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,MatScalar); \
 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,MatScalar); \
 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;
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: }

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

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

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

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

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

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

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

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

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

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

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

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

372:   return(0);
373: }

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

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

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

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

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

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

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

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

467:   base = owners[rank];

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

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

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

537:   return(0);
538: }

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

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

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

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

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

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

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


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

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

647:   return(0);
648: }

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

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

670: /*
671:   This only works correctly for square matrices where the subblock A->A is the 
672:    diagonal block
673: */
676: PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A,Vec v)
677: {
679:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

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

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

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

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

711: #if defined(PETSC_USE_LOG)
712:   PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap.N,mat->cmap.N);
713: #endif
714:   MatStashDestroy_Private(&mat->stash);
715:   MatDestroy(aij->A);
716:   MatDestroy(aij->B);
717: #if defined (PETSC_USE_CTABLE)
718:   if (aij->colmap) {PetscTableDestroy(aij->colmap);}
719: #else
720:   PetscFree(aij->colmap);
721: #endif
722:   PetscFree(aij->garray);
723:   if (aij->lvec)   {VecDestroy(aij->lvec);}
724:   if (aij->Mvctx)  {VecScatterDestroy(aij->Mvctx);}
725:   PetscFree(aij->rowvalues);
726:   PetscFree(aij->ld);
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:       PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
903:       VecScatterView(aij->Mvctx,viewer);
904:       return(0);
905:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
906:       PetscInt   inodecount,inodelimit,*inodes;
907:       MatInodeGetInodeSizes(aij->A,&inodecount,&inodes,&inodelimit);
908:       if (inodes) {
909:         PetscViewerASCIIPrintf(viewer,"using I-node (on process 0) routines: found %D nodes, limit used is %D\n",inodecount,inodelimit);
910:       } else {
911:         PetscViewerASCIIPrintf(viewer,"not using I-node (on process 0) routines\n");
912:       }
913:       return(0);
914:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
915:       return(0);
916:     }
917:   } else if (isbinary) {
918:     if (size == 1) {
919:       PetscObjectSetName((PetscObject)aij->A,mat->name);
920:       MatView(aij->A,viewer);
921:     } else {
922:       MatView_MPIAIJ_Binary(mat,viewer);
923:     }
924:     return(0);
925:   } else if (isdraw) {
926:     PetscDraw  draw;
927:     PetscTruth isnull;
928:     PetscViewerDrawGetDraw(viewer,0,&draw);
929:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
930:   }

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

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

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

965:     /* copy over the B part */
966:     Aloc = (Mat_SeqAIJ*)aij->B->data;
967:     m    = aij->B->rmap.n;  ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
968:     row  = mat->rmap.rstart;
969:     PetscMalloc((ai[m]+1)*sizeof(PetscInt),&cols);
970:     ct   = cols;
971:     for (i=0; i<ai[m]; i++) {cols[i] = aij->garray[aj[i]];}
972:     for (i=0; i<m; i++) {
973:       MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);
974:       row++; a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
975:     }
976:     PetscFree(ct);
977:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
978:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
979:     /* 
980:        Everyone has to call to draw the matrix since the graphics waits are
981:        synchronized across all processors that share the PetscDraw object
982:     */
983:     PetscViewerGetSingleton(viewer,&sviewer);
984:     if (!rank) {
985:       PetscObjectSetName((PetscObject)((Mat_MPIAIJ*)(A->data))->A,mat->name);
986:       MatView(((Mat_MPIAIJ*)(A->data))->A,sviewer);
987:     }
988:     PetscViewerRestoreSingleton(viewer,&sviewer);
989:     MatDestroy(A);
990:   }
991:   return(0);
992: }

996: PetscErrorCode MatView_MPIAIJ(Mat mat,PetscViewer viewer)
997: {
999:   PetscTruth     iascii,isdraw,issocket,isbinary;
1000: 
1002:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1003:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1004:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1005:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1006:   if (iascii || isdraw || isbinary || issocket) {
1007:     MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);
1008:   } else {
1009:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIAIJ matrices",((PetscObject)viewer)->type_name);
1010:   }
1011:   return(0);
1012: }

1016: PetscErrorCode MatRelax_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1017: {
1018:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1020:   Vec            bb1;

1023:   if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);

1025:   VecDuplicate(bb,&bb1);

1027:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
1028:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1029:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
1030:       its--;
1031:     }
1032: 
1033:     while (its--) {
1034:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1035:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

1037:       /* update rhs: bb1 = bb - B*x */
1038:       VecScale(mat->lvec,-1.0);
1039:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1041:       /* local sweep */
1042:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
1043: 
1044:     }
1045:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP){
1046:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1047:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1048:       its--;
1049:     }
1050:     while (its--) {
1051:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1052:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

1054:       /* update rhs: bb1 = bb - B*x */
1055:       VecScale(mat->lvec,-1.0);
1056:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1058:       /* local sweep */
1059:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1060: 
1061:     }
1062:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP){
1063:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1064:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1065:       its--;
1066:     }
1067:     while (its--) {
1068:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1069:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

1071:       /* update rhs: bb1 = bb - B*x */
1072:       VecScale(mat->lvec,-1.0);
1073:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1075:       /* local sweep */
1076:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1077: 
1078:     }
1079:   } else {
1080:     SETERRQ(PETSC_ERR_SUP,"Parallel SOR not supported");
1081:   }

1083:   VecDestroy(bb1);
1084:   return(0);
1085: }

1089: PetscErrorCode MatPermute_MPIAIJ(Mat A,IS rowp,IS colp,Mat *B)
1090: {
1091:   MPI_Comm       comm,pcomm;
1092:   PetscInt       first,local_size,nrows,*rows;
1093:   int            ntids;
1094:   IS             crowp,growp,irowp,lrowp,lcolp,icolp;

1098:   PetscObjectGetComm((PetscObject)A,&comm);
1099:   /* make a collective version of 'rowp' */
1100:   PetscObjectGetComm((PetscObject)rowp,&pcomm);
1101:   if (pcomm==comm) {
1102:     crowp = rowp;
1103:   } else {
1104:     ISGetSize(rowp,&nrows);
1105:     ISGetIndices(rowp,&rows);
1106:     ISCreateGeneral(comm,nrows,rows,&crowp);
1107:     ISRestoreIndices(rowp,&rows);
1108:   }
1109:   /* collect the global row permutation and invert it */
1110:   ISAllGather(crowp,&growp);
1111:   ISSetPermutation(growp);
1112:   if (pcomm!=comm) {
1113:     ISDestroy(crowp);
1114:   }
1115:   ISInvertPermutation(growp,PETSC_DECIDE,&irowp);
1116:   /* get the local target indices */
1117:   MatGetOwnershipRange(A,&first,PETSC_NULL);
1118:   MatGetLocalSize(A,&local_size,PETSC_NULL);
1119:   ISGetIndices(irowp,&rows);
1120:   ISCreateGeneral(MPI_COMM_SELF,local_size,rows+first,&lrowp);
1121:   ISRestoreIndices(irowp,&rows);
1122:   ISDestroy(irowp);
1123:   /* the column permutation is so much easier;
1124:      make a local version of 'colp' and invert it */
1125:   PetscObjectGetComm((PetscObject)colp,&pcomm);
1126:   MPI_Comm_size(pcomm,&ntids);
1127:   if (ntids==1) {
1128:     lcolp = colp;
1129:   } else {
1130:     ISGetSize(colp,&nrows);
1131:     ISGetIndices(colp,&rows);
1132:     ISCreateGeneral(MPI_COMM_SELF,nrows,rows,&lcolp);
1133:   }
1134:   ISInvertPermutation(lcolp,PETSC_DECIDE,&icolp);
1135:   ISSetPermutation(lcolp);
1136:   if (ntids>1) {
1137:     ISRestoreIndices(colp,&rows);
1138:     ISDestroy(lcolp);
1139:   }
1140:   /* now we just get the submatrix */
1141:   MatGetSubMatrix(A,lrowp,icolp,local_size,MAT_INITIAL_MATRIX,B);
1142:   /* clean up */
1143:   ISDestroy(lrowp);
1144:   ISDestroy(icolp);
1145:   return(0);
1146: }

1150: PetscErrorCode MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1151: {
1152:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1153:   Mat            A = mat->A,B = mat->B;
1155:   PetscReal      isend[5],irecv[5];

1158:   info->block_size     = 1.0;
1159:   MatGetInfo(A,MAT_LOCAL,info);
1160:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1161:   isend[3] = info->memory;  isend[4] = info->mallocs;
1162:   MatGetInfo(B,MAT_LOCAL,info);
1163:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1164:   isend[3] += info->memory;  isend[4] += info->mallocs;
1165:   if (flag == MAT_LOCAL) {
1166:     info->nz_used      = isend[0];
1167:     info->nz_allocated = isend[1];
1168:     info->nz_unneeded  = isend[2];
1169:     info->memory       = isend[3];
1170:     info->mallocs      = isend[4];
1171:   } else if (flag == MAT_GLOBAL_MAX) {
1172:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1173:     info->nz_used      = irecv[0];
1174:     info->nz_allocated = irecv[1];
1175:     info->nz_unneeded  = irecv[2];
1176:     info->memory       = irecv[3];
1177:     info->mallocs      = irecv[4];
1178:   } else if (flag == MAT_GLOBAL_SUM) {
1179:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1180:     info->nz_used      = irecv[0];
1181:     info->nz_allocated = irecv[1];
1182:     info->nz_unneeded  = irecv[2];
1183:     info->memory       = irecv[3];
1184:     info->mallocs      = irecv[4];
1185:   }
1186:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1187:   info->fill_ratio_needed = 0;
1188:   info->factor_mallocs    = 0;
1189:   info->rows_global       = (double)matin->rmap.N;
1190:   info->columns_global    = (double)matin->cmap.N;
1191:   info->rows_local        = (double)matin->rmap.n;
1192:   info->columns_local     = (double)matin->cmap.N;

1194:   return(0);
1195: }

1199: PetscErrorCode MatSetOption_MPIAIJ(Mat A,MatOption op)
1200: {
1201:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

1205:   switch (op) {
1206:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1207:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1208:   case MAT_COLUMNS_UNSORTED:
1209:   case MAT_COLUMNS_SORTED:
1210:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1211:   case MAT_KEEP_ZEROED_ROWS:
1212:   case MAT_NEW_NONZERO_LOCATION_ERR:
1213:   case MAT_USE_INODES:
1214:   case MAT_DO_NOT_USE_INODES:
1215:   case MAT_IGNORE_ZERO_ENTRIES:
1216:     MatSetOption(a->A,op);
1217:     MatSetOption(a->B,op);
1218:     break;
1219:   case MAT_ROW_ORIENTED:
1220:     a->roworiented = PETSC_TRUE;
1221:     MatSetOption(a->A,op);
1222:     MatSetOption(a->B,op);
1223:     break;
1224:   case MAT_ROWS_SORTED:
1225:   case MAT_ROWS_UNSORTED:
1226:   case MAT_YES_NEW_DIAGONALS:
1227:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1228:     break;
1229:   case MAT_COLUMN_ORIENTED:
1230:     a->roworiented = PETSC_FALSE;
1231:     MatSetOption(a->A,op);
1232:     MatSetOption(a->B,op);
1233:     break;
1234:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1235:     a->donotstash = PETSC_TRUE;
1236:     break;
1237:   case MAT_NO_NEW_DIAGONALS:
1238:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1239:   case MAT_SYMMETRIC:
1240:     MatSetOption(a->A,op);
1241:     break;
1242:   case MAT_STRUCTURALLY_SYMMETRIC:
1243:   case MAT_HERMITIAN:
1244:   case MAT_SYMMETRY_ETERNAL:
1245:     MatSetOption(a->A,op);
1246:     break;
1247:   case MAT_NOT_SYMMETRIC:
1248:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1249:   case MAT_NOT_HERMITIAN:
1250:   case MAT_NOT_SYMMETRY_ETERNAL:
1251:     break;
1252:   default:
1253:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
1254:   }
1255:   return(0);
1256: }

1260: PetscErrorCode MatGetRow_MPIAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1261: {
1262:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1263:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1265:   PetscInt       i,*cworkA,*cworkB,**pcA,**pcB,cstart = matin->cmap.rstart;
1266:   PetscInt       nztot,nzA,nzB,lrow,rstart = matin->rmap.rstart,rend = matin->rmap.rend;
1267:   PetscInt       *cmap,*idx_p;

1270:   if (mat->getrowactive) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1271:   mat->getrowactive = PETSC_TRUE;

1273:   if (!mat->rowvalues && (idx || v)) {
1274:     /*
1275:         allocate enough space to hold information from the longest row.
1276:     */
1277:     Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data;
1278:     PetscInt     max = 1,tmp;
1279:     for (i=0; i<matin->rmap.n; i++) {
1280:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1281:       if (max < tmp) { max = tmp; }
1282:     }
1283:     PetscMalloc(max*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1284:     mat->rowindices = (PetscInt*)(mat->rowvalues + max);
1285:   }

1287:   if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Only local rows")
1288:   lrow = row - rstart;

1290:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1291:   if (!v)   {pvA = 0; pvB = 0;}
1292:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1293:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1294:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1295:   nztot = nzA + nzB;

1297:   cmap  = mat->garray;
1298:   if (v  || idx) {
1299:     if (nztot) {
1300:       /* Sort by increasing column numbers, assuming A and B already sorted */
1301:       PetscInt imark = -1;
1302:       if (v) {
1303:         *v = v_p = mat->rowvalues;
1304:         for (i=0; i<nzB; i++) {
1305:           if (cmap[cworkB[i]] < cstart)   v_p[i] = vworkB[i];
1306:           else break;
1307:         }
1308:         imark = i;
1309:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1310:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1311:       }
1312:       if (idx) {
1313:         *idx = idx_p = mat->rowindices;
1314:         if (imark > -1) {
1315:           for (i=0; i<imark; i++) {
1316:             idx_p[i] = cmap[cworkB[i]];
1317:           }
1318:         } else {
1319:           for (i=0; i<nzB; i++) {
1320:             if (cmap[cworkB[i]] < cstart)   idx_p[i] = cmap[cworkB[i]];
1321:             else break;
1322:           }
1323:           imark = i;
1324:         }
1325:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart + cworkA[i];
1326:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]];
1327:       }
1328:     } else {
1329:       if (idx) *idx = 0;
1330:       if (v)   *v   = 0;
1331:     }
1332:   }
1333:   *nz = nztot;
1334:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1335:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1336:   return(0);
1337: }

1341: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1342: {
1343:   Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;

1346:   if (!aij->getrowactive) {
1347:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1348:   }
1349:   aij->getrowactive = PETSC_FALSE;
1350:   return(0);
1351: }

1355: PetscErrorCode MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm)
1356: {
1357:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
1358:   Mat_SeqAIJ     *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data;
1360:   PetscInt       i,j,cstart = mat->cmap.rstart;
1361:   PetscReal      sum = 0.0;
1362:   PetscScalar    *v;

1365:   if (aij->size == 1) {
1366:      MatNorm(aij->A,type,norm);
1367:   } else {
1368:     if (type == NORM_FROBENIUS) {
1369:       v = amat->a;
1370:       for (i=0; i<amat->nz; i++) {
1371: #if defined(PETSC_USE_COMPLEX)
1372:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1373: #else
1374:         sum += (*v)*(*v); v++;
1375: #endif
1376:       }
1377:       v = bmat->a;
1378:       for (i=0; i<bmat->nz; i++) {
1379: #if defined(PETSC_USE_COMPLEX)
1380:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1381: #else
1382:         sum += (*v)*(*v); v++;
1383: #endif
1384:       }
1385:       MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPI_SUM,mat->comm);
1386:       *norm = sqrt(*norm);
1387:     } else if (type == NORM_1) { /* max column norm */
1388:       PetscReal *tmp,*tmp2;
1389:       PetscInt    *jj,*garray = aij->garray;
1390:       PetscMalloc((mat->cmap.N+1)*sizeof(PetscReal),&tmp);
1391:       PetscMalloc((mat->cmap.N+1)*sizeof(PetscReal),&tmp2);
1392:       PetscMemzero(tmp,mat->cmap.N*sizeof(PetscReal));
1393:       *norm = 0.0;
1394:       v = amat->a; jj = amat->j;
1395:       for (j=0; j<amat->nz; j++) {
1396:         tmp[cstart + *jj++ ] += PetscAbsScalar(*v);  v++;
1397:       }
1398:       v = bmat->a; jj = bmat->j;
1399:       for (j=0; j<bmat->nz; j++) {
1400:         tmp[garray[*jj++]] += PetscAbsScalar(*v); v++;
1401:       }
1402:       MPI_Allreduce(tmp,tmp2,mat->cmap.N,MPIU_REAL,MPI_SUM,mat->comm);
1403:       for (j=0; j<mat->cmap.N; j++) {
1404:         if (tmp2[j] > *norm) *norm = tmp2[j];
1405:       }
1406:       PetscFree(tmp);
1407:       PetscFree(tmp2);
1408:     } else if (type == NORM_INFINITY) { /* max row norm */
1409:       PetscReal ntemp = 0.0;
1410:       for (j=0; j<aij->A->rmap.n; j++) {
1411:         v = amat->a + amat->i[j];
1412:         sum = 0.0;
1413:         for (i=0; i<amat->i[j+1]-amat->i[j]; i++) {
1414:           sum += PetscAbsScalar(*v); v++;
1415:         }
1416:         v = bmat->a + bmat->i[j];
1417:         for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) {
1418:           sum += PetscAbsScalar(*v); v++;
1419:         }
1420:         if (sum > ntemp) ntemp = sum;
1421:       }
1422:       MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPI_MAX,mat->comm);
1423:     } else {
1424:       SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1425:     }
1426:   }
1427:   return(0);
1428: }

1432: PetscErrorCode MatTranspose_MPIAIJ(Mat A,Mat *matout)
1433: {
1434:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
1435:   Mat_SeqAIJ     *Aloc=(Mat_SeqAIJ*)a->A->data,*Bloc=(Mat_SeqAIJ*)a->B->data;
1437:   PetscInt       M = A->rmap.N,N = A->cmap.N,ma,na,mb,*ai,*aj,*bi,*bj,row,*cols,i,*d_nnz;
1438:   PetscInt       cstart=A->cmap.rstart,ncol;
1439:   Mat            B;
1440:   PetscScalar    *array;

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

1445:   /* compute d_nnz for preallocation; o_nnz is approximated by d_nnz to avoid communication */
1446:   ma = A->rmap.n; na = A->cmap.n; mb = a->B->rmap.n;
1447:   ai = Aloc->i; aj = Aloc->j;
1448:   bi = Bloc->i; bj = Bloc->j;
1449:   PetscMalloc((1+na+bi[mb])*sizeof(PetscInt),&d_nnz);
1450:   cols = d_nnz + na + 1; /* work space to be used by B part */
1451:   PetscMemzero(d_nnz,(1+na)*sizeof(PetscInt));
1452:   for (i=0; i<ai[ma]; i++){
1453:     d_nnz[aj[i]] ++;
1454:     aj[i] += cstart; /* global col index to be used by MatSetValues() */
1455:   }

1457:   MatCreate(A->comm,&B);
1458:   MatSetSizes(B,A->cmap.n,A->rmap.n,N,M);
1459:   MatSetType(B,A->type_name);
1460:   MatMPIAIJSetPreallocation(B,0,d_nnz,0,d_nnz);

1462:   /* copy over the A part */
1463:   array = Aloc->a;
1464:   row = A->rmap.rstart;
1465:   for (i=0; i<ma; i++) {
1466:     ncol = ai[i+1]-ai[i];
1467:     MatSetValues(B,ncol,aj,1,&row,array,INSERT_VALUES);
1468:     row++; array += ncol; aj += ncol;
1469:   }
1470:   aj = Aloc->j;
1471:   for (i=0; i<ai[ma]; i++) aj[i] -= cstart; /* resume local col index */

1473:   /* copy over the B part */
1474:   array = Bloc->a;
1475:   row = A->rmap.rstart;
1476:   for (i=0; i<bi[mb]; i++) {cols[i] = a->garray[bj[i]];}
1477:   for (i=0; i<mb; i++) {
1478:     ncol = bi[i+1]-bi[i];
1479:     MatSetValues(B,ncol,cols,1,&row,array,INSERT_VALUES);
1480:     row++; array += ncol; cols += ncol;
1481:   }
1482:   PetscFree(d_nnz);
1483:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1484:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1485:   if (matout) {
1486:     *matout = B;
1487:   } else {
1488:     MatHeaderCopy(A,B);
1489:   }
1490:   return(0);
1491: }

1495: PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr)
1496: {
1497:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
1498:   Mat            a = aij->A,b = aij->B;
1500:   PetscInt       s1,s2,s3;

1503:   MatGetLocalSize(mat,&s2,&s3);
1504:   if (rr) {
1505:     VecGetLocalSize(rr,&s1);
1506:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1507:     /* Overlap communication with computation. */
1508:     VecScatterBegin(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1509:   }
1510:   if (ll) {
1511:     VecGetLocalSize(ll,&s1);
1512:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1513:     (*b->ops->diagonalscale)(b,ll,0);
1514:   }
1515:   /* scale  the diagonal block */
1516:   (*a->ops->diagonalscale)(a,ll,rr);

1518:   if (rr) {
1519:     /* Do a scatter end and then right scale the off-diagonal block */
1520:     VecScatterEnd(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1521:     (*b->ops->diagonalscale)(b,0,aij->lvec);
1522:   }
1523: 
1524:   return(0);
1525: }

1529: PetscErrorCode MatSetBlockSize_MPIAIJ(Mat A,PetscInt bs)
1530: {
1531:   Mat_MPIAIJ     *a   = (Mat_MPIAIJ*)A->data;

1535:   MatSetBlockSize(a->A,bs);
1536:   MatSetBlockSize(a->B,bs);
1537:   return(0);
1538: }
1541: PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
1542: {
1543:   Mat_MPIAIJ     *a   = (Mat_MPIAIJ*)A->data;

1547:   MatSetUnfactored(a->A);
1548:   return(0);
1549: }

1553: PetscErrorCode MatEqual_MPIAIJ(Mat A,Mat B,PetscTruth *flag)
1554: {
1555:   Mat_MPIAIJ     *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data;
1556:   Mat            a,b,c,d;
1557:   PetscTruth     flg;

1561:   a = matA->A; b = matA->B;
1562:   c = matB->A; d = matB->B;

1564:   MatEqual(a,c,&flg);
1565:   if (flg) {
1566:     MatEqual(b,d,&flg);
1567:   }
1568:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1569:   return(0);
1570: }

1574: PetscErrorCode MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str)
1575: {
1577:   Mat_MPIAIJ     *a = (Mat_MPIAIJ *)A->data;
1578:   Mat_MPIAIJ     *b = (Mat_MPIAIJ *)B->data;

1581:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1582:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1583:     /* because of the column compression in the off-processor part of the matrix a->B,
1584:        the number of columns in a->B and b->B may be different, hence we cannot call
1585:        the MatCopy() directly on the two parts. If need be, we can provide a more 
1586:        efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
1587:        then copying the submatrices */
1588:     MatCopy_Basic(A,B,str);
1589:   } else {
1590:     MatCopy(a->A,b->A,str);
1591:     MatCopy(a->B,b->B,str);
1592:   }
1593:   return(0);
1594: }

1598: PetscErrorCode MatSetUpPreallocation_MPIAIJ(Mat A)
1599: {

1603:    MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1604:   return(0);
1605: }

1607:  #include petscblaslapack.h
1610: PetscErrorCode MatAXPY_MPIAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1611: {
1613:   PetscInt       i;
1614:   Mat_MPIAIJ     *xx = (Mat_MPIAIJ *)X->data,*yy = (Mat_MPIAIJ *)Y->data;
1615:   PetscBLASInt   bnz,one=1;
1616:   Mat_SeqAIJ     *x,*y;

1619:   if (str == SAME_NONZERO_PATTERN) {
1620:     PetscScalar alpha = a;
1621:     x = (Mat_SeqAIJ *)xx->A->data;
1622:     y = (Mat_SeqAIJ *)yy->A->data;
1623:     bnz = (PetscBLASInt)x->nz;
1624:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1625:     x = (Mat_SeqAIJ *)xx->B->data;
1626:     y = (Mat_SeqAIJ *)yy->B->data;
1627:     bnz = (PetscBLASInt)x->nz;
1628:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1629:   } else if (str == SUBSET_NONZERO_PATTERN) {
1630:     MatAXPY_SeqAIJ(yy->A,a,xx->A,str);

1632:     x = (Mat_SeqAIJ *)xx->B->data;
1633:     y = (Mat_SeqAIJ *)yy->B->data;
1634:     if (y->xtoy && y->XtoY != xx->B) {
1635:       PetscFree(y->xtoy);
1636:       MatDestroy(y->XtoY);
1637:     }
1638:     if (!y->xtoy) { /* get xtoy */
1639:       MatAXPYGetxtoy_Private(xx->B->rmap.n,x->i,x->j,xx->garray,y->i,y->j,yy->garray,&y->xtoy);
1640:       y->XtoY = xx->B;
1641:       PetscObjectReference((PetscObject)xx->B);
1642:     }
1643:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
1644:   } else {
1645:     MatAXPY_Basic(Y,a,X,str);
1646:   }
1647:   return(0);
1648: }

1650: EXTERN PetscErrorCode  MatConjugate_SeqAIJ(Mat);

1654: PetscErrorCode  MatConjugate_MPIAIJ(Mat mat)
1655: {
1656: #if defined(PETSC_USE_COMPLEX)
1658:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

1661:   MatConjugate_SeqAIJ(aij->A);
1662:   MatConjugate_SeqAIJ(aij->B);
1663: #else
1665: #endif
1666:   return(0);
1667: }

1671: PetscErrorCode MatRealPart_MPIAIJ(Mat A)
1672: {
1673:   Mat_MPIAIJ   *a = (Mat_MPIAIJ*)A->data;

1677:   MatRealPart(a->A);
1678:   MatRealPart(a->B);
1679:   return(0);
1680: }

1684: PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
1685: {
1686:   Mat_MPIAIJ   *a = (Mat_MPIAIJ*)A->data;

1690:   MatImaginaryPart(a->A);
1691:   MatImaginaryPart(a->B);
1692:   return(0);
1693: }

1695: #ifdef PETSC_HAVE_PBGL

1697: #include <boost/parallel/mpi/bsp_process_group.hpp>
1698: #include <boost/graph/distributed/ilu_default_graph.hpp>
1699: #include <boost/graph/distributed/ilu_0_block.hpp>
1700: #include <boost/graph/distributed/ilu_preconditioner.hpp>
1701: #include <boost/graph/distributed/petsc/interface.hpp>
1702: #include <boost/multi_array.hpp>
1703: #include <boost/parallel/distributed_property_map.hpp>

1707: /*
1708:   This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
1709: */
1710: PetscErrorCode MatILUFactorSymbolic_MPIAIJ(Mat A, IS isrow, IS iscol, MatFactorInfo *info, Mat *fact)
1711: {
1712:   namespace petsc = boost::distributed::petsc;
1713: 
1714:   namespace graph_dist = boost::graph::distributed;
1715:   using boost::graph::distributed::ilu_default::process_group_type;
1716:   using boost::graph::ilu_permuted;

1718:   PetscTruth      row_identity, col_identity;
1719:   PetscContainer  c;
1720:   PetscInt        m, n, M, N;
1721:   PetscErrorCode  ierr;

1724:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels = 0 supported for parallel ilu");
1725:   ISIdentity(isrow, &row_identity);
1726:   ISIdentity(iscol, &col_identity);
1727:   if (!row_identity || !col_identity) {
1728:     SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for parallel ILU");
1729:   }

1731:   process_group_type pg;
1732:   typedef graph_dist::ilu_default::ilu_level_graph_type  lgraph_type;
1733:   lgraph_type*   lgraph_p = new lgraph_type(petsc::num_global_vertices(A), pg, petsc::matrix_distribution(A, pg));
1734:   lgraph_type&   level_graph = *lgraph_p;
1735:   graph_dist::ilu_default::graph_type&            graph(level_graph.graph);

1737:   petsc::read_matrix(A, graph, get(boost::edge_weight, graph));
1738:   ilu_permuted(level_graph);

1740:   /* put together the new matrix */
1741:   MatCreate(A->comm, fact);
1742:   MatGetLocalSize(A, &m, &n);
1743:   MatGetSize(A, &M, &N);
1744:   MatSetSizes(*fact, m, n, M, N);
1745:   MatSetType(*fact, A->type_name);
1746:   MatAssemblyBegin(*fact, MAT_FINAL_ASSEMBLY);
1747:   MatAssemblyEnd(*fact, MAT_FINAL_ASSEMBLY);
1748:   (*fact)->factor = FACTOR_LU;

1750:   PetscContainerCreate(A->comm, &c);
1751:   PetscContainerSetPointer(c, lgraph_p);
1752:   PetscObjectCompose((PetscObject) (*fact), "graph", (PetscObject) c);
1753:   return(0);
1754: }

1758: PetscErrorCode MatLUFactorNumeric_MPIAIJ(Mat A, MatFactorInfo *info, Mat *B)
1759: {
1761:   return(0);
1762: }

1766: /*
1767:   This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
1768: */
1769: PetscErrorCode MatSolve_MPIAIJ(Mat A, Vec b, Vec x)
1770: {
1771:   namespace graph_dist = boost::graph::distributed;

1773:   typedef graph_dist::ilu_default::ilu_level_graph_type  lgraph_type;
1774:   lgraph_type*   lgraph_p;
1775:   PetscContainer c;

1779:   PetscObjectQuery((PetscObject) A, "graph", (PetscObject *) &c);
1780:   PetscContainerGetPointer(c, (void **) &lgraph_p);
1781:   VecCopy(b, x);

1783:   PetscScalar* array_x;
1784:   VecGetArray(x, &array_x);
1785:   PetscInt sx;
1786:   VecGetSize(x, &sx);
1787: 
1788:   PetscScalar* array_b;
1789:   VecGetArray(b, &array_b);
1790:   PetscInt sb;
1791:   VecGetSize(b, &sb);

1793:   lgraph_type&   level_graph = *lgraph_p;
1794:   graph_dist::ilu_default::graph_type&            graph(level_graph.graph);

1796:   typedef boost::multi_array_ref<PetscScalar, 1> array_ref_type;
1797:   array_ref_type                                 ref_b(array_b, boost::extents[num_vertices(graph)]),
1798:                                                  ref_x(array_x, boost::extents[num_vertices(graph)]);

1800:   typedef boost::iterator_property_map<array_ref_type::iterator,
1801:                                 boost::property_map<graph_dist::ilu_default::graph_type, boost::vertex_index_t>::type>  gvector_type;
1802:   gvector_type                                   vector_b(ref_b.begin(), get(boost::vertex_index, graph)),
1803:                                                  vector_x(ref_x.begin(), get(boost::vertex_index, graph));
1804: 
1805:   ilu_set_solve(*lgraph_p, vector_b, vector_x);

1807:   return(0);
1808: }
1809: #endif

1811: typedef struct { /* used by MatGetRedundantMatrix() for reusing matredundant */
1812:   PetscInt       nzlocal,nsends,nrecvs;
1813:   PetscMPIInt    *send_rank;
1814:   PetscInt       *sbuf_nz,*sbuf_j,**rbuf_j;
1815:   PetscScalar    *sbuf_a,**rbuf_a;
1816:   PetscErrorCode (*MatDestroy)(Mat);
1817: } Mat_Redundant;

1821: PetscErrorCode PetscContainerDestroy_MatRedundant(void *ptr)
1822: {
1823:   PetscErrorCode       ierr;
1824:   Mat_Redundant        *redund=(Mat_Redundant*)ptr;
1825:   PetscInt             i;

1828:   PetscFree(redund->send_rank);
1829:   PetscFree(redund->sbuf_j);
1830:   PetscFree(redund->sbuf_a);
1831:   for (i=0; i<redund->nrecvs; i++){
1832:     PetscFree(redund->rbuf_j[i]);
1833:     PetscFree(redund->rbuf_a[i]);
1834:   }
1835:   PetscFree3(redund->sbuf_nz,redund->rbuf_j,redund->rbuf_a);
1836:   PetscFree(redund);
1837:   return(0);
1838: }

1842: PetscErrorCode MatDestroy_MatRedundant(Mat A)
1843: {
1844:   PetscErrorCode  ierr;
1845:   PetscContainer  container;
1846:   Mat_Redundant   *redund=PETSC_NULL;

1849:   PetscObjectQuery((PetscObject)A,"Mat_Redundant",(PetscObject *)&container);
1850:   if (container) {
1851:     PetscContainerGetPointer(container,(void **)&redund);
1852:   } else {
1853:     SETERRQ(PETSC_ERR_PLIB,"Container does not exit");
1854:   }
1855:   A->ops->destroy = redund->MatDestroy;
1856:   PetscObjectCompose((PetscObject)A,"Mat_Redundant",0);
1857:   (*A->ops->destroy)(A);
1858:   PetscContainerDestroy(container);
1859:   return(0);
1860: }

1864: PetscErrorCode MatGetRedundantMatrix_MPIAIJ(Mat mat,PetscInt nsubcomm,MPI_Comm subcomm,PetscInt mlocal_sub,MatReuse reuse,Mat *matredundant)
1865: {
1866:   PetscMPIInt    rank,size;
1867:   MPI_Comm       comm=mat->comm;
1869:   PetscInt       nsends=0,nrecvs=0,i,rownz_max=0;
1870:   PetscMPIInt    *send_rank=PETSC_NULL,*recv_rank=PETSC_NULL;
1871:   PetscInt       *rowrange=mat->rmap.range;
1872:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
1873:   Mat            A=aij->A,B=aij->B,C=*matredundant;
1874:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data;
1875:   PetscScalar    *sbuf_a;
1876:   PetscInt       nzlocal=a->nz+b->nz;
1877:   PetscInt       j,cstart=mat->cmap.rstart,cend=mat->cmap.rend,row,nzA,nzB,ncols,*cworkA,*cworkB;
1878:   PetscInt       rstart=mat->rmap.rstart,rend=mat->rmap.rend,*bmap=aij->garray,M,N;
1879:   PetscInt       *cols,ctmp,lwrite,*rptr,l,*sbuf_j;
1880:   PetscScalar    *vals,*aworkA,*aworkB;
1881:   PetscMPIInt    tag1,tag2,tag3,imdex;
1882:   MPI_Request    *s_waits1=PETSC_NULL,*s_waits2=PETSC_NULL,*s_waits3=PETSC_NULL,
1883:                  *r_waits1=PETSC_NULL,*r_waits2=PETSC_NULL,*r_waits3=PETSC_NULL;
1884:   MPI_Status     recv_status,*send_status;
1885:   PetscInt       *sbuf_nz=PETSC_NULL,*rbuf_nz=PETSC_NULL,count;
1886:   PetscInt       **rbuf_j=PETSC_NULL;
1887:   PetscScalar    **rbuf_a=PETSC_NULL;
1888:   Mat_Redundant  *redund=PETSC_NULL;
1889:   PetscContainer container;

1892:   MPI_Comm_rank(comm,&rank);
1893:   MPI_Comm_size(comm,&size);

1895:   if (reuse == MAT_REUSE_MATRIX) {
1896:     MatGetSize(C,&M,&N);
1897:     if (M != N || M != mat->rmap.N) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong global size");
1898:     MatGetLocalSize(C,&M,&N);
1899:     if (M != N || M != mlocal_sub) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong local size");
1900:     PetscObjectQuery((PetscObject)C,"Mat_Redundant",(PetscObject *)&container);
1901:     if (container) {
1902:       PetscContainerGetPointer(container,(void **)&redund);
1903:     } else {
1904:       SETERRQ(PETSC_ERR_PLIB,"Container does not exit");
1905:     }
1906:     if (nzlocal != redund->nzlocal) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong nzlocal");

1908:     nsends    = redund->nsends;
1909:     nrecvs    = redund->nrecvs;
1910:     send_rank = redund->send_rank; recv_rank = send_rank + size;
1911:     sbuf_nz   = redund->sbuf_nz;     rbuf_nz = sbuf_nz + nsends;
1912:     sbuf_j    = redund->sbuf_j;
1913:     sbuf_a    = redund->sbuf_a;
1914:     rbuf_j    = redund->rbuf_j;
1915:     rbuf_a    = redund->rbuf_a;
1916:   }

1918:   if (reuse == MAT_INITIAL_MATRIX){
1919:     PetscMPIInt  subrank,subsize;
1920:     PetscInt     nleftover,np_subcomm;
1921:     /* get the destination processors' id send_rank, nsends and nrecvs */
1922:     MPI_Comm_rank(subcomm,&subrank);
1923:     MPI_Comm_size(subcomm,&subsize);
1924:     PetscMalloc((2*size+1)*sizeof(PetscMPIInt),&send_rank);
1925:     recv_rank = send_rank + size;
1926:     np_subcomm = size/nsubcomm;
1927:     nleftover  = size - nsubcomm*np_subcomm;
1928:     nsends = 0; nrecvs = 0;
1929:     for (i=0; i<size; i++){ /* i=rank*/
1930:       if (subrank == i/nsubcomm && rank != i){ /* my_subrank == other's subrank */
1931:         send_rank[nsends] = i; nsends++;
1932:         recv_rank[nrecvs++] = i;
1933:       }
1934:     }
1935:     if (rank >= size - nleftover){/* this proc is a leftover processor */
1936:       i = size-nleftover-1;
1937:       j = 0;
1938:       while (j < nsubcomm - nleftover){
1939:         send_rank[nsends++] = i;
1940:         i--; j++;
1941:       }
1942:     }

1944:     if (nleftover && subsize == size/nsubcomm && subrank==subsize-1){ /* this proc recvs from leftover processors */
1945:       for (i=0; i<nleftover; i++){
1946:         recv_rank[nrecvs++] = size-nleftover+i;
1947:       }
1948:     }

1950:     /* allocate sbuf_j, sbuf_a */
1951:     i = nzlocal + rowrange[rank+1] - rowrange[rank] + 2;
1952:     PetscMalloc(i*sizeof(PetscInt),&sbuf_j);
1953:     PetscMalloc((nzlocal+1)*sizeof(PetscScalar),&sbuf_a);
1954:   } /* endof if (reuse == MAT_INITIAL_MATRIX) */
1955: 
1956:   /* copy mat's local entries into the buffers */
1957:   if (reuse == MAT_INITIAL_MATRIX){
1958:     rownz_max = 0;
1959:     rptr = sbuf_j;
1960:     cols = sbuf_j + rend-rstart + 1;
1961:     vals = sbuf_a;
1962:     rptr[0] = 0;
1963:     for (i=0; i<rend-rstart; i++){
1964:       row = i + rstart;
1965:       nzA    = a->i[i+1] - a->i[i]; nzB = b->i[i+1] - b->i[i];
1966:       ncols  = nzA + nzB;
1967:       cworkA = a->j + a->i[i]; cworkB = b->j + b->i[i];
1968:       aworkA = a->a + a->i[i]; aworkB = b->a + b->i[i];
1969:       /* load the column indices for this row into cols */
1970:       lwrite = 0;
1971:       for (l=0; l<nzB; l++) {
1972:         if ((ctmp = bmap[cworkB[l]]) < cstart){
1973:           vals[lwrite]   = aworkB[l];
1974:           cols[lwrite++] = ctmp;
1975:         }
1976:       }
1977:       for (l=0; l<nzA; l++){
1978:         vals[lwrite]   = aworkA[l];
1979:         cols[lwrite++] = cstart + cworkA[l];
1980:       }
1981:       for (l=0; l<nzB; l++) {
1982:         if ((ctmp = bmap[cworkB[l]]) >= cend){
1983:           vals[lwrite]   = aworkB[l];
1984:           cols[lwrite++] = ctmp;
1985:         }
1986:       }
1987:       vals += ncols;
1988:       cols += ncols;
1989:       rptr[i+1] = rptr[i] + ncols;
1990:       if (rownz_max < ncols) rownz_max = ncols;
1991:     }
1992:     if (rptr[rend-rstart] != a->nz + b->nz) SETERRQ4(1, "rptr[%d] %d != %d + %d",rend-rstart,rptr[rend-rstart+1],a->nz,b->nz);
1993:   } else { /* only copy matrix values into sbuf_a */
1994:     rptr = sbuf_j;
1995:     vals = sbuf_a;
1996:     rptr[0] = 0;
1997:     for (i=0; i<rend-rstart; i++){
1998:       row = i + rstart;
1999:       nzA    = a->i[i+1] - a->i[i]; nzB = b->i[i+1] - b->i[i];
2000:       ncols  = nzA + nzB;
2001:       cworkA = a->j + a->i[i]; cworkB = b->j + b->i[i];
2002:       aworkA = a->a + a->i[i]; aworkB = b->a + b->i[i];
2003:       lwrite = 0;
2004:       for (l=0; l<nzB; l++) {
2005:         if ((ctmp = bmap[cworkB[l]]) < cstart) vals[lwrite++] = aworkB[l];
2006:       }
2007:       for (l=0; l<nzA; l++) vals[lwrite++] = aworkA[l];
2008:       for (l=0; l<nzB; l++) {
2009:         if ((ctmp = bmap[cworkB[l]]) >= cend) vals[lwrite++] = aworkB[l];
2010:       }
2011:       vals += ncols;
2012:       rptr[i+1] = rptr[i] + ncols;
2013:     }
2014:   } /* endof if (reuse == MAT_INITIAL_MATRIX) */

2016:   /* send nzlocal to others, and recv other's nzlocal */
2017:   /*--------------------------------------------------*/
2018:   if (reuse == MAT_INITIAL_MATRIX){
2019:     PetscMalloc2(3*(nsends + nrecvs)+1,MPI_Request,&s_waits3,nsends+1,MPI_Status,&send_status);
2020:     s_waits2 = s_waits3 + nsends;
2021:     s_waits1 = s_waits2 + nsends;
2022:     r_waits1 = s_waits1 + nsends;
2023:     r_waits2 = r_waits1 + nrecvs;
2024:     r_waits3 = r_waits2 + nrecvs;
2025:   } else {
2026:     PetscMalloc2(nsends + nrecvs +1,MPI_Request,&s_waits3,nsends+1,MPI_Status,&send_status);
2027:     r_waits3 = s_waits3 + nsends;
2028:   }

2030:   PetscObjectGetNewTag((PetscObject)mat,&tag3);
2031:   if (reuse == MAT_INITIAL_MATRIX){
2032:     /* get new tags to keep the communication clean */
2033:     PetscObjectGetNewTag((PetscObject)mat,&tag1);
2034:     PetscObjectGetNewTag((PetscObject)mat,&tag2);
2035:     PetscMalloc3(nsends+nrecvs+1,PetscInt,&sbuf_nz,nrecvs,PetscInt*,&rbuf_j,nrecvs,PetscScalar*,&rbuf_a);
2036:     rbuf_nz = sbuf_nz + nsends;
2037: 
2038:     /* post receives of other's nzlocal */
2039:     for (i=0; i<nrecvs; i++){
2040:       MPI_Irecv(rbuf_nz+i,1,MPIU_INT,MPI_ANY_SOURCE,tag1,comm,r_waits1+i);
2041:     }
2042:     /* send nzlocal to others */
2043:     for (i=0; i<nsends; i++){
2044:       sbuf_nz[i] = nzlocal;
2045:       MPI_Isend(sbuf_nz+i,1,MPIU_INT,send_rank[i],tag1,comm,s_waits1+i);
2046:     }
2047:     /* wait on receives of nzlocal; allocate space for rbuf_j, rbuf_a */
2048:     count = nrecvs;
2049:     while (count) {
2050:       MPI_Waitany(nrecvs,r_waits1,&imdex,&recv_status);
2051:       recv_rank[imdex] = recv_status.MPI_SOURCE;
2052:       /* allocate rbuf_a and rbuf_j; then post receives of rbuf_j */
2053:       PetscMalloc((rbuf_nz[imdex]+1)*sizeof(PetscScalar),&rbuf_a[imdex]);

2055:       i = rowrange[recv_status.MPI_SOURCE+1] - rowrange[recv_status.MPI_SOURCE]; /* number of expected mat->i */
2056:       rbuf_nz[imdex] += i + 2;
2057:       PetscMalloc(rbuf_nz[imdex]*sizeof(PetscInt),&rbuf_j[imdex]);
2058:       MPI_Irecv(rbuf_j[imdex],rbuf_nz[imdex],MPIU_INT,recv_status.MPI_SOURCE,tag2,comm,r_waits2+imdex);
2059:       count--;
2060:     }
2061:     /* wait on sends of nzlocal */
2062:     if (nsends) {MPI_Waitall(nsends,s_waits1,send_status);}
2063:     /* send mat->i,j to others, and recv from other's */
2064:     /*------------------------------------------------*/
2065:     for (i=0; i<nsends; i++){
2066:       j = nzlocal + rowrange[rank+1] - rowrange[rank] + 1;
2067:       MPI_Isend(sbuf_j,j,MPIU_INT,send_rank[i],tag2,comm,s_waits2+i);
2068:     }
2069:     /* wait on receives of mat->i,j */
2070:     /*------------------------------*/
2071:     count = nrecvs;
2072:     while (count) {
2073:       MPI_Waitany(nrecvs,r_waits2,&imdex,&recv_status);
2074:       if (recv_rank[imdex] != recv_status.MPI_SOURCE) SETERRQ2(1, "recv_rank %d != MPI_SOURCE %d",recv_rank[imdex],recv_status.MPI_SOURCE);
2075:       count--;
2076:     }
2077:     /* wait on sends of mat->i,j */
2078:     /*---------------------------*/
2079:     if (nsends) {
2080:       MPI_Waitall(nsends,s_waits2,send_status);
2081:     }
2082:   } /* endof if (reuse == MAT_INITIAL_MATRIX) */

2084:   /* post receives, send and receive mat->a */
2085:   /*----------------------------------------*/
2086:   for (imdex=0; imdex<nrecvs; imdex++) {
2087:     MPI_Irecv(rbuf_a[imdex],rbuf_nz[imdex],MPIU_SCALAR,recv_rank[imdex],tag3,comm,r_waits3+imdex);
2088:   }
2089:   for (i=0; i<nsends; i++){
2090:     MPI_Isend(sbuf_a,nzlocal,MPIU_SCALAR,send_rank[i],tag3,comm,s_waits3+i);
2091:   }
2092:   count = nrecvs;
2093:   while (count) {
2094:     MPI_Waitany(nrecvs,r_waits3,&imdex,&recv_status);
2095:     if (recv_rank[imdex] != recv_status.MPI_SOURCE) SETERRQ2(1, "recv_rank %d != MPI_SOURCE %d",recv_rank[imdex],recv_status.MPI_SOURCE);
2096:     count--;
2097:   }
2098:   if (nsends) {
2099:     MPI_Waitall(nsends,s_waits3,send_status);
2100:   }

2102:   PetscFree2(s_waits3,send_status);
2103: 
2104:   /* create redundant matrix */
2105:   /*-------------------------*/
2106:   if (reuse == MAT_INITIAL_MATRIX){
2107:     /* compute rownz_max for preallocation */
2108:     for (imdex=0; imdex<nrecvs; imdex++){
2109:       j = rowrange[recv_rank[imdex]+1] - rowrange[recv_rank[imdex]];
2110:       rptr = rbuf_j[imdex];
2111:       for (i=0; i<j; i++){
2112:         ncols = rptr[i+1] - rptr[i];
2113:         if (rownz_max < ncols) rownz_max = ncols;
2114:       }
2115:     }
2116: 
2117:     MatCreate(subcomm,&C);
2118:     MatSetSizes(C,mlocal_sub,mlocal_sub,PETSC_DECIDE,PETSC_DECIDE);
2119:     MatSetFromOptions(C);
2120:     MatSeqAIJSetPreallocation(C,rownz_max,PETSC_NULL);
2121:     MatMPIAIJSetPreallocation(C,rownz_max,PETSC_NULL,rownz_max,PETSC_NULL);
2122:   } else {
2123:     C = *matredundant;
2124:   }

2126:   /* insert local matrix entries */
2127:   rptr = sbuf_j;
2128:   cols = sbuf_j + rend-rstart + 1;
2129:   vals = sbuf_a;
2130:   for (i=0; i<rend-rstart; i++){
2131:     row   = i + rstart;
2132:     ncols = rptr[i+1] - rptr[i];
2133:     MatSetValues(C,1,&row,ncols,cols,vals,INSERT_VALUES);
2134:     vals += ncols;
2135:     cols += ncols;
2136:   }
2137:   /* insert received matrix entries */
2138:   for (imdex=0; imdex<nrecvs; imdex++){
2139:     rstart = rowrange[recv_rank[imdex]];
2140:     rend   = rowrange[recv_rank[imdex]+1];
2141:     rptr = rbuf_j[imdex];
2142:     cols = rbuf_j[imdex] + rend-rstart + 1;
2143:     vals = rbuf_a[imdex];
2144:     for (i=0; i<rend-rstart; i++){
2145:       row   = i + rstart;
2146:       ncols = rptr[i+1] - rptr[i];
2147:       MatSetValues(C,1,&row,ncols,cols,vals,INSERT_VALUES);
2148:       vals += ncols;
2149:       cols += ncols;
2150:     }
2151:   }
2152:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2153:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2154:   MatGetSize(C,&M,&N);
2155:   if (M != mat->rmap.N || N != mat->cmap.N) SETERRQ2(PETSC_ERR_ARG_INCOMP,"redundant mat size %d != input mat size %d",M,mat->rmap.N);
2156:   if (reuse == MAT_INITIAL_MATRIX){
2157:     PetscContainer container;
2158:     *matredundant = C;
2159:     /* create a supporting struct and attach it to C for reuse */
2160:     PetscNew(Mat_Redundant,&redund);
2161:     PetscContainerCreate(PETSC_COMM_SELF,&container);
2162:     PetscContainerSetPointer(container,redund);
2163:     PetscObjectCompose((PetscObject)C,"Mat_Redundant",(PetscObject)container);
2164:     PetscContainerSetUserDestroy(container,PetscContainerDestroy_MatRedundant);
2165: 
2166:     redund->nzlocal = nzlocal;
2167:     redund->nsends  = nsends;
2168:     redund->nrecvs  = nrecvs;
2169:     redund->send_rank = send_rank;
2170:     redund->sbuf_nz = sbuf_nz;
2171:     redund->sbuf_j  = sbuf_j;
2172:     redund->sbuf_a  = sbuf_a;
2173:     redund->rbuf_j  = rbuf_j;
2174:     redund->rbuf_a  = rbuf_a;

2176:     redund->MatDestroy = C->ops->destroy;
2177:     C->ops->destroy    = MatDestroy_MatRedundant;
2178:   }
2179:   return(0);
2180: }

2182: /* -------------------------------------------------------------------*/
2183: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2184:        MatGetRow_MPIAIJ,
2185:        MatRestoreRow_MPIAIJ,
2186:        MatMult_MPIAIJ,
2187: /* 4*/ MatMultAdd_MPIAIJ,
2188:        MatMultTranspose_MPIAIJ,
2189:        MatMultTransposeAdd_MPIAIJ,
2190: #ifdef PETSC_HAVE_PBGL
2191:        MatSolve_MPIAIJ,
2192: #else
2193:        0,
2194: #endif
2195:        0,
2196:        0,
2197: /*10*/ 0,
2198:        0,
2199:        0,
2200:        MatRelax_MPIAIJ,
2201:        MatTranspose_MPIAIJ,
2202: /*15*/ MatGetInfo_MPIAIJ,
2203:        MatEqual_MPIAIJ,
2204:        MatGetDiagonal_MPIAIJ,
2205:        MatDiagonalScale_MPIAIJ,
2206:        MatNorm_MPIAIJ,
2207: /*20*/ MatAssemblyBegin_MPIAIJ,
2208:        MatAssemblyEnd_MPIAIJ,
2209:        0,
2210:        MatSetOption_MPIAIJ,
2211:        MatZeroEntries_MPIAIJ,
2212: /*25*/ MatZeroRows_MPIAIJ,
2213:        0,
2214: #ifdef PETSC_HAVE_PBGL
2215:        MatLUFactorNumeric_MPIAIJ,
2216: #else
2217:        0,
2218: #endif
2219:        0,
2220:        0,
2221: /*30*/ MatSetUpPreallocation_MPIAIJ,
2222: #ifdef PETSC_HAVE_PBGL
2223:        MatILUFactorSymbolic_MPIAIJ,
2224: #else
2225:        0,
2226: #endif
2227:        0,
2228:        0,
2229:        0,
2230: /*35*/ MatDuplicate_MPIAIJ,
2231:        0,
2232:        0,
2233:        0,
2234:        0,
2235: /*40*/ MatAXPY_MPIAIJ,
2236:        MatGetSubMatrices_MPIAIJ,
2237:        MatIncreaseOverlap_MPIAIJ,
2238:        MatGetValues_MPIAIJ,
2239:        MatCopy_MPIAIJ,
2240: /*45*/ 0,
2241:        MatScale_MPIAIJ,
2242:        0,
2243:        0,
2244:        0,
2245: /*50*/ MatSetBlockSize_MPIAIJ,
2246:        0,
2247:        0,
2248:        0,
2249:        0,
2250: /*55*/ MatFDColoringCreate_MPIAIJ,
2251:        0,
2252:        MatSetUnfactored_MPIAIJ,
2253:        MatPermute_MPIAIJ,
2254:        0,
2255: /*60*/ MatGetSubMatrix_MPIAIJ,
2256:        MatDestroy_MPIAIJ,
2257:        MatView_MPIAIJ,
2258:        0,
2259:        0,
2260: /*65*/ 0,
2261:        0,
2262:        0,
2263:        0,
2264:        0,
2265: /*70*/ 0,
2266:        0,
2267:        MatSetColoring_MPIAIJ,
2268: #if defined(PETSC_HAVE_ADIC)
2269:        MatSetValuesAdic_MPIAIJ,
2270: #else
2271:        0,
2272: #endif
2273:        MatSetValuesAdifor_MPIAIJ,
2274: /*75*/ 0,
2275:        0,
2276:        0,
2277:        0,
2278:        0,
2279: /*80*/ 0,
2280:        0,
2281:        0,
2282:        0,
2283: /*84*/ MatLoad_MPIAIJ,
2284:        0,
2285:        0,
2286:        0,
2287:        0,
2288:        0,
2289: /*90*/ MatMatMult_MPIAIJ_MPIAIJ,
2290:        MatMatMultSymbolic_MPIAIJ_MPIAIJ,
2291:        MatMatMultNumeric_MPIAIJ_MPIAIJ,
2292:        MatPtAP_Basic,
2293:        MatPtAPSymbolic_MPIAIJ,
2294: /*95*/ MatPtAPNumeric_MPIAIJ,
2295:        0,
2296:        0,
2297:        0,
2298:        0,
2299: /*100*/0,
2300:        MatPtAPSymbolic_MPIAIJ_MPIAIJ,
2301:        MatPtAPNumeric_MPIAIJ_MPIAIJ,
2302:        MatConjugate_MPIAIJ,
2303:        0,
2304: /*105*/MatSetValuesRow_MPIAIJ,
2305:        MatRealPart_MPIAIJ,
2306:        MatImaginaryPart_MPIAIJ,
2307:        0,
2308:        0,
2309: /*110*/0,
2310:        MatGetRedundantMatrix_MPIAIJ};

2312: /* ----------------------------------------------------------------------------------------*/

2317: PetscErrorCode  MatStoreValues_MPIAIJ(Mat mat)
2318: {
2319:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

2323:   MatStoreValues(aij->A);
2324:   MatStoreValues(aij->B);
2325:   return(0);
2326: }

2332: PetscErrorCode  MatRetrieveValues_MPIAIJ(Mat mat)
2333: {
2334:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

2338:   MatRetrieveValues(aij->A);
2339:   MatRetrieveValues(aij->B);
2340:   return(0);
2341: }

2344:  #include petscpc.h
2348: PetscErrorCode  MatMPIAIJSetPreallocation_MPIAIJ(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2349: {
2350:   Mat_MPIAIJ     *b;
2352:   PetscInt       i;

2355:   B->preallocated = PETSC_TRUE;
2356:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2357:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2358:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
2359:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);

2361:   B->rmap.bs = B->cmap.bs = 1;
2362:   PetscMapSetUp(&B->rmap);
2363:   PetscMapSetUp(&B->cmap);
2364:   if (d_nnz) {
2365:     for (i=0; i<B->rmap.n; i++) {
2366:       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]);
2367:     }
2368:   }
2369:   if (o_nnz) {
2370:     for (i=0; i<B->rmap.n; i++) {
2371:       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]);
2372:     }
2373:   }
2374:   b = (Mat_MPIAIJ*)B->data;

2376:   /* Explicitly create 2 MATSEQAIJ matrices. */
2377:   MatCreate(PETSC_COMM_SELF,&b->A);
2378:   MatSetSizes(b->A,B->rmap.n,B->cmap.n,B->rmap.n,B->cmap.n);
2379:   MatSetType(b->A,MATSEQAIJ);
2380:   PetscLogObjectParent(B,b->A);
2381:   MatCreate(PETSC_COMM_SELF,&b->B);
2382:   MatSetSizes(b->B,B->rmap.n,B->cmap.N,B->rmap.n,B->cmap.N);
2383:   MatSetType(b->B,MATSEQAIJ);
2384:   PetscLogObjectParent(B,b->B);

2386:   MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
2387:   MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);

2389:   return(0);
2390: }

2395: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2396: {
2397:   Mat            mat;
2398:   Mat_MPIAIJ     *a,*oldmat = (Mat_MPIAIJ*)matin->data;

2402:   *newmat       = 0;
2403:   MatCreate(matin->comm,&mat);
2404:   MatSetSizes(mat,matin->rmap.n,matin->cmap.n,matin->rmap.N,matin->cmap.N);
2405:   MatSetType(mat,matin->type_name);
2406:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
2407:   a    = (Mat_MPIAIJ*)mat->data;
2408: 
2409:   mat->factor       = matin->factor;
2410:   mat->rmap.bs      = matin->rmap.bs;
2411:   mat->assembled    = PETSC_TRUE;
2412:   mat->insertmode   = NOT_SET_VALUES;
2413:   mat->preallocated = PETSC_TRUE;

2415:   a->size           = oldmat->size;
2416:   a->rank           = oldmat->rank;
2417:   a->donotstash     = oldmat->donotstash;
2418:   a->roworiented    = oldmat->roworiented;
2419:   a->rowindices     = 0;
2420:   a->rowvalues      = 0;
2421:   a->getrowactive   = PETSC_FALSE;

2423:   PetscMapCopy(mat->comm,&matin->rmap,&mat->rmap);
2424:   PetscMapCopy(mat->comm,&matin->cmap,&mat->cmap);

2426:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2427:   if (oldmat->colmap) {
2428: #if defined (PETSC_USE_CTABLE)
2429:     PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2430: #else
2431:     PetscMalloc((mat->cmap.N)*sizeof(PetscInt),&a->colmap);
2432:     PetscLogObjectMemory(mat,(mat->cmap.N)*sizeof(PetscInt));
2433:     PetscMemcpy(a->colmap,oldmat->colmap,(mat->cmap.N)*sizeof(PetscInt));
2434: #endif
2435:   } else a->colmap = 0;
2436:   if (oldmat->garray) {
2437:     PetscInt len;
2438:     len  = oldmat->B->cmap.n;
2439:     PetscMalloc((len+1)*sizeof(PetscInt),&a->garray);
2440:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2441:     if (len) { PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt)); }
2442:   } else a->garray = 0;
2443: 
2444:   VecDuplicate(oldmat->lvec,&a->lvec);
2445:   PetscLogObjectParent(mat,a->lvec);
2446:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2447:   PetscLogObjectParent(mat,a->Mvctx);
2448:   MatDuplicate(oldmat->A,cpvalues,&a->A);
2449:   PetscLogObjectParent(mat,a->A);
2450:   MatDuplicate(oldmat->B,cpvalues,&a->B);
2451:   PetscLogObjectParent(mat,a->B);
2452:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2453:   *newmat = mat;
2454:   return(0);
2455: }

2457:  #include petscsys.h

2461: PetscErrorCode MatLoad_MPIAIJ(PetscViewer viewer, MatType type,Mat *newmat)
2462: {
2463:   Mat            A;
2464:   PetscScalar    *vals,*svals;
2465:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2466:   MPI_Status     status;
2468:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag,maxnz;
2469:   PetscInt       i,nz,j,rstart,rend,mmax;
2470:   PetscInt       header[4],*rowlengths = 0,M,N,m,*cols;
2471:   PetscInt       *ourlens = PETSC_NULL,*procsnz = PETSC_NULL,*offlens = PETSC_NULL,jj,*mycols,*smycols;
2472:   PetscInt       cend,cstart,n,*rowners;
2473:   int            fd;

2476:   MPI_Comm_size(comm,&size);
2477:   MPI_Comm_rank(comm,&rank);
2478:   if (!rank) {
2479:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2480:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2481:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2482:   }

2484:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2485:   M = header[1]; N = header[2];
2486:   /* determine ownership of all rows */
2487:   m    = M/size + ((M % size) > rank);
2488:   PetscMalloc((size+1)*sizeof(PetscInt),&rowners);
2489:   MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);

2491:   /* First process needs enough room for process with most rows */
2492:   if (!rank) {
2493:     mmax       = rowners[1];
2494:     for (i=2; i<size; i++) {
2495:       mmax = PetscMax(mmax,rowners[i]);
2496:     }
2497:   } else mmax = m;

2499:   rowners[0] = 0;
2500:   for (i=2; i<=size; i++) {
2501:     rowners[i] += rowners[i-1];
2502:   }
2503:   rstart = rowners[rank];
2504:   rend   = rowners[rank+1];

2506:   /* distribute row lengths to all processors */
2507:   PetscMalloc2(mmax,PetscInt,&ourlens,mmax,PetscInt,&offlens);
2508:   if (!rank) {
2509:     PetscBinaryRead(fd,ourlens,m,PETSC_INT);
2510:     PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2511:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2512:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2513:     for (j=0; j<m; j++) {
2514:       procsnz[0] += ourlens[j];
2515:     }
2516:     for (i=1; i<size; i++) {
2517:       PetscBinaryRead(fd,rowlengths,rowners[i+1]-rowners[i],PETSC_INT);
2518:       /* calculate the number of nonzeros on each processor */
2519:       for (j=0; j<rowners[i+1]-rowners[i]; j++) {
2520:         procsnz[i] += rowlengths[j];
2521:       }
2522:       MPI_Send(rowlengths,rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
2523:     }
2524:     PetscFree(rowlengths);
2525:   } else {
2526:     MPI_Recv(ourlens,m,MPIU_INT,0,tag,comm,&status);
2527:   }

2529:   if (!rank) {
2530:     /* determine max buffer needed and allocate it */
2531:     maxnz = 0;
2532:     for (i=0; i<size; i++) {
2533:       maxnz = PetscMax(maxnz,procsnz[i]);
2534:     }
2535:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2537:     /* read in my part of the matrix column indices  */
2538:     nz   = procsnz[0];
2539:     PetscMalloc(nz*sizeof(PetscInt),&mycols);
2540:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);

2542:     /* read in every one elses and ship off */
2543:     for (i=1; i<size; i++) {
2544:       nz   = procsnz[i];
2545:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2546:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2547:     }
2548:     PetscFree(cols);
2549:   } else {
2550:     /* determine buffer space needed for message */
2551:     nz = 0;
2552:     for (i=0; i<m; i++) {
2553:       nz += ourlens[i];
2554:     }
2555:     PetscMalloc(nz*sizeof(PetscInt),&mycols);

2557:     /* receive message of column indices*/
2558:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2559:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2560:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2561:   }

2563:   /* determine column ownership if matrix is not square */
2564:   if (N != M) {
2565:     n      = N/size + ((N % size) > rank);
2566:     MPI_Scan(&n,&cend,1,MPIU_INT,MPI_SUM,comm);
2567:     cstart = cend - n;
2568:   } else {
2569:     cstart = rstart;
2570:     cend   = rend;
2571:     n      = cend - cstart;
2572:   }

2574:   /* loop over local rows, determining number of off diagonal entries */
2575:   PetscMemzero(offlens,m*sizeof(PetscInt));
2576:   jj = 0;
2577:   for (i=0; i<m; i++) {
2578:     for (j=0; j<ourlens[i]; j++) {
2579:       if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++;
2580:       jj++;
2581:     }
2582:   }

2584:   /* create our matrix */
2585:   for (i=0; i<m; i++) {
2586:     ourlens[i] -= offlens[i];
2587:   }
2588:   MatCreate(comm,&A);
2589:   MatSetSizes(A,m,n,M,N);
2590:   MatSetType(A,type);
2591:   MatMPIAIJSetPreallocation(A,0,ourlens,0,offlens);

2593:   MatSetOption(A,MAT_COLUMNS_SORTED);
2594:   for (i=0; i<m; i++) {
2595:     ourlens[i] += offlens[i];
2596:   }

2598:   if (!rank) {
2599:     PetscMalloc((maxnz+1)*sizeof(PetscScalar),&vals);

2601:     /* read in my part of the matrix numerical values  */
2602:     nz   = procsnz[0];
2603:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2604: 
2605:     /* insert into matrix */
2606:     jj      = rstart;
2607:     smycols = mycols;
2608:     svals   = vals;
2609:     for (i=0; i<m; i++) {
2610:       MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2611:       smycols += ourlens[i];
2612:       svals   += ourlens[i];
2613:       jj++;
2614:     }

2616:     /* read in other processors and ship out */
2617:     for (i=1; i<size; i++) {
2618:       nz   = procsnz[i];
2619:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2620:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2621:     }
2622:     PetscFree(procsnz);
2623:   } else {
2624:     /* receive numeric values */
2625:     PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);

2627:     /* receive message of values*/
2628:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2629:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2630:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2632:     /* insert into matrix */
2633:     jj      = rstart;
2634:     smycols = mycols;
2635:     svals   = vals;
2636:     for (i=0; i<m; i++) {
2637:       MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2638:       smycols += ourlens[i];
2639:       svals   += ourlens[i];
2640:       jj++;
2641:     }
2642:   }
2643:   PetscFree2(ourlens,offlens);
2644:   PetscFree(vals);
2645:   PetscFree(mycols);
2646:   PetscFree(rowners);

2648:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2649:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2650:   *newmat = A;
2651:   return(0);
2652: }

2656: /*
2657:     Not great since it makes two copies of the submatrix, first an SeqAIJ 
2658:   in local and then by concatenating the local matrices the end result.
2659:   Writing it directly would be much like MatGetSubMatrices_MPIAIJ()
2660: */
2661: PetscErrorCode MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
2662: {
2664:   PetscMPIInt    rank,size;
2665:   PetscInt       i,m,n,rstart,row,rend,nz,*cwork,j;
2666:   PetscInt       *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal;
2667:   Mat            *local,M,Mreuse;
2668:   PetscScalar    *vwork,*aa;
2669:   MPI_Comm       comm = mat->comm;
2670:   Mat_SeqAIJ     *aij;


2674:   MPI_Comm_rank(comm,&rank);
2675:   MPI_Comm_size(comm,&size);

2677:   if (call ==  MAT_REUSE_MATRIX) {
2678:     PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject *)&Mreuse);
2679:     if (!Mreuse) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
2680:     local = &Mreuse;
2681:     MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&local);
2682:   } else {
2683:     MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);
2684:     Mreuse = *local;
2685:     PetscFree(local);
2686:   }

2688:   /* 
2689:       m - number of local rows
2690:       n - number of columns (same on all processors)
2691:       rstart - first row in new global matrix generated
2692:   */
2693:   MatGetSize(Mreuse,&m,&n);
2694:   if (call == MAT_INITIAL_MATRIX) {
2695:     aij = (Mat_SeqAIJ*)(Mreuse)->data;
2696:     ii  = aij->i;
2697:     jj  = aij->j;

2699:     /*
2700:         Determine the number of non-zeros in the diagonal and off-diagonal 
2701:         portions of the matrix in order to do correct preallocation
2702:     */

2704:     /* first get start and end of "diagonal" columns */
2705:     if (csize == PETSC_DECIDE) {
2706:       ISGetSize(isrow,&mglobal);
2707:       if (mglobal == n) { /* square matrix */
2708:         nlocal = m;
2709:       } else {
2710:         nlocal = n/size + ((n % size) > rank);
2711:       }
2712:     } else {
2713:       nlocal = csize;
2714:     }
2715:     MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
2716:     rstart = rend - nlocal;
2717:     if (rank == size - 1 && rend != n) {
2718:       SETERRQ2(PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);
2719:     }

2721:     /* next, compute all the lengths */
2722:     PetscMalloc((2*m+1)*sizeof(PetscInt),&dlens);
2723:     olens = dlens + m;
2724:     for (i=0; i<m; i++) {
2725:       jend = ii[i+1] - ii[i];
2726:       olen = 0;
2727:       dlen = 0;
2728:       for (j=0; j<jend; j++) {
2729:         if (*jj < rstart || *jj >= rend) olen++;
2730:         else dlen++;
2731:         jj++;
2732:       }
2733:       olens[i] = olen;
2734:       dlens[i] = dlen;
2735:     }
2736:     MatCreate(comm,&M);
2737:     MatSetSizes(M,m,nlocal,PETSC_DECIDE,n);
2738:     MatSetType(M,mat->type_name);
2739:     MatMPIAIJSetPreallocation(M,0,dlens,0,olens);
2740:     PetscFree(dlens);
2741:   } else {
2742:     PetscInt ml,nl;

2744:     M = *newmat;
2745:     MatGetLocalSize(M,&ml,&nl);
2746:     if (ml != m) SETERRQ(PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
2747:     MatZeroEntries(M);
2748:     /*
2749:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2750:        rather than the slower MatSetValues().
2751:     */
2752:     M->was_assembled = PETSC_TRUE;
2753:     M->assembled     = PETSC_FALSE;
2754:   }
2755:   MatGetOwnershipRange(M,&rstart,&rend);
2756:   aij = (Mat_SeqAIJ*)(Mreuse)->data;
2757:   ii  = aij->i;
2758:   jj  = aij->j;
2759:   aa  = aij->a;
2760:   for (i=0; i<m; i++) {
2761:     row   = rstart + i;
2762:     nz    = ii[i+1] - ii[i];
2763:     cwork = jj;     jj += nz;
2764:     vwork = aa;     aa += nz;
2765:     MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
2766:   }

2768:   MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
2769:   MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
2770:   *newmat = M;

2772:   /* save submatrix used in processor for next request */
2773:   if (call ==  MAT_INITIAL_MATRIX) {
2774:     PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
2775:     PetscObjectDereference((PetscObject)Mreuse);
2776:   }

2778:   return(0);
2779: }

2784: PetscErrorCode  MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
2785: {
2786:   PetscInt       m,cstart, cend,j,nnz,i,d;
2787:   PetscInt       *d_nnz,*o_nnz,nnz_max = 0,rstart,ii;
2788:   const PetscInt *JJ;
2789:   PetscScalar    *values;

2793:   if (Ii[0]) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Ii[0] must be 0 it is %D",Ii[0]);

2795:   B->rmap.bs = B->cmap.bs = 1;
2796:   PetscMapSetUp(&B->rmap);
2797:   PetscMapSetUp(&B->cmap);
2798:   m      = B->rmap.n;
2799:   cstart = B->cmap.rstart;
2800:   cend   = B->cmap.rend;
2801:   rstart = B->rmap.rstart;

2803:   PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
2804:   o_nnz = d_nnz + m;

2806:   for (i=0; i<m; i++) {
2807:     nnz     = Ii[i+1]- Ii[i];
2808:     JJ      = J + Ii[i];
2809:     nnz_max = PetscMax(nnz_max,nnz);
2810:     if (nnz < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative %D number of columns",i,nnz);
2811:     for (j=0; j<nnz; j++) {
2812:       if (*JJ >= cstart) break;
2813:       JJ++;
2814:     }
2815:     d = 0;
2816:     for (; j<nnz; j++) {
2817:       if (*JJ++ >= cend) break;
2818:       d++;
2819:     }
2820:     d_nnz[i] = d;
2821:     o_nnz[i] = nnz - d;
2822:   }
2823:   MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
2824:   PetscFree(d_nnz);

2826:   if (v) values = (PetscScalar*)v;
2827:   else {
2828:     PetscMalloc((nnz_max+1)*sizeof(PetscScalar),&values);
2829:     PetscMemzero(values,nnz_max*sizeof(PetscScalar));
2830:   }

2832:   MatSetOption(B,MAT_COLUMNS_SORTED);
2833:   for (i=0; i<m; i++) {
2834:     ii   = i + rstart;
2835:     nnz  = Ii[i+1]- Ii[i];
2836:     MatSetValues_MPIAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
2837:   }
2838:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2839:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2840:   MatSetOption(B,MAT_COLUMNS_UNSORTED);

2842:   if (!v) {
2843:     PetscFree(values);
2844:   }
2845:   return(0);
2846: }

2851: /*@
2852:    MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
2853:    (the default parallel PETSc format).  

2855:    Collective on MPI_Comm

2857:    Input Parameters:
2858: +  B - the matrix 
2859: .  i - the indices into j for the start of each local row (starts with zero)
2860: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
2861: -  v - optional values in the matrix

2863:    Level: developer

2865:    Notes: this actually copies the values from i[], j[], and a[] to put them into PETSc's internal
2866:      storage format. Thus changing the values in a[] after this call will not effect the matrix values.

2868: .keywords: matrix, aij, compressed row, sparse, parallel

2870: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ,
2871:           MatCreateSeqAIJWithArrays(), MatCreateMPIAIJWithSplitArrays()
2872: @*/
2873: PetscErrorCode  MatMPIAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2874: {
2875:   PetscErrorCode ierr,(*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);

2878:   PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",(void (**)(void))&f);
2879:   if (f) {
2880:     (*f)(B,i,j,v);
2881:   }
2882:   return(0);
2883: }

2887: /*@C
2888:    MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in AIJ format
2889:    (the default parallel PETSc format).  For good matrix assembly performance
2890:    the user should preallocate the matrix storage by setting the parameters 
2891:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2892:    performance can be increased by more than a factor of 50.

2894:    Collective on MPI_Comm

2896:    Input Parameters:
2897: +  A - the matrix 
2898: .  d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
2899:            (same value is used for all local rows)
2900: .  d_nnz - array containing the number of nonzeros in the various rows of the 
2901:            DIAGONAL portion of the local submatrix (possibly different for each row)
2902:            or PETSC_NULL, if d_nz is used to specify the nonzero structure. 
2903:            The size of this array is equal to the number of local rows, i.e 'm'. 
2904:            You must leave room for the diagonal entry even if it is zero.
2905: .  o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
2906:            submatrix (same value is used for all local rows).
2907: -  o_nnz - array containing the number of nonzeros in the various rows of the
2908:            OFF-DIAGONAL portion of the local submatrix (possibly different for
2909:            each row) or PETSC_NULL, if o_nz is used to specify the nonzero 
2910:            structure. The size of this array is equal to the number 
2911:            of local rows, i.e 'm'. 

2913:    If the *_nnz parameter is given then the *_nz parameter is ignored

2915:    The AIJ format (also called the Yale sparse matrix format or
2916:    compressed row storage (CSR)), is fully compatible with standard Fortran 77
2917:    storage.  The stored row and column indices begin with zero.  See the users manual for details.

2919:    The parallel matrix is partitioned such that the first m0 rows belong to 
2920:    process 0, the next m1 rows belong to process 1, the next m2 rows belong 
2921:    to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.

2923:    The DIAGONAL portion of the local submatrix of a processor can be defined 
2924:    as the submatrix which is obtained by extraction the part corresponding 
2925:    to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 
2926:    first row that belongs to the processor, and r2 is the last row belonging 
2927:    to the this processor. This is a square mxm matrix. The remaining portion 
2928:    of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.

2930:    If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.

2932:    Example usage:
2933:   
2934:    Consider the following 8x8 matrix with 34 non-zero values, that is 
2935:    assembled across 3 processors. Lets assume that proc0 owns 3 rows,
2936:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 
2937:    as follows:

2939: .vb
2940:             1  2  0  |  0  3  0  |  0  4
2941:     Proc0   0  5  6  |  7  0  0  |  8  0
2942:             9  0 10  | 11  0  0  | 12  0
2943:     -------------------------------------
2944:            13  0 14  | 15 16 17  |  0  0
2945:     Proc1   0 18  0  | 19 20 21  |  0  0 
2946:             0  0  0  | 22 23  0  | 24  0
2947:     -------------------------------------
2948:     Proc2  25 26 27  |  0  0 28  | 29  0
2949:            30  0  0  | 31 32 33  |  0 34
2950: .ve

2952:    This can be represented as a collection of submatrices as:

2954: .vb
2955:       A B C
2956:       D E F
2957:       G H I
2958: .ve

2960:    Where the submatrices A,B,C are owned by proc0, D,E,F are
2961:    owned by proc1, G,H,I are owned by proc2.

2963:    The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2964:    The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2965:    The 'M','N' parameters are 8,8, and have the same values on all procs.

2967:    The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
2968:    submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
2969:    corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
2970:    Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
2971:    part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
2972:    matrix, ans [DF] as another SeqAIJ matrix.

2974:    When d_nz, o_nz parameters are specified, d_nz storage elements are
2975:    allocated for every row of the local diagonal submatrix, and o_nz
2976:    storage locations are allocated for every row of the OFF-DIAGONAL submat.
2977:    One way to choose d_nz and o_nz is to use the max nonzerors per local 
2978:    rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 
2979:    In this case, the values of d_nz,o_nz are:
2980: .vb
2981:      proc0 : dnz = 2, o_nz = 2
2982:      proc1 : dnz = 3, o_nz = 2
2983:      proc2 : dnz = 1, o_nz = 4
2984: .ve
2985:    We are allocating m*(d_nz+o_nz) storage locations for every proc. This
2986:    translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
2987:    for proc3. i.e we are using 12+15+10=37 storage locations to store 
2988:    34 values.

2990:    When d_nnz, o_nnz parameters are specified, the storage is specified
2991:    for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
2992:    In the above case the values for d_nnz,o_nnz are:
2993: .vb
2994:      proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
2995:      proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
2996:      proc2: d_nnz = [1,1]   and o_nnz = [4,4]
2997: .ve
2998:    Here the space allocated is sum of all the above values i.e 34, and
2999:    hence pre-allocation is perfect.

3001:    Level: intermediate

3003: .keywords: matrix, aij, compressed row, sparse, parallel

3005: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIAIJ(), MatMPIAIJSetPreallocationCSR(),
3006:           MPIAIJ
3007: @*/
3008: PetscErrorCode  MatMPIAIJSetPreallocation(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
3009: {
3010:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

3013:   PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocation_C",(void (**)(void))&f);
3014:   if (f) {
3015:     (*f)(B,d_nz,d_nnz,o_nz,o_nnz);
3016:   }
3017:   return(0);
3018: }

3022: /*@C
3023:      MatCreateMPIAIJWithArrays - creates a MPI AIJ matrix using arrays that contain in standard
3024:          CSR format the local rows.

3026:    Collective on MPI_Comm

3028:    Input Parameters:
3029: +  comm - MPI communicator
3030: .  m - number of local rows (Cannot be PETSC_DECIDE)
3031: .  n - This value should be the same as the local size used in creating the 
3032:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3033:        calculated if N is given) For square matrices n is almost always m.
3034: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3035: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3036: .   i - row indices
3037: .   j - column indices
3038: -   a - matrix values

3040:    Output Parameter:
3041: .   mat - the matrix

3043:    Level: intermediate

3045:    Notes:
3046:        The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3047:      thus you CANNOT change the matrix entries by changing the values of a[] after you have 
3048:      called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.

3050:        The i and j indices are 0 based

3052: .keywords: matrix, aij, compressed row, sparse, parallel

3054: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3055:           MPIAIJ, MatCreateMPIAIJ(), MatCreateMPIAIJWithSplitArrays()
3056: @*/
3057: PetscErrorCode  MatCreateMPIAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
3058: {

3062:   if (i[0]) {
3063:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3064:   }
3065:   if (m < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
3066:   MatCreate(comm,mat);
3067:   MatSetSizes(*mat,m,n,M,N);
3068:   MatSetType(*mat,MATMPIAIJ);
3069:   MatMPIAIJSetPreallocationCSR(*mat,i,j,a);
3070:   return(0);
3071: }

3075: /*@C
3076:    MatCreateMPIAIJ - Creates a sparse parallel matrix in AIJ format
3077:    (the default parallel PETSc format).  For good matrix assembly performance
3078:    the user should preallocate the matrix storage by setting the parameters 
3079:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
3080:    performance can be increased by more than a factor of 50.

3082:    Collective on MPI_Comm

3084:    Input Parameters:
3085: +  comm - MPI communicator
3086: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
3087:            This value should be the same as the local size used in creating the 
3088:            y vector for the matrix-vector product y = Ax.
3089: .  n - This value should be the same as the local size used in creating the 
3090:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3091:        calculated if N is given) For square matrices n is almost always m.
3092: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3093: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3094: .  d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
3095:            (same value is used for all local rows)
3096: .  d_nnz - array containing the number of nonzeros in the various rows of the 
3097:            DIAGONAL portion of the local submatrix (possibly different for each row)
3098:            or PETSC_NULL, if d_nz is used to specify the nonzero structure. 
3099:            The size of this array is equal to the number of local rows, i.e 'm'. 
3100:            You must leave room for the diagonal entry even if it is zero.
3101: .  o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
3102:            submatrix (same value is used for all local rows).
3103: -  o_nnz - array containing the number of nonzeros in the various rows of the
3104:            OFF-DIAGONAL portion of the local submatrix (possibly different for
3105:            each row) or PETSC_NULL, if o_nz is used to specify the nonzero 
3106:            structure. The size of this array is equal to the number 
3107:            of local rows, i.e 'm'. 

3109:    Output Parameter:
3110: .  A - the matrix 

3112:    Notes:
3113:    If the *_nnz parameter is given then the *_nz parameter is ignored

3115:    m,n,M,N parameters specify the size of the matrix, and its partitioning across
3116:    processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate
3117:    storage requirements for this matrix.

3119:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one 
3120:    processor than it must be used on all processors that share the object for 
3121:    that argument.

3123:    The user MUST specify either the local or global matrix dimensions
3124:    (possibly both).

3126:    The parallel matrix is partitioned across processors such that the
3127:    first m0 rows belong to process 0, the next m1 rows belong to
3128:    process 1, the next m2 rows belong to process 2 etc.. where
3129:    m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
3130:    values corresponding to [m x N] submatrix.

3132:    The columns are logically partitioned with the n0 columns belonging
3133:    to 0th partition, the next n1 columns belonging to the next
3134:    partition etc.. where n0,n1,n2... are the the input parameter 'n'.

3136:    The DIAGONAL portion of the local submatrix on any given processor
3137:    is the submatrix corresponding to the rows and columns m,n
3138:    corresponding to the given processor. i.e diagonal matrix on
3139:    process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
3140:    etc. The remaining portion of the local submatrix [m x (N-n)]
3141:    constitute the OFF-DIAGONAL portion. The example below better
3142:    illustrates this concept.

3144:    For a square global matrix we define each processor's diagonal portion 
3145:    to be its local rows and the corresponding columns (a square submatrix);  
3146:    each processor's off-diagonal portion encompasses the remainder of the
3147:    local matrix (a rectangular submatrix). 

3149:    If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.

3151:    When calling this routine with a single process communicator, a matrix of
3152:    type SEQAIJ is returned.  If a matrix of type MPIAIJ is desired for this
3153:    type of communicator, use the construction mechanism:
3154:      MatCreate(...,&A); MatSetType(A,MPIAIJ); MatMPIAIJSetPreallocation(A,...);

3156:    By default, this format uses inodes (identical nodes) when possible.
3157:    We search for consecutive rows with the same nonzero structure, thereby
3158:    reusing matrix information to achieve increased efficiency.

3160:    Options Database Keys:
3161: +  -mat_no_inode  - Do not use inodes
3162: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3163: -  -mat_aij_oneindex - Internally use indexing starting at 1
3164:         rather than 0.  Note that when calling MatSetValues(),
3165:         the user still MUST index entries starting at 0!


3168:    Example usage:
3169:   
3170:    Consider the following 8x8 matrix with 34 non-zero values, that is 
3171:    assembled across 3 processors. Lets assume that proc0 owns 3 rows,
3172:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 
3173:    as follows:

3175: .vb
3176:             1  2  0  |  0  3  0  |  0  4
3177:     Proc0   0  5  6  |  7  0  0  |  8  0
3178:             9  0 10  | 11  0  0  | 12  0
3179:     -------------------------------------
3180:            13  0 14  | 15 16 17  |  0  0
3181:     Proc1   0 18  0  | 19 20 21  |  0  0 
3182:             0  0  0  | 22 23  0  | 24  0
3183:     -------------------------------------
3184:     Proc2  25 26 27  |  0  0 28  | 29  0
3185:            30  0  0  | 31 32 33  |  0 34
3186: .ve

3188:    This can be represented as a collection of submatrices as:

3190: .vb
3191:       A B C
3192:       D E F
3193:       G H I
3194: .ve

3196:    Where the submatrices A,B,C are owned by proc0, D,E,F are
3197:    owned by proc1, G,H,I are owned by proc2.

3199:    The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3200:    The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3201:    The 'M','N' parameters are 8,8, and have the same values on all procs.

3203:    The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
3204:    submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
3205:    corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
3206:    Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
3207:    part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
3208:    matrix, ans [DF] as another SeqAIJ matrix.

3210:    When d_nz, o_nz parameters are specified, d_nz storage elements are
3211:    allocated for every row of the local diagonal submatrix, and o_nz
3212:    storage locations are allocated for every row of the OFF-DIAGONAL submat.
3213:    One way to choose d_nz and o_nz is to use the max nonzerors per local 
3214:    rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 
3215:    In this case, the values of d_nz,o_nz are:
3216: .vb
3217:      proc0 : dnz = 2, o_nz = 2
3218:      proc1 : dnz = 3, o_nz = 2
3219:      proc2 : dnz = 1, o_nz = 4
3220: .ve
3221:    We are allocating m*(d_nz+o_nz) storage locations for every proc. This
3222:    translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
3223:    for proc3. i.e we are using 12+15+10=37 storage locations to store 
3224:    34 values.

3226:    When d_nnz, o_nnz parameters are specified, the storage is specified
3227:    for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
3228:    In the above case the values for d_nnz,o_nnz are:
3229: .vb
3230:      proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
3231:      proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
3232:      proc2: d_nnz = [1,1]   and o_nnz = [4,4]
3233: .ve
3234:    Here the space allocated is sum of all the above values i.e 34, and
3235:    hence pre-allocation is perfect.

3237:    Level: intermediate

3239: .keywords: matrix, aij, compressed row, sparse, parallel

3241: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3242:           MPIAIJ, MatCreateMPIAIJWithArrays()
3243: @*/
3244: 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)
3245: {
3247:   PetscMPIInt    size;

3250:   MatCreate(comm,A);
3251:   MatSetSizes(*A,m,n,M,N);
3252:   MPI_Comm_size(comm,&size);
3253:   if (size > 1) {
3254:     MatSetType(*A,MATMPIAIJ);
3255:     MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
3256:   } else {
3257:     MatSetType(*A,MATSEQAIJ);
3258:     MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
3259:   }
3260:   return(0);
3261: }

3265: PetscErrorCode  MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
3266: {
3267:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

3270:   *Ad     = a->A;
3271:   *Ao     = a->B;
3272:   *colmap = a->garray;
3273:   return(0);
3274: }

3278: PetscErrorCode MatSetColoring_MPIAIJ(Mat A,ISColoring coloring)
3279: {
3281:   PetscInt       i;
3282:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

3285:   if (coloring->ctype == IS_COLORING_GLOBAL) {
3286:     ISColoringValue *allcolors,*colors;
3287:     ISColoring      ocoloring;

3289:     /* set coloring for diagonal portion */
3290:     MatSetColoring_SeqAIJ(a->A,coloring);

3292:     /* set coloring for off-diagonal portion */
3293:     ISAllGatherColors(A->comm,coloring->n,coloring->colors,PETSC_NULL,&allcolors);
3294:     PetscMalloc((a->B->cmap.n+1)*sizeof(ISColoringValue),&colors);
3295:     for (i=0; i<a->B->cmap.n; i++) {
3296:       colors[i] = allcolors[a->garray[i]];
3297:     }
3298:     PetscFree(allcolors);
3299:     ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap.n,colors,&ocoloring);
3300:     MatSetColoring_SeqAIJ(a->B,ocoloring);
3301:     ISColoringDestroy(ocoloring);
3302:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3303:     ISColoringValue *colors;
3304:     PetscInt        *larray;
3305:     ISColoring      ocoloring;

3307:     /* set coloring for diagonal portion */
3308:     PetscMalloc((a->A->cmap.n+1)*sizeof(PetscInt),&larray);
3309:     for (i=0; i<a->A->cmap.n; i++) {
3310:       larray[i] = i + A->cmap.rstart;
3311:     }
3312:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->A->cmap.n,larray,PETSC_NULL,larray);
3313:     PetscMalloc((a->A->cmap.n+1)*sizeof(ISColoringValue),&colors);
3314:     for (i=0; i<a->A->cmap.n; i++) {
3315:       colors[i] = coloring->colors[larray[i]];
3316:     }
3317:     PetscFree(larray);
3318:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,a->A->cmap.n,colors,&ocoloring);
3319:     MatSetColoring_SeqAIJ(a->A,ocoloring);
3320:     ISColoringDestroy(ocoloring);

3322:     /* set coloring for off-diagonal portion */
3323:     PetscMalloc((a->B->cmap.n+1)*sizeof(PetscInt),&larray);
3324:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->B->cmap.n,a->garray,PETSC_NULL,larray);
3325:     PetscMalloc((a->B->cmap.n+1)*sizeof(ISColoringValue),&colors);
3326:     for (i=0; i<a->B->cmap.n; i++) {
3327:       colors[i] = coloring->colors[larray[i]];
3328:     }
3329:     PetscFree(larray);
3330:     ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap.n,colors,&ocoloring);
3331:     MatSetColoring_SeqAIJ(a->B,ocoloring);
3332:     ISColoringDestroy(ocoloring);
3333:   } else {
3334:     SETERRQ1(PETSC_ERR_SUP,"No support ISColoringType %d",(int)coloring->ctype);
3335:   }

3337:   return(0);
3338: }

3340: #if defined(PETSC_HAVE_ADIC)
3343: PetscErrorCode MatSetValuesAdic_MPIAIJ(Mat A,void *advalues)
3344: {
3345:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

3349:   MatSetValuesAdic_SeqAIJ(a->A,advalues);
3350:   MatSetValuesAdic_SeqAIJ(a->B,advalues);
3351:   return(0);
3352: }
3353: #endif

3357: PetscErrorCode MatSetValuesAdifor_MPIAIJ(Mat A,PetscInt nl,void *advalues)
3358: {
3359:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

3363:   MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);
3364:   MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);
3365:   return(0);
3366: }

3370: /*@C
3371:       MatMerge - Creates a single large PETSc matrix by concatinating sequential
3372:                  matrices from each processor

3374:     Collective on MPI_Comm

3376:    Input Parameters:
3377: +    comm - the communicators the parallel matrix will live on
3378: .    inmat - the input sequential matrices
3379: .    n - number of local columns (or PETSC_DECIDE)
3380: -    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

3382:    Output Parameter:
3383: .    outmat - the parallel matrix generated

3385:     Level: advanced

3387:    Notes: The number of columns of the matrix in EACH processor MUST be the same.

3389: @*/
3390: PetscErrorCode  MatMerge(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
3391: {
3393:   PetscInt       m,N,i,rstart,nnz,Ii,*dnz,*onz;
3394:   PetscInt       *indx;
3395:   PetscScalar    *values;

3398:   MatGetSize(inmat,&m,&N);
3399:   if (scall == MAT_INITIAL_MATRIX){
3400:     /* count nonzeros in each row, for diagonal and off diagonal portion of matrix */
3401:     if (n == PETSC_DECIDE){
3402:       PetscSplitOwnership(comm,&n,&N);
3403:     }
3404:     MPI_Scan(&m, &rstart,1,MPIU_INT,MPI_SUM,comm);
3405:     rstart -= m;

3407:     MatPreallocateInitialize(comm,m,n,dnz,onz);
3408:     for (i=0;i<m;i++) {
3409:       MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
3410:       MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);
3411:       MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
3412:     }
3413:     /* This routine will ONLY return MPIAIJ type matrix */
3414:     MatCreate(comm,outmat);
3415:     MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3416:     MatSetType(*outmat,MATMPIAIJ);
3417:     MatMPIAIJSetPreallocation(*outmat,0,dnz,0,onz);
3418:     MatPreallocateFinalize(dnz,onz);
3419: 
3420:   } else if (scall == MAT_REUSE_MATRIX){
3421:     MatGetOwnershipRange(*outmat,&rstart,PETSC_NULL);
3422:   } else {
3423:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
3424:   }

3426:   for (i=0;i<m;i++) {
3427:     MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3428:     Ii    = i + rstart;
3429:     MatSetValues(*outmat,1,&Ii,nnz,indx,values,INSERT_VALUES);
3430:     MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3431:   }
3432:   MatDestroy(inmat);
3433:   MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
3434:   MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);

3436:   return(0);
3437: }

3441: PetscErrorCode MatFileSplit(Mat A,char *outfile)
3442: {
3443:   PetscErrorCode    ierr;
3444:   PetscMPIInt       rank;
3445:   PetscInt          m,N,i,rstart,nnz;
3446:   size_t            len;
3447:   const PetscInt    *indx;
3448:   PetscViewer       out;
3449:   char              *name;
3450:   Mat               B;
3451:   const PetscScalar *values;

3454:   MatGetLocalSize(A,&m,0);
3455:   MatGetSize(A,0,&N);
3456:   /* Should this be the type of the diagonal block of A? */
3457:   MatCreate(PETSC_COMM_SELF,&B);
3458:   MatSetSizes(B,m,N,m,N);
3459:   MatSetType(B,MATSEQAIJ);
3460:   MatSeqAIJSetPreallocation(B,0,PETSC_NULL);
3461:   MatGetOwnershipRange(A,&rstart,0);
3462:   for (i=0;i<m;i++) {
3463:     MatGetRow(A,i+rstart,&nnz,&indx,&values);
3464:     MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);
3465:     MatRestoreRow(A,i+rstart,&nnz,&indx,&values);
3466:   }
3467:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3468:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3470:   MPI_Comm_rank(A->comm,&rank);
3471:   PetscStrlen(outfile,&len);
3472:   PetscMalloc((len+5)*sizeof(char),&name);
3473:   sprintf(name,"%s.%d",outfile,rank);
3474:   PetscViewerBinaryOpen(PETSC_COMM_SELF,name,FILE_MODE_APPEND,&out);
3475:   PetscFree(name);
3476:   MatView(B,out);
3477:   PetscViewerDestroy(out);
3478:   MatDestroy(B);
3479:   return(0);
3480: }

3482: EXTERN PetscErrorCode MatDestroy_MPIAIJ(Mat);
3485: PetscErrorCode  MatDestroy_MPIAIJ_SeqsToMPI(Mat A)
3486: {
3487:   PetscErrorCode       ierr;
3488:   Mat_Merge_SeqsToMPI  *merge;
3489:   PetscContainer       container;

3492:   PetscObjectQuery((PetscObject)A,"MatMergeSeqsToMPI",(PetscObject *)&container);
3493:   if (container) {
3494:     PetscContainerGetPointer(container,(void **)&merge);
3495:     PetscFree(merge->id_r);
3496:     PetscFree(merge->len_s);
3497:     PetscFree(merge->len_r);
3498:     PetscFree(merge->bi);
3499:     PetscFree(merge->bj);
3500:     PetscFree(merge->buf_ri);
3501:     PetscFree(merge->buf_rj);
3502:     PetscFree(merge->coi);
3503:     PetscFree(merge->coj);
3504:     PetscFree(merge->owners_co);
3505:     PetscFree(merge->rowmap.range);
3506: 
3507:     PetscContainerDestroy(container);
3508:     PetscObjectCompose((PetscObject)A,"MatMergeSeqsToMPI",0);
3509:   }
3510:   PetscFree(merge);

3512:   MatDestroy_MPIAIJ(A);
3513:   return(0);
3514: }

3516:  #include src/mat/utils/freespace.h
3517:  #include petscbt.h
3518: static PetscEvent logkey_seqstompinum = 0;
3521: /*@C
3522:       MatMerge_SeqsToMPI - Creates a MPIAIJ matrix by adding sequential
3523:                  matrices from each processor

3525:     Collective on MPI_Comm

3527:    Input Parameters:
3528: +    comm - the communicators the parallel matrix will live on
3529: .    seqmat - the input sequential matrices
3530: .    m - number of local rows (or PETSC_DECIDE)
3531: .    n - number of local columns (or PETSC_DECIDE)
3532: -    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

3534:    Output Parameter:
3535: .    mpimat - the parallel matrix generated

3537:     Level: advanced

3539:    Notes: 
3540:      The dimensions of the sequential matrix in each processor MUST be the same.
3541:      The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
3542:      destroyed when mpimat is destroyed. Call PetscObjectQuery() to access seqmat.
3543: @*/
3544: PetscErrorCode  MatMerge_SeqsToMPINumeric(Mat seqmat,Mat mpimat)
3545: {
3546:   PetscErrorCode       ierr;
3547:   MPI_Comm             comm=mpimat->comm;
3548:   Mat_SeqAIJ           *a=(Mat_SeqAIJ*)seqmat->data;
3549:   PetscMPIInt          size,rank,taga,*len_s;
3550:   PetscInt             N=mpimat->cmap.N,i,j,*owners,*ai=a->i,*aj=a->j;
3551:   PetscInt             proc,m;
3552:   PetscInt             **buf_ri,**buf_rj;
3553:   PetscInt             k,anzi,*bj_i,*bi,*bj,arow,bnzi,nextaj;
3554:   PetscInt             nrows,**buf_ri_k,**nextrow,**nextai;
3555:   MPI_Request          *s_waits,*r_waits;
3556:   MPI_Status           *status;
3557:   MatScalar            *aa=a->a,**abuf_r,*ba_i;
3558:   Mat_Merge_SeqsToMPI  *merge;
3559:   PetscContainer       container;
3560: 
3562:   if (!logkey_seqstompinum) {
3564:   }

3567:   MPI_Comm_size(comm,&size);
3568:   MPI_Comm_rank(comm,&rank);

3570:   PetscObjectQuery((PetscObject)mpimat,"MatMergeSeqsToMPI",(PetscObject *)&container);
3571:   if (container) {
3572:     PetscContainerGetPointer(container,(void **)&merge);
3573:   }
3574:   bi     = merge->bi;
3575:   bj     = merge->bj;
3576:   buf_ri = merge->buf_ri;
3577:   buf_rj = merge->buf_rj;

3579:   PetscMalloc(size*sizeof(MPI_Status),&status);
3580:   owners = merge->rowmap.range;
3581:   len_s  = merge->len_s;

3583:   /* send and recv matrix values */
3584:   /*-----------------------------*/
3585:   PetscObjectGetNewTag((PetscObject)mpimat,&taga);
3586:   PetscPostIrecvScalar(comm,taga,merge->nrecv,merge->id_r,merge->len_r,&abuf_r,&r_waits);

3588:   PetscMalloc((merge->nsend+1)*sizeof(MPI_Request),&s_waits);
3589:   for (proc=0,k=0; proc<size; proc++){
3590:     if (!len_s[proc]) continue;
3591:     i = owners[proc];
3592:     MPI_Isend(aa+ai[i],len_s[proc],MPIU_MATSCALAR,proc,taga,comm,s_waits+k);
3593:     k++;
3594:   }

3596:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,r_waits,status);}
3597:   if (merge->nsend) {MPI_Waitall(merge->nsend,s_waits,status);}
3598:   PetscFree(status);

3600:   PetscFree(s_waits);
3601:   PetscFree(r_waits);

3603:   /* insert mat values of mpimat */
3604:   /*----------------------------*/
3605:   PetscMalloc(N*sizeof(MatScalar),&ba_i);
3606:   PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
3607:   nextrow = buf_ri_k + merge->nrecv;
3608:   nextai  = nextrow + merge->nrecv;

3610:   for (k=0; k<merge->nrecv; k++){
3611:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
3612:     nrows = *(buf_ri_k[k]);
3613:     nextrow[k]  = buf_ri_k[k]+1;  /* next row number of k-th recved i-structure */
3614:     nextai[k]   = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure  */
3615:   }

3617:   /* set values of ba */
3618:   m = merge->rowmap.n;
3619:   for (i=0; i<m; i++) {
3620:     arow = owners[rank] + i;
3621:     bj_i = bj+bi[i];  /* col indices of the i-th row of mpimat */
3622:     bnzi = bi[i+1] - bi[i];
3623:     PetscMemzero(ba_i,bnzi*sizeof(MatScalar));

3625:     /* add local non-zero vals of this proc's seqmat into ba */
3626:     anzi = ai[arow+1] - ai[arow];
3627:     aj   = a->j + ai[arow];
3628:     aa   = a->a + ai[arow];
3629:     nextaj = 0;
3630:     for (j=0; nextaj<anzi; j++){
3631:       if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
3632:         ba_i[j] += aa[nextaj++];
3633:       }
3634:     }

3636:     /* add received vals into ba */
3637:     for (k=0; k<merge->nrecv; k++){ /* k-th received message */
3638:       /* i-th row */
3639:       if (i == *nextrow[k]) {
3640:         anzi = *(nextai[k]+1) - *nextai[k];
3641:         aj   = buf_rj[k] + *(nextai[k]);
3642:         aa   = abuf_r[k] + *(nextai[k]);
3643:         nextaj = 0;
3644:         for (j=0; nextaj<anzi; j++){
3645:           if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
3646:             ba_i[j] += aa[nextaj++];
3647:           }
3648:         }
3649:         nextrow[k]++; nextai[k]++;
3650:       }
3651:     }
3652:     MatSetValues(mpimat,1,&arow,bnzi,bj_i,ba_i,INSERT_VALUES);
3653:   }
3654:   MatAssemblyBegin(mpimat,MAT_FINAL_ASSEMBLY);
3655:   MatAssemblyEnd(mpimat,MAT_FINAL_ASSEMBLY);

3657:   PetscFree(abuf_r);
3658:   PetscFree(ba_i);
3659:   PetscFree(buf_ri_k);
3661:   return(0);
3662: }

3664: static PetscEvent logkey_seqstompisym = 0;
3667: PetscErrorCode  MatMerge_SeqsToMPISymbolic(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,Mat *mpimat)
3668: {
3669:   PetscErrorCode       ierr;
3670:   Mat                  B_mpi;
3671:   Mat_SeqAIJ           *a=(Mat_SeqAIJ*)seqmat->data;
3672:   PetscMPIInt          size,rank,tagi,tagj,*len_s,*len_si,*len_ri;
3673:   PetscInt             **buf_rj,**buf_ri,**buf_ri_k;
3674:   PetscInt             M=seqmat->rmap.n,N=seqmat->cmap.n,i,*owners,*ai=a->i,*aj=a->j;
3675:   PetscInt             len,proc,*dnz,*onz;
3676:   PetscInt             k,anzi,*bi,*bj,*lnk,nlnk,arow,bnzi,nspacedouble=0;
3677:   PetscInt             nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextai;
3678:   MPI_Request          *si_waits,*sj_waits,*ri_waits,*rj_waits;
3679:   MPI_Status           *status;
3680:   PetscFreeSpaceList   free_space=PETSC_NULL,current_space=PETSC_NULL;
3681:   PetscBT              lnkbt;
3682:   Mat_Merge_SeqsToMPI  *merge;
3683:   PetscContainer       container;

3686:   if (!logkey_seqstompisym) {
3688:   }

3691:   /* make sure it is a PETSc comm */
3692:   PetscCommDuplicate(comm,&comm,PETSC_NULL);
3693:   MPI_Comm_size(comm,&size);
3694:   MPI_Comm_rank(comm,&rank);
3695: 
3696:   PetscNew(Mat_Merge_SeqsToMPI,&merge);
3697:   PetscMalloc(size*sizeof(MPI_Status),&status);

3699:   /* determine row ownership */
3700:   /*---------------------------------------------------------*/
3701:   PetscMapInitialize(comm,&merge->rowmap);
3702:   merge->rowmap.n = m;
3703:   merge->rowmap.N = M;
3704:   merge->rowmap.bs = 1;
3705:   PetscMapSetUp(&merge->rowmap);
3706:   PetscMalloc(size*sizeof(PetscMPIInt),&len_si);
3707:   PetscMalloc(size*sizeof(PetscMPIInt),&merge->len_s);
3708: 
3709:   m      = merge->rowmap.n;
3710:   M      = merge->rowmap.N;
3711:   owners = merge->rowmap.range;

3713:   /* determine the number of messages to send, their lengths */
3714:   /*---------------------------------------------------------*/
3715:   len_s  = merge->len_s;

3717:   len = 0;  /* length of buf_si[] */
3718:   merge->nsend = 0;
3719:   for (proc=0; proc<size; proc++){
3720:     len_si[proc] = 0;
3721:     if (proc == rank){
3722:       len_s[proc] = 0;
3723:     } else {
3724:       len_si[proc] = owners[proc+1] - owners[proc] + 1;
3725:       len_s[proc] = ai[owners[proc+1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
3726:     }
3727:     if (len_s[proc]) {
3728:       merge->nsend++;
3729:       nrows = 0;
3730:       for (i=owners[proc]; i<owners[proc+1]; i++){
3731:         if (ai[i+1] > ai[i]) nrows++;
3732:       }
3733:       len_si[proc] = 2*(nrows+1);
3734:       len += len_si[proc];
3735:     }
3736:   }

3738:   /* determine the number and length of messages to receive for ij-structure */
3739:   /*-------------------------------------------------------------------------*/
3740:   PetscGatherNumberOfMessages(comm,PETSC_NULL,len_s,&merge->nrecv);
3741:   PetscGatherMessageLengths2(comm,merge->nsend,merge->nrecv,len_s,len_si,&merge->id_r,&merge->len_r,&len_ri);

3743:   /* post the Irecv of j-structure */
3744:   /*-------------------------------*/
3745:   PetscCommGetNewTag(comm,&tagj);
3746:   PetscPostIrecvInt(comm,tagj,merge->nrecv,merge->id_r,merge->len_r,&buf_rj,&rj_waits);

3748:   /* post the Isend of j-structure */
3749:   /*--------------------------------*/
3750:   PetscMalloc((2*merge->nsend+1)*sizeof(MPI_Request),&si_waits);
3751:   sj_waits = si_waits + merge->nsend;

3753:   for (proc=0, k=0; proc<size; proc++){
3754:     if (!len_s[proc]) continue;
3755:     i = owners[proc];
3756:     MPI_Isend(aj+ai[i],len_s[proc],MPIU_INT,proc,tagj,comm,sj_waits+k);
3757:     k++;
3758:   }

3760:   /* receives and sends of j-structure are complete */
3761:   /*------------------------------------------------*/
3762:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,rj_waits,status);}
3763:   if (merge->nsend) {MPI_Waitall(merge->nsend,sj_waits,status);}
3764: 
3765:   /* send and recv i-structure */
3766:   /*---------------------------*/
3767:   PetscCommGetNewTag(comm,&tagi);
3768:   PetscPostIrecvInt(comm,tagi,merge->nrecv,merge->id_r,len_ri,&buf_ri,&ri_waits);
3769: 
3770:   PetscMalloc((len+1)*sizeof(PetscInt),&buf_s);
3771:   buf_si = buf_s;  /* points to the beginning of k-th msg to be sent */
3772:   for (proc=0,k=0; proc<size; proc++){
3773:     if (!len_s[proc]) continue;
3774:     /* form outgoing message for i-structure: 
3775:          buf_si[0]:                 nrows to be sent
3776:                [1:nrows]:           row index (global)
3777:                [nrows+1:2*nrows+1]: i-structure index
3778:     */
3779:     /*-------------------------------------------*/
3780:     nrows = len_si[proc]/2 - 1;
3781:     buf_si_i    = buf_si + nrows+1;
3782:     buf_si[0]   = nrows;
3783:     buf_si_i[0] = 0;
3784:     nrows = 0;
3785:     for (i=owners[proc]; i<owners[proc+1]; i++){
3786:       anzi = ai[i+1] - ai[i];
3787:       if (anzi) {
3788:         buf_si_i[nrows+1] = buf_si_i[nrows] + anzi; /* i-structure */
3789:         buf_si[nrows+1] = i-owners[proc]; /* local row index */
3790:         nrows++;
3791:       }
3792:     }
3793:     MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,si_waits+k);
3794:     k++;
3795:     buf_si += len_si[proc];
3796:   }

3798:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,ri_waits,status);}
3799:   if (merge->nsend) {MPI_Waitall(merge->nsend,si_waits,status);}

3801:   PetscInfo2(seqmat,"nsend: %D, nrecv: %D\n",merge->nsend,merge->nrecv);
3802:   for (i=0; i<merge->nrecv; i++){
3803:     PetscInfo3(seqmat,"recv len_ri=%D, len_rj=%D from [%D]\n",len_ri[i],merge->len_r[i],merge->id_r[i]);
3804:   }

3806:   PetscFree(len_si);
3807:   PetscFree(len_ri);
3808:   PetscFree(rj_waits);
3809:   PetscFree(si_waits);
3810:   PetscFree(ri_waits);
3811:   PetscFree(buf_s);
3812:   PetscFree(status);

3814:   /* compute a local seq matrix in each processor */
3815:   /*----------------------------------------------*/
3816:   /* allocate bi array and free space for accumulating nonzero column info */
3817:   PetscMalloc((m+1)*sizeof(PetscInt),&bi);
3818:   bi[0] = 0;

3820:   /* create and initialize a linked list */
3821:   nlnk = N+1;
3822:   PetscLLCreate(N,N,nlnk,lnk,lnkbt);
3823: 
3824:   /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
3825:   len = 0;
3826:   len  = ai[owners[rank+1]] - ai[owners[rank]];
3827:   PetscFreeSpaceGet((PetscInt)(2*len+1),&free_space);
3828:   current_space = free_space;

3830:   /* determine symbolic info for each local row */
3831:   PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
3832:   nextrow = buf_ri_k + merge->nrecv;
3833:   nextai  = nextrow + merge->nrecv;
3834:   for (k=0; k<merge->nrecv; k++){
3835:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
3836:     nrows = *buf_ri_k[k];
3837:     nextrow[k]  = buf_ri_k[k] + 1;  /* next row number of k-th recved i-structure */
3838:     nextai[k]   = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure  */
3839:   }

3841:   MatPreallocateInitialize(comm,m,n,dnz,onz);
3842:   len = 0;
3843:   for (i=0;i<m;i++) {
3844:     bnzi   = 0;
3845:     /* add local non-zero cols of this proc's seqmat into lnk */
3846:     arow   = owners[rank] + i;
3847:     anzi   = ai[arow+1] - ai[arow];
3848:     aj     = a->j + ai[arow];
3849:     PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
3850:     bnzi += nlnk;
3851:     /* add received col data into lnk */
3852:     for (k=0; k<merge->nrecv; k++){ /* k-th received message */
3853:       if (i == *nextrow[k]) { /* i-th row */
3854:         anzi = *(nextai[k]+1) - *nextai[k];
3855:         aj   = buf_rj[k] + *nextai[k];
3856:         PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
3857:         bnzi += nlnk;
3858:         nextrow[k]++; nextai[k]++;
3859:       }
3860:     }
3861:     if (len < bnzi) len = bnzi;  /* =max(bnzi) */

3863:     /* if free space is not available, make more free space */
3864:     if (current_space->local_remaining<bnzi) {
3865:       PetscFreeSpaceGet(current_space->total_array_size,&current_space);
3866:       nspacedouble++;
3867:     }
3868:     /* copy data into free space, then initialize lnk */
3869:     PetscLLClean(N,N,bnzi,lnk,current_space->array,lnkbt);
3870:     MatPreallocateSet(i+owners[rank],bnzi,current_space->array,dnz,onz);

3872:     current_space->array           += bnzi;
3873:     current_space->local_used      += bnzi;
3874:     current_space->local_remaining -= bnzi;
3875: 
3876:     bi[i+1] = bi[i] + bnzi;
3877:   }
3878: 
3879:   PetscFree(buf_ri_k);

3881:   PetscMalloc((bi[m]+1)*sizeof(PetscInt),&bj);
3882:   PetscFreeSpaceContiguous(&free_space,bj);
3883:   PetscLLDestroy(lnk,lnkbt);

3885:   /* create symbolic parallel matrix B_mpi */
3886:   /*---------------------------------------*/
3887:   MatCreate(comm,&B_mpi);
3888:   if (n==PETSC_DECIDE) {
3889:     MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,N);
3890:   } else {
3891:     MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3892:   }
3893:   MatSetType(B_mpi,MATMPIAIJ);
3894:   MatMPIAIJSetPreallocation(B_mpi,0,dnz,0,onz);
3895:   MatPreallocateFinalize(dnz,onz);

3897:   /* B_mpi is not ready for use - assembly will be done by MatMerge_SeqsToMPINumeric() */
3898:   B_mpi->assembled     = PETSC_FALSE;
3899:   B_mpi->ops->destroy  = MatDestroy_MPIAIJ_SeqsToMPI;
3900:   merge->bi            = bi;
3901:   merge->bj            = bj;
3902:   merge->buf_ri        = buf_ri;
3903:   merge->buf_rj        = buf_rj;
3904:   merge->coi           = PETSC_NULL;
3905:   merge->coj           = PETSC_NULL;
3906:   merge->owners_co     = PETSC_NULL;

3908:   /* attach the supporting struct to B_mpi for reuse */
3909:   PetscContainerCreate(PETSC_COMM_SELF,&container);
3910:   PetscContainerSetPointer(container,merge);
3911:   PetscObjectCompose((PetscObject)B_mpi,"MatMergeSeqsToMPI",(PetscObject)container);
3912:   *mpimat = B_mpi;

3914:   PetscCommDestroy(&comm);
3916:   return(0);
3917: }

3919: static PetscEvent logkey_seqstompi = 0;
3922: PetscErrorCode  MatMerge_SeqsToMPI(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,MatReuse scall,Mat *mpimat)
3923: {
3924:   PetscErrorCode   ierr;

3927:   if (!logkey_seqstompi) {
3929:   }
3931:   if (scall == MAT_INITIAL_MATRIX){
3932:     MatMerge_SeqsToMPISymbolic(comm,seqmat,m,n,mpimat);
3933:   }
3934:   MatMerge_SeqsToMPINumeric(seqmat,*mpimat);
3936:   return(0);
3937: }
3938: static PetscEvent logkey_getlocalmat = 0;
3941: /*@C
3942:      MatGetLocalMat - Creates a SeqAIJ matrix by taking all its local rows

3944:     Not Collective

3946:    Input Parameters:
3947: +    A - the matrix 
3948: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 

3950:    Output Parameter:
3951: .    A_loc - the local sequential matrix generated

3953:     Level: developer

3955: @*/
3956: PetscErrorCode  MatGetLocalMat(Mat A,MatReuse scall,Mat *A_loc)
3957: {
3958:   PetscErrorCode  ierr;
3959:   Mat_MPIAIJ      *mpimat=(Mat_MPIAIJ*)A->data;
3960:   Mat_SeqAIJ      *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
3961:   PetscInt        *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,*cmap=mpimat->garray;
3962:   PetscScalar     *aa=a->a,*ba=b->a,*ca;
3963:   PetscInt        am=A->rmap.n,i,j,k,cstart=A->cmap.rstart;
3964:   PetscInt        *ci,*cj,col,ncols_d,ncols_o,jo;

3967:   if (!logkey_getlocalmat) {
3969:   }
3971:   if (scall == MAT_INITIAL_MATRIX){
3972:     PetscMalloc((1+am)*sizeof(PetscInt),&ci);
3973:     ci[0] = 0;
3974:     for (i=0; i<am; i++){
3975:       ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
3976:     }
3977:     PetscMalloc((1+ci[am])*sizeof(PetscInt),&cj);
3978:     PetscMalloc((1+ci[am])*sizeof(PetscScalar),&ca);
3979:     k = 0;
3980:     for (i=0; i<am; i++) {
3981:       ncols_o = bi[i+1] - bi[i];
3982:       ncols_d = ai[i+1] - ai[i];
3983:       /* off-diagonal portion of A */
3984:       for (jo=0; jo<ncols_o; jo++) {
3985:         col = cmap[*bj];
3986:         if (col >= cstart) break;
3987:         cj[k]   = col; bj++;
3988:         ca[k++] = *ba++;
3989:       }
3990:       /* diagonal portion of A */
3991:       for (j=0; j<ncols_d; j++) {
3992:         cj[k]   = cstart + *aj++;
3993:         ca[k++] = *aa++;
3994:       }
3995:       /* off-diagonal portion of A */
3996:       for (j=jo; j<ncols_o; j++) {
3997:         cj[k]   = cmap[*bj++];
3998:         ca[k++] = *ba++;
3999:       }
4000:     }
4001:     /* put together the new matrix */
4002:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,A->cmap.N,ci,cj,ca,A_loc);
4003:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4004:     /* Since these are PETSc arrays, change flags to free them as necessary. */
4005:     mat          = (Mat_SeqAIJ*)(*A_loc)->data;
4006:     mat->free_a  = PETSC_TRUE;
4007:     mat->free_ij = PETSC_TRUE;
4008:     mat->nonew   = 0;
4009:   } else if (scall == MAT_REUSE_MATRIX){
4010:     mat=(Mat_SeqAIJ*)(*A_loc)->data;
4011:     ci = mat->i; cj = mat->j; ca = mat->a;
4012:     for (i=0; i<am; i++) {
4013:       /* off-diagonal portion of A */
4014:       ncols_o = bi[i+1] - bi[i];
4015:       for (jo=0; jo<ncols_o; jo++) {
4016:         col = cmap[*bj];
4017:         if (col >= cstart) break;
4018:         *ca++ = *ba++; bj++;
4019:       }
4020:       /* diagonal portion of A */
4021:       ncols_d = ai[i+1] - ai[i];
4022:       for (j=0; j<ncols_d; j++) *ca++ = *aa++;
4023:       /* off-diagonal portion of A */
4024:       for (j=jo; j<ncols_o; j++) {
4025:         *ca++ = *ba++; bj++;
4026:       }
4027:     }
4028:   } else {
4029:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
4030:   }

4033:   return(0);
4034: }

4036: static PetscEvent logkey_getlocalmatcondensed = 0;
4039: /*@C
4040:      MatGetLocalMatCondensed - Creates a SeqAIJ matrix by taking all its local rows and NON-ZERO columns

4042:     Not Collective

4044:    Input Parameters:
4045: +    A - the matrix 
4046: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4047: -    row, col - index sets of rows and columns to extract (or PETSC_NULL)  

4049:    Output Parameter:
4050: .    A_loc - the local sequential matrix generated

4052:     Level: developer

4054: @*/
4055: PetscErrorCode  MatGetLocalMatCondensed(Mat A,MatReuse scall,IS *row,IS *col,Mat *A_loc)
4056: {
4057:   Mat_MPIAIJ        *a=(Mat_MPIAIJ*)A->data;
4058:   PetscErrorCode    ierr;
4059:   PetscInt          i,start,end,ncols,nzA,nzB,*cmap,imark,*idx;
4060:   IS                isrowa,iscola;
4061:   Mat               *aloc;

4064:   if (!logkey_getlocalmatcondensed) {
4066:   }
4068:   if (!row){
4069:     start = A->rmap.rstart; end = A->rmap.rend;
4070:     ISCreateStride(PETSC_COMM_SELF,end-start,start,1,&isrowa);
4071:   } else {
4072:     isrowa = *row;
4073:   }
4074:   if (!col){
4075:     start = A->cmap.rstart;
4076:     cmap  = a->garray;
4077:     nzA   = a->A->cmap.n;
4078:     nzB   = a->B->cmap.n;
4079:     PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
4080:     ncols = 0;
4081:     for (i=0; i<nzB; i++) {
4082:       if (cmap[i] < start) idx[ncols++] = cmap[i];
4083:       else break;
4084:     }
4085:     imark = i;
4086:     for (i=0; i<nzA; i++) idx[ncols++] = start + i;
4087:     for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i];
4088:     ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&iscola);
4089:     PetscFree(idx);
4090:   } else {
4091:     iscola = *col;
4092:   }
4093:   if (scall != MAT_INITIAL_MATRIX){
4094:     PetscMalloc(sizeof(Mat),&aloc);
4095:     aloc[0] = *A_loc;
4096:   }
4097:   MatGetSubMatrices(A,1,&isrowa,&iscola,scall,&aloc);
4098:   *A_loc = aloc[0];
4099:   PetscFree(aloc);
4100:   if (!row){
4101:     ISDestroy(isrowa);
4102:   }
4103:   if (!col){
4104:     ISDestroy(iscola);
4105:   }
4107:   return(0);
4108: }

4110: static PetscEvent logkey_GetBrowsOfAcols = 0;
4113: /*@C
4114:     MatGetBrowsOfAcols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A 

4116:     Collective on Mat

4118:    Input Parameters:
4119: +    A,B - the matrices in mpiaij format
4120: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4121: -    rowb, colb - index sets of rows and columns of B to extract (or PETSC_NULL)   

4123:    Output Parameter:
4124: +    rowb, colb - index sets of rows and columns of B to extract 
4125: .    brstart - row index of B_seq from which next B->rmap.n rows are taken from B's local rows
4126: -    B_seq - the sequential matrix generated

4128:     Level: developer

4130: @*/
4131: PetscErrorCode  MatGetBrowsOfAcols(Mat A,Mat B,MatReuse scall,IS *rowb,IS *colb,PetscInt *brstart,Mat *B_seq)
4132: {
4133:   Mat_MPIAIJ        *a=(Mat_MPIAIJ*)A->data;
4134:   PetscErrorCode    ierr;
4135:   PetscInt          *idx,i,start,ncols,nzA,nzB,*cmap,imark;
4136:   IS                isrowb,iscolb;
4137:   Mat               *bseq;
4138: 
4140:   if (A->cmap.rstart != B->rmap.rstart || A->cmap.rend != B->rmap.rend){
4141:     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);
4142:   }
4143:   if (!logkey_GetBrowsOfAcols) {
4145:   }
4147: 
4148:   if (scall == MAT_INITIAL_MATRIX){
4149:     start = A->cmap.rstart;
4150:     cmap  = a->garray;
4151:     nzA   = a->A->cmap.n;
4152:     nzB   = a->B->cmap.n;
4153:     PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
4154:     ncols = 0;
4155:     for (i=0; i<nzB; i++) {  /* row < local row index */
4156:       if (cmap[i] < start) idx[ncols++] = cmap[i];
4157:       else break;
4158:     }
4159:     imark = i;
4160:     for (i=0; i<nzA; i++) idx[ncols++] = start + i;  /* local rows */
4161:     for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
4162:     ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&isrowb);
4163:     PetscFree(idx);
4164:     *brstart = imark;
4165:     ISCreateStride(PETSC_COMM_SELF,B->cmap.N,0,1,&iscolb);
4166:   } else {
4167:     if (!rowb || !colb) SETERRQ(PETSC_ERR_SUP,"IS rowb and colb must be provided for MAT_REUSE_MATRIX");
4168:     isrowb = *rowb; iscolb = *colb;
4169:     PetscMalloc(sizeof(Mat),&bseq);
4170:     bseq[0] = *B_seq;
4171:   }
4172:   MatGetSubMatrices(B,1,&isrowb,&iscolb,scall,&bseq);
4173:   *B_seq = bseq[0];
4174:   PetscFree(bseq);
4175:   if (!rowb){
4176:     ISDestroy(isrowb);
4177:   } else {
4178:     *rowb = isrowb;
4179:   }
4180:   if (!colb){
4181:     ISDestroy(iscolb);
4182:   } else {
4183:     *colb = iscolb;
4184:   }
4186:   return(0);
4187: }

4189: static PetscEvent logkey_GetBrowsOfAocols = 0;
4192: /*@C
4193:     MatGetBrowsOfAoCols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns
4194:     of the OFF-DIAGONAL portion of local A 

4196:     Collective on Mat

4198:    Input Parameters:
4199: +    A,B - the matrices in mpiaij format
4200: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4201: .    startsj - starting point in B's sending and receiving j-arrays, saved for MAT_REUSE (or PETSC_NULL) 
4202: -    bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or PETSC_NULL) 

4204:    Output Parameter:
4205: +    B_oth - the sequential matrix generated

4207:     Level: developer

4209: @*/
4210: PetscErrorCode  MatGetBrowsOfAoCols(Mat A,Mat B,MatReuse scall,PetscInt **startsj,PetscScalar **bufa_ptr,Mat *B_oth)
4211: {
4212:   VecScatter_MPI_General *gen_to,*gen_from;
4213:   PetscErrorCode         ierr;
4214:   Mat_MPIAIJ             *a=(Mat_MPIAIJ*)A->data;
4215:   Mat_SeqAIJ             *b_oth;
4216:   VecScatter             ctx=a->Mvctx;
4217:   MPI_Comm               comm=ctx->comm;
4218:   PetscMPIInt            *rprocs,*sprocs,tag=ctx->tag,rank;
4219:   PetscInt               *rowlen,*bufj,*bufJ,ncols,aBn=a->B->cmap.n,row,*b_othi,*b_othj;
4220:   PetscScalar            *rvalues,*svalues,*b_otha,*bufa,*bufA;
4221:   PetscInt               i,j,k,l,ll,nrecvs,nsends,nrows,*srow,*rstarts,*rstartsj = 0,*sstarts,*sstartsj,len;
4222:   MPI_Request            *rwaits = PETSC_NULL,*swaits = PETSC_NULL;
4223:   MPI_Status             *sstatus,rstatus;
4224:   PetscMPIInt            jj;
4225:   PetscInt               *cols,sbs,rbs;
4226:   PetscScalar            *vals;

4229:   if (A->cmap.rstart != B->rmap.rstart || A->cmap.rend != B->rmap.rend){
4230:     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);
4231:   }
4232:   if (!logkey_GetBrowsOfAocols) {
4234:   }
4236:   MPI_Comm_rank(comm,&rank);

4238:   gen_to   = (VecScatter_MPI_General*)ctx->todata;
4239:   gen_from = (VecScatter_MPI_General*)ctx->fromdata;
4240:   rvalues  = gen_from->values; /* holds the length of receiving row */
4241:   svalues  = gen_to->values;   /* holds the length of sending row */
4242:   nrecvs   = gen_from->n;
4243:   nsends   = gen_to->n;

4245:   PetscMalloc2(nrecvs,MPI_Request,&rwaits,nsends,MPI_Request,&swaits);
4246:   srow     = gen_to->indices;   /* local row index to be sent */
4247:   sstarts  = gen_to->starts;
4248:   sprocs   = gen_to->procs;
4249:   sstatus  = gen_to->sstatus;
4250:   sbs      = gen_to->bs;
4251:   rstarts  = gen_from->starts;
4252:   rprocs   = gen_from->procs;
4253:   rbs      = gen_from->bs;

4255:   if (!startsj || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
4256:   if (scall == MAT_INITIAL_MATRIX){
4257:     /* i-array */
4258:     /*---------*/
4259:     /*  post receives */
4260:     for (i=0; i<nrecvs; i++){
4261:       rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
4262:       nrows = (rstarts[i+1]-rstarts[i])*rbs; /* num of indices to be received */
4263:       MPI_Irecv(rowlen,nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
4264:     }

4266:     /* pack the outgoing message */
4267:     PetscMalloc((nsends+nrecvs+3)*sizeof(PetscInt),&sstartsj);
4268:     rstartsj = sstartsj + nsends +1;
4269:     sstartsj[0] = 0;  rstartsj[0] = 0;
4270:     len = 0; /* total length of j or a array to be sent */
4271:     k = 0;
4272:     for (i=0; i<nsends; i++){
4273:       rowlen = (PetscInt*)svalues + sstarts[i]*sbs;
4274:       nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4275:       for (j=0; j<nrows; j++) {
4276:         row = srow[k] + B->rmap.range[rank]; /* global row idx */
4277:         for (l=0; l<sbs; l++){
4278:           MatGetRow_MPIAIJ(B,row+l,&ncols,PETSC_NULL,PETSC_NULL); /* rowlength */
4279:           rowlen[j*sbs+l] = ncols;
4280:           len += ncols;
4281:           MatRestoreRow_MPIAIJ(B,row+l,&ncols,PETSC_NULL,PETSC_NULL);
4282:         }
4283:         k++;
4284:       }
4285:       MPI_Isend(rowlen,nrows*sbs,MPIU_INT,sprocs[i],tag,comm,swaits+i);
4286:       sstartsj[i+1] = len;  /* starting point of (i+1)-th outgoing msg in bufj and bufa */
4287:     }
4288:     /* recvs and sends of i-array are completed */
4289:     i = nrecvs;
4290:     while (i--) {
4291:       MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4292:     }
4293:     if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}

4295:     /* allocate buffers for sending j and a arrays */
4296:     PetscMalloc((len+1)*sizeof(PetscInt),&bufj);
4297:     PetscMalloc((len+1)*sizeof(PetscScalar),&bufa);

4299:     /* create i-array of B_oth */
4300:     PetscMalloc((aBn+2)*sizeof(PetscInt),&b_othi);
4301:     b_othi[0] = 0;
4302:     len = 0; /* total length of j or a array to be received */
4303:     k = 0;
4304:     for (i=0; i<nrecvs; i++){
4305:       rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
4306:       nrows = rbs*(rstarts[i+1]-rstarts[i]); /* num of rows to be recieved */
4307:       for (j=0; j<nrows; j++) {
4308:         b_othi[k+1] = b_othi[k] + rowlen[j];
4309:         len += rowlen[j]; k++;
4310:       }
4311:       rstartsj[i+1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
4312:     }

4314:     /* allocate space for j and a arrrays of B_oth */
4315:     PetscMalloc((b_othi[aBn]+1)*sizeof(PetscInt),&b_othj);
4316:     PetscMalloc((b_othi[aBn]+1)*sizeof(PetscScalar),&b_otha);

4318:     /* j-array */
4319:     /*---------*/
4320:     /*  post receives of j-array */
4321:     for (i=0; i<nrecvs; i++){
4322:       nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
4323:       MPI_Irecv(b_othj+rstartsj[i],nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
4324:     }

4326:     /* pack the outgoing message j-array */
4327:     k = 0;
4328:     for (i=0; i<nsends; i++){
4329:       nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4330:       bufJ = bufj+sstartsj[i];
4331:       for (j=0; j<nrows; j++) {
4332:         row  = srow[k++] + B->rmap.range[rank]; /* global row idx */
4333:         for (ll=0; ll<sbs; ll++){
4334:           MatGetRow_MPIAIJ(B,row+ll,&ncols,&cols,PETSC_NULL);
4335:           for (l=0; l<ncols; l++){
4336:             *bufJ++ = cols[l];
4337:           }
4338:           MatRestoreRow_MPIAIJ(B,row+ll,&ncols,&cols,PETSC_NULL);
4339:         }
4340:       }
4341:       MPI_Isend(bufj+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_INT,sprocs[i],tag,comm,swaits+i);
4342:     }

4344:     /* recvs and sends of j-array are completed */
4345:     i = nrecvs;
4346:     while (i--) {
4347:       MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4348:     }
4349:     if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4350:   } else if (scall == MAT_REUSE_MATRIX){
4351:     sstartsj = *startsj;
4352:     rstartsj = sstartsj + nsends +1;
4353:     bufa     = *bufa_ptr;
4354:     b_oth    = (Mat_SeqAIJ*)(*B_oth)->data;
4355:     b_otha   = b_oth->a;
4356:   } else {
4357:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not posses an object container");
4358:   }

4360:   /* a-array */
4361:   /*---------*/
4362:   /*  post receives of a-array */
4363:   for (i=0; i<nrecvs; i++){
4364:     nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
4365:     MPI_Irecv(b_otha+rstartsj[i],nrows,MPIU_SCALAR,rprocs[i],tag,comm,rwaits+i);
4366:   }

4368:   /* pack the outgoing message a-array */
4369:   k = 0;
4370:   for (i=0; i<nsends; i++){
4371:     nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4372:     bufA = bufa+sstartsj[i];
4373:     for (j=0; j<nrows; j++) {
4374:       row  = srow[k++] + B->rmap.range[rank]; /* global row idx */
4375:       for (ll=0; ll<sbs; ll++){
4376:         MatGetRow_MPIAIJ(B,row+ll,&ncols,PETSC_NULL,&vals);
4377:         for (l=0; l<ncols; l++){
4378:           *bufA++ = vals[l];
4379:         }
4380:         MatRestoreRow_MPIAIJ(B,row+ll,&ncols,PETSC_NULL,&vals);
4381:       }
4382:     }
4383:     MPI_Isend(bufa+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_SCALAR,sprocs[i],tag,comm,swaits+i);
4384:   }
4385:   /* recvs and sends of a-array are completed */
4386:   i = nrecvs;
4387:   while (i--) {
4388:     MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4389:   }
4390:   if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4391:   PetscFree2(rwaits,swaits);

4393:   if (scall == MAT_INITIAL_MATRIX){
4394:     /* put together the new matrix */
4395:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,aBn,B->cmap.N,b_othi,b_othj,b_otha,B_oth);

4397:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4398:     /* Since these are PETSc arrays, change flags to free them as necessary. */
4399:     b_oth          = (Mat_SeqAIJ *)(*B_oth)->data;
4400:     b_oth->free_a  = PETSC_TRUE;
4401:     b_oth->free_ij = PETSC_TRUE;
4402:     b_oth->nonew   = 0;

4404:     PetscFree(bufj);
4405:     if (!startsj || !bufa_ptr){
4406:       PetscFree(sstartsj);
4407:       PetscFree(bufa_ptr);
4408:     } else {
4409:       *startsj  = sstartsj;
4410:       *bufa_ptr = bufa;
4411:     }
4412:   }
4414:   return(0);
4415: }

4419: /*@C
4420:   MatGetCommunicationStructs - Provides access to the communication structures used in matrix-vector multiplication.

4422:   Not Collective

4424:   Input Parameters:
4425: . A - The matrix in mpiaij format

4427:   Output Parameter:
4428: + lvec - The local vector holding off-process values from the argument to a matrix-vector product
4429: . colmap - A map from global column index to local index into lvec
4430: - multScatter - A scatter from the argument of a matrix-vector product to lvec

4432:   Level: developer

4434: @*/
4435: #if defined (PETSC_USE_CTABLE)
4436: PetscErrorCode  MatGetCommunicationStructs(Mat A, Vec *lvec, PetscTable *colmap, VecScatter *multScatter)
4437: #else
4438: PetscErrorCode  MatGetCommunicationStructs(Mat A, Vec *lvec, PetscInt *colmap[], VecScatter *multScatter)
4439: #endif
4440: {
4441:   Mat_MPIAIJ *a;

4448:   a = (Mat_MPIAIJ *) A->data;
4449:   if (lvec) *lvec = a->lvec;
4450:   if (colmap) *colmap = a->colmap;
4451:   if (multScatter) *multScatter = a->Mvctx;
4452:   return(0);
4453: }


4460: /*MC
4461:    MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.

4463:    Options Database Keys:
4464: . -mat_type mpiaij - sets the matrix type to "mpiaij" during a call to MatSetFromOptions()

4466:   Level: beginner

4468: .seealso: MatCreateMPIAIJ
4469: M*/

4474: PetscErrorCode  MatCreate_MPIAIJ(Mat B)
4475: {
4476:   Mat_MPIAIJ     *b;
4478:   PetscMPIInt    size;

4481:   MPI_Comm_size(B->comm,&size);

4483:   PetscNew(Mat_MPIAIJ,&b);
4484:   B->data         = (void*)b;
4485:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4486:   B->factor       = 0;
4487:   B->rmap.bs      = 1;
4488:   B->assembled    = PETSC_FALSE;
4489:   B->mapping      = 0;

4491:   B->insertmode      = NOT_SET_VALUES;
4492:   b->size            = size;
4493:   MPI_Comm_rank(B->comm,&b->rank);

4495:   /* build cache for off array entries formed */
4496:   MatStashCreate_Private(B->comm,1,&B->stash);
4497:   b->donotstash  = PETSC_FALSE;
4498:   b->colmap      = 0;
4499:   b->garray      = 0;
4500:   b->roworiented = PETSC_TRUE;

4502:   /* stuff used for matrix vector multiply */
4503:   b->lvec      = PETSC_NULL;
4504:   b->Mvctx     = PETSC_NULL;

4506:   /* stuff for MatGetRow() */
4507:   b->rowindices   = 0;
4508:   b->rowvalues    = 0;
4509:   b->getrowactive = PETSC_FALSE;


4512:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
4513:                                      "MatStoreValues_MPIAIJ",
4514:                                      MatStoreValues_MPIAIJ);
4515:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
4516:                                      "MatRetrieveValues_MPIAIJ",
4517:                                      MatRetrieveValues_MPIAIJ);
4518:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
4519:                                      "MatGetDiagonalBlock_MPIAIJ",
4520:                                      MatGetDiagonalBlock_MPIAIJ);
4521:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
4522:                                      "MatIsTranspose_MPIAIJ",
4523:                                      MatIsTranspose_MPIAIJ);
4524:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocation_C",
4525:                                      "MatMPIAIJSetPreallocation_MPIAIJ",
4526:                                      MatMPIAIJSetPreallocation_MPIAIJ);
4527:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",
4528:                                      "MatMPIAIJSetPreallocationCSR_MPIAIJ",
4529:                                      MatMPIAIJSetPreallocationCSR_MPIAIJ);
4530:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
4531:                                      "MatDiagonalScaleLocal_MPIAIJ",
4532:                                      MatDiagonalScaleLocal_MPIAIJ);
4533:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpicsrperm_C",
4534:                                      "MatConvert_MPIAIJ_MPICSRPERM",
4535:                                       MatConvert_MPIAIJ_MPICSRPERM);
4536:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpicrl_C",
4537:                                      "MatConvert_MPIAIJ_MPICRL",
4538:                                       MatConvert_MPIAIJ_MPICRL);
4539:   PetscObjectChangeTypeName((PetscObject)B,MATMPIAIJ);
4540:   return(0);
4541: }

4546: /*@C
4547:      MatCreateMPIAIJWithSplitArrays - creates a MPI AIJ matrix using arrays that contain the "diagonal"
4548:          and "off-diagonal" part of the matrix in CSR format.

4550:    Collective on MPI_Comm

4552:    Input Parameters:
4553: +  comm - MPI communicator
4554: .  m - number of local rows (Cannot be PETSC_DECIDE)
4555: .  n - This value should be the same as the local size used in creating the 
4556:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
4557:        calculated if N is given) For square matrices n is almost always m.
4558: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
4559: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
4560: .   i - row indices for "diagonal" portion of matrix
4561: .   j - column indices
4562: .   a - matrix values
4563: .   oi - row indices for "off-diagonal" portion of matrix
4564: .   oj - column indices
4565: -   oa - matrix values

4567:    Output Parameter:
4568: .   mat - the matrix

4570:    Level: advanced

4572:    Notes:
4573:        The i, j, and a arrays ARE NOT copied by this routine into the internal format used by PETSc.

4575:        The i and j indices are 0 based
4576:  
4577:        See MatCreateMPIAIJ() for the definition of "diagonal" and "off-diagonal" portion of the matrix


4580: .keywords: matrix, aij, compressed row, sparse, parallel

4582: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
4583:           MPIAIJ, MatCreateMPIAIJ(), MatCreateMPIAIJWithArrays()
4584: @*/
4585: PetscErrorCode  MatCreateMPIAIJWithSplitArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt i[],PetscInt j[],PetscScalar a[],
4586:                                                                 PetscInt oi[], PetscInt oj[],PetscScalar oa[],Mat *mat)
4587: {
4589:   Mat_MPIAIJ     *maij;

4592:   if (m < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
4593:   if (i[0]) {
4594:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4595:   }
4596:   if (oi[0]) {
4597:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"oi (row indices) must start with 0");
4598:   }
4599:   MatCreate(comm,mat);
4600:   MatSetSizes(*mat,m,n,M,N);
4601:   MatSetType(*mat,MATMPIAIJ);
4602:   maij = (Mat_MPIAIJ*) (*mat)->data;
4603:   maij->donotstash     = PETSC_TRUE;
4604:   (*mat)->preallocated = PETSC_TRUE;

4606:   (*mat)->rmap.bs = (*mat)->cmap.bs = 1;
4607:   PetscMapSetUp(&(*mat)->rmap);
4608:   PetscMapSetUp(&(*mat)->cmap);

4610:   MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,i,j,a,&maij->A);
4611:   MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,(*mat)->cmap.N,oi,oj,oa,&maij->B);

4613:   MatAssemblyBegin(maij->A,MAT_FINAL_ASSEMBLY);
4614:   MatAssemblyEnd(maij->A,MAT_FINAL_ASSEMBLY);
4615:   MatAssemblyBegin(maij->B,MAT_FINAL_ASSEMBLY);
4616:   MatAssemblyEnd(maij->B,MAT_FINAL_ASSEMBLY);

4618:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4619:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4620:   return(0);
4621: }

4623: /*
4624:     Special version for direct calls from Fortran 
4625: */
4626: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4627: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
4628: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4629: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
4630: #endif

4632: /* Change these macros so can be used in void function */
4633: #undef CHKERRQ
4634: #define CHKERRQ(ierr) CHKERRABORT(mat->comm,ierr) 
4635: #undef SETERRQ2
4636: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(mat->comm,ierr) 
4637: #undef SETERRQ
4638: #define SETERRQ(ierr,b) CHKERRABORT(mat->comm,ierr) 

4643: void PETSC_STDCALL matsetvaluesmpiaij_(Mat *mmat,PetscInt *mm,const PetscInt im[],PetscInt *mn,const PetscInt in[],const PetscScalar v[],InsertMode *maddv,PetscErrorCode *_ierr)
4644: {
4645:   Mat            mat = *mmat;
4646:   PetscInt       m = *mm, n = *mn;
4647:   InsertMode     addv = *maddv;
4648:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
4649:   PetscScalar    value;

4652:   MatPreallocated(mat);
4653:   if (mat->insertmode == NOT_SET_VALUES) {
4654:     mat->insertmode = addv;
4655:   }
4656: #if defined(PETSC_USE_DEBUG)
4657:   else if (mat->insertmode != addv) {
4658:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
4659:   }
4660: #endif
4661:   {
4662:   PetscInt       i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend;
4663:   PetscInt       cstart = mat->cmap.rstart,cend = mat->cmap.rend,row,col;
4664:   PetscTruth     roworiented = aij->roworiented;

4666:   /* Some Variables required in the macro */
4667:   Mat            A = aij->A;
4668:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
4669:   PetscInt       *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
4670:   PetscScalar    *aa = a->a;
4671:   PetscTruth     ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES))?PETSC_TRUE:PETSC_FALSE);
4672:   Mat            B = aij->B;
4673:   Mat_SeqAIJ     *b = (Mat_SeqAIJ*)B->data;
4674:   PetscInt       *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap.n,am = aij->A->rmap.n;
4675:   PetscScalar    *ba = b->a;

4677:   PetscInt       *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
4678:   PetscInt       nonew = a->nonew;
4679:   PetscScalar    *ap1,*ap2;

4682:   for (i=0; i<m; i++) {
4683:     if (im[i] < 0) continue;
4684: #if defined(PETSC_USE_DEBUG)
4685:     if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
4686: #endif
4687:     if (im[i] >= rstart && im[i] < rend) {
4688:       row      = im[i] - rstart;
4689:       lastcol1 = -1;
4690:       rp1      = aj + ai[row];
4691:       ap1      = aa + ai[row];
4692:       rmax1    = aimax[row];
4693:       nrow1    = ailen[row];
4694:       low1     = 0;
4695:       high1    = nrow1;
4696:       lastcol2 = -1;
4697:       rp2      = bj + bi[row];
4698:       ap2      = ba + bi[row];
4699:       rmax2    = bimax[row];
4700:       nrow2    = bilen[row];
4701:       low2     = 0;
4702:       high2    = nrow2;

4704:       for (j=0; j<n; j++) {
4705:         if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
4706:         if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
4707:         if (in[j] >= cstart && in[j] < cend){
4708:           col = in[j] - cstart;
4709:           MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
4710:         } else if (in[j] < 0) continue;
4711: #if defined(PETSC_USE_DEBUG)
4712:         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);}
4713: #endif
4714:         else {
4715:           if (mat->was_assembled) {
4716:             if (!aij->colmap) {
4717:               CreateColmap_MPIAIJ_Private(mat);
4718:             }
4719: #if defined (PETSC_USE_CTABLE)
4720:             PetscTableFind(aij->colmap,in[j]+1,&col);
4721:             col--;
4722: #else
4723:             col = aij->colmap[in[j]] - 1;
4724: #endif
4725:             if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
4726:               DisAssemble_MPIAIJ(mat);
4727:               col =  in[j];
4728:               /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
4729:               B = aij->B;
4730:               b = (Mat_SeqAIJ*)B->data;
4731:               bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
4732:               rp2      = bj + bi[row];
4733:               ap2      = ba + bi[row];
4734:               rmax2    = bimax[row];
4735:               nrow2    = bilen[row];
4736:               low2     = 0;
4737:               high2    = nrow2;
4738:               bm       = aij->B->rmap.n;
4739:               ba = b->a;
4740:             }
4741:           } else col = in[j];
4742:           MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
4743:         }
4744:       }
4745:     } else {
4746:       if (!aij->donotstash) {
4747:         if (roworiented) {
4748:           if (ignorezeroentries && v[i*n] == 0.0) continue;
4749:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
4750:         } else {
4751:           if (ignorezeroentries && v[i] == 0.0) continue;
4752:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
4753:         }
4754:       }
4755:     }
4756:   }}
4757:   PetscFunctionReturnVoid();
4758: }