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 MatZeroRows_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscScalar);

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

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

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

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

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

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

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

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

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

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

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

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

127: #define CHUNKSIZE  10

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

527: 
528:         idx = h1;
529: #if defined(PETSC_USE_DEBUG)
530:         insert_ct++;
531:         total_ct++;
532:         if (HT[idx] != key) {
533:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
534:           if (idx == size) {
535:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
536:             if (idx == h1) {
537:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
538:             }
539:           }
540:         }
541: #else
542:         if (HT[idx] != key) {
543:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
544:           if (idx == size) {
545:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
546:             if (idx == h1) {
547:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
548:             }
549:           }
550:         }
551: #endif
552:         /* A HASH table entry is found, so insert the values at the correct address */
553:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
554:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
555:       }
556:     } else {
557:       if (!baij->donotstash) {
558:         if (roworiented) {
559:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
560:         } else {
561:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
562:         }
563:       }
564:     }
565:   }
566: #if defined(PETSC_USE_DEBUG)
567:   baij->ht_total_ct = total_ct;
568:   baij->ht_insert_ct = insert_ct;
569: #endif
570:   return(0);
571: }

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

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

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

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

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

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

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

840: /*
841:   Creates the hash table, and sets the table 
842:   This table is created only once. 
843:   If new entried need to be added to the matrix
844:   then the hash table has to be destroyed and
845:   recreated.
846: */
849: PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
850: {
851:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
852:   Mat            A = baij->A,B=baij->B;
853:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ *)A->data,*b=(Mat_SeqBAIJ *)B->data;
854:   PetscInt       i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
856:   PetscInt       size,bs2=baij->bs2,rstart=baij->rstartbs;
857:   PetscInt       cstart=baij->cstartbs,*garray=baij->garray,row,col,Nbs=baij->Nbs;
858:   PetscInt       *HT,key;
859:   MatScalar      **HD;
860:   PetscReal      tmp;
861: #if defined(PETSC_USE_INFO)
862:   PetscInt       ct=0,max=0;
863: #endif

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

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


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

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

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

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

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

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

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

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

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

1034:   /* determine if any processor has disassembled, if so we must 
1035:      also disassemble ourselfs, in order that we may reassemble. */
1036:   /*
1037:      if nonzero structure of submatrix B cannot change then we know that
1038:      no processor disassembled thus we can skip this stuff
1039:   */
1040:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
1041:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
1042:     if (mat->was_assembled && !other_disassembled) {
1043:       DisAssemble_MPIBAIJ(mat);
1044:     }
1045:   }

1047:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
1048:     MatSetUpMultiply_MPIBAIJ(mat);
1049:   }
1050:   ((Mat_SeqBAIJ*)baij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
1051:   MatAssemblyBegin(baij->B,mode);
1052:   MatAssemblyEnd(baij->B,mode);
1053: 
1054: #if defined(PETSC_USE_INFO)
1055:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
1056:     PetscInfo1(0,"Average Hash Table Search in MatSetValues = %5.2f\n",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
1057:     baij->ht_total_ct  = 0;
1058:     baij->ht_insert_ct = 0;
1059:   }
1060: #endif
1061:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
1062:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);
1063:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
1064:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
1065:   }

1067:   PetscFree(baij->rowvalues);
1068:   baij->rowvalues = 0;
1069:   return(0);
1070: }

1074: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1075: {
1076:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
1077:   PetscErrorCode    ierr;
1078:   PetscMPIInt       size = baij->size,rank = baij->rank;
1079:   PetscInt          bs = mat->rmap.bs;
1080:   PetscTruth        iascii,isdraw;
1081:   PetscViewer       sviewer;
1082:   PetscViewerFormat format;

1085:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1086:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1087:   if (iascii) {
1088:     PetscViewerGetFormat(viewer,&format);
1089:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1090:       MatInfo info;
1091:       MPI_Comm_rank(mat->comm,&rank);
1092:       MatGetInfo(mat,MAT_LOCAL,&info);
1093:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",
1094:               rank,mat->rmap.N,(PetscInt)info.nz_used*bs,(PetscInt)info.nz_allocated*bs,
1095:               mat->rmap.bs,(PetscInt)info.memory);
1096:       MatGetInfo(baij->A,MAT_LOCAL,&info);
1097:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
1098:       MatGetInfo(baij->B,MAT_LOCAL,&info);
1099:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
1100:       PetscViewerFlush(viewer);
1101:       VecScatterView(baij->Mvctx,viewer);
1102:       return(0);
1103:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1104:       PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
1105:       return(0);
1106:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1107:       return(0);
1108:     }
1109:   }

1111:   if (isdraw) {
1112:     PetscDraw       draw;
1113:     PetscTruth isnull;
1114:     PetscViewerDrawGetDraw(viewer,0,&draw);
1115:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1116:   }

1118:   if (size == 1) {
1119:     PetscObjectSetName((PetscObject)baij->A,mat->name);
1120:     MatView(baij->A,viewer);
1121:   } else {
1122:     /* assemble the entire matrix onto first processor. */
1123:     Mat         A;
1124:     Mat_SeqBAIJ *Aloc;
1125:     PetscInt    M = mat->rmap.N,N = mat->cmap.N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
1126:     MatScalar   *a;

1128:     /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1129:     /* Perhaps this should be the type of mat? */
1130:     MatCreate(mat->comm,&A);
1131:     if (!rank) {
1132:       MatSetSizes(A,M,N,M,N);
1133:     } else {
1134:       MatSetSizes(A,0,0,M,N);
1135:     }
1136:     MatSetType(A,MATMPIBAIJ);
1137:     MatMPIBAIJSetPreallocation(A,mat->rmap.bs,0,PETSC_NULL,0,PETSC_NULL);
1138:     PetscLogObjectParent(mat,A);

1140:     /* copy over the A part */
1141:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
1142:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1143:     PetscMalloc(bs*sizeof(PetscInt),&rvals);

1145:     for (i=0; i<mbs; i++) {
1146:       rvals[0] = bs*(baij->rstartbs + i);
1147:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1148:       for (j=ai[i]; j<ai[i+1]; j++) {
1149:         col = (baij->cstartbs+aj[j])*bs;
1150:         for (k=0; k<bs; k++) {
1151:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1152:           col++; a += bs;
1153:         }
1154:       }
1155:     }
1156:     /* copy over the B part */
1157:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
1158:     ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1159:     for (i=0; i<mbs; i++) {
1160:       rvals[0] = bs*(baij->rstartbs + i);
1161:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1162:       for (j=ai[i]; j<ai[i+1]; j++) {
1163:         col = baij->garray[aj[j]]*bs;
1164:         for (k=0; k<bs; k++) {
1165:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1166:           col++; a += bs;
1167:         }
1168:       }
1169:     }
1170:     PetscFree(rvals);
1171:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1172:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1173:     /* 
1174:        Everyone has to call to draw the matrix since the graphics waits are
1175:        synchronized across all processors that share the PetscDraw object
1176:     */
1177:     PetscViewerGetSingleton(viewer,&sviewer);
1178:     if (!rank) {
1179:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,mat->name);
1180:       MatView(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1181:     }
1182:     PetscViewerRestoreSingleton(viewer,&sviewer);
1183:     MatDestroy(A);
1184:   }
1185:   return(0);
1186: }

1190: PetscErrorCode MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1191: {
1193:   PetscTruth     iascii,isdraw,issocket,isbinary;

1196:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1197:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1198:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1199:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1200:   if (iascii || isdraw || issocket || isbinary) {
1201:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1202:   } else {
1203:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIBAIJ matrices",((PetscObject)viewer)->type_name);
1204:   }
1205:   return(0);
1206: }

1210: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
1211: {
1212:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;

1216: #if defined(PETSC_USE_LOG)
1217:   PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap.N,mat->cmap.N);
1218: #endif
1219:   MatStashDestroy_Private(&mat->stash);
1220:   MatStashDestroy_Private(&mat->bstash);
1221:   MatDestroy(baij->A);
1222:   MatDestroy(baij->B);
1223: #if defined (PETSC_USE_CTABLE)
1224:   if (baij->colmap) {PetscTableDelete(baij->colmap);}
1225: #else
1226:   PetscFree(baij->colmap);
1227: #endif
1228:   PetscFree(baij->garray);
1229:   if (baij->lvec)   {VecDestroy(baij->lvec);}
1230:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
1231:   PetscFree(baij->rowvalues);
1232:   PetscFree(baij->barray);
1233:   PetscFree(baij->hd);
1234: #if defined(PETSC_USE_MAT_SINGLE)
1235:   PetscFree(baij->setvaluescopy);
1236: #endif
1237:   PetscFree(baij->rangebs);
1238:   PetscFree(baij);

1240:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
1241:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
1242:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
1243:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C","",PETSC_NULL);
1244:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C","",PETSC_NULL);
1245:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
1246:   PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C","",PETSC_NULL);
1247:   return(0);
1248: }

1252: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1253: {
1254:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1256:   PetscInt       nt;

1259:   VecGetLocalSize(xx,&nt);
1260:   if (nt != A->cmap.n) {
1261:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1262:   }
1263:   VecGetLocalSize(yy,&nt);
1264:   if (nt != A->rmap.n) {
1265:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1266:   }
1267:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1268:   (*a->A->ops->mult)(a->A,xx,yy);
1269:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1270:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1271:   return(0);
1272: }

1276: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1277: {
1278:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1282:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1283:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1284:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1285:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1286:   return(0);
1287: }

1291: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1292: {
1293:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1295:   PetscTruth     merged;

1298:   VecScatterGetMerged(a->Mvctx,&merged);
1299:   /* do nondiagonal part */
1300:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1301:   if (!merged) {
1302:     /* send it on its way */
1303:     VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1304:     /* do local part */
1305:     (*a->A->ops->multtranspose)(a->A,xx,yy);
1306:     /* receive remote parts: note this assumes the values are not actually */
1307:     /* inserted in yy until the next line */
1308:     VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1309:   } else {
1310:     /* do local part */
1311:     (*a->A->ops->multtranspose)(a->A,xx,yy);
1312:     /* send it on its way */
1313:     VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1314:     /* values actually were received in the Begin() but we need to call this nop */
1315:     VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1316:   }
1317:   return(0);
1318: }

1322: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1323: {
1324:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1328:   /* do nondiagonal part */
1329:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1330:   /* send it on its way */
1331:   VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1332:   /* do local part */
1333:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1334:   /* receive remote parts: note this assumes the values are not actually */
1335:   /* inserted in yy until the next line, which is true for my implementation*/
1336:   /* but is not perhaps always true. */
1337:   VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1338:   return(0);
1339: }

1341: /*
1342:   This only works correctly for square matrices where the subblock A->A is the 
1343:    diagonal block
1344: */
1347: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1348: {
1349:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1353:   if (A->rmap.N != A->cmap.N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1354:   MatGetDiagonal(a->A,v);
1355:   return(0);
1356: }

1360: PetscErrorCode MatScale_MPIBAIJ(Mat A,PetscScalar aa)
1361: {
1362:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1366:   MatScale(a->A,aa);
1367:   MatScale(a->B,aa);
1368:   return(0);
1369: }

1373: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1374: {
1375:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
1376:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1378:   PetscInt       bs = matin->rmap.bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
1379:   PetscInt       nztot,nzA,nzB,lrow,brstart = matin->rmap.rstart,brend = matin->rmap.rend;
1380:   PetscInt       *cmap,*idx_p,cstart = mat->cstartbs;

1383:   if (mat->getrowactive) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1384:   mat->getrowactive = PETSC_TRUE;

1386:   if (!mat->rowvalues && (idx || v)) {
1387:     /*
1388:         allocate enough space to hold information from the longest row.
1389:     */
1390:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1391:     PetscInt     max = 1,mbs = mat->mbs,tmp;
1392:     for (i=0; i<mbs; i++) {
1393:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1394:       if (max < tmp) { max = tmp; }
1395:     }
1396:     PetscMalloc(max*bs2*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1397:     mat->rowindices = (PetscInt*)(mat->rowvalues + max*bs2);
1398:   }
1399: 
1400:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1401:   lrow = row - brstart;

1403:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1404:   if (!v)   {pvA = 0; pvB = 0;}
1405:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1406:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1407:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1408:   nztot = nzA + nzB;

1410:   cmap  = mat->garray;
1411:   if (v  || idx) {
1412:     if (nztot) {
1413:       /* Sort by increasing column numbers, assuming A and B already sorted */
1414:       PetscInt imark = -1;
1415:       if (v) {
1416:         *v = v_p = mat->rowvalues;
1417:         for (i=0; i<nzB; i++) {
1418:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1419:           else break;
1420:         }
1421:         imark = i;
1422:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1423:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1424:       }
1425:       if (idx) {
1426:         *idx = idx_p = mat->rowindices;
1427:         if (imark > -1) {
1428:           for (i=0; i<imark; i++) {
1429:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1430:           }
1431:         } else {
1432:           for (i=0; i<nzB; i++) {
1433:             if (cmap[cworkB[i]/bs] < cstart)
1434:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1435:             else break;
1436:           }
1437:           imark = i;
1438:         }
1439:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1440:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1441:       }
1442:     } else {
1443:       if (idx) *idx = 0;
1444:       if (v)   *v   = 0;
1445:     }
1446:   }
1447:   *nz = nztot;
1448:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1449:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1450:   return(0);
1451: }

1455: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1456: {
1457:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1460:   if (!baij->getrowactive) {
1461:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1462:   }
1463:   baij->getrowactive = PETSC_FALSE;
1464:   return(0);
1465: }

1469: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1470: {
1471:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;

1475:   MatZeroEntries(l->A);
1476:   MatZeroEntries(l->B);
1477:   return(0);
1478: }

1482: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1483: {
1484:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)matin->data;
1485:   Mat            A = a->A,B = a->B;
1487:   PetscReal      isend[5],irecv[5];

1490:   info->block_size     = (PetscReal)matin->rmap.bs;
1491:   MatGetInfo(A,MAT_LOCAL,info);
1492:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1493:   isend[3] = info->memory;  isend[4] = info->mallocs;
1494:   MatGetInfo(B,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:   if (flag == MAT_LOCAL) {
1498:     info->nz_used      = isend[0];
1499:     info->nz_allocated = isend[1];
1500:     info->nz_unneeded  = isend[2];
1501:     info->memory       = isend[3];
1502:     info->mallocs      = isend[4];
1503:   } else if (flag == MAT_GLOBAL_MAX) {
1504:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1505:     info->nz_used      = irecv[0];
1506:     info->nz_allocated = irecv[1];
1507:     info->nz_unneeded  = irecv[2];
1508:     info->memory       = irecv[3];
1509:     info->mallocs      = irecv[4];
1510:   } else if (flag == MAT_GLOBAL_SUM) {
1511:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1512:     info->nz_used      = irecv[0];
1513:     info->nz_allocated = irecv[1];
1514:     info->nz_unneeded  = irecv[2];
1515:     info->memory       = irecv[3];
1516:     info->mallocs      = irecv[4];
1517:   } else {
1518:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1519:   }
1520:   info->rows_global       = (PetscReal)A->rmap.N;
1521:   info->columns_global    = (PetscReal)A->cmap.N;
1522:   info->rows_local        = (PetscReal)A->rmap.N;
1523:   info->columns_local     = (PetscReal)A->cmap.N;
1524:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1525:   info->fill_ratio_needed = 0;
1526:   info->factor_mallocs    = 0;
1527:   return(0);
1528: }

1532: PetscErrorCode MatSetOption_MPIBAIJ(Mat A,MatOption op)
1533: {
1534:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1538:   switch (op) {
1539:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1540:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1541:   case MAT_COLUMNS_UNSORTED:
1542:   case MAT_COLUMNS_SORTED:
1543:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1544:   case MAT_KEEP_ZEROED_ROWS:
1545:   case MAT_NEW_NONZERO_LOCATION_ERR:
1546:     MatSetOption(a->A,op);
1547:     MatSetOption(a->B,op);
1548:     break;
1549:   case MAT_ROW_ORIENTED:
1550:     a->roworiented = PETSC_TRUE;
1551:     MatSetOption(a->A,op);
1552:     MatSetOption(a->B,op);
1553:     break;
1554:   case MAT_ROWS_SORTED:
1555:   case MAT_ROWS_UNSORTED:
1556:   case MAT_YES_NEW_DIAGONALS:
1557:     PetscInfo(A,"Option ignored\n");
1558:     break;
1559:   case MAT_COLUMN_ORIENTED:
1560:     a->roworiented = PETSC_FALSE;
1561:     MatSetOption(a->A,op);
1562:     MatSetOption(a->B,op);
1563:     break;
1564:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1565:     a->donotstash = PETSC_TRUE;
1566:     break;
1567:   case MAT_NO_NEW_DIAGONALS:
1568:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1569:   case MAT_USE_HASH_TABLE:
1570:     a->ht_flag = PETSC_TRUE;
1571:     break;
1572:   case MAT_SYMMETRIC:
1573:   case MAT_STRUCTURALLY_SYMMETRIC:
1574:   case MAT_HERMITIAN:
1575:   case MAT_SYMMETRY_ETERNAL:
1576:     MatSetOption(a->A,op);
1577:     break;
1578:   case MAT_NOT_SYMMETRIC:
1579:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1580:   case MAT_NOT_HERMITIAN:
1581:   case MAT_NOT_SYMMETRY_ETERNAL:
1582:     break;
1583:   default:
1584:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
1585:   }
1586:   return(0);
1587: }

1591: PetscErrorCode MatTranspose_MPIBAIJ(Mat A,Mat *matout)
1592: {
1593:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)A->data;
1594:   Mat_SeqBAIJ    *Aloc;
1595:   Mat            B;
1597:   PetscInt       M=A->rmap.N,N=A->cmap.N,*ai,*aj,i,*rvals,j,k,col;
1598:   PetscInt       bs=A->rmap.bs,mbs=baij->mbs;
1599:   MatScalar      *a;
1600: 
1602:   if (!matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1603:   MatCreate(A->comm,&B);
1604:   MatSetSizes(B,A->cmap.n,A->rmap.n,N,M);
1605:   MatSetType(B,A->type_name);
1606:   MatMPIBAIJSetPreallocation(B,A->rmap.bs,0,PETSC_NULL,0,PETSC_NULL);
1607: 
1608:   /* copy over the A part */
1609:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1610:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1611:   PetscMalloc(bs*sizeof(PetscInt),&rvals);
1612: 
1613:   for (i=0; i<mbs; i++) {
1614:     rvals[0] = bs*(baij->rstartbs + i);
1615:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1616:     for (j=ai[i]; j<ai[i+1]; j++) {
1617:       col = (baij->cstartbs+aj[j])*bs;
1618:       for (k=0; k<bs; k++) {
1619:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1620:         col++; a += bs;
1621:       }
1622:     }
1623:   }
1624:   /* copy over the B part */
1625:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1626:   ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1627:   for (i=0; i<mbs; i++) {
1628:     rvals[0] = bs*(baij->rstartbs + i);
1629:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1630:     for (j=ai[i]; j<ai[i+1]; j++) {
1631:       col = baij->garray[aj[j]]*bs;
1632:       for (k=0; k<bs; k++) {
1633:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1634:         col++; a += bs;
1635:       }
1636:     }
1637:   }
1638:   PetscFree(rvals);
1639:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1640:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1641: 
1642:   if (matout) {
1643:     *matout = B;
1644:   } else {
1645:     MatHeaderCopy(A,B);
1646:   }
1647:   return(0);
1648: }

1652: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1653: {
1654:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
1655:   Mat            a = baij->A,b = baij->B;
1657:   PetscInt       s1,s2,s3;

1660:   MatGetLocalSize(mat,&s2,&s3);
1661:   if (rr) {
1662:     VecGetLocalSize(rr,&s1);
1663:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1664:     /* Overlap communication with computation. */
1665:     VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1666:   }
1667:   if (ll) {
1668:     VecGetLocalSize(ll,&s1);
1669:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1670:     (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1671:   }
1672:   /* scale  the diagonal block */
1673:   (*a->ops->diagonalscale)(a,ll,rr);

1675:   if (rr) {
1676:     /* Do a scatter end and then right scale the off-diagonal block */
1677:     VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1678:     (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1679:   }
1680: 
1681:   return(0);
1682: }

1686: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1687: {
1688:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;
1690:   PetscMPIInt    imdex,size = l->size,n,rank = l->rank;
1691:   PetscInt       i,*owners = A->rmap.range;
1692:   PetscInt       *nprocs,j,idx,nsends,row;
1693:   PetscInt       nmax,*svalues,*starts,*owner,nrecvs;
1694:   PetscInt       *rvalues,tag = A->tag,count,base,slen,*source,lastidx = -1;
1695:   PetscInt       *lens,*lrows,*values,rstart_bs=A->rmap.rstart;
1696:   MPI_Comm       comm = A->comm;
1697:   MPI_Request    *send_waits,*recv_waits;
1698:   MPI_Status     recv_status,*send_status;
1699: #if defined(PETSC_DEBUG)
1700:   PetscTruth     found = PETSC_FALSE;
1701: #endif
1702: 
1704:   /*  first count number of contributors to each processor */
1705:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
1706:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
1707:   PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
1708:   j = 0;
1709:   for (i=0; i<N; i++) {
1710:     if (lastidx > (idx = rows[i])) j = 0;
1711:     lastidx = idx;
1712:     for (; j<size; j++) {
1713:       if (idx >= owners[j] && idx < owners[j+1]) {
1714:         nprocs[2*j]++;
1715:         nprocs[2*j+1] = 1;
1716:         owner[i] = j;
1717: #if defined(PETSC_DEBUG)
1718:         found = PETSC_TRUE;
1719: #endif
1720:         break;
1721:       }
1722:     }
1723: #if defined(PETSC_DEBUG)
1724:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1725:     found = PETSC_FALSE;
1726: #endif
1727:   }
1728:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
1729: 
1730:   /* inform other processors of number of messages and max length*/
1731:   PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
1732: 
1733:   /* post receives:   */
1734:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
1735:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1736:   for (i=0; i<nrecvs; i++) {
1737:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1738:   }
1739: 
1740:   /* do sends:
1741:      1) starts[i] gives the starting index in svalues for stuff going to 
1742:      the ith processor
1743:   */
1744:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
1745:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1746:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
1747:   starts[0]  = 0;
1748:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1749:   for (i=0; i<N; i++) {
1750:     svalues[starts[owner[i]]++] = rows[i];
1751:   }
1752: 
1753:   starts[0] = 0;
1754:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1755:   count = 0;
1756:   for (i=0; i<size; i++) {
1757:     if (nprocs[2*i+1]) {
1758:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
1759:     }
1760:   }
1761:   PetscFree(starts);

1763:   base = owners[rank];
1764: 
1765:   /*  wait on receives */
1766:   PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
1767:   source = lens + nrecvs;
1768:   count  = nrecvs; slen = 0;
1769:   while (count) {
1770:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1771:     /* unpack receives into our local space */
1772:     MPI_Get_count(&recv_status,MPIU_INT,&n);
1773:     source[imdex]  = recv_status.MPI_SOURCE;
1774:     lens[imdex]    = n;
1775:     slen          += n;
1776:     count--;
1777:   }
1778:   PetscFree(recv_waits);
1779: 
1780:   /* move the data into the send scatter */
1781:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
1782:   count = 0;
1783:   for (i=0; i<nrecvs; i++) {
1784:     values = rvalues + i*nmax;
1785:     for (j=0; j<lens[i]; j++) {
1786:       lrows[count++] = values[j] - base;
1787:     }
1788:   }
1789:   PetscFree(rvalues);
1790:   PetscFree(lens);
1791:   PetscFree(owner);
1792:   PetscFree(nprocs);
1793: 
1794:   /* actually zap the local rows */
1795:   /*
1796:         Zero the required rows. If the "diagonal block" of the matrix
1797:      is square and the user wishes to set the diagonal we use separate
1798:      code so that MatSetValues() is not called for each diagonal allocating
1799:      new memory, thus calling lots of mallocs and slowing things down.

1801:        Contributed by: Matthew Knepley
1802:   */
1803:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1804:   MatZeroRows_SeqBAIJ(l->B,slen,lrows,0.0);
1805:   if ((diag != 0.0) && (l->A->rmap.N == l->A->cmap.N)) {
1806:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,diag);
1807:   } else if (diag != 0.0) {
1808:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0);
1809:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) {
1810:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1811: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1812:     }
1813:     for (i=0; i<slen; i++) {
1814:       row  = lrows[i] + rstart_bs;
1815:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
1816:     }
1817:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1818:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1819:   } else {
1820:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0);
1821:   }

1823:   PetscFree(lrows);

1825:   /* wait on sends */
1826:   if (nsends) {
1827:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1828:     MPI_Waitall(nsends,send_waits,send_status);
1829:     PetscFree(send_status);
1830:   }
1831:   PetscFree(send_waits);
1832:   PetscFree(svalues);

1834:   return(0);
1835: }

1839: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1840: {
1841:   Mat_MPIBAIJ    *a   = (Mat_MPIBAIJ*)A->data;

1845:   MatSetUnfactored(a->A);
1846:   return(0);
1847: }

1849: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat *);

1853: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscTruth *flag)
1854: {
1855:   Mat_MPIBAIJ    *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1856:   Mat            a,b,c,d;
1857:   PetscTruth     flg;

1861:   a = matA->A; b = matA->B;
1862:   c = matB->A; d = matB->B;

1864:   MatEqual(a,c,&flg);
1865:   if (flg) {
1866:     MatEqual(b,d,&flg);
1867:   }
1868:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1869:   return(0);
1870: }

1874: PetscErrorCode MatCopy_MPIBAIJ(Mat A,Mat B,MatStructure str)
1875: {
1877:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ *)A->data;
1878:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ *)B->data;

1881:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1882:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1883:     MatCopy_Basic(A,B,str);
1884:   } else {
1885:     MatCopy(a->A,b->A,str);
1886:     MatCopy(a->B,b->B,str);
1887:   }
1888:   return(0);
1889: }

1893: PetscErrorCode MatSetUpPreallocation_MPIBAIJ(Mat A)
1894: {

1898:    MatMPIBAIJSetPreallocation(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1899:   return(0);
1900: }

1902:  #include petscblaslapack.h
1905: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1906: {
1908:   Mat_MPIBAIJ    *xx=(Mat_MPIBAIJ *)X->data,*yy=(Mat_MPIBAIJ *)Y->data;
1909:   PetscBLASInt   bnz,one=1;
1910:   Mat_SeqBAIJ    *x,*y;

1913:   if (str == SAME_NONZERO_PATTERN) {
1914:     PetscScalar alpha = a;
1915:     x = (Mat_SeqBAIJ *)xx->A->data;
1916:     y = (Mat_SeqBAIJ *)yy->A->data;
1917:     bnz = (PetscBLASInt)x->nz;
1918:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1919:     x = (Mat_SeqBAIJ *)xx->B->data;
1920:     y = (Mat_SeqBAIJ *)yy->B->data;
1921:     bnz = (PetscBLASInt)x->nz;
1922:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1923:   } else {
1924:     MatAXPY_Basic(Y,a,X,str);
1925:   }
1926:   return(0);
1927: }

1931: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1932: {
1933:   Mat_MPIBAIJ   *a = (Mat_MPIBAIJ*)A->data;

1937:   MatRealPart(a->A);
1938:   MatRealPart(a->B);
1939:   return(0);
1940: }

1944: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1945: {
1946:   Mat_MPIBAIJ   *a = (Mat_MPIBAIJ*)A->data;

1950:   MatImaginaryPart(a->A);
1951:   MatImaginaryPart(a->B);
1952:   return(0);
1953: }

1955: /* -------------------------------------------------------------------*/
1956: static struct _MatOps MatOps_Values = {
1957:        MatSetValues_MPIBAIJ,
1958:        MatGetRow_MPIBAIJ,
1959:        MatRestoreRow_MPIBAIJ,
1960:        MatMult_MPIBAIJ,
1961: /* 4*/ MatMultAdd_MPIBAIJ,
1962:        MatMultTranspose_MPIBAIJ,
1963:        MatMultTransposeAdd_MPIBAIJ,
1964:        0,
1965:        0,
1966:        0,
1967: /*10*/ 0,
1968:        0,
1969:        0,
1970:        0,
1971:        MatTranspose_MPIBAIJ,
1972: /*15*/ MatGetInfo_MPIBAIJ,
1973:        MatEqual_MPIBAIJ,
1974:        MatGetDiagonal_MPIBAIJ,
1975:        MatDiagonalScale_MPIBAIJ,
1976:        MatNorm_MPIBAIJ,
1977: /*20*/ MatAssemblyBegin_MPIBAIJ,
1978:        MatAssemblyEnd_MPIBAIJ,
1979:        0,
1980:        MatSetOption_MPIBAIJ,
1981:        MatZeroEntries_MPIBAIJ,
1982: /*25*/ MatZeroRows_MPIBAIJ,
1983:        0,
1984:        0,
1985:        0,
1986:        0,
1987: /*30*/ MatSetUpPreallocation_MPIBAIJ,
1988:        0,
1989:        0,
1990:        0,
1991:        0,
1992: /*35*/ MatDuplicate_MPIBAIJ,
1993:        0,
1994:        0,
1995:        0,
1996:        0,
1997: /*40*/ MatAXPY_MPIBAIJ,
1998:        MatGetSubMatrices_MPIBAIJ,
1999:        MatIncreaseOverlap_MPIBAIJ,
2000:        MatGetValues_MPIBAIJ,
2001:        MatCopy_MPIBAIJ,
2002: /*45*/ 0,
2003:        MatScale_MPIBAIJ,
2004:        0,
2005:        0,
2006:        0,
2007: /*50*/ 0,
2008:        0,
2009:        0,
2010:        0,
2011:        0,
2012: /*55*/ 0,
2013:        0,
2014:        MatSetUnfactored_MPIBAIJ,
2015:        0,
2016:        MatSetValuesBlocked_MPIBAIJ,
2017: /*60*/ 0,
2018:        MatDestroy_MPIBAIJ,
2019:        MatView_MPIBAIJ,
2020:        0,
2021:        0,
2022: /*65*/ 0,
2023:        0,
2024:        0,
2025:        0,
2026:        0,
2027: /*70*/ MatGetRowMax_MPIBAIJ,
2028:        0,
2029:        0,
2030:        0,
2031:        0,
2032: /*75*/ 0,
2033:        0,
2034:        0,
2035:        0,
2036:        0,
2037: /*80*/ 0,
2038:        0,
2039:        0,
2040:        0,
2041:        MatLoad_MPIBAIJ,
2042: /*85*/ 0,
2043:        0,
2044:        0,
2045:        0,
2046:        0,
2047: /*90*/ 0,
2048:        0,
2049:        0,
2050:        0,
2051:        0,
2052: /*95*/ 0,
2053:        0,
2054:        0,
2055:        0,
2056:        0,
2057: /*100*/0,
2058:        0,
2059:        0,
2060:        0,
2061:        0,
2062: /*105*/0,
2063:        MatRealPart_MPIBAIJ,
2064:        MatImaginaryPart_MPIBAIJ};


2070: PetscErrorCode  MatGetDiagonalBlock_MPIBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
2071: {
2073:   *a      = ((Mat_MPIBAIJ *)A->data)->A;
2074:   *iscopy = PETSC_FALSE;
2075:   return(0);
2076: }


2085: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
2086: {
2087:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)B->data;
2088:   PetscInt       m = B->rmap.n/bs,cstart = baij->cstartbs, cend = baij->cendbs,j,nnz,i,d;
2089:   PetscInt       *d_nnz,*o_nnz,nnz_max = 0,rstart = baij->rstartbs,ii;
2090:   const PetscInt *JJ;
2091:   PetscScalar    *values;

2095: #if defined(PETSC_OPT_g)
2096:   if (Ii[0]) SETERRQ1(PETSC_ERR_ARG_RANGE,"Ii[0] must be 0 it is %D",Ii[0]);
2097: #endif
2098:   PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
2099:   o_nnz = d_nnz + m;

2101:   for (i=0; i<m; i++) {
2102:     nnz     = Ii[i+1]- Ii[i];
2103:     JJ      = J + Ii[i];
2104:     nnz_max = PetscMax(nnz_max,nnz);
2105: #if defined(PETSC_OPT_g)
2106:     if (nnz < 0) SETERRQ1(PETSC_ERR_ARG_RANGE,"Local row %D has a negative %D number of columns",i,nnz);
2107: #endif
2108:     for (j=0; j<nnz; j++) {
2109:       if (*JJ >= cstart) break;
2110:       JJ++;
2111:     }
2112:     d = 0;
2113:     for (; j<nnz; j++) {
2114:       if (*JJ++ >= cend) break;
2115:       d++;
2116:     }
2117:     d_nnz[i] = d;
2118:     o_nnz[i] = nnz - d;
2119:   }
2120:   MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
2121:   PetscFree(d_nnz);

2123:   if (v) values = (PetscScalar*)v;
2124:   else {
2125:     PetscMalloc(bs*bs*(nnz_max+1)*sizeof(PetscScalar),&values);
2126:     PetscMemzero(values,bs*bs*nnz_max*sizeof(PetscScalar));
2127:   }

2129:   MatSetOption(B,MAT_COLUMNS_SORTED);
2130:   for (i=0; i<m; i++) {
2131:     ii   = i + rstart;
2132:     nnz  = Ii[i+1]- Ii[i];
2133:     MatSetValuesBlocked_MPIBAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
2134:   }
2135:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2136:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2137:   MatSetOption(B,MAT_COLUMNS_UNSORTED);

2139:   if (!v) {
2140:     PetscFree(values);
2141:   }
2142:   return(0);
2143: }

2147: /*@C
2148:    MatMPIBAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
2149:    (the default parallel PETSc format).  

2151:    Collective on MPI_Comm

2153:    Input Parameters:
2154: +  A - the matrix 
2155: .  i - the indices into j for the start of each local row (starts with zero)
2156: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
2157: -  v - optional values in the matrix

2159:    Level: developer

2161: .keywords: matrix, aij, compressed row, sparse, parallel

2163: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ
2164: @*/
2165: PetscErrorCode  MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2166: {
2167:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]);

2170:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",(void (**)(void))&f);
2171:   if (f) {
2172:     (*f)(B,bs,i,j,v);
2173:   }
2174:   return(0);
2175: }

2180: PetscErrorCode  MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,PetscInt *d_nnz,PetscInt o_nz,PetscInt *o_nnz)
2181: {
2182:   Mat_MPIBAIJ    *b;
2184:   PetscInt       i;

2187:   B->preallocated = PETSC_TRUE;
2188:   PetscOptionsBegin(B->comm,B->prefix,"Options for MPIBAIJ matrix","Mat");
2189:     PetscOptionsInt("-mat_block_size","Set the blocksize used to store the matrix","MatMPIBAIJSetPreallocation",bs,&bs,PETSC_NULL);
2190:   PetscOptionsEnd();

2192:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
2193:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2194:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2195:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
2196:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);
2197: 
2198:   B->rmap.bs  = bs;
2199:   B->cmap.bs  = bs;
2200:   PetscMapInitialize(B->comm,&B->rmap);
2201:   PetscMapInitialize(B->comm,&B->cmap);

2203:   if (d_nnz) {
2204:     for (i=0; i<B->rmap.n/bs; i++) {
2205:       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]);
2206:     }
2207:   }
2208:   if (o_nnz) {
2209:     for (i=0; i<B->rmap.n/bs; i++) {
2210:       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]);
2211:     }
2212:   }

2214:   b = (Mat_MPIBAIJ*)B->data;
2215:   b->bs2 = bs*bs;
2216:   b->mbs = B->rmap.n/bs;
2217:   b->nbs = B->cmap.n/bs;
2218:   b->Mbs = B->rmap.N/bs;
2219:   b->Nbs = B->cmap.N/bs;

2221:   for (i=0; i<=b->size; i++) {
2222:     b->rangebs[i] = B->rmap.range[i]/bs;
2223:   }
2224:   b->rstartbs = B->rmap.rstart/bs;
2225:   b->rendbs   = B->rmap.rend/bs;
2226:   b->cstartbs = B->cmap.rstart/bs;
2227:   b->cendbs   = B->cmap.rend/bs;

2229:   MatCreate(PETSC_COMM_SELF,&b->A);
2230:   MatSetSizes(b->A,B->rmap.n,B->cmap.n,B->rmap.n,B->cmap.n);
2231:   MatSetType(b->A,MATSEQBAIJ);
2232:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
2233:   PetscLogObjectParent(B,b->A);
2234:   MatCreate(PETSC_COMM_SELF,&b->B);
2235:   MatSetSizes(b->B,B->rmap.n,B->cmap.N,B->rmap.n,B->cmap.N);
2236:   MatSetType(b->B,MATSEQBAIJ);
2237:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
2238:   PetscLogObjectParent(B,b->B);

2240:   MatStashCreate_Private(B->comm,bs,&B->bstash);

2242:   return(0);
2243: }

2247: EXTERN PetscErrorCode  MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2248: EXTERN PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);

2251: /*MC
2252:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

2254:    Options Database Keys:
2255: + -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()
2256: . -mat_block_size <bs> - set the blocksize used to store the matrix
2257: - -mat_use_hash_table <fact>

2259:   Level: beginner

2261: .seealso: MatCreateMPIBAIJ
2262: M*/

2267: PetscErrorCode  MatCreate_MPIBAIJ(Mat B)
2268: {
2269:   Mat_MPIBAIJ    *b;
2271:   PetscTruth     flg;

2274:   PetscNew(Mat_MPIBAIJ,&b);
2275:   B->data = (void*)b;


2278:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2279:   B->mapping    = 0;
2280:   B->factor     = 0;
2281:   B->assembled  = PETSC_FALSE;

2283:   B->insertmode = NOT_SET_VALUES;
2284:   MPI_Comm_rank(B->comm,&b->rank);
2285:   MPI_Comm_size(B->comm,&b->size);

2287:   /* build local table of row and column ownerships */
2288:   PetscMalloc((b->size+1)*sizeof(PetscInt),&b->rangebs);

2290:   /* build cache for off array entries formed */
2291:   MatStashCreate_Private(B->comm,1,&B->stash);
2292:   b->donotstash  = PETSC_FALSE;
2293:   b->colmap      = PETSC_NULL;
2294:   b->garray      = PETSC_NULL;
2295:   b->roworiented = PETSC_TRUE;

2297: #if defined(PETSC_USE_MAT_SINGLE)
2298:   /* stuff for MatSetValues_XXX in single precision */
2299:   b->setvalueslen     = 0;
2300:   b->setvaluescopy    = PETSC_NULL;
2301: #endif

2303:   /* stuff used in block assembly */
2304:   b->barray       = 0;

2306:   /* stuff used for matrix vector multiply */
2307:   b->lvec         = 0;
2308:   b->Mvctx        = 0;

2310:   /* stuff for MatGetRow() */
2311:   b->rowindices   = 0;
2312:   b->rowvalues    = 0;
2313:   b->getrowactive = PETSC_FALSE;

2315:   /* hash table stuff */
2316:   b->ht           = 0;
2317:   b->hd           = 0;
2318:   b->ht_size      = 0;
2319:   b->ht_flag      = PETSC_FALSE;
2320:   b->ht_fact      = 0;
2321:   b->ht_total_ct  = 0;
2322:   b->ht_insert_ct = 0;

2324:   PetscOptionsBegin(B->comm,PETSC_NULL,"Options for loading MPIBAIJ matrix","Mat");
2325:     PetscOptionsTruth("-mat_use_hash_table","Use hash table to save memory in constructing matrix","MatSetOption",PETSC_FALSE,&flg,PETSC_NULL);
2326:     if (flg) {
2327:       PetscReal fact = 1.39;
2328:       MatSetOption(B,MAT_USE_HASH_TABLE);
2329:       PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,PETSC_NULL);
2330:       if (fact <= 1.0) fact = 1.39;
2331:       MatMPIBAIJSetHashTableFactor(B,fact);
2332:       PetscInfo1(0,"Hash table Factor used %5.2f\n",fact);
2333:     }
2334:   PetscOptionsEnd();

2336:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2337:                                      "MatStoreValues_MPIBAIJ",
2338:                                      MatStoreValues_MPIBAIJ);
2339:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2340:                                      "MatRetrieveValues_MPIBAIJ",
2341:                                      MatRetrieveValues_MPIBAIJ);
2342:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
2343:                                      "MatGetDiagonalBlock_MPIBAIJ",
2344:                                      MatGetDiagonalBlock_MPIBAIJ);
2345:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocation_C",
2346:                                      "MatMPIBAIJSetPreallocation_MPIBAIJ",
2347:                                      MatMPIBAIJSetPreallocation_MPIBAIJ);
2348:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",
2349:                                      "MatMPIBAIJSetPreallocationCSR_MPIAIJ",
2350:                                      MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
2351:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
2352:                                      "MatDiagonalScaleLocal_MPIBAIJ",
2353:                                      MatDiagonalScaleLocal_MPIBAIJ);
2354:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetHashTableFactor_C",
2355:                                      "MatSetHashTableFactor_MPIBAIJ",
2356:                                      MatSetHashTableFactor_MPIBAIJ);
2357:   PetscObjectChangeTypeName((PetscObject)B,MATMPIBAIJ);
2358:   return(0);
2359: }

2362: /*MC
2363:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

2365:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
2366:    and MATMPIBAIJ otherwise.

2368:    Options Database Keys:
2369: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

2371:   Level: beginner

2373: .seealso: MatCreateMPIBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2374: M*/

2379: PetscErrorCode  MatCreate_BAIJ(Mat A)
2380: {
2382:   PetscMPIInt    size;

2385:   MPI_Comm_size(A->comm,&size);
2386:   if (size == 1) {
2387:     MatSetType(A,MATSEQBAIJ);
2388:   } else {
2389:     MatSetType(A,MATMPIBAIJ);
2390:   }
2391:   return(0);
2392: }

2397: /*@C
2398:    MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
2399:    (block compressed row).  For good matrix assembly performance
2400:    the user should preallocate the matrix storage by setting the parameters 
2401:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2402:    performance can be increased by more than a factor of 50.

2404:    Collective on Mat

2406:    Input Parameters:
2407: +  A - the matrix 
2408: .  bs   - size of blockk
2409: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
2410:            submatrix  (same for all local rows)
2411: .  d_nnz - array containing the number of block nonzeros in the various block rows 
2412:            of the in diagonal portion of the local (possibly different for each block
2413:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2414: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
2415:            submatrix (same for all local rows).
2416: -  o_nnz - array containing the number of nonzeros in the various block rows of the
2417:            off-diagonal portion of the local submatrix (possibly different for
2418:            each block row) or PETSC_NULL.

2420:    If the *_nnz parameter is given then the *_nz parameter is ignored

2422:    Options Database Keys:
2423: +   -mat_block_size - size of the blocks to use
2424: -   -mat_use_hash_table <fact>

2426:    Notes:
2427:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2428:    than it must be used on all processors that share the object for that argument.

2430:    Storage Information:
2431:    For a square global matrix we define each processor's diagonal portion 
2432:    to be its local rows and the corresponding columns (a square submatrix);  
2433:    each processor's off-diagonal portion encompasses the remainder of the
2434:    local matrix (a rectangular submatrix). 

2436:    The user can specify preallocated storage for the diagonal part of
2437:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2438:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2439:    memory allocation.  Likewise, specify preallocated storage for the
2440:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2442:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2443:    the figure below we depict these three local rows and all columns (0-11).

2445: .vb
2446:            0 1 2 3 4 5 6 7 8 9 10 11
2447:           -------------------
2448:    row 3  |  o o o d d d o o o o o o
2449:    row 4  |  o o o d d d o o o o o o
2450:    row 5  |  o o o d d d o o o o o o
2451:           -------------------
2452: .ve
2453:   
2454:    Thus, any entries in the d locations are stored in the d (diagonal) 
2455:    submatrix, and any entries in the o locations are stored in the
2456:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2457:    stored simply in the MATSEQBAIJ format for compressed row storage.

2459:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2460:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2461:    In general, for PDE problems in which most nonzeros are near the diagonal,
2462:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2463:    or you will get TERRIBLE performance; see the users' manual chapter on
2464:    matrices.

2466:    Level: intermediate

2468: .keywords: matrix, block, aij, compressed row, sparse, parallel

2470: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocationCSR()
2471: @*/
2472: PetscErrorCode  MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2473: {
2474:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

2477:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",(void (**)(void))&f);
2478:   if (f) {
2479:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
2480:   }
2481:   return(0);
2482: }

2486: /*@C
2487:    MatCreateMPIBAIJ - Creates a sparse parallel matrix in block AIJ format
2488:    (block compressed row).  For good matrix assembly performance
2489:    the user should preallocate the matrix storage by setting the parameters 
2490:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2491:    performance can be increased by more than a factor of 50.

2493:    Collective on MPI_Comm

2495:    Input Parameters:
2496: +  comm - MPI communicator
2497: .  bs   - size of blockk
2498: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2499:            This value should be the same as the local size used in creating the 
2500:            y vector for the matrix-vector product y = Ax.
2501: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
2502:            This value should be the same as the local size used in creating the 
2503:            x vector for the matrix-vector product y = Ax.
2504: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2505: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2506: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local 
2507:            submatrix  (same for all local rows)
2508: .  d_nnz - array containing the number of nonzero blocks in the various block rows 
2509:            of the in diagonal portion of the local (possibly different for each block
2510:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2511: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
2512:            submatrix (same for all local rows).
2513: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
2514:            off-diagonal portion of the local submatrix (possibly different for
2515:            each block row) or PETSC_NULL.

2517:    Output Parameter:
2518: .  A - the matrix 

2520:    Options Database Keys:
2521: +   -mat_block_size - size of the blocks to use
2522: -   -mat_use_hash_table <fact>

2524:    Notes:
2525:    If the *_nnz parameter is given then the *_nz parameter is ignored

2527:    A nonzero block is any block that as 1 or more nonzeros in it

2529:    The user MUST specify either the local or global matrix dimensions
2530:    (possibly both).

2532:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2533:    than it must be used on all processors that share the object for that argument.

2535:    Storage Information:
2536:    For a square global matrix we define each processor's diagonal portion 
2537:    to be its local rows and the corresponding columns (a square submatrix);  
2538:    each processor's off-diagonal portion encompasses the remainder of the
2539:    local matrix (a rectangular submatrix). 

2541:    The user can specify preallocated storage for the diagonal part of
2542:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2543:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2544:    memory allocation.  Likewise, specify preallocated storage for the
2545:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2547:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2548:    the figure below we depict these three local rows and all columns (0-11).

2550: .vb
2551:            0 1 2 3 4 5 6 7 8 9 10 11
2552:           -------------------
2553:    row 3  |  o o o d d d o o o o o o
2554:    row 4  |  o o o d d d o o o o o o
2555:    row 5  |  o o o d d d o o o o o o
2556:           -------------------
2557: .ve
2558:   
2559:    Thus, any entries in the d locations are stored in the d (diagonal) 
2560:    submatrix, and any entries in the o locations are stored in the
2561:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2562:    stored simply in the MATSEQBAIJ format for compressed row storage.

2564:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2565:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2566:    In general, for PDE problems in which most nonzeros are near the diagonal,
2567:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2568:    or you will get TERRIBLE performance; see the users' manual chapter on
2569:    matrices.

2571:    Level: intermediate

2573: .keywords: matrix, block, aij, compressed row, sparse, parallel

2575: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2576: @*/
2577: PetscErrorCode  MatCreateMPIBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
2578: {
2580:   PetscMPIInt    size;

2583:   MatCreate(comm,A);
2584:   MatSetSizes(*A,m,n,M,N);
2585:   MPI_Comm_size(comm,&size);
2586:   if (size > 1) {
2587:     MatSetType(*A,MATMPIBAIJ);
2588:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2589:   } else {
2590:     MatSetType(*A,MATSEQBAIJ);
2591:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2592:   }
2593:   return(0);
2594: }

2598: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2599: {
2600:   Mat            mat;
2601:   Mat_MPIBAIJ    *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
2603:   PetscInt       len=0;

2606:   *newmat       = 0;
2607:   MatCreate(matin->comm,&mat);
2608:   MatSetSizes(mat,matin->rmap.n,matin->cmap.n,matin->rmap.N,matin->cmap.N);
2609:   MatSetType(mat,matin->type_name);
2610:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));

2612:   mat->factor       = matin->factor;
2613:   mat->preallocated = PETSC_TRUE;
2614:   mat->assembled    = PETSC_TRUE;
2615:   mat->insertmode   = NOT_SET_VALUES;

2617:   a      = (Mat_MPIBAIJ*)mat->data;
2618:   mat->rmap.bs  = matin->rmap.bs;
2619:   a->bs2   = oldmat->bs2;
2620:   a->mbs   = oldmat->mbs;
2621:   a->nbs   = oldmat->nbs;
2622:   a->Mbs   = oldmat->Mbs;
2623:   a->Nbs   = oldmat->Nbs;
2624: 
2625:   PetscMapCopy(matin->comm,&matin->rmap,&mat->rmap);
2626:   PetscMapCopy(matin->comm,&matin->cmap,&mat->cmap);

2628:   a->size         = oldmat->size;
2629:   a->rank         = oldmat->rank;
2630:   a->donotstash   = oldmat->donotstash;
2631:   a->roworiented  = oldmat->roworiented;
2632:   a->rowindices   = 0;
2633:   a->rowvalues    = 0;
2634:   a->getrowactive = PETSC_FALSE;
2635:   a->barray       = 0;
2636:   a->rstartbs     = oldmat->rstartbs;
2637:   a->rendbs       = oldmat->rendbs;
2638:   a->cstartbs     = oldmat->cstartbs;
2639:   a->cendbs       = oldmat->cendbs;

2641:   /* hash table stuff */
2642:   a->ht           = 0;
2643:   a->hd           = 0;
2644:   a->ht_size      = 0;
2645:   a->ht_flag      = oldmat->ht_flag;
2646:   a->ht_fact      = oldmat->ht_fact;
2647:   a->ht_total_ct  = 0;
2648:   a->ht_insert_ct = 0;

2650:   PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+1)*sizeof(PetscInt));
2651:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2652:   MatStashCreate_Private(matin->comm,matin->rmap.bs,&mat->bstash);
2653:   if (oldmat->colmap) {
2654: #if defined (PETSC_USE_CTABLE)
2655:   PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2656: #else
2657:   PetscMalloc((a->Nbs)*sizeof(PetscInt),&a->colmap);
2658:   PetscLogObjectMemory(mat,(a->Nbs)*sizeof(PetscInt));
2659:   PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
2660: #endif
2661:   } else a->colmap = 0;

2663:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2664:     PetscMalloc(len*sizeof(PetscInt),&a->garray);
2665:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2666:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
2667:   } else a->garray = 0;
2668: 
2669:   VecDuplicate(oldmat->lvec,&a->lvec);
2670:   PetscLogObjectParent(mat,a->lvec);
2671:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2672:   PetscLogObjectParent(mat,a->Mvctx);

2674:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2675:   PetscLogObjectParent(mat,a->A);
2676:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2677:   PetscLogObjectParent(mat,a->B);
2678:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2679:   *newmat = mat;

2681:   return(0);
2682: }

2684:  #include petscsys.h

2688: PetscErrorCode MatLoad_MPIBAIJ(PetscViewer viewer, MatType type,Mat *newmat)
2689: {
2690:   Mat            A;
2692:   int            fd;
2693:   PetscInt       i,nz,j,rstart,rend;
2694:   PetscScalar    *vals,*buf;
2695:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2696:   MPI_Status     status;
2697:   PetscMPIInt    rank,size,maxnz;
2698:   PetscInt       header[4],*rowlengths = 0,M,N,m,*rowners,*cols;
2699:   PetscInt       *locrowlens = PETSC_NULL,*procsnz = PETSC_NULL,*browners = PETSC_NULL;
2700:   PetscInt       jj,*mycols,*ibuf,bs=1,Mbs,mbs,extra_rows,mmax;
2701:   PetscMPIInt    tag = ((PetscObject)viewer)->tag;
2702:   PetscInt       *dlens = PETSC_NULL,*odlens = PETSC_NULL,*mask = PETSC_NULL,*masked1 = PETSC_NULL,*masked2 = PETSC_NULL,rowcount,odcount;
2703:   PetscInt       dcount,kmax,k,nzcount,tmp,mend;

2706:   PetscOptionsBegin(comm,PETSC_NULL,"Options for loading MPIBAIJ matrix","Mat");
2707:     PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
2708:   PetscOptionsEnd();

2710:   MPI_Comm_size(comm,&size);
2711:   MPI_Comm_rank(comm,&rank);
2712:   if (!rank) {
2713:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2714:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2715:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2716:   }

2718:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2719:   M = header[1]; N = header[2];

2721:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2723:   /* 
2724:      This code adds extra rows to make sure the number of rows is 
2725:      divisible by the blocksize
2726:   */
2727:   Mbs        = M/bs;
2728:   extra_rows = bs - M + bs*Mbs;
2729:   if (extra_rows == bs) extra_rows = 0;
2730:   else                  Mbs++;
2731:   if (extra_rows && !rank) {
2732:     PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2733:   }

2735:   /* determine ownership of all rows */
2736:   mbs        = Mbs/size + ((Mbs % size) > rank);
2737:   m          = mbs*bs;
2738:   PetscMalloc2(size+1,PetscInt,&rowners,size+1,PetscInt,&browners);
2739:   MPI_Allgather(&mbs,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);

2741:   /* process 0 needs enough room for process with most rows */
2742:   if (!rank) {
2743:     mmax = rowners[1];
2744:     for (i=2; i<size; i++) {
2745:       mmax = PetscMax(mmax,rowners[i]);
2746:     }
2747:     mmax*=bs;
2748:   } else mmax = m;

2750:   rowners[0] = 0;
2751:   for (i=2; i<=size; i++)  rowners[i] += rowners[i-1];
2752:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2753:   rstart = rowners[rank];
2754:   rend   = rowners[rank+1];

2756:   /* distribute row lengths to all processors */
2757:   PetscMalloc((mmax+1)*sizeof(PetscInt),&locrowlens);
2758:   if (!rank) {
2759:     mend = m;
2760:     if (size == 1) mend = mend - extra_rows;
2761:     PetscBinaryRead(fd,locrowlens,mend,PETSC_INT);
2762:     for (j=mend; j<m; j++) locrowlens[j] = 1;
2763:     PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2764:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2765:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2766:     for (j=0; j<m; j++) {
2767:       procsnz[0] += locrowlens[j];
2768:     }
2769:     for (i=1; i<size; i++) {
2770:       mend = browners[i+1] - browners[i];
2771:       if (i == size-1) mend = mend - extra_rows;
2772:       PetscBinaryRead(fd,rowlengths,mend,PETSC_INT);
2773:       for (j=mend; j<browners[i+1] - browners[i]; j++) rowlengths[j] = 1;
2774:       /* calculate the number of nonzeros on each processor */
2775:       for (j=0; j<browners[i+1]-browners[i]; j++) {
2776:         procsnz[i] += rowlengths[j];
2777:       }
2778:       MPI_Send(rowlengths,browners[i+1]-browners[i],MPIU_INT,i,tag,comm);
2779:     }
2780:     PetscFree(rowlengths);
2781:   } else {
2782:     MPI_Recv(locrowlens,m,MPIU_INT,0,tag,comm,&status);
2783:   }

2785:   if (!rank) {
2786:     /* determine max buffer needed and allocate it */
2787:     maxnz = procsnz[0];
2788:     for (i=1; i<size; i++) {
2789:       maxnz = PetscMax(maxnz,procsnz[i]);
2790:     }
2791:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2793:     /* read in my part of the matrix column indices  */
2794:     nz     = procsnz[0];
2795:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
2796:     mycols = ibuf;
2797:     if (size == 1)  nz -= extra_rows;
2798:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2799:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2801:     /* read in every ones (except the last) and ship off */
2802:     for (i=1; i<size-1; i++) {
2803:       nz   = procsnz[i];
2804:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2805:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2806:     }
2807:     /* read in the stuff for the last proc */
2808:     if (size != 1) {
2809:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2810:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2811:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2812:       MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
2813:     }
2814:     PetscFree(cols);
2815:   } else {
2816:     /* determine buffer space needed for message */
2817:     nz = 0;
2818:     for (i=0; i<m; i++) {
2819:       nz += locrowlens[i];
2820:     }
2821:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
2822:     mycols = ibuf;
2823:     /* receive message of column indices*/
2824:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2825:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2826:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2827:   }
2828: 
2829:   /* loop over local rows, determining number of off diagonal entries */
2830:   PetscMalloc2(rend-rstart,PetscInt,&dlens,rend-rstart,PetscInt,&odlens);
2831:   PetscMalloc3(Mbs,PetscInt,&mask,Mbs,PetscInt,&masked1,Mbs,PetscInt,&masked2);
2832:   PetscMemzero(mask,Mbs*sizeof(PetscInt));
2833:   PetscMemzero(masked1,Mbs*sizeof(PetscInt));
2834:   PetscMemzero(masked2,Mbs*sizeof(PetscInt));
2835:   rowcount = 0; nzcount = 0;
2836:   for (i=0; i<mbs; i++) {
2837:     dcount  = 0;
2838:     odcount = 0;
2839:     for (j=0; j<bs; j++) {
2840:       kmax = locrowlens[rowcount];
2841:       for (k=0; k<kmax; k++) {
2842:         tmp = mycols[nzcount++]/bs;
2843:         if (!mask[tmp]) {
2844:           mask[tmp] = 1;
2845:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
2846:           else masked1[dcount++] = tmp;
2847:         }
2848:       }
2849:       rowcount++;
2850:     }
2851: 
2852:     dlens[i]  = dcount;
2853:     odlens[i] = odcount;

2855:     /* zero out the mask elements we set */
2856:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2857:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2858:   }

2860:   /* create our matrix */
2861:   MatCreate(comm,&A);
2862:   MatSetSizes(A,m,m,M+extra_rows,N+extra_rows);
2863:   MatSetType(A,type);CHKERRQ(ierr)
2864:   MatMPIBAIJSetPreallocation(A,bs,0,dlens,0,odlens);

2866:   /* Why doesn't this called using MatSetOption(A,MAT_COLUMNS_SORTED); */
2867:   MatSetOption(A,MAT_COLUMNS_SORTED);
2868: 
2869:   if (!rank) {
2870:     PetscMalloc((maxnz+1)*sizeof(PetscScalar),&buf);
2871:     /* read in my part of the matrix numerical values  */
2872:     nz = procsnz[0];
2873:     vals = buf;
2874:     mycols = ibuf;
2875:     if (size == 1)  nz -= extra_rows;
2876:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2877:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2879:     /* insert into matrix */
2880:     jj      = rstart*bs;
2881:     for (i=0; i<m; i++) {
2882:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2883:       mycols += locrowlens[i];
2884:       vals   += locrowlens[i];
2885:       jj++;
2886:     }
2887:     /* read in other processors (except the last one) and ship out */
2888:     for (i=1; i<size-1; i++) {
2889:       nz   = procsnz[i];
2890:       vals = buf;
2891:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2892:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2893:     }
2894:     /* the last proc */
2895:     if (size != 1){
2896:       nz   = procsnz[i] - extra_rows;
2897:       vals = buf;
2898:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2899:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2900:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2901:     }
2902:     PetscFree(procsnz);
2903:   } else {
2904:     /* receive numeric values */
2905:     PetscMalloc((nz+1)*sizeof(PetscScalar),&buf);

2907:     /* receive message of values*/
2908:     vals   = buf;
2909:     mycols = ibuf;
2910:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2911:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2912:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2914:     /* insert into matrix */
2915:     jj      = rstart*bs;
2916:     for (i=0; i<m; i++) {
2917:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2918:       mycols += locrowlens[i];
2919:       vals   += locrowlens[i];
2920:       jj++;
2921:     }
2922:   }
2923:   PetscFree(locrowlens);
2924:   PetscFree(buf);
2925:   PetscFree(ibuf);
2926:   PetscFree2(rowners,browners);
2927:   PetscFree2(dlens,odlens);
2928:   PetscFree3(mask,masked1,masked2);
2929:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2930:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

2932:   *newmat = A;
2933:   return(0);
2934: }

2938: /*@
2939:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2941:    Input Parameters:
2942: .  mat  - the matrix
2943: .  fact - factor

2945:    Collective on Mat

2947:    Level: advanced

2949:   Notes:
2950:    This can also be set by the command line option: -mat_use_hash_table <fact>

2952: .keywords: matrix, hashtable, factor, HT

2954: .seealso: MatSetOption()
2955: @*/
2956: PetscErrorCode  MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2957: {
2958:   PetscErrorCode ierr,(*f)(Mat,PetscReal);

2961:   PetscObjectQueryFunction((PetscObject)mat,"MatSetHashTableFactor_C",(void (**)(void))&f);
2962:   if (f) {
2963:     (*f)(mat,fact);
2964:   }
2965:   return(0);
2966: }

2971: PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
2972: {
2973:   Mat_MPIBAIJ *baij;

2976:   baij = (Mat_MPIBAIJ*)mat->data;
2977:   baij->ht_fact = fact;
2978:   return(0);
2979: }

2984: PetscErrorCode  MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
2985: {
2986:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2988:   *Ad     = a->A;
2989:   *Ao     = a->B;
2990:   *colmap = a->garray;
2991:   return(0);
2992: }