Actual source code: mpibaij.c

  1: #define PETSCMAT_DLL

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

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

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

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

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

 53:   VecCreateSeq(PETSC_COMM_SELF,A->rmap.n,&vtmp);
 54:   MatGetRowMax(a->B,vtmp);
 55:   VecGetArray(vtmp,&vb);

 57:   for (i=0; i<A->rmap.n; i++){
 58:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) va[i] = vb[i];
 59:   }

 61:   VecRestoreArray(v,&va);
 62:   VecRestoreArray(vtmp,&vb);
 63:   VecDestroy(vtmp);
 64: 
 65:   return(0);
 66: }

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

 77:   MatStoreValues(aij->A);
 78:   MatStoreValues(aij->B);
 79:   return(0);
 80: }

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

 92:   MatRetrieveValues(aij->A);
 93:   MatRetrieveValues(aij->B);
 94:   return(0);
 95: }

 98: /* 
 99:      Local utility routine that creates a mapping from the global column 
100:    number to the local number in the off-diagonal part of the local 
101:    storage of the matrix.  This is done in a non scable way since the 
102:    length of colmap equals the global matrix length. 
103: */
106: PetscErrorCode CreateColmap_MPIBAIJ_Private(Mat mat)
107: {
108:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
109:   Mat_SeqBAIJ    *B = (Mat_SeqBAIJ*)baij->B->data;
111:   PetscInt       nbs = B->nbs,i,bs=mat->rmap.bs;

114: #if defined (PETSC_USE_CTABLE)
115:   PetscTableCreate(baij->nbs,&baij->colmap);
116:   for (i=0; i<nbs; i++){
117:     PetscTableAdd(baij->colmap,baij->garray[i]+1,i*bs+1);
118:   }
119: #else
120:   PetscMalloc((baij->Nbs+1)*sizeof(PetscInt),&baij->colmap);
121:   PetscLogObjectMemory(mat,baij->Nbs*sizeof(PetscInt));
122:   PetscMemzero(baij->colmap,baij->Nbs*sizeof(PetscInt));
123:   for (i=0; i<nbs; i++) baij->colmap[baij->garray[i]] = i*bs+1;
124: #endif
125:   return(0);
126: }

128: #define CHUNKSIZE  10

130: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv) \
131: { \
132:  \
133:     brow = row/bs;  \
134:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
135:     rmax = aimax[brow]; nrow = ailen[brow]; \
136:       bcol = col/bs; \
137:       ridx = row % bs; cidx = col % bs; \
138:       low = 0; high = nrow; \
139:       while (high-low > 3) { \
140:         t = (low+high)/2; \
141:         if (rp[t] > bcol) high = t; \
142:         else              low  = t; \
143:       } \
144:       for (_i=low; _i<high; _i++) { \
145:         if (rp[_i] > bcol) break; \
146:         if (rp[_i] == bcol) { \
147:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
148:           if (addv == ADD_VALUES) *bap += value;  \
149:           else                    *bap  = value;  \
150:           goto a_noinsert; \
151:         } \
152:       } \
153:       if (a->nonew == 1) goto a_noinsert; \
154:       if (a->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
155:       MatSeqXAIJReallocateAIJ(a,bs2,nrow,brow,bcol,rmax,aa,ai,aj,a->mbs,rp,ap,aimax,a->nonew); \
156:       N = nrow++ - 1;  \
157:       /* shift up all the later entries in this row */ \
158:       for (ii=N; ii>=_i; ii--) { \
159:         rp[ii+1] = rp[ii]; \
160:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
161:       } \
162:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  \
163:       rp[_i]                      = bcol;  \
164:       ap[bs2*_i + bs*cidx + ridx] = value;  \
165:       a_noinsert:; \
166:     ailen[brow] = nrow; \
167: } 

169: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv) \
170: { \
171:     brow = row/bs;  \
172:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
173:     rmax = bimax[brow]; nrow = bilen[brow]; \
174:       bcol = col/bs; \
175:       ridx = row % bs; cidx = col % bs; \
176:       low = 0; high = nrow; \
177:       while (high-low > 3) { \
178:         t = (low+high)/2; \
179:         if (rp[t] > bcol) high = t; \
180:         else              low  = t; \
181:       } \
182:       for (_i=low; _i<high; _i++) { \
183:         if (rp[_i] > bcol) break; \
184:         if (rp[_i] == bcol) { \
185:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
186:           if (addv == ADD_VALUES) *bap += value;  \
187:           else                    *bap  = value;  \
188:           goto b_noinsert; \
189:         } \
190:       } \
191:       if (b->nonew == 1) goto b_noinsert; \
192:       if (b->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
193:       MatSeqXAIJReallocateAIJ(b,bs2,nrow,brow,bcol,rmax,ba,bi,bj,b->mbs,rp,ap,bimax,b->nonew); \
194:       CHKMEMQ;\
195:       N = nrow++ - 1;  \
196:       /* shift up all the later entries in this row */ \
197:       for (ii=N; ii>=_i; ii--) { \
198:         rp[ii+1] = rp[ii]; \
199:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
200:       } \
201:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  \
202:       rp[_i]                      = bcol;  \
203:       ap[bs2*_i + bs*cidx + ridx] = value;  \
204:       b_noinsert:; \
205:     bilen[brow] = nrow; \
206: } 

208: #if defined(PETSC_USE_MAT_SINGLE)
211: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
212: {
213:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
215:   PetscInt       i,N = m*n;
216:   MatScalar      *vsingle;

219:   if (N > b->setvalueslen) {
220:     PetscFree(b->setvaluescopy);
221:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
222:     b->setvalueslen  = N;
223:   }
224:   vsingle = b->setvaluescopy;

226:   for (i=0; i<N; i++) {
227:     vsingle[i] = v[i];
228:   }
229:   MatSetValues_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
230:   return(0);
231: }

235: PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
236: {
237:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
239:   PetscInt       i,N = m*n*b->bs2;
240:   MatScalar      *vsingle;

243:   if (N > b->setvalueslen) {
244:     PetscFree(b->setvaluescopy);
245:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
246:     b->setvalueslen  = N;
247:   }
248:   vsingle = b->setvaluescopy;
249:   for (i=0; i<N; i++) {
250:     vsingle[i] = v[i];
251:   }
252:   MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
253:   return(0);
254: }

258: PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
259: {
260:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
262:   PetscInt       i,N = m*n;
263:   MatScalar      *vsingle;

266:   if (N > b->setvalueslen) {
267:     PetscFree(b->setvaluescopy);
268:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
269:     b->setvalueslen  = N;
270:   }
271:   vsingle = b->setvaluescopy;
272:   for (i=0; i<N; i++) {
273:     vsingle[i] = v[i];
274:   }
275:   MatSetValues_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
276:   return(0);
277: }

281: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
282: {
283:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
285:   PetscInt       i,N = m*n*b->bs2;
286:   MatScalar      *vsingle;

289:   if (N > b->setvalueslen) {
290:     PetscFree(b->setvaluescopy);
291:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
292:     b->setvalueslen  = N;
293:   }
294:   vsingle = b->setvaluescopy;
295:   for (i=0; i<N; i++) {
296:     vsingle[i] = v[i];
297:   }
298:   MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
299:   return(0);
300: }
301: #endif

305: PetscErrorCode MatSetValues_MPIBAIJ_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
306: {
307:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
308:   MatScalar      value;
309:   PetscTruth     roworiented = baij->roworiented;
311:   PetscInt       i,j,row,col;
312:   PetscInt       rstart_orig=mat->rmap.rstart;
313:   PetscInt       rend_orig=mat->rmap.rend,cstart_orig=mat->cmap.rstart;
314:   PetscInt       cend_orig=mat->cmap.rend,bs=mat->rmap.bs;

316:   /* Some Variables required in the macro */
317:   Mat            A = baij->A;
318:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)(A)->data;
319:   PetscInt       *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
320:   MatScalar      *aa=a->a;

322:   Mat            B = baij->B;
323:   Mat_SeqBAIJ    *b = (Mat_SeqBAIJ*)(B)->data;
324:   PetscInt       *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
325:   MatScalar      *ba=b->a;

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

332:   for (i=0; i<m; i++) {
333:     if (im[i] < 0) continue;
334: #if defined(PETSC_USE_DEBUG)
335:     if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
336: #endif
337:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
338:       row = im[i] - rstart_orig;
339:       for (j=0; j<n; j++) {
340:         if (in[j] >= cstart_orig && in[j] < cend_orig){
341:           col = in[j] - cstart_orig;
342:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
343:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv);
344:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
345:         } else if (in[j] < 0) continue;
346: #if defined(PETSC_USE_DEBUG)
347:         else if (in[j] >= mat->cmap.N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[i],mat->cmap.N-1);}
348: #endif
349:         else {
350:           if (mat->was_assembled) {
351:             if (!baij->colmap) {
352:               CreateColmap_MPIBAIJ_Private(mat);
353:             }
354: #if defined (PETSC_USE_CTABLE)
355:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
356:             col  = col - 1;
357: #else
358:             col = baij->colmap[in[j]/bs] - 1;
359: #endif
360:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
361:               DisAssemble_MPIBAIJ(mat);
362:               col =  in[j];
363:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
364:               B = baij->B;
365:               b = (Mat_SeqBAIJ*)(B)->data;
366:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
367:               ba=b->a;
368:             } else col += in[j]%bs;
369:           } else col = in[j];
370:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
371:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv);
372:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
373:         }
374:       }
375:     } else {
376:       if (!baij->donotstash) {
377:         if (roworiented) {
378:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
379:         } else {
380:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
381:         }
382:       }
383:     }
384:   }
385:   return(0);
386: }

390: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
391: {
392:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
393:   const MatScalar *value;
394:   MatScalar       *barray=baij->barray;
395:   PetscTruth      roworiented = baij->roworiented;
396:   PetscErrorCode  ierr;
397:   PetscInt        i,j,ii,jj,row,col,rstart=baij->rstartbs;
398:   PetscInt        rend=baij->rendbs,cstart=baij->cstartbs,stepval;
399:   PetscInt        cend=baij->cendbs,bs=mat->rmap.bs,bs2=baij->bs2;
400: 
402:   if(!barray) {
403:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
404:     baij->barray = barray;
405:   }

407:   if (roworiented) {
408:     stepval = (n-1)*bs;
409:   } else {
410:     stepval = (m-1)*bs;
411:   }
412:   for (i=0; i<m; i++) {
413:     if (im[i] < 0) continue;
414: #if defined(PETSC_USE_DEBUG)
415:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
416: #endif
417:     if (im[i] >= rstart && im[i] < rend) {
418:       row = im[i] - rstart;
419:       for (j=0; j<n; j++) {
420:         /* If NumCol = 1 then a copy is not required */
421:         if ((roworiented) && (n == 1)) {
422:           barray = (MatScalar*)v + i*bs2;
423:         } else if((!roworiented) && (m == 1)) {
424:           barray = (MatScalar*)v + j*bs2;
425:         } else { /* Here a copy is required */
426:           if (roworiented) {
427:             value = v + i*(stepval+bs)*bs + j*bs;
428:           } else {
429:             value = v + j*(stepval+bs)*bs + i*bs;
430:           }
431:           for (ii=0; ii<bs; ii++,value+=stepval) {
432:             for (jj=0; jj<bs; jj++) {
433:               *barray++  = *value++;
434:             }
435:           }
436:           barray -=bs2;
437:         }
438: 
439:         if (in[j] >= cstart && in[j] < cend){
440:           col  = in[j] - cstart;
441:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->A,1,&row,1,&col,barray,addv);
442:         }
443:         else if (in[j] < 0) continue;
444: #if defined(PETSC_USE_DEBUG)
445:         else if (in[j] >= baij->Nbs) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);}
446: #endif
447:         else {
448:           if (mat->was_assembled) {
449:             if (!baij->colmap) {
450:               CreateColmap_MPIBAIJ_Private(mat);
451:             }

453: #if defined(PETSC_USE_DEBUG)
454: #if defined (PETSC_USE_CTABLE)
455:             { PetscInt data;
456:               PetscTableFind(baij->colmap,in[j]+1,&data);
457:               if ((data - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
458:             }
459: #else
460:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
461: #endif
462: #endif
463: #if defined (PETSC_USE_CTABLE)
464:             PetscTableFind(baij->colmap,in[j]+1,&col);
465:             col  = (col - 1)/bs;
466: #else
467:             col = (baij->colmap[in[j]] - 1)/bs;
468: #endif
469:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
470:               DisAssemble_MPIBAIJ(mat);
471:               col =  in[j];
472:             }
473:           }
474:           else col = in[j];
475:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->B,1,&row,1,&col,barray,addv);
476:         }
477:       }
478:     } else {
479:       if (!baij->donotstash) {
480:         if (roworiented) {
481:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
482:         } else {
483:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
484:         }
485:       }
486:     }
487:   }
488:   return(0);
489: }

491: #define HASH_KEY 0.6180339887
492: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(PetscInt)((size)*(tmp-(PetscInt)tmp)))
493: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
494: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
497: PetscErrorCode MatSetValues_MPIBAIJ_HT_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
498: {
499:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
500:   PetscTruth     roworiented = baij->roworiented;
502:   PetscInt       i,j,row,col;
503:   PetscInt       rstart_orig=mat->rmap.rstart;
504:   PetscInt       rend_orig=mat->rmap.rend,Nbs=baij->Nbs;
505:   PetscInt       h1,key,size=baij->ht_size,bs=mat->rmap.bs,*HT=baij->ht,idx;
506:   PetscReal      tmp;
507:   MatScalar      **HD = baij->hd,value;
508: #if defined(PETSC_USE_DEBUG)
509:   PetscInt       total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
510: #endif


514:   for (i=0; i<m; i++) {
515: #if defined(PETSC_USE_DEBUG)
516:     if (im[i] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
517:     if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
518: #endif
519:       row = im[i];
520:     if (row >= rstart_orig && row < rend_orig) {
521:       for (j=0; j<n; j++) {
522:         col = in[j];
523:         if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
524:         /* Look up PetscInto the Hash Table */
525:         key = (row/bs)*Nbs+(col/bs)+1;
526:         h1  = HASH(size,key,tmp);

528: 
529:         idx = h1;
530: #if defined(PETSC_USE_DEBUG)
531:         insert_ct++;
532:         total_ct++;
533:         if (HT[idx] != key) {
534:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
535:           if (idx == size) {
536:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
537:             if (idx == h1) {
538:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
539:             }
540:           }
541:         }
542: #else
543:         if (HT[idx] != key) {
544:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
545:           if (idx == size) {
546:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
547:             if (idx == h1) {
548:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
549:             }
550:           }
551:         }
552: #endif
553:         /* A HASH table entry is found, so insert the values at the correct address */
554:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
555:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
556:       }
557:     } else {
558:       if (!baij->donotstash) {
559:         if (roworiented) {
560:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
561:         } else {
562:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
563:         }
564:       }
565:     }
566:   }
567: #if defined(PETSC_USE_DEBUG)
568:   baij->ht_total_ct = total_ct;
569:   baij->ht_insert_ct = insert_ct;
570: #endif
571:   return(0);
572: }

576: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
577: {
578:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
579:   PetscTruth      roworiented = baij->roworiented;
580:   PetscErrorCode  ierr;
581:   PetscInt        i,j,ii,jj,row,col;
582:   PetscInt        rstart=baij->rstartbs;
583:   PetscInt        rend=mat->rmap.rend,stepval,bs=mat->rmap.bs,bs2=baij->bs2;
584:   PetscInt        h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
585:   PetscReal       tmp;
586:   MatScalar       **HD = baij->hd,*baij_a;
587:   const MatScalar *v_t,*value;
588: #if defined(PETSC_USE_DEBUG)
589:   PetscInt        total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
590: #endif
591: 

594:   if (roworiented) {
595:     stepval = (n-1)*bs;
596:   } else {
597:     stepval = (m-1)*bs;
598:   }
599:   for (i=0; i<m; i++) {
600: #if defined(PETSC_USE_DEBUG)
601:     if (im[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",im[i]);
602:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],baij->Mbs-1);
603: #endif
604:     row   = im[i];
605:     v_t   = v + i*bs2;
606:     if (row >= rstart && row < rend) {
607:       for (j=0; j<n; j++) {
608:         col = in[j];

610:         /* Look up into the Hash Table */
611:         key = row*Nbs+col+1;
612:         h1  = HASH(size,key,tmp);
613: 
614:         idx = h1;
615: #if defined(PETSC_USE_DEBUG)
616:         total_ct++;
617:         insert_ct++;
618:        if (HT[idx] != key) {
619:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
620:           if (idx == size) {
621:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
622:             if (idx == h1) {
623:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
624:             }
625:           }
626:         }
627: #else  
628:         if (HT[idx] != key) {
629:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
630:           if (idx == size) {
631:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
632:             if (idx == h1) {
633:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
634:             }
635:           }
636:         }
637: #endif
638:         baij_a = HD[idx];
639:         if (roworiented) {
640:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
641:           /* value = v + (i*(stepval+bs)+j)*bs; */
642:           value = v_t;
643:           v_t  += bs;
644:           if (addv == ADD_VALUES) {
645:             for (ii=0; ii<bs; ii++,value+=stepval) {
646:               for (jj=ii; jj<bs2; jj+=bs) {
647:                 baij_a[jj]  += *value++;
648:               }
649:             }
650:           } else {
651:             for (ii=0; ii<bs; ii++,value+=stepval) {
652:               for (jj=ii; jj<bs2; jj+=bs) {
653:                 baij_a[jj]  = *value++;
654:               }
655:             }
656:           }
657:         } else {
658:           value = v + j*(stepval+bs)*bs + i*bs;
659:           if (addv == ADD_VALUES) {
660:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
661:               for (jj=0; jj<bs; jj++) {
662:                 baij_a[jj]  += *value++;
663:               }
664:             }
665:           } else {
666:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
667:               for (jj=0; jj<bs; jj++) {
668:                 baij_a[jj]  = *value++;
669:               }
670:             }
671:           }
672:         }
673:       }
674:     } else {
675:       if (!baij->donotstash) {
676:         if (roworiented) {
677:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
678:         } else {
679:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
680:         }
681:       }
682:     }
683:   }
684: #if defined(PETSC_USE_DEBUG)
685:   baij->ht_total_ct = total_ct;
686:   baij->ht_insert_ct = insert_ct;
687: #endif
688:   return(0);
689: }

693: PetscErrorCode MatGetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
694: {
695:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
697:   PetscInt       bs=mat->rmap.bs,i,j,bsrstart = mat->rmap.rstart,bsrend = mat->rmap.rend;
698:   PetscInt       bscstart = mat->cmap.rstart,bscend = mat->cmap.rend,row,col,data;

701:   for (i=0; i<m; i++) {
702:     if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);
703:     if (idxm[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap.N-1);
704:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
705:       row = idxm[i] - bsrstart;
706:       for (j=0; j<n; j++) {
707:         if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]);
708:         if (idxn[j] >= mat->cmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap.N-1);
709:         if (idxn[j] >= bscstart && idxn[j] < bscend){
710:           col = idxn[j] - bscstart;
711:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
712:         } else {
713:           if (!baij->colmap) {
714:             CreateColmap_MPIBAIJ_Private(mat);
715:           }
716: #if defined (PETSC_USE_CTABLE)
717:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
718:           data --;
719: #else
720:           data = baij->colmap[idxn[j]/bs]-1;
721: #endif
722:           if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
723:           else {
724:             col  = data + idxn[j]%bs;
725:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
726:           }
727:         }
728:       }
729:     } else {
730:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
731:     }
732:   }
733:  return(0);
734: }

738: PetscErrorCode MatNorm_MPIBAIJ(Mat mat,NormType type,PetscReal *nrm)
739: {
740:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
741:   Mat_SeqBAIJ    *amat = (Mat_SeqBAIJ*)baij->A->data,*bmat = (Mat_SeqBAIJ*)baij->B->data;
743:   PetscInt       i,j,bs2=baij->bs2,bs=baij->A->rmap.bs,nz,row,col;
744:   PetscReal      sum = 0.0;
745:   MatScalar      *v;

748:   if (baij->size == 1) {
749:      MatNorm(baij->A,type,nrm);
750:   } else {
751:     if (type == NORM_FROBENIUS) {
752:       v = amat->a;
753:       nz = amat->nz*bs2;
754:       for (i=0; i<nz; i++) {
755: #if defined(PETSC_USE_COMPLEX)
756:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
757: #else
758:         sum += (*v)*(*v); v++;
759: #endif
760:       }
761:       v = bmat->a;
762:       nz = bmat->nz*bs2;
763:       for (i=0; i<nz; i++) {
764: #if defined(PETSC_USE_COMPLEX)
765:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
766: #else
767:         sum += (*v)*(*v); v++;
768: #endif
769:       }
770:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPI_SUM,mat->comm);
771:       *nrm = sqrt(*nrm);
772:     } else if (type == NORM_1) { /* max column sum */
773:       PetscReal *tmp,*tmp2;
774:       PetscInt  *jj,*garray=baij->garray,cstart=baij->rstartbs;
775:       PetscMalloc((2*mat->cmap.N+1)*sizeof(PetscReal),&tmp);
776:       tmp2 = tmp + mat->cmap.N;
777:       PetscMemzero(tmp,mat->cmap.N*sizeof(PetscReal));
778:       v = amat->a; jj = amat->j;
779:       for (i=0; i<amat->nz; i++) {
780:         for (j=0; j<bs; j++){
781:           col = bs*(cstart + *jj) + j; /* column index */
782:           for (row=0; row<bs; row++){
783:             tmp[col] += PetscAbsScalar(*v);  v++;
784:           }
785:         }
786:         jj++;
787:       }
788:       v = bmat->a; jj = bmat->j;
789:       for (i=0; i<bmat->nz; i++) {
790:         for (j=0; j<bs; j++){
791:           col = bs*garray[*jj] + j;
792:           for (row=0; row<bs; row++){
793:             tmp[col] += PetscAbsScalar(*v); v++;
794:           }
795:         }
796:         jj++;
797:       }
798:       MPI_Allreduce(tmp,tmp2,mat->cmap.N,MPIU_REAL,MPI_SUM,mat->comm);
799:       *nrm = 0.0;
800:       for (j=0; j<mat->cmap.N; j++) {
801:         if (tmp2[j] > *nrm) *nrm = tmp2[j];
802:       }
803:       PetscFree(tmp);
804:     } else if (type == NORM_INFINITY) { /* max row sum */
805:       PetscReal *sums;
806:       PetscMalloc(bs*sizeof(PetscReal),&sums);CHKERRQ(ierr)
807:       sum = 0.0;
808:       for (j=0; j<amat->mbs; j++) {
809:         for (row=0; row<bs; row++) sums[row] = 0.0;
810:         v = amat->a + bs2*amat->i[j];
811:         nz = amat->i[j+1]-amat->i[j];
812:         for (i=0; i<nz; i++) {
813:           for (col=0; col<bs; col++){
814:             for (row=0; row<bs; row++){
815:               sums[row] += PetscAbsScalar(*v); v++;
816:             }
817:           }
818:         }
819:         v = bmat->a + bs2*bmat->i[j];
820:         nz = bmat->i[j+1]-bmat->i[j];
821:         for (i=0; i<nz; i++) {
822:           for (col=0; col<bs; col++){
823:             for (row=0; row<bs; row++){
824:               sums[row] += PetscAbsScalar(*v); v++;
825:             }
826:           }
827:         }
828:         for (row=0; row<bs; row++){
829:           if (sums[row] > sum) sum = sums[row];
830:         }
831:       }
832:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPI_MAX,mat->comm);
833:       PetscFree(sums);
834:     } else {
835:       SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
836:     }
837:   }
838:   return(0);
839: }

841: /*
842:   Creates the hash table, and sets the table 
843:   This table is created only once. 
844:   If new entried need to be added to the matrix
845:   then the hash table has to be destroyed and
846:   recreated.
847: */
850: PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
851: {
852:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
853:   Mat            A = baij->A,B=baij->B;
854:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ *)A->data,*b=(Mat_SeqBAIJ *)B->data;
855:   PetscInt       i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
857:   PetscInt       size,bs2=baij->bs2,rstart=baij->rstartbs;
858:   PetscInt       cstart=baij->cstartbs,*garray=baij->garray,row,col,Nbs=baij->Nbs;
859:   PetscInt       *HT,key;
860:   MatScalar      **HD;
861:   PetscReal      tmp;
862: #if defined(PETSC_USE_INFO)
863:   PetscInt       ct=0,max=0;
864: #endif

867:   baij->ht_size=(PetscInt)(factor*nz);
868:   size = baij->ht_size;

870:   if (baij->ht) {
871:     return(0);
872:   }
873: 
874:   /* Allocate Memory for Hash Table */
875:   PetscMalloc((size)*(sizeof(PetscInt)+sizeof(MatScalar*))+1,&baij->hd);
876:   baij->ht = (PetscInt*)(baij->hd + size);
877:   HD       = baij->hd;
878:   HT       = baij->ht;


881:   PetscMemzero(HD,size*(sizeof(PetscInt)+sizeof(PetscScalar*)));
882: 

884:   /* Loop Over A */
885:   for (i=0; i<a->mbs; i++) {
886:     for (j=ai[i]; j<ai[i+1]; j++) {
887:       row = i+rstart;
888:       col = aj[j]+cstart;
889: 
890:       key = row*Nbs + col + 1;
891:       h1  = HASH(size,key,tmp);
892:       for (k=0; k<size; k++){
893:         if (!HT[(h1+k)%size]) {
894:           HT[(h1+k)%size] = key;
895:           HD[(h1+k)%size] = a->a + j*bs2;
896:           break;
897: #if defined(PETSC_USE_INFO)
898:         } else {
899:           ct++;
900: #endif
901:         }
902:       }
903: #if defined(PETSC_USE_INFO)
904:       if (k> max) max = k;
905: #endif
906:     }
907:   }
908:   /* Loop Over B */
909:   for (i=0; i<b->mbs; i++) {
910:     for (j=bi[i]; j<bi[i+1]; j++) {
911:       row = i+rstart;
912:       col = garray[bj[j]];
913:       key = row*Nbs + col + 1;
914:       h1  = HASH(size,key,tmp);
915:       for (k=0; k<size; k++){
916:         if (!HT[(h1+k)%size]) {
917:           HT[(h1+k)%size] = key;
918:           HD[(h1+k)%size] = b->a + j*bs2;
919:           break;
920: #if defined(PETSC_USE_INFO)
921:         } else {
922:           ct++;
923: #endif
924:         }
925:       }
926: #if defined(PETSC_USE_INFO)
927:       if (k> max) max = k;
928: #endif
929:     }
930:   }
931: 
932:   /* Print Summary */
933: #if defined(PETSC_USE_INFO)
934:   for (i=0,j=0; i<size; i++) {
935:     if (HT[i]) {j++;}
936:   }
937:   PetscInfo2(0,"Average Search = %5.2f,max search = %D\n",(!j)? 0.0:((PetscReal)(ct+j))/j,max);
938: #endif
939:   return(0);
940: }

944: PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat,MatAssemblyType mode)
945: {
946:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
948:   PetscInt       nstash,reallocs;
949:   InsertMode     addv;

952:   if (baij->donotstash) {
953:     return(0);
954:   }

956:   /* make sure all processors are either in INSERTMODE or ADDMODE */
957:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
958:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
959:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
960:   }
961:   mat->insertmode = addv; /* in case this processor had no cache */

963:   MatStashScatterBegin_Private(&mat->stash,mat->rmap.range);
964:   MatStashScatterBegin_Private(&mat->bstash,baij->rangebs);
965:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
966:   PetscInfo2(0,"Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
967:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
968:   PetscInfo2(0,"Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
969:   return(0);
970: }

974: PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat,MatAssemblyType mode)
975: {
976:   Mat_MPIBAIJ    *baij=(Mat_MPIBAIJ*)mat->data;
977:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ*)baij->A->data;
979:   PetscInt       i,j,rstart,ncols,flg,bs2=baij->bs2;
980:   PetscInt       *row,*col,other_disassembled;
981:   PetscTruth     r1,r2,r3;
982:   MatScalar      *val;
983:   InsertMode     addv = mat->insertmode;
984:   PetscMPIInt    n;

986:   /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
988:   if (!baij->donotstash) {
989:     while (1) {
990:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
991:       if (!flg) break;

993:       for (i=0; i<n;) {
994:         /* Now identify the consecutive vals belonging to the same row */
995:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
996:         if (j < n) ncols = j-i;
997:         else       ncols = n-i;
998:         /* Now assemble all these values with a single function call */
999:         MatSetValues_MPIBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i,addv);
1000:         i = j;
1001:       }
1002:     }
1003:     MatStashScatterEnd_Private(&mat->stash);
1004:     /* Now process the block-stash. Since the values are stashed column-oriented,
1005:        set the roworiented flag to column oriented, and after MatSetValues() 
1006:        restore the original flags */
1007:     r1 = baij->roworiented;
1008:     r2 = a->roworiented;
1009:     r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;
1010:     baij->roworiented = PETSC_FALSE;
1011:     a->roworiented    = PETSC_FALSE;
1012:     (((Mat_SeqBAIJ*)baij->B->data))->roworiented    = PETSC_FALSE; /* b->roworiented */
1013:     while (1) {
1014:       MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
1015:       if (!flg) break;
1016: 
1017:       for (i=0; i<n;) {
1018:         /* Now identify the consecutive vals belonging to the same row */
1019:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
1020:         if (j < n) ncols = j-i;
1021:         else       ncols = n-i;
1022:         MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
1023:         i = j;
1024:       }
1025:     }
1026:     MatStashScatterEnd_Private(&mat->bstash);
1027:     baij->roworiented = r1;
1028:     a->roworiented    = r2;
1029:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented    = r3; /* b->roworiented */
1030:   }
1031: 
1032:   MatAssemblyBegin(baij->A,mode);
1033:   MatAssemblyEnd(baij->A,mode);

1035:   /* determine if any processor has disassembled, if so we must 
1036:      also disassemble ourselfs, in order that we may reassemble. */
1037:   /*
1038:      if nonzero structure of submatrix B cannot change then we know that
1039:      no processor disassembled thus we can skip this stuff
1040:   */
1041:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
1042:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
1043:     if (mat->was_assembled && !other_disassembled) {
1044:       DisAssemble_MPIBAIJ(mat);
1045:     }
1046:   }

1048:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
1049:     MatSetUpMultiply_MPIBAIJ(mat);
1050:   }
1051:   ((Mat_SeqBAIJ*)baij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
1052:   MatAssemblyBegin(baij->B,mode);
1053:   MatAssemblyEnd(baij->B,mode);
1054: 
1055: #if defined(PETSC_USE_INFO)
1056:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
1057:     PetscInfo1(0,"Average Hash Table Search in MatSetValues = %5.2f\n",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
1058:     baij->ht_total_ct  = 0;
1059:     baij->ht_insert_ct = 0;
1060:   }
1061: #endif
1062:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
1063:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);
1064:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
1065:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
1066:   }

1068:   PetscFree(baij->rowvalues);
1069:   baij->rowvalues = 0;
1070:   return(0);
1071: }

1075: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1076: {
1077:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
1078:   PetscErrorCode    ierr;
1079:   PetscMPIInt       size = baij->size,rank = baij->rank;
1080:   PetscInt          bs = mat->rmap.bs;
1081:   PetscTruth        iascii,isdraw;
1082:   PetscViewer       sviewer;
1083:   PetscViewerFormat format;

1086:   /* printf(" MatView_MPIBAIJ_ASCIIorDraworSocket is called ...\n"); */
1087:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1088:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1089:   if (iascii) {
1090:     PetscViewerGetFormat(viewer,&format);
1091:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1092:       MatInfo info;
1093:       MPI_Comm_rank(mat->comm,&rank);
1094:       MatGetInfo(mat,MAT_LOCAL,&info);
1095:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",
1096:               rank,mat->rmap.N,(PetscInt)info.nz_used*bs,(PetscInt)info.nz_allocated*bs,
1097:               mat->rmap.bs,(PetscInt)info.memory);
1098:       MatGetInfo(baij->A,MAT_LOCAL,&info);
1099:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
1100:       MatGetInfo(baij->B,MAT_LOCAL,&info);
1101:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
1102:       PetscViewerFlush(viewer);
1103:       VecScatterView(baij->Mvctx,viewer);
1104:       return(0);
1105:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1106:       PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
1107:       return(0);
1108:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1109:       return(0);
1110:     }
1111:   }

1113:   if (isdraw) {
1114:     PetscDraw       draw;
1115:     PetscTruth isnull;
1116:     PetscViewerDrawGetDraw(viewer,0,&draw);
1117:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1118:   }

1120:   if (size == 1) {
1121:     PetscObjectSetName((PetscObject)baij->A,mat->name);
1122:     MatView(baij->A,viewer);
1123:   } else {
1124:     /* assemble the entire matrix onto first processor. */
1125:     Mat         A;
1126:     Mat_SeqBAIJ *Aloc;
1127:     PetscInt    M = mat->rmap.N,N = mat->cmap.N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
1128:     MatScalar   *a;

1130:     /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1131:     /* Perhaps this should be the type of mat? */
1132:     MatCreate(mat->comm,&A);
1133:     if (!rank) {
1134:       MatSetSizes(A,M,N,M,N);
1135:     } else {
1136:       MatSetSizes(A,0,0,M,N);
1137:     }
1138:     MatSetType(A,MATMPIBAIJ);
1139:     MatMPIBAIJSetPreallocation(A,mat->rmap.bs,0,PETSC_NULL,0,PETSC_NULL);
1140:     PetscLogObjectParent(mat,A);

1142:     /* copy over the A part */
1143:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
1144:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1145:     PetscMalloc(bs*sizeof(PetscInt),&rvals);

1147:     for (i=0; i<mbs; i++) {
1148:       rvals[0] = bs*(baij->rstartbs + i);
1149:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1150:       for (j=ai[i]; j<ai[i+1]; j++) {
1151:         col = (baij->cstartbs+aj[j])*bs;
1152:         for (k=0; k<bs; k++) {
1153:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1154:           col++; a += bs;
1155:         }
1156:       }
1157:     }
1158:     /* copy over the B part */
1159:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
1160:     ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1161:     for (i=0; i<mbs; i++) {
1162:       rvals[0] = bs*(baij->rstartbs + i);
1163:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1164:       for (j=ai[i]; j<ai[i+1]; j++) {
1165:         col = baij->garray[aj[j]]*bs;
1166:         for (k=0; k<bs; k++) {
1167:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1168:           col++; a += bs;
1169:         }
1170:       }
1171:     }
1172:     PetscFree(rvals);
1173:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1174:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1175:     /* 
1176:        Everyone has to call to draw the matrix since the graphics waits are
1177:        synchronized across all processors that share the PetscDraw object
1178:     */
1179:     PetscViewerGetSingleton(viewer,&sviewer);
1180:     if (!rank) {
1181:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,mat->name);
1182:       MatView(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1183:     }
1184:     PetscViewerRestoreSingleton(viewer,&sviewer);
1185:     MatDestroy(A);
1186:   }
1187:   return(0);
1188: }

1192: PetscErrorCode MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1193: {
1195:   PetscTruth     iascii,isdraw,issocket,isbinary;

1198:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1199:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1200:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1201:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1202:   if (iascii || isdraw || issocket || isbinary) {
1203:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1204:   } else {
1205:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIBAIJ matrices",((PetscObject)viewer)->type_name);
1206:   }
1207:   return(0);
1208: }

1212: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
1213: {
1214:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;

1218: #if defined(PETSC_USE_LOG)
1219:   PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap.N,mat->cmap.N);
1220: #endif
1221:   MatStashDestroy_Private(&mat->stash);
1222:   MatStashDestroy_Private(&mat->bstash);
1223:   MatDestroy(baij->A);
1224:   MatDestroy(baij->B);
1225: #if defined (PETSC_USE_CTABLE)
1226:   if (baij->colmap) {PetscTableDelete(baij->colmap);}
1227: #else
1228:   PetscFree(baij->colmap);
1229: #endif
1230:   PetscFree(baij->garray);
1231:   if (baij->lvec)   {VecDestroy(baij->lvec);}
1232:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
1233:   PetscFree(baij->rowvalues);
1234:   PetscFree(baij->barray);
1235:   PetscFree(baij->hd);
1236: #if defined(PETSC_USE_MAT_SINGLE)
1237:   PetscFree(baij->setvaluescopy);
1238: #endif
1239:   PetscFree(baij->rangebs);
1240:   PetscFree(baij);

1242:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
1243:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
1244:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
1245:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C","",PETSC_NULL);
1246:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C","",PETSC_NULL);
1247:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
1248:   PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C","",PETSC_NULL);
1249:   return(0);
1250: }

1254: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1255: {
1256:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1258:   PetscInt       nt;

1261:   VecGetLocalSize(xx,&nt);
1262:   if (nt != A->cmap.n) {
1263:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1264:   }
1265:   VecGetLocalSize(yy,&nt);
1266:   if (nt != A->rmap.n) {
1267:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1268:   }
1269:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1270:   (*a->A->ops->mult)(a->A,xx,yy);
1271:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1272:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1273:   VecScatterPostRecvs(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1274:   return(0);
1275: }

1279: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1280: {
1281:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1285:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1286:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1287:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1288:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1289:   return(0);
1290: }

1294: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1295: {
1296:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1298:   PetscTruth     merged;

1301:   VecScatterGetMerged(a->Mvctx,&merged);
1302:   /* do nondiagonal part */
1303:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1304:   if (!merged) {
1305:     /* send it on its way */
1306:     VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1307:     /* do local part */
1308:     (*a->A->ops->multtranspose)(a->A,xx,yy);
1309:     /* receive remote parts: note this assumes the values are not actually */
1310:     /* inserted in yy until the next line */
1311:     VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1312:   } else {
1313:     /* do local part */
1314:     (*a->A->ops->multtranspose)(a->A,xx,yy);
1315:     /* send it on its way */
1316:     VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1317:     /* values actually were received in the Begin() but we need to call this nop */
1318:     VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1319:   }
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->rmap.N != A->cmap.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(Mat A,PetscScalar aa)
1364: {
1365:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1369:   MatScale(a->A,aa);
1370:   MatScale(a->B,aa);
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->rmap.bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
1382:   PetscInt       nztot,nzA,nzB,lrow,brstart = matin->rmap.rstart,brend = matin->rmap.rend;
1383:   PetscInt       *cmap,*idx_p,cstart = mat->cstartbs;

1386:   if (mat->getrowactive) 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) {
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->rmap.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->rmap.N;
1524:   info->columns_global    = (PetscReal)A->cmap.N;
1525:   info->rows_local        = (PetscReal)A->rmap.N;
1526:   info->columns_local     = (PetscReal)A->cmap.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:     PetscInfo(A,"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->rmap.N,N=A->cmap.N,*ai,*aj,i,*rvals,j,k,col;
1601:   PetscInt       bs=A->rmap.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,&B);
1607:   MatSetSizes(B,A->cmap.n,A->rmap.n,N,M);
1608:   MatSetType(B,A->type_name);
1609:   MatMPIBAIJSetPreallocation(B,A->rmap.bs,0,PETSC_NULL,0,PETSC_NULL);
1610: 
1611:   /* copy over the A part */
1612:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1613:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1614:   PetscMalloc(bs*sizeof(PetscInt),&rvals);
1615: 
1616:   for (i=0; i<mbs; i++) {
1617:     rvals[0] = bs*(baij->rstartbs + i);
1618:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1619:     for (j=ai[i]; j<ai[i+1]; j++) {
1620:       col = (baij->cstartbs+aj[j])*bs;
1621:       for (k=0; k<bs; k++) {
1622:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1623:         col++; a += bs;
1624:       }
1625:     }
1626:   }
1627:   /* copy over the B part */
1628:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1629:   ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1630:   for (i=0; i<mbs; i++) {
1631:     rvals[0] = bs*(baij->rstartbs + i);
1632:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1633:     for (j=ai[i]; j<ai[i+1]; j++) {
1634:       col = baij->garray[aj[j]]*bs;
1635:       for (k=0; k<bs; k++) {
1636:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1637:         col++; a += bs;
1638:       }
1639:     }
1640:   }
1641:   PetscFree(rvals);
1642:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1643:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1644: 
1645:   if (matout) {
1646:     *matout = B;
1647:   } else {
1648:     MatHeaderCopy(A,B);
1649:   }
1650:   return(0);
1651: }

1655: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1656: {
1657:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
1658:   Mat            a = baij->A,b = baij->B;
1660:   PetscInt       s1,s2,s3;

1663:   MatGetLocalSize(mat,&s2,&s3);
1664:   if (rr) {
1665:     VecGetLocalSize(rr,&s1);
1666:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1667:     /* Overlap communication with computation. */
1668:     VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1669:   }
1670:   if (ll) {
1671:     VecGetLocalSize(ll,&s1);
1672:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1673:     (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1674:   }
1675:   /* scale  the diagonal block */
1676:   (*a->ops->diagonalscale)(a,ll,rr);

1678:   if (rr) {
1679:     /* Do a scatter end and then right scale the off-diagonal block */
1680:     VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1681:     (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1682:   }
1683: 
1684:   return(0);
1685: }

1689: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1690: {
1691:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;
1693:   PetscMPIInt    imdex,size = l->size,n,rank = l->rank;
1694:   PetscInt       i,*owners = A->rmap.range;
1695:   PetscInt       *nprocs,j,idx,nsends,row;
1696:   PetscInt       nmax,*svalues,*starts,*owner,nrecvs;
1697:   PetscInt       *rvalues,tag = A->tag,count,base,slen,*source,lastidx = -1;
1698:   PetscInt       *lens,*lrows,*values,rstart_bs=A->rmap.rstart;
1699:   MPI_Comm       comm = A->comm;
1700:   MPI_Request    *send_waits,*recv_waits;
1701:   MPI_Status     recv_status,*send_status;
1702: #if defined(PETSC_DEBUG)
1703:   PetscTruth     found = PETSC_FALSE;
1704: #endif
1705: 
1707:   /*  first count number of contributors to each processor */
1708:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
1709:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
1710:   PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
1711:   j = 0;
1712:   for (i=0; i<N; i++) {
1713:     if (lastidx > (idx = rows[i])) j = 0;
1714:     lastidx = idx;
1715:     for (; j<size; j++) {
1716:       if (idx >= owners[j] && idx < owners[j+1]) {
1717:         nprocs[2*j]++;
1718:         nprocs[2*j+1] = 1;
1719:         owner[i] = j;
1720: #if defined(PETSC_DEBUG)
1721:         found = PETSC_TRUE;
1722: #endif
1723:         break;
1724:       }
1725:     }
1726: #if defined(PETSC_DEBUG)
1727:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1728:     found = PETSC_FALSE;
1729: #endif
1730:   }
1731:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
1732: 
1733:   /* inform other processors of number of messages and max length*/
1734:   PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
1735: 
1736:   /* post receives:   */
1737:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
1738:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1739:   for (i=0; i<nrecvs; i++) {
1740:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1741:   }
1742: 
1743:   /* do sends:
1744:      1) starts[i] gives the starting index in svalues for stuff going to 
1745:      the ith processor
1746:   */
1747:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
1748:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1749:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
1750:   starts[0]  = 0;
1751:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1752:   for (i=0; i<N; i++) {
1753:     svalues[starts[owner[i]]++] = rows[i];
1754:   }
1755: 
1756:   starts[0] = 0;
1757:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1758:   count = 0;
1759:   for (i=0; i<size; i++) {
1760:     if (nprocs[2*i+1]) {
1761:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
1762:     }
1763:   }
1764:   PetscFree(starts);

1766:   base = owners[rank];
1767: 
1768:   /*  wait on receives */
1769:   PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
1770:   source = lens + nrecvs;
1771:   count  = nrecvs; slen = 0;
1772:   while (count) {
1773:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1774:     /* unpack receives into our local space */
1775:     MPI_Get_count(&recv_status,MPIU_INT,&n);
1776:     source[imdex]  = recv_status.MPI_SOURCE;
1777:     lens[imdex]    = n;
1778:     slen          += n;
1779:     count--;
1780:   }
1781:   PetscFree(recv_waits);
1782: 
1783:   /* move the data into the send scatter */
1784:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
1785:   count = 0;
1786:   for (i=0; i<nrecvs; i++) {
1787:     values = rvalues + i*nmax;
1788:     for (j=0; j<lens[i]; j++) {
1789:       lrows[count++] = values[j] - base;
1790:     }
1791:   }
1792:   PetscFree(rvalues);
1793:   PetscFree(lens);
1794:   PetscFree(owner);
1795:   PetscFree(nprocs);
1796: 
1797:   /* actually zap the local rows */
1798:   /*
1799:         Zero the required rows. If the "diagonal block" of the matrix
1800:      is square and the user wishes to set the diagonal we use separate
1801:      code so that MatSetValues() is not called for each diagonal allocating
1802:      new memory, thus calling lots of mallocs and slowing things down.

1804:        Contributed by: Matthew Knepley
1805:   */
1806:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1807:   MatZeroRows_SeqBAIJ(l->B,slen,lrows,0.0);
1808:   if ((diag != 0.0) && (l->A->rmap.N == l->A->cmap.N)) {
1809:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,diag);
1810:   } else if (diag != 0.0) {
1811:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0);
1812:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) {
1813:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1814: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1815:     }
1816:     for (i=0; i<slen; i++) {
1817:       row  = lrows[i] + rstart_bs;
1818:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
1819:     }
1820:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1821:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1822:   } else {
1823:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0);
1824:   }

1826:   PetscFree(lrows);

1828:   /* wait on sends */
1829:   if (nsends) {
1830:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1831:     MPI_Waitall(nsends,send_waits,send_status);
1832:     PetscFree(send_status);
1833:   }
1834:   PetscFree(send_waits);
1835:   PetscFree(svalues);

1837:   return(0);
1838: }

1842: PetscErrorCode MatPrintHelp_MPIBAIJ(Mat A)
1843: {
1844:   Mat_MPIBAIJ       *a   = (Mat_MPIBAIJ*)A->data;
1845:   MPI_Comm          comm = A->comm;
1846:   static PetscTruth called = PETSC_FALSE;
1847:   PetscErrorCode    ierr;

1850:   if (!a->rank) {
1851:     MatPrintHelp_SeqBAIJ(a->A);
1852:   }
1853:   if (called) {return(0);} else called = PETSC_TRUE;
1854:   (*PetscHelpPrintf)(comm," Options for MATMPIBAIJ matrix format (the defaults):\n");
1855:   (*PetscHelpPrintf)(comm,"  -mat_use_hash_table <factor>: Use hashtable for efficient matrix assembly\n");
1856:   return(0);
1857: }

1861: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1862: {
1863:   Mat_MPIBAIJ    *a   = (Mat_MPIBAIJ*)A->data;

1867:   MatSetUnfactored(a->A);
1868:   return(0);
1869: }

1871: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat *);

1875: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscTruth *flag)
1876: {
1877:   Mat_MPIBAIJ    *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1878:   Mat            a,b,c,d;
1879:   PetscTruth     flg;

1883:   a = matA->A; b = matA->B;
1884:   c = matB->A; d = matB->B;

1886:   MatEqual(a,c,&flg);
1887:   if (flg) {
1888:     MatEqual(b,d,&flg);
1889:   }
1890:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1891:   return(0);
1892: }

1896: PetscErrorCode MatCopy_MPIBAIJ(Mat A,Mat B,MatStructure str)
1897: {
1899:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ *)A->data;
1900:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ *)B->data;

1903:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1904:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1905:     MatCopy_Basic(A,B,str);
1906:   } else {
1907:     MatCopy(a->A,b->A,str);
1908:     MatCopy(a->B,b->B,str);
1909:   }
1910:   return(0);
1911: }

1915: PetscErrorCode MatSetUpPreallocation_MPIBAIJ(Mat A)
1916: {

1920:    MatMPIBAIJSetPreallocation(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1921:   return(0);
1922: }

1924:  #include petscblaslapack.h
1927: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1928: {
1930:   Mat_MPIBAIJ    *xx=(Mat_MPIBAIJ *)X->data,*yy=(Mat_MPIBAIJ *)Y->data;
1931:   PetscBLASInt   bnz,one=1;
1932:   Mat_SeqBAIJ    *x,*y;

1935:   if (str == SAME_NONZERO_PATTERN) {
1936:     PetscScalar alpha = a;
1937:     x = (Mat_SeqBAIJ *)xx->A->data;
1938:     y = (Mat_SeqBAIJ *)yy->A->data;
1939:     bnz = (PetscBLASInt)x->nz;
1940:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1941:     x = (Mat_SeqBAIJ *)xx->B->data;
1942:     y = (Mat_SeqBAIJ *)yy->B->data;
1943:     bnz = (PetscBLASInt)x->nz;
1944:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1945:   } else {
1946:     MatAXPY_Basic(Y,a,X,str);
1947:   }
1948:   return(0);
1949: }

1953: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1954: {
1955:   Mat_MPIBAIJ   *a = (Mat_MPIBAIJ*)A->data;

1959:   MatRealPart(a->A);
1960:   MatRealPart(a->B);
1961:   return(0);
1962: }

1966: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1967: {
1968:   Mat_MPIBAIJ   *a = (Mat_MPIBAIJ*)A->data;

1972:   MatImaginaryPart(a->A);
1973:   MatImaginaryPart(a->B);
1974:   return(0);
1975: }

1977: /* -------------------------------------------------------------------*/
1978: static struct _MatOps MatOps_Values = {
1979:        MatSetValues_MPIBAIJ,
1980:        MatGetRow_MPIBAIJ,
1981:        MatRestoreRow_MPIBAIJ,
1982:        MatMult_MPIBAIJ,
1983: /* 4*/ MatMultAdd_MPIBAIJ,
1984:        MatMultTranspose_MPIBAIJ,
1985:        MatMultTransposeAdd_MPIBAIJ,
1986:        0,
1987:        0,
1988:        0,
1989: /*10*/ 0,
1990:        0,
1991:        0,
1992:        0,
1993:        MatTranspose_MPIBAIJ,
1994: /*15*/ MatGetInfo_MPIBAIJ,
1995:        MatEqual_MPIBAIJ,
1996:        MatGetDiagonal_MPIBAIJ,
1997:        MatDiagonalScale_MPIBAIJ,
1998:        MatNorm_MPIBAIJ,
1999: /*20*/ MatAssemblyBegin_MPIBAIJ,
2000:        MatAssemblyEnd_MPIBAIJ,
2001:        0,
2002:        MatSetOption_MPIBAIJ,
2003:        MatZeroEntries_MPIBAIJ,
2004: /*25*/ MatZeroRows_MPIBAIJ,
2005:        0,
2006:        0,
2007:        0,
2008:        0,
2009: /*30*/ MatSetUpPreallocation_MPIBAIJ,
2010:        0,
2011:        0,
2012:        0,
2013:        0,
2014: /*35*/ MatDuplicate_MPIBAIJ,
2015:        0,
2016:        0,
2017:        0,
2018:        0,
2019: /*40*/ MatAXPY_MPIBAIJ,
2020:        MatGetSubMatrices_MPIBAIJ,
2021:        MatIncreaseOverlap_MPIBAIJ,
2022:        MatGetValues_MPIBAIJ,
2023:        MatCopy_MPIBAIJ,
2024: /*45*/ MatPrintHelp_MPIBAIJ,
2025:        MatScale_MPIBAIJ,
2026:        0,
2027:        0,
2028:        0,
2029: /*50*/ 0,
2030:        0,
2031:        0,
2032:        0,
2033:        0,
2034: /*55*/ 0,
2035:        0,
2036:        MatSetUnfactored_MPIBAIJ,
2037:        0,
2038:        MatSetValuesBlocked_MPIBAIJ,
2039: /*60*/ 0,
2040:        MatDestroy_MPIBAIJ,
2041:        MatView_MPIBAIJ,
2042:        0,
2043:        0,
2044: /*65*/ 0,
2045:        0,
2046:        0,
2047:        0,
2048:        0,
2049: /*70*/ MatGetRowMax_MPIBAIJ,
2050:        0,
2051:        0,
2052:        0,
2053:        0,
2054: /*75*/ 0,
2055:        0,
2056:        0,
2057:        0,
2058:        0,
2059: /*80*/ 0,
2060:        0,
2061:        0,
2062:        0,
2063:        MatLoad_MPIBAIJ,
2064: /*85*/ 0,
2065:        0,
2066:        0,
2067:        0,
2068:        0,
2069: /*90*/ 0,
2070:        0,
2071:        0,
2072:        0,
2073:        0,
2074: /*95*/ 0,
2075:        0,
2076:        0,
2077:        0,
2078:        0,
2079: /*100*/0,
2080:        0,
2081:        0,
2082:        0,
2083:        0,
2084: /*105*/0,
2085:        MatRealPart_MPIBAIJ,
2086:        MatImaginaryPart_MPIBAIJ};


2092: PetscErrorCode PETSCMAT_DLLEXPORT MatGetDiagonalBlock_MPIBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
2093: {
2095:   *a      = ((Mat_MPIBAIJ *)A->data)->A;
2096:   *iscopy = PETSC_FALSE;
2097:   return(0);
2098: }


2107: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt I[],const PetscInt J[],const PetscScalar v[])
2108: {
2109:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)B->data;
2110:   PetscInt       m = B->rmap.n/bs,cstart = baij->cstartbs, cend = baij->cendbs,j,nnz,i,d;
2111:   PetscInt       *d_nnz,*o_nnz,nnz_max = 0,rstart = baij->rstartbs,ii;
2112:   const PetscInt *JJ;
2113:   PetscScalar    *values;

2117: #if defined(PETSC_OPT_g)
2118:   if (I[0]) SETERRQ1(PETSC_ERR_ARG_RANGE,"I[0] must be 0 it is %D",I[0]);
2119: #endif
2120:   PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
2121:   o_nnz = d_nnz + m;

2123:   for (i=0; i<m; i++) {
2124:     nnz     = I[i+1]- I[i];
2125:     JJ      = J + I[i];
2126:     nnz_max = PetscMax(nnz_max,nnz);
2127: #if defined(PETSC_OPT_g)
2128:     if (nnz < 0) SETERRQ1(PETSC_ERR_ARG_RANGE,"Local row %D has a negative %D number of columns",i,nnz);
2129: #endif
2130:     for (j=0; j<nnz; j++) {
2131:       if (*JJ >= cstart) break;
2132:       JJ++;
2133:     }
2134:     d = 0;
2135:     for (; j<nnz; j++) {
2136:       if (*JJ++ >= cend) break;
2137:       d++;
2138:     }
2139:     d_nnz[i] = d;
2140:     o_nnz[i] = nnz - d;
2141:   }
2142:   MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
2143:   PetscFree(d_nnz);

2145:   if (v) values = (PetscScalar*)v;
2146:   else {
2147:     PetscMalloc(bs*bs*(nnz_max+1)*sizeof(PetscScalar),&values);
2148:     PetscMemzero(values,bs*bs*nnz_max*sizeof(PetscScalar));
2149:   }

2151:   MatSetOption(B,MAT_COLUMNS_SORTED);
2152:   for (i=0; i<m; i++) {
2153:     ii   = i + rstart;
2154:     nnz  = I[i+1]- I[i];
2155:     MatSetValuesBlocked_MPIBAIJ(B,1,&ii,nnz,J+I[i],values+(v ? I[i] : 0),INSERT_VALUES);
2156:   }
2157:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2158:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2159:   MatSetOption(B,MAT_COLUMNS_UNSORTED);

2161:   if (!v) {
2162:     PetscFree(values);
2163:   }
2164:   return(0);
2165: }

2169: /*@C
2170:    MatMPIBAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
2171:    (the default parallel PETSc format).  

2173:    Collective on MPI_Comm

2175:    Input Parameters:
2176: +  A - the matrix 
2177: .  i - the indices into j for the start of each local row (starts with zero)
2178: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
2179: -  v - optional values in the matrix

2181:    Level: developer

2183: .keywords: matrix, aij, compressed row, sparse, parallel

2185: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ
2186: @*/
2187: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2188: {
2189:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]);

2192:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",(void (**)(void))&f);
2193:   if (f) {
2194:     (*f)(B,bs,i,j,v);
2195:   }
2196:   return(0);
2197: }

2202: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,PetscInt *d_nnz,PetscInt o_nz,PetscInt *o_nnz)
2203: {
2204:   Mat_MPIBAIJ    *b;
2206:   PetscInt       i;

2209:   B->preallocated = PETSC_TRUE;
2210:   PetscOptionsGetInt(PETSC_NULL,"-mat_block_size",&bs,PETSC_NULL);

2212:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
2213:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2214:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2215:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
2216:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);
2217: 
2218:   B->rmap.bs  = bs;
2219:   B->cmap.bs  = bs;
2220:   PetscMapInitialize(B->comm,&B->rmap);
2221:   PetscMapInitialize(B->comm,&B->cmap);

2223:   if (d_nnz) {
2224:     for (i=0; i<B->rmap.n/bs; i++) {
2225:       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]);
2226:     }
2227:   }
2228:   if (o_nnz) {
2229:     for (i=0; i<B->rmap.n/bs; i++) {
2230:       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]);
2231:     }
2232:   }

2234:   b = (Mat_MPIBAIJ*)B->data;
2235:   b->bs2 = bs*bs;
2236:   b->mbs = B->rmap.n/bs;
2237:   b->nbs = B->cmap.n/bs;
2238:   b->Mbs = B->rmap.N/bs;
2239:   b->Nbs = B->cmap.N/bs;

2241:   for (i=0; i<=b->size; i++) {
2242:     b->rangebs[i] = B->rmap.range[i]/bs;
2243:   }
2244:   b->rstartbs = B->rmap.rstart/bs;
2245:   b->rendbs   = B->rmap.rend/bs;
2246:   b->cstartbs = B->cmap.rstart/bs;
2247:   b->cendbs   = B->cmap.rend/bs;

2249:   MatCreate(PETSC_COMM_SELF,&b->A);
2250:   MatSetSizes(b->A,B->rmap.n,B->cmap.n,B->rmap.n,B->cmap.n);
2251:   MatSetType(b->A,MATSEQBAIJ);
2252:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
2253:   PetscLogObjectParent(B,b->A);
2254:   MatCreate(PETSC_COMM_SELF,&b->B);
2255:   MatSetSizes(b->B,B->rmap.n,B->cmap.N,B->rmap.n,B->cmap.N);
2256:   MatSetType(b->B,MATSEQBAIJ);
2257:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
2258:   PetscLogObjectParent(B,b->B);

2260:   MatStashCreate_Private(B->comm,bs,&B->bstash);

2262:   return(0);
2263: }

2267: EXTERN PetscErrorCode PETSCMAT_DLLEXPORT MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2268: EXTERN PetscErrorCode PETSCMAT_DLLEXPORT MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);

2271: /*MC
2272:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

2274:    Options Database Keys:
2275: . -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()

2277:   Level: beginner

2279: .seealso: MatCreateMPIBAIJ
2280: M*/

2285: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_MPIBAIJ(Mat B)
2286: {
2287:   Mat_MPIBAIJ    *b;
2289:   PetscTruth     flg;

2292:   PetscNew(Mat_MPIBAIJ,&b);
2293:   B->data = (void*)b;


2296:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2297:   B->mapping    = 0;
2298:   B->factor     = 0;
2299:   B->assembled  = PETSC_FALSE;

2301:   B->insertmode = NOT_SET_VALUES;
2302:   MPI_Comm_rank(B->comm,&b->rank);
2303:   MPI_Comm_size(B->comm,&b->size);

2305:   /* build local table of row and column ownerships */
2306:   PetscMalloc((b->size+1)*sizeof(PetscInt),&b->rangebs);

2308:   /* build cache for off array entries formed */
2309:   MatStashCreate_Private(B->comm,1,&B->stash);
2310:   b->donotstash  = PETSC_FALSE;
2311:   b->colmap      = PETSC_NULL;
2312:   b->garray      = PETSC_NULL;
2313:   b->roworiented = PETSC_TRUE;

2315: #if defined(PETSC_USE_MAT_SINGLE)
2316:   /* stuff for MatSetValues_XXX in single precision */
2317:   b->setvalueslen     = 0;
2318:   b->setvaluescopy    = PETSC_NULL;
2319: #endif

2321:   /* stuff used in block assembly */
2322:   b->barray       = 0;

2324:   /* stuff used for matrix vector multiply */
2325:   b->lvec         = 0;
2326:   b->Mvctx        = 0;

2328:   /* stuff for MatGetRow() */
2329:   b->rowindices   = 0;
2330:   b->rowvalues    = 0;
2331:   b->getrowactive = PETSC_FALSE;

2333:   /* hash table stuff */
2334:   b->ht           = 0;
2335:   b->hd           = 0;
2336:   b->ht_size      = 0;
2337:   b->ht_flag      = PETSC_FALSE;
2338:   b->ht_fact      = 0;
2339:   b->ht_total_ct  = 0;
2340:   b->ht_insert_ct = 0;

2342:   PetscOptionsHasName(PETSC_NULL,"-mat_use_hash_table",&flg);
2343:   if (flg) {
2344:     PetscReal fact = 1.39;
2345:     MatSetOption(B,MAT_USE_HASH_TABLE);
2346:     PetscOptionsGetReal(PETSC_NULL,"-mat_use_hash_table",&fact,PETSC_NULL);
2347:     if (fact <= 1.0) fact = 1.39;
2348:     MatMPIBAIJSetHashTableFactor(B,fact);
2349:     PetscInfo1(0,"Hash table Factor used %5.2f\n",fact);
2350:   }
2351:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2352:                                      "MatStoreValues_MPIBAIJ",
2353:                                      MatStoreValues_MPIBAIJ);
2354:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2355:                                      "MatRetrieveValues_MPIBAIJ",
2356:                                      MatRetrieveValues_MPIBAIJ);
2357:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
2358:                                      "MatGetDiagonalBlock_MPIBAIJ",
2359:                                      MatGetDiagonalBlock_MPIBAIJ);
2360:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocation_C",
2361:                                      "MatMPIBAIJSetPreallocation_MPIBAIJ",
2362:                                      MatMPIBAIJSetPreallocation_MPIBAIJ);
2363:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",
2364:                                      "MatMPIBAIJSetPreallocationCSR_MPIAIJ",
2365:                                      MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
2366:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
2367:                                      "MatDiagonalScaleLocal_MPIBAIJ",
2368:                                      MatDiagonalScaleLocal_MPIBAIJ);
2369:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetHashTableFactor_C",
2370:                                      "MatSetHashTableFactor_MPIBAIJ",
2371:                                      MatSetHashTableFactor_MPIBAIJ);
2372:   return(0);
2373: }

2376: /*MC
2377:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

2379:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
2380:    and MATMPIBAIJ otherwise.

2382:    Options Database Keys:
2383: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

2385:   Level: beginner

2387: .seealso: MatCreateMPIBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2388: M*/

2393: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_BAIJ(Mat A)
2394: {
2396:   PetscMPIInt    size;

2399:   PetscObjectChangeTypeName((PetscObject)A,MATBAIJ);
2400:   MPI_Comm_size(A->comm,&size);
2401:   if (size == 1) {
2402:     MatSetType(A,MATSEQBAIJ);
2403:   } else {
2404:     MatSetType(A,MATMPIBAIJ);
2405:   }
2406:   return(0);
2407: }

2412: /*@C
2413:    MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
2414:    (block compressed row).  For good matrix assembly performance
2415:    the user should preallocate the matrix storage by setting the parameters 
2416:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2417:    performance can be increased by more than a factor of 50.

2419:    Collective on Mat

2421:    Input Parameters:
2422: +  A - the matrix 
2423: .  bs   - size of blockk
2424: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
2425:            submatrix  (same for all local rows)
2426: .  d_nnz - array containing the number of block nonzeros in the various block rows 
2427:            of the in diagonal portion of the local (possibly different for each block
2428:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2429: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
2430:            submatrix (same for all local rows).
2431: -  o_nnz - array containing the number of nonzeros in the various block rows of the
2432:            off-diagonal portion of the local submatrix (possibly different for
2433:            each block row) or PETSC_NULL.

2435:    If the *_nnz parameter is given then the *_nz parameter is ignored

2437:    Options Database Keys:
2438: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2439:                      block calculations (much slower)
2440: .   -mat_block_size - size of the blocks to use

2442:    Notes:
2443:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2444:    than it must be used on all processors that share the object for that argument.

2446:    Storage Information:
2447:    For a square global matrix we define each processor's diagonal portion 
2448:    to be its local rows and the corresponding columns (a square submatrix);  
2449:    each processor's off-diagonal portion encompasses the remainder of the
2450:    local matrix (a rectangular submatrix). 

2452:    The user can specify preallocated storage for the diagonal part of
2453:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2454:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2455:    memory allocation.  Likewise, specify preallocated storage for the
2456:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2458:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2459:    the figure below we depict these three local rows and all columns (0-11).

2461: .vb
2462:            0 1 2 3 4 5 6 7 8 9 10 11
2463:           -------------------
2464:    row 3  |  o o o d d d o o o o o o
2465:    row 4  |  o o o d d d o o o o o o
2466:    row 5  |  o o o d d d o o o o o o
2467:           -------------------
2468: .ve
2469:   
2470:    Thus, any entries in the d locations are stored in the d (diagonal) 
2471:    submatrix, and any entries in the o locations are stored in the
2472:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2473:    stored simply in the MATSEQBAIJ format for compressed row storage.

2475:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2476:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2477:    In general, for PDE problems in which most nonzeros are near the diagonal,
2478:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2479:    or you will get TERRIBLE performance; see the users' manual chapter on
2480:    matrices.

2482:    Level: intermediate

2484: .keywords: matrix, block, aij, compressed row, sparse, parallel

2486: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocationCSR()
2487: @*/
2488: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2489: {
2490:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

2493:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",(void (**)(void))&f);
2494:   if (f) {
2495:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
2496:   }
2497:   return(0);
2498: }

2502: /*@C
2503:    MatCreateMPIBAIJ - Creates a sparse parallel matrix in block AIJ format
2504:    (block compressed row).  For good matrix assembly performance
2505:    the user should preallocate the matrix storage by setting the parameters 
2506:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2507:    performance can be increased by more than a factor of 50.

2509:    Collective on MPI_Comm

2511:    Input Parameters:
2512: +  comm - MPI communicator
2513: .  bs   - size of blockk
2514: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2515:            This value should be the same as the local size used in creating the 
2516:            y vector for the matrix-vector product y = Ax.
2517: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
2518:            This value should be the same as the local size used in creating the 
2519:            x vector for the matrix-vector product y = Ax.
2520: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2521: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2522: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local 
2523:            submatrix  (same for all local rows)
2524: .  d_nnz - array containing the number of nonzero blocks in the various block rows 
2525:            of the in diagonal portion of the local (possibly different for each block
2526:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2527: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
2528:            submatrix (same for all local rows).
2529: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
2530:            off-diagonal portion of the local submatrix (possibly different for
2531:            each block row) or PETSC_NULL.

2533:    Output Parameter:
2534: .  A - the matrix 

2536:    Options Database Keys:
2537: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2538:                      block calculations (much slower)
2539: .   -mat_block_size - size of the blocks to use

2541:    Notes:
2542:    If the *_nnz parameter is given then the *_nz parameter is ignored

2544:    A nonzero block is any block that as 1 or more nonzeros in it

2546:    The user MUST specify either the local or global matrix dimensions
2547:    (possibly both).

2549:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2550:    than it must be used on all processors that share the object for that argument.

2552:    Storage Information:
2553:    For a square global matrix we define each processor's diagonal portion 
2554:    to be its local rows and the corresponding columns (a square submatrix);  
2555:    each processor's off-diagonal portion encompasses the remainder of the
2556:    local matrix (a rectangular submatrix). 

2558:    The user can specify preallocated storage for the diagonal part of
2559:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2560:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2561:    memory allocation.  Likewise, specify preallocated storage for the
2562:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2564:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2565:    the figure below we depict these three local rows and all columns (0-11).

2567: .vb
2568:            0 1 2 3 4 5 6 7 8 9 10 11
2569:           -------------------
2570:    row 3  |  o o o d d d o o o o o o
2571:    row 4  |  o o o d d d o o o o o o
2572:    row 5  |  o o o d d d o o o o o o
2573:           -------------------
2574: .ve
2575:   
2576:    Thus, any entries in the d locations are stored in the d (diagonal) 
2577:    submatrix, and any entries in the o locations are stored in the
2578:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2579:    stored simply in the MATSEQBAIJ format for compressed row storage.

2581:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2582:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2583:    In general, for PDE problems in which most nonzeros are near the diagonal,
2584:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2585:    or you will get TERRIBLE performance; see the users' manual chapter on
2586:    matrices.

2588:    Level: intermediate

2590: .keywords: matrix, block, aij, compressed row, sparse, parallel

2592: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2593: @*/
2594: PetscErrorCode PETSCMAT_DLLEXPORT 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)
2595: {
2597:   PetscMPIInt    size;

2600:   MatCreate(comm,A);
2601:   MatSetSizes(*A,m,n,M,N);
2602:   MPI_Comm_size(comm,&size);
2603:   if (size > 1) {
2604:     MatSetType(*A,MATMPIBAIJ);
2605:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2606:   } else {
2607:     MatSetType(*A,MATSEQBAIJ);
2608:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2609:   }
2610:   return(0);
2611: }

2615: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2616: {
2617:   Mat            mat;
2618:   Mat_MPIBAIJ    *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
2620:   PetscInt       len=0;

2623:   *newmat       = 0;
2624:   MatCreate(matin->comm,&mat);
2625:   MatSetSizes(mat,matin->rmap.n,matin->cmap.n,matin->rmap.N,matin->cmap.N);
2626:   MatSetType(mat,matin->type_name);
2627:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));

2629:   mat->factor       = matin->factor;
2630:   mat->preallocated = PETSC_TRUE;
2631:   mat->assembled    = PETSC_TRUE;
2632:   mat->insertmode   = NOT_SET_VALUES;

2634:   a      = (Mat_MPIBAIJ*)mat->data;
2635:   mat->rmap.bs  = matin->rmap.bs;
2636:   a->bs2   = oldmat->bs2;
2637:   a->mbs   = oldmat->mbs;
2638:   a->nbs   = oldmat->nbs;
2639:   a->Mbs   = oldmat->Mbs;
2640:   a->Nbs   = oldmat->Nbs;
2641: 
2642:   PetscMapCopy(matin->comm,&matin->rmap,&mat->rmap);
2643:   PetscMapCopy(matin->comm,&matin->cmap,&mat->cmap);

2645:   a->size         = oldmat->size;
2646:   a->rank         = oldmat->rank;
2647:   a->donotstash   = oldmat->donotstash;
2648:   a->roworiented  = oldmat->roworiented;
2649:   a->rowindices   = 0;
2650:   a->rowvalues    = 0;
2651:   a->getrowactive = PETSC_FALSE;
2652:   a->barray       = 0;
2653:   a->rstartbs     = oldmat->rstartbs;
2654:   a->rendbs       = oldmat->rendbs;
2655:   a->cstartbs     = oldmat->cstartbs;
2656:   a->cendbs       = oldmat->cendbs;

2658:   /* hash table stuff */
2659:   a->ht           = 0;
2660:   a->hd           = 0;
2661:   a->ht_size      = 0;
2662:   a->ht_flag      = oldmat->ht_flag;
2663:   a->ht_fact      = oldmat->ht_fact;
2664:   a->ht_total_ct  = 0;
2665:   a->ht_insert_ct = 0;

2667:   PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+1)*sizeof(PetscInt));
2668:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2669:   MatStashCreate_Private(matin->comm,matin->rmap.bs,&mat->bstash);
2670:   if (oldmat->colmap) {
2671: #if defined (PETSC_USE_CTABLE)
2672:   PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2673: #else
2674:   PetscMalloc((a->Nbs)*sizeof(PetscInt),&a->colmap);
2675:   PetscLogObjectMemory(mat,(a->Nbs)*sizeof(PetscInt));
2676:   PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
2677: #endif
2678:   } else a->colmap = 0;

2680:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2681:     PetscMalloc(len*sizeof(PetscInt),&a->garray);
2682:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2683:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
2684:   } else a->garray = 0;
2685: 
2686:   VecDuplicate(oldmat->lvec,&a->lvec);
2687:   PetscLogObjectParent(mat,a->lvec);
2688:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2689:   PetscLogObjectParent(mat,a->Mvctx);

2691:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2692:   PetscLogObjectParent(mat,a->A);
2693:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2694:   PetscLogObjectParent(mat,a->B);
2695:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2696:   *newmat = mat;

2698:   return(0);
2699: }

2701:  #include petscsys.h

2705: PetscErrorCode MatLoad_MPIBAIJ(PetscViewer viewer, MatType type,Mat *newmat)
2706: {
2707:   Mat            A;
2709:   int            fd;
2710:   PetscInt       i,nz,j,rstart,rend;
2711:   PetscScalar    *vals,*buf;
2712:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2713:   MPI_Status     status;
2714:   PetscMPIInt    rank,size,maxnz;
2715:   PetscInt       header[4],*rowlengths = 0,M,N,m,*rowners,*cols;
2716:   PetscInt       *locrowlens,*procsnz = 0,*browners;
2717:   PetscInt       jj,*mycols,*ibuf,bs=1,Mbs,mbs,extra_rows,mmax;
2718:   PetscMPIInt    tag = ((PetscObject)viewer)->tag;
2719:   PetscInt       *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
2720:   PetscInt       dcount,kmax,k,nzcount,tmp,mend;
2721: 
2723:   PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);

2725:   MPI_Comm_size(comm,&size);
2726:   MPI_Comm_rank(comm,&rank);
2727:   if (!rank) {
2728:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2729:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2730:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2731:   }

2733:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2734:   M = header[1]; N = header[2];

2736:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2738:   /* 
2739:      This code adds extra rows to make sure the number of rows is 
2740:      divisible by the blocksize
2741:   */
2742:   Mbs        = M/bs;
2743:   extra_rows = bs - M + bs*Mbs;
2744:   if (extra_rows == bs) extra_rows = 0;
2745:   else                  Mbs++;
2746:   if (extra_rows && !rank) {
2747:     PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2748:   }

2750:   /* determine ownership of all rows */
2751:   mbs        = Mbs/size + ((Mbs % size) > rank);
2752:   m          = mbs*bs;
2753:   PetscMalloc2(size+1,PetscInt,&rowners,size+1,PetscInt,&browners);
2754:   MPI_Allgather(&mbs,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);

2756:   /* process 0 needs enough room for process with most rows */
2757:   if (!rank) {
2758:     mmax = rowners[1];
2759:     for (i=2; i<size; i++) {
2760:       mmax = PetscMax(mmax,rowners[i]);
2761:     }
2762:     mmax*=bs;
2763:   } else mmax = m;

2765:   rowners[0] = 0;
2766:   for (i=2; i<=size; i++)  rowners[i] += rowners[i-1];
2767:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2768:   rstart = rowners[rank];
2769:   rend   = rowners[rank+1];

2771:   /* distribute row lengths to all processors */
2772:   PetscMalloc((mmax+1)*sizeof(PetscInt),&locrowlens);
2773:   if (!rank) {
2774:     mend = m;
2775:     if (size == 1) mend = mend - extra_rows;
2776:     PetscBinaryRead(fd,locrowlens,mend,PETSC_INT);
2777:     for (j=mend; j<m; j++) locrowlens[j] = 1;
2778:     PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2779:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2780:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2781:     for (j=0; j<m; j++) {
2782:       procsnz[0] += locrowlens[j];
2783:     }
2784:     for (i=1; i<size; i++) {
2785:       mend = browners[i+1] - browners[i];
2786:       if (i == size-1) mend = mend - extra_rows;
2787:       PetscBinaryRead(fd,rowlengths,mend,PETSC_INT);
2788:       for (j=mend; j<browners[i+1] - browners[i]; j++) rowlengths[j] = 1;
2789:       /* calculate the number of nonzeros on each processor */
2790:       for (j=0; j<browners[i+1]-browners[i]; j++) {
2791:         procsnz[i] += rowlengths[j];
2792:       }
2793:       MPI_Send(rowlengths,browners[i+1]-browners[i],MPIU_INT,i,tag,comm);
2794:     }
2795:     PetscFree(rowlengths);
2796:   } else {
2797:     MPI_Recv(locrowlens,m,MPIU_INT,0,tag,comm,&status);
2798:   }

2800:   if (!rank) {
2801:     /* determine max buffer needed and allocate it */
2802:     maxnz = procsnz[0];
2803:     for (i=1; i<size; i++) {
2804:       maxnz = PetscMax(maxnz,procsnz[i]);
2805:     }
2806:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2808:     /* read in my part of the matrix column indices  */
2809:     nz     = procsnz[0];
2810:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
2811:     mycols = ibuf;
2812:     if (size == 1)  nz -= extra_rows;
2813:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2814:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2816:     /* read in every ones (except the last) and ship off */
2817:     for (i=1; i<size-1; i++) {
2818:       nz   = procsnz[i];
2819:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2820:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2821:     }
2822:     /* read in the stuff for the last proc */
2823:     if (size != 1) {
2824:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2825:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2826:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2827:       MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
2828:     }
2829:     PetscFree(cols);
2830:   } else {
2831:     /* determine buffer space needed for message */
2832:     nz = 0;
2833:     for (i=0; i<m; i++) {
2834:       nz += locrowlens[i];
2835:     }
2836:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
2837:     mycols = ibuf;
2838:     /* receive message of column indices*/
2839:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2840:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2841:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2842:   }
2843: 
2844:   /* loop over local rows, determining number of off diagonal entries */
2845:   PetscMalloc2(rend-rstart,PetscInt,&dlens,rend-rstart,PetscInt,&odlens);
2846:   PetscMalloc3(Mbs,PetscInt,&mask,Mbs,PetscInt,&masked1,Mbs,PetscInt,&masked2);
2847:   PetscMemzero(mask,Mbs*sizeof(PetscInt));
2848:   PetscMemzero(masked1,Mbs*sizeof(PetscInt));
2849:   PetscMemzero(masked2,Mbs*sizeof(PetscInt));
2850:   rowcount = 0; nzcount = 0;
2851:   for (i=0; i<mbs; i++) {
2852:     dcount  = 0;
2853:     odcount = 0;
2854:     for (j=0; j<bs; j++) {
2855:       kmax = locrowlens[rowcount];
2856:       for (k=0; k<kmax; k++) {
2857:         tmp = mycols[nzcount++]/bs;
2858:         if (!mask[tmp]) {
2859:           mask[tmp] = 1;
2860:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
2861:           else masked1[dcount++] = tmp;
2862:         }
2863:       }
2864:       rowcount++;
2865:     }
2866: 
2867:     dlens[i]  = dcount;
2868:     odlens[i] = odcount;

2870:     /* zero out the mask elements we set */
2871:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2872:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2873:   }

2875:   /* create our matrix */
2876:   MatCreate(comm,&A);
2877:   MatSetSizes(A,m,m,M+extra_rows,N+extra_rows);
2878:   MatSetType(A,type);CHKERRQ(ierr)
2879:   MatMPIBAIJSetPreallocation(A,bs,0,dlens,0,odlens);

2881:   /* Why doesn't this called using MatSetOption(A,MAT_COLUMNS_SORTED); */
2882:   MatSetOption(A,MAT_COLUMNS_SORTED);
2883: 
2884:   if (!rank) {
2885:     PetscMalloc((maxnz+1)*sizeof(PetscScalar),&buf);
2886:     /* read in my part of the matrix numerical values  */
2887:     nz = procsnz[0];
2888:     vals = buf;
2889:     mycols = ibuf;
2890:     if (size == 1)  nz -= extra_rows;
2891:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2892:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2894:     /* insert into matrix */
2895:     jj      = rstart*bs;
2896:     for (i=0; i<m; i++) {
2897:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2898:       mycols += locrowlens[i];
2899:       vals   += locrowlens[i];
2900:       jj++;
2901:     }
2902:     /* read in other processors (except the last one) and ship out */
2903:     for (i=1; i<size-1; i++) {
2904:       nz   = procsnz[i];
2905:       vals = buf;
2906:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2907:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2908:     }
2909:     /* the last proc */
2910:     if (size != 1){
2911:       nz   = procsnz[i] - extra_rows;
2912:       vals = buf;
2913:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2914:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2915:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2916:     }
2917:     PetscFree(procsnz);
2918:   } else {
2919:     /* receive numeric values */
2920:     PetscMalloc((nz+1)*sizeof(PetscScalar),&buf);

2922:     /* receive message of values*/
2923:     vals   = buf;
2924:     mycols = ibuf;
2925:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2926:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2927:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2929:     /* insert into matrix */
2930:     jj      = rstart*bs;
2931:     for (i=0; i<m; i++) {
2932:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2933:       mycols += locrowlens[i];
2934:       vals   += locrowlens[i];
2935:       jj++;
2936:     }
2937:   }
2938:   PetscFree(locrowlens);
2939:   PetscFree(buf);
2940:   PetscFree(ibuf);
2941:   PetscFree2(rowners,browners);
2942:   PetscFree2(dlens,odlens);
2943:   PetscFree3(mask,masked1,masked2);
2944:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2945:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

2947:   *newmat = A;
2948:   return(0);
2949: }

2953: /*@
2954:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2956:    Input Parameters:
2957: .  mat  - the matrix
2958: .  fact - factor

2960:    Collective on Mat

2962:    Level: advanced

2964:   Notes:
2965:    This can also be set by the command line option: -mat_use_hash_table fact

2967: .keywords: matrix, hashtable, factor, HT

2969: .seealso: MatSetOption()
2970: @*/
2971: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2972: {
2973:   PetscErrorCode ierr,(*f)(Mat,PetscReal);

2976:   PetscObjectQueryFunction((PetscObject)mat,"MatSetHashTableFactor_C",(void (**)(void))&f);
2977:   if (f) {
2978:     (*f)(mat,fact);
2979:   }
2980:   return(0);
2981: }

2986: PetscErrorCode PETSCMAT_DLLEXPORT MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
2987: {
2988:   Mat_MPIBAIJ *baij;

2991:   baij = (Mat_MPIBAIJ*)mat->data;
2992:   baij->ht_fact = fact;
2993:   return(0);
2994: }

2999: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
3000: {
3001:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
3003:   *Ad     = a->A;
3004:   *Ao     = a->B;
3005:   *colmap = a->garray;
3006:   return(0);
3007: }