Actual source code: mpiaij.c

  1: #define PETSCMAT_DLL

 3:  #include src/mat/impls/aij/mpi/mpiaij.h
 4:  #include src/inline/spops.h

  6: /* 
  7:   Local utility routine that creates a mapping from the global column 
  8: number to the local number in the off-diagonal part of the local 
  9: storage of the matrix.  When PETSC_USE_CTABLE is used this is scalable at 
 10: a slightly higher hash table cost; without it it is not scalable (each processor
 11: has an order N integer array but is fast to acess.
 12: */
 15: PetscErrorCode CreateColmap_MPIAIJ_Private(Mat mat)
 16: {
 17:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
 19:   PetscInt       n = aij->B->cmap.n,i;

 22: #if defined (PETSC_USE_CTABLE)
 23:   PetscTableCreate(n,&aij->colmap);
 24:   for (i=0; i<n; i++){
 25:     PetscTableAdd(aij->colmap,aij->garray[i]+1,i+1);
 26:   }
 27: #else
 28:   PetscMalloc((mat->cmap.N+1)*sizeof(PetscInt),&aij->colmap);
 29:   PetscLogObjectMemory(mat,mat->cmap.N*sizeof(PetscInt));
 30:   PetscMemzero(aij->colmap,mat->cmap.N*sizeof(PetscInt));
 31:   for (i=0; i<n; i++) aij->colmap[aij->garray[i]] = i+1;
 32: #endif
 33:   return(0);
 34: }


 37: #define CHUNKSIZE   15
 38: #define MatSetValues_SeqAIJ_A_Private(row,col,value,addv) \
 39: { \
 40:     if (col <= lastcol1) low1 = 0; else high1 = nrow1; \
 41:     lastcol1 = col;\
 42:     while (high1-low1 > 5) { \
 43:       t = (low1+high1)/2; \
 44:       if (rp1[t] > col) high1 = t; \
 45:       else             low1  = t; \
 46:     } \
 47:       for (_i=low1; _i<high1; _i++) { \
 48:         if (rp1[_i] > col) break; \
 49:         if (rp1[_i] == col) { \
 50:           if (addv == ADD_VALUES) ap1[_i] += value;   \
 51:           else                    ap1[_i] = value; \
 52:           goto a_noinsert; \
 53:         } \
 54:       }  \
 55:       if (value == 0.0 && ignorezeroentries) goto a_noinsert; \
 56:       if (nonew == 1) goto a_noinsert; \
 57:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
 58:       MatSeqXAIJReallocateAIJ(A,am,1,nrow1,row,col,rmax1,aa,ai,aj,rp1,ap1,aimax,nonew); \
 59:       N = nrow1++ - 1; a->nz++; high1++; \
 60:       /* shift up all the later entries in this row */ \
 61:       for (ii=N; ii>=_i; ii--) { \
 62:         rp1[ii+1] = rp1[ii]; \
 63:         ap1[ii+1] = ap1[ii]; \
 64:       } \
 65:       rp1[_i] = col;  \
 66:       ap1[_i] = value;  \
 67:       a_noinsert: ; \
 68:       ailen[row] = nrow1; \
 69: } 


 72: #define MatSetValues_SeqAIJ_B_Private(row,col,value,addv) \
 73: { \
 74:     if (col <= lastcol2) low2 = 0; else high2 = nrow2; \
 75:     lastcol2 = col;\
 76:     while (high2-low2 > 5) { \
 77:       t = (low2+high2)/2; \
 78:       if (rp2[t] > col) high2 = t; \
 79:       else             low2  = t; \
 80:     } \
 81:        for (_i=low2; _i<high2; _i++) { \
 82:         if (rp2[_i] > col) break; \
 83:         if (rp2[_i] == col) { \
 84:           if (addv == ADD_VALUES) ap2[_i] += value;   \
 85:           else                    ap2[_i] = value; \
 86:           goto b_noinsert; \
 87:         } \
 88:       }  \
 89:       if (value == 0.0 && ignorezeroentries) goto b_noinsert; \
 90:       if (nonew == 1) goto b_noinsert; \
 91:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
 92:       MatSeqXAIJReallocateAIJ(B,bm,1,nrow2,row,col,rmax2,ba,bi,bj,rp2,ap2,bimax,nonew); \
 93:       N = nrow2++ - 1; b->nz++; high2++;\
 94:       /* shift up all the later entries in this row */ \
 95:       for (ii=N; ii>=_i; ii--) { \
 96:         rp2[ii+1] = rp2[ii]; \
 97:         ap2[ii+1] = ap2[ii]; \
 98:       } \
 99:       rp2[_i] = col;  \
100:       ap2[_i] = value;  \
101:       b_noinsert: ; \
102:       bilen[row] = nrow2; \
103: }

107: PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A,PetscInt row,const PetscScalar v[])
108: {
109:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)A->data;
110:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)mat->A->data,*b = (Mat_SeqAIJ*)mat->B->data;
112:   PetscInt       l,*garray = mat->garray,diag;

115:   /* code only works for square matrices A */

117:   /* find size of row to the left of the diagonal part */
118:   MatGetOwnershipRange(A,&diag,0);
119:   row  = row - diag;
120:   for (l=0; l<b->i[row+1]-b->i[row]; l++) {
121:     if (garray[b->j[b->i[row]+l]] > diag) break;
122:   }
123:   PetscMemcpy(b->a+b->i[row],v,l*sizeof(PetscScalar));

125:   /* diagonal part */
126:   PetscMemcpy(a->a+a->i[row],v+l,(a->i[row+1]-a->i[row])*sizeof(PetscScalar));

128:   /* right of diagonal part */
129:   PetscMemcpy(b->a+b->i[row]+l,v+l+a->i[row+1]-a->i[row],(b->i[row+1]-b->i[row]-l)*sizeof(PetscScalar));
130:   return(0);
131: }

135: PetscErrorCode MatSetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
136: {
137:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
138:   PetscScalar    value;
140:   PetscInt       i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend;
141:   PetscInt       cstart = mat->cmap.rstart,cend = mat->cmap.rend,row,col;
142:   PetscTruth     roworiented = aij->roworiented;

144:   /* Some Variables required in the macro */
145:   Mat            A = aij->A;
146:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
147:   PetscInt       *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
148:   PetscScalar    *aa = a->a;
149:   PetscTruth     ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES))?PETSC_TRUE:PETSC_FALSE);
150:   Mat            B = aij->B;
151:   Mat_SeqAIJ     *b = (Mat_SeqAIJ*)B->data;
152:   PetscInt       *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap.n,am = aij->A->rmap.n;
153:   PetscScalar    *ba = b->a;

155:   PetscInt       *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
156:   PetscInt       nonew = a->nonew;
157:   PetscScalar    *ap1,*ap2;

160:   for (i=0; i<m; i++) {
161:     if (im[i] < 0) continue;
162: #if defined(PETSC_USE_DEBUG)
163:     if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
164: #endif
165:     if (im[i] >= rstart && im[i] < rend) {
166:       row      = im[i] - rstart;
167:       lastcol1 = -1;
168:       rp1      = aj + ai[row];
169:       ap1      = aa + ai[row];
170:       rmax1    = aimax[row];
171:       nrow1    = ailen[row];
172:       low1     = 0;
173:       high1    = nrow1;
174:       lastcol2 = -1;
175:       rp2      = bj + bi[row];
176:       ap2      = ba + bi[row];
177:       rmax2    = bimax[row];
178:       nrow2    = bilen[row];
179:       low2     = 0;
180:       high2    = nrow2;

182:       for (j=0; j<n; j++) {
183:         if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
184:         if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
185:         if (in[j] >= cstart && in[j] < cend){
186:           col = in[j] - cstart;
187:           MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
188:         } else if (in[j] < 0) continue;
189: #if defined(PETSC_USE_DEBUG)
190:         else if (in[j] >= mat->cmap.N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap.N-1);}
191: #endif
192:         else {
193:           if (mat->was_assembled) {
194:             if (!aij->colmap) {
195:               CreateColmap_MPIAIJ_Private(mat);
196:             }
197: #if defined (PETSC_USE_CTABLE)
198:             PetscTableFind(aij->colmap,in[j]+1,&col);
199:             col--;
200: #else
201:             col = aij->colmap[in[j]] - 1;
202: #endif
203:             if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
204:               DisAssemble_MPIAIJ(mat);
205:               col =  in[j];
206:               /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
207:               B = aij->B;
208:               b = (Mat_SeqAIJ*)B->data;
209:               bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
210:               rp2      = bj + bi[row];
211:               ap2      = ba + bi[row];
212:               rmax2    = bimax[row];
213:               nrow2    = bilen[row];
214:               low2     = 0;
215:               high2    = nrow2;
216:               bm       = aij->B->rmap.n;
217:               ba = b->a;
218:             }
219:           } else col = in[j];
220:           MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
221:         }
222:       }
223:     } else {
224:       if (!aij->donotstash) {
225:         if (roworiented) {
226:           if (ignorezeroentries && v[i*n] == 0.0) continue;
227:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
228:         } else {
229:           if (ignorezeroentries && v[i] == 0.0) continue;
230:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
231:         }
232:       }
233:     }
234:   }
235:   return(0);
236: }


241: PetscErrorCode MatGetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
242: {
243:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
245:   PetscInt       i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend;
246:   PetscInt       cstart = mat->cmap.rstart,cend = mat->cmap.rend,row,col;

249:   for (i=0; i<m; i++) {
250:     if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);
251:     if (idxm[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap.N-1);
252:     if (idxm[i] >= rstart && idxm[i] < rend) {
253:       row = idxm[i] - rstart;
254:       for (j=0; j<n; j++) {
255:         if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]);
256:         if (idxn[j] >= mat->cmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap.N-1);
257:         if (idxn[j] >= cstart && idxn[j] < cend){
258:           col = idxn[j] - cstart;
259:           MatGetValues(aij->A,1,&row,1,&col,v+i*n+j);
260:         } else {
261:           if (!aij->colmap) {
262:             CreateColmap_MPIAIJ_Private(mat);
263:           }
264: #if defined (PETSC_USE_CTABLE)
265:           PetscTableFind(aij->colmap,idxn[j]+1,&col);
266:           col --;
267: #else
268:           col = aij->colmap[idxn[j]] - 1;
269: #endif
270:           if ((col < 0) || (aij->garray[col] != idxn[j])) *(v+i*n+j) = 0.0;
271:           else {
272:             MatGetValues(aij->B,1,&row,1,&col,v+i*n+j);
273:           }
274:         }
275:       }
276:     } else {
277:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
278:     }
279:   }
280:   return(0);
281: }

285: PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat,MatAssemblyType mode)
286: {
287:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
289:   PetscInt       nstash,reallocs;
290:   InsertMode     addv;

293:   if (aij->donotstash) {
294:     return(0);
295:   }

297:   /* make sure all processors are either in INSERTMODE or ADDMODE */
298:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
299:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
300:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
301:   }
302:   mat->insertmode = addv; /* in case this processor had no cache */

304:   MatStashScatterBegin_Private(&mat->stash,mat->rmap.range);
305:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
306:   PetscInfo2(aij->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
307:   return(0);
308: }

312: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat,MatAssemblyType mode)
313: {
314:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
315:   Mat_SeqAIJ     *a=(Mat_SeqAIJ *)aij->A->data;
317:   PetscMPIInt    n;
318:   PetscInt       i,j,rstart,ncols,flg;
319:   PetscInt       *row,*col,other_disassembled;
320:   PetscScalar    *val;
321:   InsertMode     addv = mat->insertmode;

323:   /* do not use 'b = (Mat_SeqAIJ *)aij->B->data' as B can be reset in disassembly */
325:   if (!aij->donotstash) {
326:     while (1) {
327:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
328:       if (!flg) break;

330:       for (i=0; i<n;) {
331:         /* Now identify the consecutive vals belonging to the same row */
332:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
333:         if (j < n) ncols = j-i;
334:         else       ncols = n-i;
335:         /* Now assemble all these values with a single function call */
336:         MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
337:         i = j;
338:       }
339:     }
340:     MatStashScatterEnd_Private(&mat->stash);
341:   }
342:   a->compressedrow.use     = PETSC_FALSE;
343:   MatAssemblyBegin(aij->A,mode);
344:   MatAssemblyEnd(aij->A,mode);

346:   /* determine if any processor has disassembled, if so we must 
347:      also disassemble ourselfs, in order that we may reassemble. */
348:   /*
349:      if nonzero structure of submatrix B cannot change then we know that
350:      no processor disassembled thus we can skip this stuff
351:   */
352:   if (!((Mat_SeqAIJ*)aij->B->data)->nonew)  {
353:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
354:     if (mat->was_assembled && !other_disassembled) {
355:       DisAssemble_MPIAIJ(mat);
356:     }
357:   }
358:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
359:     MatSetUpMultiply_MPIAIJ(mat);
360:   }
361:   MatSetOption(aij->B,MAT_DO_NOT_USE_INODES);
362:   ((Mat_SeqAIJ *)aij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
363:   MatAssemblyBegin(aij->B,mode);
364:   MatAssemblyEnd(aij->B,mode);

366:   PetscFree(aij->rowvalues);
367:   aij->rowvalues = 0;

369:   /* used by MatAXPY() */
370:   a->xtoy = 0; ((Mat_SeqAIJ *)aij->B->data)->xtoy = 0;  /* b->xtoy = 0 */
371:   a->XtoY = 0; ((Mat_SeqAIJ *)aij->B->data)->XtoY = 0;  /* b->XtoY = 0 */

373:   return(0);
374: }

378: PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
379: {
380:   Mat_MPIAIJ     *l = (Mat_MPIAIJ*)A->data;

384:   MatZeroEntries(l->A);
385:   MatZeroEntries(l->B);
386:   return(0);
387: }

391: PetscErrorCode MatZeroRows_MPIAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
392: {
393:   Mat_MPIAIJ     *l = (Mat_MPIAIJ*)A->data;
395:   PetscMPIInt    size = l->size,imdex,n,rank = l->rank,tag = A->tag,lastidx = -1;
396:   PetscInt       i,*owners = A->rmap.range;
397:   PetscInt       *nprocs,j,idx,nsends,row;
398:   PetscInt       nmax,*svalues,*starts,*owner,nrecvs;
399:   PetscInt       *rvalues,count,base,slen,*source;
400:   PetscInt       *lens,*lrows,*values,rstart=A->rmap.rstart;
401:   MPI_Comm       comm = A->comm;
402:   MPI_Request    *send_waits,*recv_waits;
403:   MPI_Status     recv_status,*send_status;
404: #if defined(PETSC_DEBUG)
405:   PetscTruth     found = PETSC_FALSE;
406: #endif

409:   /*  first count number of contributors to each processor */
410:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
411:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
412:   PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
413:   j = 0;
414:   for (i=0; i<N; i++) {
415:     if (lastidx > (idx = rows[i])) j = 0;
416:     lastidx = idx;
417:     for (; j<size; j++) {
418:       if (idx >= owners[j] && idx < owners[j+1]) {
419:         nprocs[2*j]++;
420:         nprocs[2*j+1] = 1;
421:         owner[i] = j;
422: #if defined(PETSC_DEBUG)
423:         found = PETSC_TRUE;
424: #endif
425:         break;
426:       }
427:     }
428: #if defined(PETSC_DEBUG)
429:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
430:     found = PETSC_FALSE;
431: #endif
432:   }
433:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}

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

438:   /* post receives:   */
439:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
440:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
441:   for (i=0; i<nrecvs; i++) {
442:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
443:   }

445:   /* do sends:
446:       1) starts[i] gives the starting index in svalues for stuff going to 
447:          the ith processor
448:   */
449:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
450:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
451:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
452:   starts[0] = 0;
453:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
454:   for (i=0; i<N; i++) {
455:     svalues[starts[owner[i]]++] = rows[i];
456:   }

458:   starts[0] = 0;
459:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
460:   count = 0;
461:   for (i=0; i<size; i++) {
462:     if (nprocs[2*i+1]) {
463:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
464:     }
465:   }
466:   PetscFree(starts);

468:   base = owners[rank];

470:   /*  wait on receives */
471:   PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
472:   source = lens + nrecvs;
473:   count  = nrecvs; slen = 0;
474:   while (count) {
475:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
476:     /* unpack receives into our local space */
477:     MPI_Get_count(&recv_status,MPIU_INT,&n);
478:     source[imdex]  = recv_status.MPI_SOURCE;
479:     lens[imdex]    = n;
480:     slen          += n;
481:     count--;
482:   }
483:   PetscFree(recv_waits);
484: 
485:   /* move the data into the send scatter */
486:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
487:   count = 0;
488:   for (i=0; i<nrecvs; i++) {
489:     values = rvalues + i*nmax;
490:     for (j=0; j<lens[i]; j++) {
491:       lrows[count++] = values[j] - base;
492:     }
493:   }
494:   PetscFree(rvalues);
495:   PetscFree(lens);
496:   PetscFree(owner);
497:   PetscFree(nprocs);
498: 
499:   /* actually zap the local rows */
500:   /*
501:         Zero the required rows. If the "diagonal block" of the matrix
502:      is square and the user wishes to set the diagonal we use separate
503:      code so that MatSetValues() is not called for each diagonal allocating
504:      new memory, thus calling lots of mallocs and slowing things down.

506:        Contributed by: Matthew Knepley
507:   */
508:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
509:   MatZeroRows(l->B,slen,lrows,0.0);
510:   if ((diag != 0.0) && (l->A->rmap.N == l->A->cmap.N)) {
511:     MatZeroRows(l->A,slen,lrows,diag);
512:   } else if (diag != 0.0) {
513:     MatZeroRows(l->A,slen,lrows,0.0);
514:     if (((Mat_SeqAIJ*)l->A->data)->nonew) {
515:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options\n\
516: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
517:     }
518:     for (i = 0; i < slen; i++) {
519:       row  = lrows[i] + rstart;
520:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
521:     }
522:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
523:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
524:   } else {
525:     MatZeroRows(l->A,slen,lrows,0.0);
526:   }
527:   PetscFree(lrows);

529:   /* wait on sends */
530:   if (nsends) {
531:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
532:     MPI_Waitall(nsends,send_waits,send_status);
533:     PetscFree(send_status);
534:   }
535:   PetscFree(send_waits);
536:   PetscFree(svalues);

538:   return(0);
539: }

543: PetscErrorCode MatMult_MPIAIJ(Mat A,Vec xx,Vec yy)
544: {
545:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
547:   PetscInt       nt;

550:   VecGetLocalSize(xx,&nt);
551:   if (nt != A->cmap.n) {
552:     SETERRQ2(PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%D) and xx (%D)",A->cmap.n,nt);
553:   }
554:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
555:   (*a->A->ops->mult)(a->A,xx,yy);
556:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
557:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
558:   return(0);
559: }

563: PetscErrorCode MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
564: {
565:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

569:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
570:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
571:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
572:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
573:   return(0);
574: }

578: PetscErrorCode MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy)
579: {
580:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
582:   PetscTruth     merged;

585:   VecScatterGetMerged(a->Mvctx,&merged);
586:   /* do nondiagonal part */
587:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
588:   if (!merged) {
589:     /* send it on its way */
590:     VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
591:     /* do local part */
592:     (*a->A->ops->multtranspose)(a->A,xx,yy);
593:     /* receive remote parts: note this assumes the values are not actually */
594:     /* added in yy until the next line, */
595:     VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
596:   } else {
597:     /* do local part */
598:     (*a->A->ops->multtranspose)(a->A,xx,yy);
599:     /* send it on its way */
600:     VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
601:     /* values actually were received in the Begin() but we need to call this nop */
602:     VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
603:   }
604:   return(0);
605: }

610: PetscErrorCode  MatIsTranspose_MPIAIJ(Mat Amat,Mat Bmat,PetscReal tol,PetscTruth *f)
611: {
612:   MPI_Comm       comm;
613:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *) Amat->data, *Bij;
614:   Mat            Adia = Aij->A, Bdia, Aoff,Boff,*Aoffs,*Boffs;
615:   IS             Me,Notme;
617:   PetscInt       M,N,first,last,*notme,i;
618:   PetscMPIInt    size;


622:   /* Easy test: symmetric diagonal block */
623:   Bij = (Mat_MPIAIJ *) Bmat->data; Bdia = Bij->A;
624:   MatIsTranspose(Adia,Bdia,tol,f);
625:   if (!*f) return(0);
626:   PetscObjectGetComm((PetscObject)Amat,&comm);
627:   MPI_Comm_size(comm,&size);
628:   if (size == 1) return(0);

630:   /* Hard test: off-diagonal block. This takes a MatGetSubMatrix. */
631:   MatGetSize(Amat,&M,&N);
632:   MatGetOwnershipRange(Amat,&first,&last);
633:   PetscMalloc((N-last+first)*sizeof(PetscInt),&notme);
634:   for (i=0; i<first; i++) notme[i] = i;
635:   for (i=last; i<M; i++) notme[i-last+first] = i;
636:   ISCreateGeneral(MPI_COMM_SELF,N-last+first,notme,&Notme);
637:   ISCreateStride(MPI_COMM_SELF,last-first,first,1,&Me);
638:   MatGetSubMatrices(Amat,1,&Me,&Notme,MAT_INITIAL_MATRIX,&Aoffs);
639:   Aoff = Aoffs[0];
640:   MatGetSubMatrices(Bmat,1,&Notme,&Me,MAT_INITIAL_MATRIX,&Boffs);
641:   Boff = Boffs[0];
642:   MatIsTranspose(Aoff,Boff,tol,f);
643:   MatDestroyMatrices(1,&Aoffs);
644:   MatDestroyMatrices(1,&Boffs);
645:   ISDestroy(Me);
646:   ISDestroy(Notme);

648:   return(0);
649: }

654: PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
655: {
656:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

660:   /* do nondiagonal part */
661:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
662:   /* send it on its way */
663:   VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
664:   /* do local part */
665:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
666:   /* receive remote parts */
667:   VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
668:   return(0);
669: }

671: /*
672:   This only works correctly for square matrices where the subblock A->A is the 
673:    diagonal block
674: */
677: PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A,Vec v)
678: {
680:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

683:   if (A->rmap.N != A->cmap.N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
684:   if (A->rmap.rstart != A->cmap.rstart || A->rmap.rend != A->cmap.rend) {
685:     SETERRQ(PETSC_ERR_ARG_SIZ,"row partition must equal col partition");
686:   }
687:   MatGetDiagonal(a->A,v);
688:   return(0);
689: }

693: PetscErrorCode MatScale_MPIAIJ(Mat A,PetscScalar aa)
694: {
695:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

699:   MatScale(a->A,aa);
700:   MatScale(a->B,aa);
701:   return(0);
702: }

706: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
707: {
708:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;

712: #if defined(PETSC_USE_LOG)
713:   PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap.N,mat->cmap.N);
714: #endif
715:   MatStashDestroy_Private(&mat->stash);
716:   MatDestroy(aij->A);
717:   MatDestroy(aij->B);
718: #if defined (PETSC_USE_CTABLE)
719:   if (aij->colmap) {PetscTableDelete(aij->colmap);}
720: #else
721:   PetscFree(aij->colmap);
722: #endif
723:   PetscFree(aij->garray);
724:   if (aij->lvec)   {VecDestroy(aij->lvec);}
725:   if (aij->Mvctx)  {VecScatterDestroy(aij->Mvctx);}
726:   PetscFree(aij->rowvalues);
727:   PetscFree(aij);

729:   PetscObjectChangeTypeName((PetscObject)mat,0);
730:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
731:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
732:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
733:   PetscObjectComposeFunction((PetscObject)mat,"MatIsTranspose_C","",PETSC_NULL);
734:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocation_C","",PETSC_NULL);
735:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocationCSR_C","",PETSC_NULL);
736:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
737:   return(0);
738: }

742: PetscErrorCode MatView_MPIAIJ_Binary(Mat mat,PetscViewer viewer)
743: {
744:   Mat_MPIAIJ        *aij = (Mat_MPIAIJ*)mat->data;
745:   Mat_SeqAIJ*       A = (Mat_SeqAIJ*)aij->A->data;
746:   Mat_SeqAIJ*       B = (Mat_SeqAIJ*)aij->B->data;
747:   PetscErrorCode    ierr;
748:   PetscMPIInt       rank,size,tag = ((PetscObject)viewer)->tag;
749:   int               fd;
750:   PetscInt          nz,header[4],*row_lengths,*range=0,rlen,i;
751:   PetscInt          nzmax,*column_indices,j,k,col,*garray = aij->garray,cnt,cstart = mat->cmap.rstart,rnz;
752:   PetscScalar       *column_values;

755:   MPI_Comm_rank(mat->comm,&rank);
756:   MPI_Comm_size(mat->comm,&size);
757:   nz   = A->nz + B->nz;
758:   if (!rank) {
759:     header[0] = MAT_FILE_COOKIE;
760:     header[1] = mat->rmap.N;
761:     header[2] = mat->cmap.N;
762:     MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,mat->comm);
763:     PetscViewerBinaryGetDescriptor(viewer,&fd);
764:     PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
765:     /* get largest number of rows any processor has */
766:     rlen = mat->rmap.n;
767:     range = mat->rmap.range;
768:     for (i=1; i<size; i++) {
769:       rlen = PetscMax(rlen,range[i+1] - range[i]);
770:     }
771:   } else {
772:     MPI_Reduce(&nz,0,1,MPIU_INT,MPI_SUM,0,mat->comm);
773:     rlen = mat->rmap.n;
774:   }

776:   /* load up the local row counts */
777:   PetscMalloc((rlen+1)*sizeof(PetscInt),&row_lengths);
778:   for (i=0; i<mat->rmap.n; i++) {
779:     row_lengths[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i];
780:   }

782:   /* store the row lengths to the file */
783:   if (!rank) {
784:     MPI_Status status;
785:     PetscBinaryWrite(fd,row_lengths,mat->rmap.n,PETSC_INT,PETSC_TRUE);
786:     for (i=1; i<size; i++) {
787:       rlen = range[i+1] - range[i];
788:       MPI_Recv(row_lengths,rlen,MPIU_INT,i,tag,mat->comm,&status);
789:       PetscBinaryWrite(fd,row_lengths,rlen,PETSC_INT,PETSC_TRUE);
790:     }
791:   } else {
792:     MPI_Send(row_lengths,mat->rmap.n,MPIU_INT,0,tag,mat->comm);
793:   }
794:   PetscFree(row_lengths);

796:   /* load up the local column indices */
797:   nzmax = nz; /* )th processor needs space a largest processor needs */
798:   MPI_Reduce(&nz,&nzmax,1,MPIU_INT,MPI_MAX,0,mat->comm);
799:   PetscMalloc((nzmax+1)*sizeof(PetscInt),&column_indices);
800:   cnt  = 0;
801:   for (i=0; i<mat->rmap.n; i++) {
802:     for (j=B->i[i]; j<B->i[i+1]; j++) {
803:       if ( (col = garray[B->j[j]]) > cstart) break;
804:       column_indices[cnt++] = col;
805:     }
806:     for (k=A->i[i]; k<A->i[i+1]; k++) {
807:       column_indices[cnt++] = A->j[k] + cstart;
808:     }
809:     for (; j<B->i[i+1]; j++) {
810:       column_indices[cnt++] = garray[B->j[j]];
811:     }
812:   }
813:   if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);

815:   /* store the column indices to the file */
816:   if (!rank) {
817:     MPI_Status status;
818:     PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,PETSC_TRUE);
819:     for (i=1; i<size; i++) {
820:       MPI_Recv(&rnz,1,MPIU_INT,i,tag,mat->comm,&status);
821:       if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
822:       MPI_Recv(column_indices,rnz,MPIU_INT,i,tag,mat->comm,&status);
823:       PetscBinaryWrite(fd,column_indices,rnz,PETSC_INT,PETSC_TRUE);
824:     }
825:   } else {
826:     MPI_Send(&nz,1,MPIU_INT,0,tag,mat->comm);
827:     MPI_Send(column_indices,nz,MPIU_INT,0,tag,mat->comm);
828:   }
829:   PetscFree(column_indices);

831:   /* load up the local column values */
832:   PetscMalloc((nzmax+1)*sizeof(PetscScalar),&column_values);
833:   cnt  = 0;
834:   for (i=0; i<mat->rmap.n; i++) {
835:     for (j=B->i[i]; j<B->i[i+1]; j++) {
836:       if ( garray[B->j[j]] > cstart) break;
837:       column_values[cnt++] = B->a[j];
838:     }
839:     for (k=A->i[i]; k<A->i[i+1]; k++) {
840:       column_values[cnt++] = A->a[k];
841:     }
842:     for (; j<B->i[i+1]; j++) {
843:       column_values[cnt++] = B->a[j];
844:     }
845:   }
846:   if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_PLIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);

848:   /* store the column values to the file */
849:   if (!rank) {
850:     MPI_Status status;
851:     PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,PETSC_TRUE);
852:     for (i=1; i<size; i++) {
853:       MPI_Recv(&rnz,1,MPIU_INT,i,tag,mat->comm,&status);
854:       if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
855:       MPI_Recv(column_values,rnz,MPIU_SCALAR,i,tag,mat->comm,&status);
856:       PetscBinaryWrite(fd,column_values,rnz,PETSC_SCALAR,PETSC_TRUE);
857:     }
858:   } else {
859:     MPI_Send(&nz,1,MPIU_INT,0,tag,mat->comm);
860:     MPI_Send(column_values,nz,MPIU_SCALAR,0,tag,mat->comm);
861:   }
862:   PetscFree(column_values);
863:   return(0);
864: }

868: PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
869: {
870:   Mat_MPIAIJ        *aij = (Mat_MPIAIJ*)mat->data;
871:   PetscErrorCode    ierr;
872:   PetscMPIInt       rank = aij->rank,size = aij->size;
873:   PetscTruth        isdraw,iascii,isbinary;
874:   PetscViewer       sviewer;
875:   PetscViewerFormat format;

878:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
879:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
880:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
881:   if (iascii) {
882:     PetscViewerGetFormat(viewer,&format);
883:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
884:       MatInfo    info;
885:       PetscTruth inodes;

887:       MPI_Comm_rank(mat->comm,&rank);
888:       MatGetInfo(mat,MAT_LOCAL,&info);
889:       MatInodeGetInodeSizes(aij->A,PETSC_NULL,(PetscInt **)&inodes,PETSC_NULL);
890:       if (!inodes) {
891:         PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, not using I-node routines\n",
892:                                               rank,mat->rmap.n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
893:       } else {
894:         PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, using I-node routines\n",
895:                     rank,mat->rmap.n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
896:       }
897:       MatGetInfo(aij->A,MAT_LOCAL,&info);
898:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
899:       MatGetInfo(aij->B,MAT_LOCAL,&info);
900:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
901:       PetscViewerFlush(viewer);
902:       VecScatterView(aij->Mvctx,viewer);
903:       return(0);
904:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
905:       PetscInt   inodecount,inodelimit,*inodes;
906:       MatInodeGetInodeSizes(aij->A,&inodecount,&inodes,&inodelimit);
907:       if (inodes) {
908:         PetscViewerASCIIPrintf(viewer,"using I-node (on process 0) routines: found %D nodes, limit used is %D\n",inodecount,inodelimit);
909:       } else {
910:         PetscViewerASCIIPrintf(viewer,"not using I-node (on process 0) routines\n");
911:       }
912:       return(0);
913:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
914:       return(0);
915:     }
916:   } else if (isbinary) {
917:     if (size == 1) {
918:       PetscObjectSetName((PetscObject)aij->A,mat->name);
919:       MatView(aij->A,viewer);
920:     } else {
921:       MatView_MPIAIJ_Binary(mat,viewer);
922:     }
923:     return(0);
924:   } else if (isdraw) {
925:     PetscDraw  draw;
926:     PetscTruth isnull;
927:     PetscViewerDrawGetDraw(viewer,0,&draw);
928:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
929:   }

931:   if (size == 1) {
932:     PetscObjectSetName((PetscObject)aij->A,mat->name);
933:     MatView(aij->A,viewer);
934:   } else {
935:     /* assemble the entire matrix onto first processor. */
936:     Mat         A;
937:     Mat_SeqAIJ  *Aloc;
938:     PetscInt    M = mat->rmap.N,N = mat->cmap.N,m,*ai,*aj,row,*cols,i,*ct;
939:     PetscScalar *a;

941:     MatCreate(mat->comm,&A);
942:     if (!rank) {
943:       MatSetSizes(A,M,N,M,N);
944:     } else {
945:       MatSetSizes(A,0,0,M,N);
946:     }
947:     /* This is just a temporary matrix, so explicitly using MATMPIAIJ is probably best */
948:     MatSetType(A,MATMPIAIJ);
949:     MatMPIAIJSetPreallocation(A,0,PETSC_NULL,0,PETSC_NULL);
950:     PetscLogObjectParent(mat,A);

952:     /* copy over the A part */
953:     Aloc = (Mat_SeqAIJ*)aij->A->data;
954:     m = aij->A->rmap.n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
955:     row = mat->rmap.rstart;
956:     for (i=0; i<ai[m]; i++) {aj[i] += mat->cmap.rstart ;}
957:     for (i=0; i<m; i++) {
958:       MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);
959:       row++; a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i];
960:     }
961:     aj = Aloc->j;
962:     for (i=0; i<ai[m]; i++) {aj[i] -= mat->cmap.rstart;}

964:     /* copy over the B part */
965:     Aloc = (Mat_SeqAIJ*)aij->B->data;
966:     m    = aij->B->rmap.n;  ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
967:     row  = mat->rmap.rstart;
968:     PetscMalloc((ai[m]+1)*sizeof(PetscInt),&cols);
969:     ct   = cols;
970:     for (i=0; i<ai[m]; i++) {cols[i] = aij->garray[aj[i]];}
971:     for (i=0; i<m; i++) {
972:       MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);
973:       row++; a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
974:     }
975:     PetscFree(ct);
976:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
977:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
978:     /* 
979:        Everyone has to call to draw the matrix since the graphics waits are
980:        synchronized across all processors that share the PetscDraw object
981:     */
982:     PetscViewerGetSingleton(viewer,&sviewer);
983:     if (!rank) {
984:       PetscObjectSetName((PetscObject)((Mat_MPIAIJ*)(A->data))->A,mat->name);
985:       MatView(((Mat_MPIAIJ*)(A->data))->A,sviewer);
986:     }
987:     PetscViewerRestoreSingleton(viewer,&sviewer);
988:     MatDestroy(A);
989:   }
990:   return(0);
991: }

995: PetscErrorCode MatView_MPIAIJ(Mat mat,PetscViewer viewer)
996: {
998:   PetscTruth     iascii,isdraw,issocket,isbinary;
999: 
1001:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1002:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1003:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1004:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1005:   if (iascii || isdraw || isbinary || issocket) {
1006:     MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);
1007:   } else {
1008:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIAIJ matrices",((PetscObject)viewer)->type_name);
1009:   }
1010:   return(0);
1011: }



1017: PetscErrorCode MatRelax_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1018: {
1019:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1021:   Vec            bb1;

1024:   if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);

1026:   VecDuplicate(bb,&bb1);

1028:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
1029:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1030:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
1031:       its--;
1032:     }
1033: 
1034:     while (its--) {
1035:       VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1036:       VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);

1038:       /* update rhs: bb1 = bb - B*x */
1039:       VecScale(mat->lvec,-1.0);
1040:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1042:       /* local sweep */
1043:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
1044: 
1045:     }
1046:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP){
1047:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1048:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1049:       its--;
1050:     }
1051:     while (its--) {
1052:       VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1053:       VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);

1055:       /* update rhs: bb1 = bb - B*x */
1056:       VecScale(mat->lvec,-1.0);
1057:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1059:       /* local sweep */
1060:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1061: 
1062:     }
1063:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP){
1064:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1065:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1066:       its--;
1067:     }
1068:     while (its--) {
1069:       VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1070:       VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);

1072:       /* update rhs: bb1 = bb - B*x */
1073:       VecScale(mat->lvec,-1.0);
1074:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1076:       /* local sweep */
1077:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1078: 
1079:     }
1080:   } else {
1081:     SETERRQ(PETSC_ERR_SUP,"Parallel SOR not supported");
1082:   }

1084:   VecDestroy(bb1);
1085:   return(0);
1086: }

1090: PetscErrorCode MatPermute_MPIAIJ(Mat A,IS rowp,IS colp,Mat *B)
1091: {
1092:   MPI_Comm       comm,pcomm;
1093:   PetscInt       first,local_size,nrows,*rows;
1094:   int            ntids;
1095:   IS             crowp,growp,irowp,lrowp,lcolp,icolp;

1099:   PetscObjectGetComm((PetscObject)A,&comm);
1100:   /* make a collective version of 'rowp' */
1101:   PetscObjectGetComm((PetscObject)rowp,&pcomm);
1102:   if (pcomm==comm) {
1103:     crowp = rowp;
1104:   } else {
1105:     ISGetSize(rowp,&nrows);
1106:     ISGetIndices(rowp,&rows);
1107:     ISCreateGeneral(comm,nrows,rows,&crowp);
1108:     ISRestoreIndices(rowp,&rows);
1109:   }
1110:   /* collect the global row permutation and invert it */
1111:   ISAllGather(crowp,&growp);
1112:   ISSetPermutation(growp);
1113:   if (pcomm!=comm) {
1114:     ISDestroy(crowp);
1115:   }
1116:   ISInvertPermutation(growp,PETSC_DECIDE,&irowp);
1117:   /* get the local target indices */
1118:   MatGetOwnershipRange(A,&first,PETSC_NULL);
1119:   MatGetLocalSize(A,&local_size,PETSC_NULL);
1120:   ISGetIndices(irowp,&rows);
1121:   ISCreateGeneral(MPI_COMM_SELF,local_size,rows+first,&lrowp);
1122:   ISRestoreIndices(irowp,&rows);
1123:   ISDestroy(irowp);
1124:   /* the column permutation is so much easier;
1125:      make a local version of 'colp' and invert it */
1126:   PetscObjectGetComm((PetscObject)colp,&pcomm);
1127:   MPI_Comm_size(pcomm,&ntids);
1128:   if (ntids==1) {
1129:     lcolp = colp;
1130:   } else {
1131:     ISGetSize(colp,&nrows);
1132:     ISGetIndices(colp,&rows);
1133:     ISCreateGeneral(MPI_COMM_SELF,nrows,rows,&lcolp);
1134:   }
1135:   ISInvertPermutation(lcolp,PETSC_DECIDE,&icolp);
1136:   ISSetPermutation(lcolp);
1137:   if (ntids>1) {
1138:     ISRestoreIndices(colp,&rows);
1139:     ISDestroy(lcolp);
1140:   }
1141:   /* now we just get the submatrix */
1142:   MatGetSubMatrix(A,lrowp,icolp,local_size,MAT_INITIAL_MATRIX,B);
1143:   /* clean up */
1144:   ISDestroy(lrowp);
1145:   ISDestroy(icolp);
1146:   return(0);
1147: }

1151: PetscErrorCode MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1152: {
1153:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1154:   Mat            A = mat->A,B = mat->B;
1156:   PetscReal      isend[5],irecv[5];

1159:   info->block_size     = 1.0;
1160:   MatGetInfo(A,MAT_LOCAL,info);
1161:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1162:   isend[3] = info->memory;  isend[4] = info->mallocs;
1163:   MatGetInfo(B,MAT_LOCAL,info);
1164:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1165:   isend[3] += info->memory;  isend[4] += info->mallocs;
1166:   if (flag == MAT_LOCAL) {
1167:     info->nz_used      = isend[0];
1168:     info->nz_allocated = isend[1];
1169:     info->nz_unneeded  = isend[2];
1170:     info->memory       = isend[3];
1171:     info->mallocs      = isend[4];
1172:   } else if (flag == MAT_GLOBAL_MAX) {
1173:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1174:     info->nz_used      = irecv[0];
1175:     info->nz_allocated = irecv[1];
1176:     info->nz_unneeded  = irecv[2];
1177:     info->memory       = irecv[3];
1178:     info->mallocs      = irecv[4];
1179:   } else if (flag == MAT_GLOBAL_SUM) {
1180:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1181:     info->nz_used      = irecv[0];
1182:     info->nz_allocated = irecv[1];
1183:     info->nz_unneeded  = irecv[2];
1184:     info->memory       = irecv[3];
1185:     info->mallocs      = irecv[4];
1186:   }
1187:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1188:   info->fill_ratio_needed = 0;
1189:   info->factor_mallocs    = 0;
1190:   info->rows_global       = (double)matin->rmap.N;
1191:   info->columns_global    = (double)matin->cmap.N;
1192:   info->rows_local        = (double)matin->rmap.n;
1193:   info->columns_local     = (double)matin->cmap.N;

1195:   return(0);
1196: }

1200: PetscErrorCode MatSetOption_MPIAIJ(Mat A,MatOption op)
1201: {
1202:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

1206:   switch (op) {
1207:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1208:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1209:   case MAT_COLUMNS_UNSORTED:
1210:   case MAT_COLUMNS_SORTED:
1211:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1212:   case MAT_KEEP_ZEROED_ROWS:
1213:   case MAT_NEW_NONZERO_LOCATION_ERR:
1214:   case MAT_USE_INODES:
1215:   case MAT_DO_NOT_USE_INODES:
1216:   case MAT_IGNORE_ZERO_ENTRIES:
1217:     MatSetOption(a->A,op);
1218:     MatSetOption(a->B,op);
1219:     break;
1220:   case MAT_ROW_ORIENTED:
1221:     a->roworiented = PETSC_TRUE;
1222:     MatSetOption(a->A,op);
1223:     MatSetOption(a->B,op);
1224:     break;
1225:   case MAT_ROWS_SORTED:
1226:   case MAT_ROWS_UNSORTED:
1227:   case MAT_YES_NEW_DIAGONALS:
1228:     PetscInfo(A,"Option ignored\n");
1229:     break;
1230:   case MAT_COLUMN_ORIENTED:
1231:     a->roworiented = PETSC_FALSE;
1232:     MatSetOption(a->A,op);
1233:     MatSetOption(a->B,op);
1234:     break;
1235:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1236:     a->donotstash = PETSC_TRUE;
1237:     break;
1238:   case MAT_NO_NEW_DIAGONALS:
1239:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1240:   case MAT_SYMMETRIC:
1241:     MatSetOption(a->A,op);
1242:     break;
1243:   case MAT_STRUCTURALLY_SYMMETRIC:
1244:   case MAT_HERMITIAN:
1245:   case MAT_SYMMETRY_ETERNAL:
1246:     MatSetOption(a->A,op);
1247:     break;
1248:   case MAT_NOT_SYMMETRIC:
1249:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1250:   case MAT_NOT_HERMITIAN:
1251:   case MAT_NOT_SYMMETRY_ETERNAL:
1252:     break;
1253:   default:
1254:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
1255:   }
1256:   return(0);
1257: }

1261: PetscErrorCode MatGetRow_MPIAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1262: {
1263:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1264:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1266:   PetscInt       i,*cworkA,*cworkB,**pcA,**pcB,cstart = matin->cmap.rstart;
1267:   PetscInt       nztot,nzA,nzB,lrow,rstart = matin->rmap.rstart,rend = matin->rmap.rend;
1268:   PetscInt       *cmap,*idx_p;

1271:   if (mat->getrowactive) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1272:   mat->getrowactive = PETSC_TRUE;

1274:   if (!mat->rowvalues && (idx || v)) {
1275:     /*
1276:         allocate enough space to hold information from the longest row.
1277:     */
1278:     Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data;
1279:     PetscInt     max = 1,tmp;
1280:     for (i=0; i<matin->rmap.n; i++) {
1281:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1282:       if (max < tmp) { max = tmp; }
1283:     }
1284:     PetscMalloc(max*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1285:     mat->rowindices = (PetscInt*)(mat->rowvalues + max);
1286:   }

1288:   if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Only local rows")
1289:   lrow = row - rstart;

1291:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1292:   if (!v)   {pvA = 0; pvB = 0;}
1293:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1294:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1295:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1296:   nztot = nzA + nzB;

1298:   cmap  = mat->garray;
1299:   if (v  || idx) {
1300:     if (nztot) {
1301:       /* Sort by increasing column numbers, assuming A and B already sorted */
1302:       PetscInt imark = -1;
1303:       if (v) {
1304:         *v = v_p = mat->rowvalues;
1305:         for (i=0; i<nzB; i++) {
1306:           if (cmap[cworkB[i]] < cstart)   v_p[i] = vworkB[i];
1307:           else break;
1308:         }
1309:         imark = i;
1310:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1311:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1312:       }
1313:       if (idx) {
1314:         *idx = idx_p = mat->rowindices;
1315:         if (imark > -1) {
1316:           for (i=0; i<imark; i++) {
1317:             idx_p[i] = cmap[cworkB[i]];
1318:           }
1319:         } else {
1320:           for (i=0; i<nzB; i++) {
1321:             if (cmap[cworkB[i]] < cstart)   idx_p[i] = cmap[cworkB[i]];
1322:             else break;
1323:           }
1324:           imark = i;
1325:         }
1326:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart + cworkA[i];
1327:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]];
1328:       }
1329:     } else {
1330:       if (idx) *idx = 0;
1331:       if (v)   *v   = 0;
1332:     }
1333:   }
1334:   *nz = nztot;
1335:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1336:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1337:   return(0);
1338: }

1342: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1343: {
1344:   Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;

1347:   if (!aij->getrowactive) {
1348:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1349:   }
1350:   aij->getrowactive = PETSC_FALSE;
1351:   return(0);
1352: }

1356: PetscErrorCode MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm)
1357: {
1358:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
1359:   Mat_SeqAIJ     *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data;
1361:   PetscInt       i,j,cstart = mat->cmap.rstart;
1362:   PetscReal      sum = 0.0;
1363:   PetscScalar    *v;

1366:   if (aij->size == 1) {
1367:      MatNorm(aij->A,type,norm);
1368:   } else {
1369:     if (type == NORM_FROBENIUS) {
1370:       v = amat->a;
1371:       for (i=0; i<amat->nz; i++) {
1372: #if defined(PETSC_USE_COMPLEX)
1373:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1374: #else
1375:         sum += (*v)*(*v); v++;
1376: #endif
1377:       }
1378:       v = bmat->a;
1379:       for (i=0; i<bmat->nz; i++) {
1380: #if defined(PETSC_USE_COMPLEX)
1381:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1382: #else
1383:         sum += (*v)*(*v); v++;
1384: #endif
1385:       }
1386:       MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPI_SUM,mat->comm);
1387:       *norm = sqrt(*norm);
1388:     } else if (type == NORM_1) { /* max column norm */
1389:       PetscReal *tmp,*tmp2;
1390:       PetscInt    *jj,*garray = aij->garray;
1391:       PetscMalloc((mat->cmap.N+1)*sizeof(PetscReal),&tmp);
1392:       PetscMalloc((mat->cmap.N+1)*sizeof(PetscReal),&tmp2);
1393:       PetscMemzero(tmp,mat->cmap.N*sizeof(PetscReal));
1394:       *norm = 0.0;
1395:       v = amat->a; jj = amat->j;
1396:       for (j=0; j<amat->nz; j++) {
1397:         tmp[cstart + *jj++ ] += PetscAbsScalar(*v);  v++;
1398:       }
1399:       v = bmat->a; jj = bmat->j;
1400:       for (j=0; j<bmat->nz; j++) {
1401:         tmp[garray[*jj++]] += PetscAbsScalar(*v); v++;
1402:       }
1403:       MPI_Allreduce(tmp,tmp2,mat->cmap.N,MPIU_REAL,MPI_SUM,mat->comm);
1404:       for (j=0; j<mat->cmap.N; j++) {
1405:         if (tmp2[j] > *norm) *norm = tmp2[j];
1406:       }
1407:       PetscFree(tmp);
1408:       PetscFree(tmp2);
1409:     } else if (type == NORM_INFINITY) { /* max row norm */
1410:       PetscReal ntemp = 0.0;
1411:       for (j=0; j<aij->A->rmap.n; j++) {
1412:         v = amat->a + amat->i[j];
1413:         sum = 0.0;
1414:         for (i=0; i<amat->i[j+1]-amat->i[j]; i++) {
1415:           sum += PetscAbsScalar(*v); v++;
1416:         }
1417:         v = bmat->a + bmat->i[j];
1418:         for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) {
1419:           sum += PetscAbsScalar(*v); v++;
1420:         }
1421:         if (sum > ntemp) ntemp = sum;
1422:       }
1423:       MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPI_MAX,mat->comm);
1424:     } else {
1425:       SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1426:     }
1427:   }
1428:   return(0);
1429: }

1433: PetscErrorCode MatTranspose_MPIAIJ(Mat A,Mat *matout)
1434: {
1435:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
1436:   Mat_SeqAIJ     *Aloc = (Mat_SeqAIJ*)a->A->data;
1438:   PetscInt       M = A->rmap.N,N = A->cmap.N,m,*ai,*aj,row,*cols,i,*ct;
1439:   Mat            B;
1440:   PetscScalar    *array;

1443:   if (!matout && M != N) {
1444:     SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1445:   }

1447:   MatCreate(A->comm,&B);
1448:   MatSetSizes(B,A->cmap.n,A->rmap.n,N,M);
1449:   MatSetType(B,A->type_name);
1450:   MatMPIAIJSetPreallocation(B,0,PETSC_NULL,0,PETSC_NULL);

1452:   /* copy over the A part */
1453:   Aloc = (Mat_SeqAIJ*)a->A->data;
1454:   m = a->A->rmap.n; ai = Aloc->i; aj = Aloc->j; array = Aloc->a;
1455:   row = A->rmap.rstart;
1456:   for (i=0; i<ai[m]; i++) {aj[i] += A->cmap.rstart ;}
1457:   for (i=0; i<m; i++) {
1458:     MatSetValues(B,ai[i+1]-ai[i],aj,1,&row,array,INSERT_VALUES);
1459:     row++; array += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i];
1460:   }
1461:   aj = Aloc->j;
1462:   for (i=0; i<ai[m]; i++) {aj[i] -= A->cmap.rstart ;}

1464:   /* copy over the B part */
1465:   Aloc = (Mat_SeqAIJ*)a->B->data;
1466:   m = a->B->rmap.n;  ai = Aloc->i; aj = Aloc->j; array = Aloc->a;
1467:   row  = A->rmap.rstart;
1468:   PetscMalloc((1+ai[m])*sizeof(PetscInt),&cols);
1469:   ct   = cols;
1470:   for (i=0; i<ai[m]; i++) {cols[i] = a->garray[aj[i]];}
1471:   for (i=0; i<m; i++) {
1472:     MatSetValues(B,ai[i+1]-ai[i],cols,1,&row,array,INSERT_VALUES);
1473:     row++; array += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
1474:   }
1475:   PetscFree(ct);
1476:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1477:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1478:   if (matout) {
1479:     *matout = B;
1480:   } else {
1481:     MatHeaderCopy(A,B);
1482:   }
1483:   return(0);
1484: }

1488: PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr)
1489: {
1490:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
1491:   Mat            a = aij->A,b = aij->B;
1493:   PetscInt       s1,s2,s3;

1496:   MatGetLocalSize(mat,&s2,&s3);
1497:   if (rr) {
1498:     VecGetLocalSize(rr,&s1);
1499:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1500:     /* Overlap communication with computation. */
1501:     VecScatterBegin(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);
1502:   }
1503:   if (ll) {
1504:     VecGetLocalSize(ll,&s1);
1505:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1506:     (*b->ops->diagonalscale)(b,ll,0);
1507:   }
1508:   /* scale  the diagonal block */
1509:   (*a->ops->diagonalscale)(a,ll,rr);

1511:   if (rr) {
1512:     /* Do a scatter end and then right scale the off-diagonal block */
1513:     VecScatterEnd(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);
1514:     (*b->ops->diagonalscale)(b,0,aij->lvec);
1515:   }
1516: 
1517:   return(0);
1518: }

1522: PetscErrorCode MatSetBlockSize_MPIAIJ(Mat A,PetscInt bs)
1523: {
1524:   Mat_MPIAIJ     *a   = (Mat_MPIAIJ*)A->data;

1528:   MatSetBlockSize(a->A,bs);
1529:   MatSetBlockSize(a->B,bs);
1530:   return(0);
1531: }
1534: PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
1535: {
1536:   Mat_MPIAIJ     *a   = (Mat_MPIAIJ*)A->data;

1540:   MatSetUnfactored(a->A);
1541:   return(0);
1542: }

1546: PetscErrorCode MatEqual_MPIAIJ(Mat A,Mat B,PetscTruth *flag)
1547: {
1548:   Mat_MPIAIJ     *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data;
1549:   Mat            a,b,c,d;
1550:   PetscTruth     flg;

1554:   a = matA->A; b = matA->B;
1555:   c = matB->A; d = matB->B;

1557:   MatEqual(a,c,&flg);
1558:   if (flg) {
1559:     MatEqual(b,d,&flg);
1560:   }
1561:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1562:   return(0);
1563: }

1567: PetscErrorCode MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str)
1568: {
1570:   Mat_MPIAIJ     *a = (Mat_MPIAIJ *)A->data;
1571:   Mat_MPIAIJ     *b = (Mat_MPIAIJ *)B->data;

1574:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1575:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1576:     /* because of the column compression in the off-processor part of the matrix a->B,
1577:        the number of columns in a->B and b->B may be different, hence we cannot call
1578:        the MatCopy() directly on the two parts. If need be, we can provide a more 
1579:        efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
1580:        then copying the submatrices */
1581:     MatCopy_Basic(A,B,str);
1582:   } else {
1583:     MatCopy(a->A,b->A,str);
1584:     MatCopy(a->B,b->B,str);
1585:   }
1586:   return(0);
1587: }

1591: PetscErrorCode MatSetUpPreallocation_MPIAIJ(Mat A)
1592: {

1596:    MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1597:   return(0);
1598: }

1600:  #include petscblaslapack.h
1603: PetscErrorCode MatAXPY_MPIAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1604: {
1606:   PetscInt       i;
1607:   Mat_MPIAIJ     *xx = (Mat_MPIAIJ *)X->data,*yy = (Mat_MPIAIJ *)Y->data;
1608:   PetscBLASInt   bnz,one=1;
1609:   Mat_SeqAIJ     *x,*y;

1612:   if (str == SAME_NONZERO_PATTERN) {
1613:     PetscScalar alpha = a;
1614:     x = (Mat_SeqAIJ *)xx->A->data;
1615:     y = (Mat_SeqAIJ *)yy->A->data;
1616:     bnz = (PetscBLASInt)x->nz;
1617:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1618:     x = (Mat_SeqAIJ *)xx->B->data;
1619:     y = (Mat_SeqAIJ *)yy->B->data;
1620:     bnz = (PetscBLASInt)x->nz;
1621:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1622:   } else if (str == SUBSET_NONZERO_PATTERN) {
1623:     MatAXPY_SeqAIJ(yy->A,a,xx->A,str);

1625:     x = (Mat_SeqAIJ *)xx->B->data;
1626:     y = (Mat_SeqAIJ *)yy->B->data;
1627:     if (y->xtoy && y->XtoY != xx->B) {
1628:       PetscFree(y->xtoy);
1629:       MatDestroy(y->XtoY);
1630:     }
1631:     if (!y->xtoy) { /* get xtoy */
1632:       MatAXPYGetxtoy_Private(xx->B->rmap.n,x->i,x->j,xx->garray,y->i,y->j,yy->garray,&y->xtoy);
1633:       y->XtoY = xx->B;
1634:     }
1635:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
1636:   } else {
1637:     MatAXPY_Basic(Y,a,X,str);
1638:   }
1639:   return(0);
1640: }

1642: EXTERN PetscErrorCode  MatConjugate_SeqAIJ(Mat);

1646: PetscErrorCode  MatConjugate_MPIAIJ(Mat mat)
1647: {
1648: #if defined(PETSC_USE_COMPLEX)
1650:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

1653:   MatConjugate_SeqAIJ(aij->A);
1654:   MatConjugate_SeqAIJ(aij->B);
1655: #else
1657: #endif
1658:   return(0);
1659: }

1663: PetscErrorCode MatRealPart_MPIAIJ(Mat A)
1664: {
1665:   Mat_MPIAIJ   *a = (Mat_MPIAIJ*)A->data;

1669:   MatRealPart(a->A);
1670:   MatRealPart(a->B);
1671:   return(0);
1672: }

1676: PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
1677: {
1678:   Mat_MPIAIJ   *a = (Mat_MPIAIJ*)A->data;

1682:   MatImaginaryPart(a->A);
1683:   MatImaginaryPart(a->B);
1684:   return(0);
1685: }

1687: #ifdef PETSC_HAVE_PBGL
1688: #include <boost/parallel/mpi/bsp_process_group.hpp>
1689: typedef boost::parallel::mpi::bsp_process_group            process_group_type;

1691: #include <boost/graph/distributed/adjacency_list.hpp>
1692: #include <boost/parallel/mpi/bsp_process_group.hpp>

1694: #include <boost/graph/distributed/petsc/interface.hpp>
1695: #include <boost/graph/distributed/ilu_0.hpp>

1697: namespace petsc = boost::distributed::petsc;
1698: using namespace std;
1699: typedef double                                             value_type;
1700: typedef boost::graph::distributed::ilu_elimination_state   elimination_state;
1701: typedef boost::adjacency_list<boost::listS,
1702:                        boost::distributedS<process_group_type, boost::vecS>,
1703:                        boost::bidirectionalS,
1704:                        // Vertex properties
1705:                        boost::no_property,
1706:                        // Edge properties
1707:                        boost::property<boost::edge_weight_t, value_type,
1708:                          boost::property<boost::edge_finished_t, elimination_state> > > graph_type;

1710: typedef boost::graph_traits<graph_type>::vertex_descriptor        vertex_type;
1711: typedef boost::graph_traits<graph_type>::edge_descriptor          edge_type;
1712: typedef boost::property_map<graph_type, boost::edge_weight_t>::type      weight_map_type;

1716: /*
1717:   This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
1718: */
1719: PetscErrorCode MatILUFactorSymbolic_MPIAIJ(Mat A, IS isrow, IS iscol, MatFactorInfo *info, Mat *fact)
1720: {
1721:   PetscTruth           row_identity, col_identity;
1722:   PetscObjectContainer c;
1723:   PetscInt             m, n, M, N;
1724:   PetscErrorCode       ierr;

1727:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels = 0 supported for parallel ilu");
1728:   ISIdentity(isrow, &row_identity);
1729:   ISIdentity(iscol, &col_identity);
1730:   if (!row_identity || !col_identity) {
1731:     SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for parallel ILU");
1732:   }

1734:   process_group_type pg;
1735:   graph_type*        graph_p = new graph_type(petsc::num_global_vertices(A), pg, petsc::matrix_distribution(A, pg));
1736:   graph_type&        graph   = *graph_p;
1737:   petsc::read_matrix(A, graph, get(boost::edge_weight, graph));

1739:   //write_graphviz("petsc_matrix_as_graph.dot", graph, default_writer(), matrix_graph_writer<graph_type>(graph));
1740:   boost::property_map<graph_type, boost::edge_finished_t>::type finished = get(boost::edge_finished, graph);
1741:   BGL_FORALL_EDGES(e, graph, graph_type)
1742:     put(finished, e, boost::graph::distributed::unseen);

1744:   ilu_0(graph, get(boost::edge_weight, graph), get(boost::edge_finished, graph));

1746:   /* put together the new matrix */
1747:   MatCreate(A->comm, fact);
1748:   MatGetLocalSize(A, &m, &n);
1749:   MatGetSize(A, &M, &N);
1750:   MatSetSizes(*fact, m, n, M, N);
1751:   MatSetType(*fact, A->type_name);
1752:   MatAssemblyBegin(*fact, MAT_FINAL_ASSEMBLY);
1753:   MatAssemblyEnd(*fact, MAT_FINAL_ASSEMBLY);
1754:   (*fact)->factor = FACTOR_LU;

1756:   PetscObjectContainerCreate(A->comm, &c);
1757:   PetscObjectContainerSetPointer(c, graph_p);
1758:   PetscObjectCompose((PetscObject) (*fact), "graph", (PetscObject) c);
1759:   return(0);
1760: }

1764: PetscErrorCode MatLUFactorNumeric_MPIAIJ(Mat A, MatFactorInfo *info, Mat *B)
1765: {
1767:   return(0);
1768: }

1772: /*
1773:   This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
1774: */
1775: PetscErrorCode MatSolve_MPIAIJ(Mat A, Vec b, Vec x)
1776: {
1777:   graph_type*          graph_p;
1778:   PetscObjectContainer c;
1779:   PetscErrorCode       ierr;

1782:   PetscObjectQuery((PetscObject) A, "graph", (PetscObject *) &c);
1783:   PetscObjectContainerGetPointer(c, (void **) &graph_p);
1784:   VecCopy(b, x);
1785:   return(0);
1786: }
1787: #endif

1789: /* -------------------------------------------------------------------*/
1790: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
1791:        MatGetRow_MPIAIJ,
1792:        MatRestoreRow_MPIAIJ,
1793:        MatMult_MPIAIJ,
1794: /* 4*/ MatMultAdd_MPIAIJ,
1795:        MatMultTranspose_MPIAIJ,
1796:        MatMultTransposeAdd_MPIAIJ,
1797: #ifdef PETSC_HAVE_PBGL
1798:        MatSolve_MPIAIJ,
1799: #else
1800:        0,
1801: #endif
1802:        0,
1803:        0,
1804: /*10*/ 0,
1805:        0,
1806:        0,
1807:        MatRelax_MPIAIJ,
1808:        MatTranspose_MPIAIJ,
1809: /*15*/ MatGetInfo_MPIAIJ,
1810:        MatEqual_MPIAIJ,
1811:        MatGetDiagonal_MPIAIJ,
1812:        MatDiagonalScale_MPIAIJ,
1813:        MatNorm_MPIAIJ,
1814: /*20*/ MatAssemblyBegin_MPIAIJ,
1815:        MatAssemblyEnd_MPIAIJ,
1816:        0,
1817:        MatSetOption_MPIAIJ,
1818:        MatZeroEntries_MPIAIJ,
1819: /*25*/ MatZeroRows_MPIAIJ,
1820:        0,
1821: #ifdef PETSC_HAVE_PBGL
1822:        MatLUFactorNumeric_MPIAIJ,
1823: #else
1824:        0,
1825: #endif
1826:        0,
1827:        0,
1828: /*30*/ MatSetUpPreallocation_MPIAIJ,
1829: #ifdef PETSC_HAVE_PBGL
1830:        MatILUFactorSymbolic_MPIAIJ,
1831: #else
1832:        0,
1833: #endif
1834:        0,
1835:        0,
1836:        0,
1837: /*35*/ MatDuplicate_MPIAIJ,
1838:        0,
1839:        0,
1840:        0,
1841:        0,
1842: /*40*/ MatAXPY_MPIAIJ,
1843:        MatGetSubMatrices_MPIAIJ,
1844:        MatIncreaseOverlap_MPIAIJ,
1845:        MatGetValues_MPIAIJ,
1846:        MatCopy_MPIAIJ,
1847: /*45*/ 0,
1848:        MatScale_MPIAIJ,
1849:        0,
1850:        0,
1851:        0,
1852: /*50*/ MatSetBlockSize_MPIAIJ,
1853:        0,
1854:        0,
1855:        0,
1856:        0,
1857: /*55*/ MatFDColoringCreate_MPIAIJ,
1858:        0,
1859:        MatSetUnfactored_MPIAIJ,
1860:        MatPermute_MPIAIJ,
1861:        0,
1862: /*60*/ MatGetSubMatrix_MPIAIJ,
1863:        MatDestroy_MPIAIJ,
1864:        MatView_MPIAIJ,
1865:        0,
1866:        0,
1867: /*65*/ 0,
1868:        0,
1869:        0,
1870:        0,
1871:        0,
1872: /*70*/ 0,
1873:        0,
1874:        MatSetColoring_MPIAIJ,
1875: #if defined(PETSC_HAVE_ADIC)
1876:        MatSetValuesAdic_MPIAIJ,
1877: #else
1878:        0,
1879: #endif
1880:        MatSetValuesAdifor_MPIAIJ,
1881: /*75*/ 0,
1882:        0,
1883:        0,
1884:        0,
1885:        0,
1886: /*80*/ 0,
1887:        0,
1888:        0,
1889:        0,
1890: /*84*/ MatLoad_MPIAIJ,
1891:        0,
1892:        0,
1893:        0,
1894:        0,
1895:        0,
1896: /*90*/ MatMatMult_MPIAIJ_MPIAIJ,
1897:        MatMatMultSymbolic_MPIAIJ_MPIAIJ,
1898:        MatMatMultNumeric_MPIAIJ_MPIAIJ,
1899:        MatPtAP_Basic,
1900:        MatPtAPSymbolic_MPIAIJ,
1901: /*95*/ MatPtAPNumeric_MPIAIJ,
1902:        0,
1903:        0,
1904:        0,
1905:        0,
1906: /*100*/0,
1907:        MatPtAPSymbolic_MPIAIJ_MPIAIJ,
1908:        MatPtAPNumeric_MPIAIJ_MPIAIJ,
1909:        MatConjugate_MPIAIJ,
1910:        0,
1911: /*105*/MatSetValuesRow_MPIAIJ,
1912:        MatRealPart_MPIAIJ,
1913:        MatImaginaryPart_MPIAIJ};

1915: /* ----------------------------------------------------------------------------------------*/

1920: PetscErrorCode  MatStoreValues_MPIAIJ(Mat mat)
1921: {
1922:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

1926:   MatStoreValues(aij->A);
1927:   MatStoreValues(aij->B);
1928:   return(0);
1929: }

1935: PetscErrorCode  MatRetrieveValues_MPIAIJ(Mat mat)
1936: {
1937:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

1941:   MatRetrieveValues(aij->A);
1942:   MatRetrieveValues(aij->B);
1943:   return(0);
1944: }

1947:  #include petscpc.h
1951: PetscErrorCode  MatMPIAIJSetPreallocation_MPIAIJ(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
1952: {
1953:   Mat_MPIAIJ     *b;
1955:   PetscInt       i;

1958:   B->preallocated = PETSC_TRUE;
1959:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
1960:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
1961:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
1962:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);

1964:   B->rmap.bs = B->cmap.bs = 1;
1965:   PetscMapInitialize(B->comm,&B->rmap);
1966:   PetscMapInitialize(B->comm,&B->cmap);
1967:   if (d_nnz) {
1968:     for (i=0; i<B->rmap.n; i++) {
1969:       if (d_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than 0: local row %D value %D",i,d_nnz[i]);
1970:     }
1971:   }
1972:   if (o_nnz) {
1973:     for (i=0; i<B->rmap.n; i++) {
1974:       if (o_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than 0: local row %D value %D",i,o_nnz[i]);
1975:     }
1976:   }
1977:   b = (Mat_MPIAIJ*)B->data;

1979:   /* Explicitly create 2 MATSEQAIJ matrices. */
1980:   MatCreate(PETSC_COMM_SELF,&b->A);
1981:   MatSetSizes(b->A,B->rmap.n,B->cmap.n,B->rmap.n,B->cmap.n);
1982:   MatSetType(b->A,MATSEQAIJ);
1983:   PetscLogObjectParent(B,b->A);
1984:   MatCreate(PETSC_COMM_SELF,&b->B);
1985:   MatSetSizes(b->B,B->rmap.n,B->cmap.N,B->rmap.n,B->cmap.N);
1986:   MatSetType(b->B,MATSEQAIJ);
1987:   PetscLogObjectParent(B,b->B);

1989:   MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
1990:   MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);

1992:   return(0);
1993: }

1998: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
1999: {
2000:   Mat            mat;
2001:   Mat_MPIAIJ     *a,*oldmat = (Mat_MPIAIJ*)matin->data;

2005:   *newmat       = 0;
2006:   MatCreate(matin->comm,&mat);
2007:   MatSetSizes(mat,matin->rmap.n,matin->cmap.n,matin->rmap.N,matin->cmap.N);
2008:   MatSetType(mat,matin->type_name);
2009:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
2010:   a    = (Mat_MPIAIJ*)mat->data;
2011: 
2012:   mat->factor       = matin->factor;
2013:   mat->rmap.bs      = matin->rmap.bs;
2014:   mat->assembled    = PETSC_TRUE;
2015:   mat->insertmode   = NOT_SET_VALUES;
2016:   mat->preallocated = PETSC_TRUE;

2018:   a->size           = oldmat->size;
2019:   a->rank           = oldmat->rank;
2020:   a->donotstash     = oldmat->donotstash;
2021:   a->roworiented    = oldmat->roworiented;
2022:   a->rowindices     = 0;
2023:   a->rowvalues      = 0;
2024:   a->getrowactive   = PETSC_FALSE;

2026:   PetscMapCopy(mat->comm,&matin->rmap,&mat->rmap);
2027:   PetscMapCopy(mat->comm,&matin->cmap,&mat->cmap);

2029:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2030:   if (oldmat->colmap) {
2031: #if defined (PETSC_USE_CTABLE)
2032:     PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2033: #else
2034:     PetscMalloc((mat->cmap.N)*sizeof(PetscInt),&a->colmap);
2035:     PetscLogObjectMemory(mat,(mat->cmap.N)*sizeof(PetscInt));
2036:     PetscMemcpy(a->colmap,oldmat->colmap,(mat->cmap.N)*sizeof(PetscInt));
2037: #endif
2038:   } else a->colmap = 0;
2039:   if (oldmat->garray) {
2040:     PetscInt len;
2041:     len  = oldmat->B->cmap.n;
2042:     PetscMalloc((len+1)*sizeof(PetscInt),&a->garray);
2043:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2044:     if (len) { PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt)); }
2045:   } else a->garray = 0;
2046: 
2047:   VecDuplicate(oldmat->lvec,&a->lvec);
2048:   PetscLogObjectParent(mat,a->lvec);
2049:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2050:   PetscLogObjectParent(mat,a->Mvctx);
2051:   MatDuplicate(oldmat->A,cpvalues,&a->A);
2052:   PetscLogObjectParent(mat,a->A);
2053:   MatDuplicate(oldmat->B,cpvalues,&a->B);
2054:   PetscLogObjectParent(mat,a->B);
2055:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2056:   *newmat = mat;
2057:   return(0);
2058: }

2060:  #include petscsys.h

2064: PetscErrorCode MatLoad_MPIAIJ(PetscViewer viewer, MatType type,Mat *newmat)
2065: {
2066:   Mat            A;
2067:   PetscScalar    *vals,*svals;
2068:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2069:   MPI_Status     status;
2071:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag,maxnz;
2072:   PetscInt       i,nz,j,rstart,rend,mmax;
2073:   PetscInt       header[4],*rowlengths = 0,M,N,m,*cols;
2074:   PetscInt       *ourlens = PETSC_NULL,*procsnz = PETSC_NULL,*offlens = PETSC_NULL,jj,*mycols,*smycols;
2075:   PetscInt       cend,cstart,n,*rowners;
2076:   int            fd;

2079:   MPI_Comm_size(comm,&size);
2080:   MPI_Comm_rank(comm,&rank);
2081:   if (!rank) {
2082:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2083:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2084:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2085:   }

2087:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2088:   M = header[1]; N = header[2];
2089:   /* determine ownership of all rows */
2090:   m    = M/size + ((M % size) > rank);
2091:   PetscMalloc((size+1)*sizeof(PetscInt),&rowners);
2092:   MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);

2094:   /* First process needs enough room for process with most rows */
2095:   if (!rank) {
2096:     mmax       = rowners[1];
2097:     for (i=2; i<size; i++) {
2098:       mmax = PetscMax(mmax,rowners[i]);
2099:     }
2100:   } else mmax = m;

2102:   rowners[0] = 0;
2103:   for (i=2; i<=size; i++) {
2104:     mmax       = PetscMax(mmax,rowners[i]);
2105:     rowners[i] += rowners[i-1];
2106:   }
2107:   rstart = rowners[rank];
2108:   rend   = rowners[rank+1];

2110:   /* distribute row lengths to all processors */
2111:   PetscMalloc2(mmax,PetscInt,&ourlens,mmax,PetscInt,&offlens);
2112:   if (!rank) {
2113:     PetscBinaryRead(fd,ourlens,m,PETSC_INT);
2114:     PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2115:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2116:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2117:     for (j=0; j<m; j++) {
2118:       procsnz[0] += ourlens[j];
2119:     }
2120:     for (i=1; i<size; i++) {
2121:       PetscBinaryRead(fd,rowlengths,rowners[i+1]-rowners[i],PETSC_INT);
2122:       /* calculate the number of nonzeros on each processor */
2123:       for (j=0; j<rowners[i+1]-rowners[i]; j++) {
2124:         procsnz[i] += rowlengths[j];
2125:       }
2126:       MPI_Send(rowlengths,rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
2127:     }
2128:     PetscFree(rowlengths);
2129:   } else {
2130:     MPI_Recv(ourlens,m,MPIU_INT,0,tag,comm,&status);
2131:   }

2133:   if (!rank) {
2134:     /* determine max buffer needed and allocate it */
2135:     maxnz = 0;
2136:     for (i=0; i<size; i++) {
2137:       maxnz = PetscMax(maxnz,procsnz[i]);
2138:     }
2139:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2141:     /* read in my part of the matrix column indices  */
2142:     nz   = procsnz[0];
2143:     PetscMalloc(nz*sizeof(PetscInt),&mycols);
2144:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);

2146:     /* read in every one elses and ship off */
2147:     for (i=1; i<size; i++) {
2148:       nz   = procsnz[i];
2149:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2150:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2151:     }
2152:     PetscFree(cols);
2153:   } else {
2154:     /* determine buffer space needed for message */
2155:     nz = 0;
2156:     for (i=0; i<m; i++) {
2157:       nz += ourlens[i];
2158:     }
2159:     PetscMalloc(nz*sizeof(PetscInt),&mycols);

2161:     /* receive message of column indices*/
2162:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2163:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2164:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2165:   }

2167:   /* determine column ownership if matrix is not square */
2168:   if (N != M) {
2169:     n      = N/size + ((N % size) > rank);
2170:     MPI_Scan(&n,&cend,1,MPIU_INT,MPI_SUM,comm);
2171:     cstart = cend - n;
2172:   } else {
2173:     cstart = rstart;
2174:     cend   = rend;
2175:     n      = cend - cstart;
2176:   }

2178:   /* loop over local rows, determining number of off diagonal entries */
2179:   PetscMemzero(offlens,m*sizeof(PetscInt));
2180:   jj = 0;
2181:   for (i=0; i<m; i++) {
2182:     for (j=0; j<ourlens[i]; j++) {
2183:       if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++;
2184:       jj++;
2185:     }
2186:   }

2188:   /* create our matrix */
2189:   for (i=0; i<m; i++) {
2190:     ourlens[i] -= offlens[i];
2191:   }
2192:   MatCreate(comm,&A);
2193:   MatSetSizes(A,m,n,M,N);
2194:   MatSetType(A,type);
2195:   MatMPIAIJSetPreallocation(A,0,ourlens,0,offlens);

2197:   MatSetOption(A,MAT_COLUMNS_SORTED);
2198:   for (i=0; i<m; i++) {
2199:     ourlens[i] += offlens[i];
2200:   }

2202:   if (!rank) {
2203:     PetscMalloc((maxnz+1)*sizeof(PetscScalar),&vals);

2205:     /* read in my part of the matrix numerical values  */
2206:     nz   = procsnz[0];
2207:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2208: 
2209:     /* insert into matrix */
2210:     jj      = rstart;
2211:     smycols = mycols;
2212:     svals   = vals;
2213:     for (i=0; i<m; i++) {
2214:       MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2215:       smycols += ourlens[i];
2216:       svals   += ourlens[i];
2217:       jj++;
2218:     }

2220:     /* read in other processors and ship out */
2221:     for (i=1; i<size; i++) {
2222:       nz   = procsnz[i];
2223:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2224:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2225:     }
2226:     PetscFree(procsnz);
2227:   } else {
2228:     /* receive numeric values */
2229:     PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);

2231:     /* receive message of values*/
2232:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2233:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2234:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2236:     /* insert into matrix */
2237:     jj      = rstart;
2238:     smycols = mycols;
2239:     svals   = vals;
2240:     for (i=0; i<m; i++) {
2241:       MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2242:       smycols += ourlens[i];
2243:       svals   += ourlens[i];
2244:       jj++;
2245:     }
2246:   }
2247:   PetscFree2(ourlens,offlens);
2248:   PetscFree(vals);
2249:   PetscFree(mycols);
2250:   PetscFree(rowners);

2252:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2253:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2254:   *newmat = A;
2255:   return(0);
2256: }

2260: /*
2261:     Not great since it makes two copies of the submatrix, first an SeqAIJ 
2262:   in local and then by concatenating the local matrices the end result.
2263:   Writing it directly would be much like MatGetSubMatrices_MPIAIJ()
2264: */
2265: PetscErrorCode MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
2266: {
2268:   PetscMPIInt    rank,size;
2269:   PetscInt       i,m,n,rstart,row,rend,nz,*cwork,j;
2270:   PetscInt       *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal;
2271:   Mat            *local,M,Mreuse;
2272:   PetscScalar    *vwork,*aa;
2273:   MPI_Comm       comm = mat->comm;
2274:   Mat_SeqAIJ     *aij;


2278:   MPI_Comm_rank(comm,&rank);
2279:   MPI_Comm_size(comm,&size);

2281:   if (call ==  MAT_REUSE_MATRIX) {
2282:     PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject *)&Mreuse);
2283:     if (!Mreuse) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
2284:     local = &Mreuse;
2285:     MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&local);
2286:   } else {
2287:     MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);
2288:     Mreuse = *local;
2289:     PetscFree(local);
2290:   }

2292:   /* 
2293:       m - number of local rows
2294:       n - number of columns (same on all processors)
2295:       rstart - first row in new global matrix generated
2296:   */
2297:   MatGetSize(Mreuse,&m,&n);
2298:   if (call == MAT_INITIAL_MATRIX) {
2299:     aij = (Mat_SeqAIJ*)(Mreuse)->data;
2300:     ii  = aij->i;
2301:     jj  = aij->j;

2303:     /*
2304:         Determine the number of non-zeros in the diagonal and off-diagonal 
2305:         portions of the matrix in order to do correct preallocation
2306:     */

2308:     /* first get start and end of "diagonal" columns */
2309:     if (csize == PETSC_DECIDE) {
2310:       ISGetSize(isrow,&mglobal);
2311:       if (mglobal == n) { /* square matrix */
2312:         nlocal = m;
2313:       } else {
2314:         nlocal = n/size + ((n % size) > rank);
2315:       }
2316:     } else {
2317:       nlocal = csize;
2318:     }
2319:     MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
2320:     rstart = rend - nlocal;
2321:     if (rank == size - 1 && rend != n) {
2322:       SETERRQ2(PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);
2323:     }

2325:     /* next, compute all the lengths */
2326:     PetscMalloc((2*m+1)*sizeof(PetscInt),&dlens);
2327:     olens = dlens + m;
2328:     for (i=0; i<m; i++) {
2329:       jend = ii[i+1] - ii[i];
2330:       olen = 0;
2331:       dlen = 0;
2332:       for (j=0; j<jend; j++) {
2333:         if (*jj < rstart || *jj >= rend) olen++;
2334:         else dlen++;
2335:         jj++;
2336:       }
2337:       olens[i] = olen;
2338:       dlens[i] = dlen;
2339:     }
2340:     MatCreate(comm,&M);
2341:     MatSetSizes(M,m,nlocal,PETSC_DECIDE,n);
2342:     MatSetType(M,mat->type_name);
2343:     MatMPIAIJSetPreallocation(M,0,dlens,0,olens);
2344:     PetscFree(dlens);
2345:   } else {
2346:     PetscInt ml,nl;

2348:     M = *newmat;
2349:     MatGetLocalSize(M,&ml,&nl);
2350:     if (ml != m) SETERRQ(PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
2351:     MatZeroEntries(M);
2352:     /*
2353:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2354:        rather than the slower MatSetValues().
2355:     */
2356:     M->was_assembled = PETSC_TRUE;
2357:     M->assembled     = PETSC_FALSE;
2358:   }
2359:   MatGetOwnershipRange(M,&rstart,&rend);
2360:   aij = (Mat_SeqAIJ*)(Mreuse)->data;
2361:   ii  = aij->i;
2362:   jj  = aij->j;
2363:   aa  = aij->a;
2364:   for (i=0; i<m; i++) {
2365:     row   = rstart + i;
2366:     nz    = ii[i+1] - ii[i];
2367:     cwork = jj;     jj += nz;
2368:     vwork = aa;     aa += nz;
2369:     MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
2370:   }

2372:   MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
2373:   MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
2374:   *newmat = M;

2376:   /* save submatrix used in processor for next request */
2377:   if (call ==  MAT_INITIAL_MATRIX) {
2378:     PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
2379:     PetscObjectDereference((PetscObject)Mreuse);
2380:   }

2382:   return(0);
2383: }

2388: PetscErrorCode  MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
2389: {
2390:   PetscInt       m,cstart, cend,j,nnz,i,d;
2391:   PetscInt       *d_nnz,*o_nnz,nnz_max = 0,rstart,ii;
2392:   const PetscInt *JJ;
2393:   PetscScalar    *values;

2397:   if (Ii[0]) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Ii[0] must be 0 it is %D",Ii[0]);

2399:   B->rmap.bs = B->cmap.bs = 1;
2400:   PetscMapInitialize(B->comm,&B->rmap);
2401:   PetscMapInitialize(B->comm,&B->cmap);
2402:   m      = B->rmap.n;
2403:   cstart = B->cmap.rstart;
2404:   cend   = B->cmap.rend;
2405:   rstart = B->rmap.rstart;

2407:   PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
2408:   o_nnz = d_nnz + m;

2410:   for (i=0; i<m; i++) {
2411:     nnz     = Ii[i+1]- Ii[i];
2412:     JJ      = J + Ii[i];
2413:     nnz_max = PetscMax(nnz_max,nnz);
2414:     if (nnz < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative %D number of columns",i,nnz);
2415:     for (j=0; j<nnz; j++) {
2416:       if (*JJ >= cstart) break;
2417:       JJ++;
2418:     }
2419:     d = 0;
2420:     for (; j<nnz; j++) {
2421:       if (*JJ++ >= cend) break;
2422:       d++;
2423:     }
2424:     d_nnz[i] = d;
2425:     o_nnz[i] = nnz - d;
2426:   }
2427:   MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
2428:   PetscFree(d_nnz);

2430:   if (v) values = (PetscScalar*)v;
2431:   else {
2432:     PetscMalloc((nnz_max+1)*sizeof(PetscScalar),&values);
2433:     PetscMemzero(values,nnz_max*sizeof(PetscScalar));
2434:   }

2436:   MatSetOption(B,MAT_COLUMNS_SORTED);
2437:   for (i=0; i<m; i++) {
2438:     ii   = i + rstart;
2439:     nnz  = Ii[i+1]- Ii[i];
2440:     MatSetValues_MPIAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
2441:   }
2442:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2443:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2444:   MatSetOption(B,MAT_COLUMNS_UNSORTED);

2446:   if (!v) {
2447:     PetscFree(values);
2448:   }
2449:   return(0);
2450: }

2455: /*@
2456:    MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
2457:    (the default parallel PETSc format).  

2459:    Collective on MPI_Comm

2461:    Input Parameters:
2462: +  B - the matrix 
2463: .  i - the indices into j for the start of each local row (starts with zero)
2464: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
2465: -  v - optional values in the matrix

2467:    Level: developer

2469:    Notes: this actually copies the values from i[], j[], and a[] to put them into PETSc's internal
2470:      storage format. Thus changing the values in a[] after this call will not effect the matrix values.

2472: .keywords: matrix, aij, compressed row, sparse, parallel

2474: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ,
2475:           MatCreateSeqAIJWithArrays()
2476: @*/
2477: PetscErrorCode  MatMPIAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2478: {
2479:   PetscErrorCode ierr,(*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);

2482:   PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",(void (**)(void))&f);
2483:   if (f) {
2484:     (*f)(B,i,j,v);
2485:   }
2486:   return(0);
2487: }

2491: /*@C
2492:    MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in AIJ format
2493:    (the default parallel PETSc format).  For good matrix assembly performance
2494:    the user should preallocate the matrix storage by setting the parameters 
2495:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2496:    performance can be increased by more than a factor of 50.

2498:    Collective on MPI_Comm

2500:    Input Parameters:
2501: +  A - the matrix 
2502: .  d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
2503:            (same value is used for all local rows)
2504: .  d_nnz - array containing the number of nonzeros in the various rows of the 
2505:            DIAGONAL portion of the local submatrix (possibly different for each row)
2506:            or PETSC_NULL, if d_nz is used to specify the nonzero structure. 
2507:            The size of this array is equal to the number of local rows, i.e 'm'. 
2508:            You must leave room for the diagonal entry even if it is zero.
2509: .  o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
2510:            submatrix (same value is used for all local rows).
2511: -  o_nnz - array containing the number of nonzeros in the various rows of the
2512:            OFF-DIAGONAL portion of the local submatrix (possibly different for
2513:            each row) or PETSC_NULL, if o_nz is used to specify the nonzero 
2514:            structure. The size of this array is equal to the number 
2515:            of local rows, i.e 'm'. 

2517:    If the *_nnz parameter is given then the *_nz parameter is ignored

2519:    The AIJ format (also called the Yale sparse matrix format or
2520:    compressed row storage (CSR)), is fully compatible with standard Fortran 77
2521:    storage.  The stored row and column indices begin with zero.  See the users manual for details.

2523:    The parallel matrix is partitioned such that the first m0 rows belong to 
2524:    process 0, the next m1 rows belong to process 1, the next m2 rows belong 
2525:    to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.

2527:    The DIAGONAL portion of the local submatrix of a processor can be defined 
2528:    as the submatrix which is obtained by extraction the part corresponding 
2529:    to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 
2530:    first row that belongs to the processor, and r2 is the last row belonging 
2531:    to the this processor. This is a square mxm matrix. The remaining portion 
2532:    of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.

2534:    If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.

2536:    Example usage:
2537:   
2538:    Consider the following 8x8 matrix with 34 non-zero values, that is 
2539:    assembled across 3 processors. Lets assume that proc0 owns 3 rows,
2540:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 
2541:    as follows:

2543: .vb
2544:             1  2  0  |  0  3  0  |  0  4
2545:     Proc0   0  5  6  |  7  0  0  |  8  0
2546:             9  0 10  | 11  0  0  | 12  0
2547:     -------------------------------------
2548:            13  0 14  | 15 16 17  |  0  0
2549:     Proc1   0 18  0  | 19 20 21  |  0  0 
2550:             0  0  0  | 22 23  0  | 24  0
2551:     -------------------------------------
2552:     Proc2  25 26 27  |  0  0 28  | 29  0
2553:            30  0  0  | 31 32 33  |  0 34
2554: .ve

2556:    This can be represented as a collection of submatrices as:

2558: .vb
2559:       A B C
2560:       D E F
2561:       G H I
2562: .ve

2564:    Where the submatrices A,B,C are owned by proc0, D,E,F are
2565:    owned by proc1, G,H,I are owned by proc2.

2567:    The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2568:    The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2569:    The 'M','N' parameters are 8,8, and have the same values on all procs.

2571:    The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
2572:    submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
2573:    corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
2574:    Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
2575:    part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
2576:    matrix, ans [DF] as another SeqAIJ matrix.

2578:    When d_nz, o_nz parameters are specified, d_nz storage elements are
2579:    allocated for every row of the local diagonal submatrix, and o_nz
2580:    storage locations are allocated for every row of the OFF-DIAGONAL submat.
2581:    One way to choose d_nz and o_nz is to use the max nonzerors per local 
2582:    rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 
2583:    In this case, the values of d_nz,o_nz are:
2584: .vb
2585:      proc0 : dnz = 2, o_nz = 2
2586:      proc1 : dnz = 3, o_nz = 2
2587:      proc2 : dnz = 1, o_nz = 4
2588: .ve
2589:    We are allocating m*(d_nz+o_nz) storage locations for every proc. This
2590:    translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
2591:    for proc3. i.e we are using 12+15+10=37 storage locations to store 
2592:    34 values.

2594:    When d_nnz, o_nnz parameters are specified, the storage is specified
2595:    for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
2596:    In the above case the values for d_nnz,o_nnz are:
2597: .vb
2598:      proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
2599:      proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
2600:      proc2: d_nnz = [1,1]   and o_nnz = [4,4]
2601: .ve
2602:    Here the space allocated is sum of all the above values i.e 34, and
2603:    hence pre-allocation is perfect.

2605:    Level: intermediate

2607: .keywords: matrix, aij, compressed row, sparse, parallel

2609: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIAIJ(), MatMPIAIJSetPreallocationCSR(),
2610:           MPIAIJ
2611: @*/
2612: PetscErrorCode  MatMPIAIJSetPreallocation(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2613: {
2614:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

2617:   PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocation_C",(void (**)(void))&f);
2618:   if (f) {
2619:     (*f)(B,d_nz,d_nnz,o_nz,o_nnz);
2620:   }
2621:   return(0);
2622: }

2626: /*@C
2627:      MatCreateMPIAIJWithArrays - creates a MPI AIJ matrix using arrays that contain in standard
2628:          CSR format the local rows.

2630:    Collective on MPI_Comm

2632:    Input Parameters:
2633: +  comm - MPI communicator
2634: .  m - number of local rows (Cannot be PETSC_DECIDE)
2635: .  n - This value should be the same as the local size used in creating the 
2636:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
2637:        calculated if N is given) For square matrices n is almost always m.
2638: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2639: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2640: .   i - row indices
2641: .   j - column indices
2642: -   a - matrix values

2644:    Output Parameter:
2645: .   mat - the matrix
2646:    Level: intermediate

2648:    Notes:
2649:        The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
2650:      thus you CANNOT change the matrix entries by changing the values of a[] after you have 
2651:      called this routine.

2653:        The i and j indices are 0 based

2655: .keywords: matrix, aij, compressed row, sparse, parallel

2657: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
2658:           MPIAIJ, MatCreateMPIAIJ()
2659: @*/
2660: PetscErrorCode  MatCreateMPIAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
2661: {

2665:   if (i[0]) {
2666:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
2667:   }
2668:   if (m < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
2669:   MatCreate(comm,mat);
2670:   MatSetType(*mat,MATMPIAIJ);
2671:   MatMPIAIJSetPreallocationCSR(*mat,i,j,a);
2672:   return(0);
2673: }

2677: /*@C
2678:    MatCreateMPIAIJ - Creates a sparse parallel matrix in AIJ format
2679:    (the default parallel PETSc format).  For good matrix assembly performance
2680:    the user should preallocate the matrix storage by setting the parameters 
2681:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2682:    performance can be increased by more than a factor of 50.

2684:    Collective on MPI_Comm

2686:    Input Parameters:
2687: +  comm - MPI communicator
2688: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2689:            This value should be the same as the local size used in creating the 
2690:            y vector for the matrix-vector product y = Ax.
2691: .  n - This value should be the same as the local size used in creating the 
2692:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
2693:        calculated if N is given) For square matrices n is almost always m.
2694: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2695: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2696: .  d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
2697:            (same value is used for all local rows)
2698: .  d_nnz - array containing the number of nonzeros in the various rows of the 
2699:            DIAGONAL portion of the local submatrix (possibly different for each row)
2700:            or PETSC_NULL, if d_nz is used to specify the nonzero structure. 
2701:            The size of this array is equal to the number of local rows, i.e 'm'. 
2702:            You must leave room for the diagonal entry even if it is zero.
2703: .  o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
2704:            submatrix (same value is used for all local rows).
2705: -  o_nnz - array containing the number of nonzeros in the various rows of the
2706:            OFF-DIAGONAL portion of the local submatrix (possibly different for
2707:            each row) or PETSC_NULL, if o_nz is used to specify the nonzero 
2708:            structure. The size of this array is equal to the number 
2709:            of local rows, i.e 'm'. 

2711:    Output Parameter:
2712: .  A - the matrix 

2714:    Notes:
2715:    If the *_nnz parameter is given then the *_nz parameter is ignored

2717:    m,n,M,N parameters specify the size of the matrix, and its partitioning across
2718:    processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate
2719:    storage requirements for this matrix.

2721:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one 
2722:    processor than it must be used on all processors that share the object for 
2723:    that argument.

2725:    The user MUST specify either the local or global matrix dimensions
2726:    (possibly both).

2728:    The parallel matrix is partitioned such that the first m0 rows belong to 
2729:    process 0, the next m1 rows belong to process 1, the next m2 rows belong 
2730:    to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.

2732:    The DIAGONAL portion of the local submatrix of a processor can be defined 
2733:    as the submatrix which is obtained by extraction the part corresponding 
2734:    to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 
2735:    first row that belongs to the processor, and r2 is the last row belonging 
2736:    to the this processor. This is a square mxm matrix. The remaining portion 
2737:    of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.

2739:    If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.

2741:    When calling this routine with a single process communicator, a matrix of
2742:    type SEQAIJ is returned.  If a matrix of type MPIAIJ is desired for this
2743:    type of communicator, use the construction mechanism:
2744:      MatCreate(...,&A); MatSetType(A,MPIAIJ); MatMPIAIJSetPreallocation(A,...);

2746:    By default, this format uses inodes (identical nodes) when possible.
2747:    We search for consecutive rows with the same nonzero structure, thereby
2748:    reusing matrix information to achieve increased efficiency.

2750:    Options Database Keys:
2751: +  -mat_no_inode  - Do not use inodes
2752: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2753: -  -mat_aij_oneindex - Internally use indexing starting at 1
2754:         rather than 0.  Note that when calling MatSetValues(),
2755:         the user still MUST index entries starting at 0!


2758:    Example usage:
2759:   
2760:    Consider the following 8x8 matrix with 34 non-zero values, that is 
2761:    assembled across 3 processors. Lets assume that proc0 owns 3 rows,
2762:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 
2763:    as follows:

2765: .vb
2766:             1  2  0  |  0  3  0  |  0  4
2767:     Proc0   0  5  6  |  7  0  0  |  8  0
2768:             9  0 10  | 11  0  0  | 12  0
2769:     -------------------------------------
2770:            13  0 14  | 15 16 17  |  0  0
2771:     Proc1   0 18  0  | 19 20 21  |  0  0 
2772:             0  0  0  | 22 23  0  | 24  0
2773:     -------------------------------------
2774:     Proc2  25 26 27  |  0  0 28  | 29  0
2775:            30  0  0  | 31 32 33  |  0 34
2776: .ve

2778:    This can be represented as a collection of submatrices as:

2780: .vb
2781:       A B C
2782:       D E F
2783:       G H I
2784: .ve

2786:    Where the submatrices A,B,C are owned by proc0, D,E,F are
2787:    owned by proc1, G,H,I are owned by proc2.

2789:    The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2790:    The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2791:    The 'M','N' parameters are 8,8, and have the same values on all procs.

2793:    The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
2794:    submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
2795:    corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
2796:    Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
2797:    part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
2798:    matrix, ans [DF] as another SeqAIJ matrix.

2800:    When d_nz, o_nz parameters are specified, d_nz storage elements are
2801:    allocated for every row of the local diagonal submatrix, and o_nz
2802:    storage locations are allocated for every row of the OFF-DIAGONAL submat.
2803:    One way to choose d_nz and o_nz is to use the max nonzerors per local 
2804:    rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 
2805:    In this case, the values of d_nz,o_nz are:
2806: .vb
2807:      proc0 : dnz = 2, o_nz = 2
2808:      proc1 : dnz = 3, o_nz = 2
2809:      proc2 : dnz = 1, o_nz = 4
2810: .ve
2811:    We are allocating m*(d_nz+o_nz) storage locations for every proc. This
2812:    translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
2813:    for proc3. i.e we are using 12+15+10=37 storage locations to store 
2814:    34 values.

2816:    When d_nnz, o_nnz parameters are specified, the storage is specified
2817:    for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
2818:    In the above case the values for d_nnz,o_nnz are:
2819: .vb
2820:      proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
2821:      proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
2822:      proc2: d_nnz = [1,1]   and o_nnz = [4,4]
2823: .ve
2824:    Here the space allocated is sum of all the above values i.e 34, and
2825:    hence pre-allocation is perfect.

2827:    Level: intermediate

2829: .keywords: matrix, aij, compressed row, sparse, parallel

2831: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
2832:           MPIAIJ, MatCreateMPIAIJWithArrays()
2833: @*/
2834: PetscErrorCode  MatCreateMPIAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
2835: {
2837:   PetscMPIInt    size;

2840:   MatCreate(comm,A);
2841:   MatSetSizes(*A,m,n,M,N);
2842:   MPI_Comm_size(comm,&size);
2843:   if (size > 1) {
2844:     MatSetType(*A,MATMPIAIJ);
2845:     MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
2846:   } else {
2847:     MatSetType(*A,MATSEQAIJ);
2848:     MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
2849:   }
2850:   return(0);
2851: }

2855: PetscErrorCode  MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
2856: {
2857:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2860:   *Ad     = a->A;
2861:   *Ao     = a->B;
2862:   *colmap = a->garray;
2863:   return(0);
2864: }

2868: PetscErrorCode MatSetColoring_MPIAIJ(Mat A,ISColoring coloring)
2869: {
2871:   PetscInt       i;
2872:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

2875:   if (coloring->ctype == IS_COLORING_LOCAL) {
2876:     ISColoringValue *allcolors,*colors;
2877:     ISColoring      ocoloring;

2879:     /* set coloring for diagonal portion */
2880:     MatSetColoring_SeqAIJ(a->A,coloring);

2882:     /* set coloring for off-diagonal portion */
2883:     ISAllGatherColors(A->comm,coloring->n,coloring->colors,PETSC_NULL,&allcolors);
2884:     PetscMalloc((a->B->cmap.n+1)*sizeof(ISColoringValue),&colors);
2885:     for (i=0; i<a->B->cmap.n; i++) {
2886:       colors[i] = allcolors[a->garray[i]];
2887:     }
2888:     PetscFree(allcolors);
2889:     ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap.n,colors,&ocoloring);
2890:     MatSetColoring_SeqAIJ(a->B,ocoloring);
2891:     ISColoringDestroy(ocoloring);
2892:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
2893:     ISColoringValue *colors;
2894:     PetscInt        *larray;
2895:     ISColoring      ocoloring;

2897:     /* set coloring for diagonal portion */
2898:     PetscMalloc((a->A->cmap.n+1)*sizeof(PetscInt),&larray);
2899:     for (i=0; i<a->A->cmap.n; i++) {
2900:       larray[i] = i + A->cmap.rstart;
2901:     }
2902:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->A->cmap.n,larray,PETSC_NULL,larray);
2903:     PetscMalloc((a->A->cmap.n+1)*sizeof(ISColoringValue),&colors);
2904:     for (i=0; i<a->A->cmap.n; i++) {
2905:       colors[i] = coloring->colors[larray[i]];
2906:     }
2907:     PetscFree(larray);
2908:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,a->A->cmap.n,colors,&ocoloring);
2909:     MatSetColoring_SeqAIJ(a->A,ocoloring);
2910:     ISColoringDestroy(ocoloring);

2912:     /* set coloring for off-diagonal portion */
2913:     PetscMalloc((a->B->cmap.n+1)*sizeof(PetscInt),&larray);
2914:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->B->cmap.n,a->garray,PETSC_NULL,larray);
2915:     PetscMalloc((a->B->cmap.n+1)*sizeof(ISColoringValue),&colors);
2916:     for (i=0; i<a->B->cmap.n; i++) {
2917:       colors[i] = coloring->colors[larray[i]];
2918:     }
2919:     PetscFree(larray);
2920:     ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap.n,colors,&ocoloring);
2921:     MatSetColoring_SeqAIJ(a->B,ocoloring);
2922:     ISColoringDestroy(ocoloring);
2923:   } else {
2924:     SETERRQ1(PETSC_ERR_SUP,"No support ISColoringType %d",(int)coloring->ctype);
2925:   }

2927:   return(0);
2928: }

2930: #if defined(PETSC_HAVE_ADIC)
2933: PetscErrorCode MatSetValuesAdic_MPIAIJ(Mat A,void *advalues)
2934: {
2935:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

2939:   MatSetValuesAdic_SeqAIJ(a->A,advalues);
2940:   MatSetValuesAdic_SeqAIJ(a->B,advalues);
2941:   return(0);
2942: }
2943: #endif

2947: PetscErrorCode MatSetValuesAdifor_MPIAIJ(Mat A,PetscInt nl,void *advalues)
2948: {
2949:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

2953:   MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);
2954:   MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);
2955:   return(0);
2956: }

2960: /*@C
2961:       MatMerge - Creates a single large PETSc matrix by concatinating sequential
2962:                  matrices from each processor

2964:     Collective on MPI_Comm

2966:    Input Parameters:
2967: +    comm - the communicators the parallel matrix will live on
2968: .    inmat - the input sequential matrices
2969: .    n - number of local columns (or PETSC_DECIDE)
2970: -    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

2972:    Output Parameter:
2973: .    outmat - the parallel matrix generated

2975:     Level: advanced

2977:    Notes: The number of columns of the matrix in EACH processor MUST be the same.

2979: @*/
2980: PetscErrorCode  MatMerge(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
2981: {
2983:   PetscInt       m,N,i,rstart,nnz,Ii,*dnz,*onz;
2984:   PetscInt       *indx;
2985:   PetscScalar    *values;

2988:   MatGetSize(inmat,&m,&N);
2989:   if (scall == MAT_INITIAL_MATRIX){
2990:     /* count nonzeros in each row, for diagonal and off diagonal portion of matrix */
2991:     if (n == PETSC_DECIDE){
2992:       PetscSplitOwnership(comm,&n,&N);
2993:     }
2994:     MPI_Scan(&m, &rstart,1,MPIU_INT,MPI_SUM,comm);
2995:     rstart -= m;

2997:     MatPreallocateInitialize(comm,m,n,dnz,onz);
2998:     for (i=0;i<m;i++) {
2999:       MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
3000:       MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);
3001:       MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
3002:     }
3003:     /* This routine will ONLY return MPIAIJ type matrix */
3004:     MatCreate(comm,outmat);
3005:     MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3006:     MatSetType(*outmat,MATMPIAIJ);
3007:     MatMPIAIJSetPreallocation(*outmat,0,dnz,0,onz);
3008:     MatPreallocateFinalize(dnz,onz);
3009: 
3010:   } else if (scall == MAT_REUSE_MATRIX){
3011:     MatGetOwnershipRange(*outmat,&rstart,PETSC_NULL);
3012:   } else {
3013:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
3014:   }

3016:   for (i=0;i<m;i++) {
3017:     MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3018:     Ii    = i + rstart;
3019:     MatSetValues(*outmat,1,&Ii,nnz,indx,values,INSERT_VALUES);
3020:     MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3021:   }
3022:   MatDestroy(inmat);
3023:   MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
3024:   MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);

3026:   return(0);
3027: }

3031: PetscErrorCode MatFileSplit(Mat A,char *outfile)
3032: {
3033:   PetscErrorCode    ierr;
3034:   PetscMPIInt       rank;
3035:   PetscInt          m,N,i,rstart,nnz;
3036:   size_t            len;
3037:   const PetscInt    *indx;
3038:   PetscViewer       out;
3039:   char              *name;
3040:   Mat               B;
3041:   const PetscScalar *values;

3044:   MatGetLocalSize(A,&m,0);
3045:   MatGetSize(A,0,&N);
3046:   /* Should this be the type of the diagonal block of A? */
3047:   MatCreate(PETSC_COMM_SELF,&B);
3048:   MatSetSizes(B,m,N,m,N);
3049:   MatSetType(B,MATSEQAIJ);
3050:   MatSeqAIJSetPreallocation(B,0,PETSC_NULL);
3051:   MatGetOwnershipRange(A,&rstart,0);
3052:   for (i=0;i<m;i++) {
3053:     MatGetRow(A,i+rstart,&nnz,&indx,&values);
3054:     MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);
3055:     MatRestoreRow(A,i+rstart,&nnz,&indx,&values);
3056:   }
3057:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3058:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3060:   MPI_Comm_rank(A->comm,&rank);
3061:   PetscStrlen(outfile,&len);
3062:   PetscMalloc((len+5)*sizeof(char),&name);
3063:   sprintf(name,"%s.%d",outfile,rank);
3064:   PetscViewerBinaryOpen(PETSC_COMM_SELF,name,FILE_MODE_APPEND,&out);
3065:   PetscFree(name);
3066:   MatView(B,out);
3067:   PetscViewerDestroy(out);
3068:   MatDestroy(B);
3069:   return(0);
3070: }

3072: EXTERN PetscErrorCode MatDestroy_MPIAIJ(Mat);
3075: PetscErrorCode  MatDestroy_MPIAIJ_SeqsToMPI(Mat A)
3076: {
3077:   PetscErrorCode       ierr;
3078:   Mat_Merge_SeqsToMPI  *merge;
3079:   PetscObjectContainer container;

3082:   PetscObjectQuery((PetscObject)A,"MatMergeSeqsToMPI",(PetscObject *)&container);
3083:   if (container) {
3084:     PetscObjectContainerGetPointer(container,(void **)&merge);
3085:     PetscFree(merge->id_r);
3086:     PetscFree(merge->len_s);
3087:     PetscFree(merge->len_r);
3088:     PetscFree(merge->bi);
3089:     PetscFree(merge->bj);
3090:     PetscFree(merge->buf_ri);
3091:     PetscFree(merge->buf_rj);
3092:     PetscFree(merge->coi);
3093:     PetscFree(merge->coj);
3094:     PetscFree(merge->owners_co);
3095:     PetscFree(merge->rowmap.range);
3096: 
3097:     PetscObjectContainerDestroy(container);
3098:     PetscObjectCompose((PetscObject)A,"MatMergeSeqsToMPI",0);
3099:   }
3100:   PetscFree(merge);

3102:   MatDestroy_MPIAIJ(A);
3103:   return(0);
3104: }

3106:  #include src/mat/utils/freespace.h
3107:  #include petscbt.h
3108: static PetscEvent logkey_seqstompinum = 0;
3111: /*@C
3112:       MatMerge_SeqsToMPI - Creates a MPIAIJ matrix by adding sequential
3113:                  matrices from each processor

3115:     Collective on MPI_Comm

3117:    Input Parameters:
3118: +    comm - the communicators the parallel matrix will live on
3119: .    seqmat - the input sequential matrices
3120: .    m - number of local rows (or PETSC_DECIDE)
3121: .    n - number of local columns (or PETSC_DECIDE)
3122: -    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

3124:    Output Parameter:
3125: .    mpimat - the parallel matrix generated

3127:     Level: advanced

3129:    Notes: 
3130:      The dimensions of the sequential matrix in each processor MUST be the same.
3131:      The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
3132:      destroyed when mpimat is destroyed. Call PetscObjectQuery() to access seqmat.
3133: @*/
3134: PetscErrorCode  MatMerge_SeqsToMPINumeric(Mat seqmat,Mat mpimat)
3135: {
3136:   PetscErrorCode       ierr;
3137:   MPI_Comm             comm=mpimat->comm;
3138:   Mat_SeqAIJ           *a=(Mat_SeqAIJ*)seqmat->data;
3139:   PetscMPIInt          size,rank,taga,*len_s;
3140:   PetscInt             N=mpimat->cmap.N,i,j,*owners,*ai=a->i,*aj=a->j;
3141:   PetscInt             proc,m;
3142:   PetscInt             **buf_ri,**buf_rj;
3143:   PetscInt             k,anzi,*bj_i,*bi,*bj,arow,bnzi,nextaj;
3144:   PetscInt             nrows,**buf_ri_k,**nextrow,**nextai;
3145:   MPI_Request          *s_waits,*r_waits;
3146:   MPI_Status           *status;
3147:   MatScalar            *aa=a->a,**abuf_r,*ba_i;
3148:   Mat_Merge_SeqsToMPI  *merge;
3149:   PetscObjectContainer container;
3150: 
3152:   if (!logkey_seqstompinum) {
3154:   }

3157:   MPI_Comm_size(comm,&size);
3158:   MPI_Comm_rank(comm,&rank);

3160:   PetscObjectQuery((PetscObject)mpimat,"MatMergeSeqsToMPI",(PetscObject *)&container);
3161:   if (container) {
3162:     PetscObjectContainerGetPointer(container,(void **)&merge);
3163:   }
3164:   bi     = merge->bi;
3165:   bj     = merge->bj;
3166:   buf_ri = merge->buf_ri;
3167:   buf_rj = merge->buf_rj;

3169:   PetscMalloc(size*sizeof(MPI_Status),&status);
3170:   owners = merge->rowmap.range;
3171:   len_s  = merge->len_s;

3173:   /* send and recv matrix values */
3174:   /*-----------------------------*/
3175:   PetscObjectGetNewTag((PetscObject)mpimat,&taga);
3176:   PetscPostIrecvScalar(comm,taga,merge->nrecv,merge->id_r,merge->len_r,&abuf_r,&r_waits);

3178:   PetscMalloc((merge->nsend+1)*sizeof(MPI_Request),&s_waits);
3179:   for (proc=0,k=0; proc<size; proc++){
3180:     if (!len_s[proc]) continue;
3181:     i = owners[proc];
3182:     MPI_Isend(aa+ai[i],len_s[proc],MPIU_MATSCALAR,proc,taga,comm,s_waits+k);
3183:     k++;
3184:   }

3186:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,r_waits,status);}
3187:   if (merge->nsend) {MPI_Waitall(merge->nsend,s_waits,status);}
3188:   PetscFree(status);

3190:   PetscFree(s_waits);
3191:   PetscFree(r_waits);

3193:   /* insert mat values of mpimat */
3194:   /*----------------------------*/
3195:   PetscMalloc(N*sizeof(MatScalar),&ba_i);
3196:   PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
3197:   nextrow = buf_ri_k + merge->nrecv;
3198:   nextai  = nextrow + merge->nrecv;

3200:   for (k=0; k<merge->nrecv; k++){
3201:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
3202:     nrows = *(buf_ri_k[k]);
3203:     nextrow[k]  = buf_ri_k[k]+1;  /* next row number of k-th recved i-structure */
3204:     nextai[k]   = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure  */
3205:   }

3207:   /* set values of ba */
3208:   m = merge->rowmap.n;
3209:   for (i=0; i<m; i++) {
3210:     arow = owners[rank] + i;
3211:     bj_i = bj+bi[i];  /* col indices of the i-th row of mpimat */
3212:     bnzi = bi[i+1] - bi[i];
3213:     PetscMemzero(ba_i,bnzi*sizeof(MatScalar));

3215:     /* add local non-zero vals of this proc's seqmat into ba */
3216:     anzi = ai[arow+1] - ai[arow];
3217:     aj   = a->j + ai[arow];
3218:     aa   = a->a + ai[arow];
3219:     nextaj = 0;
3220:     for (j=0; nextaj<anzi; j++){
3221:       if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
3222:         ba_i[j] += aa[nextaj++];
3223:       }
3224:     }

3226:     /* add received vals into ba */
3227:     for (k=0; k<merge->nrecv; k++){ /* k-th received message */
3228:       /* i-th row */
3229:       if (i == *nextrow[k]) {
3230:         anzi = *(nextai[k]+1) - *nextai[k];
3231:         aj   = buf_rj[k] + *(nextai[k]);
3232:         aa   = abuf_r[k] + *(nextai[k]);
3233:         nextaj = 0;
3234:         for (j=0; nextaj<anzi; j++){
3235:           if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
3236:             ba_i[j] += aa[nextaj++];
3237:           }
3238:         }
3239:         nextrow[k]++; nextai[k]++;
3240:       }
3241:     }
3242:     MatSetValues(mpimat,1,&arow,bnzi,bj_i,ba_i,INSERT_VALUES);
3243:   }
3244:   MatAssemblyBegin(mpimat,MAT_FINAL_ASSEMBLY);
3245:   MatAssemblyEnd(mpimat,MAT_FINAL_ASSEMBLY);

3247:   PetscFree(abuf_r);
3248:   PetscFree(ba_i);
3249:   PetscFree(buf_ri_k);
3251:   return(0);
3252: }

3254: static PetscEvent logkey_seqstompisym = 0;
3257: PetscErrorCode  MatMerge_SeqsToMPISymbolic(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,Mat *mpimat)
3258: {
3259:   PetscErrorCode       ierr;
3260:   Mat                  B_mpi;
3261:   Mat_SeqAIJ           *a=(Mat_SeqAIJ*)seqmat->data;
3262:   PetscMPIInt          size,rank,tagi,tagj,*len_s,*len_si,*len_ri;
3263:   PetscInt             **buf_rj,**buf_ri,**buf_ri_k;
3264:   PetscInt             M=seqmat->rmap.n,N=seqmat->cmap.n,i,*owners,*ai=a->i,*aj=a->j;
3265:   PetscInt             len,proc,*dnz,*onz;
3266:   PetscInt             k,anzi,*bi,*bj,*lnk,nlnk,arow,bnzi,nspacedouble=0;
3267:   PetscInt             nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextai;
3268:   MPI_Request          *si_waits,*sj_waits,*ri_waits,*rj_waits;
3269:   MPI_Status           *status;
3270:   PetscFreeSpaceList   free_space=PETSC_NULL,current_space=PETSC_NULL;
3271:   PetscBT              lnkbt;
3272:   Mat_Merge_SeqsToMPI  *merge;
3273:   PetscObjectContainer container;

3276:   if (!logkey_seqstompisym) {
3278:   }

3281:   /* make sure it is a PETSc comm */
3282:   PetscCommDuplicate(comm,&comm,PETSC_NULL);
3283:   MPI_Comm_size(comm,&size);
3284:   MPI_Comm_rank(comm,&rank);
3285: 
3286:   PetscNew(Mat_Merge_SeqsToMPI,&merge);
3287:   PetscMalloc(size*sizeof(MPI_Status),&status);

3289:   /* determine row ownership */
3290:   /*---------------------------------------------------------*/
3291:   merge->rowmap.n = m;
3292:   merge->rowmap.N = M;
3293:   merge->rowmap.bs = 1;
3294:   PetscMapInitialize(comm,&merge->rowmap);
3295:   PetscMalloc(size*sizeof(PetscMPIInt),&len_si);
3296:   PetscMalloc(size*sizeof(PetscMPIInt),&merge->len_s);
3297: 
3298:   m      = merge->rowmap.n;
3299:   M      = merge->rowmap.N;
3300:   owners = merge->rowmap.range;

3302:   /* determine the number of messages to send, their lengths */
3303:   /*---------------------------------------------------------*/
3304:   len_s  = merge->len_s;

3306:   len = 0;  /* length of buf_si[] */
3307:   merge->nsend = 0;
3308:   for (proc=0; proc<size; proc++){
3309:     len_si[proc] = 0;
3310:     if (proc == rank){
3311:       len_s[proc] = 0;
3312:     } else {
3313:       len_si[proc] = owners[proc+1] - owners[proc] + 1;
3314:       len_s[proc] = ai[owners[proc+1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
3315:     }
3316:     if (len_s[proc]) {
3317:       merge->nsend++;
3318:       nrows = 0;
3319:       for (i=owners[proc]; i<owners[proc+1]; i++){
3320:         if (ai[i+1] > ai[i]) nrows++;
3321:       }
3322:       len_si[proc] = 2*(nrows+1);
3323:       len += len_si[proc];
3324:     }
3325:   }

3327:   /* determine the number and length of messages to receive for ij-structure */
3328:   /*-------------------------------------------------------------------------*/
3329:   PetscGatherNumberOfMessages(comm,PETSC_NULL,len_s,&merge->nrecv);
3330:   PetscGatherMessageLengths2(comm,merge->nsend,merge->nrecv,len_s,len_si,&merge->id_r,&merge->len_r,&len_ri);

3332:   /* post the Irecv of j-structure */
3333:   /*-------------------------------*/
3334:   PetscCommGetNewTag(comm,&tagj);
3335:   PetscPostIrecvInt(comm,tagj,merge->nrecv,merge->id_r,merge->len_r,&buf_rj,&rj_waits);

3337:   /* post the Isend of j-structure */
3338:   /*--------------------------------*/
3339:   PetscMalloc((2*merge->nsend+1)*sizeof(MPI_Request),&si_waits);
3340:   sj_waits = si_waits + merge->nsend;

3342:   for (proc=0, k=0; proc<size; proc++){
3343:     if (!len_s[proc]) continue;
3344:     i = owners[proc];
3345:     MPI_Isend(aj+ai[i],len_s[proc],MPIU_INT,proc,tagj,comm,sj_waits+k);
3346:     k++;
3347:   }

3349:   /* receives and sends of j-structure are complete */
3350:   /*------------------------------------------------*/
3351:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,rj_waits,status);}
3352:   if (merge->nsend) {MPI_Waitall(merge->nsend,sj_waits,status);}
3353: 
3354:   /* send and recv i-structure */
3355:   /*---------------------------*/
3356:   PetscCommGetNewTag(comm,&tagi);
3357:   PetscPostIrecvInt(comm,tagi,merge->nrecv,merge->id_r,len_ri,&buf_ri,&ri_waits);
3358: 
3359:   PetscMalloc((len+1)*sizeof(PetscInt),&buf_s);
3360:   buf_si = buf_s;  /* points to the beginning of k-th msg to be sent */
3361:   for (proc=0,k=0; proc<size; proc++){
3362:     if (!len_s[proc]) continue;
3363:     /* form outgoing message for i-structure: 
3364:          buf_si[0]:                 nrows to be sent
3365:                [1:nrows]:           row index (global)
3366:                [nrows+1:2*nrows+1]: i-structure index
3367:     */
3368:     /*-------------------------------------------*/
3369:     nrows = len_si[proc]/2 - 1;
3370:     buf_si_i    = buf_si + nrows+1;
3371:     buf_si[0]   = nrows;
3372:     buf_si_i[0] = 0;
3373:     nrows = 0;
3374:     for (i=owners[proc]; i<owners[proc+1]; i++){
3375:       anzi = ai[i+1] - ai[i];
3376:       if (anzi) {
3377:         buf_si_i[nrows+1] = buf_si_i[nrows] + anzi; /* i-structure */
3378:         buf_si[nrows+1] = i-owners[proc]; /* local row index */
3379:         nrows++;
3380:       }
3381:     }
3382:     MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,si_waits+k);
3383:     k++;
3384:     buf_si += len_si[proc];
3385:   }

3387:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,ri_waits,status);}
3388:   if (merge->nsend) {MPI_Waitall(merge->nsend,si_waits,status);}

3390:   PetscInfo2(seqmat,"nsend: %D, nrecv: %D\n",merge->nsend,merge->nrecv);
3391:   for (i=0; i<merge->nrecv; i++){
3392:     PetscInfo3(seqmat,"recv len_ri=%D, len_rj=%D from [%D]\n",len_ri[i],merge->len_r[i],merge->id_r[i]);
3393:   }

3395:   PetscFree(len_si);
3396:   PetscFree(len_ri);
3397:   PetscFree(rj_waits);
3398:   PetscFree(si_waits);
3399:   PetscFree(ri_waits);
3400:   PetscFree(buf_s);
3401:   PetscFree(status);

3403:   /* compute a local seq matrix in each processor */
3404:   /*----------------------------------------------*/
3405:   /* allocate bi array and free space for accumulating nonzero column info */
3406:   PetscMalloc((m+1)*sizeof(PetscInt),&bi);
3407:   bi[0] = 0;

3409:   /* create and initialize a linked list */
3410:   nlnk = N+1;
3411:   PetscLLCreate(N,N,nlnk,lnk,lnkbt);
3412: 
3413:   /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
3414:   len = 0;
3415:   len  = ai[owners[rank+1]] - ai[owners[rank]];
3416:   PetscFreeSpaceGet((PetscInt)(2*len+1),&free_space);
3417:   current_space = free_space;

3419:   /* determine symbolic info for each local row */
3420:   PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
3421:   nextrow = buf_ri_k + merge->nrecv;
3422:   nextai  = nextrow + merge->nrecv;
3423:   for (k=0; k<merge->nrecv; k++){
3424:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
3425:     nrows = *buf_ri_k[k];
3426:     nextrow[k]  = buf_ri_k[k] + 1;  /* next row number of k-th recved i-structure */
3427:     nextai[k]   = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure  */
3428:   }

3430:   MatPreallocateInitialize(comm,m,n,dnz,onz);
3431:   len = 0;
3432:   for (i=0;i<m;i++) {
3433:     bnzi   = 0;
3434:     /* add local non-zero cols of this proc's seqmat into lnk */
3435:     arow   = owners[rank] + i;
3436:     anzi   = ai[arow+1] - ai[arow];
3437:     aj     = a->j + ai[arow];
3438:     PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
3439:     bnzi += nlnk;
3440:     /* add received col data into lnk */
3441:     for (k=0; k<merge->nrecv; k++){ /* k-th received message */
3442:       if (i == *nextrow[k]) { /* i-th row */
3443:         anzi = *(nextai[k]+1) - *nextai[k];
3444:         aj   = buf_rj[k] + *nextai[k];
3445:         PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
3446:         bnzi += nlnk;
3447:         nextrow[k]++; nextai[k]++;
3448:       }
3449:     }
3450:     if (len < bnzi) len = bnzi;  /* =max(bnzi) */

3452:     /* if free space is not available, make more free space */
3453:     if (current_space->local_remaining<bnzi) {
3454:       PetscFreeSpaceGet(current_space->total_array_size,&current_space);
3455:       nspacedouble++;
3456:     }
3457:     /* copy data into free space, then initialize lnk */
3458:     PetscLLClean(N,N,bnzi,lnk,current_space->array,lnkbt);
3459:     MatPreallocateSet(i+owners[rank],bnzi,current_space->array,dnz,onz);

3461:     current_space->array           += bnzi;
3462:     current_space->local_used      += bnzi;
3463:     current_space->local_remaining -= bnzi;
3464: 
3465:     bi[i+1] = bi[i] + bnzi;
3466:   }
3467: 
3468:   PetscFree(buf_ri_k);

3470:   PetscMalloc((bi[m]+1)*sizeof(PetscInt),&bj);
3471:   PetscFreeSpaceContiguous(&free_space,bj);
3472:   PetscLLDestroy(lnk,lnkbt);

3474:   /* create symbolic parallel matrix B_mpi */
3475:   /*---------------------------------------*/
3476:   MatCreate(comm,&B_mpi);
3477:   if (n==PETSC_DECIDE) {
3478:     MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,N);
3479:   } else {
3480:     MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3481:   }
3482:   MatSetType(B_mpi,MATMPIAIJ);
3483:   MatMPIAIJSetPreallocation(B_mpi,0,dnz,0,onz);
3484:   MatPreallocateFinalize(dnz,onz);

3486:   /* B_mpi is not ready for use - assembly will be done by MatMerge_SeqsToMPINumeric() */
3487:   B_mpi->assembled     = PETSC_FALSE;
3488:   B_mpi->ops->destroy  = MatDestroy_MPIAIJ_SeqsToMPI;
3489:   merge->bi            = bi;
3490:   merge->bj            = bj;
3491:   merge->buf_ri        = buf_ri;
3492:   merge->buf_rj        = buf_rj;
3493:   merge->coi           = PETSC_NULL;
3494:   merge->coj           = PETSC_NULL;
3495:   merge->owners_co     = PETSC_NULL;

3497:   /* attach the supporting struct to B_mpi for reuse */
3498:   PetscObjectContainerCreate(PETSC_COMM_SELF,&container);
3499:   PetscObjectContainerSetPointer(container,merge);
3500:   PetscObjectCompose((PetscObject)B_mpi,"MatMergeSeqsToMPI",(PetscObject)container);
3501:   *mpimat = B_mpi;

3503:   PetscCommDestroy(&comm);
3505:   return(0);
3506: }

3508: static PetscEvent logkey_seqstompi = 0;
3511: PetscErrorCode  MatMerge_SeqsToMPI(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,MatReuse scall,Mat *mpimat)
3512: {
3513:   PetscErrorCode   ierr;

3516:   if (!logkey_seqstompi) {
3518:   }
3520:   if (scall == MAT_INITIAL_MATRIX){
3521:     MatMerge_SeqsToMPISymbolic(comm,seqmat,m,n,mpimat);
3522:   }
3523:   MatMerge_SeqsToMPINumeric(seqmat,*mpimat);
3525:   return(0);
3526: }
3527: static PetscEvent logkey_getlocalmat = 0;
3530: /*@C
3531:      MatGetLocalMat - Creates a SeqAIJ matrix by taking all its local rows

3533:     Not Collective

3535:    Input Parameters:
3536: +    A - the matrix 
3537: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 

3539:    Output Parameter:
3540: .    A_loc - the local sequential matrix generated

3542:     Level: developer

3544: @*/
3545: PetscErrorCode  MatGetLocalMat(Mat A,MatReuse scall,Mat *A_loc)
3546: {
3547:   PetscErrorCode  ierr;
3548:   Mat_MPIAIJ      *mpimat=(Mat_MPIAIJ*)A->data;
3549:   Mat_SeqAIJ      *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
3550:   PetscInt        *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,*cmap=mpimat->garray;
3551:   PetscScalar     *aa=a->a,*ba=b->a,*ca;
3552:   PetscInt        am=A->rmap.n,i,j,k,cstart=A->cmap.rstart;
3553:   PetscInt        *ci,*cj,col,ncols_d,ncols_o,jo;

3556:   if (!logkey_getlocalmat) {
3558:   }
3560:   if (scall == MAT_INITIAL_MATRIX){
3561:     PetscMalloc((1+am)*sizeof(PetscInt),&ci);
3562:     ci[0] = 0;
3563:     for (i=0; i<am; i++){
3564:       ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
3565:     }
3566:     PetscMalloc((1+ci[am])*sizeof(PetscInt),&cj);
3567:     PetscMalloc((1+ci[am])*sizeof(PetscScalar),&ca);
3568:     k = 0;
3569:     for (i=0; i<am; i++) {
3570:       ncols_o = bi[i+1] - bi[i];
3571:       ncols_d = ai[i+1] - ai[i];
3572:       /* off-diagonal portion of A */
3573:       for (jo=0; jo<ncols_o; jo++) {
3574:         col = cmap[*bj];
3575:         if (col >= cstart) break;
3576:         cj[k]   = col; bj++;
3577:         ca[k++] = *ba++;
3578:       }
3579:       /* diagonal portion of A */
3580:       for (j=0; j<ncols_d; j++) {
3581:         cj[k]   = cstart + *aj++;
3582:         ca[k++] = *aa++;
3583:       }
3584:       /* off-diagonal portion of A */
3585:       for (j=jo; j<ncols_o; j++) {
3586:         cj[k]   = cmap[*bj++];
3587:         ca[k++] = *ba++;
3588:       }
3589:     }
3590:     /* put together the new matrix */
3591:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,A->cmap.N,ci,cj,ca,A_loc);
3592:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
3593:     /* Since these are PETSc arrays, change flags to free them as necessary. */
3594:     mat          = (Mat_SeqAIJ*)(*A_loc)->data;
3595:     mat->free_a  = PETSC_TRUE;
3596:     mat->free_ij = PETSC_TRUE;
3597:     mat->nonew   = 0;
3598:   } else if (scall == MAT_REUSE_MATRIX){
3599:     mat=(Mat_SeqAIJ*)(*A_loc)->data;
3600:     ci = mat->i; cj = mat->j; ca = mat->a;
3601:     for (i=0; i<am; i++) {
3602:       /* off-diagonal portion of A */
3603:       ncols_o = bi[i+1] - bi[i];
3604:       for (jo=0; jo<ncols_o; jo++) {
3605:         col = cmap[*bj];
3606:         if (col >= cstart) break;
3607:         *ca++ = *ba++; bj++;
3608:       }
3609:       /* diagonal portion of A */
3610:       ncols_d = ai[i+1] - ai[i];
3611:       for (j=0; j<ncols_d; j++) *ca++ = *aa++;
3612:       /* off-diagonal portion of A */
3613:       for (j=jo; j<ncols_o; j++) {
3614:         *ca++ = *ba++; bj++;
3615:       }
3616:     }
3617:   } else {
3618:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
3619:   }

3622:   return(0);
3623: }

3625: static PetscEvent logkey_getlocalmatcondensed = 0;
3628: /*@C
3629:      MatGetLocalMatCondensed - Creates a SeqAIJ matrix by taking all its local rows and NON-ZERO columns

3631:     Not Collective

3633:    Input Parameters:
3634: +    A - the matrix 
3635: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3636: -    row, col - index sets of rows and columns to extract (or PETSC_NULL)  

3638:    Output Parameter:
3639: .    A_loc - the local sequential matrix generated

3641:     Level: developer

3643: @*/
3644: PetscErrorCode  MatGetLocalMatCondensed(Mat A,MatReuse scall,IS *row,IS *col,Mat *A_loc)
3645: {
3646:   Mat_MPIAIJ        *a=(Mat_MPIAIJ*)A->data;
3647:   PetscErrorCode    ierr;
3648:   PetscInt          i,start,end,ncols,nzA,nzB,*cmap,imark,*idx;
3649:   IS                isrowa,iscola;
3650:   Mat               *aloc;

3653:   if (!logkey_getlocalmatcondensed) {
3655:   }
3657:   if (!row){
3658:     start = A->rmap.rstart; end = A->rmap.rend;
3659:     ISCreateStride(PETSC_COMM_SELF,end-start,start,1,&isrowa);
3660:   } else {
3661:     isrowa = *row;
3662:   }
3663:   if (!col){
3664:     start = A->cmap.rstart;
3665:     cmap  = a->garray;
3666:     nzA   = a->A->cmap.n;
3667:     nzB   = a->B->cmap.n;
3668:     PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
3669:     ncols = 0;
3670:     for (i=0; i<nzB; i++) {
3671:       if (cmap[i] < start) idx[ncols++] = cmap[i];
3672:       else break;
3673:     }
3674:     imark = i;
3675:     for (i=0; i<nzA; i++) idx[ncols++] = start + i;
3676:     for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i];
3677:     ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&iscola);
3678:     PetscFree(idx);
3679:   } else {
3680:     iscola = *col;
3681:   }
3682:   if (scall != MAT_INITIAL_MATRIX){
3683:     PetscMalloc(sizeof(Mat),&aloc);
3684:     aloc[0] = *A_loc;
3685:   }
3686:   MatGetSubMatrices(A,1,&isrowa,&iscola,scall,&aloc);
3687:   *A_loc = aloc[0];
3688:   PetscFree(aloc);
3689:   if (!row){
3690:     ISDestroy(isrowa);
3691:   }
3692:   if (!col){
3693:     ISDestroy(iscola);
3694:   }
3696:   return(0);
3697: }

3699: static PetscEvent logkey_GetBrowsOfAcols = 0;
3702: /*@C
3703:     MatGetBrowsOfAcols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A 

3705:     Collective on Mat

3707:    Input Parameters:
3708: +    A,B - the matrices in mpiaij format
3709: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3710: -    rowb, colb - index sets of rows and columns of B to extract (or PETSC_NULL)   

3712:    Output Parameter:
3713: +    rowb, colb - index sets of rows and columns of B to extract 
3714: .    brstart - row index of B_seq from which next B->rmap.n rows are taken from B's local rows
3715: -    B_seq - the sequential matrix generated

3717:     Level: developer

3719: @*/
3720: PetscErrorCode  MatGetBrowsOfAcols(Mat A,Mat B,MatReuse scall,IS *rowb,IS *colb,PetscInt *brstart,Mat *B_seq)
3721: {
3722:   Mat_MPIAIJ        *a=(Mat_MPIAIJ*)A->data;
3723:   PetscErrorCode    ierr;
3724:   PetscInt          *idx,i,start,ncols,nzA,nzB,*cmap,imark;
3725:   IS                isrowb,iscolb;
3726:   Mat               *bseq;
3727: 
3729:   if (A->cmap.rstart != B->rmap.rstart || A->cmap.rend != B->rmap.rend){
3730:     SETERRQ4(PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, (%D, %D) != (%D,%D)",A->cmap.rstart,A->cmap.rend,B->rmap.rstart,B->rmap.rend);
3731:   }
3732:   if (!logkey_GetBrowsOfAcols) {
3734:   }
3736: 
3737:   if (scall == MAT_INITIAL_MATRIX){
3738:     start = A->cmap.rstart;
3739:     cmap  = a->garray;
3740:     nzA   = a->A->cmap.n;
3741:     nzB   = a->B->cmap.n;
3742:     PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
3743:     ncols = 0;
3744:     for (i=0; i<nzB; i++) {  /* row < local row index */
3745:       if (cmap[i] < start) idx[ncols++] = cmap[i];
3746:       else break;
3747:     }
3748:     imark = i;
3749:     for (i=0; i<nzA; i++) idx[ncols++] = start + i;  /* local rows */
3750:     for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
3751:     ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&isrowb);
3752:     PetscFree(idx);
3753:     *brstart = imark;
3754:     ISCreateStride(PETSC_COMM_SELF,B->cmap.N,0,1,&iscolb);
3755:   } else {
3756:     if (!rowb || !colb) SETERRQ(PETSC_ERR_SUP,"IS rowb and colb must be provided for MAT_REUSE_MATRIX");
3757:     isrowb = *rowb; iscolb = *colb;
3758:     PetscMalloc(sizeof(Mat),&bseq);
3759:     bseq[0] = *B_seq;
3760:   }
3761:   MatGetSubMatrices(B,1,&isrowb,&iscolb,scall,&bseq);
3762:   *B_seq = bseq[0];
3763:   PetscFree(bseq);
3764:   if (!rowb){
3765:     ISDestroy(isrowb);
3766:   } else {
3767:     *rowb = isrowb;
3768:   }
3769:   if (!colb){
3770:     ISDestroy(iscolb);
3771:   } else {
3772:     *colb = iscolb;
3773:   }
3775:   return(0);
3776: }

3778: static PetscEvent logkey_GetBrowsOfAocols = 0;
3781: /*@C
3782:     MatGetBrowsOfAoCols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns
3783:     of the OFF-DIAGONAL portion of local A 

3785:     Collective on Mat

3787:    Input Parameters:
3788: +    A,B - the matrices in mpiaij format
3789: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3790: .    startsj - starting point in B's sending and receiving j-arrays, saved for MAT_REUSE (or PETSC_NULL) 
3791: -    bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or PETSC_NULL) 

3793:    Output Parameter:
3794: +    B_oth - the sequential matrix generated

3796:     Level: developer

3798: @*/
3799: PetscErrorCode  MatGetBrowsOfAoCols(Mat A,Mat B,MatReuse scall,PetscInt **startsj,PetscScalar **bufa_ptr,Mat *B_oth)
3800: {
3801:   VecScatter_MPI_General *gen_to,*gen_from;
3802:   PetscErrorCode         ierr;
3803:   Mat_MPIAIJ             *a=(Mat_MPIAIJ*)A->data;
3804:   Mat_SeqAIJ             *b_oth;
3805:   VecScatter             ctx=a->Mvctx;
3806:   MPI_Comm               comm=ctx->comm;
3807:   PetscMPIInt            *rprocs,*sprocs,tag=ctx->tag,rank;
3808:   PetscInt               *rowlen,*bufj,*bufJ,ncols,aBn=a->B->cmap.n,row,*b_othi,*b_othj;
3809:   PetscScalar            *rvalues,*svalues,*b_otha,*bufa,*bufA;
3810:   PetscInt               i,k,l,nrecvs,nsends,nrows,*srow,*rstarts,*rstartsj = 0,*sstarts,*sstartsj,len;
3811:   MPI_Request            *rwaits = PETSC_NULL,*swaits = PETSC_NULL;
3812:   MPI_Status             *sstatus,rstatus;
3813:   PetscInt               *cols;
3814:   PetscScalar            *vals;
3815:   PetscMPIInt            j;
3816: 
3818:   if (A->cmap.rstart != B->rmap.rstart || A->cmap.rend != B->rmap.rend){
3819:     SETERRQ4(PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, (%d, %d) != (%d,%d)",A->cmap.rstart,A->cmap.rend,B->rmap.rstart,B->rmap.rend);
3820:   }
3821:   if (!logkey_GetBrowsOfAocols) {
3823:   }
3825:   MPI_Comm_rank(comm,&rank);

3827:   gen_to   = (VecScatter_MPI_General*)ctx->todata;
3828:   gen_from = (VecScatter_MPI_General*)ctx->fromdata;
3829:   rvalues  = gen_from->values; /* holds the length of sending row */
3830:   svalues  = gen_to->values;   /* holds the length of receiving row */
3831:   nrecvs   = gen_from->n;
3832:   nsends   = gen_to->n;

3834:   PetscMalloc2(nrecvs,MPI_Request,&rwaits,nsends,MPI_Request,&swaits);
3835:   srow     = gen_to->indices;   /* local row index to be sent */
3836:   rstarts  = gen_from->starts;
3837:   sstarts  = gen_to->starts;
3838:   rprocs   = gen_from->procs;
3839:   sprocs   = gen_to->procs;
3840:   sstatus  = gen_to->sstatus;

3842:   if (!startsj || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
3843:   if (scall == MAT_INITIAL_MATRIX){
3844:     /* i-array */
3845:     /*---------*/
3846:     /*  post receives */
3847:     for (i=0; i<nrecvs; i++){
3848:       rowlen = (PetscInt*)rvalues + rstarts[i];
3849:       nrows = rstarts[i+1]-rstarts[i];
3850:       MPI_Irecv(rowlen,nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
3851:     }

3853:     /* pack the outgoing message */
3854:     PetscMalloc((nsends+nrecvs+3)*sizeof(PetscInt),&sstartsj);
3855:     rstartsj = sstartsj + nsends +1;
3856:     sstartsj[0] = 0;  rstartsj[0] = 0;
3857:     len = 0; /* total length of j or a array to be sent */
3858:     k = 0;
3859:     for (i=0; i<nsends; i++){
3860:       rowlen = (PetscInt*)svalues + sstarts[i];
3861:       nrows = sstarts[i+1]-sstarts[i]; /* num of rows */
3862:       for (j=0; j<nrows; j++) {
3863:         row = srow[k] + B->rmap.range[rank]; /* global row idx */
3864:         MatGetRow_MPIAIJ(B,row,&rowlen[j],PETSC_NULL,PETSC_NULL); /* rowlength */
3865:         len += rowlen[j];
3866:         MatRestoreRow_MPIAIJ(B,row,&ncols,PETSC_NULL,PETSC_NULL);
3867:         k++;
3868:       }
3869:       MPI_Isend(rowlen,nrows,MPIU_INT,sprocs[i],tag,comm,swaits+i);
3870:        sstartsj[i+1] = len;  /* starting point of (i+1)-th outgoing msg in bufj and bufa */
3871:     }
3872:     /* recvs and sends of i-array are completed */
3873:     i = nrecvs;
3874:     while (i--) {
3875:       MPI_Waitany(nrecvs,rwaits,&j,&rstatus);
3876:     }
3877:     if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
3878:     /* allocate buffers for sending j and a arrays */
3879:     PetscMalloc((len+1)*sizeof(PetscInt),&bufj);
3880:     PetscMalloc((len+1)*sizeof(PetscScalar),&bufa);

3882:     /* create i-array of B_oth */
3883:     PetscMalloc((aBn+2)*sizeof(PetscInt),&b_othi);
3884:     b_othi[0] = 0;
3885:     len = 0; /* total length of j or a array to be received */
3886:     k = 0;
3887:     for (i=0; i<nrecvs; i++){
3888:       rowlen = (PetscInt*)rvalues + rstarts[i];
3889:       nrows = rstarts[i+1]-rstarts[i];
3890:       for (j=0; j<nrows; j++) {
3891:         b_othi[k+1] = b_othi[k] + rowlen[j];
3892:         len += rowlen[j]; k++;
3893:       }
3894:       rstartsj[i+1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
3895:     }

3897:     /* allocate space for j and a arrrays of B_oth */
3898:     PetscMalloc((b_othi[aBn]+1)*sizeof(PetscInt),&b_othj);
3899:     PetscMalloc((b_othi[aBn]+1)*sizeof(PetscScalar),&b_otha);

3901:     /* j-array */
3902:     /*---------*/
3903:     /*  post receives of j-array */
3904:     for (i=0; i<nrecvs; i++){
3905:       nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
3906:       MPI_Irecv(b_othj+rstartsj[i],nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
3907:     }
3908:     k = 0;
3909:     for (i=0; i<nsends; i++){
3910:       nrows = sstarts[i+1]-sstarts[i]; /* num of rows */
3911:       bufJ = bufj+sstartsj[i];
3912:       for (j=0; j<nrows; j++) {
3913:         row  = srow[k++] + B->rmap.range[rank]; /* global row idx */
3914:         MatGetRow_MPIAIJ(B,row,&ncols,&cols,PETSC_NULL);
3915:         for (l=0; l<ncols; l++){
3916:           *bufJ++ = cols[l];
3917:         }
3918:         MatRestoreRow_MPIAIJ(B,row,&ncols,&cols,PETSC_NULL);
3919:       }
3920:       MPI_Isend(bufj+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_INT,sprocs[i],tag,comm,swaits+i);
3921:     }

3923:     /* recvs and sends of j-array are completed */
3924:     i = nrecvs;
3925:     while (i--) {
3926:       MPI_Waitany(nrecvs,rwaits,&j,&rstatus);
3927:     }
3928:     if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
3929:   } else if (scall == MAT_REUSE_MATRIX){
3930:     sstartsj = *startsj;
3931:     rstartsj = sstartsj + nsends +1;
3932:     bufa     = *bufa_ptr;
3933:     b_oth    = (Mat_SeqAIJ*)(*B_oth)->data;
3934:     b_otha   = b_oth->a;
3935:   } else {
3936:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not posses an object container");
3937:   }

3939:   /* a-array */
3940:   /*---------*/
3941:   /*  post receives of a-array */
3942:   for (i=0; i<nrecvs; i++){
3943:     nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
3944:     MPI_Irecv(b_otha+rstartsj[i],nrows,MPIU_SCALAR,rprocs[i],tag,comm,rwaits+i);
3945:   }
3946:   k = 0;
3947:   for (i=0; i<nsends; i++){
3948:     nrows = sstarts[i+1]-sstarts[i];
3949:     bufA = bufa+sstartsj[i];
3950:     for (j=0; j<nrows; j++) {
3951:       row  = srow[k++] + B->rmap.range[rank]; /* global row idx */
3952:       MatGetRow_MPIAIJ(B,row,&ncols,PETSC_NULL,&vals);
3953:       for (l=0; l<ncols; l++){
3954:         *bufA++ = vals[l];
3955:       }
3956:       MatRestoreRow_MPIAIJ(B,row,&ncols,PETSC_NULL,&vals);

3958:     }
3959:     MPI_Isend(bufa+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_SCALAR,sprocs[i],tag,comm,swaits+i);
3960:   }
3961:   /* recvs and sends of a-array are completed */
3962:   i = nrecvs;
3963:   while (i--) {
3964:     MPI_Waitany(nrecvs,rwaits,&j,&rstatus);
3965:   }
3966:   if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
3967:   PetscFree2(rwaits,swaits);

3969:   if (scall == MAT_INITIAL_MATRIX){
3970:     /* put together the new matrix */
3971:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,aBn,B->cmap.N,b_othi,b_othj,b_otha,B_oth);

3973:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
3974:     /* Since these are PETSc arrays, change flags to free them as necessary. */
3975:     b_oth          = (Mat_SeqAIJ *)(*B_oth)->data;
3976:     b_oth->free_a  = PETSC_TRUE;
3977:     b_oth->free_ij = PETSC_TRUE;
3978:     b_oth->nonew   = 0;

3980:     PetscFree(bufj);
3981:     if (!startsj || !bufa_ptr){
3982:       PetscFree(sstartsj);
3983:       PetscFree(bufa_ptr);
3984:     } else {
3985:       *startsj  = sstartsj;
3986:       *bufa_ptr = bufa;
3987:     }
3988:   }
3990: 
3991:   return(0);
3992: }

3996: /*@C
3997:   MatGetCommunicationStructs - Provides access to the communication structures used in matrix-vector multiplication.

3999:   Not Collective

4001:   Input Parameters:
4002: . A - The matrix in mpiaij format

4004:   Output Parameter:
4005: + lvec - The local vector holding off-process values from the argument to a matrix-vector product
4006: . colmap - A map from global column index to local index into lvec
4007: - multScatter - A scatter from the argument of a matrix-vector product to lvec

4009:   Level: developer

4011: @*/
4012: #if defined (PETSC_USE_CTABLE)
4013: PetscErrorCode  MatGetCommunicationStructs(Mat A, Vec *lvec, PetscTable *colmap, VecScatter *multScatter)
4014: #else
4015: PetscErrorCode  MatGetCommunicationStructs(Mat A, Vec *lvec, PetscInt *colmap[], VecScatter *multScatter)
4016: #endif
4017: {
4018:   Mat_MPIAIJ *a;

4025:   a = (Mat_MPIAIJ *) A->data;
4026:   if (lvec) *lvec = a->lvec;
4027:   if (colmap) *colmap = a->colmap;
4028:   if (multScatter) *multScatter = a->Mvctx;
4029:   return(0);
4030: }


4037: /*MC
4038:    MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.

4040:    Options Database Keys:
4041: . -mat_type mpiaij - sets the matrix type to "mpiaij" during a call to MatSetFromOptions()

4043:   Level: beginner

4045: .seealso: MatCreateMPIAIJ
4046: M*/

4051: PetscErrorCode  MatCreate_MPIAIJ(Mat B)
4052: {
4053:   Mat_MPIAIJ     *b;
4055:   PetscMPIInt    size;

4058:   MPI_Comm_size(B->comm,&size);

4060:   PetscNew(Mat_MPIAIJ,&b);
4061:   B->data         = (void*)b;
4062:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4063:   B->factor       = 0;
4064:   B->rmap.bs      = 1;
4065:   B->assembled    = PETSC_FALSE;
4066:   B->mapping      = 0;

4068:   B->insertmode      = NOT_SET_VALUES;
4069:   b->size            = size;
4070:   MPI_Comm_rank(B->comm,&b->rank);

4072:   /* build cache for off array entries formed */
4073:   MatStashCreate_Private(B->comm,1,&B->stash);
4074:   b->donotstash  = PETSC_FALSE;
4075:   b->colmap      = 0;
4076:   b->garray      = 0;
4077:   b->roworiented = PETSC_TRUE;

4079:   /* stuff used for matrix vector multiply */
4080:   b->lvec      = PETSC_NULL;
4081:   b->Mvctx     = PETSC_NULL;

4083:   /* stuff for MatGetRow() */
4084:   b->rowindices   = 0;
4085:   b->rowvalues    = 0;
4086:   b->getrowactive = PETSC_FALSE;


4089:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
4090:                                      "MatStoreValues_MPIAIJ",
4091:                                      MatStoreValues_MPIAIJ);
4092:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
4093:                                      "MatRetrieveValues_MPIAIJ",
4094:                                      MatRetrieveValues_MPIAIJ);
4095:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
4096:                                      "MatGetDiagonalBlock_MPIAIJ",
4097:                                      MatGetDiagonalBlock_MPIAIJ);
4098:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
4099:                                      "MatIsTranspose_MPIAIJ",
4100:                                      MatIsTranspose_MPIAIJ);
4101:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocation_C",
4102:                                      "MatMPIAIJSetPreallocation_MPIAIJ",
4103:                                      MatMPIAIJSetPreallocation_MPIAIJ);
4104:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",
4105:                                      "MatMPIAIJSetPreallocationCSR_MPIAIJ",
4106:                                      MatMPIAIJSetPreallocationCSR_MPIAIJ);
4107:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
4108:                                      "MatDiagonalScaleLocal_MPIAIJ",
4109:                                      MatDiagonalScaleLocal_MPIAIJ);
4110:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpicsrperm_C",
4111:                                      "MatConvert_MPIAIJ_MPICSRPERM",
4112:                                       MatConvert_MPIAIJ_MPICSRPERM);
4113:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpicrl_C",
4114:                                      "MatConvert_MPIAIJ_MPICRL",
4115:                                       MatConvert_MPIAIJ_MPICRL);
4116:   PetscObjectChangeTypeName((PetscObject)B,MATMPIAIJ);
4117:   return(0);
4118: }

4121: /*
4122:     Special version for direct calls from Fortran 
4123: */
4124: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4125: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
4126: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4127: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
4128: #endif

4130: /* Change these macros so can be used in void function */
4131: #undef CHKERRQ
4132: #define CHKERRQ(ierr) CHKERRABORT(mat->comm,ierr) 
4133: #undef SETERRQ2
4134: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(mat->comm,ierr) 
4135: #undef SETERRQ
4136: #define SETERRQ(ierr,b) CHKERRABORT(mat->comm,ierr) 

4141: void PETSC_STDCALL matsetvaluesmpiaij_(Mat *mmat,PetscInt *mm,const PetscInt im[],PetscInt *mn,const PetscInt in[],const PetscScalar v[],InsertMode *maddv,PetscErrorCode *_ierr)
4142: {
4143:   Mat            mat = *mmat;
4144:   PetscInt       m = *mm, n = *mn;
4145:   InsertMode     addv = *maddv;
4146:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
4147:   PetscScalar    value;

4150:   MatPreallocated(mat);
4151:   if (mat->insertmode == NOT_SET_VALUES) {
4152:     mat->insertmode = addv;
4153:   }
4154: #if defined(PETSC_USE_DEBUG)
4155:   else if (mat->insertmode != addv) {
4156:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
4157:   }
4158: #endif
4159:   {
4160:   PetscInt       i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend;
4161:   PetscInt       cstart = mat->cmap.rstart,cend = mat->cmap.rend,row,col;
4162:   PetscTruth     roworiented = aij->roworiented;

4164:   /* Some Variables required in the macro */
4165:   Mat            A = aij->A;
4166:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
4167:   PetscInt       *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
4168:   PetscScalar    *aa = a->a;
4169:   PetscTruth     ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES))?PETSC_TRUE:PETSC_FALSE);
4170:   Mat            B = aij->B;
4171:   Mat_SeqAIJ     *b = (Mat_SeqAIJ*)B->data;
4172:   PetscInt       *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap.n,am = aij->A->rmap.n;
4173:   PetscScalar    *ba = b->a;

4175:   PetscInt       *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
4176:   PetscInt       nonew = a->nonew;
4177:   PetscScalar    *ap1,*ap2;

4180:   for (i=0; i<m; i++) {
4181:     if (im[i] < 0) continue;
4182: #if defined(PETSC_USE_DEBUG)
4183:     if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
4184: #endif
4185:     if (im[i] >= rstart && im[i] < rend) {
4186:       row      = im[i] - rstart;
4187:       lastcol1 = -1;
4188:       rp1      = aj + ai[row];
4189:       ap1      = aa + ai[row];
4190:       rmax1    = aimax[row];
4191:       nrow1    = ailen[row];
4192:       low1     = 0;
4193:       high1    = nrow1;
4194:       lastcol2 = -1;
4195:       rp2      = bj + bi[row];
4196:       ap2      = ba + bi[row];
4197:       rmax2    = bimax[row];
4198:       nrow2    = bilen[row];
4199:       low2     = 0;
4200:       high2    = nrow2;

4202:       for (j=0; j<n; j++) {
4203:         if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
4204:         if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
4205:         if (in[j] >= cstart && in[j] < cend){
4206:           col = in[j] - cstart;
4207:           MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
4208:         } else if (in[j] < 0) continue;
4209: #if defined(PETSC_USE_DEBUG)
4210:         else if (in[j] >= mat->cmap.N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap.N-1);}
4211: #endif
4212:         else {
4213:           if (mat->was_assembled) {
4214:             if (!aij->colmap) {
4215:               CreateColmap_MPIAIJ_Private(mat);
4216:             }
4217: #if defined (PETSC_USE_CTABLE)
4218:             PetscTableFind(aij->colmap,in[j]+1,&col);
4219:             col--;
4220: #else
4221:             col = aij->colmap[in[j]] - 1;
4222: #endif
4223:             if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
4224:               DisAssemble_MPIAIJ(mat);
4225:               col =  in[j];
4226:               /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
4227:               B = aij->B;
4228:               b = (Mat_SeqAIJ*)B->data;
4229:               bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
4230:               rp2      = bj + bi[row];
4231:               ap2      = ba + bi[row];
4232:               rmax2    = bimax[row];
4233:               nrow2    = bilen[row];
4234:               low2     = 0;
4235:               high2    = nrow2;
4236:               bm       = aij->B->rmap.n;
4237:               ba = b->a;
4238:             }
4239:           } else col = in[j];
4240:           MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
4241:         }
4242:       }
4243:     } else {
4244:       if (!aij->donotstash) {
4245:         if (roworiented) {
4246:           if (ignorezeroentries && v[i*n] == 0.0) continue;
4247:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
4248:         } else {
4249:           if (ignorezeroentries && v[i] == 0.0) continue;
4250:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
4251:         }
4252:       }
4253:     }
4254:   }}
4255:   PetscFunctionReturnVoid();
4256: }