Actual source code: mpidense.c
petsc-3.7.3 2016-08-01
2: /*
3: Basic functions for basic parallel dense matrices.
4: */
7: #include <../src/mat/impls/dense/mpi/mpidense.h> /*I "petscmat.h" I*/
8: #include <../src/mat/impls/aij/mpi/mpiaij.h>
9: #include <petscblaslapack.h>
13: /*@
15: MatDenseGetLocalMatrix - For a MATMPIDENSE or MATSEQDENSE matrix returns the sequential
16: matrix that represents the operator. For sequential matrices it returns itself.
18: Input Parameter:
19: . A - the Seq or MPI dense matrix
21: Output Parameter:
22: . B - the inner matrix
24: Level: intermediate
26: @*/
27: PetscErrorCode MatDenseGetLocalMatrix(Mat A,Mat *B)
28: {
29: Mat_MPIDense *mat = (Mat_MPIDense*)A->data;
31: PetscBool flg;
34: PetscObjectTypeCompare((PetscObject)A,MATMPIDENSE,&flg);
35: if (flg) *B = mat->A;
36: else *B = A;
37: return(0);
38: }
42: PetscErrorCode MatGetRow_MPIDense(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
43: {
44: Mat_MPIDense *mat = (Mat_MPIDense*)A->data;
46: PetscInt lrow,rstart = A->rmap->rstart,rend = A->rmap->rend;
49: if (row < rstart || row >= rend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"only local rows");
50: lrow = row - rstart;
51: MatGetRow(mat->A,lrow,nz,(const PetscInt**)idx,(const PetscScalar**)v);
52: return(0);
53: }
57: PetscErrorCode MatRestoreRow_MPIDense(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
58: {
62: if (idx) {PetscFree(*idx);}
63: if (v) {PetscFree(*v);}
64: return(0);
65: }
69: PetscErrorCode MatGetDiagonalBlock_MPIDense(Mat A,Mat *a)
70: {
71: Mat_MPIDense *mdn = (Mat_MPIDense*)A->data;
73: PetscInt m = A->rmap->n,rstart = A->rmap->rstart;
74: PetscScalar *array;
75: MPI_Comm comm;
76: Mat B;
79: if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only square matrices supported.");
81: PetscObjectQuery((PetscObject)A,"DiagonalBlock",(PetscObject*)&B);
82: if (!B) {
83: PetscObjectGetComm((PetscObject)(mdn->A),&comm);
84: MatCreate(comm,&B);
85: MatSetSizes(B,m,m,m,m);
86: MatSetType(B,((PetscObject)mdn->A)->type_name);
87: MatDenseGetArray(mdn->A,&array);
88: MatSeqDenseSetPreallocation(B,array+m*rstart);
89: MatDenseRestoreArray(mdn->A,&array);
90: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
91: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
92: PetscObjectCompose((PetscObject)A,"DiagonalBlock",(PetscObject)B);
93: *a = B;
94: MatDestroy(&B);
95: } else *a = B;
96: return(0);
97: }
101: PetscErrorCode MatSetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
102: {
103: Mat_MPIDense *A = (Mat_MPIDense*)mat->data;
105: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row;
106: PetscBool roworiented = A->roworiented;
109: for (i=0; i<m; i++) {
110: if (idxm[i] < 0) continue;
111: if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
112: if (idxm[i] >= rstart && idxm[i] < rend) {
113: row = idxm[i] - rstart;
114: if (roworiented) {
115: MatSetValues(A->A,1,&row,n,idxn,v+i*n,addv);
116: } else {
117: for (j=0; j<n; j++) {
118: if (idxn[j] < 0) continue;
119: if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
120: MatSetValues(A->A,1,&row,1,&idxn[j],v+i+j*m,addv);
121: }
122: }
123: } else if (!A->donotstash) {
124: mat->assembled = PETSC_FALSE;
125: if (roworiented) {
126: MatStashValuesRow_Private(&mat->stash,idxm[i],n,idxn,v+i*n,PETSC_FALSE);
127: } else {
128: MatStashValuesCol_Private(&mat->stash,idxm[i],n,idxn,v+i,m,PETSC_FALSE);
129: }
130: }
131: }
132: return(0);
133: }
137: PetscErrorCode MatGetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
138: {
139: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
141: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row;
144: for (i=0; i<m; i++) {
145: if (idxm[i] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */
146: if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
147: if (idxm[i] >= rstart && idxm[i] < rend) {
148: row = idxm[i] - rstart;
149: for (j=0; j<n; j++) {
150: if (idxn[j] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */
151: if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
152: MatGetValues(mdn->A,1,&row,1,&idxn[j],v+i*n+j);
153: }
154: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
155: }
156: return(0);
157: }
161: PetscErrorCode MatDenseGetArray_MPIDense(Mat A,PetscScalar *array[])
162: {
163: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
167: MatDenseGetArray(a->A,array);
168: return(0);
169: }
173: static PetscErrorCode MatGetSubMatrix_MPIDense(Mat A,IS isrow,IS iscol,MatReuse scall,Mat *B)
174: {
175: Mat_MPIDense *mat = (Mat_MPIDense*)A->data,*newmatd;
176: Mat_SeqDense *lmat = (Mat_SeqDense*)mat->A->data;
178: PetscInt i,j,rstart,rend,nrows,ncols,Ncols,nlrows,nlcols;
179: const PetscInt *irow,*icol;
180: PetscScalar *av,*bv,*v = lmat->v;
181: Mat newmat;
182: IS iscol_local;
185: ISAllGather(iscol,&iscol_local);
186: ISGetIndices(isrow,&irow);
187: ISGetIndices(iscol_local,&icol);
188: ISGetLocalSize(isrow,&nrows);
189: ISGetLocalSize(iscol,&ncols);
190: ISGetSize(iscol,&Ncols); /* global number of columns, size of iscol_local */
192: /* No parallel redistribution currently supported! Should really check each index set
193: to comfirm that it is OK. ... Currently supports only submatrix same partitioning as
194: original matrix! */
196: MatGetLocalSize(A,&nlrows,&nlcols);
197: MatGetOwnershipRange(A,&rstart,&rend);
199: /* Check submatrix call */
200: if (scall == MAT_REUSE_MATRIX) {
201: /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); */
202: /* Really need to test rows and column sizes! */
203: newmat = *B;
204: } else {
205: /* Create and fill new matrix */
206: MatCreate(PetscObjectComm((PetscObject)A),&newmat);
207: MatSetSizes(newmat,nrows,ncols,PETSC_DECIDE,Ncols);
208: MatSetType(newmat,((PetscObject)A)->type_name);
209: MatMPIDenseSetPreallocation(newmat,NULL);
210: }
212: /* Now extract the data pointers and do the copy, column at a time */
213: newmatd = (Mat_MPIDense*)newmat->data;
214: bv = ((Mat_SeqDense*)newmatd->A->data)->v;
216: for (i=0; i<Ncols; i++) {
217: av = v + ((Mat_SeqDense*)mat->A->data)->lda*icol[i];
218: for (j=0; j<nrows; j++) {
219: *bv++ = av[irow[j] - rstart];
220: }
221: }
223: /* Assemble the matrices so that the correct flags are set */
224: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
225: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
227: /* Free work space */
228: ISRestoreIndices(isrow,&irow);
229: ISRestoreIndices(iscol_local,&icol);
230: ISDestroy(&iscol_local);
231: *B = newmat;
232: return(0);
233: }
237: PetscErrorCode MatDenseRestoreArray_MPIDense(Mat A,PetscScalar *array[])
238: {
239: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
243: MatDenseRestoreArray(a->A,array);
244: return(0);
245: }
249: PetscErrorCode MatAssemblyBegin_MPIDense(Mat mat,MatAssemblyType mode)
250: {
251: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
252: MPI_Comm comm;
254: PetscInt nstash,reallocs;
255: InsertMode addv;
258: PetscObjectGetComm((PetscObject)mat,&comm);
259: /* make sure all processors are either in INSERTMODE or ADDMODE */
260: MPIU_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,comm);
261: if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot mix adds/inserts on different procs");
262: mat->insertmode = addv; /* in case this processor had no cache */
264: MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
265: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
266: PetscInfo2(mdn->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
267: return(0);
268: }
272: PetscErrorCode MatAssemblyEnd_MPIDense(Mat mat,MatAssemblyType mode)
273: {
274: Mat_MPIDense *mdn=(Mat_MPIDense*)mat->data;
276: PetscInt i,*row,*col,flg,j,rstart,ncols;
277: PetscMPIInt n;
278: PetscScalar *val;
281: /* wait on receives */
282: while (1) {
283: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
284: if (!flg) break;
286: for (i=0; i<n;) {
287: /* Now identify the consecutive vals belonging to the same row */
288: for (j=i,rstart=row[j]; j<n; j++) {
289: if (row[j] != rstart) break;
290: }
291: if (j < n) ncols = j-i;
292: else ncols = n-i;
293: /* Now assemble all these values with a single function call */
294: MatSetValues_MPIDense(mat,1,row+i,ncols,col+i,val+i,mat->insertmode);
295: i = j;
296: }
297: }
298: MatStashScatterEnd_Private(&mat->stash);
300: MatAssemblyBegin(mdn->A,mode);
301: MatAssemblyEnd(mdn->A,mode);
303: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
304: MatSetUpMultiply_MPIDense(mat);
305: }
306: return(0);
307: }
311: PetscErrorCode MatZeroEntries_MPIDense(Mat A)
312: {
314: Mat_MPIDense *l = (Mat_MPIDense*)A->data;
317: MatZeroEntries(l->A);
318: return(0);
319: }
321: /* the code does not do the diagonal entries correctly unless the
322: matrix is square and the column and row owerships are identical.
323: This is a BUG. The only way to fix it seems to be to access
324: mdn->A and mdn->B directly and not through the MatZeroRows()
325: routine.
326: */
329: PetscErrorCode MatZeroRows_MPIDense(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
330: {
331: Mat_MPIDense *l = (Mat_MPIDense*)A->data;
332: PetscErrorCode ierr;
333: PetscInt i,*owners = A->rmap->range;
334: PetscInt *sizes,j,idx,nsends;
335: PetscInt nmax,*svalues,*starts,*owner,nrecvs;
336: PetscInt *rvalues,tag = ((PetscObject)A)->tag,count,base,slen,*source;
337: PetscInt *lens,*lrows,*values;
338: PetscMPIInt n,imdex,rank = l->rank,size = l->size;
339: MPI_Comm comm;
340: MPI_Request *send_waits,*recv_waits;
341: MPI_Status recv_status,*send_status;
342: PetscBool found;
343: const PetscScalar *xx;
344: PetscScalar *bb;
347: PetscObjectGetComm((PetscObject)A,&comm);
348: if (A->rmap->N != A->cmap->N) SETERRQ(comm,PETSC_ERR_SUP,"Only handles square matrices");
349: if (A->rmap->n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only handles matrices with identical column and row ownership");
350: /* first count number of contributors to each processor */
351: PetscCalloc1(2*size,&sizes);
352: PetscMalloc1(N+1,&owner); /* see note*/
353: for (i=0; i<N; i++) {
354: idx = rows[i];
355: found = PETSC_FALSE;
356: for (j=0; j<size; j++) {
357: if (idx >= owners[j] && idx < owners[j+1]) {
358: sizes[2*j]++; sizes[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break;
359: }
360: }
361: if (!found) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
362: }
363: nsends = 0;
364: for (i=0; i<size; i++) nsends += sizes[2*i+1];
366: /* inform other processors of number of messages and max length*/
367: PetscMaxSum(comm,sizes,&nmax,&nrecvs);
369: /* post receives: */
370: PetscMalloc1((nrecvs+1)*(nmax+1),&rvalues);
371: PetscMalloc1(nrecvs+1,&recv_waits);
372: for (i=0; i<nrecvs; i++) {
373: MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
374: }
376: /* do sends:
377: 1) starts[i] gives the starting index in svalues for stuff going to
378: the ith processor
379: */
380: PetscMalloc1(N+1,&svalues);
381: PetscMalloc1(nsends+1,&send_waits);
382: PetscMalloc1(size+1,&starts);
384: starts[0] = 0;
385: for (i=1; i<size; i++) starts[i] = starts[i-1] + sizes[2*i-2];
386: for (i=0; i<N; i++) svalues[starts[owner[i]]++] = rows[i];
388: starts[0] = 0;
389: for (i=1; i<size+1; i++) starts[i] = starts[i-1] + sizes[2*i-2];
390: count = 0;
391: for (i=0; i<size; i++) {
392: if (sizes[2*i+1]) {
393: MPI_Isend(svalues+starts[i],sizes[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
394: }
395: }
396: PetscFree(starts);
398: base = owners[rank];
400: /* wait on receives */
401: PetscMalloc2(nrecvs,&lens,nrecvs,&source);
402: count = nrecvs;
403: slen = 0;
404: while (count) {
405: MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
406: /* unpack receives into our local space */
407: MPI_Get_count(&recv_status,MPIU_INT,&n);
409: source[imdex] = recv_status.MPI_SOURCE;
410: lens[imdex] = n;
411: slen += n;
412: count--;
413: }
414: PetscFree(recv_waits);
416: /* move the data into the send scatter */
417: PetscMalloc1(slen+1,&lrows);
418: count = 0;
419: for (i=0; i<nrecvs; i++) {
420: values = rvalues + i*nmax;
421: for (j=0; j<lens[i]; j++) {
422: lrows[count++] = values[j] - base;
423: }
424: }
425: PetscFree(rvalues);
426: PetscFree2(lens,source);
427: PetscFree(owner);
428: PetscFree(sizes);
430: /* fix right hand side if needed */
431: if (x && b) {
432: VecGetArrayRead(x,&xx);
433: VecGetArray(b,&bb);
434: for (i=0; i<slen; i++) {
435: bb[lrows[i]] = diag*xx[lrows[i]];
436: }
437: VecRestoreArrayRead(x,&xx);
438: VecRestoreArray(b,&bb);
439: }
441: /* actually zap the local rows */
442: MatZeroRows(l->A,slen,lrows,0.0,0,0);
443: if (diag != 0.0) {
444: Mat_SeqDense *ll = (Mat_SeqDense*)l->A->data;
445: PetscInt m = ll->lda, i;
447: for (i=0; i<slen; i++) {
448: ll->v[lrows[i] + m*(A->cmap->rstart + lrows[i])] = diag;
449: }
450: }
451: PetscFree(lrows);
453: /* wait on sends */
454: if (nsends) {
455: PetscMalloc1(nsends,&send_status);
456: MPI_Waitall(nsends,send_waits,send_status);
457: PetscFree(send_status);
458: }
459: PetscFree(send_waits);
460: PetscFree(svalues);
461: return(0);
462: }
464: PETSC_INTERN PetscErrorCode MatMult_SeqDense(Mat,Vec,Vec);
465: PETSC_INTERN PetscErrorCode MatMultAdd_SeqDense(Mat,Vec,Vec,Vec);
466: PETSC_INTERN PetscErrorCode MatMultTranspose_SeqDense(Mat,Vec,Vec);
467: PETSC_INTERN PetscErrorCode MatMultTransposeAdd_SeqDense(Mat,Vec,Vec,Vec);
471: PetscErrorCode MatMult_MPIDense(Mat mat,Vec xx,Vec yy)
472: {
473: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
477: VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
478: VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
479: MatMult_SeqDense(mdn->A,mdn->lvec,yy);
480: return(0);
481: }
485: PetscErrorCode MatMultAdd_MPIDense(Mat mat,Vec xx,Vec yy,Vec zz)
486: {
487: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
491: VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
492: VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
493: MatMultAdd_SeqDense(mdn->A,mdn->lvec,yy,zz);
494: return(0);
495: }
499: PetscErrorCode MatMultTranspose_MPIDense(Mat A,Vec xx,Vec yy)
500: {
501: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
503: PetscScalar zero = 0.0;
506: VecSet(yy,zero);
507: MatMultTranspose_SeqDense(a->A,xx,a->lvec);
508: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
509: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
510: return(0);
511: }
515: PetscErrorCode MatMultTransposeAdd_MPIDense(Mat A,Vec xx,Vec yy,Vec zz)
516: {
517: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
521: VecCopy(yy,zz);
522: MatMultTranspose_SeqDense(a->A,xx,a->lvec);
523: VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
524: VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
525: return(0);
526: }
530: PetscErrorCode MatGetDiagonal_MPIDense(Mat A,Vec v)
531: {
532: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
533: Mat_SeqDense *aloc = (Mat_SeqDense*)a->A->data;
535: PetscInt len,i,n,m = A->rmap->n,radd;
536: PetscScalar *x,zero = 0.0;
539: VecSet(v,zero);
540: VecGetArray(v,&x);
541: VecGetSize(v,&n);
542: if (n != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming mat and vec");
543: len = PetscMin(a->A->rmap->n,a->A->cmap->n);
544: radd = A->rmap->rstart*m;
545: for (i=0; i<len; i++) {
546: x[i] = aloc->v[radd + i*m + i];
547: }
548: VecRestoreArray(v,&x);
549: return(0);
550: }
554: PetscErrorCode MatDestroy_MPIDense(Mat mat)
555: {
556: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
560: #if defined(PETSC_USE_LOG)
561: PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N);
562: #endif
563: MatStashDestroy_Private(&mat->stash);
564: MatDestroy(&mdn->A);
565: VecDestroy(&mdn->lvec);
566: VecScatterDestroy(&mdn->Mvctx);
568: PetscFree(mat->data);
569: PetscObjectChangeTypeName((PetscObject)mat,0);
571: PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",NULL);
572: PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",NULL);
573: #if defined(PETSC_HAVE_ELEMENTAL)
574: PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpidense_elemental_C",NULL);
575: #endif
576: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",NULL);
577: PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",NULL);
578: PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",NULL);
579: PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",NULL);
580: PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",NULL);
581: PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMult_mpiaij_mpidense_C",NULL);
582: PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultSymbolic_mpiaij_mpidense_C",NULL);
583: PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultNumeric_mpiaij_mpidense_C",NULL);
584: return(0);
585: }
589: static PetscErrorCode MatView_MPIDense_Binary(Mat mat,PetscViewer viewer)
590: {
591: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
592: PetscErrorCode ierr;
593: PetscViewerFormat format;
594: int fd;
595: PetscInt header[4],mmax,N = mat->cmap->N,i,j,m,k;
596: PetscMPIInt rank,tag = ((PetscObject)viewer)->tag,size;
597: PetscScalar *work,*v,*vv;
598: Mat_SeqDense *a = (Mat_SeqDense*)mdn->A->data;
601: if (mdn->size == 1) {
602: MatView(mdn->A,viewer);
603: } else {
604: PetscViewerBinaryGetDescriptor(viewer,&fd);
605: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
606: MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
608: PetscViewerGetFormat(viewer,&format);
609: if (format == PETSC_VIEWER_NATIVE) {
611: if (!rank) {
612: /* store the matrix as a dense matrix */
613: header[0] = MAT_FILE_CLASSID;
614: header[1] = mat->rmap->N;
615: header[2] = N;
616: header[3] = MATRIX_BINARY_FORMAT_DENSE;
617: PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
619: /* get largest work array needed for transposing array */
620: mmax = mat->rmap->n;
621: for (i=1; i<size; i++) {
622: mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]);
623: }
624: PetscMalloc1(mmax*N,&work);
626: /* write out local array, by rows */
627: m = mat->rmap->n;
628: v = a->v;
629: for (j=0; j<N; j++) {
630: for (i=0; i<m; i++) {
631: work[j + i*N] = *v++;
632: }
633: }
634: PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
635: /* get largest work array to receive messages from other processes, excludes process zero */
636: mmax = 0;
637: for (i=1; i<size; i++) {
638: mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]);
639: }
640: PetscMalloc1(mmax*N,&vv);
641: for (k = 1; k < size; k++) {
642: v = vv;
643: m = mat->rmap->range[k+1] - mat->rmap->range[k];
644: MPIULong_Recv(v,m*N,MPIU_SCALAR,k,tag,PetscObjectComm((PetscObject)mat));
646: for (j = 0; j < N; j++) {
647: for (i = 0; i < m; i++) {
648: work[j + i*N] = *v++;
649: }
650: }
651: PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
652: }
653: PetscFree(work);
654: PetscFree(vv);
655: } else {
656: MPIULong_Send(a->v,mat->rmap->n*mat->cmap->N,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));
657: }
658: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"To store a parallel dense matrix you must first call PetscViewerPushFormat(viewer,PETSC_VIEWER_NATIVE)");
659: }
660: return(0);
661: }
663: extern PetscErrorCode MatView_SeqDense(Mat,PetscViewer);
664: #include <petscdraw.h>
667: static PetscErrorCode MatView_MPIDense_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
668: {
669: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
670: PetscErrorCode ierr;
671: PetscMPIInt rank = mdn->rank;
672: PetscViewerType vtype;
673: PetscBool iascii,isdraw;
674: PetscViewer sviewer;
675: PetscViewerFormat format;
678: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
679: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
680: if (iascii) {
681: PetscViewerGetType(viewer,&vtype);
682: PetscViewerGetFormat(viewer,&format);
683: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
684: MatInfo info;
685: MatGetInfo(mat,MAT_LOCAL,&info);
686: PetscViewerASCIIPushSynchronized(viewer);
687: PetscViewerASCIISynchronizedPrintf(viewer," [%d] local rows %D nz %D nz alloced %D mem %D \n",rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
688: PetscViewerFlush(viewer);
689: PetscViewerASCIIPopSynchronized(viewer);
690: VecScatterView(mdn->Mvctx,viewer);
691: return(0);
692: } else if (format == PETSC_VIEWER_ASCII_INFO) {
693: return(0);
694: }
695: } else if (isdraw) {
696: PetscDraw draw;
697: PetscBool isnull;
699: PetscViewerDrawGetDraw(viewer,0,&draw);
700: PetscDrawIsNull(draw,&isnull);
701: if (isnull) return(0);
702: }
704: {
705: /* assemble the entire matrix onto first processor. */
706: Mat A;
707: PetscInt M = mat->rmap->N,N = mat->cmap->N,m,row,i,nz;
708: PetscInt *cols;
709: PetscScalar *vals;
711: MatCreate(PetscObjectComm((PetscObject)mat),&A);
712: if (!rank) {
713: MatSetSizes(A,M,N,M,N);
714: } else {
715: MatSetSizes(A,0,0,M,N);
716: }
717: /* Since this is a temporary matrix, MATMPIDENSE instead of ((PetscObject)A)->type_name here is probably acceptable. */
718: MatSetType(A,MATMPIDENSE);
719: MatMPIDenseSetPreallocation(A,NULL);
720: PetscLogObjectParent((PetscObject)mat,(PetscObject)A);
722: /* Copy the matrix ... This isn't the most efficient means,
723: but it's quick for now */
724: A->insertmode = INSERT_VALUES;
726: row = mat->rmap->rstart;
727: m = mdn->A->rmap->n;
728: for (i=0; i<m; i++) {
729: MatGetRow_MPIDense(mat,row,&nz,&cols,&vals);
730: MatSetValues_MPIDense(A,1,&row,nz,cols,vals,INSERT_VALUES);
731: MatRestoreRow_MPIDense(mat,row,&nz,&cols,&vals);
732: row++;
733: }
735: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
736: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
737: PetscViewerGetSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
738: if (!rank) {
739: MatView_SeqDense(((Mat_MPIDense*)(A->data))->A,sviewer);
740: }
741: PetscViewerRestoreSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
742: PetscViewerFlush(viewer);
743: MatDestroy(&A);
744: }
745: return(0);
746: }
750: PetscErrorCode MatView_MPIDense(Mat mat,PetscViewer viewer)
751: {
753: PetscBool iascii,isbinary,isdraw,issocket;
756: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
757: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
758: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
759: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
761: if (iascii || issocket || isdraw) {
762: MatView_MPIDense_ASCIIorDraworSocket(mat,viewer);
763: } else if (isbinary) {
764: MatView_MPIDense_Binary(mat,viewer);
765: }
766: return(0);
767: }
771: PetscErrorCode MatGetInfo_MPIDense(Mat A,MatInfoType flag,MatInfo *info)
772: {
773: Mat_MPIDense *mat = (Mat_MPIDense*)A->data;
774: Mat mdn = mat->A;
776: PetscReal isend[5],irecv[5];
779: info->block_size = 1.0;
781: MatGetInfo(mdn,MAT_LOCAL,info);
783: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
784: isend[3] = info->memory; isend[4] = info->mallocs;
785: if (flag == MAT_LOCAL) {
786: info->nz_used = isend[0];
787: info->nz_allocated = isend[1];
788: info->nz_unneeded = isend[2];
789: info->memory = isend[3];
790: info->mallocs = isend[4];
791: } else if (flag == MAT_GLOBAL_MAX) {
792: MPIU_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
794: info->nz_used = irecv[0];
795: info->nz_allocated = irecv[1];
796: info->nz_unneeded = irecv[2];
797: info->memory = irecv[3];
798: info->mallocs = irecv[4];
799: } else if (flag == MAT_GLOBAL_SUM) {
800: MPIU_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
802: info->nz_used = irecv[0];
803: info->nz_allocated = irecv[1];
804: info->nz_unneeded = irecv[2];
805: info->memory = irecv[3];
806: info->mallocs = irecv[4];
807: }
808: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
809: info->fill_ratio_needed = 0;
810: info->factor_mallocs = 0;
811: return(0);
812: }
816: PetscErrorCode MatSetOption_MPIDense(Mat A,MatOption op,PetscBool flg)
817: {
818: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
822: switch (op) {
823: case MAT_NEW_NONZERO_LOCATIONS:
824: case MAT_NEW_NONZERO_LOCATION_ERR:
825: case MAT_NEW_NONZERO_ALLOCATION_ERR:
826: MatCheckPreallocated(A,1);
827: MatSetOption(a->A,op,flg);
828: break;
829: case MAT_ROW_ORIENTED:
830: MatCheckPreallocated(A,1);
831: a->roworiented = flg;
832: MatSetOption(a->A,op,flg);
833: break;
834: case MAT_NEW_DIAGONALS:
835: case MAT_KEEP_NONZERO_PATTERN:
836: case MAT_USE_HASH_TABLE:
837: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
838: break;
839: case MAT_IGNORE_OFF_PROC_ENTRIES:
840: a->donotstash = flg;
841: break;
842: case MAT_SYMMETRIC:
843: case MAT_STRUCTURALLY_SYMMETRIC:
844: case MAT_HERMITIAN:
845: case MAT_SYMMETRY_ETERNAL:
846: case MAT_IGNORE_LOWER_TRIANGULAR:
847: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
848: break;
849: default:
850: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %s",MatOptions[op]);
851: }
852: return(0);
853: }
858: PetscErrorCode MatDiagonalScale_MPIDense(Mat A,Vec ll,Vec rr)
859: {
860: Mat_MPIDense *mdn = (Mat_MPIDense*)A->data;
861: Mat_SeqDense *mat = (Mat_SeqDense*)mdn->A->data;
862: PetscScalar *l,*r,x,*v;
864: PetscInt i,j,s2a,s3a,s2,s3,m=mdn->A->rmap->n,n=mdn->A->cmap->n;
867: MatGetLocalSize(A,&s2,&s3);
868: if (ll) {
869: VecGetLocalSize(ll,&s2a);
870: if (s2a != s2) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector non-conforming local size, %d != %d.", s2a, s2);
871: VecGetArray(ll,&l);
872: for (i=0; i<m; i++) {
873: x = l[i];
874: v = mat->v + i;
875: for (j=0; j<n; j++) { (*v) *= x; v+= m;}
876: }
877: VecRestoreArray(ll,&l);
878: PetscLogFlops(n*m);
879: }
880: if (rr) {
881: VecGetLocalSize(rr,&s3a);
882: if (s3a != s3) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vec non-conforming local size, %d != %d.", s3a, s3);
883: VecScatterBegin(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
884: VecScatterEnd(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
885: VecGetArray(mdn->lvec,&r);
886: for (i=0; i<n; i++) {
887: x = r[i];
888: v = mat->v + i*m;
889: for (j=0; j<m; j++) (*v++) *= x;
890: }
891: VecRestoreArray(mdn->lvec,&r);
892: PetscLogFlops(n*m);
893: }
894: return(0);
895: }
899: PetscErrorCode MatNorm_MPIDense(Mat A,NormType type,PetscReal *nrm)
900: {
901: Mat_MPIDense *mdn = (Mat_MPIDense*)A->data;
902: Mat_SeqDense *mat = (Mat_SeqDense*)mdn->A->data;
904: PetscInt i,j;
905: PetscReal sum = 0.0;
906: PetscScalar *v = mat->v;
909: if (mdn->size == 1) {
910: MatNorm(mdn->A,type,nrm);
911: } else {
912: if (type == NORM_FROBENIUS) {
913: for (i=0; i<mdn->A->cmap->n*mdn->A->rmap->n; i++) {
914: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
915: }
916: MPIU_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
917: *nrm = PetscSqrtReal(*nrm);
918: PetscLogFlops(2.0*mdn->A->cmap->n*mdn->A->rmap->n);
919: } else if (type == NORM_1) {
920: PetscReal *tmp,*tmp2;
921: PetscMalloc2(A->cmap->N,&tmp,A->cmap->N,&tmp2);
922: PetscMemzero(tmp,A->cmap->N*sizeof(PetscReal));
923: PetscMemzero(tmp2,A->cmap->N*sizeof(PetscReal));
924: *nrm = 0.0;
925: v = mat->v;
926: for (j=0; j<mdn->A->cmap->n; j++) {
927: for (i=0; i<mdn->A->rmap->n; i++) {
928: tmp[j] += PetscAbsScalar(*v); v++;
929: }
930: }
931: MPIU_Allreduce(tmp,tmp2,A->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
932: for (j=0; j<A->cmap->N; j++) {
933: if (tmp2[j] > *nrm) *nrm = tmp2[j];
934: }
935: PetscFree2(tmp,tmp2);
936: PetscLogFlops(A->cmap->n*A->rmap->n);
937: } else if (type == NORM_INFINITY) { /* max row norm */
938: PetscReal ntemp;
939: MatNorm(mdn->A,type,&ntemp);
940: MPIU_Allreduce(&ntemp,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
941: } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for two norm");
942: }
943: return(0);
944: }
948: PetscErrorCode MatTranspose_MPIDense(Mat A,MatReuse reuse,Mat *matout)
949: {
950: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
951: Mat_SeqDense *Aloc = (Mat_SeqDense*)a->A->data;
952: Mat B;
953: PetscInt M = A->rmap->N,N = A->cmap->N,m,n,*rwork,rstart = A->rmap->rstart;
955: PetscInt j,i;
956: PetscScalar *v;
959: if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Supports square matrix only in-place");
960: if (reuse == MAT_INITIAL_MATRIX || A == *matout) {
961: MatCreate(PetscObjectComm((PetscObject)A),&B);
962: MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
963: MatSetType(B,((PetscObject)A)->type_name);
964: MatMPIDenseSetPreallocation(B,NULL);
965: } else {
966: B = *matout;
967: }
969: m = a->A->rmap->n; n = a->A->cmap->n; v = Aloc->v;
970: PetscMalloc1(m,&rwork);
971: for (i=0; i<m; i++) rwork[i] = rstart + i;
972: for (j=0; j<n; j++) {
973: MatSetValues(B,1,&j,m,rwork,v,INSERT_VALUES);
974: v += m;
975: }
976: PetscFree(rwork);
977: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
978: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
979: if (reuse == MAT_INITIAL_MATRIX || *matout != A) {
980: *matout = B;
981: } else {
982: MatHeaderMerge(A,&B);
983: }
984: return(0);
985: }
988: static PetscErrorCode MatDuplicate_MPIDense(Mat,MatDuplicateOption,Mat*);
989: extern PetscErrorCode MatScale_MPIDense(Mat,PetscScalar);
993: PetscErrorCode MatSetUp_MPIDense(Mat A)
994: {
998: MatMPIDenseSetPreallocation(A,0);
999: return(0);
1000: }
1004: PetscErrorCode MatAXPY_MPIDense(Mat Y,PetscScalar alpha,Mat X,MatStructure str)
1005: {
1007: Mat_MPIDense *A = (Mat_MPIDense*)Y->data, *B = (Mat_MPIDense*)X->data;
1010: MatAXPY(A->A,alpha,B->A,str);
1011: PetscObjectStateIncrease((PetscObject)Y);
1012: return(0);
1013: }
1017: PetscErrorCode MatConjugate_MPIDense(Mat mat)
1018: {
1019: Mat_MPIDense *a = (Mat_MPIDense*)mat->data;
1023: MatConjugate(a->A);
1024: return(0);
1025: }
1029: PetscErrorCode MatRealPart_MPIDense(Mat A)
1030: {
1031: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
1035: MatRealPart(a->A);
1036: return(0);
1037: }
1041: PetscErrorCode MatImaginaryPart_MPIDense(Mat A)
1042: {
1043: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
1047: MatImaginaryPart(a->A);
1048: return(0);
1049: }
1051: extern PetscErrorCode MatGetColumnNorms_SeqDense(Mat,NormType,PetscReal*);
1054: PetscErrorCode MatGetColumnNorms_MPIDense(Mat A,NormType type,PetscReal *norms)
1055: {
1057: PetscInt i,n;
1058: Mat_MPIDense *a = (Mat_MPIDense*) A->data;
1059: PetscReal *work;
1062: MatGetSize(A,NULL,&n);
1063: PetscMalloc1(n,&work);
1064: MatGetColumnNorms_SeqDense(a->A,type,work);
1065: if (type == NORM_2) {
1066: for (i=0; i<n; i++) work[i] *= work[i];
1067: }
1068: if (type == NORM_INFINITY) {
1069: MPIU_Allreduce(work,norms,n,MPIU_REAL,MPIU_MAX,A->hdr.comm);
1070: } else {
1071: MPIU_Allreduce(work,norms,n,MPIU_REAL,MPIU_SUM,A->hdr.comm);
1072: }
1073: PetscFree(work);
1074: if (type == NORM_2) {
1075: for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
1076: }
1077: return(0);
1078: }
1082: static PetscErrorCode MatSetRandom_MPIDense(Mat x,PetscRandom rctx)
1083: {
1084: Mat_MPIDense *d = (Mat_MPIDense*)x->data;
1086: PetscScalar *a;
1087: PetscInt m,n,i;
1090: MatGetSize(d->A,&m,&n);
1091: MatDenseGetArray(d->A,&a);
1092: for (i=0; i<m*n; i++) {
1093: PetscRandomGetValue(rctx,a+i);
1094: }
1095: MatDenseRestoreArray(d->A,&a);
1096: return(0);
1097: }
1099: extern PetscErrorCode MatMatMultNumeric_MPIDense(Mat A,Mat,Mat);
1103: static PetscErrorCode MatMissingDiagonal_MPIDense(Mat A,PetscBool *missing,PetscInt *d)
1104: {
1106: *missing = PETSC_FALSE;
1107: return(0);
1108: }
1110: /* -------------------------------------------------------------------*/
1111: static struct _MatOps MatOps_Values = { MatSetValues_MPIDense,
1112: MatGetRow_MPIDense,
1113: MatRestoreRow_MPIDense,
1114: MatMult_MPIDense,
1115: /* 4*/ MatMultAdd_MPIDense,
1116: MatMultTranspose_MPIDense,
1117: MatMultTransposeAdd_MPIDense,
1118: 0,
1119: 0,
1120: 0,
1121: /* 10*/ 0,
1122: 0,
1123: 0,
1124: 0,
1125: MatTranspose_MPIDense,
1126: /* 15*/ MatGetInfo_MPIDense,
1127: MatEqual_MPIDense,
1128: MatGetDiagonal_MPIDense,
1129: MatDiagonalScale_MPIDense,
1130: MatNorm_MPIDense,
1131: /* 20*/ MatAssemblyBegin_MPIDense,
1132: MatAssemblyEnd_MPIDense,
1133: MatSetOption_MPIDense,
1134: MatZeroEntries_MPIDense,
1135: /* 24*/ MatZeroRows_MPIDense,
1136: 0,
1137: 0,
1138: 0,
1139: 0,
1140: /* 29*/ MatSetUp_MPIDense,
1141: 0,
1142: 0,
1143: 0,
1144: 0,
1145: /* 34*/ MatDuplicate_MPIDense,
1146: 0,
1147: 0,
1148: 0,
1149: 0,
1150: /* 39*/ MatAXPY_MPIDense,
1151: MatGetSubMatrices_MPIDense,
1152: 0,
1153: MatGetValues_MPIDense,
1154: 0,
1155: /* 44*/ 0,
1156: MatScale_MPIDense,
1157: MatShift_Basic,
1158: 0,
1159: 0,
1160: /* 49*/ MatSetRandom_MPIDense,
1161: 0,
1162: 0,
1163: 0,
1164: 0,
1165: /* 54*/ 0,
1166: 0,
1167: 0,
1168: 0,
1169: 0,
1170: /* 59*/ MatGetSubMatrix_MPIDense,
1171: MatDestroy_MPIDense,
1172: MatView_MPIDense,
1173: 0,
1174: 0,
1175: /* 64*/ 0,
1176: 0,
1177: 0,
1178: 0,
1179: 0,
1180: /* 69*/ 0,
1181: 0,
1182: 0,
1183: 0,
1184: 0,
1185: /* 74*/ 0,
1186: 0,
1187: 0,
1188: 0,
1189: 0,
1190: /* 79*/ 0,
1191: 0,
1192: 0,
1193: 0,
1194: /* 83*/ MatLoad_MPIDense,
1195: 0,
1196: 0,
1197: 0,
1198: 0,
1199: 0,
1200: #if defined(PETSC_HAVE_ELEMENTAL)
1201: /* 89*/ MatMatMult_MPIDense_MPIDense,
1202: MatMatMultSymbolic_MPIDense_MPIDense,
1203: #else
1204: /* 89*/ 0,
1205: 0,
1206: #endif
1207: MatMatMultNumeric_MPIDense,
1208: 0,
1209: 0,
1210: /* 94*/ 0,
1211: 0,
1212: 0,
1213: 0,
1214: 0,
1215: /* 99*/ 0,
1216: 0,
1217: 0,
1218: MatConjugate_MPIDense,
1219: 0,
1220: /*104*/ 0,
1221: MatRealPart_MPIDense,
1222: MatImaginaryPart_MPIDense,
1223: 0,
1224: 0,
1225: /*109*/ 0,
1226: 0,
1227: 0,
1228: 0,
1229: MatMissingDiagonal_MPIDense,
1230: /*114*/ 0,
1231: 0,
1232: 0,
1233: 0,
1234: 0,
1235: /*119*/ 0,
1236: 0,
1237: 0,
1238: 0,
1239: 0,
1240: /*124*/ 0,
1241: MatGetColumnNorms_MPIDense,
1242: 0,
1243: 0,
1244: 0,
1245: /*129*/ 0,
1246: MatTransposeMatMult_MPIDense_MPIDense,
1247: MatTransposeMatMultSymbolic_MPIDense_MPIDense,
1248: MatTransposeMatMultNumeric_MPIDense_MPIDense,
1249: 0,
1250: /*134*/ 0,
1251: 0,
1252: 0,
1253: 0,
1254: 0,
1255: /*139*/ 0,
1256: 0,
1257: 0
1258: };
1262: PetscErrorCode MatMPIDenseSetPreallocation_MPIDense(Mat mat,PetscScalar *data)
1263: {
1264: Mat_MPIDense *a;
1268: mat->preallocated = PETSC_TRUE;
1269: /* Note: For now, when data is specified above, this assumes the user correctly
1270: allocates the local dense storage space. We should add error checking. */
1272: a = (Mat_MPIDense*)mat->data;
1273: PetscLayoutSetUp(mat->rmap);
1274: PetscLayoutSetUp(mat->cmap);
1275: a->nvec = mat->cmap->n;
1277: MatCreate(PETSC_COMM_SELF,&a->A);
1278: MatSetSizes(a->A,mat->rmap->n,mat->cmap->N,mat->rmap->n,mat->cmap->N);
1279: MatSetType(a->A,MATSEQDENSE);
1280: MatSeqDenseSetPreallocation(a->A,data);
1281: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
1282: return(0);
1283: }
1285: #if defined(PETSC_HAVE_ELEMENTAL)
1288: PETSC_INTERN PetscErrorCode MatConvert_MPIDense_Elemental(Mat A, MatType newtype,MatReuse reuse,Mat *newmat)
1289: {
1290: Mat mat_elemental;
1292: PetscScalar *v;
1293: PetscInt m=A->rmap->n,N=A->cmap->N,rstart=A->rmap->rstart,i,*rows,*cols;
1294:
1296: if (reuse == MAT_REUSE_MATRIX) {
1297: mat_elemental = *newmat;
1298: MatZeroEntries(*newmat);
1299: } else {
1300: MatCreate(PetscObjectComm((PetscObject)A), &mat_elemental);
1301: MatSetSizes(mat_elemental,PETSC_DECIDE,PETSC_DECIDE,A->rmap->N,A->cmap->N);
1302: MatSetType(mat_elemental,MATELEMENTAL);
1303: MatSetUp(mat_elemental);
1304: MatSetOption(mat_elemental,MAT_ROW_ORIENTED,PETSC_FALSE);
1305: }
1307: PetscMalloc2(m,&rows,N,&cols);
1308: for (i=0; i<N; i++) cols[i] = i;
1309: for (i=0; i<m; i++) rows[i] = rstart + i;
1310:
1311: /* PETSc-Elemental interaface uses axpy for setting off-processor entries, only ADD_VALUES is allowed */
1312: MatDenseGetArray(A,&v);
1313: MatSetValues(mat_elemental,m,rows,N,cols,v,ADD_VALUES);
1314: MatAssemblyBegin(mat_elemental, MAT_FINAL_ASSEMBLY);
1315: MatAssemblyEnd(mat_elemental, MAT_FINAL_ASSEMBLY);
1316: MatDenseRestoreArray(A,&v);
1317: PetscFree2(rows,cols);
1319: if (reuse == MAT_INPLACE_MATRIX) {
1320: MatHeaderReplace(A,&mat_elemental);
1321: } else {
1322: *newmat = mat_elemental;
1323: }
1324: return(0);
1325: }
1326: #endif
1330: PETSC_EXTERN PetscErrorCode MatCreate_MPIDense(Mat mat)
1331: {
1332: Mat_MPIDense *a;
1336: PetscNewLog(mat,&a);
1337: mat->data = (void*)a;
1338: PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));
1340: mat->insertmode = NOT_SET_VALUES;
1341: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&a->rank);
1342: MPI_Comm_size(PetscObjectComm((PetscObject)mat),&a->size);
1344: /* build cache for off array entries formed */
1345: a->donotstash = PETSC_FALSE;
1347: MatStashCreate_Private(PetscObjectComm((PetscObject)mat),1,&mat->stash);
1349: /* stuff used for matrix vector multiply */
1350: a->lvec = 0;
1351: a->Mvctx = 0;
1352: a->roworiented = PETSC_TRUE;
1354: PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",MatDenseGetArray_MPIDense);
1355: PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",MatDenseRestoreArray_MPIDense);
1356: #if defined(PETSC_HAVE_ELEMENTAL)
1357: PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpidense_elemental_C",MatConvert_MPIDense_Elemental);
1358: #endif
1359: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",MatGetDiagonalBlock_MPIDense);
1360: PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",MatMPIDenseSetPreallocation_MPIDense);
1361: PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",MatMatMult_MPIAIJ_MPIDense);
1362: PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",MatMatMultSymbolic_MPIAIJ_MPIDense);
1363: PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",MatMatMultNumeric_MPIAIJ_MPIDense);
1365: PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMult_mpiaij_mpidense_C",MatTransposeMatMult_MPIAIJ_MPIDense);
1366: PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultSymbolic_mpiaij_mpidense_C",MatTransposeMatMultSymbolic_MPIAIJ_MPIDense);
1367: PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultNumeric_mpiaij_mpidense_C",MatTransposeMatMultNumeric_MPIAIJ_MPIDense);
1368: PetscObjectChangeTypeName((PetscObject)mat,MATMPIDENSE);
1369: return(0);
1370: }
1372: /*MC
1373: MATDENSE - MATDENSE = "dense" - A matrix type to be used for dense matrices.
1375: This matrix type is identical to MATSEQDENSE when constructed with a single process communicator,
1376: and MATMPIDENSE otherwise.
1378: Options Database Keys:
1379: . -mat_type dense - sets the matrix type to "dense" during a call to MatSetFromOptions()
1381: Level: beginner
1384: .seealso: MatCreateMPIDense,MATSEQDENSE,MATMPIDENSE
1385: M*/
1389: /*@C
1390: MatMPIDenseSetPreallocation - Sets the array used to store the matrix entries
1392: Not collective
1394: Input Parameters:
1395: . B - the matrix
1396: - data - optional location of matrix data. Set data=NULL for PETSc
1397: to control all matrix memory allocation.
1399: Notes:
1400: The dense format is fully compatible with standard Fortran 77
1401: storage by columns.
1403: The data input variable is intended primarily for Fortran programmers
1404: who wish to allocate their own matrix memory space. Most users should
1405: set data=NULL.
1407: Level: intermediate
1409: .keywords: matrix,dense, parallel
1411: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
1412: @*/
1413: PetscErrorCode MatMPIDenseSetPreallocation(Mat B,PetscScalar *data)
1414: {
1418: PetscTryMethod(B,"MatMPIDenseSetPreallocation_C",(Mat,PetscScalar*),(B,data));
1419: return(0);
1420: }
1424: /*@C
1425: MatCreateDense - Creates a parallel matrix in dense format.
1427: Collective on MPI_Comm
1429: Input Parameters:
1430: + comm - MPI communicator
1431: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1432: . n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1433: . M - number of global rows (or PETSC_DECIDE to have calculated if m is given)
1434: . N - number of global columns (or PETSC_DECIDE to have calculated if n is given)
1435: - data - optional location of matrix data. Set data=NULL (PETSC_NULL_SCALAR for Fortran users) for PETSc
1436: to control all matrix memory allocation.
1438: Output Parameter:
1439: . A - the matrix
1441: Notes:
1442: The dense format is fully compatible with standard Fortran 77
1443: storage by columns.
1445: The data input variable is intended primarily for Fortran programmers
1446: who wish to allocate their own matrix memory space. Most users should
1447: set data=NULL (PETSC_NULL_SCALAR for Fortran users).
1449: The user MUST specify either the local or global matrix dimensions
1450: (possibly both).
1452: Level: intermediate
1454: .keywords: matrix,dense, parallel
1456: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
1457: @*/
1458: PetscErrorCode MatCreateDense(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscScalar *data,Mat *A)
1459: {
1461: PetscMPIInt size;
1464: MatCreate(comm,A);
1465: MatSetSizes(*A,m,n,M,N);
1466: MPI_Comm_size(comm,&size);
1467: if (size > 1) {
1468: MatSetType(*A,MATMPIDENSE);
1469: MatMPIDenseSetPreallocation(*A,data);
1470: if (data) { /* user provided data array, so no need to assemble */
1471: MatSetUpMultiply_MPIDense(*A);
1472: (*A)->assembled = PETSC_TRUE;
1473: }
1474: } else {
1475: MatSetType(*A,MATSEQDENSE);
1476: MatSeqDenseSetPreallocation(*A,data);
1477: }
1478: return(0);
1479: }
1483: static PetscErrorCode MatDuplicate_MPIDense(Mat A,MatDuplicateOption cpvalues,Mat *newmat)
1484: {
1485: Mat mat;
1486: Mat_MPIDense *a,*oldmat = (Mat_MPIDense*)A->data;
1490: *newmat = 0;
1491: MatCreate(PetscObjectComm((PetscObject)A),&mat);
1492: MatSetSizes(mat,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1493: MatSetType(mat,((PetscObject)A)->type_name);
1494: a = (Mat_MPIDense*)mat->data;
1495: PetscMemcpy(mat->ops,A->ops,sizeof(struct _MatOps));
1497: mat->factortype = A->factortype;
1498: mat->assembled = PETSC_TRUE;
1499: mat->preallocated = PETSC_TRUE;
1501: a->size = oldmat->size;
1502: a->rank = oldmat->rank;
1503: mat->insertmode = NOT_SET_VALUES;
1504: a->nvec = oldmat->nvec;
1505: a->donotstash = oldmat->donotstash;
1507: PetscLayoutReference(A->rmap,&mat->rmap);
1508: PetscLayoutReference(A->cmap,&mat->cmap);
1510: MatSetUpMultiply_MPIDense(mat);
1511: MatDuplicate(oldmat->A,cpvalues,&a->A);
1512: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
1514: *newmat = mat;
1515: return(0);
1516: }
1520: PetscErrorCode MatLoad_MPIDense_DenseInFile(MPI_Comm comm,PetscInt fd,PetscInt M,PetscInt N,Mat newmat)
1521: {
1523: PetscMPIInt rank,size;
1524: const PetscInt *rowners;
1525: PetscInt i,m,n,nz,j,mMax;
1526: PetscScalar *array,*vals,*vals_ptr;
1527: Mat_MPIDense *a = (Mat_MPIDense*)newmat->data;
1530: MPI_Comm_rank(comm,&rank);
1531: MPI_Comm_size(comm,&size);
1533: /* determine ownership of rows and columns */
1534: m = (newmat->rmap->n < 0) ? PETSC_DECIDE : newmat->rmap->n;
1535: n = (newmat->cmap->n < 0) ? PETSC_DECIDE : newmat->cmap->n;
1537: MatSetSizes(newmat,m,n,M,N);
1538: if (!a->A || !((Mat_SeqDense*)(a->A->data))->user_alloc) {
1539: MatMPIDenseSetPreallocation(newmat,NULL);
1540: }
1541: MatDenseGetArray(newmat,&array);
1542: MatGetLocalSize(newmat,&m,NULL);
1543: MatGetOwnershipRanges(newmat,&rowners);
1544: MPI_Reduce(&m,&mMax,1,MPIU_INT,MPI_MAX,0,comm);
1545: if (!rank) {
1546: PetscMalloc1(mMax*N,&vals);
1548: /* read in my part of the matrix numerical values */
1549: PetscBinaryRead(fd,vals,m*N,PETSC_SCALAR);
1551: /* insert into matrix-by row (this is why cannot directly read into array */
1552: vals_ptr = vals;
1553: for (i=0; i<m; i++) {
1554: for (j=0; j<N; j++) {
1555: array[i + j*m] = *vals_ptr++;
1556: }
1557: }
1559: /* read in other processors and ship out */
1560: for (i=1; i<size; i++) {
1561: nz = (rowners[i+1] - rowners[i])*N;
1562: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1563: MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)(newmat))->tag,comm);
1564: }
1565: } else {
1566: /* receive numeric values */
1567: PetscMalloc1(m*N,&vals);
1569: /* receive message of values*/
1570: MPIULong_Recv(vals,m*N,MPIU_SCALAR,0,((PetscObject)(newmat))->tag,comm);
1572: /* insert into matrix-by row (this is why cannot directly read into array */
1573: vals_ptr = vals;
1574: for (i=0; i<m; i++) {
1575: for (j=0; j<N; j++) {
1576: array[i + j*m] = *vals_ptr++;
1577: }
1578: }
1579: }
1580: MatDenseRestoreArray(newmat,&array);
1581: PetscFree(vals);
1582: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
1583: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
1584: return(0);
1585: }
1589: PetscErrorCode MatLoad_MPIDense(Mat newmat,PetscViewer viewer)
1590: {
1591: Mat_MPIDense *a;
1592: PetscScalar *vals,*svals;
1593: MPI_Comm comm;
1594: MPI_Status status;
1595: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag,*rowners,*sndcounts,m,n,maxnz;
1596: PetscInt header[4],*rowlengths = 0,M,N,*cols;
1597: PetscInt *ourlens,*procsnz = 0,jj,*mycols,*smycols;
1598: PetscInt i,nz,j,rstart,rend;
1599: int fd;
1603: /* force binary viewer to load .info file if it has not yet done so */
1604: PetscViewerSetUp(viewer);
1605: PetscObjectGetComm((PetscObject)viewer,&comm);
1606: MPI_Comm_size(comm,&size);
1607: MPI_Comm_rank(comm,&rank);
1608: PetscViewerBinaryGetDescriptor(viewer,&fd);
1609: if (!rank) {
1610: PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
1611: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
1612: }
1613: MPI_Bcast(header+1,3,MPIU_INT,0,comm);
1614: M = header[1]; N = header[2]; nz = header[3];
1616: /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */
1617: if (newmat->rmap->N < 0) newmat->rmap->N = M;
1618: if (newmat->cmap->N < 0) newmat->cmap->N = N;
1620: if (newmat->rmap->N != M) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows:Matrix in file has (%D) and input matrix has (%D)",M,newmat->rmap->N);
1621: if (newmat->cmap->N != N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of cols:Matrix in file has (%D) and input matrix has (%D)",N,newmat->cmap->N);
1623: /*
1624: Handle case where matrix is stored on disk as a dense matrix
1625: */
1626: if (nz == MATRIX_BINARY_FORMAT_DENSE) {
1627: MatLoad_MPIDense_DenseInFile(comm,fd,M,N,newmat);
1628: return(0);
1629: }
1631: /* determine ownership of all rows */
1632: if (newmat->rmap->n < 0) {
1633: PetscMPIIntCast(M/size + ((M % size) > rank),&m);
1634: } else {
1635: PetscMPIIntCast(newmat->rmap->n,&m);
1636: }
1637: if (newmat->cmap->n < 0) {
1638: n = PETSC_DECIDE;
1639: } else {
1640: PetscMPIIntCast(newmat->cmap->n,&n);
1641: }
1643: PetscMalloc1(size+2,&rowners);
1644: MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);
1645: rowners[0] = 0;
1646: for (i=2; i<=size; i++) {
1647: rowners[i] += rowners[i-1];
1648: }
1649: rstart = rowners[rank];
1650: rend = rowners[rank+1];
1652: /* distribute row lengths to all processors */
1653: PetscMalloc1(rend-rstart,&ourlens);
1654: if (!rank) {
1655: PetscMalloc1(M,&rowlengths);
1656: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
1657: PetscMalloc1(size,&sndcounts);
1658: for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i];
1659: MPI_Scatterv(rowlengths,sndcounts,rowners,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
1660: PetscFree(sndcounts);
1661: } else {
1662: MPI_Scatterv(0,0,0,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
1663: }
1665: if (!rank) {
1666: /* calculate the number of nonzeros on each processor */
1667: PetscMalloc1(size,&procsnz);
1668: PetscMemzero(procsnz,size*sizeof(PetscInt));
1669: for (i=0; i<size; i++) {
1670: for (j=rowners[i]; j< rowners[i+1]; j++) {
1671: procsnz[i] += rowlengths[j];
1672: }
1673: }
1674: PetscFree(rowlengths);
1676: /* determine max buffer needed and allocate it */
1677: maxnz = 0;
1678: for (i=0; i<size; i++) {
1679: maxnz = PetscMax(maxnz,procsnz[i]);
1680: }
1681: PetscMalloc1(maxnz,&cols);
1683: /* read in my part of the matrix column indices */
1684: nz = procsnz[0];
1685: PetscMalloc1(nz,&mycols);
1686: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
1688: /* read in every one elses and ship off */
1689: for (i=1; i<size; i++) {
1690: nz = procsnz[i];
1691: PetscBinaryRead(fd,cols,nz,PETSC_INT);
1692: MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
1693: }
1694: PetscFree(cols);
1695: } else {
1696: /* determine buffer space needed for message */
1697: nz = 0;
1698: for (i=0; i<m; i++) {
1699: nz += ourlens[i];
1700: }
1701: PetscMalloc1(nz+1,&mycols);
1703: /* receive message of column indices*/
1704: MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
1705: MPI_Get_count(&status,MPIU_INT,&maxnz);
1706: if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
1707: }
1709: MatSetSizes(newmat,m,n,M,N);
1710: a = (Mat_MPIDense*)newmat->data;
1711: if (!a->A || !((Mat_SeqDense*)(a->A->data))->user_alloc) {
1712: MatMPIDenseSetPreallocation(newmat,NULL);
1713: }
1715: if (!rank) {
1716: PetscMalloc1(maxnz,&vals);
1718: /* read in my part of the matrix numerical values */
1719: nz = procsnz[0];
1720: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1722: /* insert into matrix */
1723: jj = rstart;
1724: smycols = mycols;
1725: svals = vals;
1726: for (i=0; i<m; i++) {
1727: MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
1728: smycols += ourlens[i];
1729: svals += ourlens[i];
1730: jj++;
1731: }
1733: /* read in other processors and ship out */
1734: for (i=1; i<size; i++) {
1735: nz = procsnz[i];
1736: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1737: MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);
1738: }
1739: PetscFree(procsnz);
1740: } else {
1741: /* receive numeric values */
1742: PetscMalloc1(nz+1,&vals);
1744: /* receive message of values*/
1745: MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm,&status);
1746: MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
1747: if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
1749: /* insert into matrix */
1750: jj = rstart;
1751: smycols = mycols;
1752: svals = vals;
1753: for (i=0; i<m; i++) {
1754: MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
1755: smycols += ourlens[i];
1756: svals += ourlens[i];
1757: jj++;
1758: }
1759: }
1760: PetscFree(ourlens);
1761: PetscFree(vals);
1762: PetscFree(mycols);
1763: PetscFree(rowners);
1765: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
1766: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
1767: return(0);
1768: }
1772: PetscErrorCode MatEqual_MPIDense(Mat A,Mat B,PetscBool *flag)
1773: {
1774: Mat_MPIDense *matB = (Mat_MPIDense*)B->data,*matA = (Mat_MPIDense*)A->data;
1775: Mat a,b;
1776: PetscBool flg;
1780: a = matA->A;
1781: b = matB->A;
1782: MatEqual(a,b,&flg);
1783: MPIU_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
1784: return(0);
1785: }
1789: PetscErrorCode MatDestroy_MatTransMatMult_MPIDense_MPIDense(Mat A)
1790: {
1791: PetscErrorCode ierr;
1792: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
1793: Mat_TransMatMultDense *atb = a->atbdense;
1796: PetscFree3(atb->sendbuf,atb->atbarray,atb->recvcounts);
1797: (atb->destroy)(A);
1798: PetscFree(atb);
1799: return(0);
1800: }
1804: PetscErrorCode MatTransposeMatMultNumeric_MPIDense_MPIDense(Mat A,Mat B,Mat C)
1805: {
1806: Mat_MPIDense *a=(Mat_MPIDense*)A->data, *b=(Mat_MPIDense*)B->data, *c=(Mat_MPIDense*)C->data;
1807: Mat_SeqDense *aseq=(Mat_SeqDense*)(a->A)->data, *bseq=(Mat_SeqDense*)(b->A)->data;
1808: Mat_TransMatMultDense *atb = c->atbdense;
1810: MPI_Comm comm;
1811: PetscMPIInt rank,size,*recvcounts=atb->recvcounts;
1812: PetscScalar *carray,*atbarray=atb->atbarray,*sendbuf=atb->sendbuf;
1813: PetscInt i,cN=C->cmap->N,cM=C->rmap->N,proc,k,j;
1814: PetscScalar _DOne=1.0,_DZero=0.0;
1815: PetscBLASInt am,an,bn,aN;
1816: const PetscInt *ranges;
1819: PetscObjectGetComm((PetscObject)A,&comm);
1820: MPI_Comm_rank(comm,&rank);
1821: MPI_Comm_size(comm,&size);
1823: /* compute atbarray = aseq^T * bseq */
1824: PetscBLASIntCast(a->A->cmap->n,&an);
1825: PetscBLASIntCast(b->A->cmap->n,&bn);
1826: PetscBLASIntCast(a->A->rmap->n,&am);
1827: PetscBLASIntCast(A->cmap->N,&aN);
1828: PetscStackCallBLAS("BLASgemm",BLASgemm_("T","N",&an,&bn,&am,&_DOne,aseq->v,&aseq->lda,bseq->v,&bseq->lda,&_DZero,atbarray,&aN));
1829:
1830: MatGetOwnershipRanges(C,&ranges);
1831: for (i=0; i<size; i++) recvcounts[i] = (ranges[i+1] - ranges[i])*cN;
1832:
1833: /* arrange atbarray into sendbuf */
1834: k = 0;
1835: for (proc=0; proc<size; proc++) {
1836: for (j=0; j<cN; j++) {
1837: for (i=ranges[proc]; i<ranges[proc+1]; i++) sendbuf[k++] = atbarray[i+j*cM];
1838: }
1839: }
1840: /* sum all atbarray to local values of C */
1841: MatDenseGetArray(c->A,&carray);
1842: MPI_Reduce_scatter(sendbuf,carray,recvcounts,MPIU_SCALAR,MPIU_SUM,comm);
1843: MatDenseRestoreArray(c->A,&carray);
1844: return(0);
1845: }
1849: PetscErrorCode MatTransposeMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C)
1850: {
1851: PetscErrorCode ierr;
1852: Mat Cdense;
1853: MPI_Comm comm;
1854: PetscMPIInt size;
1855: PetscInt cm=A->cmap->n,cM,cN=B->cmap->N;
1856: Mat_MPIDense *c;
1857: Mat_TransMatMultDense *atb;
1860: PetscObjectGetComm((PetscObject)A,&comm);
1861: if (A->rmap->rstart != B->rmap->rstart || A->rmap->rend != B->rmap->rend) {
1862: SETERRQ4(comm,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A (%D, %D) != B (%D,%D)",A->rmap->rstart,A->rmap->rend,B->rmap->rstart,B->rmap->rend);
1863: }
1865: /* create matrix product Cdense */
1866: MatCreate(comm,&Cdense);
1867: MatSetSizes(Cdense,cm,B->cmap->n,PETSC_DECIDE,PETSC_DECIDE);
1868: MatSetType(Cdense,MATMPIDENSE);
1869: MatMPIDenseSetPreallocation(Cdense,NULL);
1870: MatAssemblyBegin(Cdense,MAT_FINAL_ASSEMBLY);
1871: MatAssemblyEnd(Cdense,MAT_FINAL_ASSEMBLY);
1872: *C = Cdense;
1874: /* create data structure for reuse Cdense */
1875: MPI_Comm_size(comm,&size);
1876: PetscNew(&atb);
1877: cM = Cdense->rmap->N;
1878: PetscMalloc3(cM*cN,&atb->sendbuf,cM*cN,&atb->atbarray,size,&atb->recvcounts);
1879:
1880: c = (Mat_MPIDense*)Cdense->data;
1881: c->atbdense = atb;
1882: atb->destroy = Cdense->ops->destroy;
1883: Cdense->ops->destroy = MatDestroy_MatTransMatMult_MPIDense_MPIDense;
1884: return(0);
1885: }
1889: PetscErrorCode MatTransposeMatMult_MPIDense_MPIDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
1890: {
1894: if (scall == MAT_INITIAL_MATRIX) {
1895: MatTransposeMatMultSymbolic_MPIDense_MPIDense(A,B,fill,C);
1896: }
1897: MatTransposeMatMultNumeric_MPIDense_MPIDense(A,B,*C);
1898: return(0);
1899: }
1903: PetscErrorCode MatDestroy_MatMatMult_MPIDense_MPIDense(Mat A)
1904: {
1905: PetscErrorCode ierr;
1906: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
1907: Mat_MatMultDense *ab = a->abdense;
1910: MatDestroy(&ab->Ce);
1911: MatDestroy(&ab->Ae);
1912: MatDestroy(&ab->Be);
1914: (ab->destroy)(A);
1915: PetscFree(ab);
1916: return(0);
1917: }
1919: #if defined(PETSC_HAVE_ELEMENTAL)
1922: PetscErrorCode MatMatMultNumeric_MPIDense_MPIDense(Mat A,Mat B,Mat C)
1923: {
1924: PetscErrorCode ierr;
1925: Mat_MPIDense *c=(Mat_MPIDense*)C->data;
1926: Mat_MatMultDense *ab=c->abdense;
1929: MatConvert_MPIDense_Elemental(A,MATELEMENTAL,MAT_REUSE_MATRIX, &ab->Ae);
1930: MatConvert_MPIDense_Elemental(B,MATELEMENTAL,MAT_REUSE_MATRIX, &ab->Be);
1931: MatMatMultNumeric(ab->Ae,ab->Be,ab->Ce);
1932: MatConvert(ab->Ce,MATMPIDENSE,MAT_REUSE_MATRIX,&C);
1933: return(0);
1934: }
1938: PetscErrorCode MatMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C)
1939: {
1940: PetscErrorCode ierr;
1941: Mat Ae,Be,Ce;
1942: Mat_MPIDense *c;
1943: Mat_MatMultDense *ab;
1946: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) {
1947: SETERRQ4(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A (%D, %D) != B (%D,%D)",A->rmap->rstart,A->rmap->rend,B->rmap->rstart,B->rmap->rend);
1948: }
1950: /* convert A and B to Elemental matrices Ae and Be */
1951: MatConvert(A,MATELEMENTAL,MAT_INITIAL_MATRIX, &Ae);
1952: MatConvert(B,MATELEMENTAL,MAT_INITIAL_MATRIX, &Be);
1954: /* Ce = Ae*Be */
1955: MatMatMultSymbolic(Ae,Be,fill,&Ce);
1956: MatMatMultNumeric(Ae,Be,Ce);
1957:
1958: /* convert Ce to C */
1959: MatConvert(Ce,MATMPIDENSE,MAT_INITIAL_MATRIX,C);
1961: /* create data structure for reuse Cdense */
1962: PetscNew(&ab);
1963: c = (Mat_MPIDense*)(*C)->data;
1964: c->abdense = ab;
1966: ab->Ae = Ae;
1967: ab->Be = Be;
1968: ab->Ce = Ce;
1969: ab->destroy = (*C)->ops->destroy;
1970: (*C)->ops->destroy = MatDestroy_MatMatMult_MPIDense_MPIDense;
1971: (*C)->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIDense;
1972: return(0);
1973: }
1977: PETSC_INTERN PetscErrorCode MatMatMult_MPIDense_MPIDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
1978: {
1982: if (scall == MAT_INITIAL_MATRIX) { /* simbolic product includes numeric product */
1983: PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
1984: MatMatMultSymbolic_MPIDense_MPIDense(A,B,fill,C);
1985: PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
1986: } else {
1987: PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
1988: MatMatMultNumeric_MPIDense_MPIDense(A,B,*C);
1989: PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
1990: }
1991: return(0);
1992: }
1993: #endif