Actual source code: mpidense.c
petsc-3.4.5 2014-06-29
2: /*
3: Basic functions for basic parallel dense matrices.
4: */
7: #include <../src/mat/impls/dense/mpi/mpidense.h> /*I "petscmat.h" I*/
11: /*@
13: MatDenseGetLocalMatrix - For a MATMPIDENSE or MATSEQDENSE matrix returns the sequential
14: matrix that represents the operator. For sequential matrices it returns itself.
16: Input Parameter:
17: . A - the Seq or MPI dense matrix
19: Output Parameter:
20: . B - the inner matrix
22: Level: intermediate
24: @*/
25: PetscErrorCode MatDenseGetLocalMatrix(Mat A,Mat *B)
26: {
27: Mat_MPIDense *mat = (Mat_MPIDense*)A->data;
29: PetscBool flg;
32: PetscObjectTypeCompare((PetscObject)A,MATMPIDENSE,&flg);
33: if (flg) *B = mat->A;
34: else *B = A;
35: return(0);
36: }
40: PetscErrorCode MatGetRow_MPIDense(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
41: {
42: Mat_MPIDense *mat = (Mat_MPIDense*)A->data;
44: PetscInt lrow,rstart = A->rmap->rstart,rend = A->rmap->rend;
47: if (row < rstart || row >= rend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"only local rows");
48: lrow = row - rstart;
49: MatGetRow(mat->A,lrow,nz,(const PetscInt**)idx,(const PetscScalar**)v);
50: return(0);
51: }
55: PetscErrorCode MatRestoreRow_MPIDense(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
56: {
60: if (idx) {PetscFree(*idx);}
61: if (v) {PetscFree(*v);}
62: return(0);
63: }
67: PetscErrorCode MatGetDiagonalBlock_MPIDense(Mat A,Mat *a)
68: {
69: Mat_MPIDense *mdn = (Mat_MPIDense*)A->data;
71: PetscInt m = A->rmap->n,rstart = A->rmap->rstart;
72: PetscScalar *array;
73: MPI_Comm comm;
74: Mat B;
77: if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only square matrices supported.");
79: PetscObjectQuery((PetscObject)A,"DiagonalBlock",(PetscObject*)&B);
80: if (!B) {
81: PetscObjectGetComm((PetscObject)(mdn->A),&comm);
82: MatCreate(comm,&B);
83: MatSetSizes(B,m,m,m,m);
84: MatSetType(B,((PetscObject)mdn->A)->type_name);
85: MatDenseGetArray(mdn->A,&array);
86: MatSeqDenseSetPreallocation(B,array+m*rstart);
87: MatDenseRestoreArray(mdn->A,&array);
88: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
89: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
90: PetscObjectCompose((PetscObject)A,"DiagonalBlock",(PetscObject)B);
91: *a = B;
92: MatDestroy(&B);
93: } else *a = B;
94: return(0);
95: }
99: PetscErrorCode MatSetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
100: {
101: Mat_MPIDense *A = (Mat_MPIDense*)mat->data;
103: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row;
104: PetscBool roworiented = A->roworiented;
108: for (i=0; i<m; i++) {
109: if (idxm[i] < 0) continue;
110: if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
111: if (idxm[i] >= rstart && idxm[i] < rend) {
112: row = idxm[i] - rstart;
113: if (roworiented) {
114: MatSetValues(A->A,1,&row,n,idxn,v+i*n,addv);
115: } else {
116: for (j=0; j<n; j++) {
117: if (idxn[j] < 0) continue;
118: if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
119: MatSetValues(A->A,1,&row,1,&idxn[j],v+i+j*m,addv);
120: }
121: }
122: } else if (!A->donotstash) {
123: mat->assembled = PETSC_FALSE;
124: if (roworiented) {
125: MatStashValuesRow_Private(&mat->stash,idxm[i],n,idxn,v+i*n,PETSC_FALSE);
126: } else {
127: MatStashValuesCol_Private(&mat->stash,idxm[i],n,idxn,v+i,m,PETSC_FALSE);
128: }
129: }
130: }
131: return(0);
132: }
136: PetscErrorCode MatGetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
137: {
138: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
140: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row;
143: for (i=0; i<m; i++) {
144: if (idxm[i] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */
145: if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
146: if (idxm[i] >= rstart && idxm[i] < rend) {
147: row = idxm[i] - rstart;
148: for (j=0; j<n; j++) {
149: if (idxn[j] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */
150: if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
151: MatGetValues(mdn->A,1,&row,1,&idxn[j],v+i*n+j);
152: }
153: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
154: }
155: return(0);
156: }
160: PetscErrorCode MatDenseGetArray_MPIDense(Mat A,PetscScalar *array[])
161: {
162: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
166: MatDenseGetArray(a->A,array);
167: return(0);
168: }
172: static PetscErrorCode MatGetSubMatrix_MPIDense(Mat A,IS isrow,IS iscol,MatReuse scall,Mat *B)
173: {
174: Mat_MPIDense *mat = (Mat_MPIDense*)A->data,*newmatd;
175: Mat_SeqDense *lmat = (Mat_SeqDense*)mat->A->data;
177: PetscInt i,j,rstart,rend,nrows,ncols,Ncols,nlrows,nlcols;
178: const PetscInt *irow,*icol;
179: PetscScalar *av,*bv,*v = lmat->v;
180: Mat newmat;
181: IS iscol_local;
184: ISAllGather(iscol,&iscol_local);
185: ISGetIndices(isrow,&irow);
186: ISGetIndices(iscol_local,&icol);
187: ISGetLocalSize(isrow,&nrows);
188: ISGetLocalSize(iscol,&ncols);
189: ISGetSize(iscol,&Ncols); /* global number of columns, size of iscol_local */
191: /* No parallel redistribution currently supported! Should really check each index set
192: to comfirm that it is OK. ... Currently supports only submatrix same partitioning as
193: original matrix! */
195: MatGetLocalSize(A,&nlrows,&nlcols);
196: MatGetOwnershipRange(A,&rstart,&rend);
198: /* Check submatrix call */
199: if (scall == MAT_REUSE_MATRIX) {
200: /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); */
201: /* Really need to test rows and column sizes! */
202: newmat = *B;
203: } else {
204: /* Create and fill new matrix */
205: MatCreate(PetscObjectComm((PetscObject)A),&newmat);
206: MatSetSizes(newmat,nrows,ncols,PETSC_DECIDE,Ncols);
207: MatSetType(newmat,((PetscObject)A)->type_name);
208: MatMPIDenseSetPreallocation(newmat,NULL);
209: }
211: /* Now extract the data pointers and do the copy, column at a time */
212: newmatd = (Mat_MPIDense*)newmat->data;
213: bv = ((Mat_SeqDense*)newmatd->A->data)->v;
215: for (i=0; i<Ncols; i++) {
216: av = v + ((Mat_SeqDense*)mat->A->data)->lda*icol[i];
217: for (j=0; j<nrows; j++) {
218: *bv++ = av[irow[j] - rstart];
219: }
220: }
222: /* Assemble the matrices so that the correct flags are set */
223: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
224: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
226: /* Free work space */
227: ISRestoreIndices(isrow,&irow);
228: ISRestoreIndices(iscol_local,&icol);
229: ISDestroy(&iscol_local);
230: *B = newmat;
231: return(0);
232: }
236: PetscErrorCode MatDenseRestoreArray_MPIDense(Mat A,PetscScalar *array[])
237: {
238: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
242: MatDenseRestoreArray(a->A,array);
243: return(0);
244: }
248: PetscErrorCode MatAssemblyBegin_MPIDense(Mat mat,MatAssemblyType mode)
249: {
250: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
251: MPI_Comm comm;
253: PetscInt nstash,reallocs;
254: InsertMode addv;
257: PetscObjectGetComm((PetscObject)mat,&comm);
258: /* make sure all processors are either in INSERTMODE or ADDMODE */
259: MPI_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,comm);
260: if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot mix adds/inserts on different procs");
261: mat->insertmode = addv; /* in case this processor had no cache */
263: MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
264: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
265: PetscInfo2(mdn->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
266: return(0);
267: }
271: PetscErrorCode MatAssemblyEnd_MPIDense(Mat mat,MatAssemblyType mode)
272: {
273: Mat_MPIDense *mdn=(Mat_MPIDense*)mat->data;
275: PetscInt i,*row,*col,flg,j,rstart,ncols;
276: PetscMPIInt n;
277: PetscScalar *val;
278: InsertMode addv=mat->insertmode;
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,addv);
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 *nprocs,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: PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
352: PetscMemzero(nprocs,2*size*sizeof(PetscInt));
353: PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
354: for (i=0; i<N; i++) {
355: idx = rows[i];
356: found = PETSC_FALSE;
357: for (j=0; j<size; j++) {
358: if (idx >= owners[j] && idx < owners[j+1]) {
359: nprocs[2*j]++; nprocs[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break;
360: }
361: }
362: if (!found) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
363: }
364: nsends = 0;
365: for (i=0; i<size; i++) nsends += nprocs[2*i+1];
367: /* inform other processors of number of messages and max length*/
368: PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
370: /* post receives: */
371: PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
372: PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
373: for (i=0; i<nrecvs; i++) {
374: MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
375: }
377: /* do sends:
378: 1) starts[i] gives the starting index in svalues for stuff going to
379: the ith processor
380: */
381: PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
382: PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
383: PetscMalloc((size+1)*sizeof(PetscInt),&starts);
385: starts[0] = 0;
386: for (i=1; i<size; i++) starts[i] = starts[i-1] + nprocs[2*i-2];
387: for (i=0; i<N; i++) svalues[starts[owner[i]]++] = rows[i];
389: starts[0] = 0;
390: for (i=1; i<size+1; i++) starts[i] = starts[i-1] + nprocs[2*i-2];
391: count = 0;
392: for (i=0; i<size; i++) {
393: if (nprocs[2*i+1]) {
394: MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
395: }
396: }
397: PetscFree(starts);
399: base = owners[rank];
401: /* wait on receives */
402: PetscMalloc2(nrecvs,PetscInt,&lens,nrecvs,PetscInt,&source);
403: count = nrecvs;
404: slen = 0;
405: while (count) {
406: MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
407: /* unpack receives into our local space */
408: MPI_Get_count(&recv_status,MPIU_INT,&n);
410: source[imdex] = recv_status.MPI_SOURCE;
411: lens[imdex] = n;
412: slen += n;
413: count--;
414: }
415: PetscFree(recv_waits);
417: /* move the data into the send scatter */
418: PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
419: count = 0;
420: for (i=0; i<nrecvs; i++) {
421: values = rvalues + i*nmax;
422: for (j=0; j<lens[i]; j++) {
423: lrows[count++] = values[j] - base;
424: }
425: }
426: PetscFree(rvalues);
427: PetscFree2(lens,source);
428: PetscFree(owner);
429: PetscFree(nprocs);
431: /* fix right hand side if needed */
432: if (x && b) {
433: VecGetArrayRead(x,&xx);
434: VecGetArray(b,&bb);
435: for (i=0; i<slen; i++) {
436: bb[lrows[i]] = diag*xx[lrows[i]];
437: }
438: VecRestoreArrayRead(x,&xx);
439: VecRestoreArray(b,&bb);
440: }
442: /* actually zap the local rows */
443: MatZeroRows(l->A,slen,lrows,0.0,0,0);
444: if (diag != 0.0) {
445: Mat_SeqDense *ll = (Mat_SeqDense*)l->A->data;
446: PetscInt m = ll->lda, i;
448: for (i=0; i<slen; i++) {
449: ll->v[lrows[i] + m*(A->cmap->rstart + lrows[i])] = diag;
450: }
451: }
452: PetscFree(lrows);
454: /* wait on sends */
455: if (nsends) {
456: PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
457: MPI_Waitall(nsends,send_waits,send_status);
458: PetscFree(send_status);
459: }
460: PetscFree(send_waits);
461: PetscFree(svalues);
462: return(0);
463: }
467: PetscErrorCode MatMult_MPIDense(Mat mat,Vec xx,Vec yy)
468: {
469: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
473: VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
474: VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
475: MatMult_SeqDense(mdn->A,mdn->lvec,yy);
476: return(0);
477: }
481: PetscErrorCode MatMultAdd_MPIDense(Mat mat,Vec xx,Vec yy,Vec zz)
482: {
483: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
487: VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
488: VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
489: MatMultAdd_SeqDense(mdn->A,mdn->lvec,yy,zz);
490: return(0);
491: }
495: PetscErrorCode MatMultTranspose_MPIDense(Mat A,Vec xx,Vec yy)
496: {
497: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
499: PetscScalar zero = 0.0;
502: VecSet(yy,zero);
503: MatMultTranspose_SeqDense(a->A,xx,a->lvec);
504: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
505: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
506: return(0);
507: }
511: PetscErrorCode MatMultTransposeAdd_MPIDense(Mat A,Vec xx,Vec yy,Vec zz)
512: {
513: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
517: VecCopy(yy,zz);
518: MatMultTranspose_SeqDense(a->A,xx,a->lvec);
519: VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
520: VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
521: return(0);
522: }
526: PetscErrorCode MatGetDiagonal_MPIDense(Mat A,Vec v)
527: {
528: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
529: Mat_SeqDense *aloc = (Mat_SeqDense*)a->A->data;
531: PetscInt len,i,n,m = A->rmap->n,radd;
532: PetscScalar *x,zero = 0.0;
535: VecSet(v,zero);
536: VecGetArray(v,&x);
537: VecGetSize(v,&n);
538: if (n != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming mat and vec");
539: len = PetscMin(a->A->rmap->n,a->A->cmap->n);
540: radd = A->rmap->rstart*m;
541: for (i=0; i<len; i++) {
542: x[i] = aloc->v[radd + i*m + i];
543: }
544: VecRestoreArray(v,&x);
545: return(0);
546: }
550: PetscErrorCode MatDestroy_MPIDense(Mat mat)
551: {
552: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
556: #if defined(PETSC_USE_LOG)
557: PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N);
558: #endif
559: MatStashDestroy_Private(&mat->stash);
560: MatDestroy(&mdn->A);
561: VecDestroy(&mdn->lvec);
562: VecScatterDestroy(&mdn->Mvctx);
564: PetscFree(mat->data);
565: PetscObjectChangeTypeName((PetscObject)mat,0);
566: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",NULL);
567: PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",NULL);
568: PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",NULL);
569: PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",NULL);
570: PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",NULL);
571: return(0);
572: }
576: static PetscErrorCode MatView_MPIDense_Binary(Mat mat,PetscViewer viewer)
577: {
578: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
579: PetscErrorCode ierr;
580: PetscViewerFormat format;
581: int fd;
582: PetscInt header[4],mmax,N = mat->cmap->N,i,j,m,k;
583: PetscMPIInt rank,tag = ((PetscObject)viewer)->tag,size;
584: PetscScalar *work,*v,*vv;
585: Mat_SeqDense *a = (Mat_SeqDense*)mdn->A->data;
588: if (mdn->size == 1) {
589: MatView(mdn->A,viewer);
590: } else {
591: PetscViewerBinaryGetDescriptor(viewer,&fd);
592: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
593: MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
595: PetscViewerGetFormat(viewer,&format);
596: if (format == PETSC_VIEWER_NATIVE) {
598: if (!rank) {
599: /* store the matrix as a dense matrix */
600: header[0] = MAT_FILE_CLASSID;
601: header[1] = mat->rmap->N;
602: header[2] = N;
603: header[3] = MATRIX_BINARY_FORMAT_DENSE;
604: PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
606: /* get largest work array needed for transposing array */
607: mmax = mat->rmap->n;
608: for (i=1; i<size; i++) {
609: mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]);
610: }
611: PetscMalloc(mmax*N*sizeof(PetscScalar),&work);
613: /* write out local array, by rows */
614: m = mat->rmap->n;
615: v = a->v;
616: for (j=0; j<N; j++) {
617: for (i=0; i<m; i++) {
618: work[j + i*N] = *v++;
619: }
620: }
621: PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
622: /* get largest work array to receive messages from other processes, excludes process zero */
623: mmax = 0;
624: for (i=1; i<size; i++) {
625: mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]);
626: }
627: PetscMalloc(mmax*N*sizeof(PetscScalar),&vv);
628: for (k = 1; k < size; k++) {
629: v = vv;
630: m = mat->rmap->range[k+1] - mat->rmap->range[k];
631: MPIULong_Recv(v,m*N,MPIU_SCALAR,k,tag,PetscObjectComm((PetscObject)mat));
633: for (j = 0; j < N; j++) {
634: for (i = 0; i < m; i++) {
635: work[j + i*N] = *v++;
636: }
637: }
638: PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);
639: }
640: PetscFree(work);
641: PetscFree(vv);
642: } else {
643: MPIULong_Send(a->v,mat->rmap->n*mat->cmap->N,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));
644: }
645: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"To store a parallel dense matrix you must first call PetscViewerSetFormat(viewer,PETSC_VIEWER_NATIVE)");
646: }
647: return(0);
648: }
650: #include <petscdraw.h>
653: static PetscErrorCode MatView_MPIDense_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
654: {
655: Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data;
656: PetscErrorCode ierr;
657: PetscMPIInt size = mdn->size,rank = mdn->rank;
658: PetscViewerType vtype;
659: PetscBool iascii,isdraw;
660: PetscViewer sviewer;
661: PetscViewerFormat format;
664: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
665: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
666: if (iascii) {
667: PetscViewerGetType(viewer,&vtype);
668: PetscViewerGetFormat(viewer,&format);
669: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
670: MatInfo info;
671: MatGetInfo(mat,MAT_LOCAL,&info);
672: PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);
673: 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);
674: PetscViewerFlush(viewer);
675: PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);
676: VecScatterView(mdn->Mvctx,viewer);
677: return(0);
678: } else if (format == PETSC_VIEWER_ASCII_INFO) {
679: return(0);
680: }
681: } else if (isdraw) {
682: PetscDraw draw;
683: PetscBool isnull;
685: PetscViewerDrawGetDraw(viewer,0,&draw);
686: PetscDrawIsNull(draw,&isnull);
687: if (isnull) return(0);
688: }
690: if (size == 1) {
691: MatView(mdn->A,viewer);
692: } else {
693: /* assemble the entire matrix onto first processor. */
694: Mat A;
695: PetscInt M = mat->rmap->N,N = mat->cmap->N,m,row,i,nz;
696: PetscInt *cols;
697: PetscScalar *vals;
699: MatCreate(PetscObjectComm((PetscObject)mat),&A);
700: if (!rank) {
701: MatSetSizes(A,M,N,M,N);
702: } else {
703: MatSetSizes(A,0,0,M,N);
704: }
705: /* Since this is a temporary matrix, MATMPIDENSE instead of ((PetscObject)A)->type_name here is probably acceptable. */
706: MatSetType(A,MATMPIDENSE);
707: MatMPIDenseSetPreallocation(A,NULL);
708: PetscLogObjectParent(mat,A);
710: /* Copy the matrix ... This isn't the most efficient means,
711: but it's quick for now */
712: A->insertmode = INSERT_VALUES;
714: row = mat->rmap->rstart;
715: m = mdn->A->rmap->n;
716: for (i=0; i<m; i++) {
717: MatGetRow_MPIDense(mat,row,&nz,&cols,&vals);
718: MatSetValues_MPIDense(A,1,&row,nz,cols,vals,INSERT_VALUES);
719: MatRestoreRow_MPIDense(mat,row,&nz,&cols,&vals);
720: row++;
721: }
723: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
724: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
725: PetscViewerGetSingleton(viewer,&sviewer);
726: if (!rank) {
727: PetscObjectSetName((PetscObject)((Mat_MPIDense*)(A->data))->A,((PetscObject)mat)->name);
728: /* Set the type name to MATMPIDense so that the correct type can be printed out by PetscObjectPrintClassNamePrefixType() in MatView_SeqDense_ASCII()*/
729: PetscStrcpy(((PetscObject)((Mat_MPIDense*)(A->data))->A)->type_name,MATMPIDENSE);
730: MatView(((Mat_MPIDense*)(A->data))->A,sviewer);
731: }
732: PetscViewerRestoreSingleton(viewer,&sviewer);
733: PetscViewerFlush(viewer);
734: MatDestroy(&A);
735: }
736: return(0);
737: }
741: PetscErrorCode MatView_MPIDense(Mat mat,PetscViewer viewer)
742: {
744: PetscBool iascii,isbinary,isdraw,issocket;
747: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
748: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
749: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
750: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
752: if (iascii || issocket || isdraw) {
753: MatView_MPIDense_ASCIIorDraworSocket(mat,viewer);
754: } else if (isbinary) {
755: MatView_MPIDense_Binary(mat,viewer);
756: }
757: return(0);
758: }
762: PetscErrorCode MatGetInfo_MPIDense(Mat A,MatInfoType flag,MatInfo *info)
763: {
764: Mat_MPIDense *mat = (Mat_MPIDense*)A->data;
765: Mat mdn = mat->A;
767: PetscReal isend[5],irecv[5];
770: info->block_size = 1.0;
772: MatGetInfo(mdn,MAT_LOCAL,info);
774: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
775: isend[3] = info->memory; isend[4] = info->mallocs;
776: if (flag == MAT_LOCAL) {
777: info->nz_used = isend[0];
778: info->nz_allocated = isend[1];
779: info->nz_unneeded = isend[2];
780: info->memory = isend[3];
781: info->mallocs = isend[4];
782: } else if (flag == MAT_GLOBAL_MAX) {
783: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
785: info->nz_used = irecv[0];
786: info->nz_allocated = irecv[1];
787: info->nz_unneeded = irecv[2];
788: info->memory = irecv[3];
789: info->mallocs = irecv[4];
790: } else if (flag == MAT_GLOBAL_SUM) {
791: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
793: info->nz_used = irecv[0];
794: info->nz_allocated = irecv[1];
795: info->nz_unneeded = irecv[2];
796: info->memory = irecv[3];
797: info->mallocs = irecv[4];
798: }
799: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
800: info->fill_ratio_needed = 0;
801: info->factor_mallocs = 0;
802: return(0);
803: }
807: PetscErrorCode MatSetOption_MPIDense(Mat A,MatOption op,PetscBool flg)
808: {
809: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
813: switch (op) {
814: case MAT_NEW_NONZERO_LOCATIONS:
815: case MAT_NEW_NONZERO_LOCATION_ERR:
816: case MAT_NEW_NONZERO_ALLOCATION_ERR:
817: MatSetOption(a->A,op,flg);
818: break;
819: case MAT_ROW_ORIENTED:
820: a->roworiented = flg;
822: MatSetOption(a->A,op,flg);
823: break;
824: case MAT_NEW_DIAGONALS:
825: case MAT_KEEP_NONZERO_PATTERN:
826: case MAT_USE_HASH_TABLE:
827: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
828: break;
829: case MAT_IGNORE_OFF_PROC_ENTRIES:
830: a->donotstash = flg;
831: break;
832: case MAT_SYMMETRIC:
833: case MAT_STRUCTURALLY_SYMMETRIC:
834: case MAT_HERMITIAN:
835: case MAT_SYMMETRY_ETERNAL:
836: case MAT_IGNORE_LOWER_TRIANGULAR:
837: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
838: break;
839: default:
840: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %s",MatOptions[op]);
841: }
842: return(0);
843: }
848: PetscErrorCode MatDiagonalScale_MPIDense(Mat A,Vec ll,Vec rr)
849: {
850: Mat_MPIDense *mdn = (Mat_MPIDense*)A->data;
851: Mat_SeqDense *mat = (Mat_SeqDense*)mdn->A->data;
852: PetscScalar *l,*r,x,*v;
854: PetscInt i,j,s2a,s3a,s2,s3,m=mdn->A->rmap->n,n=mdn->A->cmap->n;
857: MatGetLocalSize(A,&s2,&s3);
858: if (ll) {
859: VecGetLocalSize(ll,&s2a);
860: if (s2a != s2) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector non-conforming local size, %d != %d.", s2a, s2);
861: VecGetArray(ll,&l);
862: for (i=0; i<m; i++) {
863: x = l[i];
864: v = mat->v + i;
865: for (j=0; j<n; j++) { (*v) *= x; v+= m;}
866: }
867: VecRestoreArray(ll,&l);
868: PetscLogFlops(n*m);
869: }
870: if (rr) {
871: VecGetLocalSize(rr,&s3a);
872: if (s3a != s3) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vec non-conforming local size, %d != %d.", s3a, s3);
873: VecScatterBegin(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
874: VecScatterEnd(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);
875: VecGetArray(mdn->lvec,&r);
876: for (i=0; i<n; i++) {
877: x = r[i];
878: v = mat->v + i*m;
879: for (j=0; j<m; j++) (*v++) *= x;
880: }
881: VecRestoreArray(mdn->lvec,&r);
882: PetscLogFlops(n*m);
883: }
884: return(0);
885: }
889: PetscErrorCode MatNorm_MPIDense(Mat A,NormType type,PetscReal *nrm)
890: {
891: Mat_MPIDense *mdn = (Mat_MPIDense*)A->data;
892: Mat_SeqDense *mat = (Mat_SeqDense*)mdn->A->data;
894: PetscInt i,j;
895: PetscReal sum = 0.0;
896: PetscScalar *v = mat->v;
899: if (mdn->size == 1) {
900: MatNorm(mdn->A,type,nrm);
901: } else {
902: if (type == NORM_FROBENIUS) {
903: for (i=0; i<mdn->A->cmap->n*mdn->A->rmap->n; i++) {
904: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
905: }
906: MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
907: *nrm = PetscSqrtReal(*nrm);
908: PetscLogFlops(2.0*mdn->A->cmap->n*mdn->A->rmap->n);
909: } else if (type == NORM_1) {
910: PetscReal *tmp,*tmp2;
911: PetscMalloc2(A->cmap->N,PetscReal,&tmp,A->cmap->N,PetscReal,&tmp2);
912: PetscMemzero(tmp,A->cmap->N*sizeof(PetscReal));
913: PetscMemzero(tmp2,A->cmap->N*sizeof(PetscReal));
914: *nrm = 0.0;
915: v = mat->v;
916: for (j=0; j<mdn->A->cmap->n; j++) {
917: for (i=0; i<mdn->A->rmap->n; i++) {
918: tmp[j] += PetscAbsScalar(*v); v++;
919: }
920: }
921: MPI_Allreduce(tmp,tmp2,A->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
922: for (j=0; j<A->cmap->N; j++) {
923: if (tmp2[j] > *nrm) *nrm = tmp2[j];
924: }
925: PetscFree2(tmp,tmp);
926: PetscLogFlops(A->cmap->n*A->rmap->n);
927: } else if (type == NORM_INFINITY) { /* max row norm */
928: PetscReal ntemp;
929: MatNorm(mdn->A,type,&ntemp);
930: MPI_Allreduce(&ntemp,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
931: } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for two norm");
932: }
933: return(0);
934: }
938: PetscErrorCode MatTranspose_MPIDense(Mat A,MatReuse reuse,Mat *matout)
939: {
940: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
941: Mat_SeqDense *Aloc = (Mat_SeqDense*)a->A->data;
942: Mat B;
943: PetscInt M = A->rmap->N,N = A->cmap->N,m,n,*rwork,rstart = A->rmap->rstart;
945: PetscInt j,i;
946: PetscScalar *v;
949: if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Supports square matrix only in-place");
950: if (reuse == MAT_INITIAL_MATRIX || A == *matout) {
951: MatCreate(PetscObjectComm((PetscObject)A),&B);
952: MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
953: MatSetType(B,((PetscObject)A)->type_name);
954: MatMPIDenseSetPreallocation(B,NULL);
955: } else {
956: B = *matout;
957: }
959: m = a->A->rmap->n; n = a->A->cmap->n; v = Aloc->v;
960: PetscMalloc(m*sizeof(PetscInt),&rwork);
961: for (i=0; i<m; i++) rwork[i] = rstart + i;
962: for (j=0; j<n; j++) {
963: MatSetValues(B,1,&j,m,rwork,v,INSERT_VALUES);
964: v += m;
965: }
966: PetscFree(rwork);
967: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
968: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
969: if (reuse == MAT_INITIAL_MATRIX || *matout != A) {
970: *matout = B;
971: } else {
972: MatHeaderMerge(A,B);
973: }
974: return(0);
975: }
978: static PetscErrorCode MatDuplicate_MPIDense(Mat,MatDuplicateOption,Mat*);
979: extern PetscErrorCode MatScale_MPIDense(Mat,PetscScalar);
983: PetscErrorCode MatSetUp_MPIDense(Mat A)
984: {
988: MatMPIDenseSetPreallocation(A,0);
989: return(0);
990: }
994: PetscErrorCode MatAXPY_MPIDense(Mat Y,PetscScalar alpha,Mat X,MatStructure str)
995: {
997: Mat_MPIDense *A = (Mat_MPIDense*)Y->data, *B = (Mat_MPIDense*)X->data;
1000: MatAXPY(A->A,alpha,B->A,str);
1001: return(0);
1002: }
1006: PetscErrorCode MatConjugate_MPIDense(Mat mat)
1007: {
1008: Mat_MPIDense *a = (Mat_MPIDense*)mat->data;
1012: MatConjugate(a->A);
1013: return(0);
1014: }
1018: PetscErrorCode MatRealPart_MPIDense(Mat A)
1019: {
1020: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
1024: MatRealPart(a->A);
1025: return(0);
1026: }
1030: PetscErrorCode MatImaginaryPart_MPIDense(Mat A)
1031: {
1032: Mat_MPIDense *a = (Mat_MPIDense*)A->data;
1036: MatImaginaryPart(a->A);
1037: return(0);
1038: }
1040: extern PetscErrorCode MatGetColumnNorms_SeqDense(Mat,NormType,PetscReal*);
1043: PetscErrorCode MatGetColumnNorms_MPIDense(Mat A,NormType type,PetscReal *norms)
1044: {
1046: PetscInt i,n;
1047: Mat_MPIDense *a = (Mat_MPIDense*) A->data;
1048: PetscReal *work;
1051: MatGetSize(A,NULL,&n);
1052: PetscMalloc(n*sizeof(PetscReal),&work);
1053: MatGetColumnNorms_SeqDense(a->A,type,work);
1054: if (type == NORM_2) {
1055: for (i=0; i<n; i++) work[i] *= work[i];
1056: }
1057: if (type == NORM_INFINITY) {
1058: MPI_Allreduce(work,norms,n,MPIU_REAL,MPIU_MAX,A->hdr.comm);
1059: } else {
1060: MPI_Allreduce(work,norms,n,MPIU_REAL,MPIU_SUM,A->hdr.comm);
1061: }
1062: PetscFree(work);
1063: if (type == NORM_2) {
1064: for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
1065: }
1066: return(0);
1067: }
1071: static PetscErrorCode MatSetRandom_MPIDense(Mat x,PetscRandom rctx)
1072: {
1073: Mat_MPIDense *d = (Mat_MPIDense*)x->data;
1075: PetscScalar *a;
1076: PetscInt m,n,i;
1079: MatGetSize(d->A,&m,&n);
1080: MatDenseGetArray(d->A,&a);
1081: for (i=0; i<m*n; i++) {
1082: PetscRandomGetValue(rctx,a+i);
1083: }
1084: MatDenseRestoreArray(d->A,&a);
1085: return(0);
1086: }
1088: /* -------------------------------------------------------------------*/
1089: static struct _MatOps MatOps_Values = { MatSetValues_MPIDense,
1090: MatGetRow_MPIDense,
1091: MatRestoreRow_MPIDense,
1092: MatMult_MPIDense,
1093: /* 4*/ MatMultAdd_MPIDense,
1094: MatMultTranspose_MPIDense,
1095: MatMultTransposeAdd_MPIDense,
1096: 0,
1097: 0,
1098: 0,
1099: /* 10*/ 0,
1100: 0,
1101: 0,
1102: 0,
1103: MatTranspose_MPIDense,
1104: /* 15*/ MatGetInfo_MPIDense,
1105: MatEqual_MPIDense,
1106: MatGetDiagonal_MPIDense,
1107: MatDiagonalScale_MPIDense,
1108: MatNorm_MPIDense,
1109: /* 20*/ MatAssemblyBegin_MPIDense,
1110: MatAssemblyEnd_MPIDense,
1111: MatSetOption_MPIDense,
1112: MatZeroEntries_MPIDense,
1113: /* 24*/ MatZeroRows_MPIDense,
1114: 0,
1115: 0,
1116: 0,
1117: 0,
1118: /* 29*/ MatSetUp_MPIDense,
1119: 0,
1120: 0,
1121: 0,
1122: 0,
1123: /* 34*/ MatDuplicate_MPIDense,
1124: 0,
1125: 0,
1126: 0,
1127: 0,
1128: /* 39*/ MatAXPY_MPIDense,
1129: MatGetSubMatrices_MPIDense,
1130: 0,
1131: MatGetValues_MPIDense,
1132: 0,
1133: /* 44*/ 0,
1134: MatScale_MPIDense,
1135: 0,
1136: 0,
1137: 0,
1138: /* 49*/ MatSetRandom_MPIDense,
1139: 0,
1140: 0,
1141: 0,
1142: 0,
1143: /* 54*/ 0,
1144: 0,
1145: 0,
1146: 0,
1147: 0,
1148: /* 59*/ MatGetSubMatrix_MPIDense,
1149: MatDestroy_MPIDense,
1150: MatView_MPIDense,
1151: 0,
1152: 0,
1153: /* 64*/ 0,
1154: 0,
1155: 0,
1156: 0,
1157: 0,
1158: /* 69*/ 0,
1159: 0,
1160: 0,
1161: 0,
1162: 0,
1163: /* 74*/ 0,
1164: 0,
1165: 0,
1166: 0,
1167: 0,
1168: /* 79*/ 0,
1169: 0,
1170: 0,
1171: 0,
1172: /* 83*/ MatLoad_MPIDense,
1173: 0,
1174: 0,
1175: 0,
1176: 0,
1177: 0,
1178: /* 89*/
1179: 0,
1180: 0,
1181: 0,
1182: 0,
1183: 0,
1184: /* 94*/ 0,
1185: 0,
1186: 0,
1187: 0,
1188: 0,
1189: /* 99*/ 0,
1190: 0,
1191: 0,
1192: MatConjugate_MPIDense,
1193: 0,
1194: /*104*/ 0,
1195: MatRealPart_MPIDense,
1196: MatImaginaryPart_MPIDense,
1197: 0,
1198: 0,
1199: /*109*/ 0,
1200: 0,
1201: 0,
1202: 0,
1203: 0,
1204: /*114*/ 0,
1205: 0,
1206: 0,
1207: 0,
1208: 0,
1209: /*119*/ 0,
1210: 0,
1211: 0,
1212: 0,
1213: 0,
1214: /*124*/ 0,
1215: MatGetColumnNorms_MPIDense,
1216: 0,
1217: 0,
1218: 0,
1219: /*129*/ 0,
1220: 0,
1221: 0,
1222: 0,
1223: 0,
1224: /*134*/ 0,
1225: 0,
1226: 0,
1227: 0,
1228: 0,
1229: /*139*/ 0,
1230: 0
1231: };
1235: PetscErrorCode MatMPIDenseSetPreallocation_MPIDense(Mat mat,PetscScalar *data)
1236: {
1237: Mat_MPIDense *a;
1241: mat->preallocated = PETSC_TRUE;
1242: /* Note: For now, when data is specified above, this assumes the user correctly
1243: allocates the local dense storage space. We should add error checking. */
1245: a = (Mat_MPIDense*)mat->data;
1246: PetscLayoutSetUp(mat->rmap);
1247: PetscLayoutSetUp(mat->cmap);
1248: a->nvec = mat->cmap->n;
1250: MatCreate(PETSC_COMM_SELF,&a->A);
1251: MatSetSizes(a->A,mat->rmap->n,mat->cmap->N,mat->rmap->n,mat->cmap->N);
1252: MatSetType(a->A,MATSEQDENSE);
1253: MatSeqDenseSetPreallocation(a->A,data);
1254: PetscLogObjectParent(mat,a->A);
1255: return(0);
1256: }
1260: PETSC_EXTERN PetscErrorCode MatCreate_MPIDense(Mat mat)
1261: {
1262: Mat_MPIDense *a;
1266: PetscNewLog(mat,Mat_MPIDense,&a);
1267: mat->data = (void*)a;
1268: PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));
1270: mat->insertmode = NOT_SET_VALUES;
1271: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&a->rank);
1272: MPI_Comm_size(PetscObjectComm((PetscObject)mat),&a->size);
1274: /* build cache for off array entries formed */
1275: a->donotstash = PETSC_FALSE;
1277: MatStashCreate_Private(PetscObjectComm((PetscObject)mat),1,&mat->stash);
1279: /* stuff used for matrix vector multiply */
1280: a->lvec = 0;
1281: a->Mvctx = 0;
1282: a->roworiented = PETSC_TRUE;
1284: PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",MatDenseGetArray_MPIDense);
1285: PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",MatDenseRestoreArray_MPIDense);
1287: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",MatGetDiagonalBlock_MPIDense);
1288: PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",MatMPIDenseSetPreallocation_MPIDense);
1289: PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",MatMatMult_MPIAIJ_MPIDense);
1290: PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",MatMatMultSymbolic_MPIAIJ_MPIDense);
1291: PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",MatMatMultNumeric_MPIAIJ_MPIDense);
1292: PetscObjectChangeTypeName((PetscObject)mat,MATMPIDENSE);
1293: return(0);
1294: }
1296: /*MC
1297: MATDENSE - MATDENSE = "dense" - A matrix type to be used for dense matrices.
1299: This matrix type is identical to MATSEQDENSE when constructed with a single process communicator,
1300: and MATMPIDENSE otherwise.
1302: Options Database Keys:
1303: . -mat_type dense - sets the matrix type to "dense" during a call to MatSetFromOptions()
1305: Level: beginner
1308: .seealso: MatCreateMPIDense,MATSEQDENSE,MATMPIDENSE
1309: M*/
1313: /*@C
1314: MatMPIDenseSetPreallocation - Sets the array used to store the matrix entries
1316: Not collective
1318: Input Parameters:
1319: . A - the matrix
1320: - data - optional location of matrix data. Set data=NULL for PETSc
1321: to control all matrix memory allocation.
1323: Notes:
1324: The dense format is fully compatible with standard Fortran 77
1325: storage by columns.
1327: The data input variable is intended primarily for Fortran programmers
1328: who wish to allocate their own matrix memory space. Most users should
1329: set data=NULL.
1331: Level: intermediate
1333: .keywords: matrix,dense, parallel
1335: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
1336: @*/
1337: PetscErrorCode MatMPIDenseSetPreallocation(Mat mat,PetscScalar *data)
1338: {
1342: PetscTryMethod(mat,"MatMPIDenseSetPreallocation_C",(Mat,PetscScalar*),(mat,data));
1343: return(0);
1344: }
1348: /*@C
1349: MatCreateDense - Creates a parallel matrix in dense format.
1351: Collective on MPI_Comm
1353: Input Parameters:
1354: + comm - MPI communicator
1355: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1356: . n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1357: . M - number of global rows (or PETSC_DECIDE to have calculated if m is given)
1358: . N - number of global columns (or PETSC_DECIDE to have calculated if n is given)
1359: - data - optional location of matrix data. Set data=NULL (NULL_SCALAR for Fortran users) for PETSc
1360: to control all matrix memory allocation.
1362: Output Parameter:
1363: . A - the matrix
1365: Notes:
1366: The dense format is fully compatible with standard Fortran 77
1367: storage by columns.
1369: The data input variable is intended primarily for Fortran programmers
1370: who wish to allocate their own matrix memory space. Most users should
1371: set data=NULL (NULL_SCALAR for Fortran users).
1373: The user MUST specify either the local or global matrix dimensions
1374: (possibly both).
1376: Level: intermediate
1378: .keywords: matrix,dense, parallel
1380: .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues()
1381: @*/
1382: PetscErrorCode MatCreateDense(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscScalar *data,Mat *A)
1383: {
1385: PetscMPIInt size;
1388: MatCreate(comm,A);
1389: MatSetSizes(*A,m,n,M,N);
1390: MPI_Comm_size(comm,&size);
1391: if (size > 1) {
1392: MatSetType(*A,MATMPIDENSE);
1393: MatMPIDenseSetPreallocation(*A,data);
1394: } else {
1395: MatSetType(*A,MATSEQDENSE);
1396: MatSeqDenseSetPreallocation(*A,data);
1397: }
1398: return(0);
1399: }
1403: static PetscErrorCode MatDuplicate_MPIDense(Mat A,MatDuplicateOption cpvalues,Mat *newmat)
1404: {
1405: Mat mat;
1406: Mat_MPIDense *a,*oldmat = (Mat_MPIDense*)A->data;
1410: *newmat = 0;
1411: MatCreate(PetscObjectComm((PetscObject)A),&mat);
1412: MatSetSizes(mat,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1413: MatSetType(mat,((PetscObject)A)->type_name);
1414: a = (Mat_MPIDense*)mat->data;
1415: PetscMemcpy(mat->ops,A->ops,sizeof(struct _MatOps));
1417: mat->factortype = A->factortype;
1418: mat->assembled = PETSC_TRUE;
1419: mat->preallocated = PETSC_TRUE;
1421: a->size = oldmat->size;
1422: a->rank = oldmat->rank;
1423: mat->insertmode = NOT_SET_VALUES;
1424: a->nvec = oldmat->nvec;
1425: a->donotstash = oldmat->donotstash;
1427: PetscLayoutReference(A->rmap,&mat->rmap);
1428: PetscLayoutReference(A->cmap,&mat->cmap);
1430: MatSetUpMultiply_MPIDense(mat);
1431: MatDuplicate(oldmat->A,cpvalues,&a->A);
1432: PetscLogObjectParent(mat,a->A);
1434: *newmat = mat;
1435: return(0);
1436: }
1440: PetscErrorCode MatLoad_MPIDense_DenseInFile(MPI_Comm comm,PetscInt fd,PetscInt M,PetscInt N,Mat newmat,PetscInt sizesset)
1441: {
1443: PetscMPIInt rank,size;
1444: PetscInt *rowners,i,m,nz,j;
1445: PetscScalar *array,*vals,*vals_ptr;
1448: MPI_Comm_rank(comm,&rank);
1449: MPI_Comm_size(comm,&size);
1451: /* determine ownership of all rows */
1452: if (newmat->rmap->n < 0) m = M/size + ((M % size) > rank);
1453: else m = newmat->rmap->n;
1454: PetscMalloc((size+2)*sizeof(PetscInt),&rowners);
1455: MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
1456: rowners[0] = 0;
1457: for (i=2; i<=size; i++) {
1458: rowners[i] += rowners[i-1];
1459: }
1461: if (!sizesset) {
1462: MatSetSizes(newmat,m,PETSC_DECIDE,M,N);
1463: }
1464: MatMPIDenseSetPreallocation(newmat,NULL);
1465: MatDenseGetArray(newmat,&array);
1467: if (!rank) {
1468: PetscMalloc(m*N*sizeof(PetscScalar),&vals);
1470: /* read in my part of the matrix numerical values */
1471: PetscBinaryRead(fd,vals,m*N,PETSC_SCALAR);
1473: /* insert into matrix-by row (this is why cannot directly read into array */
1474: vals_ptr = vals;
1475: for (i=0; i<m; i++) {
1476: for (j=0; j<N; j++) {
1477: array[i + j*m] = *vals_ptr++;
1478: }
1479: }
1481: /* read in other processors and ship out */
1482: for (i=1; i<size; i++) {
1483: nz = (rowners[i+1] - rowners[i])*N;
1484: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1485: MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)(newmat))->tag,comm);
1486: }
1487: } else {
1488: /* receive numeric values */
1489: PetscMalloc(m*N*sizeof(PetscScalar),&vals);
1491: /* receive message of values*/
1492: MPIULong_Recv(vals,m*N,MPIU_SCALAR,0,((PetscObject)(newmat))->tag,comm);
1494: /* insert into matrix-by row (this is why cannot directly read into array */
1495: vals_ptr = vals;
1496: for (i=0; i<m; i++) {
1497: for (j=0; j<N; j++) {
1498: array[i + j*m] = *vals_ptr++;
1499: }
1500: }
1501: }
1502: MatDenseRestoreArray(newmat,&array);
1503: PetscFree(rowners);
1504: PetscFree(vals);
1505: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
1506: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
1507: return(0);
1508: }
1512: PetscErrorCode MatLoad_MPIDense(Mat newmat,PetscViewer viewer)
1513: {
1514: PetscScalar *vals,*svals;
1515: MPI_Comm comm;
1516: MPI_Status status;
1517: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag,*rowners,*sndcounts,m,maxnz;
1518: PetscInt header[4],*rowlengths = 0,M,N,*cols;
1519: PetscInt *ourlens,*procsnz = 0,*offlens,jj,*mycols,*smycols;
1520: PetscInt i,nz,j,rstart,rend,sizesset=1,grows,gcols;
1521: int fd;
1525: PetscObjectGetComm((PetscObject)viewer,&comm);
1526: MPI_Comm_size(comm,&size);
1527: MPI_Comm_rank(comm,&rank);
1528: if (!rank) {
1529: PetscViewerBinaryGetDescriptor(viewer,&fd);
1530: PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
1531: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
1532: }
1533: if (newmat->rmap->n < 0 && newmat->rmap->N < 0 && newmat->cmap->n < 0 && newmat->cmap->N < 0) sizesset = 0;
1535: MPI_Bcast(header+1,3,MPIU_INT,0,comm);
1536: M = header[1]; N = header[2]; nz = header[3];
1538: /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */
1539: if (sizesset && newmat->rmap->N < 0) newmat->rmap->N = M;
1540: if (sizesset && newmat->cmap->N < 0) newmat->cmap->N = N;
1542: /* If global sizes are set, check if they are consistent with that given in the file */
1543: if (sizesset) {
1544: MatGetSize(newmat,&grows,&gcols);
1545: }
1546: if (sizesset && newmat->rmap->N != grows) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows:Matrix in file has (%d) and input matrix has (%d)",M,grows);
1547: if (sizesset && newmat->cmap->N != gcols) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of cols:Matrix in file has (%d) and input matrix has (%d)",N,gcols);
1549: /*
1550: Handle case where matrix is stored on disk as a dense matrix
1551: */
1552: if (nz == MATRIX_BINARY_FORMAT_DENSE) {
1553: MatLoad_MPIDense_DenseInFile(comm,fd,M,N,newmat,sizesset);
1554: return(0);
1555: }
1557: /* determine ownership of all rows */
1558: if (newmat->rmap->n < 0) {
1559: PetscMPIIntCast(M/size + ((M % size) > rank),&m);
1560: } else {
1561: PetscMPIIntCast(newmat->rmap->n,&m);
1562: }
1563: PetscMalloc((size+2)*sizeof(PetscMPIInt),&rowners);
1564: MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);
1565: rowners[0] = 0;
1566: for (i=2; i<=size; i++) {
1567: rowners[i] += rowners[i-1];
1568: }
1569: rstart = rowners[rank];
1570: rend = rowners[rank+1];
1572: /* distribute row lengths to all processors */
1573: PetscMalloc2(rend-rstart,PetscInt,&ourlens,rend-rstart,PetscInt,&offlens);
1574: if (!rank) {
1575: PetscMalloc(M*sizeof(PetscInt),&rowlengths);
1576: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
1577: PetscMalloc(size*sizeof(PetscMPIInt),&sndcounts);
1578: for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i];
1579: MPI_Scatterv(rowlengths,sndcounts,rowners,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
1580: PetscFree(sndcounts);
1581: } else {
1582: MPI_Scatterv(0,0,0,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);
1583: }
1585: if (!rank) {
1586: /* calculate the number of nonzeros on each processor */
1587: PetscMalloc(size*sizeof(PetscInt),&procsnz);
1588: PetscMemzero(procsnz,size*sizeof(PetscInt));
1589: for (i=0; i<size; i++) {
1590: for (j=rowners[i]; j< rowners[i+1]; j++) {
1591: procsnz[i] += rowlengths[j];
1592: }
1593: }
1594: PetscFree(rowlengths);
1596: /* determine max buffer needed and allocate it */
1597: maxnz = 0;
1598: for (i=0; i<size; i++) {
1599: maxnz = PetscMax(maxnz,procsnz[i]);
1600: }
1601: PetscMalloc(maxnz*sizeof(PetscInt),&cols);
1603: /* read in my part of the matrix column indices */
1604: nz = procsnz[0];
1605: PetscMalloc(nz*sizeof(PetscInt),&mycols);
1606: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
1608: /* read in every one elses and ship off */
1609: for (i=1; i<size; i++) {
1610: nz = procsnz[i];
1611: PetscBinaryRead(fd,cols,nz,PETSC_INT);
1612: MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
1613: }
1614: PetscFree(cols);
1615: } else {
1616: /* determine buffer space needed for message */
1617: nz = 0;
1618: for (i=0; i<m; i++) {
1619: nz += ourlens[i];
1620: }
1621: PetscMalloc((nz+1)*sizeof(PetscInt),&mycols);
1623: /* receive message of column indices*/
1624: MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
1625: MPI_Get_count(&status,MPIU_INT,&maxnz);
1626: if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
1627: }
1629: /* loop over local rows, determining number of off diagonal entries */
1630: PetscMemzero(offlens,m*sizeof(PetscInt));
1631: jj = 0;
1632: for (i=0; i<m; i++) {
1633: for (j=0; j<ourlens[i]; j++) {
1634: if (mycols[jj] < rstart || mycols[jj] >= rend) offlens[i]++;
1635: jj++;
1636: }
1637: }
1639: /* create our matrix */
1640: for (i=0; i<m; i++) ourlens[i] -= offlens[i];
1642: if (!sizesset) {
1643: MatSetSizes(newmat,m,PETSC_DECIDE,M,N);
1644: }
1645: MatMPIDenseSetPreallocation(newmat,NULL);
1646: for (i=0; i<m; i++) ourlens[i] += offlens[i];
1648: if (!rank) {
1649: PetscMalloc(maxnz*sizeof(PetscScalar),&vals);
1651: /* read in my part of the matrix numerical values */
1652: nz = procsnz[0];
1653: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1655: /* insert into matrix */
1656: jj = rstart;
1657: smycols = mycols;
1658: svals = vals;
1659: for (i=0; i<m; i++) {
1660: MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
1661: smycols += ourlens[i];
1662: svals += ourlens[i];
1663: jj++;
1664: }
1666: /* read in other processors and ship out */
1667: for (i=1; i<size; i++) {
1668: nz = procsnz[i];
1669: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
1670: MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);
1671: }
1672: PetscFree(procsnz);
1673: } else {
1674: /* receive numeric values */
1675: PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);
1677: /* receive message of values*/
1678: MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm,&status);
1679: MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
1680: if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
1682: /* insert into matrix */
1683: jj = rstart;
1684: smycols = mycols;
1685: svals = vals;
1686: for (i=0; i<m; i++) {
1687: MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
1688: smycols += ourlens[i];
1689: svals += ourlens[i];
1690: jj++;
1691: }
1692: }
1693: PetscFree2(ourlens,offlens);
1694: PetscFree(vals);
1695: PetscFree(mycols);
1696: PetscFree(rowners);
1698: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
1699: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
1700: return(0);
1701: }
1705: PetscErrorCode MatEqual_MPIDense(Mat A,Mat B,PetscBool *flag)
1706: {
1707: Mat_MPIDense *matB = (Mat_MPIDense*)B->data,*matA = (Mat_MPIDense*)A->data;
1708: Mat a,b;
1709: PetscBool flg;
1713: a = matA->A;
1714: b = matB->A;
1715: MatEqual(a,b,&flg);
1716: MPI_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
1717: return(0);
1718: }