Actual source code: mpiaij.c

  1: #define PETSCMAT_DLL

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

  8: /*
  9:     Distributes a SeqAIJ matrix across a set of processes. Code stolen from
 10:     MatLoad_MPIAIJ(). Horrible lack of reuse. Should be a routine for each matrix type.

 12:     Only for square matrices
 13: */
 14: PetscErrorCode MatDistribute_MPIAIJ(MPI_Comm comm,Mat gmat,PetscInt m,MatReuse reuse,Mat *inmat)
 15: {
 16:   PetscMPIInt    rank,size;
 17:   PetscInt       *rowners,*dlens,*olens,i,rstart,rend,j,jj,nz,*gmataj,cnt,row,*ld;
 19:   Mat            mat;
 20:   Mat_SeqAIJ     *gmata;
 21:   PetscMPIInt    tag;
 22:   MPI_Status     status;
 23:   PetscTruth     aij;
 24:   MatScalar      *gmataa,*ao,*ad,*gmataarestore=0;

 27:   CHKMEMQ;
 28:   MPI_Comm_rank(comm,&rank);
 29:   MPI_Comm_size(comm,&size);
 30:   if (!rank) {
 31:     PetscTypeCompare((PetscObject)gmat,MATSEQAIJ,&aij);
 32:     if (!aij) SETERRQ1(PETSC_ERR_SUP,"Currently no support for input matrix of type %s\n",((PetscObject)gmat)->type_name);
 33:   }
 34:   if (reuse == MAT_INITIAL_MATRIX) {
 35:     MatCreate(comm,&mat);
 36:     MatSetSizes(mat,m,m,PETSC_DETERMINE,PETSC_DETERMINE);
 37:     MatSetType(mat,MATAIJ);
 38:     PetscMalloc((size+1)*sizeof(PetscInt),&rowners);
 39:     PetscMalloc2(m,PetscInt,&dlens,m,PetscInt,&olens);
 40:     MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
 41:     rowners[0] = 0;
 42:     for (i=2; i<=size; i++) {
 43:       rowners[i] += rowners[i-1];
 44:     }
 45:     rstart = rowners[rank];
 46:     rend   = rowners[rank+1];
 47:     PetscObjectGetNewTag((PetscObject)mat,&tag);
 48:     if (!rank) {
 49:       gmata = (Mat_SeqAIJ*) gmat->data;
 50:       /* send row lengths to all processors */
 51:       for (i=0; i<m; i++) dlens[i] = gmata->ilen[i];
 52:       for (i=1; i<size; i++) {
 53:         MPI_Send(gmata->ilen + rowners[i],rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
 54:       }
 55:       /* determine number diagonal and off-diagonal counts */
 56:       PetscMemzero(olens,m*sizeof(PetscInt));
 57:       PetscMalloc(m*sizeof(PetscInt),&ld);
 58:       PetscMemzero(ld,m*sizeof(PetscInt));
 59:       jj = 0;
 60:       for (i=0; i<m; i++) {
 61:         for (j=0; j<dlens[i]; j++) {
 62:           if (gmata->j[jj] < rstart) ld[i]++;
 63:           if (gmata->j[jj] < rstart || gmata->j[jj] >= rend) olens[i]++;
 64:           jj++;
 65:         }
 66:       }
 67:       /* send column indices to other processes */
 68:       for (i=1; i<size; i++) {
 69:         nz   = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
 70:         MPI_Send(&nz,1,MPIU_INT,i,tag,comm);
 71:         MPI_Send(gmata->j + gmata->i[rowners[i]],nz,MPIU_INT,i,tag,comm);
 72:       }

 74:       /* send numerical values to other processes */
 75:       for (i=1; i<size; i++) {
 76:         nz   = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
 77:         MPI_Send(gmata->a + gmata->i[rowners[i]],nz,MPIU_SCALAR,i,tag,comm);
 78:       }
 79:       gmataa = gmata->a;
 80:       gmataj = gmata->j;

 82:     } else {
 83:       /* receive row lengths */
 84:       MPI_Recv(dlens,m,MPIU_INT,0,tag,comm,&status);
 85:       /* receive column indices */
 86:       MPI_Recv(&nz,1,MPIU_INT,0,tag,comm,&status);
 87:       PetscMalloc2(nz,PetscScalar,&gmataa,nz,PetscInt,&gmataj);
 88:       MPI_Recv(gmataj,nz,MPIU_INT,0,tag,comm,&status);
 89:       /* determine number diagonal and off-diagonal counts */
 90:       PetscMemzero(olens,m*sizeof(PetscInt));
 91:       PetscMalloc(m*sizeof(PetscInt),&ld);
 92:       PetscMemzero(ld,m*sizeof(PetscInt));
 93:       jj = 0;
 94:       for (i=0; i<m; i++) {
 95:         for (j=0; j<dlens[i]; j++) {
 96:           if (gmataj[jj] < rstart) ld[i]++;
 97:           if (gmataj[jj] < rstart || gmataj[jj] >= rend) olens[i]++;
 98:           jj++;
 99:         }
100:       }
101:       /* receive numerical values */
102:       PetscMemzero(gmataa,nz*sizeof(PetscScalar));
103:       MPI_Recv(gmataa,nz,MPIU_SCALAR,0,tag,comm,&status);
104:     }
105:     /* set preallocation */
106:     for (i=0; i<m; i++) {
107:       dlens[i] -= olens[i];
108:     }
109:     MatSeqAIJSetPreallocation(mat,0,dlens);
110:     MatMPIAIJSetPreallocation(mat,0,dlens,0,olens);
111: 
112:     for (i=0; i<m; i++) {
113:       dlens[i] += olens[i];
114:     }
115:     cnt  = 0;
116:     for (i=0; i<m; i++) {
117:       row  = rstart + i;
118:       MatSetValues(mat,1,&row,dlens[i],gmataj+cnt,gmataa+cnt,INSERT_VALUES);
119:       cnt += dlens[i];
120:     }
121:     if (rank) {
122:       PetscFree2(gmataa,gmataj);
123:     }
124:     PetscFree2(dlens,olens);
125:     PetscFree(rowners);
126:     ((Mat_MPIAIJ*)(mat->data))->ld = ld;
127:     *inmat = mat;
128:   } else {   /* column indices are already set; only need to move over numerical values from process 0 */
129:     Mat_SeqAIJ *Ad = (Mat_SeqAIJ*)((Mat_MPIAIJ*)((*inmat)->data))->A->data;
130:     Mat_SeqAIJ *Ao = (Mat_SeqAIJ*)((Mat_MPIAIJ*)((*inmat)->data))->B->data;
131:     mat   = *inmat;
132:     PetscObjectGetNewTag((PetscObject)mat,&tag);
133:     if (!rank) {
134:       /* send numerical values to other processes */
135:       gmata = (Mat_SeqAIJ*) gmat->data;
136:       MatGetOwnershipRanges(mat,(const PetscInt**)&rowners);
137:       gmataa = gmata->a;
138:       for (i=1; i<size; i++) {
139:         nz   = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
140:         MPI_Send(gmataa + gmata->i[rowners[i]],nz,MPIU_SCALAR,i,tag,comm);
141:       }
142:       nz   = gmata->i[rowners[1]]-gmata->i[rowners[0]];
143:     } else {
144:       /* receive numerical values from process 0*/
145:       nz   = Ad->nz + Ao->nz;
146:       PetscMalloc(nz*sizeof(PetscScalar),&gmataa); gmataarestore = gmataa;
147:       MPI_Recv(gmataa,nz,MPIU_SCALAR,0,tag,comm,&status);
148:     }
149:     /* transfer numerical values into the diagonal A and off diagonal B parts of mat */
150:     ld = ((Mat_MPIAIJ*)(mat->data))->ld;
151:     ad = Ad->a;
152:     ao = Ao->a;
153:     if (mat->rmap->n) {
154:       i  = 0;
155:       nz = ld[i];                                   PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
156:       nz = Ad->i[i+1] - Ad->i[i];                   PetscMemcpy(ad,gmataa,nz*sizeof(PetscScalar)); ad += nz; gmataa += nz;
157:     }
158:     for (i=1; i<mat->rmap->n; i++) {
159:       nz = Ao->i[i] - Ao->i[i-1] - ld[i-1] + ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
160:       nz = Ad->i[i+1] - Ad->i[i];                   PetscMemcpy(ad,gmataa,nz*sizeof(PetscScalar)); ad += nz; gmataa += nz;
161:     }
162:     i--;
163:     if (mat->rmap->n) {
164:       nz = Ao->i[i+1] - Ao->i[i] - ld[i];           PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
165:     }
166:     if (rank) {
167:       PetscFree(gmataarestore);
168:     }
169:   }
170:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
171:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
172:   CHKMEMQ;
173:   return(0);
174: }

176: /* 
177:   Local utility routine that creates a mapping from the global column 
178: number to the local number in the off-diagonal part of the local 
179: storage of the matrix.  When PETSC_USE_CTABLE is used this is scalable at 
180: a slightly higher hash table cost; without it it is not scalable (each processor
181: has an order N integer array but is fast to acess.
182: */
185: PetscErrorCode CreateColmap_MPIAIJ_Private(Mat mat)
186: {
187:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
189:   PetscInt       n = aij->B->cmap->n,i;

192: #if defined (PETSC_USE_CTABLE)
193:   PetscTableCreate(n,&aij->colmap);
194:   for (i=0; i<n; i++){
195:     PetscTableAdd(aij->colmap,aij->garray[i]+1,i+1);
196:   }
197: #else
198:   PetscMalloc((mat->cmap->N+1)*sizeof(PetscInt),&aij->colmap);
199:   PetscLogObjectMemory(mat,mat->cmap->N*sizeof(PetscInt));
200:   PetscMemzero(aij->colmap,mat->cmap->N*sizeof(PetscInt));
201:   for (i=0; i<n; i++) aij->colmap[aij->garray[i]] = i+1;
202: #endif
203:   return(0);
204: }


207: #define CHUNKSIZE   15
208: #define MatSetValues_SeqAIJ_A_Private(row,col,value,addv) \
209: { \
210:     if (col <= lastcol1) low1 = 0; else high1 = nrow1; \
211:     lastcol1 = col;\
212:     while (high1-low1 > 5) { \
213:       t = (low1+high1)/2; \
214:       if (rp1[t] > col) high1 = t; \
215:       else             low1  = t; \
216:     } \
217:       for (_i=low1; _i<high1; _i++) { \
218:         if (rp1[_i] > col) break; \
219:         if (rp1[_i] == col) { \
220:           if (addv == ADD_VALUES) ap1[_i] += value;   \
221:           else                    ap1[_i] = value; \
222:           goto a_noinsert; \
223:         } \
224:       }  \
225:       if (value == 0.0 && ignorezeroentries) {low1 = 0; high1 = nrow1;goto a_noinsert;} \
226:       if (nonew == 1) {low1 = 0; high1 = nrow1; goto a_noinsert;}                \
227:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
228:       MatSeqXAIJReallocateAIJ(A,am,1,nrow1,row,col,rmax1,aa,ai,aj,rp1,ap1,aimax,nonew,MatScalar); \
229:       N = nrow1++ - 1; a->nz++; high1++; \
230:       /* shift up all the later entries in this row */ \
231:       for (ii=N; ii>=_i; ii--) { \
232:         rp1[ii+1] = rp1[ii]; \
233:         ap1[ii+1] = ap1[ii]; \
234:       } \
235:       rp1[_i] = col;  \
236:       ap1[_i] = value;  \
237:       a_noinsert: ; \
238:       ailen[row] = nrow1; \
239: } 


242: #define MatSetValues_SeqAIJ_B_Private(row,col,value,addv) \
243: { \
244:     if (col <= lastcol2) low2 = 0; else high2 = nrow2; \
245:     lastcol2 = col;\
246:     while (high2-low2 > 5) { \
247:       t = (low2+high2)/2; \
248:       if (rp2[t] > col) high2 = t; \
249:       else             low2  = t; \
250:     } \
251:     for (_i=low2; _i<high2; _i++) {                \
252:       if (rp2[_i] > col) break;                        \
253:       if (rp2[_i] == col) {                              \
254:         if (addv == ADD_VALUES) ap2[_i] += value;     \
255:         else                    ap2[_i] = value;      \
256:         goto b_noinsert;                              \
257:       }                                                      \
258:     }                                                              \
259:     if (value == 0.0 && ignorezeroentries) {low2 = 0; high2 = nrow2; goto b_noinsert;} \
260:     if (nonew == 1) {low2 = 0; high2 = nrow2; goto b_noinsert;}                \
261:     if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
262:     MatSeqXAIJReallocateAIJ(B,bm,1,nrow2,row,col,rmax2,ba,bi,bj,rp2,ap2,bimax,nonew,MatScalar); \
263:     N = nrow2++ - 1; b->nz++; high2++;                                        \
264:     /* shift up all the later entries in this row */                        \
265:     for (ii=N; ii>=_i; ii--) {                                                \
266:       rp2[ii+1] = rp2[ii];                                                \
267:       ap2[ii+1] = ap2[ii];                                                \
268:     }                                                                        \
269:     rp2[_i] = col;                                                        \
270:     ap2[_i] = value;                                                        \
271:     b_noinsert: ;                                                                \
272:     bilen[row] = nrow2;                                                        \
273: }

277: PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A,PetscInt row,const PetscScalar v[])
278: {
279:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)A->data;
280:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)mat->A->data,*b = (Mat_SeqAIJ*)mat->B->data;
282:   PetscInt       l,*garray = mat->garray,diag;

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

287:   /* find size of row to the left of the diagonal part */
288:   MatGetOwnershipRange(A,&diag,0);
289:   row  = row - diag;
290:   for (l=0; l<b->i[row+1]-b->i[row]; l++) {
291:     if (garray[b->j[b->i[row]+l]] > diag) break;
292:   }
293:   PetscMemcpy(b->a+b->i[row],v,l*sizeof(PetscScalar));

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

298:   /* right of diagonal part */
299:   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));
300:   return(0);
301: }

305: PetscErrorCode MatSetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
306: {
307:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
308:   PetscScalar    value;
310:   PetscInt       i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
311:   PetscInt       cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
312:   PetscTruth     roworiented = aij->roworiented;

314:   /* Some Variables required in the macro */
315:   Mat            A = aij->A;
316:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
317:   PetscInt       *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
318:   MatScalar      *aa = a->a;
319:   PetscTruth     ignorezeroentries = a->ignorezeroentries;
320:   Mat            B = aij->B;
321:   Mat_SeqAIJ     *b = (Mat_SeqAIJ*)B->data;
322:   PetscInt       *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap->n,am = aij->A->rmap->n;
323:   MatScalar      *ba = b->a;

325:   PetscInt       *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
326:   PetscInt       nonew = a->nonew;
327:   MatScalar      *ap1,*ap2;

330:   for (i=0; i<m; i++) {
331:     if (im[i] < 0) continue;
332: #if defined(PETSC_USE_DEBUG)
333:     if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
334: #endif
335:     if (im[i] >= rstart && im[i] < rend) {
336:       row      = im[i] - rstart;
337:       lastcol1 = -1;
338:       rp1      = aj + ai[row];
339:       ap1      = aa + ai[row];
340:       rmax1    = aimax[row];
341:       nrow1    = ailen[row];
342:       low1     = 0;
343:       high1    = nrow1;
344:       lastcol2 = -1;
345:       rp2      = bj + bi[row];
346:       ap2      = ba + bi[row];
347:       rmax2    = bimax[row];
348:       nrow2    = bilen[row];
349:       low2     = 0;
350:       high2    = nrow2;

352:       for (j=0; j<n; j++) {
353:         if (v) {if (roworiented) value = v[i*n+j]; else value = v[i+j*m];} else value = 0.0;
354:         if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
355:         if (in[j] >= cstart && in[j] < cend){
356:           col = in[j] - cstart;
357:           MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
358:         } else if (in[j] < 0) continue;
359: #if defined(PETSC_USE_DEBUG)
360:         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);}
361: #endif
362:         else {
363:           if (mat->was_assembled) {
364:             if (!aij->colmap) {
365:               CreateColmap_MPIAIJ_Private(mat);
366:             }
367: #if defined (PETSC_USE_CTABLE)
368:             PetscTableFind(aij->colmap,in[j]+1,&col);
369:             col--;
370: #else
371:             col = aij->colmap[in[j]] - 1;
372: #endif
373:             if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
374:               DisAssemble_MPIAIJ(mat);
375:               col =  in[j];
376:               /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
377:               B = aij->B;
378:               b = (Mat_SeqAIJ*)B->data;
379:               bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j; ba = b->a;
380:               rp2      = bj + bi[row];
381:               ap2      = ba + bi[row];
382:               rmax2    = bimax[row];
383:               nrow2    = bilen[row];
384:               low2     = 0;
385:               high2    = nrow2;
386:               bm       = aij->B->rmap->n;
387:               ba = b->a;
388:             }
389:           } else col = in[j];
390:           MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
391:         }
392:       }
393:     } else {
394:       if (!aij->donotstash) {
395:         if (roworiented) {
396:           if (ignorezeroentries && v[i*n] == 0.0) continue;
397:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
398:         } else {
399:           if (ignorezeroentries && v[i] == 0.0) continue;
400:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
401:         }
402:       }
403:     }
404:   }
405:   return(0);
406: }

410: PetscErrorCode MatGetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
411: {
412:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
414:   PetscInt       i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
415:   PetscInt       cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;

418:   for (i=0; i<m; i++) {
419:     if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);*/
420:     if (idxm[i] >= mat->rmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap->N-1);
421:     if (idxm[i] >= rstart && idxm[i] < rend) {
422:       row = idxm[i] - rstart;
423:       for (j=0; j<n; j++) {
424:         if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]); */
425:         if (idxn[j] >= mat->cmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap->N-1);
426:         if (idxn[j] >= cstart && idxn[j] < cend){
427:           col = idxn[j] - cstart;
428:           MatGetValues(aij->A,1,&row,1,&col,v+i*n+j);
429:         } else {
430:           if (!aij->colmap) {
431:             CreateColmap_MPIAIJ_Private(mat);
432:           }
433: #if defined (PETSC_USE_CTABLE)
434:           PetscTableFind(aij->colmap,idxn[j]+1,&col);
435:           col --;
436: #else
437:           col = aij->colmap[idxn[j]] - 1;
438: #endif
439:           if ((col < 0) || (aij->garray[col] != idxn[j])) *(v+i*n+j) = 0.0;
440:           else {
441:             MatGetValues(aij->B,1,&row,1,&col,v+i*n+j);
442:           }
443:         }
444:       }
445:     } else {
446:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
447:     }
448:   }
449:   return(0);
450: }

454: PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat,MatAssemblyType mode)
455: {
456:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
458:   PetscInt       nstash,reallocs;
459:   InsertMode     addv;

462:   if (aij->donotstash) {
463:     return(0);
464:   }

466:   /* make sure all processors are either in INSERTMODE or ADDMODE */
467:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,((PetscObject)mat)->comm);
468:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
469:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
470:   }
471:   mat->insertmode = addv; /* in case this processor had no cache */

473:   MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
474:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
475:   PetscInfo2(aij->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
476:   return(0);
477: }

481: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat,MatAssemblyType mode)
482: {
483:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
484:   Mat_SeqAIJ     *a=(Mat_SeqAIJ *)aij->A->data;
486:   PetscMPIInt    n;
487:   PetscInt       i,j,rstart,ncols,flg;
488:   PetscInt       *row,*col;
489:   PetscTruth     other_disassembled;
490:   PetscScalar    *val;
491:   InsertMode     addv = mat->insertmode;

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

500:       for (i=0; i<n;) {
501:         /* Now identify the consecutive vals belonging to the same row */
502:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
503:         if (j < n) ncols = j-i;
504:         else       ncols = n-i;
505:         /* Now assemble all these values with a single function call */
506:         MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
507:         i = j;
508:       }
509:     }
510:     MatStashScatterEnd_Private(&mat->stash);
511:   }
512:   a->compressedrow.use     = PETSC_FALSE;
513:   MatAssemblyBegin(aij->A,mode);
514:   MatAssemblyEnd(aij->A,mode);

516:   /* determine if any processor has disassembled, if so we must 
517:      also disassemble ourselfs, in order that we may reassemble. */
518:   /*
519:      if nonzero structure of submatrix B cannot change then we know that
520:      no processor disassembled thus we can skip this stuff
521:   */
522:   if (!((Mat_SeqAIJ*)aij->B->data)->nonew)  {
523:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,((PetscObject)mat)->comm);
524:     if (mat->was_assembled && !other_disassembled) {
525:       DisAssemble_MPIAIJ(mat);
526:     }
527:   }
528:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
529:     MatSetUpMultiply_MPIAIJ(mat);
530:   }
531:   MatSetOption(aij->B,MAT_USE_INODES,PETSC_FALSE);
532:   ((Mat_SeqAIJ *)aij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
533:   MatAssemblyBegin(aij->B,mode);
534:   MatAssemblyEnd(aij->B,mode);

536:   PetscFree(aij->rowvalues);
537:   aij->rowvalues = 0;

539:   /* used by MatAXPY() */
540:   a->xtoy = 0; ((Mat_SeqAIJ *)aij->B->data)->xtoy = 0;  /* b->xtoy = 0 */
541:   a->XtoY = 0; ((Mat_SeqAIJ *)aij->B->data)->XtoY = 0;  /* b->XtoY = 0 */

543:   return(0);
544: }

548: PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
549: {
550:   Mat_MPIAIJ     *l = (Mat_MPIAIJ*)A->data;

554:   MatZeroEntries(l->A);
555:   MatZeroEntries(l->B);
556:   return(0);
557: }

561: PetscErrorCode MatZeroRows_MPIAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
562: {
563:   Mat_MPIAIJ     *l = (Mat_MPIAIJ*)A->data;
565:   PetscMPIInt    size = l->size,imdex,n,rank = l->rank,tag = ((PetscObject)A)->tag,lastidx = -1;
566:   PetscInt       i,*owners = A->rmap->range;
567:   PetscInt       *nprocs,j,idx,nsends,row;
568:   PetscInt       nmax,*svalues,*starts,*owner,nrecvs;
569:   PetscInt       *rvalues,count,base,slen,*source;
570:   PetscInt       *lens,*lrows,*values,rstart=A->rmap->rstart;
571:   MPI_Comm       comm = ((PetscObject)A)->comm;
572:   MPI_Request    *send_waits,*recv_waits;
573:   MPI_Status     recv_status,*send_status;
574: #if defined(PETSC_DEBUG)
575:   PetscTruth     found = PETSC_FALSE;
576: #endif

579:   /*  first count number of contributors to each processor */
580:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
581:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
582:   PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
583:   j = 0;
584:   for (i=0; i<N; i++) {
585:     if (lastidx > (idx = rows[i])) j = 0;
586:     lastidx = idx;
587:     for (; j<size; j++) {
588:       if (idx >= owners[j] && idx < owners[j+1]) {
589:         nprocs[2*j]++;
590:         nprocs[2*j+1] = 1;
591:         owner[i] = j;
592: #if defined(PETSC_DEBUG)
593:         found = PETSC_TRUE;
594: #endif
595:         break;
596:       }
597:     }
598: #if defined(PETSC_DEBUG)
599:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
600:     found = PETSC_FALSE;
601: #endif
602:   }
603:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}

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

608:   /* post receives:   */
609:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
610:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
611:   for (i=0; i<nrecvs; i++) {
612:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
613:   }

615:   /* do sends:
616:       1) starts[i] gives the starting index in svalues for stuff going to 
617:          the ith processor
618:   */
619:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
620:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
621:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
622:   starts[0] = 0;
623:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
624:   for (i=0; i<N; i++) {
625:     svalues[starts[owner[i]]++] = rows[i];
626:   }

628:   starts[0] = 0;
629:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
630:   count = 0;
631:   for (i=0; i<size; i++) {
632:     if (nprocs[2*i+1]) {
633:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
634:     }
635:   }
636:   PetscFree(starts);

638:   base = owners[rank];

640:   /*  wait on receives */
641:   PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
642:   source = lens + nrecvs;
643:   count  = nrecvs; slen = 0;
644:   while (count) {
645:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
646:     /* unpack receives into our local space */
647:     MPI_Get_count(&recv_status,MPIU_INT,&n);
648:     source[imdex]  = recv_status.MPI_SOURCE;
649:     lens[imdex]    = n;
650:     slen          += n;
651:     count--;
652:   }
653:   PetscFree(recv_waits);
654: 
655:   /* move the data into the send scatter */
656:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
657:   count = 0;
658:   for (i=0; i<nrecvs; i++) {
659:     values = rvalues + i*nmax;
660:     for (j=0; j<lens[i]; j++) {
661:       lrows[count++] = values[j] - base;
662:     }
663:   }
664:   PetscFree(rvalues);
665:   PetscFree(lens);
666:   PetscFree(owner);
667:   PetscFree(nprocs);
668: 
669:   /* actually zap the local rows */
670:   /*
671:         Zero the required rows. If the "diagonal block" of the matrix
672:      is square and the user wishes to set the diagonal we use separate
673:      code so that MatSetValues() is not called for each diagonal allocating
674:      new memory, thus calling lots of mallocs and slowing things down.

676:        Contributed by: Matthew Knepley
677:   */
678:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
679:   MatZeroRows(l->B,slen,lrows,0.0);
680:   if ((diag != 0.0) && (l->A->rmap->N == l->A->cmap->N)) {
681:     MatZeroRows(l->A,slen,lrows,diag);
682:   } else if (diag != 0.0) {
683:     MatZeroRows(l->A,slen,lrows,0.0);
684:     if (((Mat_SeqAIJ*)l->A->data)->nonew) {
685:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options\n\
686: MAT_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
687:     }
688:     for (i = 0; i < slen; i++) {
689:       row  = lrows[i] + rstart;
690:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
691:     }
692:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
693:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
694:   } else {
695:     MatZeroRows(l->A,slen,lrows,0.0);
696:   }
697:   PetscFree(lrows);

699:   /* wait on sends */
700:   if (nsends) {
701:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
702:     MPI_Waitall(nsends,send_waits,send_status);
703:     PetscFree(send_status);
704:   }
705:   PetscFree(send_waits);
706:   PetscFree(svalues);

708:   return(0);
709: }

713: PetscErrorCode MatMult_MPIAIJ(Mat A,Vec xx,Vec yy)
714: {
715:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
717:   PetscInt       nt;

720:   VecGetLocalSize(xx,&nt);
721:   if (nt != A->cmap->n) {
722:     SETERRQ2(PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%D) and xx (%D)",A->cmap->n,nt);
723:   }
724:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
725:   (*a->A->ops->mult)(a->A,xx,yy);
726:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
727:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
728:   return(0);
729: }

733: PetscErrorCode MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
734: {
735:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

739:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
740:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
741:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
742:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
743:   return(0);
744: }

748: PetscErrorCode MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy)
749: {
750:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
752:   PetscTruth     merged;

755:   VecScatterGetMerged(a->Mvctx,&merged);
756:   /* do nondiagonal part */
757:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
758:   if (!merged) {
759:     /* send it on its way */
760:     VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
761:     /* do local part */
762:     (*a->A->ops->multtranspose)(a->A,xx,yy);
763:     /* receive remote parts: note this assumes the values are not actually */
764:     /* added in yy until the next line, */
765:     VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
766:   } else {
767:     /* do local part */
768:     (*a->A->ops->multtranspose)(a->A,xx,yy);
769:     /* send it on its way */
770:     VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
771:     /* values actually were received in the Begin() but we need to call this nop */
772:     VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
773:   }
774:   return(0);
775: }

780: PetscErrorCode  MatIsTranspose_MPIAIJ(Mat Amat,Mat Bmat,PetscReal tol,PetscTruth *f)
781: {
782:   MPI_Comm       comm;
783:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *) Amat->data, *Bij;
784:   Mat            Adia = Aij->A, Bdia, Aoff,Boff,*Aoffs,*Boffs;
785:   IS             Me,Notme;
787:   PetscInt       M,N,first,last,*notme,i;
788:   PetscMPIInt    size;


792:   /* Easy test: symmetric diagonal block */
793:   Bij = (Mat_MPIAIJ *) Bmat->data; Bdia = Bij->A;
794:   MatIsTranspose(Adia,Bdia,tol,f);
795:   if (!*f) return(0);
796:   PetscObjectGetComm((PetscObject)Amat,&comm);
797:   MPI_Comm_size(comm,&size);
798:   if (size == 1) return(0);

800:   /* Hard test: off-diagonal block. This takes a MatGetSubMatrix. */
801:   MatGetSize(Amat,&M,&N);
802:   MatGetOwnershipRange(Amat,&first,&last);
803:   PetscMalloc((N-last+first)*sizeof(PetscInt),&notme);
804:   for (i=0; i<first; i++) notme[i] = i;
805:   for (i=last; i<M; i++) notme[i-last+first] = i;
806:   ISCreateGeneral(MPI_COMM_SELF,N-last+first,notme,&Notme);
807:   ISCreateStride(MPI_COMM_SELF,last-first,first,1,&Me);
808:   MatGetSubMatrices(Amat,1,&Me,&Notme,MAT_INITIAL_MATRIX,&Aoffs);
809:   Aoff = Aoffs[0];
810:   MatGetSubMatrices(Bmat,1,&Notme,&Me,MAT_INITIAL_MATRIX,&Boffs);
811:   Boff = Boffs[0];
812:   MatIsTranspose(Aoff,Boff,tol,f);
813:   MatDestroyMatrices(1,&Aoffs);
814:   MatDestroyMatrices(1,&Boffs);
815:   ISDestroy(Me);
816:   ISDestroy(Notme);

818:   return(0);
819: }

824: PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
825: {
826:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

830:   /* do nondiagonal part */
831:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
832:   /* send it on its way */
833:   VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
834:   /* do local part */
835:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
836:   /* receive remote parts */
837:   VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
838:   return(0);
839: }

841: /*
842:   This only works correctly for square matrices where the subblock A->A is the 
843:    diagonal block
844: */
847: PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A,Vec v)
848: {
850:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

853:   if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
854:   if (A->rmap->rstart != A->cmap->rstart || A->rmap->rend != A->cmap->rend) {
855:     SETERRQ(PETSC_ERR_ARG_SIZ,"row partition must equal col partition");
856:   }
857:   MatGetDiagonal(a->A,v);
858:   return(0);
859: }

863: PetscErrorCode MatScale_MPIAIJ(Mat A,PetscScalar aa)
864: {
865:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

869:   MatScale(a->A,aa);
870:   MatScale(a->B,aa);
871:   return(0);
872: }

876: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
877: {
878:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;

882: #if defined(PETSC_USE_LOG)
883:   PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N);
884: #endif
885:   MatStashDestroy_Private(&mat->stash);
886:   MatDestroy(aij->A);
887:   MatDestroy(aij->B);
888: #if defined (PETSC_USE_CTABLE)
889:   if (aij->colmap) {PetscTableDestroy(aij->colmap);}
890: #else
891:   PetscFree(aij->colmap);
892: #endif
893:   PetscFree(aij->garray);
894:   if (aij->lvec)   {VecDestroy(aij->lvec);}
895:   if (aij->Mvctx)  {VecScatterDestroy(aij->Mvctx);}
896:   PetscFree(aij->rowvalues);
897:   PetscFree(aij->ld);
898:   PetscFree(aij);

900:   PetscObjectChangeTypeName((PetscObject)mat,0);
901:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
902:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
903:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
904:   PetscObjectComposeFunction((PetscObject)mat,"MatIsTranspose_C","",PETSC_NULL);
905:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocation_C","",PETSC_NULL);
906:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocationCSR_C","",PETSC_NULL);
907:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
908:   return(0);
909: }

913: PetscErrorCode MatView_MPIAIJ_Binary(Mat mat,PetscViewer viewer)
914: {
915:   Mat_MPIAIJ        *aij = (Mat_MPIAIJ*)mat->data;
916:   Mat_SeqAIJ*       A = (Mat_SeqAIJ*)aij->A->data;
917:   Mat_SeqAIJ*       B = (Mat_SeqAIJ*)aij->B->data;
918:   PetscErrorCode    ierr;
919:   PetscMPIInt       rank,size,tag = ((PetscObject)viewer)->tag;
920:   int               fd;
921:   PetscInt          nz,header[4],*row_lengths,*range=0,rlen,i;
922:   PetscInt          nzmax,*column_indices,j,k,col,*garray = aij->garray,cnt,cstart = mat->cmap->rstart,rnz;
923:   PetscScalar       *column_values;

926:   MPI_Comm_rank(((PetscObject)mat)->comm,&rank);
927:   MPI_Comm_size(((PetscObject)mat)->comm,&size);
928:   nz   = A->nz + B->nz;
929:   if (!rank) {
930:     header[0] = MAT_FILE_COOKIE;
931:     header[1] = mat->rmap->N;
932:     header[2] = mat->cmap->N;
933:     MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,((PetscObject)mat)->comm);
934:     PetscViewerBinaryGetDescriptor(viewer,&fd);
935:     PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
936:     /* get largest number of rows any processor has */
937:     rlen = mat->rmap->n;
938:     range = mat->rmap->range;
939:     for (i=1; i<size; i++) {
940:       rlen = PetscMax(rlen,range[i+1] - range[i]);
941:     }
942:   } else {
943:     MPI_Reduce(&nz,0,1,MPIU_INT,MPI_SUM,0,((PetscObject)mat)->comm);
944:     rlen = mat->rmap->n;
945:   }

947:   /* load up the local row counts */
948:   PetscMalloc((rlen+1)*sizeof(PetscInt),&row_lengths);
949:   for (i=0; i<mat->rmap->n; i++) {
950:     row_lengths[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i];
951:   }

953:   /* store the row lengths to the file */
954:   if (!rank) {
955:     MPI_Status status;
956:     PetscBinaryWrite(fd,row_lengths,mat->rmap->n,PETSC_INT,PETSC_TRUE);
957:     for (i=1; i<size; i++) {
958:       rlen = range[i+1] - range[i];
959:       MPI_Recv(row_lengths,rlen,MPIU_INT,i,tag,((PetscObject)mat)->comm,&status);
960:       PetscBinaryWrite(fd,row_lengths,rlen,PETSC_INT,PETSC_TRUE);
961:     }
962:   } else {
963:     MPI_Send(row_lengths,mat->rmap->n,MPIU_INT,0,tag,((PetscObject)mat)->comm);
964:   }
965:   PetscFree(row_lengths);

967:   /* load up the local column indices */
968:   nzmax = nz; /* )th processor needs space a largest processor needs */
969:   MPI_Reduce(&nz,&nzmax,1,MPIU_INT,MPI_MAX,0,((PetscObject)mat)->comm);
970:   PetscMalloc((nzmax+1)*sizeof(PetscInt),&column_indices);
971:   cnt  = 0;
972:   for (i=0; i<mat->rmap->n; i++) {
973:     for (j=B->i[i]; j<B->i[i+1]; j++) {
974:       if ( (col = garray[B->j[j]]) > cstart) break;
975:       column_indices[cnt++] = col;
976:     }
977:     for (k=A->i[i]; k<A->i[i+1]; k++) {
978:       column_indices[cnt++] = A->j[k] + cstart;
979:     }
980:     for (; j<B->i[i+1]; j++) {
981:       column_indices[cnt++] = garray[B->j[j]];
982:     }
983:   }
984:   if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);

986:   /* store the column indices to the file */
987:   if (!rank) {
988:     MPI_Status status;
989:     PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,PETSC_TRUE);
990:     for (i=1; i<size; i++) {
991:       MPI_Recv(&rnz,1,MPIU_INT,i,tag,((PetscObject)mat)->comm,&status);
992:       if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
993:       MPI_Recv(column_indices,rnz,MPIU_INT,i,tag,((PetscObject)mat)->comm,&status);
994:       PetscBinaryWrite(fd,column_indices,rnz,PETSC_INT,PETSC_TRUE);
995:     }
996:   } else {
997:     MPI_Send(&nz,1,MPIU_INT,0,tag,((PetscObject)mat)->comm);
998:     MPI_Send(column_indices,nz,MPIU_INT,0,tag,((PetscObject)mat)->comm);
999:   }
1000:   PetscFree(column_indices);

1002:   /* load up the local column values */
1003:   PetscMalloc((nzmax+1)*sizeof(PetscScalar),&column_values);
1004:   cnt  = 0;
1005:   for (i=0; i<mat->rmap->n; i++) {
1006:     for (j=B->i[i]; j<B->i[i+1]; j++) {
1007:       if ( garray[B->j[j]] > cstart) break;
1008:       column_values[cnt++] = B->a[j];
1009:     }
1010:     for (k=A->i[i]; k<A->i[i+1]; k++) {
1011:       column_values[cnt++] = A->a[k];
1012:     }
1013:     for (; j<B->i[i+1]; j++) {
1014:       column_values[cnt++] = B->a[j];
1015:     }
1016:   }
1017:   if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_PLIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);

1019:   /* store the column values to the file */
1020:   if (!rank) {
1021:     MPI_Status status;
1022:     PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,PETSC_TRUE);
1023:     for (i=1; i<size; i++) {
1024:       MPI_Recv(&rnz,1,MPIU_INT,i,tag,((PetscObject)mat)->comm,&status);
1025:       if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
1026:       MPI_Recv(column_values,rnz,MPIU_SCALAR,i,tag,((PetscObject)mat)->comm,&status);
1027:       PetscBinaryWrite(fd,column_values,rnz,PETSC_SCALAR,PETSC_TRUE);
1028:     }
1029:   } else {
1030:     MPI_Send(&nz,1,MPIU_INT,0,tag,((PetscObject)mat)->comm);
1031:     MPI_Send(column_values,nz,MPIU_SCALAR,0,tag,((PetscObject)mat)->comm);
1032:   }
1033:   PetscFree(column_values);
1034:   return(0);
1035: }

1039: PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1040: {
1041:   Mat_MPIAIJ        *aij = (Mat_MPIAIJ*)mat->data;
1042:   PetscErrorCode    ierr;
1043:   PetscMPIInt       rank = aij->rank,size = aij->size;
1044:   PetscTruth        isdraw,iascii,isbinary;
1045:   PetscViewer       sviewer;
1046:   PetscViewerFormat format;

1049:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1050:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1051:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1052:   if (iascii) {
1053:     PetscViewerGetFormat(viewer,&format);
1054:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1055:       MatInfo    info;
1056:       PetscTruth inodes;

1058:       MPI_Comm_rank(((PetscObject)mat)->comm,&rank);
1059:       MatGetInfo(mat,MAT_LOCAL,&info);
1060:       MatInodeGetInodeSizes(aij->A,PETSC_NULL,(PetscInt **)&inodes,PETSC_NULL);
1061:       if (!inodes) {
1062:         PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, not using I-node routines\n",
1063:                                               rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
1064:       } else {
1065:         PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, using I-node routines\n",
1066:                     rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
1067:       }
1068:       MatGetInfo(aij->A,MAT_LOCAL,&info);
1069:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1070:       MatGetInfo(aij->B,MAT_LOCAL,&info);
1071:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1072:       PetscViewerFlush(viewer);
1073:       PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
1074:       VecScatterView(aij->Mvctx,viewer);
1075:       return(0);
1076:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1077:       PetscInt   inodecount,inodelimit,*inodes;
1078:       MatInodeGetInodeSizes(aij->A,&inodecount,&inodes,&inodelimit);
1079:       if (inodes) {
1080:         PetscViewerASCIIPrintf(viewer,"using I-node (on process 0) routines: found %D nodes, limit used is %D\n",inodecount,inodelimit);
1081:       } else {
1082:         PetscViewerASCIIPrintf(viewer,"not using I-node (on process 0) routines\n");
1083:       }
1084:       return(0);
1085:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1086:       return(0);
1087:     }
1088:   } else if (isbinary) {
1089:     if (size == 1) {
1090:       PetscObjectSetName((PetscObject)aij->A,((PetscObject)mat)->name);
1091:       MatView(aij->A,viewer);
1092:     } else {
1093:       MatView_MPIAIJ_Binary(mat,viewer);
1094:     }
1095:     return(0);
1096:   } else if (isdraw) {
1097:     PetscDraw  draw;
1098:     PetscTruth isnull;
1099:     PetscViewerDrawGetDraw(viewer,0,&draw);
1100:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1101:   }

1103:   if (size == 1) {
1104:     PetscObjectSetName((PetscObject)aij->A,((PetscObject)mat)->name);
1105:     MatView(aij->A,viewer);
1106:   } else {
1107:     /* assemble the entire matrix onto first processor. */
1108:     Mat         A;
1109:     Mat_SeqAIJ  *Aloc;
1110:     PetscInt    M = mat->rmap->N,N = mat->cmap->N,m,*ai,*aj,row,*cols,i,*ct;
1111:     MatScalar   *a;

1113:     if (mat->rmap->N > 1024) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"ASCII matrix output not allowed for matrices with more than 512 rows, use binary format instead");

1115:     MatCreate(((PetscObject)mat)->comm,&A);
1116:     if (!rank) {
1117:       MatSetSizes(A,M,N,M,N);
1118:     } else {
1119:       MatSetSizes(A,0,0,M,N);
1120:     }
1121:     /* This is just a temporary matrix, so explicitly using MATMPIAIJ is probably best */
1122:     MatSetType(A,MATMPIAIJ);
1123:     MatMPIAIJSetPreallocation(A,0,PETSC_NULL,0,PETSC_NULL);
1124:     PetscLogObjectParent(mat,A);

1126:     /* copy over the A part */
1127:     Aloc = (Mat_SeqAIJ*)aij->A->data;
1128:     m = aij->A->rmap->n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1129:     row = mat->rmap->rstart;
1130:     for (i=0; i<ai[m]; i++) {aj[i] += mat->cmap->rstart ;}
1131:     for (i=0; i<m; i++) {
1132:       MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);
1133:       row++; a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i];
1134:     }
1135:     aj = Aloc->j;
1136:     for (i=0; i<ai[m]; i++) {aj[i] -= mat->cmap->rstart;}

1138:     /* copy over the B part */
1139:     Aloc = (Mat_SeqAIJ*)aij->B->data;
1140:     m    = aij->B->rmap->n;  ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1141:     row  = mat->rmap->rstart;
1142:     PetscMalloc((ai[m]+1)*sizeof(PetscInt),&cols);
1143:     ct   = cols;
1144:     for (i=0; i<ai[m]; i++) {cols[i] = aij->garray[aj[i]];}
1145:     for (i=0; i<m; i++) {
1146:       MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);
1147:       row++; a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
1148:     }
1149:     PetscFree(ct);
1150:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1151:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1152:     /* 
1153:        Everyone has to call to draw the matrix since the graphics waits are
1154:        synchronized across all processors that share the PetscDraw object
1155:     */
1156:     PetscViewerGetSingleton(viewer,&sviewer);
1157:     if (!rank) {
1158:       PetscObjectSetName((PetscObject)((Mat_MPIAIJ*)(A->data))->A,((PetscObject)mat)->name);
1159:       MatView(((Mat_MPIAIJ*)(A->data))->A,sviewer);
1160:     }
1161:     PetscViewerRestoreSingleton(viewer,&sviewer);
1162:     MatDestroy(A);
1163:   }
1164:   return(0);
1165: }

1169: PetscErrorCode MatView_MPIAIJ(Mat mat,PetscViewer viewer)
1170: {
1172:   PetscTruth     iascii,isdraw,issocket,isbinary;
1173: 
1175:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1176:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1177:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1178:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1179:   if (iascii || isdraw || isbinary || issocket) {
1180:     MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);
1181:   } else {
1182:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIAIJ matrices",((PetscObject)viewer)->type_name);
1183:   }
1184:   return(0);
1185: }

1189: PetscErrorCode MatRelax_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1190: {
1191:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1193:   Vec            bb1;

1196:   VecDuplicate(bb,&bb1);

1198:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
1199:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1200:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
1201:       its--;
1202:     }
1203: 
1204:     while (its--) {
1205:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1206:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

1208:       /* update rhs: bb1 = bb - B*x */
1209:       VecScale(mat->lvec,-1.0);
1210:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1212:       /* local sweep */
1213:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
1214:     }
1215:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP){
1216:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1217:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1218:       its--;
1219:     }
1220:     while (its--) {
1221:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1222:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

1224:       /* update rhs: bb1 = bb - B*x */
1225:       VecScale(mat->lvec,-1.0);
1226:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1228:       /* local sweep */
1229:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1230:     }
1231:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP){
1232:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1233:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1234:       its--;
1235:     }
1236:     while (its--) {
1237:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1238:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

1240:       /* update rhs: bb1 = bb - B*x */
1241:       VecScale(mat->lvec,-1.0);
1242:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

1244:       /* local sweep */
1245:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1246:     }
1247:   } else {
1248:     SETERRQ(PETSC_ERR_SUP,"Parallel SOR not supported");
1249:   }

1251:   VecDestroy(bb1);
1252:   return(0);
1253: }

1257: PetscErrorCode MatPermute_MPIAIJ(Mat A,IS rowp,IS colp,Mat *B)
1258: {
1259:   MPI_Comm       comm,pcomm;
1260:   PetscInt       first,local_size,nrows;
1261:   const PetscInt *rows;
1262:   int            ntids;
1263:   IS             crowp,growp,irowp,lrowp,lcolp,icolp;

1267:   PetscObjectGetComm((PetscObject)A,&comm);
1268:   /* make a collective version of 'rowp' */
1269:   PetscObjectGetComm((PetscObject)rowp,&pcomm);
1270:   if (pcomm==comm) {
1271:     crowp = rowp;
1272:   } else {
1273:     ISGetSize(rowp,&nrows);
1274:     ISGetIndices(rowp,&rows);
1275:     ISCreateGeneral(comm,nrows,rows,&crowp);
1276:     ISRestoreIndices(rowp,&rows);
1277:   }
1278:   /* collect the global row permutation and invert it */
1279:   ISAllGather(crowp,&growp);
1280:   ISSetPermutation(growp);
1281:   if (pcomm!=comm) {
1282:     ISDestroy(crowp);
1283:   }
1284:   ISInvertPermutation(growp,PETSC_DECIDE,&irowp);
1285:   /* get the local target indices */
1286:   MatGetOwnershipRange(A,&first,PETSC_NULL);
1287:   MatGetLocalSize(A,&local_size,PETSC_NULL);
1288:   ISGetIndices(irowp,&rows);
1289:   ISCreateGeneral(MPI_COMM_SELF,local_size,rows+first,&lrowp);
1290:   ISRestoreIndices(irowp,&rows);
1291:   ISDestroy(irowp);
1292:   /* the column permutation is so much easier;
1293:      make a local version of 'colp' and invert it */
1294:   PetscObjectGetComm((PetscObject)colp,&pcomm);
1295:   MPI_Comm_size(pcomm,&ntids);
1296:   if (ntids==1) {
1297:     lcolp = colp;
1298:   } else {
1299:     ISGetSize(colp,&nrows);
1300:     ISGetIndices(colp,&rows);
1301:     ISCreateGeneral(MPI_COMM_SELF,nrows,rows,&lcolp);
1302:   }
1303:   ISInvertPermutation(lcolp,PETSC_DECIDE,&icolp);
1304:   ISSetPermutation(lcolp);
1305:   if (ntids>1) {
1306:     ISRestoreIndices(colp,&rows);
1307:     ISDestroy(lcolp);
1308:   }
1309:   /* now we just get the submatrix */
1310:   MatGetSubMatrix(A,lrowp,icolp,local_size,MAT_INITIAL_MATRIX,B);
1311:   /* clean up */
1312:   ISDestroy(lrowp);
1313:   ISDestroy(icolp);
1314:   return(0);
1315: }

1319: PetscErrorCode MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1320: {
1321:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1322:   Mat            A = mat->A,B = mat->B;
1324:   PetscReal      isend[5],irecv[5];

1327:   info->block_size     = 1.0;
1328:   MatGetInfo(A,MAT_LOCAL,info);
1329:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1330:   isend[3] = info->memory;  isend[4] = info->mallocs;
1331:   MatGetInfo(B,MAT_LOCAL,info);
1332:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1333:   isend[3] += info->memory;  isend[4] += info->mallocs;
1334:   if (flag == MAT_LOCAL) {
1335:     info->nz_used      = isend[0];
1336:     info->nz_allocated = isend[1];
1337:     info->nz_unneeded  = isend[2];
1338:     info->memory       = isend[3];
1339:     info->mallocs      = isend[4];
1340:   } else if (flag == MAT_GLOBAL_MAX) {
1341:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,((PetscObject)matin)->comm);
1342:     info->nz_used      = irecv[0];
1343:     info->nz_allocated = irecv[1];
1344:     info->nz_unneeded  = irecv[2];
1345:     info->memory       = irecv[3];
1346:     info->mallocs      = irecv[4];
1347:   } else if (flag == MAT_GLOBAL_SUM) {
1348:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,((PetscObject)matin)->comm);
1349:     info->nz_used      = irecv[0];
1350:     info->nz_allocated = irecv[1];
1351:     info->nz_unneeded  = irecv[2];
1352:     info->memory       = irecv[3];
1353:     info->mallocs      = irecv[4];
1354:   }
1355:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1356:   info->fill_ratio_needed = 0;
1357:   info->factor_mallocs    = 0;

1359:   return(0);
1360: }

1364: PetscErrorCode MatSetOption_MPIAIJ(Mat A,MatOption op,PetscTruth flg)
1365: {
1366:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

1370:   switch (op) {
1371:   case MAT_NEW_NONZERO_LOCATIONS:
1372:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1373:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1374:   case MAT_KEEP_ZEROED_ROWS:
1375:   case MAT_NEW_NONZERO_LOCATION_ERR:
1376:   case MAT_USE_INODES:
1377:   case MAT_IGNORE_ZERO_ENTRIES:
1378:     MatSetOption(a->A,op,flg);
1379:     MatSetOption(a->B,op,flg);
1380:     break;
1381:   case MAT_ROW_ORIENTED:
1382:     a->roworiented = flg;
1383:     MatSetOption(a->A,op,flg);
1384:     MatSetOption(a->B,op,flg);
1385:     break;
1386:   case MAT_NEW_DIAGONALS:
1387:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1388:     break;
1389:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1390:     a->donotstash = PETSC_TRUE;
1391:     break;
1392:   case MAT_SYMMETRIC:
1393:     MatSetOption(a->A,op,flg);
1394:     break;
1395:   case MAT_STRUCTURALLY_SYMMETRIC:
1396:   case MAT_HERMITIAN:
1397:   case MAT_SYMMETRY_ETERNAL:
1398:     MatSetOption(a->A,op,flg);
1399:     break;
1400:   default:
1401:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
1402:   }
1403:   return(0);
1404: }

1408: PetscErrorCode MatGetRow_MPIAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1409: {
1410:   Mat_MPIAIJ     *mat = (Mat_MPIAIJ*)matin->data;
1411:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1413:   PetscInt       i,*cworkA,*cworkB,**pcA,**pcB,cstart = matin->cmap->rstart;
1414:   PetscInt       nztot,nzA,nzB,lrow,rstart = matin->rmap->rstart,rend = matin->rmap->rend;
1415:   PetscInt       *cmap,*idx_p;

1418:   if (mat->getrowactive) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1419:   mat->getrowactive = PETSC_TRUE;

1421:   if (!mat->rowvalues && (idx || v)) {
1422:     /*
1423:         allocate enough space to hold information from the longest row.
1424:     */
1425:     Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data;
1426:     PetscInt     max = 1,tmp;
1427:     for (i=0; i<matin->rmap->n; i++) {
1428:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1429:       if (max < tmp) { max = tmp; }
1430:     }
1431:     PetscMalloc(max*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1432:     mat->rowindices = (PetscInt*)(mat->rowvalues + max);
1433:   }

1435:   if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Only local rows")
1436:   lrow = row - rstart;

1438:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1439:   if (!v)   {pvA = 0; pvB = 0;}
1440:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1441:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1442:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1443:   nztot = nzA + nzB;

1445:   cmap  = mat->garray;
1446:   if (v  || idx) {
1447:     if (nztot) {
1448:       /* Sort by increasing column numbers, assuming A and B already sorted */
1449:       PetscInt imark = -1;
1450:       if (v) {
1451:         *v = v_p = mat->rowvalues;
1452:         for (i=0; i<nzB; i++) {
1453:           if (cmap[cworkB[i]] < cstart)   v_p[i] = vworkB[i];
1454:           else break;
1455:         }
1456:         imark = i;
1457:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1458:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1459:       }
1460:       if (idx) {
1461:         *idx = idx_p = mat->rowindices;
1462:         if (imark > -1) {
1463:           for (i=0; i<imark; i++) {
1464:             idx_p[i] = cmap[cworkB[i]];
1465:           }
1466:         } else {
1467:           for (i=0; i<nzB; i++) {
1468:             if (cmap[cworkB[i]] < cstart)   idx_p[i] = cmap[cworkB[i]];
1469:             else break;
1470:           }
1471:           imark = i;
1472:         }
1473:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart + cworkA[i];
1474:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]];
1475:       }
1476:     } else {
1477:       if (idx) *idx = 0;
1478:       if (v)   *v   = 0;
1479:     }
1480:   }
1481:   *nz = nztot;
1482:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1483:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1484:   return(0);
1485: }

1489: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1490: {
1491:   Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;

1494:   if (!aij->getrowactive) {
1495:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1496:   }
1497:   aij->getrowactive = PETSC_FALSE;
1498:   return(0);
1499: }

1503: PetscErrorCode MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm)
1504: {
1505:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
1506:   Mat_SeqAIJ     *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data;
1508:   PetscInt       i,j,cstart = mat->cmap->rstart;
1509:   PetscReal      sum = 0.0;
1510:   MatScalar      *v;

1513:   if (aij->size == 1) {
1514:      MatNorm(aij->A,type,norm);
1515:   } else {
1516:     if (type == NORM_FROBENIUS) {
1517:       v = amat->a;
1518:       for (i=0; i<amat->nz; i++) {
1519: #if defined(PETSC_USE_COMPLEX)
1520:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1521: #else
1522:         sum += (*v)*(*v); v++;
1523: #endif
1524:       }
1525:       v = bmat->a;
1526:       for (i=0; i<bmat->nz; i++) {
1527: #if defined(PETSC_USE_COMPLEX)
1528:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1529: #else
1530:         sum += (*v)*(*v); v++;
1531: #endif
1532:       }
1533:       MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPI_SUM,((PetscObject)mat)->comm);
1534:       *norm = sqrt(*norm);
1535:     } else if (type == NORM_1) { /* max column norm */
1536:       PetscReal *tmp,*tmp2;
1537:       PetscInt  *jj,*garray = aij->garray;
1538:       PetscMalloc((mat->cmap->N+1)*sizeof(PetscReal),&tmp);
1539:       PetscMalloc((mat->cmap->N+1)*sizeof(PetscReal),&tmp2);
1540:       PetscMemzero(tmp,mat->cmap->N*sizeof(PetscReal));
1541:       *norm = 0.0;
1542:       v = amat->a; jj = amat->j;
1543:       for (j=0; j<amat->nz; j++) {
1544:         tmp[cstart + *jj++ ] += PetscAbsScalar(*v);  v++;
1545:       }
1546:       v = bmat->a; jj = bmat->j;
1547:       for (j=0; j<bmat->nz; j++) {
1548:         tmp[garray[*jj++]] += PetscAbsScalar(*v); v++;
1549:       }
1550:       MPI_Allreduce(tmp,tmp2,mat->cmap->N,MPIU_REAL,MPI_SUM,((PetscObject)mat)->comm);
1551:       for (j=0; j<mat->cmap->N; j++) {
1552:         if (tmp2[j] > *norm) *norm = tmp2[j];
1553:       }
1554:       PetscFree(tmp);
1555:       PetscFree(tmp2);
1556:     } else if (type == NORM_INFINITY) { /* max row norm */
1557:       PetscReal ntemp = 0.0;
1558:       for (j=0; j<aij->A->rmap->n; j++) {
1559:         v = amat->a + amat->i[j];
1560:         sum = 0.0;
1561:         for (i=0; i<amat->i[j+1]-amat->i[j]; i++) {
1562:           sum += PetscAbsScalar(*v); v++;
1563:         }
1564:         v = bmat->a + bmat->i[j];
1565:         for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) {
1566:           sum += PetscAbsScalar(*v); v++;
1567:         }
1568:         if (sum > ntemp) ntemp = sum;
1569:       }
1570:       MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPI_MAX,((PetscObject)mat)->comm);
1571:     } else {
1572:       SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1573:     }
1574:   }
1575:   return(0);
1576: }

1580: PetscErrorCode MatTranspose_MPIAIJ(Mat A,MatReuse reuse,Mat *matout)
1581: {
1582:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
1583:   Mat_SeqAIJ     *Aloc=(Mat_SeqAIJ*)a->A->data,*Bloc=(Mat_SeqAIJ*)a->B->data;
1585:   PetscInt       M = A->rmap->N,N = A->cmap->N,ma,na,mb,*ai,*aj,*bi,*bj,row,*cols,*cols_tmp,i,*d_nnz;
1586:   PetscInt       cstart=A->cmap->rstart,ncol;
1587:   Mat            B;
1588:   MatScalar      *array;

1591:   if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");

1593:   ma = A->rmap->n; na = A->cmap->n; mb = a->B->rmap->n;
1594:   ai = Aloc->i; aj = Aloc->j;
1595:   bi = Bloc->i; bj = Bloc->j;
1596:   if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1597:     /* compute d_nnz for preallocation; o_nnz is approximated by d_nnz to avoid communication */
1598:     PetscMalloc((1+na)*sizeof(PetscInt),&d_nnz);
1599:     PetscMemzero(d_nnz,(1+na)*sizeof(PetscInt));
1600:     for (i=0; i<ai[ma]; i++){
1601:       d_nnz[aj[i]] ++;
1602:       aj[i] += cstart; /* global col index to be used by MatSetValues() */
1603:     }

1605:     MatCreate(((PetscObject)A)->comm,&B);
1606:     MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
1607:     MatSetType(B,((PetscObject)A)->type_name);
1608:     MatMPIAIJSetPreallocation(B,0,d_nnz,0,d_nnz);
1609:     PetscFree(d_nnz);
1610:   } else {
1611:     B = *matout;
1612:   }

1614:   /* copy over the A part */
1615:   array = Aloc->a;
1616:   row = A->rmap->rstart;
1617:   for (i=0; i<ma; i++) {
1618:     ncol = ai[i+1]-ai[i];
1619:     MatSetValues(B,ncol,aj,1,&row,array,INSERT_VALUES);
1620:     row++; array += ncol; aj += ncol;
1621:   }
1622:   aj = Aloc->j;
1623:   for (i=0; i<ai[ma]; i++) aj[i] -= cstart; /* resume local col index */

1625:   /* copy over the B part */
1626:   PetscMalloc(bi[mb]*sizeof(PetscInt),&cols);
1627:   PetscMemzero(cols,bi[mb]*sizeof(PetscInt));
1628:   array = Bloc->a;
1629:   row = A->rmap->rstart;
1630:   for (i=0; i<bi[mb]; i++) {cols[i] = a->garray[bj[i]];}
1631:   cols_tmp = cols;
1632:   for (i=0; i<mb; i++) {
1633:     ncol = bi[i+1]-bi[i];
1634:     MatSetValues(B,ncol,cols_tmp,1,&row,array,INSERT_VALUES);
1635:     row++; array += ncol; cols_tmp += ncol;
1636:   }
1637:   PetscFree(cols);
1638: 
1639:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1640:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1641:   if (reuse == MAT_INITIAL_MATRIX || *matout != A) {
1642:     *matout = B;
1643:   } else {
1644:     MatHeaderCopy(A,B);
1645:   }
1646:   return(0);
1647: }

1651: PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr)
1652: {
1653:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
1654:   Mat            a = aij->A,b = aij->B;
1656:   PetscInt       s1,s2,s3;

1659:   MatGetLocalSize(mat,&s2,&s3);
1660:   if (rr) {
1661:     VecGetLocalSize(rr,&s1);
1662:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1663:     /* Overlap communication with computation. */
1664:     VecScatterBegin(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1665:   }
1666:   if (ll) {
1667:     VecGetLocalSize(ll,&s1);
1668:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1669:     (*b->ops->diagonalscale)(b,ll,0);
1670:   }
1671:   /* scale  the diagonal block */
1672:   (*a->ops->diagonalscale)(a,ll,rr);

1674:   if (rr) {
1675:     /* Do a scatter end and then right scale the off-diagonal block */
1676:     VecScatterEnd(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1677:     (*b->ops->diagonalscale)(b,0,aij->lvec);
1678:   }
1679: 
1680:   return(0);
1681: }

1685: PetscErrorCode MatSetBlockSize_MPIAIJ(Mat A,PetscInt bs)
1686: {
1687:   Mat_MPIAIJ     *a   = (Mat_MPIAIJ*)A->data;

1691:   MatSetBlockSize(a->A,bs);
1692:   MatSetBlockSize(a->B,bs);
1693:   return(0);
1694: }
1697: PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
1698: {
1699:   Mat_MPIAIJ     *a   = (Mat_MPIAIJ*)A->data;

1703:   MatSetUnfactored(a->A);
1704:   return(0);
1705: }

1709: PetscErrorCode MatEqual_MPIAIJ(Mat A,Mat B,PetscTruth *flag)
1710: {
1711:   Mat_MPIAIJ     *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data;
1712:   Mat            a,b,c,d;
1713:   PetscTruth     flg;

1717:   a = matA->A; b = matA->B;
1718:   c = matB->A; d = matB->B;

1720:   MatEqual(a,c,&flg);
1721:   if (flg) {
1722:     MatEqual(b,d,&flg);
1723:   }
1724:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,((PetscObject)A)->comm);
1725:   return(0);
1726: }

1730: PetscErrorCode MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str)
1731: {
1733:   Mat_MPIAIJ     *a = (Mat_MPIAIJ *)A->data;
1734:   Mat_MPIAIJ     *b = (Mat_MPIAIJ *)B->data;

1737:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1738:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1739:     /* because of the column compression in the off-processor part of the matrix a->B,
1740:        the number of columns in a->B and b->B may be different, hence we cannot call
1741:        the MatCopy() directly on the two parts. If need be, we can provide a more 
1742:        efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
1743:        then copying the submatrices */
1744:     MatCopy_Basic(A,B,str);
1745:   } else {
1746:     MatCopy(a->A,b->A,str);
1747:     MatCopy(a->B,b->B,str);
1748:   }
1749:   return(0);
1750: }

1754: PetscErrorCode MatSetUpPreallocation_MPIAIJ(Mat A)
1755: {

1759:    MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1760:   return(0);
1761: }

1763:  #include petscblaslapack.h
1766: PetscErrorCode MatAXPY_MPIAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1767: {
1769:   PetscInt       i;
1770:   Mat_MPIAIJ     *xx = (Mat_MPIAIJ *)X->data,*yy = (Mat_MPIAIJ *)Y->data;
1771:   PetscBLASInt   bnz,one=1;
1772:   Mat_SeqAIJ     *x,*y;

1775:   if (str == SAME_NONZERO_PATTERN) {
1776:     PetscScalar alpha = a;
1777:     x = (Mat_SeqAIJ *)xx->A->data;
1778:     y = (Mat_SeqAIJ *)yy->A->data;
1779:     bnz = PetscBLASIntCast(x->nz);
1780:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1781:     x = (Mat_SeqAIJ *)xx->B->data;
1782:     y = (Mat_SeqAIJ *)yy->B->data;
1783:     bnz = PetscBLASIntCast(x->nz);
1784:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1785:   } else if (str == SUBSET_NONZERO_PATTERN) {
1786:     MatAXPY_SeqAIJ(yy->A,a,xx->A,str);

1788:     x = (Mat_SeqAIJ *)xx->B->data;
1789:     y = (Mat_SeqAIJ *)yy->B->data;
1790:     if (y->xtoy && y->XtoY != xx->B) {
1791:       PetscFree(y->xtoy);
1792:       MatDestroy(y->XtoY);
1793:     }
1794:     if (!y->xtoy) { /* get xtoy */
1795:       MatAXPYGetxtoy_Private(xx->B->rmap->n,x->i,x->j,xx->garray,y->i,y->j,yy->garray,&y->xtoy);
1796:       y->XtoY = xx->B;
1797:       PetscObjectReference((PetscObject)xx->B);
1798:     }
1799:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
1800:   } else {
1801:     MatAXPY_Basic(Y,a,X,str);
1802:   }
1803:   return(0);
1804: }

1806: EXTERN PetscErrorCode  MatConjugate_SeqAIJ(Mat);

1810: PetscErrorCode  MatConjugate_MPIAIJ(Mat mat)
1811: {
1812: #if defined(PETSC_USE_COMPLEX)
1814:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

1817:   MatConjugate_SeqAIJ(aij->A);
1818:   MatConjugate_SeqAIJ(aij->B);
1819: #else
1821: #endif
1822:   return(0);
1823: }

1827: PetscErrorCode MatRealPart_MPIAIJ(Mat A)
1828: {
1829:   Mat_MPIAIJ   *a = (Mat_MPIAIJ*)A->data;

1833:   MatRealPart(a->A);
1834:   MatRealPart(a->B);
1835:   return(0);
1836: }

1840: PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
1841: {
1842:   Mat_MPIAIJ   *a = (Mat_MPIAIJ*)A->data;

1846:   MatImaginaryPart(a->A);
1847:   MatImaginaryPart(a->B);
1848:   return(0);
1849: }

1851: #ifdef PETSC_HAVE_PBGL

1853: #include <boost/parallel/mpi/bsp_process_group.hpp>
1854: #include <boost/graph/distributed/ilu_default_graph.hpp>
1855: #include <boost/graph/distributed/ilu_0_block.hpp>
1856: #include <boost/graph/distributed/ilu_preconditioner.hpp>
1857: #include <boost/graph/distributed/petsc/interface.hpp>
1858: #include <boost/multi_array.hpp>
1859: #include <boost/parallel/distributed_property_map->hpp>

1863: /*
1864:   This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
1865: */
1866: PetscErrorCode MatILUFactorSymbolic_MPIAIJ(Mat fact,Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
1867: {
1868:   namespace petsc = boost::distributed::petsc;
1869: 
1870:   namespace graph_dist = boost::graph::distributed;
1871:   using boost::graph::distributed::ilu_default::process_group_type;
1872:   using boost::graph::ilu_permuted;

1874:   PetscTruth      row_identity, col_identity;
1875:   PetscContainer  c;
1876:   PetscInt        m, n, M, N;
1877:   PetscErrorCode  ierr;

1880:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels = 0 supported for parallel ilu");
1881:   ISIdentity(isrow, &row_identity);
1882:   ISIdentity(iscol, &col_identity);
1883:   if (!row_identity || !col_identity) {
1884:     SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for parallel ILU");
1885:   }

1887:   process_group_type pg;
1888:   typedef graph_dist::ilu_default::ilu_level_graph_type  lgraph_type;
1889:   lgraph_type*   lgraph_p = new lgraph_type(petsc::num_global_vertices(A), pg, petsc::matrix_distribution(A, pg));
1890:   lgraph_type&   level_graph = *lgraph_p;
1891:   graph_dist::ilu_default::graph_type&            graph(level_graph.graph);

1893:   petsc::read_matrix(A, graph, get(boost::edge_weight, graph));
1894:   ilu_permuted(level_graph);

1896:   /* put together the new matrix */
1897:   MatCreate(((PetscObject)A)->comm, fact);
1898:   MatGetLocalSize(A, &m, &n);
1899:   MatGetSize(A, &M, &N);
1900:   MatSetSizes(fact, m, n, M, N);
1901:   MatSetType(fact, ((PetscObject)A)->type_name);
1902:   MatAssemblyBegin(fact, MAT_FINAL_ASSEMBLY);
1903:   MatAssemblyEnd(fact, MAT_FINAL_ASSEMBLY);

1905:   PetscContainerCreate(((PetscObject)A)->comm, &c);
1906:   PetscContainerSetPointer(c, lgraph_p);
1907:   PetscObjectCompose((PetscObject) (fact), "graph", (PetscObject) c);
1908:   return(0);
1909: }

1913: PetscErrorCode MatLUFactorNumeric_MPIAIJ(Mat B,Mat A, const MatFactorInfo *info)
1914: {
1916:   return(0);
1917: }

1921: /*
1922:   This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
1923: */
1924: PetscErrorCode MatSolve_MPIAIJ(Mat A, Vec b, Vec x)
1925: {
1926:   namespace graph_dist = boost::graph::distributed;

1928:   typedef graph_dist::ilu_default::ilu_level_graph_type  lgraph_type;
1929:   lgraph_type*   lgraph_p;
1930:   PetscContainer c;

1934:   PetscObjectQuery((PetscObject) A, "graph", (PetscObject *) &c);
1935:   PetscContainerGetPointer(c, (void **) &lgraph_p);
1936:   VecCopy(b, x);

1938:   PetscScalar* array_x;
1939:   VecGetArray(x, &array_x);
1940:   PetscInt sx;
1941:   VecGetSize(x, &sx);
1942: 
1943:   PetscScalar* array_b;
1944:   VecGetArray(b, &array_b);
1945:   PetscInt sb;
1946:   VecGetSize(b, &sb);

1948:   lgraph_type&   level_graph = *lgraph_p;
1949:   graph_dist::ilu_default::graph_type&            graph(level_graph.graph);

1951:   typedef boost::multi_array_ref<PetscScalar, 1> array_ref_type;
1952:   array_ref_type                                 ref_b(array_b, boost::extents[num_vertices(graph)]),
1953:                                                  ref_x(array_x, boost::extents[num_vertices(graph)]);

1955:   typedef boost::iterator_property_map<array_ref_type::iterator,
1956:                                 boost::property_map<graph_dist::ilu_default::graph_type, boost::vertex_index_t>::type>  gvector_type;
1957:   gvector_type                                   vector_b(ref_b.begin(), get(boost::vertex_index, graph)),
1958:                                                  vector_x(ref_x.begin(), get(boost::vertex_index, graph));
1959: 
1960:   ilu_set_solve(*lgraph_p, vector_b, vector_x);

1962:   return(0);
1963: }
1964: #endif

1966: typedef struct { /* used by MatGetRedundantMatrix() for reusing matredundant */
1967:   PetscInt       nzlocal,nsends,nrecvs;
1968:   PetscMPIInt    *send_rank;
1969:   PetscInt       *sbuf_nz,*sbuf_j,**rbuf_j;
1970:   PetscScalar    *sbuf_a,**rbuf_a;
1971:   PetscErrorCode (*MatDestroy)(Mat);
1972: } Mat_Redundant;

1976: PetscErrorCode PetscContainerDestroy_MatRedundant(void *ptr)
1977: {
1978:   PetscErrorCode       ierr;
1979:   Mat_Redundant        *redund=(Mat_Redundant*)ptr;
1980:   PetscInt             i;

1983:   PetscFree(redund->send_rank);
1984:   PetscFree(redund->sbuf_j);
1985:   PetscFree(redund->sbuf_a);
1986:   for (i=0; i<redund->nrecvs; i++){
1987:     PetscFree(redund->rbuf_j[i]);
1988:     PetscFree(redund->rbuf_a[i]);
1989:   }
1990:   PetscFree3(redund->sbuf_nz,redund->rbuf_j,redund->rbuf_a);
1991:   PetscFree(redund);
1992:   return(0);
1993: }

1997: PetscErrorCode MatDestroy_MatRedundant(Mat A)
1998: {
1999:   PetscErrorCode  ierr;
2000:   PetscContainer  container;
2001:   Mat_Redundant   *redund=PETSC_NULL;

2004:   PetscObjectQuery((PetscObject)A,"Mat_Redundant",(PetscObject *)&container);
2005:   if (container) {
2006:     PetscContainerGetPointer(container,(void **)&redund);
2007:   } else {
2008:     SETERRQ(PETSC_ERR_PLIB,"Container does not exit");
2009:   }
2010:   A->ops->destroy = redund->MatDestroy;
2011:   PetscObjectCompose((PetscObject)A,"Mat_Redundant",0);
2012:   (*A->ops->destroy)(A);
2013:   PetscContainerDestroy(container);
2014:   return(0);
2015: }

2019: PetscErrorCode MatGetRedundantMatrix_MPIAIJ(Mat mat,PetscInt nsubcomm,MPI_Comm subcomm,PetscInt mlocal_sub,MatReuse reuse,Mat *matredundant)
2020: {
2021:   PetscMPIInt    rank,size;
2022:   MPI_Comm       comm=((PetscObject)mat)->comm;
2024:   PetscInt       nsends=0,nrecvs=0,i,rownz_max=0;
2025:   PetscMPIInt    *send_rank=PETSC_NULL,*recv_rank=PETSC_NULL;
2026:   PetscInt       *rowrange=mat->rmap->range;
2027:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ*)mat->data;
2028:   Mat            A=aij->A,B=aij->B,C=*matredundant;
2029:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data;
2030:   PetscScalar    *sbuf_a;
2031:   PetscInt       nzlocal=a->nz+b->nz;
2032:   PetscInt       j,cstart=mat->cmap->rstart,cend=mat->cmap->rend,row,nzA,nzB,ncols,*cworkA,*cworkB;
2033:   PetscInt       rstart=mat->rmap->rstart,rend=mat->rmap->rend,*bmap=aij->garray,M,N;
2034:   PetscInt       *cols,ctmp,lwrite,*rptr,l,*sbuf_j;
2035:   MatScalar      *aworkA,*aworkB;
2036:   PetscScalar    *vals;
2037:   PetscMPIInt    tag1,tag2,tag3,imdex;
2038:   MPI_Request    *s_waits1=PETSC_NULL,*s_waits2=PETSC_NULL,*s_waits3=PETSC_NULL,
2039:                  *r_waits1=PETSC_NULL,*r_waits2=PETSC_NULL,*r_waits3=PETSC_NULL;
2040:   MPI_Status     recv_status,*send_status;
2041:   PetscInt       *sbuf_nz=PETSC_NULL,*rbuf_nz=PETSC_NULL,count;
2042:   PetscInt       **rbuf_j=PETSC_NULL;
2043:   PetscScalar    **rbuf_a=PETSC_NULL;
2044:   Mat_Redundant  *redund=PETSC_NULL;
2045:   PetscContainer container;

2048:   MPI_Comm_rank(comm,&rank);
2049:   MPI_Comm_size(comm,&size);

2051:   if (reuse == MAT_REUSE_MATRIX) {
2052:     MatGetSize(C,&M,&N);
2053:     if (M != N || M != mat->rmap->N) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong global size");
2054:     MatGetLocalSize(C,&M,&N);
2055:     if (M != N || M != mlocal_sub) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong local size");
2056:     PetscObjectQuery((PetscObject)C,"Mat_Redundant",(PetscObject *)&container);
2057:     if (container) {
2058:       PetscContainerGetPointer(container,(void **)&redund);
2059:     } else {
2060:       SETERRQ(PETSC_ERR_PLIB,"Container does not exit");
2061:     }
2062:     if (nzlocal != redund->nzlocal) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong nzlocal");

2064:     nsends    = redund->nsends;
2065:     nrecvs    = redund->nrecvs;
2066:     send_rank = redund->send_rank; recv_rank = send_rank + size;
2067:     sbuf_nz   = redund->sbuf_nz;     rbuf_nz = sbuf_nz + nsends;
2068:     sbuf_j    = redund->sbuf_j;
2069:     sbuf_a    = redund->sbuf_a;
2070:     rbuf_j    = redund->rbuf_j;
2071:     rbuf_a    = redund->rbuf_a;
2072:   }

2074:   if (reuse == MAT_INITIAL_MATRIX){
2075:     PetscMPIInt  subrank,subsize;
2076:     PetscInt     nleftover,np_subcomm;
2077:     /* get the destination processors' id send_rank, nsends and nrecvs */
2078:     MPI_Comm_rank(subcomm,&subrank);
2079:     MPI_Comm_size(subcomm,&subsize);
2080:     PetscMalloc((2*size+1)*sizeof(PetscMPIInt),&send_rank);
2081:     recv_rank = send_rank + size;
2082:     np_subcomm = size/nsubcomm;
2083:     nleftover  = size - nsubcomm*np_subcomm;
2084:     nsends = 0; nrecvs = 0;
2085:     for (i=0; i<size; i++){ /* i=rank*/
2086:       if (subrank == i/nsubcomm && rank != i){ /* my_subrank == other's subrank */
2087:         send_rank[nsends] = i; nsends++;
2088:         recv_rank[nrecvs++] = i;
2089:       }
2090:     }
2091:     if (rank >= size - nleftover){/* this proc is a leftover processor */
2092:       i = size-nleftover-1;
2093:       j = 0;
2094:       while (j < nsubcomm - nleftover){
2095:         send_rank[nsends++] = i;
2096:         i--; j++;
2097:       }
2098:     }

2100:     if (nleftover && subsize == size/nsubcomm && subrank==subsize-1){ /* this proc recvs from leftover processors */
2101:       for (i=0; i<nleftover; i++){
2102:         recv_rank[nrecvs++] = size-nleftover+i;
2103:       }
2104:     }

2106:     /* allocate sbuf_j, sbuf_a */
2107:     i = nzlocal + rowrange[rank+1] - rowrange[rank] + 2;
2108:     PetscMalloc(i*sizeof(PetscInt),&sbuf_j);
2109:     PetscMalloc((nzlocal+1)*sizeof(PetscScalar),&sbuf_a);
2110:   } /* endof if (reuse == MAT_INITIAL_MATRIX) */
2111: 
2112:   /* copy mat's local entries into the buffers */
2113:   if (reuse == MAT_INITIAL_MATRIX){
2114:     rownz_max = 0;
2115:     rptr = sbuf_j;
2116:     cols = sbuf_j + rend-rstart + 1;
2117:     vals = sbuf_a;
2118:     rptr[0] = 0;
2119:     for (i=0; i<rend-rstart; i++){
2120:       row = i + rstart;
2121:       nzA    = a->i[i+1] - a->i[i]; nzB = b->i[i+1] - b->i[i];
2122:       ncols  = nzA + nzB;
2123:       cworkA = a->j + a->i[i]; cworkB = b->j + b->i[i];
2124:       aworkA = a->a + a->i[i]; aworkB = b->a + b->i[i];
2125:       /* load the column indices for this row into cols */
2126:       lwrite = 0;
2127:       for (l=0; l<nzB; l++) {
2128:         if ((ctmp = bmap[cworkB[l]]) < cstart){
2129:           vals[lwrite]   = aworkB[l];
2130:           cols[lwrite++] = ctmp;
2131:         }
2132:       }
2133:       for (l=0; l<nzA; l++){
2134:         vals[lwrite]   = aworkA[l];
2135:         cols[lwrite++] = cstart + cworkA[l];
2136:       }
2137:       for (l=0; l<nzB; l++) {
2138:         if ((ctmp = bmap[cworkB[l]]) >= cend){
2139:           vals[lwrite]   = aworkB[l];
2140:           cols[lwrite++] = ctmp;
2141:         }
2142:       }
2143:       vals += ncols;
2144:       cols += ncols;
2145:       rptr[i+1] = rptr[i] + ncols;
2146:       if (rownz_max < ncols) rownz_max = ncols;
2147:     }
2148:     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);
2149:   } else { /* only copy matrix values into sbuf_a */
2150:     rptr = sbuf_j;
2151:     vals = sbuf_a;
2152:     rptr[0] = 0;
2153:     for (i=0; i<rend-rstart; i++){
2154:       row = i + rstart;
2155:       nzA    = a->i[i+1] - a->i[i]; nzB = b->i[i+1] - b->i[i];
2156:       ncols  = nzA + nzB;
2157:       cworkA = a->j + a->i[i]; cworkB = b->j + b->i[i];
2158:       aworkA = a->a + a->i[i]; aworkB = b->a + b->i[i];
2159:       lwrite = 0;
2160:       for (l=0; l<nzB; l++) {
2161:         if ((ctmp = bmap[cworkB[l]]) < cstart) vals[lwrite++] = aworkB[l];
2162:       }
2163:       for (l=0; l<nzA; l++) vals[lwrite++] = aworkA[l];
2164:       for (l=0; l<nzB; l++) {
2165:         if ((ctmp = bmap[cworkB[l]]) >= cend) vals[lwrite++] = aworkB[l];
2166:       }
2167:       vals += ncols;
2168:       rptr[i+1] = rptr[i] + ncols;
2169:     }
2170:   } /* endof if (reuse == MAT_INITIAL_MATRIX) */

2172:   /* send nzlocal to others, and recv other's nzlocal */
2173:   /*--------------------------------------------------*/
2174:   if (reuse == MAT_INITIAL_MATRIX){
2175:     PetscMalloc2(3*(nsends + nrecvs)+1,MPI_Request,&s_waits3,nsends+1,MPI_Status,&send_status);
2176:     s_waits2 = s_waits3 + nsends;
2177:     s_waits1 = s_waits2 + nsends;
2178:     r_waits1 = s_waits1 + nsends;
2179:     r_waits2 = r_waits1 + nrecvs;
2180:     r_waits3 = r_waits2 + nrecvs;
2181:   } else {
2182:     PetscMalloc2(nsends + nrecvs +1,MPI_Request,&s_waits3,nsends+1,MPI_Status,&send_status);
2183:     r_waits3 = s_waits3 + nsends;
2184:   }

2186:   PetscObjectGetNewTag((PetscObject)mat,&tag3);
2187:   if (reuse == MAT_INITIAL_MATRIX){
2188:     /* get new tags to keep the communication clean */
2189:     PetscObjectGetNewTag((PetscObject)mat,&tag1);
2190:     PetscObjectGetNewTag((PetscObject)mat,&tag2);
2191:     PetscMalloc3(nsends+nrecvs+1,PetscInt,&sbuf_nz,nrecvs,PetscInt*,&rbuf_j,nrecvs,PetscScalar*,&rbuf_a);
2192:     rbuf_nz = sbuf_nz + nsends;
2193: 
2194:     /* post receives of other's nzlocal */
2195:     for (i=0; i<nrecvs; i++){
2196:       MPI_Irecv(rbuf_nz+i,1,MPIU_INT,MPI_ANY_SOURCE,tag1,comm,r_waits1+i);
2197:     }
2198:     /* send nzlocal to others */
2199:     for (i=0; i<nsends; i++){
2200:       sbuf_nz[i] = nzlocal;
2201:       MPI_Isend(sbuf_nz+i,1,MPIU_INT,send_rank[i],tag1,comm,s_waits1+i);
2202:     }
2203:     /* wait on receives of nzlocal; allocate space for rbuf_j, rbuf_a */
2204:     count = nrecvs;
2205:     while (count) {
2206:       MPI_Waitany(nrecvs,r_waits1,&imdex,&recv_status);
2207:       recv_rank[imdex] = recv_status.MPI_SOURCE;
2208:       /* allocate rbuf_a and rbuf_j; then post receives of rbuf_j */
2209:       PetscMalloc((rbuf_nz[imdex]+1)*sizeof(PetscScalar),&rbuf_a[imdex]);

2211:       i = rowrange[recv_status.MPI_SOURCE+1] - rowrange[recv_status.MPI_SOURCE]; /* number of expected mat->i */
2212:       rbuf_nz[imdex] += i + 2;
2213:       PetscMalloc(rbuf_nz[imdex]*sizeof(PetscInt),&rbuf_j[imdex]);
2214:       MPI_Irecv(rbuf_j[imdex],rbuf_nz[imdex],MPIU_INT,recv_status.MPI_SOURCE,tag2,comm,r_waits2+imdex);
2215:       count--;
2216:     }
2217:     /* wait on sends of nzlocal */
2218:     if (nsends) {MPI_Waitall(nsends,s_waits1,send_status);}
2219:     /* send mat->i,j to others, and recv from other's */
2220:     /*------------------------------------------------*/
2221:     for (i=0; i<nsends; i++){
2222:       j = nzlocal + rowrange[rank+1] - rowrange[rank] + 1;
2223:       MPI_Isend(sbuf_j,j,MPIU_INT,send_rank[i],tag2,comm,s_waits2+i);
2224:     }
2225:     /* wait on receives of mat->i,j */
2226:     /*------------------------------*/
2227:     count = nrecvs;
2228:     while (count) {
2229:       MPI_Waitany(nrecvs,r_waits2,&imdex,&recv_status);
2230:       if (recv_rank[imdex] != recv_status.MPI_SOURCE) SETERRQ2(1, "recv_rank %d != MPI_SOURCE %d",recv_rank[imdex],recv_status.MPI_SOURCE);
2231:       count--;
2232:     }
2233:     /* wait on sends of mat->i,j */
2234:     /*---------------------------*/
2235:     if (nsends) {
2236:       MPI_Waitall(nsends,s_waits2,send_status);
2237:     }
2238:   } /* endof if (reuse == MAT_INITIAL_MATRIX) */

2240:   /* post receives, send and receive mat->a */
2241:   /*----------------------------------------*/
2242:   for (imdex=0; imdex<nrecvs; imdex++) {
2243:     MPI_Irecv(rbuf_a[imdex],rbuf_nz[imdex],MPIU_SCALAR,recv_rank[imdex],tag3,comm,r_waits3+imdex);
2244:   }
2245:   for (i=0; i<nsends; i++){
2246:     MPI_Isend(sbuf_a,nzlocal,MPIU_SCALAR,send_rank[i],tag3,comm,s_waits3+i);
2247:   }
2248:   count = nrecvs;
2249:   while (count) {
2250:     MPI_Waitany(nrecvs,r_waits3,&imdex,&recv_status);
2251:     if (recv_rank[imdex] != recv_status.MPI_SOURCE) SETERRQ2(1, "recv_rank %d != MPI_SOURCE %d",recv_rank[imdex],recv_status.MPI_SOURCE);
2252:     count--;
2253:   }
2254:   if (nsends) {
2255:     MPI_Waitall(nsends,s_waits3,send_status);
2256:   }

2258:   PetscFree2(s_waits3,send_status);
2259: 
2260:   /* create redundant matrix */
2261:   /*-------------------------*/
2262:   if (reuse == MAT_INITIAL_MATRIX){
2263:     /* compute rownz_max for preallocation */
2264:     for (imdex=0; imdex<nrecvs; imdex++){
2265:       j = rowrange[recv_rank[imdex]+1] - rowrange[recv_rank[imdex]];
2266:       rptr = rbuf_j[imdex];
2267:       for (i=0; i<j; i++){
2268:         ncols = rptr[i+1] - rptr[i];
2269:         if (rownz_max < ncols) rownz_max = ncols;
2270:       }
2271:     }
2272: 
2273:     MatCreate(subcomm,&C);
2274:     MatSetSizes(C,mlocal_sub,mlocal_sub,PETSC_DECIDE,PETSC_DECIDE);
2275:     MatSetFromOptions(C);
2276:     MatSeqAIJSetPreallocation(C,rownz_max,PETSC_NULL);
2277:     MatMPIAIJSetPreallocation(C,rownz_max,PETSC_NULL,rownz_max,PETSC_NULL);
2278:   } else {
2279:     C = *matredundant;
2280:   }

2282:   /* insert local matrix entries */
2283:   rptr = sbuf_j;
2284:   cols = sbuf_j + rend-rstart + 1;
2285:   vals = sbuf_a;
2286:   for (i=0; i<rend-rstart; i++){
2287:     row   = i + rstart;
2288:     ncols = rptr[i+1] - rptr[i];
2289:     MatSetValues(C,1,&row,ncols,cols,vals,INSERT_VALUES);
2290:     vals += ncols;
2291:     cols += ncols;
2292:   }
2293:   /* insert received matrix entries */
2294:   for (imdex=0; imdex<nrecvs; imdex++){
2295:     rstart = rowrange[recv_rank[imdex]];
2296:     rend   = rowrange[recv_rank[imdex]+1];
2297:     rptr = rbuf_j[imdex];
2298:     cols = rbuf_j[imdex] + rend-rstart + 1;
2299:     vals = rbuf_a[imdex];
2300:     for (i=0; i<rend-rstart; i++){
2301:       row   = i + rstart;
2302:       ncols = rptr[i+1] - rptr[i];
2303:       MatSetValues(C,1,&row,ncols,cols,vals,INSERT_VALUES);
2304:       vals += ncols;
2305:       cols += ncols;
2306:     }
2307:   }
2308:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2309:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2310:   MatGetSize(C,&M,&N);
2311:   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);
2312:   if (reuse == MAT_INITIAL_MATRIX){
2313:     PetscContainer container;
2314:     *matredundant = C;
2315:     /* create a supporting struct and attach it to C for reuse */
2316:     PetscNewLog(C,Mat_Redundant,&redund);
2317:     PetscContainerCreate(PETSC_COMM_SELF,&container);
2318:     PetscContainerSetPointer(container,redund);
2319:     PetscObjectCompose((PetscObject)C,"Mat_Redundant",(PetscObject)container);
2320:     PetscContainerSetUserDestroy(container,PetscContainerDestroy_MatRedundant);
2321: 
2322:     redund->nzlocal = nzlocal;
2323:     redund->nsends  = nsends;
2324:     redund->nrecvs  = nrecvs;
2325:     redund->send_rank = send_rank;
2326:     redund->sbuf_nz = sbuf_nz;
2327:     redund->sbuf_j  = sbuf_j;
2328:     redund->sbuf_a  = sbuf_a;
2329:     redund->rbuf_j  = rbuf_j;
2330:     redund->rbuf_a  = rbuf_a;

2332:     redund->MatDestroy = C->ops->destroy;
2333:     C->ops->destroy    = MatDestroy_MatRedundant;
2334:   }
2335:   return(0);
2336: }

2340: PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2341: {
2342:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
2344:   PetscInt       i,*idxb = 0;
2345:   PetscScalar    *va,*vb;
2346:   Vec            vtmp;

2349:   MatGetRowMaxAbs(a->A,v,idx);
2350:   VecGetArray(v,&va);
2351:   if (idx) {
2352:     for (i=0; i<A->rmap->n; i++) {
2353:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2354:     }
2355:   }

2357:   VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
2358:   if (idx) {
2359:     PetscMalloc(A->rmap->n*sizeof(PetscInt),&idxb);
2360:   }
2361:   MatGetRowMaxAbs(a->B,vtmp,idxb);
2362:   VecGetArray(vtmp,&vb);

2364:   for (i=0; i<A->rmap->n; i++){
2365:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2366:       va[i] = vb[i];
2367:       if (idx) idx[i] = a->garray[idxb[i]];
2368:     }
2369:   }

2371:   VecRestoreArray(v,&va);
2372:   VecRestoreArray(vtmp,&vb);
2373:   if (idxb) {
2374:     PetscFree(idxb);
2375:   }
2376:   VecDestroy(vtmp);
2377:   return(0);
2378: }

2382: PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2383: {
2384:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
2386:   PetscInt       i,*idxb = 0;
2387:   PetscScalar    *va,*vb;
2388:   Vec            vtmp;

2391:   MatGetRowMinAbs(a->A,v,idx);
2392:   VecGetArray(v,&va);
2393:   if (idx) {
2394:     for (i=0; i<A->cmap->n; i++) {
2395:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2396:     }
2397:   }

2399:   VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
2400:   if (idx) {
2401:     PetscMalloc(A->rmap->n*sizeof(PetscInt),&idxb);
2402:   }
2403:   MatGetRowMinAbs(a->B,vtmp,idxb);
2404:   VecGetArray(vtmp,&vb);

2406:   for (i=0; i<A->rmap->n; i++){
2407:     if (PetscAbsScalar(va[i]) > PetscAbsScalar(vb[i])) {
2408:       va[i] = vb[i];
2409:       if (idx) idx[i] = a->garray[idxb[i]];
2410:     }
2411:   }

2413:   VecRestoreArray(v,&va);
2414:   VecRestoreArray(vtmp,&vb);
2415:   if (idxb) {
2416:     PetscFree(idxb);
2417:   }
2418:   VecDestroy(vtmp);
2419:   return(0);
2420: }

2424: PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2425: {
2426:   Mat_MPIAIJ    *mat    = (Mat_MPIAIJ *) A->data;
2427:   PetscInt       n      = A->rmap->n;
2428:   PetscInt       cstart = A->cmap->rstart;
2429:   PetscInt      *cmap   = mat->garray;
2430:   PetscInt      *diagIdx, *offdiagIdx;
2431:   Vec            diagV, offdiagV;
2432:   PetscScalar   *a, *diagA, *offdiagA;
2433:   PetscInt       r;

2437:   PetscMalloc2(n,PetscInt,&diagIdx,n,PetscInt,&offdiagIdx);
2438:   VecCreateSeq(((PetscObject)A)->comm, n, &diagV);
2439:   VecCreateSeq(((PetscObject)A)->comm, n, &offdiagV);
2440:   MatGetRowMin(mat->A, diagV,    diagIdx);
2441:   MatGetRowMin(mat->B, offdiagV, offdiagIdx);
2442:   VecGetArray(v,        &a);
2443:   VecGetArray(diagV,    &diagA);
2444:   VecGetArray(offdiagV, &offdiagA);
2445:   for(r = 0; r < n; ++r) {
2446:     if (PetscAbsScalar(diagA[r]) <= PetscAbsScalar(offdiagA[r])) {
2447:       a[r]   = diagA[r];
2448:       idx[r] = cstart + diagIdx[r];
2449:     } else {
2450:       a[r]   = offdiagA[r];
2451:       idx[r] = cmap[offdiagIdx[r]];
2452:     }
2453:   }
2454:   VecRestoreArray(v,        &a);
2455:   VecRestoreArray(diagV,    &diagA);
2456:   VecRestoreArray(offdiagV, &offdiagA);
2457:   VecDestroy(diagV);
2458:   VecDestroy(offdiagV);
2459:   PetscFree2(diagIdx, offdiagIdx);
2460:   return(0);
2461: }

2465: PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2466: {
2467:   Mat_MPIAIJ    *mat    = (Mat_MPIAIJ *) A->data;
2468:   PetscInt       n      = A->rmap->n;
2469:   PetscInt       cstart = A->cmap->rstart;
2470:   PetscInt      *cmap   = mat->garray;
2471:   PetscInt      *diagIdx, *offdiagIdx;
2472:   Vec            diagV, offdiagV;
2473:   PetscScalar   *a, *diagA, *offdiagA;
2474:   PetscInt       r;

2478:   PetscMalloc2(n,PetscInt,&diagIdx,n,PetscInt,&offdiagIdx);
2479:   VecCreateSeq(((PetscObject)A)->comm, n, &diagV);
2480:   VecCreateSeq(((PetscObject)A)->comm, n, &offdiagV);
2481:   MatGetRowMax(mat->A, diagV,    diagIdx);
2482:   MatGetRowMax(mat->B, offdiagV, offdiagIdx);
2483:   VecGetArray(v,        &a);
2484:   VecGetArray(diagV,    &diagA);
2485:   VecGetArray(offdiagV, &offdiagA);
2486:   for(r = 0; r < n; ++r) {
2487:     if (PetscAbsScalar(diagA[r]) >= PetscAbsScalar(offdiagA[r])) {
2488:       a[r]   = diagA[r];
2489:       idx[r] = cstart + diagIdx[r];
2490:     } else {
2491:       a[r]   = offdiagA[r];
2492:       idx[r] = cmap[offdiagIdx[r]];
2493:     }
2494:   }
2495:   VecRestoreArray(v,        &a);
2496:   VecRestoreArray(diagV,    &diagA);
2497:   VecRestoreArray(offdiagV, &offdiagA);
2498:   VecDestroy(diagV);
2499:   VecDestroy(offdiagV);
2500:   PetscFree2(diagIdx, offdiagIdx);
2501:   return(0);
2502: }

2506: PetscErrorCode MatGetSeqNonzerostructure_MPIAIJ(Mat mat,Mat *newmat[])
2507: {

2511:   MatGetSubMatrix_MPIAIJ_All(mat,MAT_DO_NOT_GET_VALUES,MAT_INITIAL_MATRIX,newmat);
2512:   return(0);
2513: }

2515: /* -------------------------------------------------------------------*/
2516: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2517:        MatGetRow_MPIAIJ,
2518:        MatRestoreRow_MPIAIJ,
2519:        MatMult_MPIAIJ,
2520: /* 4*/ MatMultAdd_MPIAIJ,
2521:        MatMultTranspose_MPIAIJ,
2522:        MatMultTransposeAdd_MPIAIJ,
2523: #ifdef PETSC_HAVE_PBGL
2524:        MatSolve_MPIAIJ,
2525: #else
2526:        0,
2527: #endif
2528:        0,
2529:        0,
2530: /*10*/ 0,
2531:        0,
2532:        0,
2533:        MatRelax_MPIAIJ,
2534:        MatTranspose_MPIAIJ,
2535: /*15*/ MatGetInfo_MPIAIJ,
2536:        MatEqual_MPIAIJ,
2537:        MatGetDiagonal_MPIAIJ,
2538:        MatDiagonalScale_MPIAIJ,
2539:        MatNorm_MPIAIJ,
2540: /*20*/ MatAssemblyBegin_MPIAIJ,
2541:        MatAssemblyEnd_MPIAIJ,
2542:        0,
2543:        MatSetOption_MPIAIJ,
2544:        MatZeroEntries_MPIAIJ,
2545: /*25*/ MatZeroRows_MPIAIJ,
2546:        0,
2547: #ifdef PETSC_HAVE_PBGL
2548:        0,
2549: #else
2550:        0,
2551: #endif
2552:        0,
2553:        0,
2554: /*30*/ MatSetUpPreallocation_MPIAIJ,
2555: #ifdef PETSC_HAVE_PBGL
2556:        0,
2557: #else
2558:        0,
2559: #endif
2560:        0,
2561:        0,
2562:        0,
2563: /*35*/ MatDuplicate_MPIAIJ,
2564:        0,
2565:        0,
2566:        0,
2567:        0,
2568: /*40*/ MatAXPY_MPIAIJ,
2569:        MatGetSubMatrices_MPIAIJ,
2570:        MatIncreaseOverlap_MPIAIJ,
2571:        MatGetValues_MPIAIJ,
2572:        MatCopy_MPIAIJ,
2573: /*45*/ MatGetRowMax_MPIAIJ,
2574:        MatScale_MPIAIJ,
2575:        0,
2576:        0,
2577:        0,
2578: /*50*/ MatSetBlockSize_MPIAIJ,
2579:        0,
2580:        0,
2581:        0,
2582:        0,
2583: /*55*/ MatFDColoringCreate_MPIAIJ,
2584:        0,
2585:        MatSetUnfactored_MPIAIJ,
2586:        MatPermute_MPIAIJ,
2587:        0,
2588: /*60*/ MatGetSubMatrix_MPIAIJ,
2589:        MatDestroy_MPIAIJ,
2590:        MatView_MPIAIJ,
2591:        0,
2592:        0,
2593: /*65*/ 0,
2594:        0,
2595:        0,
2596:        0,
2597:        0,
2598: /*70*/ MatGetRowMaxAbs_MPIAIJ,
2599:        MatGetRowMinAbs_MPIAIJ,
2600:        0,
2601:        MatSetColoring_MPIAIJ,
2602: #if defined(PETSC_HAVE_ADIC)
2603:        MatSetValuesAdic_MPIAIJ,
2604: #else
2605:        0,
2606: #endif
2607:        MatSetValuesAdifor_MPIAIJ,
2608: /*75*/ 0,
2609:        0,
2610:        0,
2611:        0,
2612:        0,
2613: /*80*/ 0,
2614:        0,
2615:        0,
2616: /*84*/ MatLoad_MPIAIJ,
2617:        0,
2618:        0,
2619:        0,
2620:        0,
2621:        0,
2622: /*90*/ MatMatMult_MPIAIJ_MPIAIJ,
2623:        MatMatMultSymbolic_MPIAIJ_MPIAIJ,
2624:        MatMatMultNumeric_MPIAIJ_MPIAIJ,
2625:        MatPtAP_Basic,
2626:        MatPtAPSymbolic_MPIAIJ,
2627: /*95*/ MatPtAPNumeric_MPIAIJ,
2628:        0,
2629:        0,
2630:        0,
2631:        0,
2632: /*100*/0,
2633:        MatPtAPSymbolic_MPIAIJ_MPIAIJ,
2634:        MatPtAPNumeric_MPIAIJ_MPIAIJ,
2635:        MatConjugate_MPIAIJ,
2636:        0,
2637: /*105*/MatSetValuesRow_MPIAIJ,
2638:        MatRealPart_MPIAIJ,
2639:        MatImaginaryPart_MPIAIJ,
2640:        0,
2641:        0,
2642: /*110*/0,
2643:        MatGetRedundantMatrix_MPIAIJ,
2644:        MatGetRowMin_MPIAIJ,
2645:        0,
2646:        0,
2647: /*115*/MatGetSeqNonzerostructure_MPIAIJ};

2649: /* ----------------------------------------------------------------------------------------*/

2654: PetscErrorCode  MatStoreValues_MPIAIJ(Mat mat)
2655: {
2656:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

2660:   MatStoreValues(aij->A);
2661:   MatStoreValues(aij->B);
2662:   return(0);
2663: }

2669: PetscErrorCode  MatRetrieveValues_MPIAIJ(Mat mat)
2670: {
2671:   Mat_MPIAIJ     *aij = (Mat_MPIAIJ *)mat->data;

2675:   MatRetrieveValues(aij->A);
2676:   MatRetrieveValues(aij->B);
2677:   return(0);
2678: }

2681:  #include petscpc.h
2685: PetscErrorCode  MatMPIAIJSetPreallocation_MPIAIJ(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2686: {
2687:   Mat_MPIAIJ     *b;
2689:   PetscInt       i;

2692:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2693:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2694:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
2695:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);

2697:   PetscMapSetBlockSize(B->rmap,1);
2698:   PetscMapSetBlockSize(B->cmap,1);
2699:   PetscMapSetUp(B->rmap);
2700:   PetscMapSetUp(B->cmap);
2701:   if (d_nnz) {
2702:     for (i=0; i<B->rmap->n; i++) {
2703:       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]);
2704:     }
2705:   }
2706:   if (o_nnz) {
2707:     for (i=0; i<B->rmap->n; i++) {
2708:       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]);
2709:     }
2710:   }
2711:   b = (Mat_MPIAIJ*)B->data;

2713:   if (!B->preallocated) {
2714:     /* Explicitly create 2 MATSEQAIJ matrices. */
2715:     MatCreate(PETSC_COMM_SELF,&b->A);
2716:     MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
2717:     MatSetType(b->A,MATSEQAIJ);
2718:     PetscLogObjectParent(B,b->A);
2719:     MatCreate(PETSC_COMM_SELF,&b->B);
2720:     MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
2721:     MatSetType(b->B,MATSEQAIJ);
2722:     PetscLogObjectParent(B,b->B);
2723:   }

2725:   MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
2726:   MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
2727:   B->preallocated = PETSC_TRUE;
2728:   return(0);
2729: }

2734: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2735: {
2736:   Mat            mat;
2737:   Mat_MPIAIJ     *a,*oldmat = (Mat_MPIAIJ*)matin->data;

2741:   *newmat       = 0;
2742:   MatCreate(((PetscObject)matin)->comm,&mat);
2743:   MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
2744:   MatSetType(mat,((PetscObject)matin)->type_name);
2745:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
2746:   a    = (Mat_MPIAIJ*)mat->data;
2747: 
2748:   mat->factor       = matin->factor;
2749:   mat->rmap->bs      = matin->rmap->bs;
2750:   mat->assembled    = PETSC_TRUE;
2751:   mat->insertmode   = NOT_SET_VALUES;
2752:   mat->preallocated = PETSC_TRUE;

2754:   a->size           = oldmat->size;
2755:   a->rank           = oldmat->rank;
2756:   a->donotstash     = oldmat->donotstash;
2757:   a->roworiented    = oldmat->roworiented;
2758:   a->rowindices     = 0;
2759:   a->rowvalues      = 0;
2760:   a->getrowactive   = PETSC_FALSE;

2762:   PetscMapCopy(((PetscObject)mat)->comm,matin->rmap,mat->rmap);
2763:   PetscMapCopy(((PetscObject)mat)->comm,matin->cmap,mat->cmap);

2765:   MatStashCreate_Private(((PetscObject)matin)->comm,1,&mat->stash);
2766:   if (oldmat->colmap) {
2767: #if defined (PETSC_USE_CTABLE)
2768:     PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2769: #else
2770:     PetscMalloc((mat->cmap->N)*sizeof(PetscInt),&a->colmap);
2771:     PetscLogObjectMemory(mat,(mat->cmap->N)*sizeof(PetscInt));
2772:     PetscMemcpy(a->colmap,oldmat->colmap,(mat->cmap->N)*sizeof(PetscInt));
2773: #endif
2774:   } else a->colmap = 0;
2775:   if (oldmat->garray) {
2776:     PetscInt len;
2777:     len  = oldmat->B->cmap->n;
2778:     PetscMalloc((len+1)*sizeof(PetscInt),&a->garray);
2779:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2780:     if (len) { PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt)); }
2781:   } else a->garray = 0;
2782: 
2783:   VecDuplicate(oldmat->lvec,&a->lvec);
2784:   PetscLogObjectParent(mat,a->lvec);
2785:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2786:   PetscLogObjectParent(mat,a->Mvctx);
2787:   MatDuplicate(oldmat->A,cpvalues,&a->A);
2788:   PetscLogObjectParent(mat,a->A);
2789:   MatDuplicate(oldmat->B,cpvalues,&a->B);
2790:   PetscLogObjectParent(mat,a->B);
2791:   PetscFListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
2792:   *newmat = mat;
2793:   return(0);
2794: }

2796:  #include petscsys.h

2800: PetscErrorCode MatLoad_MPIAIJ(PetscViewer viewer, const MatType type,Mat *newmat)
2801: {
2802:   Mat            A;
2803:   PetscScalar    *vals,*svals;
2804:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2805:   MPI_Status     status;
2807:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag,mpicnt,mpimaxnz;
2808:   PetscInt       i,nz,j,rstart,rend,mmax,maxnz;
2809:   PetscInt       header[4],*rowlengths = 0,M,N,m,*cols;
2810:   PetscInt       *ourlens = PETSC_NULL,*procsnz = PETSC_NULL,*offlens = PETSC_NULL,jj,*mycols,*smycols;
2811:   PetscInt       cend,cstart,n,*rowners;
2812:   int            fd;

2815:   MPI_Comm_size(comm,&size);
2816:   MPI_Comm_rank(comm,&rank);
2817:   if (!rank) {
2818:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2819:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2820:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2821:   }

2823:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2824:   M = header[1]; N = header[2];
2825:   /* determine ownership of all rows */
2826:   m    = M/size + ((M % size) > rank);
2827:   PetscMalloc((size+1)*sizeof(PetscInt),&rowners);
2828:   MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);

2830:   /* First process needs enough room for process with most rows */
2831:   if (!rank) {
2832:     mmax       = rowners[1];
2833:     for (i=2; i<size; i++) {
2834:       mmax = PetscMax(mmax,rowners[i]);
2835:     }
2836:   } else mmax = m;

2838:   rowners[0] = 0;
2839:   for (i=2; i<=size; i++) {
2840:     rowners[i] += rowners[i-1];
2841:   }
2842:   rstart = rowners[rank];
2843:   rend   = rowners[rank+1];

2845:   /* distribute row lengths to all processors */
2846:   PetscMalloc2(mmax,PetscInt,&ourlens,mmax,PetscInt,&offlens);
2847:   if (!rank) {
2848:     PetscBinaryRead(fd,ourlens,m,PETSC_INT);
2849:     PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2850:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2851:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2852:     for (j=0; j<m; j++) {
2853:       procsnz[0] += ourlens[j];
2854:     }
2855:     for (i=1; i<size; i++) {
2856:       PetscBinaryRead(fd,rowlengths,rowners[i+1]-rowners[i],PETSC_INT);
2857:       /* calculate the number of nonzeros on each processor */
2858:       for (j=0; j<rowners[i+1]-rowners[i]; j++) {
2859:         procsnz[i] += rowlengths[j];
2860:       }
2861:       mpicnt = PetscMPIIntCast(rowners[i+1]-rowners[i]);
2862:       MPI_Send(rowlengths,mpicnt,MPIU_INT,i,tag,comm);
2863:     }
2864:     PetscFree(rowlengths);
2865:   } else {
2866:     mpicnt = PetscMPIIntCast(m);
2867:     MPI_Recv(ourlens,mpicnt,MPIU_INT,0,tag,comm,&status);
2868:   }

2870:   if (!rank) {
2871:     /* determine max buffer needed and allocate it */
2872:     maxnz = 0;
2873:     for (i=0; i<size; i++) {
2874:       maxnz = PetscMax(maxnz,procsnz[i]);
2875:     }
2876:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2878:     /* read in my part of the matrix column indices  */
2879:     nz   = procsnz[0];
2880:     PetscMalloc(nz*sizeof(PetscInt),&mycols);
2881:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);

2883:     /* read in every one elses and ship off */
2884:     for (i=1; i<size; i++) {
2885:       nz     = procsnz[i];
2886:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2887:       mpicnt = PetscMPIIntCast(nz);
2888:       MPI_Send(cols,mpicnt,MPIU_INT,i,tag,comm);
2889:     }
2890:     PetscFree(cols);
2891:   } else {
2892:     /* determine buffer space needed for message */
2893:     nz = 0;
2894:     for (i=0; i<m; i++) {
2895:       nz += ourlens[i];
2896:     }
2897:     PetscMalloc(nz*sizeof(PetscInt),&mycols);

2899:     /* receive message of column indices*/
2900:     mpicnt = PetscMPIIntCast(nz);
2901:     MPI_Recv(mycols,mpicnt,MPIU_INT,0,tag,comm,&status);
2902:     MPI_Get_count(&status,MPIU_INT,&mpimaxnz);
2903:     if (mpimaxnz == MPI_UNDEFINED) {SETERRQ1(PETSC_ERR_LIB,"MPI_Get_count() returned MPI_UNDEFINED, expected %d",mpicnt);}
2904:     else if (mpimaxnz < 0) {SETERRQ2(PETSC_ERR_LIB,"MPI_Get_count() returned impossible negative value %d, expected %d",mpimaxnz,mpicnt);}
2905:     else if (mpimaxnz != mpicnt) {SETERRQ2(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file: expected %d received %d",mpicnt,mpimaxnz);}
2906:   }

2908:   /* determine column ownership if matrix is not square */
2909:   if (N != M) {
2910:     n      = N/size + ((N % size) > rank);
2911:     MPI_Scan(&n,&cend,1,MPIU_INT,MPI_SUM,comm);
2912:     cstart = cend - n;
2913:   } else {
2914:     cstart = rstart;
2915:     cend   = rend;
2916:     n      = cend - cstart;
2917:   }

2919:   /* loop over local rows, determining number of off diagonal entries */
2920:   PetscMemzero(offlens,m*sizeof(PetscInt));
2921:   jj = 0;
2922:   for (i=0; i<m; i++) {
2923:     for (j=0; j<ourlens[i]; j++) {
2924:       if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++;
2925:       jj++;
2926:     }
2927:   }

2929:   /* create our matrix */
2930:   for (i=0; i<m; i++) {
2931:     ourlens[i] -= offlens[i];
2932:   }
2933:   MatCreate(comm,&A);
2934:   MatSetSizes(A,m,n,M,N);
2935:   MatSetType(A,type);
2936:   MatMPIAIJSetPreallocation(A,0,ourlens,0,offlens);

2938:   for (i=0; i<m; i++) {
2939:     ourlens[i] += offlens[i];
2940:   }

2942:   if (!rank) {
2943:     PetscMalloc((maxnz+1)*sizeof(PetscScalar),&vals);

2945:     /* read in my part of the matrix numerical values  */
2946:     nz   = procsnz[0];
2947:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2948: 
2949:     /* insert into matrix */
2950:     jj      = rstart;
2951:     smycols = mycols;
2952:     svals   = vals;
2953:     for (i=0; i<m; i++) {
2954:       MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2955:       smycols += ourlens[i];
2956:       svals   += ourlens[i];
2957:       jj++;
2958:     }

2960:     /* read in other processors and ship out */
2961:     for (i=1; i<size; i++) {
2962:       nz     = procsnz[i];
2963:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2964:       mpicnt = PetscMPIIntCast(nz);
2965:       MPI_Send(vals,mpicnt,MPIU_SCALAR,i,((PetscObject)A)->tag,comm);
2966:     }
2967:     PetscFree(procsnz);
2968:   } else {
2969:     /* receive numeric values */
2970:     PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);

2972:     /* receive message of values*/
2973:     mpicnt = PetscMPIIntCast(nz);
2974:     MPI_Recv(vals,mpicnt,MPIU_SCALAR,0,((PetscObject)A)->tag,comm,&status);
2975:     MPI_Get_count(&status,MPIU_SCALAR,&mpimaxnz);
2976:     if (mpimaxnz == MPI_UNDEFINED) {SETERRQ1(PETSC_ERR_LIB,"MPI_Get_count() returned MPI_UNDEFINED, expected %d",mpicnt);}
2977:     else if (mpimaxnz < 0) {SETERRQ2(PETSC_ERR_LIB,"MPI_Get_count() returned impossible negative value %d, expected %d",mpimaxnz,mpicnt);}
2978:     else if (mpimaxnz != mpicnt) {SETERRQ2(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file: expected %d received %d",mpicnt,mpimaxnz);}

2980:     /* insert into matrix */
2981:     jj      = rstart;
2982:     smycols = mycols;
2983:     svals   = vals;
2984:     for (i=0; i<m; i++) {
2985:       MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2986:       smycols += ourlens[i];
2987:       svals   += ourlens[i];
2988:       jj++;
2989:     }
2990:   }
2991:   PetscFree2(ourlens,offlens);
2992:   PetscFree(vals);
2993:   PetscFree(mycols);
2994:   PetscFree(rowners);

2996:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2997:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2998:   *newmat = A;
2999:   return(0);
3000: }

3004: /*
3005:     Not great since it makes two copies of the submatrix, first an SeqAIJ 
3006:   in local and then by concatenating the local matrices the end result.
3007:   Writing it directly would be much like MatGetSubMatrices_MPIAIJ()
3008: */
3009: PetscErrorCode MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
3010: {
3012:   PetscMPIInt    rank,size;
3013:   PetscInt       i,m,n,rstart,row,rend,nz,*cwork,j;
3014:   PetscInt       *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal;
3015:   Mat            *local,M,Mreuse;
3016:   MatScalar      *vwork,*aa;
3017:   MPI_Comm       comm = ((PetscObject)mat)->comm;
3018:   Mat_SeqAIJ     *aij;


3022:   MPI_Comm_rank(comm,&rank);
3023:   MPI_Comm_size(comm,&size);

3025:   if (call ==  MAT_REUSE_MATRIX) {
3026:     PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject *)&Mreuse);
3027:     if (!Mreuse) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
3028:     local = &Mreuse;
3029:     MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&local);
3030:   } else {
3031:     MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);
3032:     Mreuse = *local;
3033:     PetscFree(local);
3034:   }

3036:   /* 
3037:       m - number of local rows
3038:       n - number of columns (same on all processors)
3039:       rstart - first row in new global matrix generated
3040:   */
3041:   MatGetSize(Mreuse,&m,&n);
3042:   if (call == MAT_INITIAL_MATRIX) {
3043:     aij = (Mat_SeqAIJ*)(Mreuse)->data;
3044:     ii  = aij->i;
3045:     jj  = aij->j;

3047:     /*
3048:         Determine the number of non-zeros in the diagonal and off-diagonal 
3049:         portions of the matrix in order to do correct preallocation
3050:     */

3052:     /* first get start and end of "diagonal" columns */
3053:     if (csize == PETSC_DECIDE) {
3054:       ISGetSize(isrow,&mglobal);
3055:       if (mglobal == n) { /* square matrix */
3056:         nlocal = m;
3057:       } else {
3058:         nlocal = n/size + ((n % size) > rank);
3059:       }
3060:     } else {
3061:       nlocal = csize;
3062:     }
3063:     MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
3064:     rstart = rend - nlocal;
3065:     if (rank == size - 1 && rend != n) {
3066:       SETERRQ2(PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);
3067:     }

3069:     /* next, compute all the lengths */
3070:     PetscMalloc((2*m+1)*sizeof(PetscInt),&dlens);
3071:     olens = dlens + m;
3072:     for (i=0; i<m; i++) {
3073:       jend = ii[i+1] - ii[i];
3074:       olen = 0;
3075:       dlen = 0;
3076:       for (j=0; j<jend; j++) {
3077:         if (*jj < rstart || *jj >= rend) olen++;
3078:         else dlen++;
3079:         jj++;
3080:       }
3081:       olens[i] = olen;
3082:       dlens[i] = dlen;
3083:     }
3084:     MatCreate(comm,&M);
3085:     MatSetSizes(M,m,nlocal,PETSC_DECIDE,n);
3086:     MatSetType(M,((PetscObject)mat)->type_name);
3087:     MatMPIAIJSetPreallocation(M,0,dlens,0,olens);
3088:     PetscFree(dlens);
3089:   } else {
3090:     PetscInt ml,nl;

3092:     M = *newmat;
3093:     MatGetLocalSize(M,&ml,&nl);
3094:     if (ml != m) SETERRQ(PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
3095:     MatZeroEntries(M);
3096:     /*
3097:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3098:        rather than the slower MatSetValues().
3099:     */
3100:     M->was_assembled = PETSC_TRUE;
3101:     M->assembled     = PETSC_FALSE;
3102:   }
3103:   MatGetOwnershipRange(M,&rstart,&rend);
3104:   aij = (Mat_SeqAIJ*)(Mreuse)->data;
3105:   ii  = aij->i;
3106:   jj  = aij->j;
3107:   aa  = aij->a;
3108:   for (i=0; i<m; i++) {
3109:     row   = rstart + i;
3110:     nz    = ii[i+1] - ii[i];
3111:     cwork = jj;     jj += nz;
3112:     vwork = aa;     aa += nz;
3113:     MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
3114:   }

3116:   MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
3117:   MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
3118:   *newmat = M;

3120:   /* save submatrix used in processor for next request */
3121:   if (call ==  MAT_INITIAL_MATRIX) {
3122:     PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
3123:     PetscObjectDereference((PetscObject)Mreuse);
3124:   }

3126:   return(0);
3127: }

3132: PetscErrorCode  MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3133: {
3134:   PetscInt       m,cstart, cend,j,nnz,i,d;
3135:   PetscInt       *d_nnz,*o_nnz,nnz_max = 0,rstart,ii;
3136:   const PetscInt *JJ;
3137:   PetscScalar    *values;

3141:   if (Ii[0]) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Ii[0] must be 0 it is %D",Ii[0]);

3143:   PetscMapSetBlockSize(B->rmap,1);
3144:   PetscMapSetBlockSize(B->cmap,1);
3145:   PetscMapSetUp(B->rmap);
3146:   PetscMapSetUp(B->cmap);
3147:   m      = B->rmap->n;
3148:   cstart = B->cmap->rstart;
3149:   cend   = B->cmap->rend;
3150:   rstart = B->rmap->rstart;

3152:   PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
3153:   o_nnz = d_nnz + m;

3155: #if defined(PETSC_USE_DEBUGGING)
3156:   for (i=0; i<m; i++) {
3157:     nnz     = Ii[i+1]- Ii[i];
3158:     JJ      = J + Ii[i];
3159:     if (nnz < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative %D number of columns",i,nnz);
3160:     if (nnz && (JJ[0] < 0)) SETERRRQ1(PETSC_ERR_ARG_WRONGSTATE,"Row %D starts with negative column index",i,j);
3161:     if (nnz && (JJ[nnz-1] >= B->cmap->N) SETERRRQ3(PETSC_ERR_ARG_WRONGSTATE,"Row %D ends with too large a column index %D (max allowed %D)",i,JJ[nnz-1],B->cmap->N);
3162:     for (j=1; j<nnz; j++) {
3163:       if (JJ[i] <= JJ[i-1]) SETERRRQ(PETSC_ERR_ARG_WRONGSTATE,"Row %D has unsorted column index at %D location in column indices",i,j);
3164:     }
3165:   }
3166: #endif

3168:   for (i=0; i<m; i++) {
3169:     nnz     = Ii[i+1]- Ii[i];
3170:     JJ      = J + Ii[i];
3171:     nnz_max = PetscMax(nnz_max,nnz);
3172:     for (j=0; j<nnz; j++) {
3173:       if (*JJ >= cstart) break;
3174:       JJ++;
3175:     }
3176:     d = 0;
3177:     for (; j<nnz; j++) {
3178:       if (*JJ++ >= cend) break;
3179:       d++;
3180:     }
3181:     d_nnz[i] = d;
3182:     o_nnz[i] = nnz - d;
3183:   }
3184:   MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
3185:   PetscFree(d_nnz);

3187:   if (v) values = (PetscScalar*)v;
3188:   else {
3189:     PetscMalloc((nnz_max+1)*sizeof(PetscScalar),&values);
3190:     PetscMemzero(values,nnz_max*sizeof(PetscScalar));
3191:   }

3193:   for (i=0; i<m; i++) {
3194:     ii   = i + rstart;
3195:     nnz  = Ii[i+1]- Ii[i];
3196:     MatSetValues_MPIAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
3197:   }
3198:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3199:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3201:   if (!v) {
3202:     PetscFree(values);
3203:   }
3204:   return(0);
3205: }

3210: /*@
3211:    MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
3212:    (the default parallel PETSc format).  

3214:    Collective on MPI_Comm

3216:    Input Parameters:
3217: +  B - the matrix 
3218: .  i - the indices into j for the start of each local row (starts with zero)
3219: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
3220: -  v - optional values in the matrix

3222:    Level: developer

3224:    Notes:
3225:        The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3226:      thus you CANNOT change the matrix entries by changing the values of a[] after you have 
3227:      called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.

3229:        The i and j indices are 0 based, and i indices are indices corresponding to the local j array.

3231:        The format which is used for the sparse matrix input, is equivalent to a
3232:     row-major ordering.. i.e for the following matrix, the input data expected is
3233:     as shown:

3235:         1 0 0
3236:         2 0 3     P0
3237:        -------
3238:         4 5 6     P1

3240:      Process0 [P0]: rows_owned=[0,1]
3241:         i =  {0,1,3}  [size = nrow+1  = 2+1]
3242:         j =  {0,0,2}  [size = nz = 6]
3243:         v =  {1,2,3}  [size = nz = 6]

3245:      Process1 [P1]: rows_owned=[2]
3246:         i =  {0,3}    [size = nrow+1  = 1+1]
3247:         j =  {0,1,2}  [size = nz = 6]
3248:         v =  {4,5,6}  [size = nz = 6]

3250:       The column indices for each row MUST be sorted.

3252: .keywords: matrix, aij, compressed row, sparse, parallel

3254: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ,
3255:           MatCreateSeqAIJWithArrays(), MatCreateMPIAIJWithSplitArrays()
3256: @*/
3257: PetscErrorCode  MatMPIAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
3258: {
3259:   PetscErrorCode ierr,(*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);

3262:   PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",(void (**)(void))&f);
3263:   if (f) {
3264:     (*f)(B,i,j,v);
3265:   }
3266:   return(0);
3267: }

3271: /*@C
3272:    MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in AIJ format
3273:    (the default parallel PETSc format).  For good matrix assembly performance
3274:    the user should preallocate the matrix storage by setting the parameters 
3275:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
3276:    performance can be increased by more than a factor of 50.

3278:    Collective on MPI_Comm

3280:    Input Parameters:
3281: +  A - the matrix 
3282: .  d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
3283:            (same value is used for all local rows)
3284: .  d_nnz - array containing the number of nonzeros in the various rows of the 
3285:            DIAGONAL portion of the local submatrix (possibly different for each row)
3286:            or PETSC_NULL, if d_nz is used to specify the nonzero structure. 
3287:            The size of this array is equal to the number of local rows, i.e 'm'. 
3288:            You must leave room for the diagonal entry even if it is zero.
3289: .  o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
3290:            submatrix (same value is used for all local rows).
3291: -  o_nnz - array containing the number of nonzeros in the various rows of the
3292:            OFF-DIAGONAL portion of the local submatrix (possibly different for
3293:            each row) or PETSC_NULL, if o_nz is used to specify the nonzero 
3294:            structure. The size of this array is equal to the number 
3295:            of local rows, i.e 'm'. 

3297:    If the *_nnz parameter is given then the *_nz parameter is ignored

3299:    The AIJ format (also called the Yale sparse matrix format or
3300:    compressed row storage (CSR)), is fully compatible with standard Fortran 77
3301:    storage.  The stored row and column indices begin with zero.  See the users manual for details.

3303:    The parallel matrix is partitioned such that the first m0 rows belong to 
3304:    process 0, the next m1 rows belong to process 1, the next m2 rows belong 
3305:    to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.

3307:    The DIAGONAL portion of the local submatrix of a processor can be defined 
3308:    as the submatrix which is obtained by extraction the part corresponding 
3309:    to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 
3310:    first row that belongs to the processor, and r2 is the last row belonging 
3311:    to the this processor. This is a square mxm matrix. The remaining portion 
3312:    of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.

3314:    If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.

3316:    You can call MatGetInfo() to get information on how effective the preallocation was;
3317:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3318:    You can also run with the option -info and look for messages with the string 
3319:    malloc in them to see if additional memory allocation was needed.

3321:    Example usage:
3322:   
3323:    Consider the following 8x8 matrix with 34 non-zero values, that is 
3324:    assembled across 3 processors. Lets assume that proc0 owns 3 rows,
3325:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 
3326:    as follows:

3328: .vb
3329:             1  2  0  |  0  3  0  |  0  4
3330:     Proc0   0  5  6  |  7  0  0  |  8  0
3331:             9  0 10  | 11  0  0  | 12  0
3332:     -------------------------------------
3333:            13  0 14  | 15 16 17  |  0  0
3334:     Proc1   0 18  0  | 19 20 21  |  0  0 
3335:             0  0  0  | 22 23  0  | 24  0
3336:     -------------------------------------
3337:     Proc2  25 26 27  |  0  0 28  | 29  0
3338:            30  0  0  | 31 32 33  |  0 34
3339: .ve

3341:    This can be represented as a collection of submatrices as:

3343: .vb
3344:       A B C
3345:       D E F
3346:       G H I
3347: .ve

3349:    Where the submatrices A,B,C are owned by proc0, D,E,F are
3350:    owned by proc1, G,H,I are owned by proc2.

3352:    The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3353:    The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3354:    The 'M','N' parameters are 8,8, and have the same values on all procs.

3356:    The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
3357:    submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
3358:    corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
3359:    Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
3360:    part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
3361:    matrix, ans [DF] as another SeqAIJ matrix.

3363:    When d_nz, o_nz parameters are specified, d_nz storage elements are
3364:    allocated for every row of the local diagonal submatrix, and o_nz
3365:    storage locations are allocated for every row of the OFF-DIAGONAL submat.
3366:    One way to choose d_nz and o_nz is to use the max nonzerors per local 
3367:    rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 
3368:    In this case, the values of d_nz,o_nz are:
3369: .vb
3370:      proc0 : dnz = 2, o_nz = 2
3371:      proc1 : dnz = 3, o_nz = 2
3372:      proc2 : dnz = 1, o_nz = 4
3373: .ve
3374:    We are allocating m*(d_nz+o_nz) storage locations for every proc. This
3375:    translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
3376:    for proc3. i.e we are using 12+15+10=37 storage locations to store 
3377:    34 values.

3379:    When d_nnz, o_nnz parameters are specified, the storage is specified
3380:    for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
3381:    In the above case the values for d_nnz,o_nnz are:
3382: .vb
3383:      proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
3384:      proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
3385:      proc2: d_nnz = [1,1]   and o_nnz = [4,4]
3386: .ve
3387:    Here the space allocated is sum of all the above values i.e 34, and
3388:    hence pre-allocation is perfect.

3390:    Level: intermediate

3392: .keywords: matrix, aij, compressed row, sparse, parallel

3394: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIAIJ(), MatMPIAIJSetPreallocationCSR(),
3395:           MPIAIJ, MatGetInfo()
3396: @*/
3397: PetscErrorCode  MatMPIAIJSetPreallocation(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
3398: {
3399:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

3402:   PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocation_C",(void (**)(void))&f);
3403:   if (f) {
3404:     (*f)(B,d_nz,d_nnz,o_nz,o_nnz);
3405:   }
3406:   return(0);
3407: }

3411: /*@
3412:      MatCreateMPIAIJWithArrays - creates a MPI AIJ matrix using arrays that contain in standard
3413:          CSR format the local rows.

3415:    Collective on MPI_Comm

3417:    Input Parameters:
3418: +  comm - MPI communicator
3419: .  m - number of local rows (Cannot be PETSC_DECIDE)
3420: .  n - This value should be the same as the local size used in creating the 
3421:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3422:        calculated if N is given) For square matrices n is almost always m.
3423: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3424: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3425: .   i - row indices
3426: .   j - column indices
3427: -   a - matrix values

3429:    Output Parameter:
3430: .   mat - the matrix

3432:    Level: intermediate

3434:    Notes:
3435:        The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3436:      thus you CANNOT change the matrix entries by changing the values of a[] after you have 
3437:      called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.

3439:        The i and j indices are 0 based, and i indices are indices corresponding to the local j array.

3441:        The format which is used for the sparse matrix input, is equivalent to a
3442:     row-major ordering.. i.e for the following matrix, the input data expected is
3443:     as shown:

3445:         1 0 0
3446:         2 0 3     P0
3447:        -------
3448:         4 5 6     P1

3450:      Process0 [P0]: rows_owned=[0,1]
3451:         i =  {0,1,3}  [size = nrow+1  = 2+1]
3452:         j =  {0,0,2}  [size = nz = 6]
3453:         v =  {1,2,3}  [size = nz = 6]

3455:      Process1 [P1]: rows_owned=[2]
3456:         i =  {0,3}    [size = nrow+1  = 1+1]
3457:         j =  {0,1,2}  [size = nz = 6]
3458:         v =  {4,5,6}  [size = nz = 6]

3460: .keywords: matrix, aij, compressed row, sparse, parallel

3462: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3463:           MPIAIJ, MatCreateMPIAIJ(), MatCreateMPIAIJWithSplitArrays()
3464: @*/
3465: PetscErrorCode  MatCreateMPIAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
3466: {

3470:   if (i[0]) {
3471:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3472:   }
3473:   if (m < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
3474:   MatCreate(comm,mat);
3475:   MatSetSizes(*mat,m,n,M,N);
3476:   MatSetType(*mat,MATMPIAIJ);
3477:   MatMPIAIJSetPreallocationCSR(*mat,i,j,a);
3478:   return(0);
3479: }

3483: /*@C
3484:    MatCreateMPIAIJ - Creates a sparse parallel matrix in AIJ format
3485:    (the default parallel PETSc format).  For good matrix assembly performance
3486:    the user should preallocate the matrix storage by setting the parameters 
3487:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
3488:    performance can be increased by more than a factor of 50.

3490:    Collective on MPI_Comm

3492:    Input Parameters:
3493: +  comm - MPI communicator
3494: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
3495:            This value should be the same as the local size used in creating the 
3496:            y vector for the matrix-vector product y = Ax.
3497: .  n - This value should be the same as the local size used in creating the 
3498:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3499:        calculated if N is given) For square matrices n is almost always m.
3500: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3501: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3502: .  d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
3503:            (same value is used for all local rows)
3504: .  d_nnz - array containing the number of nonzeros in the various rows of the 
3505:            DIAGONAL portion of the local submatrix (possibly different for each row)
3506:            or PETSC_NULL, if d_nz is used to specify the nonzero structure. 
3507:            The size of this array is equal to the number of local rows, i.e 'm'. 
3508:            You must leave room for the diagonal entry even if it is zero.
3509: .  o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
3510:            submatrix (same value is used for all local rows).
3511: -  o_nnz - array containing the number of nonzeros in the various rows of the
3512:            OFF-DIAGONAL portion of the local submatrix (possibly different for
3513:            each row) or PETSC_NULL, if o_nz is used to specify the nonzero 
3514:            structure. The size of this array is equal to the number 
3515:            of local rows, i.e 'm'. 

3517:    Output Parameter:
3518: .  A - the matrix 

3520:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3521:    MatXXXXSetPreallocation() paradgm instead of this routine directly. 
3522:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3524:    Notes:
3525:    If the *_nnz parameter is given then the *_nz parameter is ignored

3527:    m,n,M,N parameters specify the size of the matrix, and its partitioning across
3528:    processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate
3529:    storage requirements for this matrix.

3531:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one 
3532:    processor than it must be used on all processors that share the object for 
3533:    that argument.

3535:    The user MUST specify either the local or global matrix dimensions
3536:    (possibly both).

3538:    The parallel matrix is partitioned across processors such that the
3539:    first m0 rows belong to process 0, the next m1 rows belong to
3540:    process 1, the next m2 rows belong to process 2 etc.. where
3541:    m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
3542:    values corresponding to [m x N] submatrix.

3544:    The columns are logically partitioned with the n0 columns belonging
3545:    to 0th partition, the next n1 columns belonging to the next
3546:    partition etc.. where n0,n1,n2... are the the input parameter 'n'.

3548:    The DIAGONAL portion of the local submatrix on any given processor
3549:    is the submatrix corresponding to the rows and columns m,n
3550:    corresponding to the given processor. i.e diagonal matrix on
3551:    process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
3552:    etc. The remaining portion of the local submatrix [m x (N-n)]
3553:    constitute the OFF-DIAGONAL portion. The example below better
3554:    illustrates this concept.

3556:    For a square global matrix we define each processor's diagonal portion 
3557:    to be its local rows and the corresponding columns (a square submatrix);  
3558:    each processor's off-diagonal portion encompasses the remainder of the
3559:    local matrix (a rectangular submatrix). 

3561:    If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.

3563:    When calling this routine with a single process communicator, a matrix of
3564:    type SEQAIJ is returned.  If a matrix of type MPIAIJ is desired for this
3565:    type of communicator, use the construction mechanism:
3566:      MatCreate(...,&A); MatSetType(A,MATMPIAIJ); MatSetSizes(A, m,n,M,N); MatMPIAIJSetPreallocation(A,...);
3567:  
3568:    By default, this format uses inodes (identical nodes) when possible.
3569:    We search for consecutive rows with the same nonzero structure, thereby
3570:    reusing matrix information to achieve increased efficiency.

3572:    Options Database Keys:
3573: +  -mat_no_inode  - Do not use inodes
3574: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3575: -  -mat_aij_oneindex - Internally use indexing starting at 1
3576:         rather than 0.  Note that when calling MatSetValues(),
3577:         the user still MUST index entries starting at 0!


3580:    Example usage:
3581:   
3582:    Consider the following 8x8 matrix with 34 non-zero values, that is 
3583:    assembled across 3 processors. Lets assume that proc0 owns 3 rows,
3584:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 
3585:    as follows:

3587: .vb
3588:             1  2  0  |  0  3  0  |  0  4
3589:     Proc0   0  5  6  |  7  0  0  |  8  0
3590:             9  0 10  | 11  0  0  | 12  0
3591:     -------------------------------------
3592:            13  0 14  | 15 16 17  |  0  0
3593:     Proc1   0 18  0  | 19 20 21  |  0  0 
3594:             0  0  0  | 22 23  0  | 24  0
3595:     -------------------------------------
3596:     Proc2  25 26 27  |  0  0 28  | 29  0
3597:            30  0  0  | 31 32 33  |  0 34
3598: .ve

3600:    This can be represented as a collection of submatrices as:

3602: .vb
3603:       A B C
3604:       D E F
3605:       G H I
3606: .ve

3608:    Where the submatrices A,B,C are owned by proc0, D,E,F are
3609:    owned by proc1, G,H,I are owned by proc2.

3611:    The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3612:    The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3613:    The 'M','N' parameters are 8,8, and have the same values on all procs.

3615:    The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
3616:    submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
3617:    corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
3618:    Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
3619:    part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
3620:    matrix, ans [DF] as another SeqAIJ matrix.

3622:    When d_nz, o_nz parameters are specified, d_nz storage elements are
3623:    allocated for every row of the local diagonal submatrix, and o_nz
3624:    storage locations are allocated for every row of the OFF-DIAGONAL submat.
3625:    One way to choose d_nz and o_nz is to use the max nonzerors per local 
3626:    rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 
3627:    In this case, the values of d_nz,o_nz are:
3628: .vb
3629:      proc0 : dnz = 2, o_nz = 2
3630:      proc1 : dnz = 3, o_nz = 2
3631:      proc2 : dnz = 1, o_nz = 4
3632: .ve
3633:    We are allocating m*(d_nz+o_nz) storage locations for every proc. This
3634:    translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
3635:    for proc3. i.e we are using 12+15+10=37 storage locations to store 
3636:    34 values.

3638:    When d_nnz, o_nnz parameters are specified, the storage is specified
3639:    for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
3640:    In the above case the values for d_nnz,o_nnz are:
3641: .vb
3642:      proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
3643:      proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
3644:      proc2: d_nnz = [1,1]   and o_nnz = [4,4]
3645: .ve
3646:    Here the space allocated is sum of all the above values i.e 34, and
3647:    hence pre-allocation is perfect.

3649:    Level: intermediate

3651: .keywords: matrix, aij, compressed row, sparse, parallel

3653: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3654:           MPIAIJ, MatCreateMPIAIJWithArrays()
3655: @*/
3656: 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)
3657: {
3659:   PetscMPIInt    size;

3662:   MatCreate(comm,A);
3663:   MatSetSizes(*A,m,n,M,N);
3664:   MPI_Comm_size(comm,&size);
3665:   if (size > 1) {
3666:     MatSetType(*A,MATMPIAIJ);
3667:     MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
3668:   } else {
3669:     MatSetType(*A,MATSEQAIJ);
3670:     MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
3671:   }
3672:   return(0);
3673: }

3677: PetscErrorCode  MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
3678: {
3679:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

3682:   *Ad     = a->A;
3683:   *Ao     = a->B;
3684:   *colmap = a->garray;
3685:   return(0);
3686: }

3690: PetscErrorCode MatSetColoring_MPIAIJ(Mat A,ISColoring coloring)
3691: {
3693:   PetscInt       i;
3694:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

3697:   if (coloring->ctype == IS_COLORING_GLOBAL) {
3698:     ISColoringValue *allcolors,*colors;
3699:     ISColoring      ocoloring;

3701:     /* set coloring for diagonal portion */
3702:     MatSetColoring_SeqAIJ(a->A,coloring);

3704:     /* set coloring for off-diagonal portion */
3705:     ISAllGatherColors(((PetscObject)A)->comm,coloring->n,coloring->colors,PETSC_NULL,&allcolors);
3706:     PetscMalloc((a->B->cmap->n+1)*sizeof(ISColoringValue),&colors);
3707:     for (i=0; i<a->B->cmap->n; i++) {
3708:       colors[i] = allcolors[a->garray[i]];
3709:     }
3710:     PetscFree(allcolors);
3711:     ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap->n,colors,&ocoloring);
3712:     MatSetColoring_SeqAIJ(a->B,ocoloring);
3713:     ISColoringDestroy(ocoloring);
3714:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3715:     ISColoringValue *colors;
3716:     PetscInt        *larray;
3717:     ISColoring      ocoloring;

3719:     /* set coloring for diagonal portion */
3720:     PetscMalloc((a->A->cmap->n+1)*sizeof(PetscInt),&larray);
3721:     for (i=0; i<a->A->cmap->n; i++) {
3722:       larray[i] = i + A->cmap->rstart;
3723:     }
3724:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->A->cmap->n,larray,PETSC_NULL,larray);
3725:     PetscMalloc((a->A->cmap->n+1)*sizeof(ISColoringValue),&colors);
3726:     for (i=0; i<a->A->cmap->n; i++) {
3727:       colors[i] = coloring->colors[larray[i]];
3728:     }
3729:     PetscFree(larray);
3730:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,a->A->cmap->n,colors,&ocoloring);
3731:     MatSetColoring_SeqAIJ(a->A,ocoloring);
3732:     ISColoringDestroy(ocoloring);

3734:     /* set coloring for off-diagonal portion */
3735:     PetscMalloc((a->B->cmap->n+1)*sizeof(PetscInt),&larray);
3736:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->B->cmap->n,a->garray,PETSC_NULL,larray);
3737:     PetscMalloc((a->B->cmap->n+1)*sizeof(ISColoringValue),&colors);
3738:     for (i=0; i<a->B->cmap->n; i++) {
3739:       colors[i] = coloring->colors[larray[i]];
3740:     }
3741:     PetscFree(larray);
3742:     ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap->n,colors,&ocoloring);
3743:     MatSetColoring_SeqAIJ(a->B,ocoloring);
3744:     ISColoringDestroy(ocoloring);
3745:   } else {
3746:     SETERRQ1(PETSC_ERR_SUP,"No support ISColoringType %d",(int)coloring->ctype);
3747:   }

3749:   return(0);
3750: }

3752: #if defined(PETSC_HAVE_ADIC)
3755: PetscErrorCode MatSetValuesAdic_MPIAIJ(Mat A,void *advalues)
3756: {
3757:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

3761:   MatSetValuesAdic_SeqAIJ(a->A,advalues);
3762:   MatSetValuesAdic_SeqAIJ(a->B,advalues);
3763:   return(0);
3764: }
3765: #endif

3769: PetscErrorCode MatSetValuesAdifor_MPIAIJ(Mat A,PetscInt nl,void *advalues)
3770: {
3771:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;

3775:   MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);
3776:   MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);
3777:   return(0);
3778: }

3782: /*@
3783:       MatMerge - Creates a single large PETSc matrix by concatinating sequential
3784:                  matrices from each processor

3786:     Collective on MPI_Comm

3788:    Input Parameters:
3789: +    comm - the communicators the parallel matrix will live on
3790: .    inmat - the input sequential matrices
3791: .    n - number of local columns (or PETSC_DECIDE)
3792: -    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

3794:    Output Parameter:
3795: .    outmat - the parallel matrix generated

3797:     Level: advanced

3799:    Notes: The number of columns of the matrix in EACH processor MUST be the same.

3801: @*/
3802: PetscErrorCode  MatMerge(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
3803: {
3805:   PetscInt       m,N,i,rstart,nnz,Ii,*dnz,*onz;
3806:   PetscInt       *indx;
3807:   PetscScalar    *values;

3810:   MatGetSize(inmat,&m,&N);
3811:   if (scall == MAT_INITIAL_MATRIX){
3812:     /* count nonzeros in each row, for diagonal and off diagonal portion of matrix */
3813:     if (n == PETSC_DECIDE){
3814:       PetscSplitOwnership(comm,&n,&N);
3815:     }
3816:     MPI_Scan(&m, &rstart,1,MPIU_INT,MPI_SUM,comm);
3817:     rstart -= m;

3819:     MatPreallocateInitialize(comm,m,n,dnz,onz);
3820:     for (i=0;i<m;i++) {
3821:       MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
3822:       MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);
3823:       MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
3824:     }
3825:     /* This routine will ONLY return MPIAIJ type matrix */
3826:     MatCreate(comm,outmat);
3827:     MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3828:     MatSetType(*outmat,MATMPIAIJ);
3829:     MatMPIAIJSetPreallocation(*outmat,0,dnz,0,onz);
3830:     MatPreallocateFinalize(dnz,onz);
3831: 
3832:   } else if (scall == MAT_REUSE_MATRIX){
3833:     MatGetOwnershipRange(*outmat,&rstart,PETSC_NULL);
3834:   } else {
3835:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
3836:   }

3838:   for (i=0;i<m;i++) {
3839:     MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3840:     Ii    = i + rstart;
3841:     MatSetValues(*outmat,1,&Ii,nnz,indx,values,INSERT_VALUES);
3842:     MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3843:   }
3844:   MatDestroy(inmat);
3845:   MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
3846:   MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);

3848:   return(0);
3849: }

3853: PetscErrorCode MatFileSplit(Mat A,char *outfile)
3854: {
3855:   PetscErrorCode    ierr;
3856:   PetscMPIInt       rank;
3857:   PetscInt          m,N,i,rstart,nnz;
3858:   size_t            len;
3859:   const PetscInt    *indx;
3860:   PetscViewer       out;
3861:   char              *name;
3862:   Mat               B;
3863:   const PetscScalar *values;

3866:   MatGetLocalSize(A,&m,0);
3867:   MatGetSize(A,0,&N);
3868:   /* Should this be the type of the diagonal block of A? */
3869:   MatCreate(PETSC_COMM_SELF,&B);
3870:   MatSetSizes(B,m,N,m,N);
3871:   MatSetType(B,MATSEQAIJ);
3872:   MatSeqAIJSetPreallocation(B,0,PETSC_NULL);
3873:   MatGetOwnershipRange(A,&rstart,0);
3874:   for (i=0;i<m;i++) {
3875:     MatGetRow(A,i+rstart,&nnz,&indx,&values);
3876:     MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);
3877:     MatRestoreRow(A,i+rstart,&nnz,&indx,&values);
3878:   }
3879:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3880:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3882:   MPI_Comm_rank(((PetscObject)A)->comm,&rank);
3883:   PetscStrlen(outfile,&len);
3884:   PetscMalloc((len+5)*sizeof(char),&name);
3885:   sprintf(name,"%s.%d",outfile,rank);
3886:   PetscViewerBinaryOpen(PETSC_COMM_SELF,name,FILE_MODE_APPEND,&out);
3887:   PetscFree(name);
3888:   MatView(B,out);
3889:   PetscViewerDestroy(out);
3890:   MatDestroy(B);
3891:   return(0);
3892: }

3894: EXTERN PetscErrorCode MatDestroy_MPIAIJ(Mat);
3897: PetscErrorCode  MatDestroy_MPIAIJ_SeqsToMPI(Mat A)
3898: {
3899:   PetscErrorCode       ierr;
3900:   Mat_Merge_SeqsToMPI  *merge;
3901:   PetscContainer       container;

3904:   PetscObjectQuery((PetscObject)A,"MatMergeSeqsToMPI",(PetscObject *)&container);
3905:   if (container) {
3906:     PetscContainerGetPointer(container,(void **)&merge);
3907:     PetscFree(merge->id_r);
3908:     PetscFree(merge->len_s);
3909:     PetscFree(merge->len_r);
3910:     PetscFree(merge->bi);
3911:     PetscFree(merge->bj);
3912:     PetscFree(merge->buf_ri);
3913:     PetscFree(merge->buf_rj);
3914:     PetscFree(merge->coi);
3915:     PetscFree(merge->coj);
3916:     PetscFree(merge->owners_co);
3917:     PetscFree(merge->rowmap.range);
3918: 
3919:     PetscContainerDestroy(container);
3920:     PetscObjectCompose((PetscObject)A,"MatMergeSeqsToMPI",0);
3921:   }
3922:   PetscFree(merge);

3924:   MatDestroy_MPIAIJ(A);
3925:   return(0);
3926: }

3928:  #include ../src/mat/utils/freespace.h
3929:  #include petscbt.h

3933: /*@C
3934:       MatMerge_SeqsToMPI - Creates a MPIAIJ matrix by adding sequential
3935:                  matrices from each processor

3937:     Collective on MPI_Comm

3939:    Input Parameters:
3940: +    comm - the communicators the parallel matrix will live on
3941: .    seqmat - the input sequential matrices
3942: .    m - number of local rows (or PETSC_DECIDE)
3943: .    n - number of local columns (or PETSC_DECIDE)
3944: -    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

3946:    Output Parameter:
3947: .    mpimat - the parallel matrix generated

3949:     Level: advanced

3951:    Notes: 
3952:      The dimensions of the sequential matrix in each processor MUST be the same.
3953:      The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
3954:      destroyed when mpimat is destroyed. Call PetscObjectQuery() to access seqmat.
3955: @*/
3956: PetscErrorCode  MatMerge_SeqsToMPINumeric(Mat seqmat,Mat mpimat)
3957: {
3958:   PetscErrorCode       ierr;
3959:   MPI_Comm             comm=((PetscObject)mpimat)->comm;
3960:   Mat_SeqAIJ           *a=(Mat_SeqAIJ*)seqmat->data;
3961:   PetscMPIInt          size,rank,taga,*len_s;
3962:   PetscInt             N=mpimat->cmap->N,i,j,*owners,*ai=a->i,*aj=a->j;
3963:   PetscInt             proc,m;
3964:   PetscInt             **buf_ri,**buf_rj;
3965:   PetscInt             k,anzi,*bj_i,*bi,*bj,arow,bnzi,nextaj;
3966:   PetscInt             nrows,**buf_ri_k,**nextrow,**nextai;
3967:   MPI_Request          *s_waits,*r_waits;
3968:   MPI_Status           *status;
3969:   MatScalar            *aa=a->a;
3970:   MatScalar            **abuf_r,*ba_i;
3971:   Mat_Merge_SeqsToMPI  *merge;
3972:   PetscContainer       container;
3973: 
3975:   PetscLogEventBegin(MAT_Seqstompinum,seqmat,0,0,0);

3977:   MPI_Comm_size(comm,&size);
3978:   MPI_Comm_rank(comm,&rank);

3980:   PetscObjectQuery((PetscObject)mpimat,"MatMergeSeqsToMPI",(PetscObject *)&container);
3981:   if (container) {
3982:     PetscContainerGetPointer(container,(void **)&merge);
3983:   }
3984:   bi     = merge->bi;
3985:   bj     = merge->bj;
3986:   buf_ri = merge->buf_ri;
3987:   buf_rj = merge->buf_rj;

3989:   PetscMalloc(size*sizeof(MPI_Status),&status);
3990:   owners = merge->rowmap.range;
3991:   len_s  = merge->len_s;

3993:   /* send and recv matrix values */
3994:   /*-----------------------------*/
3995:   PetscObjectGetNewTag((PetscObject)mpimat,&taga);
3996:   PetscPostIrecvScalar(comm,taga,merge->nrecv,merge->id_r,merge->len_r,&abuf_r,&r_waits);

3998:   PetscMalloc((merge->nsend+1)*sizeof(MPI_Request),&s_waits);
3999:   for (proc=0,k=0; proc<size; proc++){
4000:     if (!len_s[proc]) continue;
4001:     i = owners[proc];
4002:     MPI_Isend(aa+ai[i],len_s[proc],MPIU_MATSCALAR,proc,taga,comm,s_waits+k);
4003:     k++;
4004:   }

4006:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,r_waits,status);}
4007:   if (merge->nsend) {MPI_Waitall(merge->nsend,s_waits,status);}
4008:   PetscFree(status);

4010:   PetscFree(s_waits);
4011:   PetscFree(r_waits);

4013:   /* insert mat values of mpimat */
4014:   /*----------------------------*/
4015:   PetscMalloc(N*sizeof(PetscScalar),&ba_i);
4016:   PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
4017:   nextrow = buf_ri_k + merge->nrecv;
4018:   nextai  = nextrow + merge->nrecv;

4020:   for (k=0; k<merge->nrecv; k++){
4021:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4022:     nrows = *(buf_ri_k[k]);
4023:     nextrow[k]  = buf_ri_k[k]+1;  /* next row number of k-th recved i-structure */
4024:     nextai[k]   = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure  */
4025:   }

4027:   /* set values of ba */
4028:   m = merge->rowmap.n;
4029:   for (i=0; i<m; i++) {
4030:     arow = owners[rank] + i;
4031:     bj_i = bj+bi[i];  /* col indices of the i-th row of mpimat */
4032:     bnzi = bi[i+1] - bi[i];
4033:     PetscMemzero(ba_i,bnzi*sizeof(PetscScalar));

4035:     /* add local non-zero vals of this proc's seqmat into ba */
4036:     anzi = ai[arow+1] - ai[arow];
4037:     aj   = a->j + ai[arow];
4038:     aa   = a->a + ai[arow];
4039:     nextaj = 0;
4040:     for (j=0; nextaj<anzi; j++){
4041:       if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
4042:         ba_i[j] += aa[nextaj++];
4043:       }
4044:     }

4046:     /* add received vals into ba */
4047:     for (k=0; k<merge->nrecv; k++){ /* k-th received message */
4048:       /* i-th row */
4049:       if (i == *nextrow[k]) {
4050:         anzi = *(nextai[k]+1) - *nextai[k];
4051:         aj   = buf_rj[k] + *(nextai[k]);
4052:         aa   = abuf_r[k] + *(nextai[k]);
4053:         nextaj = 0;
4054:         for (j=0; nextaj<anzi; j++){
4055:           if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
4056:             ba_i[j] += aa[nextaj++];
4057:           }
4058:         }
4059:         nextrow[k]++; nextai[k]++;
4060:       }
4061:     }
4062:     MatSetValues(mpimat,1,&arow,bnzi,bj_i,ba_i,INSERT_VALUES);
4063:   }
4064:   MatAssemblyBegin(mpimat,MAT_FINAL_ASSEMBLY);
4065:   MatAssemblyEnd(mpimat,MAT_FINAL_ASSEMBLY);

4067:   PetscFree(abuf_r);
4068:   PetscFree(ba_i);
4069:   PetscFree(buf_ri_k);
4070:   PetscLogEventEnd(MAT_Seqstompinum,seqmat,0,0,0);
4071:   return(0);
4072: }

4076: PetscErrorCode  MatMerge_SeqsToMPISymbolic(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,Mat *mpimat)
4077: {
4078:   PetscErrorCode       ierr;
4079:   Mat                  B_mpi;
4080:   Mat_SeqAIJ           *a=(Mat_SeqAIJ*)seqmat->data;
4081:   PetscMPIInt          size,rank,tagi,tagj,*len_s,*len_si,*len_ri;
4082:   PetscInt             **buf_rj,**buf_ri,**buf_ri_k;
4083:   PetscInt             M=seqmat->rmap->n,N=seqmat->cmap->n,i,*owners,*ai=a->i,*aj=a->j;
4084:   PetscInt             len,proc,*dnz,*onz;
4085:   PetscInt             k,anzi,*bi,*bj,*lnk,nlnk,arow,bnzi,nspacedouble=0;
4086:   PetscInt             nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextai;
4087:   MPI_Request          *si_waits,*sj_waits,*ri_waits,*rj_waits;
4088:   MPI_Status           *status;
4089:   PetscFreeSpaceList   free_space=PETSC_NULL,current_space=PETSC_NULL;
4090:   PetscBT              lnkbt;
4091:   Mat_Merge_SeqsToMPI  *merge;
4092:   PetscContainer       container;

4095:   PetscLogEventBegin(MAT_Seqstompisym,seqmat,0,0,0);

4097:   /* make sure it is a PETSc comm */
4098:   PetscCommDuplicate(comm,&comm,PETSC_NULL);
4099:   MPI_Comm_size(comm,&size);
4100:   MPI_Comm_rank(comm,&rank);
4101: 
4102:   PetscNew(Mat_Merge_SeqsToMPI,&merge);
4103:   PetscMalloc(size*sizeof(MPI_Status),&status);

4105:   /* determine row ownership */
4106:   /*---------------------------------------------------------*/
4107:   PetscMapInitialize(comm,&merge->rowmap);
4108:   merge->rowmap.n = m;
4109:   merge->rowmap.N = M;
4110:   merge->rowmap.bs = 1;
4111:   PetscMapSetUp(&merge->rowmap);
4112:   PetscMalloc(size*sizeof(PetscMPIInt),&len_si);
4113:   PetscMalloc(size*sizeof(PetscMPIInt),&merge->len_s);
4114: 
4115:   m      = merge->rowmap.n;
4116:   M      = merge->rowmap.N;
4117:   owners = merge->rowmap.range;

4119:   /* determine the number of messages to send, their lengths */
4120:   /*---------------------------------------------------------*/
4121:   len_s  = merge->len_s;

4123:   len = 0;  /* length of buf_si[] */
4124:   merge->nsend = 0;
4125:   for (proc=0; proc<size; proc++){
4126:     len_si[proc] = 0;
4127:     if (proc == rank){
4128:       len_s[proc] = 0;
4129:     } else {
4130:       len_si[proc] = owners[proc+1] - owners[proc] + 1;
4131:       len_s[proc] = ai[owners[proc+1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4132:     }
4133:     if (len_s[proc]) {
4134:       merge->nsend++;
4135:       nrows = 0;
4136:       for (i=owners[proc]; i<owners[proc+1]; i++){
4137:         if (ai[i+1] > ai[i]) nrows++;
4138:       }
4139:       len_si[proc] = 2*(nrows+1);
4140:       len += len_si[proc];
4141:     }
4142:   }

4144:   /* determine the number and length of messages to receive for ij-structure */
4145:   /*-------------------------------------------------------------------------*/
4146:   PetscGatherNumberOfMessages(comm,PETSC_NULL,len_s,&merge->nrecv);
4147:   PetscGatherMessageLengths2(comm,merge->nsend,merge->nrecv,len_s,len_si,&merge->id_r,&merge->len_r,&len_ri);

4149:   /* post the Irecv of j-structure */
4150:   /*-------------------------------*/
4151:   PetscCommGetNewTag(comm,&tagj);
4152:   PetscPostIrecvInt(comm,tagj,merge->nrecv,merge->id_r,merge->len_r,&buf_rj,&rj_waits);

4154:   /* post the Isend of j-structure */
4155:   /*--------------------------------*/
4156:   PetscMalloc((2*merge->nsend+1)*sizeof(MPI_Request),&si_waits);
4157:   sj_waits = si_waits + merge->nsend;

4159:   for (proc=0, k=0; proc<size; proc++){
4160:     if (!len_s[proc]) continue;
4161:     i = owners[proc];
4162:     MPI_Isend(aj+ai[i],len_s[proc],MPIU_INT,proc,tagj,comm,sj_waits+k);
4163:     k++;
4164:   }

4166:   /* receives and sends of j-structure are complete */
4167:   /*------------------------------------------------*/
4168:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,rj_waits,status);}
4169:   if (merge->nsend) {MPI_Waitall(merge->nsend,sj_waits,status);}
4170: 
4171:   /* send and recv i-structure */
4172:   /*---------------------------*/
4173:   PetscCommGetNewTag(comm,&tagi);
4174:   PetscPostIrecvInt(comm,tagi,merge->nrecv,merge->id_r,len_ri,&buf_ri,&ri_waits);
4175: 
4176:   PetscMalloc((len+1)*sizeof(PetscInt),&buf_s);
4177:   buf_si = buf_s;  /* points to the beginning of k-th msg to be sent */
4178:   for (proc=0,k=0; proc<size; proc++){
4179:     if (!len_s[proc]) continue;
4180:     /* form outgoing message for i-structure: 
4181:          buf_si[0]:                 nrows to be sent
4182:                [1:nrows]:           row index (global)
4183:                [nrows+1:2*nrows+1]: i-structure index
4184:     */
4185:     /*-------------------------------------------*/
4186:     nrows = len_si[proc]/2 - 1;
4187:     buf_si_i    = buf_si + nrows+1;
4188:     buf_si[0]   = nrows;
4189:     buf_si_i[0] = 0;
4190:     nrows = 0;
4191:     for (i=owners[proc]; i<owners[proc+1]; i++){
4192:       anzi = ai[i+1] - ai[i];
4193:       if (anzi) {
4194:         buf_si_i[nrows+1] = buf_si_i[nrows] + anzi; /* i-structure */
4195:         buf_si[nrows+1] = i-owners[proc]; /* local row index */
4196:         nrows++;
4197:       }
4198:     }
4199:     MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,si_waits+k);
4200:     k++;
4201:     buf_si += len_si[proc];
4202:   }

4204:   if (merge->nrecv) {MPI_Waitall(merge->nrecv,ri_waits,status);}
4205:   if (merge->nsend) {MPI_Waitall(merge->nsend,si_waits,status);}

4207:   PetscInfo2(seqmat,"nsend: %D, nrecv: %D\n",merge->nsend,merge->nrecv);
4208:   for (i=0; i<merge->nrecv; i++){
4209:     PetscInfo3(seqmat,"recv len_ri=%D, len_rj=%D from [%D]\n",len_ri[i],merge->len_r[i],merge->id_r[i]);
4210:   }

4212:   PetscFree(len_si);
4213:   PetscFree(len_ri);
4214:   PetscFree(rj_waits);
4215:   PetscFree(si_waits);
4216:   PetscFree(ri_waits);
4217:   PetscFree(buf_s);
4218:   PetscFree(status);

4220:   /* compute a local seq matrix in each processor */
4221:   /*----------------------------------------------*/
4222:   /* allocate bi array and free space for accumulating nonzero column info */
4223:   PetscMalloc((m+1)*sizeof(PetscInt),&bi);
4224:   bi[0] = 0;

4226:   /* create and initialize a linked list */
4227:   nlnk = N+1;
4228:   PetscLLCreate(N,N,nlnk,lnk,lnkbt);
4229: 
4230:   /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4231:   len = 0;
4232:   len  = ai[owners[rank+1]] - ai[owners[rank]];
4233:   PetscFreeSpaceGet((PetscInt)(2*len+1),&free_space);
4234:   current_space = free_space;

4236:   /* determine symbolic info for each local row */
4237:   PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
4238:   nextrow = buf_ri_k + merge->nrecv;
4239:   nextai  = nextrow + merge->nrecv;
4240:   for (k=0; k<merge->nrecv; k++){
4241:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4242:     nrows = *buf_ri_k[k];
4243:     nextrow[k]  = buf_ri_k[k] + 1;  /* next row number of k-th recved i-structure */
4244:     nextai[k]   = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure  */
4245:   }

4247:   MatPreallocateInitialize(comm,m,n,dnz,onz);
4248:   len = 0;
4249:   for (i=0;i<m;i++) {
4250:     bnzi   = 0;
4251:     /* add local non-zero cols of this proc's seqmat into lnk */
4252:     arow   = owners[rank] + i;
4253:     anzi   = ai[arow+1] - ai[arow];
4254:     aj     = a->j + ai[arow];
4255:     PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
4256:     bnzi += nlnk;
4257:     /* add received col data into lnk */
4258:     for (k=0; k<merge->nrecv; k++){ /* k-th received message */
4259:       if (i == *nextrow[k]) { /* i-th row */
4260:         anzi = *(nextai[k]+1) - *nextai[k];
4261:         aj   = buf_rj[k] + *nextai[k];
4262:         PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
4263:         bnzi += nlnk;
4264:         nextrow[k]++; nextai[k]++;
4265:       }
4266:     }
4267:     if (len < bnzi) len = bnzi;  /* =max(bnzi) */

4269:     /* if free space is not available, make more free space */
4270:     if (current_space->local_remaining<bnzi) {
4271:       PetscFreeSpaceGet(bnzi+current_space->total_array_size,&current_space);
4272:       nspacedouble++;
4273:     }
4274:     /* copy data into free space, then initialize lnk */
4275:     PetscLLClean(N,N,bnzi,lnk,current_space->array,lnkbt);
4276:     MatPreallocateSet(i+owners[rank],bnzi,current_space->array,dnz,onz);

4278:     current_space->array           += bnzi;
4279:     current_space->local_used      += bnzi;
4280:     current_space->local_remaining -= bnzi;
4281: 
4282:     bi[i+1] = bi[i] + bnzi;
4283:   }
4284: 
4285:   PetscFree(buf_ri_k);

4287:   PetscMalloc((bi[m]+1)*sizeof(PetscInt),&bj);
4288:   PetscFreeSpaceContiguous(&free_space,bj);
4289:   PetscLLDestroy(lnk,lnkbt);

4291:   /* create symbolic parallel matrix B_mpi */
4292:   /*---------------------------------------*/
4293:   MatCreate(comm,&B_mpi);
4294:   if (n==PETSC_DECIDE) {
4295:     MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,N);
4296:   } else {
4297:     MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
4298:   }
4299:   MatSetType(B_mpi,MATMPIAIJ);
4300:   MatMPIAIJSetPreallocation(B_mpi,0,dnz,0,onz);
4301:   MatPreallocateFinalize(dnz,onz);

4303:   /* B_mpi is not ready for use - assembly will be done by MatMerge_SeqsToMPINumeric() */
4304:   B_mpi->assembled     = PETSC_FALSE;
4305:   B_mpi->ops->destroy  = MatDestroy_MPIAIJ_SeqsToMPI;
4306:   merge->bi            = bi;
4307:   merge->bj            = bj;
4308:   merge->buf_ri        = buf_ri;
4309:   merge->buf_rj        = buf_rj;
4310:   merge->coi           = PETSC_NULL;
4311:   merge->coj           = PETSC_NULL;
4312:   merge->owners_co     = PETSC_NULL;

4314:   /* attach the supporting struct to B_mpi for reuse */
4315:   PetscContainerCreate(PETSC_COMM_SELF,&container);
4316:   PetscContainerSetPointer(container,merge);
4317:   PetscObjectCompose((PetscObject)B_mpi,"MatMergeSeqsToMPI",(PetscObject)container);
4318:   *mpimat = B_mpi;

4320:   PetscCommDestroy(&comm);
4321:   PetscLogEventEnd(MAT_Seqstompisym,seqmat,0,0,0);
4322:   return(0);
4323: }

4327: PetscErrorCode  MatMerge_SeqsToMPI(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,MatReuse scall,Mat *mpimat)
4328: {
4329:   PetscErrorCode   ierr;

4332:   PetscLogEventBegin(MAT_Seqstompi,seqmat,0,0,0);
4333:   if (scall == MAT_INITIAL_MATRIX){
4334:     MatMerge_SeqsToMPISymbolic(comm,seqmat,m,n,mpimat);
4335:   }
4336:   MatMerge_SeqsToMPINumeric(seqmat,*mpimat);
4337:   PetscLogEventEnd(MAT_Seqstompi,seqmat,0,0,0);
4338:   return(0);
4339: }

4343: /*@
4344:      MatGetLocalMat - Creates a SeqAIJ matrix by taking all its local rows

4346:     Not Collective

4348:    Input Parameters:
4349: +    A - the matrix 
4350: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 

4352:    Output Parameter:
4353: .    A_loc - the local sequential matrix generated

4355:     Level: developer

4357: @*/
4358: PetscErrorCode  MatGetLocalMat(Mat A,MatReuse scall,Mat *A_loc)
4359: {
4360:   PetscErrorCode  ierr;
4361:   Mat_MPIAIJ      *mpimat=(Mat_MPIAIJ*)A->data;
4362:   Mat_SeqAIJ      *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
4363:   PetscInt        *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,*cmap=mpimat->garray;
4364:   MatScalar       *aa=a->a,*ba=b->a,*cam;
4365:   PetscScalar     *ca;
4366:   PetscInt        am=A->rmap->n,i,j,k,cstart=A->cmap->rstart;
4367:   PetscInt        *ci,*cj,col,ncols_d,ncols_o,jo;

4370:   PetscLogEventBegin(MAT_Getlocalmat,A,0,0,0);
4371:   if (scall == MAT_INITIAL_MATRIX){
4372:     PetscMalloc((1+am)*sizeof(PetscInt),&ci);
4373:     ci[0] = 0;
4374:     for (i=0; i<am; i++){
4375:       ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
4376:     }
4377:     PetscMalloc((1+ci[am])*sizeof(PetscInt),&cj);
4378:     PetscMalloc((1+ci[am])*sizeof(PetscScalar),&ca);
4379:     k = 0;
4380:     for (i=0; i<am; i++) {
4381:       ncols_o = bi[i+1] - bi[i];
4382:       ncols_d = ai[i+1] - ai[i];
4383:       /* off-diagonal portion of A */
4384:       for (jo=0; jo<ncols_o; jo++) {
4385:         col = cmap[*bj];
4386:         if (col >= cstart) break;
4387:         cj[k]   = col; bj++;
4388:         ca[k++] = *ba++;
4389:       }
4390:       /* diagonal portion of A */
4391:       for (j=0; j<ncols_d; j++) {
4392:         cj[k]   = cstart + *aj++;
4393:         ca[k++] = *aa++;
4394:       }
4395:       /* off-diagonal portion of A */
4396:       for (j=jo; j<ncols_o; j++) {
4397:         cj[k]   = cmap[*bj++];
4398:         ca[k++] = *ba++;
4399:       }
4400:     }
4401:     /* put together the new matrix */
4402:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,A->cmap->N,ci,cj,ca,A_loc);
4403:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4404:     /* Since these are PETSc arrays, change flags to free them as necessary. */
4405:     mat          = (Mat_SeqAIJ*)(*A_loc)->data;
4406:     mat->free_a  = PETSC_TRUE;
4407:     mat->free_ij = PETSC_TRUE;
4408:     mat->nonew   = 0;
4409:   } else if (scall == MAT_REUSE_MATRIX){
4410:     mat=(Mat_SeqAIJ*)(*A_loc)->data;
4411:     ci = mat->i; cj = mat->j; cam = mat->a;
4412:     for (i=0; i<am; i++) {
4413:       /* off-diagonal portion of A */
4414:       ncols_o = bi[i+1] - bi[i];
4415:       for (jo=0; jo<ncols_o; jo++) {
4416:         col = cmap[*bj];
4417:         if (col >= cstart) break;
4418:         *cam++ = *ba++; bj++;
4419:       }
4420:       /* diagonal portion of A */
4421:       ncols_d = ai[i+1] - ai[i];
4422:       for (j=0; j<ncols_d; j++) *cam++ = *aa++;
4423:       /* off-diagonal portion of A */
4424:       for (j=jo; j<ncols_o; j++) {
4425:         *cam++ = *ba++; bj++;
4426:       }
4427:     }
4428:   } else {
4429:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
4430:   }

4432:   PetscLogEventEnd(MAT_Getlocalmat,A,0,0,0);
4433:   return(0);
4434: }

4438: /*@C
4439:      MatGetLocalMatCondensed - Creates a SeqAIJ matrix by taking all its local rows and NON-ZERO columns

4441:     Not Collective

4443:    Input Parameters:
4444: +    A - the matrix 
4445: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4446: -    row, col - index sets of rows and columns to extract (or PETSC_NULL)  

4448:    Output Parameter:
4449: .    A_loc - the local sequential matrix generated

4451:     Level: developer

4453: @*/
4454: PetscErrorCode  MatGetLocalMatCondensed(Mat A,MatReuse scall,IS *row,IS *col,Mat *A_loc)
4455: {
4456:   Mat_MPIAIJ        *a=(Mat_MPIAIJ*)A->data;
4457:   PetscErrorCode    ierr;
4458:   PetscInt          i,start,end,ncols,nzA,nzB,*cmap,imark,*idx;
4459:   IS                isrowa,iscola;
4460:   Mat               *aloc;

4463:   PetscLogEventBegin(MAT_Getlocalmatcondensed,A,0,0,0);
4464:   if (!row){
4465:     start = A->rmap->rstart; end = A->rmap->rend;
4466:     ISCreateStride(PETSC_COMM_SELF,end-start,start,1,&isrowa);
4467:   } else {
4468:     isrowa = *row;
4469:   }
4470:   if (!col){
4471:     start = A->cmap->rstart;
4472:     cmap  = a->garray;
4473:     nzA   = a->A->cmap->n;
4474:     nzB   = a->B->cmap->n;
4475:     PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
4476:     ncols = 0;
4477:     for (i=0; i<nzB; i++) {
4478:       if (cmap[i] < start) idx[ncols++] = cmap[i];
4479:       else break;
4480:     }
4481:     imark = i;
4482:     for (i=0; i<nzA; i++) idx[ncols++] = start + i;
4483:     for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i];
4484:     ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&iscola);
4485:     PetscFree(idx);
4486:   } else {
4487:     iscola = *col;
4488:   }
4489:   if (scall != MAT_INITIAL_MATRIX){
4490:     PetscMalloc(sizeof(Mat),&aloc);
4491:     aloc[0] = *A_loc;
4492:   }
4493:   MatGetSubMatrices(A,1,&isrowa,&iscola,scall,&aloc);
4494:   *A_loc = aloc[0];
4495:   PetscFree(aloc);
4496:   if (!row){
4497:     ISDestroy(isrowa);
4498:   }
4499:   if (!col){
4500:     ISDestroy(iscola);
4501:   }
4502:   PetscLogEventEnd(MAT_Getlocalmatcondensed,A,0,0,0);
4503:   return(0);
4504: }

4508: /*@C
4509:     MatGetBrowsOfAcols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A 

4511:     Collective on Mat

4513:    Input Parameters:
4514: +    A,B - the matrices in mpiaij format
4515: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4516: -    rowb, colb - index sets of rows and columns of B to extract (or PETSC_NULL)   

4518:    Output Parameter:
4519: +    rowb, colb - index sets of rows and columns of B to extract 
4520: .    brstart - row index of B_seq from which next B->rmap->n rows are taken from B's local rows
4521: -    B_seq - the sequential matrix generated

4523:     Level: developer

4525: @*/
4526: PetscErrorCode  MatGetBrowsOfAcols(Mat A,Mat B,MatReuse scall,IS *rowb,IS *colb,PetscInt *brstart,Mat *B_seq)
4527: {
4528:   Mat_MPIAIJ        *a=(Mat_MPIAIJ*)A->data;
4529:   PetscErrorCode    ierr;
4530:   PetscInt          *idx,i,start,ncols,nzA,nzB,*cmap,imark;
4531:   IS                isrowb,iscolb;
4532:   Mat               *bseq;
4533: 
4535:   if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend){
4536:     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);
4537:   }
4538:   PetscLogEventBegin(MAT_GetBrowsOfAcols,A,B,0,0);
4539: 
4540:   if (scall == MAT_INITIAL_MATRIX){
4541:     start = A->cmap->rstart;
4542:     cmap  = a->garray;
4543:     nzA   = a->A->cmap->n;
4544:     nzB   = a->B->cmap->n;
4545:     PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
4546:     ncols = 0;
4547:     for (i=0; i<nzB; i++) {  /* row < local row index */
4548:       if (cmap[i] < start) idx[ncols++] = cmap[i];
4549:       else break;
4550:     }
4551:     imark = i;
4552:     for (i=0; i<nzA; i++) idx[ncols++] = start + i;  /* local rows */
4553:     for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
4554:     ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&isrowb);
4555:     PetscFree(idx);
4556:     *brstart = imark;
4557:     ISCreateStride(PETSC_COMM_SELF,B->cmap->N,0,1,&iscolb);
4558:   } else {
4559:     if (!rowb || !colb) SETERRQ(PETSC_ERR_SUP,"IS rowb and colb must be provided for MAT_REUSE_MATRIX");
4560:     isrowb = *rowb; iscolb = *colb;
4561:     PetscMalloc(sizeof(Mat),&bseq);
4562:     bseq[0] = *B_seq;
4563:   }
4564:   MatGetSubMatrices(B,1,&isrowb,&iscolb,scall,&bseq);
4565:   *B_seq = bseq[0];
4566:   PetscFree(bseq);
4567:   if (!rowb){
4568:     ISDestroy(isrowb);
4569:   } else {
4570:     *rowb = isrowb;
4571:   }
4572:   if (!colb){
4573:     ISDestroy(iscolb);
4574:   } else {
4575:     *colb = iscolb;
4576:   }
4577:   PetscLogEventEnd(MAT_GetBrowsOfAcols,A,B,0,0);
4578:   return(0);
4579: }

4583: /*@C
4584:     MatGetBrowsOfAoCols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns
4585:     of the OFF-DIAGONAL portion of local A 

4587:     Collective on Mat

4589:    Input Parameters:
4590: +    A,B - the matrices in mpiaij format
4591: .    scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4592: .    startsj - starting point in B's sending and receiving j-arrays, saved for MAT_REUSE (or PETSC_NULL) 
4593: -    bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or PETSC_NULL) 

4595:    Output Parameter:
4596: +    B_oth - the sequential matrix generated

4598:     Level: developer

4600: @*/
4601: PetscErrorCode  MatGetBrowsOfAoCols(Mat A,Mat B,MatReuse scall,PetscInt **startsj,MatScalar **bufa_ptr,Mat *B_oth)
4602: {
4603:   VecScatter_MPI_General *gen_to,*gen_from;
4604:   PetscErrorCode         ierr;
4605:   Mat_MPIAIJ             *a=(Mat_MPIAIJ*)A->data;
4606:   Mat_SeqAIJ             *b_oth;
4607:   VecScatter             ctx=a->Mvctx;
4608:   MPI_Comm               comm=((PetscObject)ctx)->comm;
4609:   PetscMPIInt            *rprocs,*sprocs,tag=((PetscObject)ctx)->tag,rank;
4610:   PetscInt               *rowlen,*bufj,*bufJ,ncols,aBn=a->B->cmap->n,row,*b_othi,*b_othj;
4611:   PetscScalar            *rvalues,*svalues;
4612:   MatScalar              *b_otha,*bufa,*bufA;
4613:   PetscInt               i,j,k,l,ll,nrecvs,nsends,nrows,*srow,*rstarts,*rstartsj = 0,*sstarts,*sstartsj,len;
4614:   MPI_Request            *rwaits = PETSC_NULL,*swaits = PETSC_NULL;
4615:   MPI_Status             *sstatus,rstatus;
4616:   PetscMPIInt            jj;
4617:   PetscInt               *cols,sbs,rbs;
4618:   PetscScalar            *vals;

4621:   if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend){
4622:     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);
4623:   }
4624:   PetscLogEventBegin(MAT_GetBrowsOfAocols,A,B,0,0);
4625:   MPI_Comm_rank(comm,&rank);

4627:   gen_to   = (VecScatter_MPI_General*)ctx->todata;
4628:   gen_from = (VecScatter_MPI_General*)ctx->fromdata;
4629:   rvalues  = gen_from->values; /* holds the length of receiving row */
4630:   svalues  = gen_to->values;   /* holds the length of sending row */
4631:   nrecvs   = gen_from->n;
4632:   nsends   = gen_to->n;

4634:   PetscMalloc2(nrecvs,MPI_Request,&rwaits,nsends,MPI_Request,&swaits);
4635:   srow     = gen_to->indices;   /* local row index to be sent */
4636:   sstarts  = gen_to->starts;
4637:   sprocs   = gen_to->procs;
4638:   sstatus  = gen_to->sstatus;
4639:   sbs      = gen_to->bs;
4640:   rstarts  = gen_from->starts;
4641:   rprocs   = gen_from->procs;
4642:   rbs      = gen_from->bs;

4644:   if (!startsj || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
4645:   if (scall == MAT_INITIAL_MATRIX){
4646:     /* i-array */
4647:     /*---------*/
4648:     /*  post receives */
4649:     for (i=0; i<nrecvs; i++){
4650:       rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
4651:       nrows = (rstarts[i+1]-rstarts[i])*rbs; /* num of indices to be received */
4652:       MPI_Irecv(rowlen,nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
4653:     }

4655:     /* pack the outgoing message */
4656:     PetscMalloc((nsends+nrecvs+3)*sizeof(PetscInt),&sstartsj);
4657:     rstartsj = sstartsj + nsends +1;
4658:     sstartsj[0] = 0;  rstartsj[0] = 0;
4659:     len = 0; /* total length of j or a array to be sent */
4660:     k = 0;
4661:     for (i=0; i<nsends; i++){
4662:       rowlen = (PetscInt*)svalues + sstarts[i]*sbs;
4663:       nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4664:       for (j=0; j<nrows; j++) {
4665:         row = srow[k] + B->rmap->range[rank]; /* global row idx */
4666:         for (l=0; l<sbs; l++){
4667:           MatGetRow_MPIAIJ(B,row+l,&ncols,PETSC_NULL,PETSC_NULL); /* rowlength */
4668:           rowlen[j*sbs+l] = ncols;
4669:           len += ncols;
4670:           MatRestoreRow_MPIAIJ(B,row+l,&ncols,PETSC_NULL,PETSC_NULL);
4671:         }
4672:         k++;
4673:       }
4674:       MPI_Isend(rowlen,nrows*sbs,MPIU_INT,sprocs[i],tag,comm,swaits+i);
4675:       sstartsj[i+1] = len;  /* starting point of (i+1)-th outgoing msg in bufj and bufa */
4676:     }
4677:     /* recvs and sends of i-array are completed */
4678:     i = nrecvs;
4679:     while (i--) {
4680:       MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4681:     }
4682:     if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}

4684:     /* allocate buffers for sending j and a arrays */
4685:     PetscMalloc((len+1)*sizeof(PetscInt),&bufj);
4686:     PetscMalloc((len+1)*sizeof(PetscScalar),&bufa);

4688:     /* create i-array of B_oth */
4689:     PetscMalloc((aBn+2)*sizeof(PetscInt),&b_othi);
4690:     b_othi[0] = 0;
4691:     len = 0; /* total length of j or a array to be received */
4692:     k = 0;
4693:     for (i=0; i<nrecvs; i++){
4694:       rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
4695:       nrows = rbs*(rstarts[i+1]-rstarts[i]); /* num of rows to be recieved */
4696:       for (j=0; j<nrows; j++) {
4697:         b_othi[k+1] = b_othi[k] + rowlen[j];
4698:         len += rowlen[j]; k++;
4699:       }
4700:       rstartsj[i+1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
4701:     }

4703:     /* allocate space for j and a arrrays of B_oth */
4704:     PetscMalloc((b_othi[aBn]+1)*sizeof(PetscInt),&b_othj);
4705:     PetscMalloc((b_othi[aBn]+1)*sizeof(MatScalar),&b_otha);

4707:     /* j-array */
4708:     /*---------*/
4709:     /*  post receives of j-array */
4710:     for (i=0; i<nrecvs; i++){
4711:       nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
4712:       MPI_Irecv(b_othj+rstartsj[i],nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
4713:     }

4715:     /* pack the outgoing message j-array */
4716:     k = 0;
4717:     for (i=0; i<nsends; i++){
4718:       nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4719:       bufJ = bufj+sstartsj[i];
4720:       for (j=0; j<nrows; j++) {
4721:         row  = srow[k++] + B->rmap->range[rank]; /* global row idx */
4722:         for (ll=0; ll<sbs; ll++){
4723:           MatGetRow_MPIAIJ(B,row+ll,&ncols,&cols,PETSC_NULL);
4724:           for (l=0; l<ncols; l++){
4725:             *bufJ++ = cols[l];
4726:           }
4727:           MatRestoreRow_MPIAIJ(B,row+ll,&ncols,&cols,PETSC_NULL);
4728:         }
4729:       }
4730:       MPI_Isend(bufj+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_INT,sprocs[i],tag,comm,swaits+i);
4731:     }

4733:     /* recvs and sends of j-array are completed */
4734:     i = nrecvs;
4735:     while (i--) {
4736:       MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4737:     }
4738:     if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4739:   } else if (scall == MAT_REUSE_MATRIX){
4740:     sstartsj = *startsj;
4741:     rstartsj = sstartsj + nsends +1;
4742:     bufa     = *bufa_ptr;
4743:     b_oth    = (Mat_SeqAIJ*)(*B_oth)->data;
4744:     b_otha   = b_oth->a;
4745:   } else {
4746:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not posses an object container");
4747:   }

4749:   /* a-array */
4750:   /*---------*/
4751:   /*  post receives of a-array */
4752:   for (i=0; i<nrecvs; i++){
4753:     nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
4754:     MPI_Irecv(b_otha+rstartsj[i],nrows,MPIU_SCALAR,rprocs[i],tag,comm,rwaits+i);
4755:   }

4757:   /* pack the outgoing message a-array */
4758:   k = 0;
4759:   for (i=0; i<nsends; i++){
4760:     nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4761:     bufA = bufa+sstartsj[i];
4762:     for (j=0; j<nrows; j++) {
4763:       row  = srow[k++] + B->rmap->range[rank]; /* global row idx */
4764:       for (ll=0; ll<sbs; ll++){
4765:         MatGetRow_MPIAIJ(B,row+ll,&ncols,PETSC_NULL,&vals);
4766:         for (l=0; l<ncols; l++){
4767:           *bufA++ = vals[l];
4768:         }
4769:         MatRestoreRow_MPIAIJ(B,row+ll,&ncols,PETSC_NULL,&vals);
4770:       }
4771:     }
4772:     MPI_Isend(bufa+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_SCALAR,sprocs[i],tag,comm,swaits+i);
4773:   }
4774:   /* recvs and sends of a-array are completed */
4775:   i = nrecvs;
4776:   while (i--) {
4777:     MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4778:   }
4779:   if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4780:   PetscFree2(rwaits,swaits);

4782:   if (scall == MAT_INITIAL_MATRIX){
4783:     /* put together the new matrix */
4784:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,aBn,B->cmap->N,b_othi,b_othj,b_otha,B_oth);

4786:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4787:     /* Since these are PETSc arrays, change flags to free them as necessary. */
4788:     b_oth          = (Mat_SeqAIJ *)(*B_oth)->data;
4789:     b_oth->free_a  = PETSC_TRUE;
4790:     b_oth->free_ij = PETSC_TRUE;
4791:     b_oth->nonew   = 0;

4793:     PetscFree(bufj);
4794:     if (!startsj || !bufa_ptr){
4795:       PetscFree(sstartsj);
4796:       PetscFree(bufa_ptr);
4797:     } else {
4798:       *startsj  = sstartsj;
4799:       *bufa_ptr = bufa;
4800:     }
4801:   }
4802:   PetscLogEventEnd(MAT_GetBrowsOfAocols,A,B,0,0);
4803:   return(0);
4804: }

4808: /*@C
4809:   MatGetCommunicationStructs - Provides access to the communication structures used in matrix-vector multiplication.

4811:   Not Collective

4813:   Input Parameters:
4814: . A - The matrix in mpiaij format

4816:   Output Parameter:
4817: + lvec - The local vector holding off-process values from the argument to a matrix-vector product
4818: . colmap - A map from global column index to local index into lvec
4819: - multScatter - A scatter from the argument of a matrix-vector product to lvec

4821:   Level: developer

4823: @*/
4824: #if defined (PETSC_USE_CTABLE)
4825: PetscErrorCode  MatGetCommunicationStructs(Mat A, Vec *lvec, PetscTable *colmap, VecScatter *multScatter)
4826: #else
4827: PetscErrorCode  MatGetCommunicationStructs(Mat A, Vec *lvec, PetscInt *colmap[], VecScatter *multScatter)
4828: #endif
4829: {
4830:   Mat_MPIAIJ *a;

4837:   a = (Mat_MPIAIJ *) A->data;
4838:   if (lvec) *lvec = a->lvec;
4839:   if (colmap) *colmap = a->colmap;
4840:   if (multScatter) *multScatter = a->Mvctx;
4841:   return(0);
4842: }


4849:  #include ../src/mat/impls/dense/mpi/mpidense.h

4853: /*
4854:     Computes (B'*A')' since computing B*A directly is untenable

4856:                n                       p                          p
4857:         (              )       (              )         (                  )
4858:       m (      A       )  *  n (       B      )   =   m (         C        )
4859:         (              )       (              )         (                  )

4861: */
4862: PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A,Mat B,Mat C)
4863: {
4864:   PetscErrorCode     ierr;
4865:   Mat                At,Bt,Ct;

4868:   MatTranspose(A,MAT_INITIAL_MATRIX,&At);
4869:   MatTranspose(B,MAT_INITIAL_MATRIX,&Bt);
4870:   MatMatMult(Bt,At,MAT_INITIAL_MATRIX,1.0,&Ct);
4871:   MatDestroy(At);
4872:   MatDestroy(Bt);
4873:   MatTranspose(Ct,MAT_REUSE_MATRIX,&C);
4874:   MatDestroy(Ct);
4875:   return(0);
4876: }

4880: PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
4881: {
4883:   PetscInt       m=A->rmap->n,n=B->cmap->n;
4884:   Mat            Cmat;

4887:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"A->cmap->n %d != B->rmap->n %d\n",A->cmap->n,B->rmap->n);
4888:   MatCreate(((PetscObject)A)->comm,&Cmat);
4889:   MatSetSizes(Cmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
4890:   MatSetType(Cmat,MATMPIDENSE);
4891:   MatMPIDenseSetPreallocation(Cmat,PETSC_NULL);
4892:   MatAssemblyBegin(Cmat,MAT_FINAL_ASSEMBLY);
4893:   MatAssemblyEnd(Cmat,MAT_FINAL_ASSEMBLY);
4894:   *C   = Cmat;
4895:   return(0);
4896: }

4898: /* ----------------------------------------------------------------*/
4901: PetscErrorCode MatMatMult_MPIDense_MPIAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
4902: {

4906:   if (scall == MAT_INITIAL_MATRIX){
4907:     MatMatMultSymbolic_MPIDense_MPIAIJ(A,B,fill,C);
4908:   }
4909:   MatMatMultNumeric_MPIDense_MPIAIJ(A,B,*C);
4910:   return(0);
4911: }

4914: #if defined(PETSC_HAVE_MUMPS)
4916: #endif
4917: #if defined(PETSC_HAVE_PASTIX)
4919: #endif
4920: #if defined(PETSC_HAVE_SUPERLU_DIST)
4922: #endif
4923: #if defined(PETSC_HAVE_SPOOLES)
4925: #endif

4928: /*MC
4929:    MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.

4931:    Options Database Keys:
4932: . -mat_type mpiaij - sets the matrix type to "mpiaij" during a call to MatSetFromOptions()

4934:   Level: beginner

4936: .seealso: MatCreateMPIAIJ()
4937: M*/

4942: PetscErrorCode  MatCreate_MPIAIJ(Mat B)
4943: {
4944:   Mat_MPIAIJ     *b;
4946:   PetscMPIInt    size;

4949:   MPI_Comm_size(((PetscObject)B)->comm,&size);

4951:   PetscNewLog(B,Mat_MPIAIJ,&b);
4952:   B->data         = (void*)b;
4953:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4954:   B->rmap->bs      = 1;
4955:   B->assembled    = PETSC_FALSE;
4956:   B->mapping      = 0;

4958:   B->insertmode      = NOT_SET_VALUES;
4959:   b->size            = size;
4960:   MPI_Comm_rank(((PetscObject)B)->comm,&b->rank);

4962:   /* build cache for off array entries formed */
4963:   MatStashCreate_Private(((PetscObject)B)->comm,1,&B->stash);
4964:   b->donotstash  = PETSC_FALSE;
4965:   b->colmap      = 0;
4966:   b->garray      = 0;
4967:   b->roworiented = PETSC_TRUE;

4969:   /* stuff used for matrix vector multiply */
4970:   b->lvec      = PETSC_NULL;
4971:   b->Mvctx     = PETSC_NULL;

4973:   /* stuff for MatGetRow() */
4974:   b->rowindices   = 0;
4975:   b->rowvalues    = 0;
4976:   b->getrowactive = PETSC_FALSE;

4978: #if defined(PETSC_HAVE_SPOOLES)
4979:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpiaij_spooles_C",
4980:                                      "MatGetFactor_mpiaij_spooles",
4981:                                      MatGetFactor_mpiaij_spooles);
4982: #endif
4983: #if defined(PETSC_HAVE_MUMPS)
4984:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpiaij_mumps_C",
4985:                                      "MatGetFactor_mpiaij_mumps",
4986:                                      MatGetFactor_mpiaij_mumps);
4987: #endif
4988: #if defined(PETSC_HAVE_PASTIX)
4989:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpiaij_pastix_C",
4990:                                            "MatGetFactor_mpiaij_pastix",
4991:                                            MatGetFactor_mpiaij_pastix);
4992: #endif
4993: #if defined(PETSC_HAVE_SUPERLU_DIST)
4994:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpiaij_superlu_dist_C",
4995:                                      "MatGetFactor_mpiaij_superlu_dist",
4996:                                      MatGetFactor_mpiaij_superlu_dist);
4997: #endif
4998:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
4999:                                      "MatStoreValues_MPIAIJ",
5000:                                      MatStoreValues_MPIAIJ);
5001:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
5002:                                      "MatRetrieveValues_MPIAIJ",
5003:                                      MatRetrieveValues_MPIAIJ);
5004:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
5005:                                      "MatGetDiagonalBlock_MPIAIJ",
5006:                                      MatGetDiagonalBlock_MPIAIJ);
5007:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
5008:                                      "MatIsTranspose_MPIAIJ",
5009:                                      MatIsTranspose_MPIAIJ);
5010:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocation_C",
5011:                                      "MatMPIAIJSetPreallocation_MPIAIJ",
5012:                                      MatMPIAIJSetPreallocation_MPIAIJ);
5013:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",
5014:                                      "MatMPIAIJSetPreallocationCSR_MPIAIJ",
5015:                                      MatMPIAIJSetPreallocationCSR_MPIAIJ);
5016:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
5017:                                      "MatDiagonalScaleLocal_MPIAIJ",
5018:                                      MatDiagonalScaleLocal_MPIAIJ);
5019:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpicsrperm_C",
5020:                                      "MatConvert_MPIAIJ_MPICSRPERM",
5021:                                       MatConvert_MPIAIJ_MPICSRPERM);
5022:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpicrl_C",
5023:                                      "MatConvert_MPIAIJ_MPICRL",
5024:                                       MatConvert_MPIAIJ_MPICRL);
5025:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMult_mpidense_mpiaij_C",
5026:                                      "MatMatMult_MPIDense_MPIAIJ",
5027:                                       MatMatMult_MPIDense_MPIAIJ);
5028:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultSymbolic_mpidense_mpiaij_C",
5029:                                      "MatMatMultSymbolic_MPIDense_MPIAIJ",
5030:                                       MatMatMultSymbolic_MPIDense_MPIAIJ);
5031:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultNumeric_mpidense_mpiaij_C",
5032:                                      "MatMatMultNumeric_MPIDense_MPIAIJ",
5033:                                       MatMatMultNumeric_MPIDense_MPIAIJ);
5034:   PetscObjectChangeTypeName((PetscObject)B,MATMPIAIJ);
5035:   return(0);
5036: }

5041: /*@
5042:      MatCreateMPIAIJWithSplitArrays - creates a MPI AIJ matrix using arrays that contain the "diagonal"
5043:          and "off-diagonal" part of the matrix in CSR format.

5045:    Collective on MPI_Comm

5047:    Input Parameters:
5048: +  comm - MPI communicator
5049: .  m - number of local rows (Cannot be PETSC_DECIDE)
5050: .  n - This value should be the same as the local size used in creating the 
5051:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
5052:        calculated if N is given) For square matrices n is almost always m.
5053: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
5054: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
5055: .   i - row indices for "diagonal" portion of matrix
5056: .   j - column indices
5057: .   a - matrix values
5058: .   oi - row indices for "off-diagonal" portion of matrix
5059: .   oj - column indices
5060: -   oa - matrix values

5062:    Output Parameter:
5063: .   mat - the matrix

5065:    Level: advanced

5067:    Notes:
5068:        The i, j, and a arrays ARE NOT copied by this routine into the internal format used by PETSc.

5070:        The i and j indices are 0 based
5071:  
5072:        See MatCreateMPIAIJ() for the definition of "diagonal" and "off-diagonal" portion of the matrix


5075: .keywords: matrix, aij, compressed row, sparse, parallel

5077: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
5078:           MPIAIJ, MatCreateMPIAIJ(), MatCreateMPIAIJWithArrays()
5079: @*/
5080: PetscErrorCode  MatCreateMPIAIJWithSplitArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt i[],PetscInt j[],PetscScalar a[],
5081:                                                                 PetscInt oi[], PetscInt oj[],PetscScalar oa[],Mat *mat)
5082: {
5084:   Mat_MPIAIJ     *maij;

5087:   if (m < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
5088:   if (i[0]) {
5089:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
5090:   }
5091:   if (oi[0]) {
5092:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"oi (row indices) must start with 0");
5093:   }
5094:   MatCreate(comm,mat);
5095:   MatSetSizes(*mat,m,n,M,N);
5096:   MatSetType(*mat,MATMPIAIJ);
5097:   maij = (Mat_MPIAIJ*) (*mat)->data;
5098:   maij->donotstash     = PETSC_TRUE;
5099:   (*mat)->preallocated = PETSC_TRUE;

5101:   PetscMapSetBlockSize((*mat)->rmap,1);
5102:   PetscMapSetBlockSize((*mat)->cmap,1);
5103:   PetscMapSetUp((*mat)->rmap);
5104:   PetscMapSetUp((*mat)->cmap);

5106:   MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,i,j,a,&maij->A);
5107:   MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,(*mat)->cmap->N,oi,oj,oa,&maij->B);

5109:   MatAssemblyBegin(maij->A,MAT_FINAL_ASSEMBLY);
5110:   MatAssemblyEnd(maij->A,MAT_FINAL_ASSEMBLY);
5111:   MatAssemblyBegin(maij->B,MAT_FINAL_ASSEMBLY);
5112:   MatAssemblyEnd(maij->B,MAT_FINAL_ASSEMBLY);

5114:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
5115:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
5116:   return(0);
5117: }

5119: /*
5120:     Special version for direct calls from Fortran 
5121: */
5122: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5123: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
5124: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5125: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
5126: #endif

5128: /* Change these macros so can be used in void function */
5129: #undef CHKERRQ
5130: #define CHKERRQ(ierr) CHKERRABORT(((PetscObject)mat)->comm,ierr) 
5131: #undef SETERRQ2
5132: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(((PetscObject)mat)->comm,ierr) 
5133: #undef SETERRQ
5134: #define SETERRQ(ierr,b) CHKERRABORT(((PetscObject)mat)->comm,ierr) 

5139: void PETSC_STDCALL matsetvaluesmpiaij_(Mat *mmat,PetscInt *mm,const PetscInt im[],PetscInt *mn,const PetscInt in[],const PetscScalar v[],InsertMode *maddv,PetscErrorCode *_ierr)
5140: {
5141:   Mat             mat = *mmat;
5142:   PetscInt        m = *mm, n = *mn;
5143:   InsertMode      addv = *maddv;
5144:   Mat_MPIAIJ      *aij = (Mat_MPIAIJ*)mat->data;
5145:   PetscScalar     value;
5146:   PetscErrorCode  ierr;

5148:   MatPreallocated(mat);
5149:   if (mat->insertmode == NOT_SET_VALUES) {
5150:     mat->insertmode = addv;
5151:   }
5152: #if defined(PETSC_USE_DEBUG)
5153:   else if (mat->insertmode != addv) {
5154:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
5155:   }
5156: #endif
5157:   {
5158:   PetscInt        i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
5159:   PetscInt        cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
5160:   PetscTruth      roworiented = aij->roworiented;

5162:   /* Some Variables required in the macro */
5163:   Mat             A = aij->A;
5164:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
5165:   PetscInt        *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
5166:   MatScalar       *aa = a->a;
5167:   PetscTruth      ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES))?PETSC_TRUE:PETSC_FALSE);
5168:   Mat             B = aij->B;
5169:   Mat_SeqAIJ      *b = (Mat_SeqAIJ*)B->data;
5170:   PetscInt        *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap->n,am = aij->A->rmap->n;
5171:   MatScalar       *ba = b->a;

5173:   PetscInt        *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
5174:   PetscInt        nonew = a->nonew;
5175:   MatScalar       *ap1,*ap2;

5178:   for (i=0; i<m; i++) {
5179:     if (im[i] < 0) continue;
5180: #if defined(PETSC_USE_DEBUG)
5181:     if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
5182: #endif
5183:     if (im[i] >= rstart && im[i] < rend) {
5184:       row      = im[i] - rstart;
5185:       lastcol1 = -1;
5186:       rp1      = aj + ai[row];
5187:       ap1      = aa + ai[row];
5188:       rmax1    = aimax[row];
5189:       nrow1    = ailen[row];
5190:       low1     = 0;
5191:       high1    = nrow1;
5192:       lastcol2 = -1;
5193:       rp2      = bj + bi[row];
5194:       ap2      = ba + bi[row];
5195:       rmax2    = bimax[row];
5196:       nrow2    = bilen[row];
5197:       low2     = 0;
5198:       high2    = nrow2;

5200:       for (j=0; j<n; j++) {
5201:         if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
5202:         if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
5203:         if (in[j] >= cstart && in[j] < cend){
5204:           col = in[j] - cstart;
5205:           MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
5206:         } else if (in[j] < 0) continue;
5207: #if defined(PETSC_USE_DEBUG)
5208:         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);}
5209: #endif
5210:         else {
5211:           if (mat->was_assembled) {
5212:             if (!aij->colmap) {
5213:               CreateColmap_MPIAIJ_Private(mat);
5214:             }
5215: #if defined (PETSC_USE_CTABLE)
5216:             PetscTableFind(aij->colmap,in[j]+1,&col);
5217:             col--;
5218: #else
5219:             col = aij->colmap[in[j]] - 1;
5220: #endif
5221:             if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
5222:               DisAssemble_MPIAIJ(mat);
5223:               col =  in[j];
5224:               /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
5225:               B = aij->B;
5226:               b = (Mat_SeqAIJ*)B->data;
5227:               bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
5228:               rp2      = bj + bi[row];
5229:               ap2      = ba + bi[row];
5230:               rmax2    = bimax[row];
5231:               nrow2    = bilen[row];
5232:               low2     = 0;
5233:               high2    = nrow2;
5234:               bm       = aij->B->rmap->n;
5235:               ba = b->a;
5236:             }
5237:           } else col = in[j];
5238:           MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
5239:         }
5240:       }
5241:     } else {
5242:       if (!aij->donotstash) {
5243:         if (roworiented) {
5244:           if (ignorezeroentries && v[i*n] == 0.0) continue;
5245:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
5246:         } else {
5247:           if (ignorezeroentries && v[i] == 0.0) continue;
5248:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
5249:         }
5250:       }
5251:     }
5252:   }}
5253:   PetscFunctionReturnVoid();
5254: }