > 1) {
1340:     MatSetType(*A,MATMPIDENSE);
1341:     MatMPIDenseSetPreallocation(*A,data);
1342:   } else {
1343:     MatSetType(*A,MATSEQDENSE);
1344:     MatSeqDenseSetPreallocation(*A,data);
1345:   }
1346:   return(0);
1347: }

1351: static PetscErrorCode MatDuplicate_MPIDense(Mat A,MatDuplicateOption cpvalues,Mat *newmat)
1352: {
1353:   Mat            mat;
1354:   Mat_MPIDense   *a,*oldmat = (Mat_MPIDense*)A->data;

1358:   *newmat       = 0;
1359:   MatCreate(((PetscObject)A)->comm,&mat);
1360:   MatSetSizes(mat,A->rmap.n,A->cmap.n,A->rmap.N,A->cmap.N);
1361:   MatSetType(mat,((PetscObject)A)->type_name);
1362:   a                 = (Mat_MPIDense*)mat->data;
1363:   PetscMemcpy(mat->ops,A->ops,sizeof(struct _MatOps));
1364:   mat->factor       = A->factor;
1365:   mat->assembled    = PETSC_TRUE;
1366:   mat->preallocated = PETSC_TRUE;

1368:   mat->rmap.rstart     = A->rmap.rstart;
1369:   mat->rmap.rend       = A->rmap.rend;
1370:   a->size              = oldmat->size;
1371:   a->rank              = oldmat->rank;
1372:   mat->insertmode      = NOT_SET_VALUES;
1373:   a->nvec              = oldmat->nvec;
1374:   a->donotstash        = oldmat->donotstash;
1375: 
1376:   PetscMemcpy(mat->rmap.range,A->rmap.range,(a->size+1)*sizeof(PetscInt));
1377:   PetscMemcpy(mat->cmap.range,A->cmap.range,(a->size+1)*sizeof(PetscInt));
1378:   MatStashCreate_Private(((PetscObject)A)->comm,1,&mat->stash);

1380:   MatSetUpMultiply_MPIDense(mat);
1381:   MatDuplicate(oldmat->A,cpvalues,&a->A);
1382:   PetscLogObjectParent(mat,a->A);
1383:   *newmat = mat;
1384:   return(0);
1385: }

1387:  #include petscsys.h

1391: PetscErrorCode MatLoad_MPIDense_DenseInFile(MPI_Comm comm,PetscInt fd,PetscInt M,PetscInt N, MatType type,Mat *newmat)
1392: {
1394:   PetscMPIInt    rank,size;
1395:   PetscInt       *rowners,i,m,nz,j;
1396:   PetscScalar    *array,*vals,*vals_ptr;
1397:   MPI_Status     status;

1400:   MPI_Comm_rank(comm,&rank);
1401:   MPI_Comm_size(comm,&size);

1403:   /* determine ownership of all rows */
1404:   m          = M/size + ((M % size) > rank);
1405:   PetscMalloc((size+2)*sizeof(PetscInt),&rowners);
1406:   MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
1407:   rowners[0] = 0;
1408:   for (i=2; i<=size; i++) {
1409:     rowners[i] += rowners[i-1];
1410:   }

1412:   MatCreate(comm,newmat);
1413:   MatSetSizes(*newmat,m,PETSC_DECIDE,M,N);
1414:   MatSetType(*newmat,type);
1415:   MatMPIDenseSetPreallocation(*newmat,PETSC_NULL);
1416:   MatGetArray(*newmat,&array);

1418:   if (!rank) {
1419:     PetscMalloc(m*N*sizeof(PetscScalar),&vals);

1421:     /* read in my part of the matrix numerical values  */
1422:     PetscBinaryRead(fd,vals,m*N,PETSC_SCALAR);
1423: 
1424:     /* insert into matrix-by row (this is why cannot directly read into array */
1425:     vals_ptr = vals;
1426:     for (i=0; i<m; i++) {
1427:       for (j=0; j<N; j++) {
1428:         array[i + j*m] = *vals_ptr++;
1429:       }
1430:     }

1432:     /* read in other processors and ship out */
1433:     for (i=1; i<size; i++) {
1434:       nz   = (rowners[i+1] - rowners[i])*N;
1435:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1436:       MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)(*newmat))->tag,comm);
1437:     }
1438:   } else {
1439:     /* receive numeric values */
1440:     PetscMalloc(m*N*sizeof(PetscScalar),&vals);

1442:     /* receive message of values*/
1443:     MPI_Recv(vals,m*N,MPIU_SCALAR,0,((PetscObject)(*newmat))->tag,comm,&status);

1445:     /* insert into matrix-by row (this is why cannot directly read into array */
1446:     vals_ptr = vals;
1447:     for (i=0; i<m; i++) {
1448:       for (j=0; j<N; j++) {
1449:         array[i + j*m] = *vals_ptr++;
1450:       }
1451:     }
1452:   }
1453:   PetscFree(rowners);
1454:   PetscFree(vals);
1455:   MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
1456:   MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);
1457:   return(0);
1458: }

1462: PetscErrorCode MatLoad_MPIDense(PetscViewer viewer, MatType type,Mat *newmat)
1463: {
1464:   Mat            A;
1465:   PetscScalar    *vals,*svals;
1466:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
1467:   MPI_Status     status;
1468:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag,*rowners,*sndcounts,m,maxnz;
1469:   PetscInt       header[4],*rowlengths = 0,M,N,*cols;
1470:   PetscInt       *ourlens,*procsnz = 0,*offlens,jj,*mycols,*smycols;
1471:   PetscInt       i,nz,j,rstart,rend;
1472:   int            fd;

1476:   MPI_Comm_size(comm,&size);
1477:   MPI_Comm_rank(comm,&rank);
1478:   if (!rank) {
1479:     PetscViewerBinaryGetDescriptor(viewer,&fd);
1480:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
1481:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
1482:   }

1484:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
1485:   M = header[1]; N = header[2]; nz = header[3];

1487:   /*
1488:        Handle case where matrix is stored on disk as a dense matrix 
1489:   */
1490:   if (nz == MATRIX_BINARY_FORMAT_DENSE) {
1491:     MatLoad_MPIDense_DenseInFile(comm,fd,M,N,type,newmat);
1492:     return(0);
1493:   }

1495:   /* determine ownership of all rows */
1496:   m          = M/size + ((M % size) > rank);
1497:   PetscMalloc((size+2)*sizeof(PetscInt),&rowners);
1498:   MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);
1499:   rowners[0] = 0;
1500:   for (i=2; i<=size; i++) {
1501:     rowners[i] += rowners[i-1];
1502:   }
1503:   rstart = rowners[rank];
1504:   rend   = rowners[rank+1];

1506:   /* distribute row lengths to all processors */
1507:   PetscMalloc(2*(rend-rstart+1)*sizeof(PetscInt),&ourlens);
1508:   offlens = ourlens + (rend-rstart);
1509:   if (!rank) {
1510:     PetscMalloc(M*sizeof(PetscInt),&rowlengths);
1511:     PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
1512:     PetscMalloc(size*sizeof(PetscMPIInt),&sndcounts);
1513:     for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i];
1514:     MPI_Scatterv(rowlengths,sndcounts,rowners,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
1515:     PetscFree(sndcounts);
1516:   } else {
1517:     MPI_Scatterv(0,0,0,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
1518:   }

1520:   if (!rank) {
1521:     /* calculate the number of nonzeros on each processor */
1522:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
1523:     PetscMemzero(procsnz,size*sizeof(PetscInt));
1524:     for (i=0; i<size; i++) {
1525:       for (j=rowners[i]; j< rowners[i+1]; j++) {
1526:         procsnz[i] += rowlengths[j];
1527:       }
1528:     }
1529:     PetscFree(rowlengths);

1531:     /* determine max buffer needed and allocate it */
1532:     maxnz = 0;
1533:     for (i=0; i<size; i++) {
1534:       maxnz = PetscMax(maxnz,procsnz[i]);
1535:     }
1536:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

1538:     /* read in my part of the matrix column indices  */
1539:     nz = procsnz[0];
1540:     PetscMalloc(nz*sizeof(PetscInt),&mycols);
1541:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);

1543:     /* read in every one elses and ship off */
1544:     for (i=1; i<size; i++) {
1545:       nz   = procsnz[i];
1546:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
1547:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
1548:     }
1549:     PetscFree(cols);
1550:   } else {
1551:     /* determine buffer space needed for message */
1552:     nz = 0;
1553:     for (i=0; i<m; i++) {
1554:       nz += ourlens[i];
1555:     }
1556:     PetscMalloc((nz+1)*sizeof(PetscInt),&mycols);

1558:     /* receive message of column indices*/
1559:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
1560:     MPI_Get_count(&status,MPIU_INT,&maxnz);
1561:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
1562:   }

1564:   /* loop over local rows, determining number of off diagonal entries */
1565:   PetscMemzero(offlens,m*sizeof(PetscInt));
1566:   jj = 0;
1567:   for (i=0; i<m; i++) {
1568:     for (j=0; j<ourlens[i]; j++) {
1569:       if (mycols[jj] < rstart || mycols[jj] >= rend) offlens[i]++;
1570:       jj++;
1571:     }
1572:   }

1574:   /* create our matrix */
1575:   for (i=0; i<m; i++) {
1576:     ourlens[i] -= offlens[i];
1577:   }
1578:   MatCreate(comm,newmat);
1579:   MatSetSizes(*newmat,m,PETSC_DECIDE,M,N);
1580:   MatSetType(*newmat,type);
1581:   MatMPIDenseSetPreallocation(*newmat,PETSC_NULL);
1582:   A = *newmat;
1583:   for (i=0; i<m; i++) {
1584:     ourlens[i] += offlens[i];
1585:   }

1587:   if (!rank) {
1588:     PetscMalloc(maxnz*sizeof(PetscScalar),&vals);

1590:     /* read in my part of the matrix numerical values  */
1591:     nz = procsnz[0];
1592:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1593: 
1594:     /* insert into matrix */
1595:     jj      = rstart;
1596:     smycols = mycols;
1597:     svals   = vals;
1598:     for (i=0; i<m; i++) {
1599:       MatSetValues(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
1600:       smycols += ourlens[i];
1601:       svals   += ourlens[i];
1602:       jj++;
1603:     }

1605:     /* read in other processors and ship out */
1606:     for (i=1; i<size; i++) {
1607:       nz   = procsnz[i];
1608:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1609:       MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)A)->tag,comm);
1610:     }
1611:     PetscFree(procsnz);
1612:   } else {
1613:     /* receive numeric values */
1614:     PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);

1616:     /* receive message of values*/
1617:     MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)A)->tag,comm,&status);
1618:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
1619:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

1621:     /* insert into matrix */
1622:     jj      = rstart;
1623:     smycols = mycols;
1624:     svals   = vals;
1625:     for (i=0; i<m; i++) {
1626:       MatSetValues(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
1627:       smycols += ourlens[i];
1628:       svals   += ourlens[i];
1629:       jj++;
1630:     }
1631:   }
1632:   PetscFree(ourlens);
1633:   PetscFree(vals);
1634:   PetscFree(mycols);
1635:   PetscFree(rowners);

1637:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1638:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1639:   return(0);
1640: }

1644: PetscErrorCode MatEqual_MPIDense(Mat A,Mat B,PetscTruth *flag)
1645: {
1646:   Mat_MPIDense   *matB = (Mat_MPIDense*)B->data,*matA = (Mat_MPIDense*)A->data;
1647:   Mat            a,b;
1648:   PetscTruth     flg;

1652:   a = matA->A;
1653:   b = matB->A;
1654:   MatEqual(a,b,&flg);
1655:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,((PetscObject)A)->comm);
1656:   return(0);
1657: }