Actual source code: mpibaij.c

 2:  #include src/mat/impls/baij/mpi/mpibaij.h

  4: EXTERN PetscErrorCode MatSetUpMultiply_MPIBAIJ(Mat);
  5: EXTERN PetscErrorCode DisAssemble_MPIBAIJ(Mat);
  6: EXTERN PetscErrorCode MatIncreaseOverlap_MPIBAIJ(Mat,PetscInt,IS[],PetscInt);
  7: EXTERN PetscErrorCode MatGetSubMatrices_MPIBAIJ(Mat,PetscInt,const IS[],const IS[],MatReuse,Mat *[]);
  8: EXTERN PetscErrorCode MatGetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt [],PetscScalar []);
  9: EXTERN PetscErrorCode MatSetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt [],const PetscScalar [],InsertMode);
 10: EXTERN PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
 11: EXTERN PetscErrorCode MatGetRow_SeqBAIJ(Mat,PetscInt,PetscInt*,PetscInt*[],PetscScalar*[]);
 12: EXTERN PetscErrorCode MatRestoreRow_SeqBAIJ(Mat,PetscInt,PetscInt*,PetscInt*[],PetscScalar*[]);
 13: EXTERN PetscErrorCode MatPrintHelp_SeqBAIJ(Mat);
 14: EXTERN PetscErrorCode MatZeroRows_SeqBAIJ(Mat,IS,const PetscScalar*);

 16: /*  UGLY, ugly, ugly
 17:    When MatScalar == PetscScalar the function MatSetValuesBlocked_MPIBAIJ_MatScalar() does 
 18:    not exist. Otherwise ..._MatScalar() takes matrix elements in single precision and 
 19:    inserts them into the single precision data structure. The function MatSetValuesBlocked_MPIBAIJ()
 20:    converts the entries into single precision and then calls ..._MatScalar() to put them
 21:    into the single precision data structures.
 22: */
 23: #if defined(PETSC_USE_MAT_SINGLE)
 24: EXTERN PetscErrorCode MatSetValuesBlocked_SeqBAIJ_MatScalar(Mat,PetscInt,const PetscInt*,PetscInt,const PetscInt*,const MatScalar*,InsertMode);
 25: EXTERN PetscErrorCode MatSetValues_MPIBAIJ_MatScalar(Mat,PetscInt,const PetscInt*,PetscInt,const PetscInt*,const MatScalar*,InsertMode);
 26: EXTERN PetscErrorCode MatSetValuesBlocked_MPIBAIJ_MatScalar(Mat,PetscInt,const PetscInt*,PetscInt,const PetscInt*,const MatScalar*,InsertMode);
 27: EXTERN PetscErrorCode MatSetValues_MPIBAIJ_HT_MatScalar(Mat,PetscInt,const PetscInt*,PetscInt,const PetscInt*,const MatScalar*,InsertMode);
 28: EXTERN PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(Mat,PetscInt,const PetscInt*,PetscInt,const PetscInt*,const MatScalar*,InsertMode);
 29: #else
 30: #define MatSetValuesBlocked_SeqBAIJ_MatScalar      MatSetValuesBlocked_SeqBAIJ
 31: #define MatSetValues_MPIBAIJ_MatScalar             MatSetValues_MPIBAIJ
 32: #define MatSetValuesBlocked_MPIBAIJ_MatScalar      MatSetValuesBlocked_MPIBAIJ
 33: #define MatSetValues_MPIBAIJ_HT_MatScalar          MatSetValues_MPIBAIJ_HT
 34: #define MatSetValuesBlocked_MPIBAIJ_HT_MatScalar   MatSetValuesBlocked_MPIBAIJ_HT
 35: #endif

 39: PetscErrorCode MatGetRowMax_MPIBAIJ(Mat A,Vec v)
 40: {
 41:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
 43:   PetscInt       i;
 44:   PetscScalar    *va,*vb;
 45:   Vec            vtmp;

 48: 
 49:   MatGetRowMax(a->A,v);
 50:   VecGetArray(v,&va);

 52:   VecCreateSeq(PETSC_COMM_SELF,A->m,&vtmp);
 53:   MatGetRowMax(a->B,vtmp);
 54:   VecGetArray(vtmp,&vb);

 56:   for (i=0; i<A->m; i++){
 57:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) va[i] = vb[i];
 58:   }

 60:   VecRestoreArray(v,&va);
 61:   VecRestoreArray(vtmp,&vb);
 62:   VecDestroy(vtmp);
 63: 
 64:   return(0);
 65: }

 70: PetscErrorCode MatStoreValues_MPIBAIJ(Mat mat)
 71: {
 72:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ *)mat->data;

 76:   MatStoreValues(aij->A);
 77:   MatStoreValues(aij->B);
 78:   return(0);
 79: }

 85: PetscErrorCode MatRetrieveValues_MPIBAIJ(Mat mat)
 86: {
 87:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ *)mat->data;

 91:   MatRetrieveValues(aij->A);
 92:   MatRetrieveValues(aij->B);
 93:   return(0);
 94: }

 97: /* 
 98:      Local utility routine that creates a mapping from the global column 
 99:    number to the local number in the off-diagonal part of the local 
100:    storage of the matrix.  This is done in a non scable way since the 
101:    length of colmap equals the global matrix length. 
102: */
105: PetscErrorCode CreateColmap_MPIBAIJ_Private(Mat mat)
106: {
107:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
108:   Mat_SeqBAIJ    *B = (Mat_SeqBAIJ*)baij->B->data;
110:   PetscInt       nbs = B->nbs,i,bs=mat->bs;

113: #if defined (PETSC_USE_CTABLE)
114:   PetscTableCreate(baij->nbs,&baij->colmap);
115:   for (i=0; i<nbs; i++){
116:     PetscTableAdd(baij->colmap,baij->garray[i]+1,i*bs+1);
117:   }
118: #else
119:   PetscMalloc((baij->Nbs+1)*sizeof(PetscInt),&baij->colmap);
120:   PetscLogObjectMemory(mat,baij->Nbs*sizeof(PetscInt));
121:   PetscMemzero(baij->colmap,baij->Nbs*sizeof(PetscInt));
122:   for (i=0; i<nbs; i++) baij->colmap[baij->garray[i]] = i*bs+1;
123: #endif
124:   return(0);
125: }

127: #define CHUNKSIZE  10

129: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv) \
130: { \
131:  \
132:     brow = row/bs;  \
133:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
134:     rmax = aimax[brow]; nrow = ailen[brow]; \
135:       bcol = col/bs; \
136:       ridx = row % bs; cidx = col % bs; \
137:       low = 0; high = nrow; \
138:       while (high-low > 3) { \
139:         t = (low+high)/2; \
140:         if (rp[t] > bcol) high = t; \
141:         else              low  = t; \
142:       } \
143:       for (_i=low; _i<high; _i++) { \
144:         if (rp[_i] > bcol) break; \
145:         if (rp[_i] == bcol) { \
146:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
147:           if (addv == ADD_VALUES) *bap += value;  \
148:           else                    *bap  = value;  \
149:           goto a_noinsert; \
150:         } \
151:       } \
152:       if (a->nonew == 1) goto a_noinsert; \
153:       else if (a->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
154:       if (nrow >= rmax) { \
155:         /* there is no extra room in row, therefore enlarge */ \
156:         PetscInt       new_nz = ai[a->mbs] + CHUNKSIZE,len,*new_i,*new_j; \
157:         MatScalar *new_a; \
158:  \
159:         if (a->nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col); \
160:  \
161:         /* malloc new storage space */ \
162:         len     = new_nz*(sizeof(PetscInt)+bs2*sizeof(MatScalar))+(a->mbs+1)*sizeof(PetscInt); \
163:         PetscMalloc(len,&new_a); \
164:         new_j   = (PetscInt*)(new_a + bs2*new_nz); \
165:         new_i   = new_j + new_nz; \
166:  \
167:         /* copy over old data into new slots */ \
168:         for (ii=0; ii<brow+1; ii++) {new_i[ii] = ai[ii];} \
169:         for (ii=brow+1; ii<a->mbs+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;} \
170:         PetscMemcpy(new_j,aj,(ai[brow]+nrow)*sizeof(PetscInt)); \
171:         len = (new_nz - CHUNKSIZE - ai[brow] - nrow); \
172:         PetscMemcpy(new_j+ai[brow]+nrow+CHUNKSIZE,aj+ai[brow]+nrow,len*sizeof(PetscInt)); \
173:         PetscMemcpy(new_a,aa,(ai[brow]+nrow)*bs2*sizeof(MatScalar)); \
174:         PetscMemzero(new_a+bs2*(ai[brow]+nrow),bs2*CHUNKSIZE*sizeof(PetscScalar)); \
175:         PetscMemcpy(new_a+bs2*(ai[brow]+nrow+CHUNKSIZE), \
176:                     aa+bs2*(ai[brow]+nrow),bs2*len*sizeof(MatScalar));  \
177:         /* free up old matrix storage */ \
178:         PetscFree(a->a);  \
179:         if (!a->singlemalloc) { \
180:           PetscFree(a->i); \
181:           PetscFree(a->j);\
182:         } \
183:         aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;  \
184:         a->singlemalloc = PETSC_TRUE; \
185:  \
186:         rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
187:         rmax = aimax[brow] = aimax[brow] + CHUNKSIZE; \
188:         PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(PetscInt) + bs2*sizeof(MatScalar))); \
189:         a->maxnz += bs2*CHUNKSIZE; \
190:         a->reallocs++; \
191:         a->nz++; \
192:       } \
193:       N = nrow++ - 1;  \
194:       /* shift up all the later entries in this row */ \
195:       for (ii=N; ii>=_i; ii--) { \
196:         rp[ii+1] = rp[ii]; \
197:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
198:       } \
199:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  \
200:       rp[_i]                      = bcol;  \
201:       ap[bs2*_i + bs*cidx + ridx] = value;  \
202:       a_noinsert:; \
203:     ailen[brow] = nrow; \
204: } 

206: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv) \
207: { \
208:     brow = row/bs;  \
209:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
210:     rmax = bimax[brow]; nrow = bilen[brow]; \
211:       bcol = col/bs; \
212:       ridx = row % bs; cidx = col % bs; \
213:       low = 0; high = nrow; \
214:       while (high-low > 3) { \
215:         t = (low+high)/2; \
216:         if (rp[t] > bcol) high = t; \
217:         else              low  = t; \
218:       } \
219:       for (_i=low; _i<high; _i++) { \
220:         if (rp[_i] > bcol) break; \
221:         if (rp[_i] == bcol) { \
222:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
223:           if (addv == ADD_VALUES) *bap += value;  \
224:           else                    *bap  = value;  \
225:           goto b_noinsert; \
226:         } \
227:       } \
228:       if (b->nonew == 1) goto b_noinsert; \
229:       else if (b->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
230:       if (nrow >= rmax) { \
231:         /* there is no extra room in row, therefore enlarge */ \
232:         PetscInt       new_nz = bi[b->mbs] + CHUNKSIZE,len,*new_i,*new_j; \
233:         MatScalar *new_a; \
234:  \
235:         if (b->nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col); \
236:  \
237:         /* malloc new storage space */ \
238:         len     = new_nz*(sizeof(PetscInt)+bs2*sizeof(MatScalar))+(b->mbs+1)*sizeof(PetscInt); \
239:         PetscMalloc(len,&new_a); \
240:         new_j   = (PetscInt*)(new_a + bs2*new_nz); \
241:         new_i   = new_j + new_nz; \
242:  \
243:         /* copy over old data into new slots */ \
244:         for (ii=0; ii<brow+1; ii++) {new_i[ii] = bi[ii];} \
245:         for (ii=brow+1; ii<b->mbs+1; ii++) {new_i[ii] = bi[ii]+CHUNKSIZE;} \
246:         PetscMemcpy(new_j,bj,(bi[brow]+nrow)*sizeof(PetscInt)); \
247:         len  = (new_nz - CHUNKSIZE - bi[brow] - nrow); \
248:         PetscMemcpy(new_j+bi[brow]+nrow+CHUNKSIZE,bj+bi[brow]+nrow,len*sizeof(PetscInt)); \
249:         PetscMemcpy(new_a,ba,(bi[brow]+nrow)*bs2*sizeof(MatScalar)); \
250:         PetscMemzero(new_a+bs2*(bi[brow]+nrow),bs2*CHUNKSIZE*sizeof(MatScalar)); \
251:         PetscMemcpy(new_a+bs2*(bi[brow]+nrow+CHUNKSIZE), \
252:                     ba+bs2*(bi[brow]+nrow),bs2*len*sizeof(MatScalar));  \
253:         /* free up old matrix storage */ \
254:         PetscFree(b->a);  \
255:         if (!b->singlemalloc) { \
256:           PetscFree(b->i); \
257:           PetscFree(b->j); \
258:         } \
259:         ba = b->a = new_a; bi = b->i = new_i; bj = b->j = new_j;  \
260:         b->singlemalloc = PETSC_TRUE; \
261:  \
262:         rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
263:         rmax = bimax[brow] = bimax[brow] + CHUNKSIZE; \
264:         PetscLogObjectMemory(B,CHUNKSIZE*(sizeof(PetscInt) + bs2*sizeof(MatScalar))); \
265:         b->maxnz += bs2*CHUNKSIZE; \
266:         b->reallocs++; \
267:         b->nz++; \
268:       } \
269:       N = nrow++ - 1;  \
270:       /* shift up all the later entries in this row */ \
271:       for (ii=N; ii>=_i; ii--) { \
272:         rp[ii+1] = rp[ii]; \
273:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
274:       } \
275:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  \
276:       rp[_i]                      = bcol;  \
277:       ap[bs2*_i + bs*cidx + ridx] = value;  \
278:       b_noinsert:; \
279:     bilen[brow] = nrow; \
280: } 

282: #if defined(PETSC_USE_MAT_SINGLE)
285: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
286: {
287:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
289:   PetscInt       i,N = m*n;
290:   MatScalar      *vsingle;

293:   if (N > b->setvalueslen) {
294:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
295:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
296:     b->setvalueslen  = N;
297:   }
298:   vsingle = b->setvaluescopy;

300:   for (i=0; i<N; i++) {
301:     vsingle[i] = v[i];
302:   }
303:   MatSetValues_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
304:   return(0);
305: }

309: PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
310: {
311:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
313:   PetscInt       i,N = m*n*b->bs2;
314:   MatScalar      *vsingle;

317:   if (N > b->setvalueslen) {
318:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
319:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
320:     b->setvalueslen  = N;
321:   }
322:   vsingle = b->setvaluescopy;
323:   for (i=0; i<N; i++) {
324:     vsingle[i] = v[i];
325:   }
326:   MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
327:   return(0);
328: }

332: PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
333: {
334:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
336:   PetscInt       i,N = m*n;
337:   MatScalar      *vsingle;

340:   if (N > b->setvalueslen) {
341:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
342:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
343:     b->setvalueslen  = N;
344:   }
345:   vsingle = b->setvaluescopy;
346:   for (i=0; i<N; i++) {
347:     vsingle[i] = v[i];
348:   }
349:   MatSetValues_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
350:   return(0);
351: }

355: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
356: {
357:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
359:   PetscInt       i,N = m*n*b->bs2;
360:   MatScalar      *vsingle;

363:   if (N > b->setvalueslen) {
364:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
365:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
366:     b->setvalueslen  = N;
367:   }
368:   vsingle = b->setvaluescopy;
369:   for (i=0; i<N; i++) {
370:     vsingle[i] = v[i];
371:   }
372:   MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
373:   return(0);
374: }
375: #endif

379: PetscErrorCode MatSetValues_MPIBAIJ_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
380: {
381:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
382:   MatScalar      value;
383:   PetscTruth     roworiented = baij->roworiented;
385:   PetscInt       i,j,row,col;
386:   PetscInt       rstart_orig=baij->rstart_bs;
387:   PetscInt       rend_orig=baij->rend_bs,cstart_orig=baij->cstart_bs;
388:   PetscInt       cend_orig=baij->cend_bs,bs=mat->bs;

390:   /* Some Variables required in the macro */
391:   Mat            A = baij->A;
392:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)(A)->data;
393:   PetscInt       *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
394:   MatScalar      *aa=a->a;

396:   Mat            B = baij->B;
397:   Mat_SeqBAIJ    *b = (Mat_SeqBAIJ*)(B)->data;
398:   PetscInt       *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
399:   MatScalar      *ba=b->a;

401:   PetscInt       *rp,ii,nrow,_i,rmax,N,brow,bcol;
402:   PetscInt       low,high,t,ridx,cidx,bs2=a->bs2;
403:   MatScalar      *ap,*bap;

406:   for (i=0; i<m; i++) {
407:     if (im[i] < 0) continue;
408: #if defined(PETSC_USE_BOPT_g)
409:     if (im[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->M-1);
410: #endif
411:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
412:       row = im[i] - rstart_orig;
413:       for (j=0; j<n; j++) {
414:         if (in[j] >= cstart_orig && in[j] < cend_orig){
415:           col = in[j] - cstart_orig;
416:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
417:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv);
418:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
419:         } else if (in[j] < 0) continue;
420: #if defined(PETSC_USE_BOPT_g)
421:         else if (in[j] >= mat->N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[i],mat->N-1);}
422: #endif
423:         else {
424:           if (mat->was_assembled) {
425:             if (!baij->colmap) {
426:               CreateColmap_MPIBAIJ_Private(mat);
427:             }
428: #if defined (PETSC_USE_CTABLE)
429:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
430:             col  = col - 1;
431: #else
432:             col = baij->colmap[in[j]/bs] - 1;
433: #endif
434:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
435:               DisAssemble_MPIBAIJ(mat);
436:               col =  in[j];
437:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
438:               B = baij->B;
439:               b = (Mat_SeqBAIJ*)(B)->data;
440:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
441:               ba=b->a;
442:             } else col += in[j]%bs;
443:           } else col = in[j];
444:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
445:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv);
446:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
447:         }
448:       }
449:     } else {
450:       if (!baij->donotstash) {
451:         if (roworiented) {
452:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
453:         } else {
454:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
455:         }
456:       }
457:     }
458:   }
459:   return(0);
460: }

464: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
465: {
466:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
467:   const MatScalar *value;
468:   MatScalar       *barray=baij->barray;
469:   PetscTruth      roworiented = baij->roworiented;
470:   PetscErrorCode  ierr;
471:   PetscInt        i,j,ii,jj,row,col,rstart=baij->rstart;
472:   PetscInt        rend=baij->rend,cstart=baij->cstart,stepval;
473:   PetscInt        cend=baij->cend,bs=mat->bs,bs2=baij->bs2;
474: 
476:   if(!barray) {
477:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
478:     baij->barray = barray;
479:   }

481:   if (roworiented) {
482:     stepval = (n-1)*bs;
483:   } else {
484:     stepval = (m-1)*bs;
485:   }
486:   for (i=0; i<m; i++) {
487:     if (im[i] < 0) continue;
488: #if defined(PETSC_USE_BOPT_g)
489:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
490: #endif
491:     if (im[i] >= rstart && im[i] < rend) {
492:       row = im[i] - rstart;
493:       for (j=0; j<n; j++) {
494:         /* If NumCol = 1 then a copy is not required */
495:         if ((roworiented) && (n == 1)) {
496:           barray = (MatScalar*)v + i*bs2;
497:         } else if((!roworiented) && (m == 1)) {
498:           barray = (MatScalar*)v + j*bs2;
499:         } else { /* Here a copy is required */
500:           if (roworiented) {
501:             value = v + i*(stepval+bs)*bs + j*bs;
502:           } else {
503:             value = v + j*(stepval+bs)*bs + i*bs;
504:           }
505:           for (ii=0; ii<bs; ii++,value+=stepval) {
506:             for (jj=0; jj<bs; jj++) {
507:               *barray++  = *value++;
508:             }
509:           }
510:           barray -=bs2;
511:         }
512: 
513:         if (in[j] >= cstart && in[j] < cend){
514:           col  = in[j] - cstart;
515:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->A,1,&row,1,&col,barray,addv);
516:         }
517:         else if (in[j] < 0) continue;
518: #if defined(PETSC_USE_BOPT_g)
519:         else if (in[j] >= baij->Nbs) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);}
520: #endif
521:         else {
522:           if (mat->was_assembled) {
523:             if (!baij->colmap) {
524:               CreateColmap_MPIBAIJ_Private(mat);
525:             }

527: #if defined(PETSC_USE_BOPT_g)
528: #if defined (PETSC_USE_CTABLE)
529:             { PetscInt data;
530:               PetscTableFind(baij->colmap,in[j]+1,&data);
531:               if ((data - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
532:             }
533: #else
534:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
535: #endif
536: #endif
537: #if defined (PETSC_USE_CTABLE)
538:             PetscTableFind(baij->colmap,in[j]+1,&col);
539:             col  = (col - 1)/bs;
540: #else
541:             col = (baij->colmap[in[j]] - 1)/bs;
542: #endif
543:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
544:               DisAssemble_MPIBAIJ(mat);
545:               col =  in[j];
546:             }
547:           }
548:           else col = in[j];
549:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->B,1,&row,1,&col,barray,addv);
550:         }
551:       }
552:     } else {
553:       if (!baij->donotstash) {
554:         if (roworiented) {
555:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
556:         } else {
557:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
558:         }
559:       }
560:     }
561:   }
562:   return(0);
563: }

565: #define HASH_KEY 0.6180339887
566: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(PetscInt)((size)*(tmp-(PetscInt)tmp)))
567: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
568: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
571: PetscErrorCode MatSetValues_MPIBAIJ_HT_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
572: {
573:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
574:   PetscTruth     roworiented = baij->roworiented;
576:   PetscInt       i,j,row,col;
577:   PetscInt       rstart_orig=baij->rstart_bs;
578:   PetscInt       rend_orig=baij->rend_bs,Nbs=baij->Nbs;
579:   PetscInt       h1,key,size=baij->ht_size,bs=mat->bs,*HT=baij->ht,idx;
580:   PetscReal      tmp;
581:   MatScalar      **HD = baij->hd,value;
582: #if defined(PETSC_USE_BOPT_g)
583:   PetscInt       total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
584: #endif


588:   for (i=0; i<m; i++) {
589: #if defined(PETSC_USE_BOPT_g)
590:     if (im[i] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
591:     if (im[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->M-1);
592: #endif
593:       row = im[i];
594:     if (row >= rstart_orig && row < rend_orig) {
595:       for (j=0; j<n; j++) {
596:         col = in[j];
597:         if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
598:         /* Look up PetscInto the Hash Table */
599:         key = (row/bs)*Nbs+(col/bs)+1;
600:         h1  = HASH(size,key,tmp);

602: 
603:         idx = h1;
604: #if defined(PETSC_USE_BOPT_g)
605:         insert_ct++;
606:         total_ct++;
607:         if (HT[idx] != key) {
608:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
609:           if (idx == size) {
610:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
611:             if (idx == h1) {
612:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
613:             }
614:           }
615:         }
616: #else
617:         if (HT[idx] != key) {
618:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
619:           if (idx == size) {
620:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
621:             if (idx == h1) {
622:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
623:             }
624:           }
625:         }
626: #endif
627:         /* A HASH table entry is found, so insert the values at the correct address */
628:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
629:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
630:       }
631:     } else {
632:       if (!baij->donotstash) {
633:         if (roworiented) {
634:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
635:         } else {
636:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
637:         }
638:       }
639:     }
640:   }
641: #if defined(PETSC_USE_BOPT_g)
642:   baij->ht_total_ct = total_ct;
643:   baij->ht_insert_ct = insert_ct;
644: #endif
645:   return(0);
646: }

650: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
651: {
652:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
653:   PetscTruth      roworiented = baij->roworiented;
654:   PetscErrorCode  ierr;
655:   PetscInt        i,j,ii,jj,row,col;
656:   PetscInt        rstart=baij->rstart ;
657:   PetscInt        rend=baij->rend,stepval,bs=mat->bs,bs2=baij->bs2;
658:   PetscInt        h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
659:   PetscReal       tmp;
660:   MatScalar       **HD = baij->hd,*baij_a;
661:   const MatScalar *v_t,*value;
662: #if defined(PETSC_USE_BOPT_g)
663:   PetscInt        total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
664: #endif
665: 

668:   if (roworiented) {
669:     stepval = (n-1)*bs;
670:   } else {
671:     stepval = (m-1)*bs;
672:   }
673:   for (i=0; i<m; i++) {
674: #if defined(PETSC_USE_BOPT_g)
675:     if (im[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",im[i]);
676:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],baij->Mbs-1);
677: #endif
678:     row   = im[i];
679:     v_t   = v + i*bs2;
680:     if (row >= rstart && row < rend) {
681:       for (j=0; j<n; j++) {
682:         col = in[j];

684:         /* Look up into the Hash Table */
685:         key = row*Nbs+col+1;
686:         h1  = HASH(size,key,tmp);
687: 
688:         idx = h1;
689: #if defined(PETSC_USE_BOPT_g)
690:         total_ct++;
691:         insert_ct++;
692:        if (HT[idx] != key) {
693:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
694:           if (idx == size) {
695:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
696:             if (idx == h1) {
697:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
698:             }
699:           }
700:         }
701: #else  
702:         if (HT[idx] != key) {
703:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
704:           if (idx == size) {
705:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
706:             if (idx == h1) {
707:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
708:             }
709:           }
710:         }
711: #endif
712:         baij_a = HD[idx];
713:         if (roworiented) {
714:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
715:           /* value = v + (i*(stepval+bs)+j)*bs; */
716:           value = v_t;
717:           v_t  += bs;
718:           if (addv == ADD_VALUES) {
719:             for (ii=0; ii<bs; ii++,value+=stepval) {
720:               for (jj=ii; jj<bs2; jj+=bs) {
721:                 baij_a[jj]  += *value++;
722:               }
723:             }
724:           } else {
725:             for (ii=0; ii<bs; ii++,value+=stepval) {
726:               for (jj=ii; jj<bs2; jj+=bs) {
727:                 baij_a[jj]  = *value++;
728:               }
729:             }
730:           }
731:         } else {
732:           value = v + j*(stepval+bs)*bs + i*bs;
733:           if (addv == ADD_VALUES) {
734:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
735:               for (jj=0; jj<bs; jj++) {
736:                 baij_a[jj]  += *value++;
737:               }
738:             }
739:           } else {
740:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
741:               for (jj=0; jj<bs; jj++) {
742:                 baij_a[jj]  = *value++;
743:               }
744:             }
745:           }
746:         }
747:       }
748:     } else {
749:       if (!baij->donotstash) {
750:         if (roworiented) {
751:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
752:         } else {
753:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
754:         }
755:       }
756:     }
757:   }
758: #if defined(PETSC_USE_BOPT_g)
759:   baij->ht_total_ct = total_ct;
760:   baij->ht_insert_ct = insert_ct;
761: #endif
762:   return(0);
763: }

767: PetscErrorCode MatGetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
768: {
769:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
771:   PetscInt       bs=mat->bs,i,j,bsrstart = baij->rstart*bs,bsrend = baij->rend*bs;
772:   PetscInt       bscstart = baij->cstart*bs,bscend = baij->cend*bs,row,col,data;

775:   for (i=0; i<m; i++) {
776:     if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);
777:     if (idxm[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->M-1);
778:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
779:       row = idxm[i] - bsrstart;
780:       for (j=0; j<n; j++) {
781:         if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]);
782:         if (idxn[j] >= mat->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->N-1);
783:         if (idxn[j] >= bscstart && idxn[j] < bscend){
784:           col = idxn[j] - bscstart;
785:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
786:         } else {
787:           if (!baij->colmap) {
788:             CreateColmap_MPIBAIJ_Private(mat);
789:           }
790: #if defined (PETSC_USE_CTABLE)
791:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
792:           data --;
793: #else
794:           data = baij->colmap[idxn[j]/bs]-1;
795: #endif
796:           if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
797:           else {
798:             col  = data + idxn[j]%bs;
799:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
800:           }
801:         }
802:       }
803:     } else {
804:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
805:     }
806:   }
807:  return(0);
808: }

812: PetscErrorCode MatNorm_MPIBAIJ(Mat mat,NormType type,PetscReal *nrm)
813: {
814:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
815:   Mat_SeqBAIJ    *amat = (Mat_SeqBAIJ*)baij->A->data,*bmat = (Mat_SeqBAIJ*)baij->B->data;
817:   PetscInt       i,bs2=baij->bs2;
818:   PetscReal      sum = 0.0;
819:   MatScalar      *v;

822:   if (baij->size == 1) {
823:      MatNorm(baij->A,type,nrm);
824:   } else {
825:     if (type == NORM_FROBENIUS) {
826:       v = amat->a;
827:       for (i=0; i<amat->nz*bs2; i++) {
828: #if defined(PETSC_USE_COMPLEX)
829:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
830: #else
831:         sum += (*v)*(*v); v++;
832: #endif
833:       }
834:       v = bmat->a;
835:       for (i=0; i<bmat->nz*bs2; i++) {
836: #if defined(PETSC_USE_COMPLEX)
837:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
838: #else
839:         sum += (*v)*(*v); v++;
840: #endif
841:       }
842:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPI_SUM,mat->comm);
843:       *nrm = sqrt(*nrm);
844:     } else {
845:       SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
846:     }
847:   }
848:   return(0);
849: }


852: /*
853:   Creates the hash table, and sets the table 
854:   This table is created only once. 
855:   If new entried need to be added to the matrix
856:   then the hash table has to be destroyed and
857:   recreated.
858: */
861: PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
862: {
863:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
864:   Mat            A = baij->A,B=baij->B;
865:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ *)A->data,*b=(Mat_SeqBAIJ *)B->data;
866:   PetscInt       i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
868:   PetscInt       size,bs2=baij->bs2,rstart=baij->rstart;
869:   PetscInt       cstart=baij->cstart,*garray=baij->garray,row,col,Nbs=baij->Nbs;
870:   PetscInt       *HT,key;
871:   MatScalar      **HD;
872:   PetscReal      tmp;
873: #if defined(PETSC_USE_BOPT_g)
874:   PetscInt       ct=0,max=0;
875: #endif

878:   baij->ht_size=(PetscInt)(factor*nz);
879:   size = baij->ht_size;

881:   if (baij->ht) {
882:     return(0);
883:   }
884: 
885:   /* Allocate Memory for Hash Table */
886:   PetscMalloc((size)*(sizeof(PetscInt)+sizeof(MatScalar*))+1,&baij->hd);
887:   baij->ht = (PetscInt*)(baij->hd + size);
888:   HD       = baij->hd;
889:   HT       = baij->ht;


892:   PetscMemzero(HD,size*(sizeof(PetscInt)+sizeof(PetscScalar*)));
893: 

895:   /* Loop Over A */
896:   for (i=0; i<a->mbs; i++) {
897:     for (j=ai[i]; j<ai[i+1]; j++) {
898:       row = i+rstart;
899:       col = aj[j]+cstart;
900: 
901:       key = row*Nbs + col + 1;
902:       h1  = HASH(size,key,tmp);
903:       for (k=0; k<size; k++){
904:         if (!HT[(h1+k)%size]) {
905:           HT[(h1+k)%size] = key;
906:           HD[(h1+k)%size] = a->a + j*bs2;
907:           break;
908: #if defined(PETSC_USE_BOPT_g)
909:         } else {
910:           ct++;
911: #endif
912:         }
913:       }
914: #if defined(PETSC_USE_BOPT_g)
915:       if (k> max) max = k;
916: #endif
917:     }
918:   }
919:   /* Loop Over B */
920:   for (i=0; i<b->mbs; i++) {
921:     for (j=bi[i]; j<bi[i+1]; j++) {
922:       row = i+rstart;
923:       col = garray[bj[j]];
924:       key = row*Nbs + col + 1;
925:       h1  = HASH(size,key,tmp);
926:       for (k=0; k<size; k++){
927:         if (!HT[(h1+k)%size]) {
928:           HT[(h1+k)%size] = key;
929:           HD[(h1+k)%size] = b->a + j*bs2;
930:           break;
931: #if defined(PETSC_USE_BOPT_g)
932:         } else {
933:           ct++;
934: #endif
935:         }
936:       }
937: #if defined(PETSC_USE_BOPT_g)
938:       if (k> max) max = k;
939: #endif
940:     }
941:   }
942: 
943:   /* Print Summary */
944: #if defined(PETSC_USE_BOPT_g)
945:   for (i=0,j=0; i<size; i++) {
946:     if (HT[i]) {j++;}
947:   }
948:   PetscLogInfo(0,"MatCreateHashTable_MPIBAIJ_Private: Average Search = %5.2f,max search = %D\n",(!j)? 0.0:((PetscReal)(ct+j))/j,max);
949: #endif
950:   return(0);
951: }

955: PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat,MatAssemblyType mode)
956: {
957:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
959:   PetscInt       nstash,reallocs;
960:   InsertMode     addv;

963:   if (baij->donotstash) {
964:     return(0);
965:   }

967:   /* make sure all processors are either in INSERTMODE or ADDMODE */
968:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
969:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
970:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
971:   }
972:   mat->insertmode = addv; /* in case this processor had no cache */

974:   MatStashScatterBegin_Private(&mat->stash,baij->rowners_bs);
975:   MatStashScatterBegin_Private(&mat->bstash,baij->rowners);
976:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
977:   PetscLogInfo(0,"MatAssemblyBegin_MPIBAIJ:Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
978:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
979:   PetscLogInfo(0,"MatAssemblyBegin_MPIBAIJ:Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
980:   return(0);
981: }

985: PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat,MatAssemblyType mode)
986: {
987:   Mat_MPIBAIJ    *baij=(Mat_MPIBAIJ*)mat->data;
988:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ*)baij->A->data,*b=(Mat_SeqBAIJ*)baij->B->data;
990:   PetscInt       i,j,rstart,ncols,flg,bs2=baij->bs2;
991:   PetscInt       *row,*col,other_disassembled;
992:   PetscTruth     r1,r2,r3;
993:   MatScalar      *val;
994:   InsertMode     addv = mat->insertmode;
995:   PetscMPIInt    n;

998:   if (!baij->donotstash) {
999:     while (1) {
1000:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
1001:       if (!flg) break;

1003:       for (i=0; i<n;) {
1004:         /* Now identify the consecutive vals belonging to the same row */
1005:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
1006:         if (j < n) ncols = j-i;
1007:         else       ncols = n-i;
1008:         /* Now assemble all these values with a single function call */
1009:         MatSetValues_MPIBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i,addv);
1010:         i = j;
1011:       }
1012:     }
1013:     MatStashScatterEnd_Private(&mat->stash);
1014:     /* Now process the block-stash. Since the values are stashed column-oriented,
1015:        set the roworiented flag to column oriented, and after MatSetValues() 
1016:        restore the original flags */
1017:     r1 = baij->roworiented;
1018:     r2 = a->roworiented;
1019:     r3 = b->roworiented;
1020:     baij->roworiented = PETSC_FALSE;
1021:     a->roworiented    = PETSC_FALSE;
1022:     b->roworiented    = PETSC_FALSE;
1023:     while (1) {
1024:       MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
1025:       if (!flg) break;
1026: 
1027:       for (i=0; i<n;) {
1028:         /* Now identify the consecutive vals belonging to the same row */
1029:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
1030:         if (j < n) ncols = j-i;
1031:         else       ncols = n-i;
1032:         MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
1033:         i = j;
1034:       }
1035:     }
1036:     MatStashScatterEnd_Private(&mat->bstash);
1037:     baij->roworiented = r1;
1038:     a->roworiented    = r2;
1039:     b->roworiented    = r3;
1040:   }

1042:   MatAssemblyBegin(baij->A,mode);
1043:   MatAssemblyEnd(baij->A,mode);

1045:   /* determine if any processor has disassembled, if so we must 
1046:      also disassemble ourselfs, in order that we may reassemble. */
1047:   /*
1048:      if nonzero structure of submatrix B cannot change then we know that
1049:      no processor disassembled thus we can skip this stuff
1050:   */
1051:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
1052:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
1053:     if (mat->was_assembled && !other_disassembled) {
1054:       DisAssemble_MPIBAIJ(mat);
1055:     }
1056:   }

1058:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
1059:     MatSetUpMultiply_MPIBAIJ(mat);
1060:   }
1061:   MatAssemblyBegin(baij->B,mode);
1062:   MatAssemblyEnd(baij->B,mode);
1063: 
1064: #if defined(PETSC_USE_BOPT_g)
1065:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
1066:     PetscLogInfo(0,"MatAssemblyEnd_MPIBAIJ:Average Hash Table Search in MatSetValues = %5.2f\n",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
1067:     baij->ht_total_ct  = 0;
1068:     baij->ht_insert_ct = 0;
1069:   }
1070: #endif
1071:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
1072:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);
1073:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
1074:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
1075:   }

1077:   if (baij->rowvalues) {
1078:     PetscFree(baij->rowvalues);
1079:     baij->rowvalues = 0;
1080:   }
1081:   return(0);
1082: }

1086: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1087: {
1088:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
1089:   PetscErrorCode    ierr;
1090:   PetscMPIInt       size = baij->size,rank = baij->rank;
1091:   PetscInt          bs = mat->bs;
1092:   PetscTruth        iascii,isdraw;
1093:   PetscViewer       sviewer;
1094:   PetscViewerFormat format;

1097:   /* printf(" MatView_MPIBAIJ_ASCIIorDraworSocket is called ...\n"); */
1098:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1099:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1100:   if (iascii) {
1101:     PetscViewerGetFormat(viewer,&format);
1102:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1103:       MatInfo info;
1104:       MPI_Comm_rank(mat->comm,&rank);
1105:       MatGetInfo(mat,MAT_LOCAL,&info);
1106:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",
1107:               rank,mat->m,(PetscInt)info.nz_used*bs,(PetscInt)info.nz_allocated*bs,
1108:               mat->bs,(PetscInt)info.memory);
1109:       MatGetInfo(baij->A,MAT_LOCAL,&info);
1110:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
1111:       MatGetInfo(baij->B,MAT_LOCAL,&info);
1112:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
1113:       PetscViewerFlush(viewer);
1114:       VecScatterView(baij->Mvctx,viewer);
1115:       return(0);
1116:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1117:       PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
1118:       return(0);
1119:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1120:       return(0);
1121:     }
1122:   }

1124:   if (isdraw) {
1125:     PetscDraw       draw;
1126:     PetscTruth isnull;
1127:     PetscViewerDrawGetDraw(viewer,0,&draw);
1128:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1129:   }

1131:   if (size == 1) {
1132:     PetscObjectSetName((PetscObject)baij->A,mat->name);
1133:     MatView(baij->A,viewer);
1134:   } else {
1135:     /* assemble the entire matrix onto first processor. */
1136:     Mat         A;
1137:     Mat_SeqBAIJ *Aloc;
1138:     PetscInt    M = mat->M,N = mat->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
1139:     MatScalar   *a;

1141:     /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1142:     /* Perhaps this should be the type of mat? */
1143:     if (!rank) {
1144:       MatCreate(mat->comm,M,N,M,N,&A);
1145:     } else {
1146:       MatCreate(mat->comm,0,0,M,N,&A);
1147:     }
1148:     MatSetType(A,MATMPIBAIJ);
1149:     MatMPIBAIJSetPreallocation(A,mat->bs,0,PETSC_NULL,0,PETSC_NULL);
1150:     PetscLogObjectParent(mat,A);

1152:     /* copy over the A part */
1153:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
1154:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1155:     PetscMalloc(bs*sizeof(PetscInt),&rvals);

1157:     for (i=0; i<mbs; i++) {
1158:       rvals[0] = bs*(baij->rstart + i);
1159:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1160:       for (j=ai[i]; j<ai[i+1]; j++) {
1161:         col = (baij->cstart+aj[j])*bs;
1162:         for (k=0; k<bs; k++) {
1163:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1164:           col++; a += bs;
1165:         }
1166:       }
1167:     }
1168:     /* copy over the B part */
1169:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
1170:     ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1171:     for (i=0; i<mbs; i++) {
1172:       rvals[0] = bs*(baij->rstart + i);
1173:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1174:       for (j=ai[i]; j<ai[i+1]; j++) {
1175:         col = baij->garray[aj[j]]*bs;
1176:         for (k=0; k<bs; k++) {
1177:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1178:           col++; a += bs;
1179:         }
1180:       }
1181:     }
1182:     PetscFree(rvals);
1183:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1184:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1185:     /* 
1186:        Everyone has to call to draw the matrix since the graphics waits are
1187:        synchronized across all processors that share the PetscDraw object
1188:     */
1189:     PetscViewerGetSingleton(viewer,&sviewer);
1190:     if (!rank) {
1191:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,mat->name);
1192:       MatView(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1193:     }
1194:     PetscViewerRestoreSingleton(viewer,&sviewer);
1195:     MatDestroy(A);
1196:   }
1197:   return(0);
1198: }

1202: PetscErrorCode MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1203: {
1205:   PetscTruth     iascii,isdraw,issocket,isbinary;

1208:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1209:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1210:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1211:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1212:   if (iascii || isdraw || issocket || isbinary) {
1213:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1214:   } else {
1215:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIBAIJ matrices",((PetscObject)viewer)->type_name);
1216:   }
1217:   return(0);
1218: }

1222: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
1223: {
1224:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;

1228: #if defined(PETSC_USE_LOG)
1229:   PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->M,mat->N);
1230: #endif
1231:   MatStashDestroy_Private(&mat->stash);
1232:   MatStashDestroy_Private(&mat->bstash);
1233:   PetscFree(baij->rowners);
1234:   MatDestroy(baij->A);
1235:   MatDestroy(baij->B);
1236: #if defined (PETSC_USE_CTABLE)
1237:   if (baij->colmap) {PetscTableDelete(baij->colmap);}
1238: #else
1239:   if (baij->colmap) {PetscFree(baij->colmap);}
1240: #endif
1241:   if (baij->garray) {PetscFree(baij->garray);}
1242:   if (baij->lvec)   {VecDestroy(baij->lvec);}
1243:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
1244:   if (baij->rowvalues) {PetscFree(baij->rowvalues);}
1245:   if (baij->barray) {PetscFree(baij->barray);}
1246:   if (baij->hd) {PetscFree(baij->hd);}
1247: #if defined(PETSC_USE_MAT_SINGLE)
1248:   if (baij->setvaluescopy) {PetscFree(baij->setvaluescopy);}
1249: #endif
1250:   PetscFree(baij);

1252:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
1253:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
1254:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
1255:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C","",PETSC_NULL);
1256:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C","",PETSC_NULL);
1257:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
1258:   PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C","",PETSC_NULL);
1259:   return(0);
1260: }

1264: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1265: {
1266:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1268:   PetscInt       nt;

1271:   VecGetLocalSize(xx,&nt);
1272:   if (nt != A->n) {
1273:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1274:   }
1275:   VecGetLocalSize(yy,&nt);
1276:   if (nt != A->m) {
1277:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1278:   }
1279:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1280:   (*a->A->ops->mult)(a->A,xx,yy);
1281:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1282:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1283:   VecScatterPostRecvs(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1284:   return(0);
1285: }

1289: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1290: {
1291:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1295:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1296:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1297:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1298:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1299:   return(0);
1300: }

1304: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1305: {
1306:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1310:   /* do nondiagonal part */
1311:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1312:   /* send it on its way */
1313:   VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1314:   /* do local part */
1315:   (*a->A->ops->multtranspose)(a->A,xx,yy);
1316:   /* receive remote parts: note this assumes the values are not actually */
1317:   /* inserted in yy until the next line, which is true for my implementation*/
1318:   /* but is not perhaps always true. */
1319:   VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1320:   return(0);
1321: }

1325: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1326: {
1327:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1331:   /* do nondiagonal part */
1332:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1333:   /* send it on its way */
1334:   VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1335:   /* do local part */
1336:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1337:   /* receive remote parts: note this assumes the values are not actually */
1338:   /* inserted in yy until the next line, which is true for my implementation*/
1339:   /* but is not perhaps always true. */
1340:   VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1341:   return(0);
1342: }

1344: /*
1345:   This only works correctly for square matrices where the subblock A->A is the 
1346:    diagonal block
1347: */
1350: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1351: {
1352:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1356:   if (A->M != A->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1357:   MatGetDiagonal(a->A,v);
1358:   return(0);
1359: }

1363: PetscErrorCode MatScale_MPIBAIJ(const PetscScalar *aa,Mat A)
1364: {
1365:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1369:   MatScale(aa,a->A);
1370:   MatScale(aa,a->B);
1371:   return(0);
1372: }

1376: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1377: {
1378:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
1379:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1381:   PetscInt       bs = matin->bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
1382:   PetscInt       nztot,nzA,nzB,lrow,brstart = mat->rstart*bs,brend = mat->rend*bs;
1383:   PetscInt       *cmap,*idx_p,cstart = mat->cstart;

1386:   if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1387:   mat->getrowactive = PETSC_TRUE;

1389:   if (!mat->rowvalues && (idx || v)) {
1390:     /*
1391:         allocate enough space to hold information from the longest row.
1392:     */
1393:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1394:     PetscInt     max = 1,mbs = mat->mbs,tmp;
1395:     for (i=0; i<mbs; i++) {
1396:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1397:       if (max < tmp) { max = tmp; }
1398:     }
1399:     PetscMalloc(max*bs2*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1400:     mat->rowindices = (PetscInt*)(mat->rowvalues + max*bs2);
1401:   }
1402: 
1403:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1404:   lrow = row - brstart;

1406:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1407:   if (!v)   {pvA = 0; pvB = 0;}
1408:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1409:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1410:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1411:   nztot = nzA + nzB;

1413:   cmap  = mat->garray;
1414:   if (v  || idx) {
1415:     if (nztot) {
1416:       /* Sort by increasing column numbers, assuming A and B already sorted */
1417:       PetscInt imark = -1;
1418:       if (v) {
1419:         *v = v_p = mat->rowvalues;
1420:         for (i=0; i<nzB; i++) {
1421:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1422:           else break;
1423:         }
1424:         imark = i;
1425:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1426:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1427:       }
1428:       if (idx) {
1429:         *idx = idx_p = mat->rowindices;
1430:         if (imark > -1) {
1431:           for (i=0; i<imark; i++) {
1432:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1433:           }
1434:         } else {
1435:           for (i=0; i<nzB; i++) {
1436:             if (cmap[cworkB[i]/bs] < cstart)
1437:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1438:             else break;
1439:           }
1440:           imark = i;
1441:         }
1442:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1443:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1444:       }
1445:     } else {
1446:       if (idx) *idx = 0;
1447:       if (v)   *v   = 0;
1448:     }
1449:   }
1450:   *nz = nztot;
1451:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1452:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1453:   return(0);
1454: }

1458: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1459: {
1460:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1463:   if (baij->getrowactive == PETSC_FALSE) {
1464:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1465:   }
1466:   baij->getrowactive = PETSC_FALSE;
1467:   return(0);
1468: }

1472: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1473: {
1474:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;

1478:   MatZeroEntries(l->A);
1479:   MatZeroEntries(l->B);
1480:   return(0);
1481: }

1485: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1486: {
1487:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)matin->data;
1488:   Mat            A = a->A,B = a->B;
1490:   PetscReal      isend[5],irecv[5];

1493:   info->block_size     = (PetscReal)matin->bs;
1494:   MatGetInfo(A,MAT_LOCAL,info);
1495:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1496:   isend[3] = info->memory;  isend[4] = info->mallocs;
1497:   MatGetInfo(B,MAT_LOCAL,info);
1498:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1499:   isend[3] += info->memory;  isend[4] += info->mallocs;
1500:   if (flag == MAT_LOCAL) {
1501:     info->nz_used      = isend[0];
1502:     info->nz_allocated = isend[1];
1503:     info->nz_unneeded  = isend[2];
1504:     info->memory       = isend[3];
1505:     info->mallocs      = isend[4];
1506:   } else if (flag == MAT_GLOBAL_MAX) {
1507:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1508:     info->nz_used      = irecv[0];
1509:     info->nz_allocated = irecv[1];
1510:     info->nz_unneeded  = irecv[2];
1511:     info->memory       = irecv[3];
1512:     info->mallocs      = irecv[4];
1513:   } else if (flag == MAT_GLOBAL_SUM) {
1514:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1515:     info->nz_used      = irecv[0];
1516:     info->nz_allocated = irecv[1];
1517:     info->nz_unneeded  = irecv[2];
1518:     info->memory       = irecv[3];
1519:     info->mallocs      = irecv[4];
1520:   } else {
1521:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1522:   }
1523:   info->rows_global       = (PetscReal)A->M;
1524:   info->columns_global    = (PetscReal)A->N;
1525:   info->rows_local        = (PetscReal)A->m;
1526:   info->columns_local     = (PetscReal)A->N;
1527:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1528:   info->fill_ratio_needed = 0;
1529:   info->factor_mallocs    = 0;
1530:   return(0);
1531: }

1535: PetscErrorCode MatSetOption_MPIBAIJ(Mat A,MatOption op)
1536: {
1537:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1541:   switch (op) {
1542:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1543:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1544:   case MAT_COLUMNS_UNSORTED:
1545:   case MAT_COLUMNS_SORTED:
1546:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1547:   case MAT_KEEP_ZEROED_ROWS:
1548:   case MAT_NEW_NONZERO_LOCATION_ERR:
1549:     MatSetOption(a->A,op);
1550:     MatSetOption(a->B,op);
1551:     break;
1552:   case MAT_ROW_ORIENTED:
1553:     a->roworiented = PETSC_TRUE;
1554:     MatSetOption(a->A,op);
1555:     MatSetOption(a->B,op);
1556:     break;
1557:   case MAT_ROWS_SORTED:
1558:   case MAT_ROWS_UNSORTED:
1559:   case MAT_YES_NEW_DIAGONALS:
1560:     PetscLogInfo(A,"Info:MatSetOption_MPIBAIJ:Option ignored\n");
1561:     break;
1562:   case MAT_COLUMN_ORIENTED:
1563:     a->roworiented = PETSC_FALSE;
1564:     MatSetOption(a->A,op);
1565:     MatSetOption(a->B,op);
1566:     break;
1567:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1568:     a->donotstash = PETSC_TRUE;
1569:     break;
1570:   case MAT_NO_NEW_DIAGONALS:
1571:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1572:   case MAT_USE_HASH_TABLE:
1573:     a->ht_flag = PETSC_TRUE;
1574:     break;
1575:   case MAT_SYMMETRIC:
1576:   case MAT_STRUCTURALLY_SYMMETRIC:
1577:   case MAT_HERMITIAN:
1578:   case MAT_SYMMETRY_ETERNAL:
1579:     MatSetOption(a->A,op);
1580:     break;
1581:   case MAT_NOT_SYMMETRIC:
1582:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1583:   case MAT_NOT_HERMITIAN:
1584:   case MAT_NOT_SYMMETRY_ETERNAL:
1585:     break;
1586:   default:
1587:     SETERRQ(PETSC_ERR_SUP,"unknown option");
1588:   }
1589:   return(0);
1590: }

1594: PetscErrorCode MatTranspose_MPIBAIJ(Mat A,Mat *matout)
1595: {
1596:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)A->data;
1597:   Mat_SeqBAIJ    *Aloc;
1598:   Mat            B;
1600:   PetscInt       M=A->M,N=A->N,*ai,*aj,i,*rvals,j,k,col;
1601:   PetscInt       bs=A->bs,mbs=baij->mbs;
1602:   MatScalar      *a;
1603: 
1605:   if (!matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1606:   MatCreate(A->comm,A->n,A->m,N,M,&B);
1607:   MatSetType(B,A->type_name);
1608:   MatMPIBAIJSetPreallocation(B,A->bs,0,PETSC_NULL,0,PETSC_NULL);
1609: 
1610:   /* copy over the A part */
1611:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1612:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1613:   PetscMalloc(bs*sizeof(PetscInt),&rvals);
1614: 
1615:   for (i=0; i<mbs; i++) {
1616:     rvals[0] = bs*(baij->rstart + i);
1617:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1618:     for (j=ai[i]; j<ai[i+1]; j++) {
1619:       col = (baij->cstart+aj[j])*bs;
1620:       for (k=0; k<bs; k++) {
1621:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1622:         col++; a += bs;
1623:       }
1624:     }
1625:   }
1626:   /* copy over the B part */
1627:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1628:   ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1629:   for (i=0; i<mbs; i++) {
1630:     rvals[0] = bs*(baij->rstart + i);
1631:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1632:     for (j=ai[i]; j<ai[i+1]; j++) {
1633:       col = baij->garray[aj[j]]*bs;
1634:       for (k=0; k<bs; k++) {
1635:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1636:         col++; a += bs;
1637:       }
1638:     }
1639:   }
1640:   PetscFree(rvals);
1641:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1642:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1643: 
1644:   if (matout) {
1645:     *matout = B;
1646:   } else {
1647:     MatHeaderCopy(A,B);
1648:   }
1649:   return(0);
1650: }

1654: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1655: {
1656:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
1657:   Mat            a = baij->A,b = baij->B;
1659:   PetscInt       s1,s2,s3;

1662:   MatGetLocalSize(mat,&s2,&s3);
1663:   if (rr) {
1664:     VecGetLocalSize(rr,&s1);
1665:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1666:     /* Overlap communication with computation. */
1667:     VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1668:   }
1669:   if (ll) {
1670:     VecGetLocalSize(ll,&s1);
1671:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1672:     (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1673:   }
1674:   /* scale  the diagonal block */
1675:   (*a->ops->diagonalscale)(a,ll,rr);

1677:   if (rr) {
1678:     /* Do a scatter end and then right scale the off-diagonal block */
1679:     VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1680:     (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1681:   }
1682: 
1683:   return(0);
1684: }

1688: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,IS is,const PetscScalar *diag)
1689: {
1690:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;
1692:   PetscMPIInt    imdex,size = l->size,n,rank = l->rank;
1693:   PetscInt       i,N,*rows,*owners = l->rowners;
1694:   PetscInt       *nprocs,j,idx,nsends,row;
1695:   PetscInt       nmax,*svalues,*starts,*owner,nrecvs;
1696:   PetscInt       *rvalues,tag = A->tag,count,base,slen,*source;
1697:   PetscInt       *lens,*lrows,*values,bs=A->bs,rstart_bs=l->rstart_bs;
1698:   MPI_Comm       comm = A->comm;
1699:   MPI_Request    *send_waits,*recv_waits;
1700:   MPI_Status     recv_status,*send_status;
1701:   IS             istmp;
1702:   PetscTruth     found;
1703: 
1705:   ISGetLocalSize(is,&N);
1706:   ISGetIndices(is,&rows);
1707: 
1708:   /*  first count number of contributors to each processor */
1709:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
1710:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
1711:   PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
1712:   for (i=0; i<N; i++) {
1713:     idx   = rows[i];
1714:     found = PETSC_FALSE;
1715:     for (j=0; j<size; j++) {
1716:       if (idx >= owners[j]*bs && idx < owners[j+1]*bs) {
1717:         nprocs[2*j]++; nprocs[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break;
1718:       }
1719:     }
1720:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1721:   }
1722:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
1723: 
1724:   /* inform other processors of number of messages and max length*/
1725:   PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
1726: 
1727:   /* post receives:   */
1728:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
1729:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1730:   for (i=0; i<nrecvs; i++) {
1731:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1732:   }
1733: 
1734:   /* do sends:
1735:      1) starts[i] gives the starting index in svalues for stuff going to 
1736:      the ith processor
1737:   */
1738:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
1739:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1740:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
1741:   starts[0]  = 0;
1742:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1743:   for (i=0; i<N; i++) {
1744:     svalues[starts[owner[i]]++] = rows[i];
1745:   }
1746:   ISRestoreIndices(is,&rows);
1747: 
1748:   starts[0] = 0;
1749:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1750:   count = 0;
1751:   for (i=0; i<size; i++) {
1752:     if (nprocs[2*i+1]) {
1753:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
1754:     }
1755:   }
1756:   PetscFree(starts);

1758:   base = owners[rank]*bs;
1759: 
1760:   /*  wait on receives */
1761:   PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
1762:   source = lens + nrecvs;
1763:   count  = nrecvs; slen = 0;
1764:   while (count) {
1765:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1766:     /* unpack receives into our local space */
1767:     MPI_Get_count(&recv_status,MPIU_INT,&n);
1768:     source[imdex]  = recv_status.MPI_SOURCE;
1769:     lens[imdex]    = n;
1770:     slen          += n;
1771:     count--;
1772:   }
1773:   PetscFree(recv_waits);
1774: 
1775:   /* move the data into the send scatter */
1776:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
1777:   count = 0;
1778:   for (i=0; i<nrecvs; i++) {
1779:     values = rvalues + i*nmax;
1780:     for (j=0; j<lens[i]; j++) {
1781:       lrows[count++] = values[j] - base;
1782:     }
1783:   }
1784:   PetscFree(rvalues);
1785:   PetscFree(lens);
1786:   PetscFree(owner);
1787:   PetscFree(nprocs);
1788: 
1789:   /* actually zap the local rows */
1790:   ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);
1791:   PetscLogObjectParent(A,istmp);

1793:   /*
1794:         Zero the required rows. If the "diagonal block" of the matrix
1795:      is square and the user wishes to set the diagonal we use seperate
1796:      code so that MatSetValues() is not called for each diagonal allocating
1797:      new memory, thus calling lots of mallocs and slowing things down.

1799:        Contributed by: Mathew Knepley
1800:   */
1801:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1802:   MatZeroRows_SeqBAIJ(l->B,istmp,0);
1803:   if (diag && (l->A->M == l->A->N)) {
1804:     MatZeroRows_SeqBAIJ(l->A,istmp,diag);
1805:   } else if (diag) {
1806:     MatZeroRows_SeqBAIJ(l->A,istmp,0);
1807:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) {
1808:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1809: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1810:     }
1811:     for (i=0; i<slen; i++) {
1812:       row  = lrows[i] + rstart_bs;
1813:       MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);
1814:     }
1815:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1816:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1817:   } else {
1818:     MatZeroRows_SeqBAIJ(l->A,istmp,0);
1819:   }

1821:   ISDestroy(istmp);
1822:   PetscFree(lrows);

1824:   /* wait on sends */
1825:   if (nsends) {
1826:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1827:     MPI_Waitall(nsends,send_waits,send_status);
1828:     PetscFree(send_status);
1829:   }
1830:   PetscFree(send_waits);
1831:   PetscFree(svalues);

1833:   return(0);
1834: }

1838: PetscErrorCode MatPrintHelp_MPIBAIJ(Mat A)
1839: {
1840:   Mat_MPIBAIJ       *a   = (Mat_MPIBAIJ*)A->data;
1841:   MPI_Comm          comm = A->comm;
1842:   static PetscTruth called = PETSC_FALSE;
1843:   PetscErrorCode    ierr;

1846:   if (!a->rank) {
1847:     MatPrintHelp_SeqBAIJ(a->A);
1848:   }
1849:   if (called) {return(0);} else called = PETSC_TRUE;
1850:   (*PetscHelpPrintf)(comm," Options for MATMPIBAIJ matrix format (the defaults):\n");
1851:   (*PetscHelpPrintf)(comm,"  -mat_use_hash_table <factor>: Use hashtable for efficient matrix assembly\n");
1852:   return(0);
1853: }

1857: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1858: {
1859:   Mat_MPIBAIJ    *a   = (Mat_MPIBAIJ*)A->data;

1863:   MatSetUnfactored(a->A);
1864:   return(0);
1865: }

1867: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat *);

1871: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscTruth *flag)
1872: {
1873:   Mat_MPIBAIJ    *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1874:   Mat            a,b,c,d;
1875:   PetscTruth     flg;

1879:   a = matA->A; b = matA->B;
1880:   c = matB->A; d = matB->B;

1882:   MatEqual(a,c,&flg);
1883:   if (flg == PETSC_TRUE) {
1884:     MatEqual(b,d,&flg);
1885:   }
1886:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1887:   return(0);
1888: }


1893: PetscErrorCode MatSetUpPreallocation_MPIBAIJ(Mat A)
1894: {

1898:    MatMPIBAIJSetPreallocation(A,1,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1899:   return(0);
1900: }

1902: /* -------------------------------------------------------------------*/
1903: static struct _MatOps MatOps_Values = {
1904:        MatSetValues_MPIBAIJ,
1905:        MatGetRow_MPIBAIJ,
1906:        MatRestoreRow_MPIBAIJ,
1907:        MatMult_MPIBAIJ,
1908: /* 4*/ MatMultAdd_MPIBAIJ,
1909:        MatMultTranspose_MPIBAIJ,
1910:        MatMultTransposeAdd_MPIBAIJ,
1911:        0,
1912:        0,
1913:        0,
1914: /*10*/ 0,
1915:        0,
1916:        0,
1917:        0,
1918:        MatTranspose_MPIBAIJ,
1919: /*15*/ MatGetInfo_MPIBAIJ,
1920:        MatEqual_MPIBAIJ,
1921:        MatGetDiagonal_MPIBAIJ,
1922:        MatDiagonalScale_MPIBAIJ,
1923:        MatNorm_MPIBAIJ,
1924: /*20*/ MatAssemblyBegin_MPIBAIJ,
1925:        MatAssemblyEnd_MPIBAIJ,
1926:        0,
1927:        MatSetOption_MPIBAIJ,
1928:        MatZeroEntries_MPIBAIJ,
1929: /*25*/ MatZeroRows_MPIBAIJ,
1930:        0,
1931:        0,
1932:        0,
1933:        0,
1934: /*30*/ MatSetUpPreallocation_MPIBAIJ,
1935:        0,
1936:        0,
1937:        0,
1938:        0,
1939: /*35*/ MatDuplicate_MPIBAIJ,
1940:        0,
1941:        0,
1942:        0,
1943:        0,
1944: /*40*/ 0,
1945:        MatGetSubMatrices_MPIBAIJ,
1946:        MatIncreaseOverlap_MPIBAIJ,
1947:        MatGetValues_MPIBAIJ,
1948:        0,
1949: /*45*/ MatPrintHelp_MPIBAIJ,
1950:        MatScale_MPIBAIJ,
1951:        0,
1952:        0,
1953:        0,
1954: /*50*/ 0,
1955:        0,
1956:        0,
1957:        0,
1958:        0,
1959: /*55*/ 0,
1960:        0,
1961:        MatSetUnfactored_MPIBAIJ,
1962:        0,
1963:        MatSetValuesBlocked_MPIBAIJ,
1964: /*60*/ 0,
1965:        MatDestroy_MPIBAIJ,
1966:        MatView_MPIBAIJ,
1967:        MatGetPetscMaps_Petsc,
1968:        0,
1969: /*65*/ 0,
1970:        0,
1971:        0,
1972:        0,
1973:        0,
1974: /*70*/ MatGetRowMax_MPIBAIJ,
1975:        0,
1976:        0,
1977:        0,
1978:        0,
1979: /*75*/ 0,
1980:        0,
1981:        0,
1982:        0,
1983:        0,
1984: /*80*/ 0,
1985:        0,
1986:        0,
1987:        0,
1988:        MatLoad_MPIBAIJ,
1989: /*85*/ 0,
1990:        0,
1991:        0,
1992:        0,
1993:        0,
1994: /*90*/ 0,
1995:        0,
1996:        0,
1997:        0,
1998:        0,
1999: /*95*/ 0,
2000:        0,
2001:        0,
2002:        0};


2008: PetscErrorCode MatGetDiagonalBlock_MPIBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
2009: {
2011:   *a      = ((Mat_MPIBAIJ *)A->data)->A;
2012:   *iscopy = PETSC_FALSE;
2013:   return(0);
2014: }


2023: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt I[],const PetscInt J[],const PetscScalar v[])
2024: {
2025:   Mat_MPIBAIJ    *b  = (Mat_MPIBAIJ *)B->data;
2026:   PetscInt       m = B->m/bs,cstart = b->cstart, cend = b->cend,j,nnz,i,d;
2027:   PetscInt       *d_nnz,*o_nnz,nnz_max = 0,rstart = b->rstart,ii;
2028:   const PetscInt *JJ;
2029:   PetscScalar    *values;

2033: #if defined(PETSC_OPT_g)
2034:   if (I[0]) SETERRQ1(PETSC_ERR_ARG_RANGE,"I[0] must be 0 it is %D",I[0]);
2035: #endif
2036:   PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
2037:   o_nnz = d_nnz + m;

2039:   for (i=0; i<m; i++) {
2040:     nnz     = I[i+1]- I[i];
2041:     JJ      = J + I[i];
2042:     nnz_max = PetscMax(nnz_max,nnz);
2043: #if defined(PETSC_OPT_g)
2044:     if (nnz < 0) SETERRQ1(PETSC_ERR_ARG_RANGE,"Local row %D has a negative %D number of columns",i,nnz);
2045: #endif
2046:     for (j=0; j<nnz; j++) {
2047:       if (*JJ >= cstart) break;
2048:       JJ++;
2049:     }
2050:     d = 0;
2051:     for (; j<nnz; j++) {
2052:       if (*JJ++ >= cend) break;
2053:       d++;
2054:     }
2055:     d_nnz[i] = d;
2056:     o_nnz[i] = nnz - d;
2057:   }
2058:   MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
2059:   PetscFree(d_nnz);

2061:   if (v) values = (PetscScalar*)v;
2062:   else {
2063:     PetscMalloc(bs*bs*(nnz_max+1)*sizeof(PetscScalar),&values);
2064:     PetscMemzero(values,bs*bs*nnz_max*sizeof(PetscScalar));
2065:   }

2067:   MatSetOption(B,MAT_COLUMNS_SORTED);
2068:   for (i=0; i<m; i++) {
2069:     ii   = i + rstart;
2070:     nnz  = I[i+1]- I[i];
2071:     MatSetValuesBlocked_MPIBAIJ(B,1,&ii,nnz,J+I[i],values,INSERT_VALUES);
2072:   }
2073:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2074:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2075:   MatSetOption(B,MAT_COLUMNS_UNSORTED);

2077:   if (!v) {
2078:     PetscFree(values);
2079:   }
2080:   return(0);
2081: }

2085: /*@C
2086:    MatMPIBAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
2087:    (the default parallel PETSc format).  

2089:    Collective on MPI_Comm

2091:    Input Parameters:
2092: +  A - the matrix 
2093: .  i - the indices into j for the start of each local row (starts with zero)
2094: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
2095: -  v - optional values in the matrix

2097:    Level: developer

2099: .keywords: matrix, aij, compressed row, sparse, parallel

2101: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ
2102: @*/
2103: PetscErrorCode MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2104: {
2105:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]);

2108:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",(void (**)(void))&f);
2109:   if (f) {
2110:     (*f)(B,bs,i,j,v);
2111:   }
2112:   return(0);
2113: }

2118: PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,PetscInt *d_nnz,PetscInt o_nz,PetscInt *o_nnz)
2119: {
2120:   Mat_MPIBAIJ    *b;
2122:   PetscInt       i;

2125:   B->preallocated = PETSC_TRUE;
2126:   PetscOptionsGetInt(PETSC_NULL,"-mat_block_size",&bs,PETSC_NULL);

2128:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
2129:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2130:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2131:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
2132:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);
2133:   if (d_nnz) {
2134:   for (i=0; i<B->m/bs; i++) {
2135:       if (d_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than -1: local row %D value %D",i,d_nnz[i]);
2136:     }
2137:   }
2138:   if (o_nnz) {
2139:     for (i=0; i<B->m/bs; i++) {
2140:       if (o_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than -1: local row %D value %D",i,o_nnz[i]);
2141:     }
2142:   }
2143: 
2144:   PetscSplitOwnershipBlock(B->comm,bs,&B->m,&B->M);
2145:   PetscSplitOwnershipBlock(B->comm,bs,&B->n,&B->N);
2146:   PetscMapCreateMPI(B->comm,B->m,B->M,&B->rmap);
2147:   PetscMapCreateMPI(B->comm,B->n,B->N,&B->cmap);

2149:   b = (Mat_MPIBAIJ*)B->data;
2150:   B->bs  = bs;
2151:   b->bs2 = bs*bs;
2152:   b->mbs = B->m/bs;
2153:   b->nbs = B->n/bs;
2154:   b->Mbs = B->M/bs;
2155:   b->Nbs = B->N/bs;

2157:   MPI_Allgather(&b->mbs,1,MPIU_INT,b->rowners+1,1,MPIU_INT,B->comm);
2158:   b->rowners[0]    = 0;
2159:   for (i=2; i<=b->size; i++) {
2160:     b->rowners[i] += b->rowners[i-1];
2161:   }
2162:   b->rstart    = b->rowners[b->rank];
2163:   b->rend      = b->rowners[b->rank+1];

2165:   MPI_Allgather(&b->nbs,1,MPIU_INT,b->cowners+1,1,MPIU_INT,B->comm);
2166:   b->cowners[0] = 0;
2167:   for (i=2; i<=b->size; i++) {
2168:     b->cowners[i] += b->cowners[i-1];
2169:   }
2170:   b->cstart    = b->cowners[b->rank];
2171:   b->cend      = b->cowners[b->rank+1];

2173:   for (i=0; i<=b->size; i++) {
2174:     b->rowners_bs[i] = b->rowners[i]*bs;
2175:   }
2176:   b->rstart_bs = b->rstart*bs;
2177:   b->rend_bs   = b->rend*bs;
2178:   b->cstart_bs = b->cstart*bs;
2179:   b->cend_bs   = b->cend*bs;

2181:   MatCreate(PETSC_COMM_SELF,B->m,B->n,B->m,B->n,&b->A);
2182:   MatSetType(b->A,MATSEQBAIJ);
2183:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
2184:   PetscLogObjectParent(B,b->A);
2185:   MatCreate(PETSC_COMM_SELF,B->m,B->N,B->m,B->N,&b->B);
2186:   MatSetType(b->B,MATSEQBAIJ);
2187:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
2188:   PetscLogObjectParent(B,b->B);

2190:   MatStashCreate_Private(B->comm,bs,&B->bstash);

2192:   return(0);
2193: }

2197: EXTERN PetscErrorCode MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2198: EXTERN PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);

2201: /*MC
2202:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

2204:    Options Database Keys:
2205: . -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()

2207:   Level: beginner

2209: .seealso: MatCreateMPIBAIJ
2210: M*/

2215: PetscErrorCode MatCreate_MPIBAIJ(Mat B)
2216: {
2217:   Mat_MPIBAIJ    *b;
2219:   PetscTruth     flg;

2222:   PetscNew(Mat_MPIBAIJ,&b);
2223:   B->data = (void*)b;

2225:   PetscMemzero(b,sizeof(Mat_MPIBAIJ));
2226:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2227:   B->mapping    = 0;
2228:   B->factor     = 0;
2229:   B->assembled  = PETSC_FALSE;

2231:   B->insertmode = NOT_SET_VALUES;
2232:   MPI_Comm_rank(B->comm,&b->rank);
2233:   MPI_Comm_size(B->comm,&b->size);

2235:   /* build local table of row and column ownerships */
2236:   PetscMalloc(3*(b->size+2)*sizeof(PetscInt),&b->rowners);
2237:   PetscLogObjectMemory(B,3*(b->size+2)*sizeof(PetscInt)+sizeof(struct _p_Mat)+sizeof(Mat_MPIBAIJ));
2238:   b->cowners    = b->rowners + b->size + 2;
2239:   b->rowners_bs = b->cowners + b->size + 2;

2241:   /* build cache for off array entries formed */
2242:   MatStashCreate_Private(B->comm,1,&B->stash);
2243:   b->donotstash  = PETSC_FALSE;
2244:   b->colmap      = PETSC_NULL;
2245:   b->garray      = PETSC_NULL;
2246:   b->roworiented = PETSC_TRUE;

2248: #if defined(PETSC_USE_MAT_SINGLE)
2249:   /* stuff for MatSetValues_XXX in single precision */
2250:   b->setvalueslen     = 0;
2251:   b->setvaluescopy    = PETSC_NULL;
2252: #endif

2254:   /* stuff used in block assembly */
2255:   b->barray       = 0;

2257:   /* stuff used for matrix vector multiply */
2258:   b->lvec         = 0;
2259:   b->Mvctx        = 0;

2261:   /* stuff for MatGetRow() */
2262:   b->rowindices   = 0;
2263:   b->rowvalues    = 0;
2264:   b->getrowactive = PETSC_FALSE;

2266:   /* hash table stuff */
2267:   b->ht           = 0;
2268:   b->hd           = 0;
2269:   b->ht_size      = 0;
2270:   b->ht_flag      = PETSC_FALSE;
2271:   b->ht_fact      = 0;
2272:   b->ht_total_ct  = 0;
2273:   b->ht_insert_ct = 0;

2275:   PetscOptionsHasName(PETSC_NULL,"-mat_use_hash_table",&flg);
2276:   if (flg) {
2277:     PetscReal fact = 1.39;
2278:     MatSetOption(B,MAT_USE_HASH_TABLE);
2279:     PetscOptionsGetReal(PETSC_NULL,"-mat_use_hash_table",&fact,PETSC_NULL);
2280:     if (fact <= 1.0) fact = 1.39;
2281:     MatMPIBAIJSetHashTableFactor(B,fact);
2282:     PetscLogInfo(0,"MatCreateMPIBAIJ:Hash table Factor used %5.2f\n",fact);
2283:   }
2284:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2285:                                      "MatStoreValues_MPIBAIJ",
2286:                                      MatStoreValues_MPIBAIJ);
2287:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2288:                                      "MatRetrieveValues_MPIBAIJ",
2289:                                      MatRetrieveValues_MPIBAIJ);
2290:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
2291:                                      "MatGetDiagonalBlock_MPIBAIJ",
2292:                                      MatGetDiagonalBlock_MPIBAIJ);
2293:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocation_C",
2294:                                      "MatMPIBAIJSetPreallocation_MPIBAIJ",
2295:                                      MatMPIBAIJSetPreallocation_MPIBAIJ);
2296:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",
2297:                                      "MatMPIBAIJSetPreallocationCSR_MPIAIJ",
2298:                                      MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
2299:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
2300:                                      "MatDiagonalScaleLocal_MPIBAIJ",
2301:                                      MatDiagonalScaleLocal_MPIBAIJ);
2302:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetHashTableFactor_C",
2303:                                      "MatSetHashTableFactor_MPIBAIJ",
2304:                                      MatSetHashTableFactor_MPIBAIJ);
2305:   return(0);
2306: }

2309: /*MC
2310:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

2312:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
2313:    and MATMPIBAIJ otherwise.

2315:    Options Database Keys:
2316: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

2318:   Level: beginner

2320: .seealso: MatCreateMPIBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2321: M*/

2326: PetscErrorCode MatCreate_BAIJ(Mat A)
2327: {
2329:   PetscMPIInt    size;

2332:   PetscObjectChangeTypeName((PetscObject)A,MATBAIJ);
2333:   MPI_Comm_size(A->comm,&size);
2334:   if (size == 1) {
2335:     MatSetType(A,MATSEQBAIJ);
2336:   } else {
2337:     MatSetType(A,MATMPIBAIJ);
2338:   }
2339:   return(0);
2340: }

2345: /*@C
2346:    MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
2347:    (block compressed row).  For good matrix assembly performance
2348:    the user should preallocate the matrix storage by setting the parameters 
2349:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2350:    performance can be increased by more than a factor of 50.

2352:    Collective on Mat

2354:    Input Parameters:
2355: +  A - the matrix 
2356: .  bs   - size of blockk
2357: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
2358:            submatrix  (same for all local rows)
2359: .  d_nnz - array containing the number of block nonzeros in the various block rows 
2360:            of the in diagonal portion of the local (possibly different for each block
2361:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2362: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
2363:            submatrix (same for all local rows).
2364: -  o_nnz - array containing the number of nonzeros in the various block rows of the
2365:            off-diagonal portion of the local submatrix (possibly different for
2366:            each block row) or PETSC_NULL.

2368:    If the *_nnz parameter is given then the *_nz parameter is ignored

2370:    Options Database Keys:
2371: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2372:                      block calculations (much slower)
2373: .   -mat_block_size - size of the blocks to use

2375:    Notes:
2376:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2377:    than it must be used on all processors that share the object for that argument.

2379:    Storage Information:
2380:    For a square global matrix we define each processor's diagonal portion 
2381:    to be its local rows and the corresponding columns (a square submatrix);  
2382:    each processor's off-diagonal portion encompasses the remainder of the
2383:    local matrix (a rectangular submatrix). 

2385:    The user can specify preallocated storage for the diagonal part of
2386:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2387:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2388:    memory allocation.  Likewise, specify preallocated storage for the
2389:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2391:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2392:    the figure below we depict these three local rows and all columns (0-11).

2394: .vb
2395:            0 1 2 3 4 5 6 7 8 9 10 11
2396:           -------------------
2397:    row 3  |  o o o d d d o o o o o o
2398:    row 4  |  o o o d d d o o o o o o
2399:    row 5  |  o o o d d d o o o o o o
2400:           -------------------
2401: .ve
2402:   
2403:    Thus, any entries in the d locations are stored in the d (diagonal) 
2404:    submatrix, and any entries in the o locations are stored in the
2405:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2406:    stored simply in the MATSEQBAIJ format for compressed row storage.

2408:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2409:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2410:    In general, for PDE problems in which most nonzeros are near the diagonal,
2411:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2412:    or you will get TERRIBLE performance; see the users' manual chapter on
2413:    matrices.

2415:    Level: intermediate

2417: .keywords: matrix, block, aij, compressed row, sparse, parallel

2419: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocationCSR()
2420: @*/
2421: PetscErrorCode MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2422: {
2423:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

2426:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",(void (**)(void))&f);
2427:   if (f) {
2428:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
2429:   }
2430:   return(0);
2431: }

2435: /*@C
2436:    MatCreateMPIBAIJ - Creates a sparse parallel matrix in block AIJ format
2437:    (block compressed row).  For good matrix assembly performance
2438:    the user should preallocate the matrix storage by setting the parameters 
2439:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2440:    performance can be increased by more than a factor of 50.

2442:    Collective on MPI_Comm

2444:    Input Parameters:
2445: +  comm - MPI communicator
2446: .  bs   - size of blockk
2447: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2448:            This value should be the same as the local size used in creating the 
2449:            y vector for the matrix-vector product y = Ax.
2450: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
2451:            This value should be the same as the local size used in creating the 
2452:            x vector for the matrix-vector product y = Ax.
2453: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2454: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2455: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local 
2456:            submatrix  (same for all local rows)
2457: .  d_nnz - array containing the number of nonzero blocks in the various block rows 
2458:            of the in diagonal portion of the local (possibly different for each block
2459:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2460: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
2461:            submatrix (same for all local rows).
2462: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
2463:            off-diagonal portion of the local submatrix (possibly different for
2464:            each block row) or PETSC_NULL.

2466:    Output Parameter:
2467: .  A - the matrix 

2469:    Options Database Keys:
2470: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2471:                      block calculations (much slower)
2472: .   -mat_block_size - size of the blocks to use

2474:    Notes:
2475:    If the *_nnz parameter is given then the *_nz parameter is ignored

2477:    A nonzero block is any block that as 1 or more nonzeros in it

2479:    The user MUST specify either the local or global matrix dimensions
2480:    (possibly both).

2482:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2483:    than it must be used on all processors that share the object for that argument.

2485:    Storage Information:
2486:    For a square global matrix we define each processor's diagonal portion 
2487:    to be its local rows and the corresponding columns (a square submatrix);  
2488:    each processor's off-diagonal portion encompasses the remainder of the
2489:    local matrix (a rectangular submatrix). 

2491:    The user can specify preallocated storage for the diagonal part of
2492:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2493:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2494:    memory allocation.  Likewise, specify preallocated storage for the
2495:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2497:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2498:    the figure below we depict these three local rows and all columns (0-11).

2500: .vb
2501:            0 1 2 3 4 5 6 7 8 9 10 11
2502:           -------------------
2503:    row 3  |  o o o d d d o o o o o o
2504:    row 4  |  o o o d d d o o o o o o
2505:    row 5  |  o o o d d d o o o o o o
2506:           -------------------
2507: .ve
2508:   
2509:    Thus, any entries in the d locations are stored in the d (diagonal) 
2510:    submatrix, and any entries in the o locations are stored in the
2511:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2512:    stored simply in the MATSEQBAIJ format for compressed row storage.

2514:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2515:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2516:    In general, for PDE problems in which most nonzeros are near the diagonal,
2517:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2518:    or you will get TERRIBLE performance; see the users' manual chapter on
2519:    matrices.

2521:    Level: intermediate

2523: .keywords: matrix, block, aij, compressed row, sparse, parallel

2525: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2526: @*/
2527: PetscErrorCode MatCreateMPIBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
2528: {
2530:   PetscMPIInt    size;

2533:   MatCreate(comm,m,n,M,N,A);
2534:   MPI_Comm_size(comm,&size);
2535:   if (size > 1) {
2536:     MatSetType(*A,MATMPIBAIJ);
2537:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2538:   } else {
2539:     MatSetType(*A,MATSEQBAIJ);
2540:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2541:   }
2542:   return(0);
2543: }

2547: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2548: {
2549:   Mat            mat;
2550:   Mat_MPIBAIJ    *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
2552:   PetscInt       len=0;

2555:   *newmat       = 0;
2556:   MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);
2557:   MatSetType(mat,matin->type_name);
2558:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));

2560:   mat->factor       = matin->factor;
2561:   mat->preallocated = PETSC_TRUE;
2562:   mat->assembled    = PETSC_TRUE;
2563:   mat->insertmode   = NOT_SET_VALUES;

2565:   a      = (Mat_MPIBAIJ*)mat->data;
2566:   mat->bs  = matin->bs;
2567:   a->bs2   = oldmat->bs2;
2568:   a->mbs   = oldmat->mbs;
2569:   a->nbs   = oldmat->nbs;
2570:   a->Mbs   = oldmat->Mbs;
2571:   a->Nbs   = oldmat->Nbs;
2572: 
2573:   a->rstart       = oldmat->rstart;
2574:   a->rend         = oldmat->rend;
2575:   a->cstart       = oldmat->cstart;
2576:   a->cend         = oldmat->cend;
2577:   a->size         = oldmat->size;
2578:   a->rank         = oldmat->rank;
2579:   a->donotstash   = oldmat->donotstash;
2580:   a->roworiented  = oldmat->roworiented;
2581:   a->rowindices   = 0;
2582:   a->rowvalues    = 0;
2583:   a->getrowactive = PETSC_FALSE;
2584:   a->barray       = 0;
2585:   a->rstart_bs    = oldmat->rstart_bs;
2586:   a->rend_bs      = oldmat->rend_bs;
2587:   a->cstart_bs    = oldmat->cstart_bs;
2588:   a->cend_bs      = oldmat->cend_bs;

2590:   /* hash table stuff */
2591:   a->ht           = 0;
2592:   a->hd           = 0;
2593:   a->ht_size      = 0;
2594:   a->ht_flag      = oldmat->ht_flag;
2595:   a->ht_fact      = oldmat->ht_fact;
2596:   a->ht_total_ct  = 0;
2597:   a->ht_insert_ct = 0;

2599:   PetscMemcpy(a->rowners,oldmat->rowners,3*(a->size+2)*sizeof(PetscInt));
2600:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2601:   MatStashCreate_Private(matin->comm,matin->bs,&mat->bstash);
2602:   if (oldmat->colmap) {
2603: #if defined (PETSC_USE_CTABLE)
2604:   PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2605: #else
2606:   PetscMalloc((a->Nbs)*sizeof(PetscInt),&a->colmap);
2607:   PetscLogObjectMemory(mat,(a->Nbs)*sizeof(PetscInt));
2608:   PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
2609: #endif
2610:   } else a->colmap = 0;

2612:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2613:     PetscMalloc(len*sizeof(PetscInt),&a->garray);
2614:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2615:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
2616:   } else a->garray = 0;
2617: 
2618:    VecDuplicate(oldmat->lvec,&a->lvec);
2619:   PetscLogObjectParent(mat,a->lvec);
2620:    VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2621:   PetscLogObjectParent(mat,a->Mvctx);

2623:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2624:   PetscLogObjectParent(mat,a->A);
2625:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2626:   PetscLogObjectParent(mat,a->B);
2627:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2628:   *newmat = mat;

2630:   return(0);
2631: }

2633:  #include petscsys.h

2637: PetscErrorCode MatLoad_MPIBAIJ(PetscViewer viewer,const MatType type,Mat *newmat)
2638: {
2639:   Mat            A;
2641:   int            fd;
2642:   PetscInt       i,nz,j,rstart,rend;
2643:   PetscScalar    *vals,*buf;
2644:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2645:   MPI_Status     status;
2646:   PetscMPIInt    rank,size,maxnz;
2647:   PetscInt       header[4],*rowlengths = 0,M,N,m,*rowners,*cols;
2648:   PetscInt       *locrowlens,*procsnz = 0,*browners;
2649:   PetscInt       jj,*mycols,*ibuf,bs=1,Mbs,mbs,extra_rows;
2650:   PetscMPIInt    tag = ((PetscObject)viewer)->tag;
2651:   PetscInt       *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
2652:   PetscInt       dcount,kmax,k,nzcount,tmp,mend;
2653: 
2655:   PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);

2657:   MPI_Comm_size(comm,&size);
2658:   MPI_Comm_rank(comm,&rank);
2659:   if (!rank) {
2660:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2661:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2662:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2663:   }

2665:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2666:   M = header[1]; N = header[2];

2668:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2670:   /* 
2671:      This code adds extra rows to make sure the number of rows is 
2672:      divisible by the blocksize
2673:   */
2674:   Mbs        = M/bs;
2675:   extra_rows = bs - M + bs*Mbs;
2676:   if (extra_rows == bs) extra_rows = 0;
2677:   else                  Mbs++;
2678:   if (extra_rows && !rank) {
2679:     PetscLogInfo(0,"MatLoad_MPIBAIJ:Padding loaded matrix to match blocksize\n");
2680:   }

2682:   /* determine ownership of all rows */
2683:   mbs        = Mbs/size + ((Mbs % size) > rank);
2684:   m          = mbs*bs;
2685:   PetscMalloc2(size+1,PetscInt,&rowners,size+1,PetscInt,&browners);
2686:   MPI_Allgather(&mbs,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
2687:   rowners[0] = 0;
2688:   for (i=2; i<=size; i++)  rowners[i] += rowners[i-1];
2689:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2690:   rstart = rowners[rank];
2691:   rend   = rowners[rank+1];

2693:   /* distribute row lengths to all processors */
2694:   PetscMalloc(m*sizeof(PetscInt),&locrowlens);
2695:   if (!rank) {
2696:     mend = m;
2697:     if (size == 1) mend = mend - extra_rows;
2698:     PetscBinaryRead(fd,locrowlens,mend,PETSC_INT);
2699:     for (j=mend; j<m; j++) locrowlens[j] = 1;
2700:     PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2701:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2702:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2703:     for (j=0; j<m; j++) {
2704:       procsnz[0] += locrowlens[j];
2705:     }
2706:     for (i=1; i<size; i++) {
2707:       mend = browners[i+1] - browners[i];
2708:       if (i == size-1) mend = mend - extra_rows;
2709:       PetscBinaryRead(fd,rowlengths,mend,PETSC_INT);
2710:       for (j=mend; j<browners[i+1] - browners[i]; j++) rowlengths[j] = 1;
2711:       /* calculate the number of nonzeros on each processor */
2712:       for (j=0; j<browners[i+1]-browners[i]; j++) {
2713:         procsnz[i] += rowlengths[j];
2714:       }
2715:       MPI_Send(rowlengths,browners[i+1]-browners[i],MPIU_INT,i,tag,comm);
2716:     }
2717:     PetscFree(rowlengths);
2718:   } else {
2719:     MPI_Recv(locrowlens,m,MPIU_INT,0,tag,comm,&status);
2720:   }

2722:   if (!rank) {
2723:     /* determine max buffer needed and allocate it */
2724:     maxnz = procsnz[0];
2725:     for (i=1; i<size; i++) {
2726:       maxnz = PetscMax(maxnz,procsnz[i]);
2727:     }
2728:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2730:     /* read in my part of the matrix column indices  */
2731:     nz     = procsnz[0];
2732:     PetscMalloc(nz*sizeof(PetscInt),&ibuf);
2733:     mycols = ibuf;
2734:     if (size == 1)  nz -= extra_rows;
2735:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2736:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2738:     /* read in every ones (except the last) and ship off */
2739:     for (i=1; i<size-1; i++) {
2740:       nz   = procsnz[i];
2741:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2742:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2743:     }
2744:     /* read in the stuff for the last proc */
2745:     if (size != 1) {
2746:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2747:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2748:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2749:       MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
2750:     }
2751:     PetscFree(cols);
2752:   } else {
2753:     /* determine buffer space needed for message */
2754:     nz = 0;
2755:     for (i=0; i<m; i++) {
2756:       nz += locrowlens[i];
2757:     }
2758:     PetscMalloc(nz*sizeof(PetscInt),&ibuf);
2759:     mycols = ibuf;
2760:     /* receive message of column indices*/
2761:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2762:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2763:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2764:   }
2765: 
2766:   /* loop over local rows, determining number of off diagonal entries */
2767:   PetscMalloc2(rend-rstart,PetscInt,&dlens,rend-rstart,PetscInt,&odlens);
2768:   PetscMalloc3(Mbs,PetscInt,&mask,Mbs,PetscInt,&masked1,Mbs,PetscInt,&masked2);
2769:   PetscMemzero(mask,Mbs*sizeof(PetscInt));
2770:   PetscMemzero(masked1,Mbs*sizeof(PetscInt));
2771:   PetscMemzero(masked2,Mbs*sizeof(PetscInt));
2772:   rowcount = 0; nzcount = 0;
2773:   for (i=0; i<mbs; i++) {
2774:     dcount  = 0;
2775:     odcount = 0;
2776:     for (j=0; j<bs; j++) {
2777:       kmax = locrowlens[rowcount];
2778:       for (k=0; k<kmax; k++) {
2779:         tmp = mycols[nzcount++]/bs;
2780:         if (!mask[tmp]) {
2781:           mask[tmp] = 1;
2782:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
2783:           else masked1[dcount++] = tmp;
2784:         }
2785:       }
2786:       rowcount++;
2787:     }
2788: 
2789:     dlens[i]  = dcount;
2790:     odlens[i] = odcount;

2792:     /* zero out the mask elements we set */
2793:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2794:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2795:   }

2797:   /* create our matrix */
2798:   MatCreate(comm,m,m,M+extra_rows,N+extra_rows,&A);
2799:   MatSetType(A,type);CHKERRQ(ierr)
2800:   MatMPIBAIJSetPreallocation(A,bs,0,dlens,0,odlens);

2802:   /* Why doesn't this called using MatSetOption(A,MAT_COLUMNS_SORTED); */
2803:   MatSetOption(A,MAT_COLUMNS_SORTED);
2804: 
2805:   if (!rank) {
2806:     PetscMalloc(maxnz*sizeof(PetscScalar),&buf);
2807:     /* read in my part of the matrix numerical values  */
2808:     nz = procsnz[0];
2809:     vals = buf;
2810:     mycols = ibuf;
2811:     if (size == 1)  nz -= extra_rows;
2812:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2813:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2815:     /* insert into matrix */
2816:     jj      = rstart*bs;
2817:     for (i=0; i<m; i++) {
2818:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2819:       mycols += locrowlens[i];
2820:       vals   += locrowlens[i];
2821:       jj++;
2822:     }
2823:     /* read in other processors (except the last one) and ship out */
2824:     for (i=1; i<size-1; i++) {
2825:       nz   = procsnz[i];
2826:       vals = buf;
2827:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2828:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2829:     }
2830:     /* the last proc */
2831:     if (size != 1){
2832:       nz   = procsnz[i] - extra_rows;
2833:       vals = buf;
2834:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2835:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2836:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2837:     }
2838:     PetscFree(procsnz);
2839:   } else {
2840:     /* receive numeric values */
2841:     PetscMalloc(nz*sizeof(PetscScalar),&buf);

2843:     /* receive message of values*/
2844:     vals   = buf;
2845:     mycols = ibuf;
2846:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2847:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2848:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2850:     /* insert into matrix */
2851:     jj      = rstart*bs;
2852:     for (i=0; i<m; i++) {
2853:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2854:       mycols += locrowlens[i];
2855:       vals   += locrowlens[i];
2856:       jj++;
2857:     }
2858:   }
2859:   PetscFree(locrowlens);
2860:   PetscFree(buf);
2861:   PetscFree(ibuf);
2862:   PetscFree2(rowners,browners);
2863:   PetscFree2(dlens,odlens);
2864:   PetscFree3(mask,masked1,masked2);
2865:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2866:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

2868:   *newmat = A;
2869:   return(0);
2870: }

2874: /*@
2875:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2877:    Input Parameters:
2878: .  mat  - the matrix
2879: .  fact - factor

2881:    Collective on Mat

2883:    Level: advanced

2885:   Notes:
2886:    This can also be set by the command line option: -mat_use_hash_table fact

2888: .keywords: matrix, hashtable, factor, HT

2890: .seealso: MatSetOption()
2891: @*/
2892: PetscErrorCode MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2893: {
2894:   PetscErrorCode ierr,(*f)(Mat,PetscReal);

2897:   PetscObjectQueryFunction((PetscObject)mat,"MatSetHashTableFactor_C",(void (**)(void))&f);
2898:   if (f) {
2899:     (*f)(mat,fact);
2900:   }
2901:   return(0);
2902: }

2906: PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
2907: {
2908:   Mat_MPIBAIJ *baij;

2911:   baij = (Mat_MPIBAIJ*)mat->data;
2912:   baij->ht_fact = fact;
2913:   return(0);
2914: }

2918: PetscErrorCode MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
2919: {
2920:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2922:   *Ad     = a->A;
2923:   *Ao     = a->B;
2924:   *colmap = a->garray;
2925:   return(0);
2926: }