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:   PetscObjectChangeTypeName((PetscObject)mat,0);
1241:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
1242:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
1243:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
1244:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C","",PETSC_NULL);
1245:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C","",PETSC_NULL);
1246:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
1247:   PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C","",PETSC_NULL);
1248:   return(0);
1249: }

1253: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1254: {
1255:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1257:   PetscInt       nt;

1260:   VecGetLocalSize(xx,&nt);
1261:   if (nt != A->cmap.n) {
1262:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1263:   }
1264:   VecGetLocalSize(yy,&nt);
1265:   if (nt != A->rmap.n) {
1266:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1267:   }
1268:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1269:   (*a->A->ops->mult)(a->A,xx,yy);
1270:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1271:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1272:   return(0);
1273: }

1277: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1278: {
1279:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1283:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1284:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1285:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1286:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1287:   return(0);
1288: }

1292: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1293: {
1294:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1296:   PetscTruth     merged;

1299:   VecScatterGetMerged(a->Mvctx,&merged);
1300:   /* do nondiagonal part */
1301:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1302:   if (!merged) {
1303:     /* send it on its way */
1304:     VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1305:     /* do local part */
1306:     (*a->A->ops->multtranspose)(a->A,xx,yy);
1307:     /* receive remote parts: note this assumes the values are not actually */
1308:     /* inserted in yy until the next line */
1309:     VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1310:   } else {
1311:     /* do local part */
1312:     (*a->A->ops->multtranspose)(a->A,xx,yy);
1313:     /* send it on its way */
1314:     VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1315:     /* values actually were received in the Begin() but we need to call this nop */
1316:     VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1317:   }
1318:   return(0);
1319: }

1323: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1324: {
1325:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1329:   /* do nondiagonal part */
1330:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1331:   /* send it on its way */
1332:   VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1333:   /* do local part */
1334:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1335:   /* receive remote parts: note this assumes the values are not actually */
1336:   /* inserted in yy until the next line, which is true for my implementation*/
1337:   /* but is not perhaps always true. */
1338:   VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1339:   return(0);
1340: }

1342: /*
1343:   This only works correctly for square matrices where the subblock A->A is the 
1344:    diagonal block
1345: */
1348: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1349: {
1350:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1354:   if (A->rmap.N != A->cmap.N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1355:   MatGetDiagonal(a->A,v);
1356:   return(0);
1357: }

1361: PetscErrorCode MatScale_MPIBAIJ(Mat A,PetscScalar aa)
1362: {
1363:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1367:   MatScale(a->A,aa);
1368:   MatScale(a->B,aa);
1369:   return(0);
1370: }

1374: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1375: {
1376:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
1377:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1379:   PetscInt       bs = matin->rmap.bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
1380:   PetscInt       nztot,nzA,nzB,lrow,brstart = matin->rmap.rstart,brend = matin->rmap.rend;
1381:   PetscInt       *cmap,*idx_p,cstart = mat->cstartbs;

1384:   if (mat->getrowactive) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1385:   mat->getrowactive = PETSC_TRUE;

1387:   if (!mat->rowvalues && (idx || v)) {
1388:     /*
1389:         allocate enough space to hold information from the longest row.
1390:     */
1391:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1392:     PetscInt     max = 1,mbs = mat->mbs,tmp;
1393:     for (i=0; i<mbs; i++) {
1394:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1395:       if (max < tmp) { max = tmp; }
1396:     }
1397:     PetscMalloc(max*bs2*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1398:     mat->rowindices = (PetscInt*)(mat->rowvalues + max*bs2);
1399:   }
1400: 
1401:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1402:   lrow = row - brstart;

1404:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1405:   if (!v)   {pvA = 0; pvB = 0;}
1406:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1407:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1408:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1409:   nztot = nzA + nzB;

1411:   cmap  = mat->garray;
1412:   if (v  || idx) {
1413:     if (nztot) {
1414:       /* Sort by increasing column numbers, assuming A and B already sorted */
1415:       PetscInt imark = -1;
1416:       if (v) {
1417:         *v = v_p = mat->rowvalues;
1418:         for (i=0; i<nzB; i++) {
1419:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1420:           else break;
1421:         }
1422:         imark = i;
1423:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1424:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1425:       }
1426:       if (idx) {
1427:         *idx = idx_p = mat->rowindices;
1428:         if (imark > -1) {
1429:           for (i=0; i<imark; i++) {
1430:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1431:           }
1432:         } else {
1433:           for (i=0; i<nzB; i++) {
1434:             if (cmap[cworkB[i]/bs] < cstart)
1435:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1436:             else break;
1437:           }
1438:           imark = i;
1439:         }
1440:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1441:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1442:       }
1443:     } else {
1444:       if (idx) *idx = 0;
1445:       if (v)   *v   = 0;
1446:     }
1447:   }
1448:   *nz = nztot;
1449:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1450:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1451:   return(0);
1452: }

1456: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1457: {
1458:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1461:   if (!baij->getrowactive) {
1462:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1463:   }
1464:   baij->getrowactive = PETSC_FALSE;
1465:   return(0);
1466: }

1470: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1471: {
1472:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;

1476:   MatZeroEntries(l->A);
1477:   MatZeroEntries(l->B);
1478:   return(0);
1479: }

1483: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1484: {
1485:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)matin->data;
1486:   Mat            A = a->A,B = a->B;
1488:   PetscReal      isend[5],irecv[5];

1491:   info->block_size     = (PetscReal)matin->rmap.bs;
1492:   MatGetInfo(A,MAT_LOCAL,info);
1493:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1494:   isend[3] = info->memory;  isend[4] = info->mallocs;
1495:   MatGetInfo(B,MAT_LOCAL,info);
1496:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1497:   isend[3] += info->memory;  isend[4] += info->mallocs;
1498:   if (flag == MAT_LOCAL) {
1499:     info->nz_used      = isend[0];
1500:     info->nz_allocated = isend[1];
1501:     info->nz_unneeded  = isend[2];
1502:     info->memory       = isend[3];
1503:     info->mallocs      = isend[4];
1504:   } else if (flag == MAT_GLOBAL_MAX) {
1505:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1506:     info->nz_used      = irecv[0];
1507:     info->nz_allocated = irecv[1];
1508:     info->nz_unneeded  = irecv[2];
1509:     info->memory       = irecv[3];
1510:     info->mallocs      = irecv[4];
1511:   } else if (flag == MAT_GLOBAL_SUM) {
1512:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1513:     info->nz_used      = irecv[0];
1514:     info->nz_allocated = irecv[1];
1515:     info->nz_unneeded  = irecv[2];
1516:     info->memory       = irecv[3];
1517:     info->mallocs      = irecv[4];
1518:   } else {
1519:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1520:   }
1521:   info->rows_global       = (PetscReal)A->rmap.N;
1522:   info->columns_global    = (PetscReal)A->cmap.N;
1523:   info->rows_local        = (PetscReal)A->rmap.N;
1524:   info->columns_local     = (PetscReal)A->cmap.N;
1525:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1526:   info->fill_ratio_needed = 0;
1527:   info->factor_mallocs    = 0;
1528:   return(0);
1529: }

1533: PetscErrorCode MatSetOption_MPIBAIJ(Mat A,MatOption op)
1534: {
1535:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1539:   switch (op) {
1540:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1541:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1542:   case MAT_COLUMNS_UNSORTED:
1543:   case MAT_COLUMNS_SORTED:
1544:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1545:   case MAT_KEEP_ZEROED_ROWS:
1546:   case MAT_NEW_NONZERO_LOCATION_ERR:
1547:     MatSetOption(a->A,op);
1548:     MatSetOption(a->B,op);
1549:     break;
1550:   case MAT_ROW_ORIENTED:
1551:     a->roworiented = PETSC_TRUE;
1552:     MatSetOption(a->A,op);
1553:     MatSetOption(a->B,op);
1554:     break;
1555:   case MAT_ROWS_SORTED:
1556:   case MAT_ROWS_UNSORTED:
1557:   case MAT_YES_NEW_DIAGONALS:
1558:     PetscInfo(A,"Option ignored\n");
1559:     break;
1560:   case MAT_COLUMN_ORIENTED:
1561:     a->roworiented = PETSC_FALSE;
1562:     MatSetOption(a->A,op);
1563:     MatSetOption(a->B,op);
1564:     break;
1565:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1566:     a->donotstash = PETSC_TRUE;
1567:     break;
1568:   case MAT_NO_NEW_DIAGONALS:
1569:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1570:   case MAT_USE_HASH_TABLE:
1571:     a->ht_flag = PETSC_TRUE;
1572:     break;
1573:   case MAT_SYMMETRIC:
1574:   case MAT_STRUCTURALLY_SYMMETRIC:
1575:   case MAT_HERMITIAN:
1576:   case MAT_SYMMETRY_ETERNAL:
1577:     MatSetOption(a->A,op);
1578:     break;
1579:   case MAT_NOT_SYMMETRIC:
1580:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1581:   case MAT_NOT_HERMITIAN:
1582:   case MAT_NOT_SYMMETRY_ETERNAL:
1583:     break;
1584:   default:
1585:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
1586:   }
1587:   return(0);
1588: }

1592: PetscErrorCode MatTranspose_MPIBAIJ(Mat A,Mat *matout)
1593: {
1594:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)A->data;
1595:   Mat_SeqBAIJ    *Aloc;
1596:   Mat            B;
1598:   PetscInt       M=A->rmap.N,N=A->cmap.N,*ai,*aj,i,*rvals,j,k,col;
1599:   PetscInt       bs=A->rmap.bs,mbs=baij->mbs;
1600:   MatScalar      *a;
1601: 
1603:   if (!matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1604:   MatCreate(A->comm,&B);
1605:   MatSetSizes(B,A->cmap.n,A->rmap.n,N,M);
1606:   MatSetType(B,A->type_name);
1607:   MatMPIBAIJSetPreallocation(B,A->rmap.bs,0,PETSC_NULL,0,PETSC_NULL);
1608: 
1609:   /* copy over the A part */
1610:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1611:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1612:   PetscMalloc(bs*sizeof(PetscInt),&rvals);
1613: 
1614:   for (i=0; i<mbs; i++) {
1615:     rvals[0] = bs*(baij->rstartbs + i);
1616:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1617:     for (j=ai[i]; j<ai[i+1]; j++) {
1618:       col = (baij->cstartbs+aj[j])*bs;
1619:       for (k=0; k<bs; k++) {
1620:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1621:         col++; a += bs;
1622:       }
1623:     }
1624:   }
1625:   /* copy over the B part */
1626:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1627:   ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1628:   for (i=0; i<mbs; i++) {
1629:     rvals[0] = bs*(baij->rstartbs + i);
1630:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1631:     for (j=ai[i]; j<ai[i+1]; j++) {
1632:       col = baij->garray[aj[j]]*bs;
1633:       for (k=0; k<bs; k++) {
1634:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1635:         col++; a += bs;
1636:       }
1637:     }
1638:   }
1639:   PetscFree(rvals);
1640:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1641:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1642: 
1643:   if (matout) {
1644:     *matout = B;
1645:   } else {
1646:     MatHeaderCopy(A,B);
1647:   }
1648:   return(0);
1649: }

1653: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1654: {
1655:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
1656:   Mat            a = baij->A,b = baij->B;
1658:   PetscInt       s1,s2,s3;

1661:   MatGetLocalSize(mat,&s2,&s3);
1662:   if (rr) {
1663:     VecGetLocalSize(rr,&s1);
1664:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1665:     /* Overlap communication with computation. */
1666:     VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1667:   }
1668:   if (ll) {
1669:     VecGetLocalSize(ll,&s1);
1670:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1671:     (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1672:   }
1673:   /* scale  the diagonal block */
1674:   (*a->ops->diagonalscale)(a,ll,rr);

1676:   if (rr) {
1677:     /* Do a scatter end and then right scale the off-diagonal block */
1678:     VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1679:     (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1680:   }
1681: 
1682:   return(0);
1683: }

1687: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1688: {
1689:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;
1691:   PetscMPIInt    imdex,size = l->size,n,rank = l->rank;
1692:   PetscInt       i,*owners = A->rmap.range;
1693:   PetscInt       *nprocs,j,idx,nsends,row;
1694:   PetscInt       nmax,*svalues,*starts,*owner,nrecvs;
1695:   PetscInt       *rvalues,tag = A->tag,count,base,slen,*source,lastidx = -1;
1696:   PetscInt       *lens,*lrows,*values,rstart_bs=A->rmap.rstart;
1697:   MPI_Comm       comm = A->comm;
1698:   MPI_Request    *send_waits,*recv_waits;
1699:   MPI_Status     recv_status,*send_status;
1700: #if defined(PETSC_DEBUG)
1701:   PetscTruth     found = PETSC_FALSE;
1702: #endif
1703: 
1705:   /*  first count number of contributors to each processor */
1706:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
1707:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
1708:   PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
1709:   j = 0;
1710:   for (i=0; i<N; i++) {
1711:     if (lastidx > (idx = rows[i])) j = 0;
1712:     lastidx = idx;
1713:     for (; j<size; j++) {
1714:       if (idx >= owners[j] && idx < owners[j+1]) {
1715:         nprocs[2*j]++;
1716:         nprocs[2*j+1] = 1;
1717:         owner[i] = j;
1718: #if defined(PETSC_DEBUG)
1719:         found = PETSC_TRUE;
1720: #endif
1721:         break;
1722:       }
1723:     }
1724: #if defined(PETSC_DEBUG)
1725:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1726:     found = PETSC_FALSE;
1727: #endif
1728:   }
1729:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
1730: 
1731:   /* inform other processors of number of messages and max length*/
1732:   PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
1733: 
1734:   /* post receives:   */
1735:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
1736:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1737:   for (i=0; i<nrecvs; i++) {
1738:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1739:   }
1740: 
1741:   /* do sends:
1742:      1) starts[i] gives the starting index in svalues for stuff going to 
1743:      the ith processor
1744:   */
1745:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
1746:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1747:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
1748:   starts[0]  = 0;
1749:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1750:   for (i=0; i<N; i++) {
1751:     svalues[starts[owner[i]]++] = rows[i];
1752:   }
1753: 
1754:   starts[0] = 0;
1755:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1756:   count = 0;
1757:   for (i=0; i<size; i++) {
1758:     if (nprocs[2*i+1]) {
1759:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
1760:     }
1761:   }
1762:   PetscFree(starts);

1764:   base = owners[rank];
1765: 
1766:   /*  wait on receives */
1767:   PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
1768:   source = lens + nrecvs;
1769:   count  = nrecvs; slen = 0;
1770:   while (count) {
1771:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1772:     /* unpack receives into our local space */
1773:     MPI_Get_count(&recv_status,MPIU_INT,&n);
1774:     source[imdex]  = recv_status.MPI_SOURCE;
1775:     lens[imdex]    = n;
1776:     slen          += n;
1777:     count--;
1778:   }
1779:   PetscFree(recv_waits);
1780: 
1781:   /* move the data into the send scatter */
1782:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
1783:   count = 0;
1784:   for (i=0; i<nrecvs; i++) {
1785:     values = rvalues + i*nmax;
1786:     for (j=0; j<lens[i]; j++) {
1787:       lrows[count++] = values[j] - base;
1788:     }
1789:   }
1790:   PetscFree(rvalues);
1791:   PetscFree(lens);
1792:   PetscFree(owner);
1793:   PetscFree(nprocs);
1794: 
1795:   /* actually zap the local rows */
1796:   /*
1797:         Zero the required rows. If the "diagonal block" of the matrix
1798:      is square and the user wishes to set the diagonal we use separate
1799:      code so that MatSetValues() is not called for each diagonal allocating
1800:      new memory, thus calling lots of mallocs and slowing things down.

1802:        Contributed by: Matthew Knepley
1803:   */
1804:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1805:   MatZeroRows_SeqBAIJ(l->B,slen,lrows,0.0);
1806:   if ((diag != 0.0) && (l->A->rmap.N == l->A->cmap.N)) {
1807:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,diag);
1808:   } else if (diag != 0.0) {
1809:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0);
1810:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) {
1811:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1812: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1813:     }
1814:     for (i=0; i<slen; i++) {
1815:       row  = lrows[i] + rstart_bs;
1816:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
1817:     }
1818:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1819:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1820:   } else {
1821:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0);
1822:   }

1824:   PetscFree(lrows);

1826:   /* wait on sends */
1827:   if (nsends) {
1828:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1829:     MPI_Waitall(nsends,send_waits,send_status);
1830:     PetscFree(send_status);
1831:   }
1832:   PetscFree(send_waits);
1833:   PetscFree(svalues);

1835:   return(0);
1836: }

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

1846:   MatSetUnfactored(a->A);
1847:   return(0);
1848: }

1850: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat *);

1854: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscTruth *flag)
1855: {
1856:   Mat_MPIBAIJ    *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1857:   Mat            a,b,c,d;
1858:   PetscTruth     flg;

1862:   a = matA->A; b = matA->B;
1863:   c = matB->A; d = matB->B;

1865:   MatEqual(a,c,&flg);
1866:   if (flg) {
1867:     MatEqual(b,d,&flg);
1868:   }
1869:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1870:   return(0);
1871: }

1875: PetscErrorCode MatCopy_MPIBAIJ(Mat A,Mat B,MatStructure str)
1876: {
1878:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ *)A->data;
1879:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ *)B->data;

1882:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1883:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1884:     MatCopy_Basic(A,B,str);
1885:   } else {
1886:     MatCopy(a->A,b->A,str);
1887:     MatCopy(a->B,b->B,str);
1888:   }
1889:   return(0);
1890: }

1894: PetscErrorCode MatSetUpPreallocation_MPIBAIJ(Mat A)
1895: {

1899:    MatMPIBAIJSetPreallocation(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1900:   return(0);
1901: }

1903:  #include petscblaslapack.h
1906: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1907: {
1909:   Mat_MPIBAIJ    *xx=(Mat_MPIBAIJ *)X->data,*yy=(Mat_MPIBAIJ *)Y->data;
1910:   PetscBLASInt   bnz,one=1;
1911:   Mat_SeqBAIJ    *x,*y;

1914:   if (str == SAME_NONZERO_PATTERN) {
1915:     PetscScalar alpha = a;
1916:     x = (Mat_SeqBAIJ *)xx->A->data;
1917:     y = (Mat_SeqBAIJ *)yy->A->data;
1918:     bnz = (PetscBLASInt)x->nz;
1919:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1920:     x = (Mat_SeqBAIJ *)xx->B->data;
1921:     y = (Mat_SeqBAIJ *)yy->B->data;
1922:     bnz = (PetscBLASInt)x->nz;
1923:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1924:   } else {
1925:     MatAXPY_Basic(Y,a,X,str);
1926:   }
1927:   return(0);
1928: }

1932: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1933: {
1934:   Mat_MPIBAIJ   *a = (Mat_MPIBAIJ*)A->data;

1938:   MatRealPart(a->A);
1939:   MatRealPart(a->B);
1940:   return(0);
1941: }

1945: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1946: {
1947:   Mat_MPIBAIJ   *a = (Mat_MPIBAIJ*)A->data;

1951:   MatImaginaryPart(a->A);
1952:   MatImaginaryPart(a->B);
1953:   return(0);
1954: }

1956: /* -------------------------------------------------------------------*/
1957: static struct _MatOps MatOps_Values = {
1958:        MatSetValues_MPIBAIJ,
1959:        MatGetRow_MPIBAIJ,
1960:        MatRestoreRow_MPIBAIJ,
1961:        MatMult_MPIBAIJ,
1962: /* 4*/ MatMultAdd_MPIBAIJ,
1963:        MatMultTranspose_MPIBAIJ,
1964:        MatMultTransposeAdd_MPIBAIJ,
1965:        0,
1966:        0,
1967:        0,
1968: /*10*/ 0,
1969:        0,
1970:        0,
1971:        0,
1972:        MatTranspose_MPIBAIJ,
1973: /*15*/ MatGetInfo_MPIBAIJ,
1974:        MatEqual_MPIBAIJ,
1975:        MatGetDiagonal_MPIBAIJ,
1976:        MatDiagonalScale_MPIBAIJ,
1977:        MatNorm_MPIBAIJ,
1978: /*20*/ MatAssemblyBegin_MPIBAIJ,
1979:        MatAssemblyEnd_MPIBAIJ,
1980:        0,
1981:        MatSetOption_MPIBAIJ,
1982:        MatZeroEntries_MPIBAIJ,
1983: /*25*/ MatZeroRows_MPIBAIJ,
1984:        0,
1985:        0,
1986:        0,
1987:        0,
1988: /*30*/ MatSetUpPreallocation_MPIBAIJ,
1989:        0,
1990:        0,
1991:        0,
1992:        0,
1993: /*35*/ MatDuplicate_MPIBAIJ,
1994:        0,
1995:        0,
1996:        0,
1997:        0,
1998: /*40*/ MatAXPY_MPIBAIJ,
1999:        MatGetSubMatrices_MPIBAIJ,
2000:        MatIncreaseOverlap_MPIBAIJ,
2001:        MatGetValues_MPIBAIJ,
2002:        MatCopy_MPIBAIJ,
2003: /*45*/ 0,
2004:        MatScale_MPIBAIJ,
2005:        0,
2006:        0,
2007:        0,
2008: /*50*/ 0,
2009:        0,
2010:        0,
2011:        0,
2012:        0,
2013: /*55*/ 0,
2014:        0,
2015:        MatSetUnfactored_MPIBAIJ,
2016:        0,
2017:        MatSetValuesBlocked_MPIBAIJ,
2018: /*60*/ 0,
2019:        MatDestroy_MPIBAIJ,
2020:        MatView_MPIBAIJ,
2021:        0,
2022:        0,
2023: /*65*/ 0,
2024:        0,
2025:        0,
2026:        0,
2027:        0,
2028: /*70*/ MatGetRowMax_MPIBAIJ,
2029:        0,
2030:        0,
2031:        0,
2032:        0,
2033: /*75*/ 0,
2034:        0,
2035:        0,
2036:        0,
2037:        0,
2038: /*80*/ 0,
2039:        0,
2040:        0,
2041:        0,
2042:        MatLoad_MPIBAIJ,
2043: /*85*/ 0,
2044:        0,
2045:        0,
2046:        0,
2047:        0,
2048: /*90*/ 0,
2049:        0,
2050:        0,
2051:        0,
2052:        0,
2053: /*95*/ 0,
2054:        0,
2055:        0,
2056:        0,
2057:        0,
2058: /*100*/0,
2059:        0,
2060:        0,
2061:        0,
2062:        0,
2063: /*105*/0,
2064:        MatRealPart_MPIBAIJ,
2065:        MatImaginaryPart_MPIBAIJ};


2071: PetscErrorCode  MatGetDiagonalBlock_MPIBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
2072: {
2074:   *a      = ((Mat_MPIBAIJ *)A->data)->A;
2075:   *iscopy = PETSC_FALSE;
2076:   return(0);
2077: }


2086: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
2087: {
2088:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)B->data;
2089:   PetscInt       m = B->rmap.n/bs,cstart = baij->cstartbs, cend = baij->cendbs,j,nnz,i,d;
2090:   PetscInt       *d_nnz,*o_nnz,nnz_max = 0,rstart = baij->rstartbs,ii;
2091:   const PetscInt *JJ;
2092:   PetscScalar    *values;

2096: #if defined(PETSC_OPT_g)
2097:   if (Ii[0]) SETERRQ1(PETSC_ERR_ARG_RANGE,"Ii[0] must be 0 it is %D",Ii[0]);
2098: #endif
2099:   PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
2100:   o_nnz = d_nnz + m;

2102:   for (i=0; i<m; i++) {
2103:     nnz     = Ii[i+1]- Ii[i];
2104:     JJ      = J + Ii[i];
2105:     nnz_max = PetscMax(nnz_max,nnz);
2106: #if defined(PETSC_OPT_g)
2107:     if (nnz < 0) SETERRQ1(PETSC_ERR_ARG_RANGE,"Local row %D has a negative %D number of columns",i,nnz);
2108: #endif
2109:     for (j=0; j<nnz; j++) {
2110:       if (*JJ >= cstart) break;
2111:       JJ++;
2112:     }
2113:     d = 0;
2114:     for (; j<nnz; j++) {
2115:       if (*JJ++ >= cend) break;
2116:       d++;
2117:     }
2118:     d_nnz[i] = d;
2119:     o_nnz[i] = nnz - d;
2120:   }
2121:   MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
2122:   PetscFree(d_nnz);

2124:   if (v) values = (PetscScalar*)v;
2125:   else {
2126:     PetscMalloc(bs*bs*(nnz_max+1)*sizeof(PetscScalar),&values);
2127:     PetscMemzero(values,bs*bs*nnz_max*sizeof(PetscScalar));
2128:   }

2130:   MatSetOption(B,MAT_COLUMNS_SORTED);
2131:   for (i=0; i<m; i++) {
2132:     ii   = i + rstart;
2133:     nnz  = Ii[i+1]- Ii[i];
2134:     MatSetValuesBlocked_MPIBAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
2135:   }
2136:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2137:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2138:   MatSetOption(B,MAT_COLUMNS_UNSORTED);

2140:   if (!v) {
2141:     PetscFree(values);
2142:   }
2143:   return(0);
2144: }

2148: /*@C
2149:    MatMPIBAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
2150:    (the default parallel PETSc format).  

2152:    Collective on MPI_Comm

2154:    Input Parameters:
2155: +  A - the matrix 
2156: .  i - the indices into j for the start of each local row (starts with zero)
2157: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
2158: -  v - optional values in the matrix

2160:    Level: developer

2162: .keywords: matrix, aij, compressed row, sparse, parallel

2164: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ
2165: @*/
2166: PetscErrorCode  MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2167: {
2168:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]);

2171:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",(void (**)(void))&f);
2172:   if (f) {
2173:     (*f)(B,bs,i,j,v);
2174:   }
2175:   return(0);
2176: }

2181: PetscErrorCode  MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,PetscInt *d_nnz,PetscInt o_nz,PetscInt *o_nnz)
2182: {
2183:   Mat_MPIBAIJ    *b;
2185:   PetscInt       i;

2188:   B->preallocated = PETSC_TRUE;
2189:   PetscOptionsBegin(B->comm,B->prefix,"Options for MPIBAIJ matrix","Mat");
2190:     PetscOptionsInt("-mat_block_size","Set the blocksize used to store the matrix","MatMPIBAIJSetPreallocation",bs,&bs,PETSC_NULL);
2191:   PetscOptionsEnd();

2193:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
2194:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2195:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2196:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
2197:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);
2198: 
2199:   B->rmap.bs  = bs;
2200:   B->cmap.bs  = bs;
2201:   PetscMapInitialize(B->comm,&B->rmap);
2202:   PetscMapInitialize(B->comm,&B->cmap);

2204:   if (d_nnz) {
2205:     for (i=0; i<B->rmap.n/bs; i++) {
2206:       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]);
2207:     }
2208:   }
2209:   if (o_nnz) {
2210:     for (i=0; i<B->rmap.n/bs; i++) {
2211:       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]);
2212:     }
2213:   }

2215:   b = (Mat_MPIBAIJ*)B->data;
2216:   b->bs2 = bs*bs;
2217:   b->mbs = B->rmap.n/bs;
2218:   b->nbs = B->cmap.n/bs;
2219:   b->Mbs = B->rmap.N/bs;
2220:   b->Nbs = B->cmap.N/bs;

2222:   for (i=0; i<=b->size; i++) {
2223:     b->rangebs[i] = B->rmap.range[i]/bs;
2224:   }
2225:   b->rstartbs = B->rmap.rstart/bs;
2226:   b->rendbs   = B->rmap.rend/bs;
2227:   b->cstartbs = B->cmap.rstart/bs;
2228:   b->cendbs   = B->cmap.rend/bs;

2230:   MatCreate(PETSC_COMM_SELF,&b->A);
2231:   MatSetSizes(b->A,B->rmap.n,B->cmap.n,B->rmap.n,B->cmap.n);
2232:   MatSetType(b->A,MATSEQBAIJ);
2233:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
2234:   PetscLogObjectParent(B,b->A);
2235:   MatCreate(PETSC_COMM_SELF,&b->B);
2236:   MatSetSizes(b->B,B->rmap.n,B->cmap.N,B->rmap.n,B->cmap.N);
2237:   MatSetType(b->B,MATSEQBAIJ);
2238:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
2239:   PetscLogObjectParent(B,b->B);

2241:   MatStashCreate_Private(B->comm,bs,&B->bstash);

2243:   return(0);
2244: }

2248: EXTERN PetscErrorCode  MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2249: EXTERN PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);

2252: /*MC
2253:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

2255:    Options Database Keys:
2256: + -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()
2257: . -mat_block_size <bs> - set the blocksize used to store the matrix
2258: - -mat_use_hash_table <fact>

2260:   Level: beginner

2262: .seealso: MatCreateMPIBAIJ
2263: M*/

2268: PetscErrorCode  MatCreate_MPIBAIJ(Mat B)
2269: {
2270:   Mat_MPIBAIJ    *b;
2272:   PetscTruth     flg;

2275:   PetscNew(Mat_MPIBAIJ,&b);
2276:   B->data = (void*)b;


2279:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2280:   B->mapping    = 0;
2281:   B->factor     = 0;
2282:   B->assembled  = PETSC_FALSE;

2284:   B->insertmode = NOT_SET_VALUES;
2285:   MPI_Comm_rank(B->comm,&b->rank);
2286:   MPI_Comm_size(B->comm,&b->size);

2288:   /* build local table of row and column ownerships */
2289:   PetscMalloc((b->size+1)*sizeof(PetscInt),&b->rangebs);

2291:   /* build cache for off array entries formed */
2292:   MatStashCreate_Private(B->comm,1,&B->stash);
2293:   b->donotstash  = PETSC_FALSE;
2294:   b->colmap      = PETSC_NULL;
2295:   b->garray      = PETSC_NULL;
2296:   b->roworiented = PETSC_TRUE;

2298: #if defined(PETSC_USE_MAT_SINGLE)
2299:   /* stuff for MatSetValues_XXX in single precision */
2300:   b->setvalueslen     = 0;
2301:   b->setvaluescopy    = PETSC_NULL;
2302: #endif

2304:   /* stuff used in block assembly */
2305:   b->barray       = 0;

2307:   /* stuff used for matrix vector multiply */
2308:   b->lvec         = 0;
2309:   b->Mvctx        = 0;

2311:   /* stuff for MatGetRow() */
2312:   b->rowindices   = 0;
2313:   b->rowvalues    = 0;
2314:   b->getrowactive = PETSC_FALSE;

2316:   /* hash table stuff */
2317:   b->ht           = 0;
2318:   b->hd           = 0;
2319:   b->ht_size      = 0;
2320:   b->ht_flag      = PETSC_FALSE;
2321:   b->ht_fact      = 0;
2322:   b->ht_total_ct  = 0;
2323:   b->ht_insert_ct = 0;

2325:   PetscOptionsBegin(B->comm,PETSC_NULL,"Options for loading MPIBAIJ matrix","Mat");
2326:     PetscOptionsTruth("-mat_use_hash_table","Use hash table to save memory in constructing matrix","MatSetOption",PETSC_FALSE,&flg,PETSC_NULL);
2327:     if (flg) {
2328:       PetscReal fact = 1.39;
2329:       MatSetOption(B,MAT_USE_HASH_TABLE);
2330:       PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,PETSC_NULL);
2331:       if (fact <= 1.0) fact = 1.39;
2332:       MatMPIBAIJSetHashTableFactor(B,fact);
2333:       PetscInfo1(0,"Hash table Factor used %5.2f\n",fact);
2334:     }
2335:   PetscOptionsEnd();

2337:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2338:                                      "MatStoreValues_MPIBAIJ",
2339:                                      MatStoreValues_MPIBAIJ);
2340:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2341:                                      "MatRetrieveValues_MPIBAIJ",
2342:                                      MatRetrieveValues_MPIBAIJ);
2343:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
2344:                                      "MatGetDiagonalBlock_MPIBAIJ",
2345:                                      MatGetDiagonalBlock_MPIBAIJ);
2346:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocation_C",
2347:                                      "MatMPIBAIJSetPreallocation_MPIBAIJ",
2348:                                      MatMPIBAIJSetPreallocation_MPIBAIJ);
2349:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",
2350:                                      "MatMPIBAIJSetPreallocationCSR_MPIAIJ",
2351:                                      MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
2352:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
2353:                                      "MatDiagonalScaleLocal_MPIBAIJ",
2354:                                      MatDiagonalScaleLocal_MPIBAIJ);
2355:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetHashTableFactor_C",
2356:                                      "MatSetHashTableFactor_MPIBAIJ",
2357:                                      MatSetHashTableFactor_MPIBAIJ);
2358:   PetscObjectChangeTypeName((PetscObject)B,MATMPIBAIJ);
2359:   return(0);
2360: }

2363: /*MC
2364:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

2366:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
2367:    and MATMPIBAIJ otherwise.

2369:    Options Database Keys:
2370: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

2372:   Level: beginner

2374: .seealso: MatCreateMPIBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2375: M*/

2380: PetscErrorCode  MatCreate_BAIJ(Mat A)
2381: {
2383:   PetscMPIInt    size;

2386:   MPI_Comm_size(A->comm,&size);
2387:   if (size == 1) {
2388:     MatSetType(A,MATSEQBAIJ);
2389:   } else {
2390:     MatSetType(A,MATMPIBAIJ);
2391:   }
2392:   return(0);
2393: }

2398: /*@C
2399:    MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
2400:    (block compressed row).  For good matrix assembly performance
2401:    the user should preallocate the matrix storage by setting the parameters 
2402:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2403:    performance can be increased by more than a factor of 50.

2405:    Collective on Mat

2407:    Input Parameters:
2408: +  A - the matrix 
2409: .  bs   - size of blockk
2410: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
2411:            submatrix  (same for all local rows)
2412: .  d_nnz - array containing the number of block nonzeros in the various block rows 
2413:            of the in diagonal portion of the local (possibly different for each block
2414:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2415: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
2416:            submatrix (same for all local rows).
2417: -  o_nnz - array containing the number of nonzeros in the various block rows of the
2418:            off-diagonal portion of the local submatrix (possibly different for
2419:            each block row) or PETSC_NULL.

2421:    If the *_nnz parameter is given then the *_nz parameter is ignored

2423:    Options Database Keys:
2424: +   -mat_block_size - size of the blocks to use
2425: -   -mat_use_hash_table <fact>

2427:    Notes:
2428:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2429:    than it must be used on all processors that share the object for that argument.

2431:    Storage Information:
2432:    For a square global matrix we define each processor's diagonal portion 
2433:    to be its local rows and the corresponding columns (a square submatrix);  
2434:    each processor's off-diagonal portion encompasses the remainder of the
2435:    local matrix (a rectangular submatrix). 

2437:    The user can specify preallocated storage for the diagonal part of
2438:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2439:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2440:    memory allocation.  Likewise, specify preallocated storage for the
2441:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2443:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2444:    the figure below we depict these three local rows and all columns (0-11).

2446: .vb
2447:            0 1 2 3 4 5 6 7 8 9 10 11
2448:           -------------------
2449:    row 3  |  o o o d d d o o o o o o
2450:    row 4  |  o o o d d d o o o o o o
2451:    row 5  |  o o o d d d o o o o o o
2452:           -------------------
2453: .ve
2454:   
2455:    Thus, any entries in the d locations are stored in the d (diagonal) 
2456:    submatrix, and any entries in the o locations are stored in the
2457:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2458:    stored simply in the MATSEQBAIJ format for compressed row storage.

2460:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2461:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2462:    In general, for PDE problems in which most nonzeros are near the diagonal,
2463:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2464:    or you will get TERRIBLE performance; see the users' manual chapter on
2465:    matrices.

2467:    Level: intermediate

2469: .keywords: matrix, block, aij, compressed row, sparse, parallel

2471: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocationCSR()
2472: @*/
2473: PetscErrorCode  MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2474: {
2475:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

2478:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",(void (**)(void))&f);
2479:   if (f) {
2480:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
2481:   }
2482:   return(0);
2483: }

2487: /*@C
2488:    MatCreateMPIBAIJ - Creates a sparse parallel matrix in block AIJ format
2489:    (block compressed row).  For good matrix assembly performance
2490:    the user should preallocate the matrix storage by setting the parameters 
2491:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2492:    performance can be increased by more than a factor of 50.

2494:    Collective on MPI_Comm

2496:    Input Parameters:
2497: +  comm - MPI communicator
2498: .  bs   - size of blockk
2499: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2500:            This value should be the same as the local size used in creating the 
2501:            y vector for the matrix-vector product y = Ax.
2502: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
2503:            This value should be the same as the local size used in creating the 
2504:            x vector for the matrix-vector product y = Ax.
2505: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2506: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2507: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local 
2508:            submatrix  (same for all local rows)
2509: .  d_nnz - array containing the number of nonzero blocks in the various block rows 
2510:            of the in diagonal portion of the local (possibly different for each block
2511:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2512: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
2513:            submatrix (same for all local rows).
2514: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
2515:            off-diagonal portion of the local submatrix (possibly different for
2516:            each block row) or PETSC_NULL.

2518:    Output Parameter:
2519: .  A - the matrix 

2521:    Options Database Keys:
2522: +   -mat_block_size - size of the blocks to use
2523: -   -mat_use_hash_table <fact>

2525:    Notes:
2526:    If the *_nnz parameter is given then the *_nz parameter is ignored

2528:    A nonzero block is any block that as 1 or more nonzeros in it

2530:    The user MUST specify either the local or global matrix dimensions
2531:    (possibly both).

2533:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2534:    than it must be used on all processors that share the object for that argument.

2536:    Storage Information:
2537:    For a square global matrix we define each processor's diagonal portion 
2538:    to be its local rows and the corresponding columns (a square submatrix);  
2539:    each processor's off-diagonal portion encompasses the remainder of the
2540:    local matrix (a rectangular submatrix). 

2542:    The user can specify preallocated storage for the diagonal part of
2543:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2544:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2545:    memory allocation.  Likewise, specify preallocated storage for the
2546:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2548:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2549:    the figure below we depict these three local rows and all columns (0-11).

2551: .vb
2552:            0 1 2 3 4 5 6 7 8 9 10 11
2553:           -------------------
2554:    row 3  |  o o o d d d o o o o o o
2555:    row 4  |  o o o d d d o o o o o o
2556:    row 5  |  o o o d d d o o o o o o
2557:           -------------------
2558: .ve
2559:   
2560:    Thus, any entries in the d locations are stored in the d (diagonal) 
2561:    submatrix, and any entries in the o locations are stored in the
2562:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2563:    stored simply in the MATSEQBAIJ format for compressed row storage.

2565:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2566:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2567:    In general, for PDE problems in which most nonzeros are near the diagonal,
2568:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2569:    or you will get TERRIBLE performance; see the users' manual chapter on
2570:    matrices.

2572:    Level: intermediate

2574: .keywords: matrix, block, aij, compressed row, sparse, parallel

2576: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2577: @*/
2578: 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)
2579: {
2581:   PetscMPIInt    size;

2584:   MatCreate(comm,A);
2585:   MatSetSizes(*A,m,n,M,N);
2586:   MPI_Comm_size(comm,&size);
2587:   if (size > 1) {
2588:     MatSetType(*A,MATMPIBAIJ);
2589:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2590:   } else {
2591:     MatSetType(*A,MATSEQBAIJ);
2592:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2593:   }
2594:   return(0);
2595: }

2599: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2600: {
2601:   Mat            mat;
2602:   Mat_MPIBAIJ    *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
2604:   PetscInt       len=0;

2607:   *newmat       = 0;
2608:   MatCreate(matin->comm,&mat);
2609:   MatSetSizes(mat,matin->rmap.n,matin->cmap.n,matin->rmap.N,matin->cmap.N);
2610:   MatSetType(mat,matin->type_name);
2611:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));

2613:   mat->factor       = matin->factor;
2614:   mat->preallocated = PETSC_TRUE;
2615:   mat->assembled    = PETSC_TRUE;
2616:   mat->insertmode   = NOT_SET_VALUES;

2618:   a      = (Mat_MPIBAIJ*)mat->data;
2619:   mat->rmap.bs  = matin->rmap.bs;
2620:   a->bs2   = oldmat->bs2;
2621:   a->mbs   = oldmat->mbs;
2622:   a->nbs   = oldmat->nbs;
2623:   a->Mbs   = oldmat->Mbs;
2624:   a->Nbs   = oldmat->Nbs;
2625: 
2626:   PetscMapCopy(matin->comm,&matin->rmap,&mat->rmap);
2627:   PetscMapCopy(matin->comm,&matin->cmap,&mat->cmap);

2629:   a->size         = oldmat->size;
2630:   a->rank         = oldmat->rank;
2631:   a->donotstash   = oldmat->donotstash;
2632:   a->roworiented  = oldmat->roworiented;
2633:   a->rowindices   = 0;
2634:   a->rowvalues    = 0;
2635:   a->getrowactive = PETSC_FALSE;
2636:   a->barray       = 0;
2637:   a->rstartbs     = oldmat->rstartbs;
2638:   a->rendbs       = oldmat->rendbs;
2639:   a->cstartbs     = oldmat->cstartbs;
2640:   a->cendbs       = oldmat->cendbs;

2642:   /* hash table stuff */
2643:   a->ht           = 0;
2644:   a->hd           = 0;
2645:   a->ht_size      = 0;
2646:   a->ht_flag      = oldmat->ht_flag;
2647:   a->ht_fact      = oldmat->ht_fact;
2648:   a->ht_total_ct  = 0;
2649:   a->ht_insert_ct = 0;

2651:   PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+1)*sizeof(PetscInt));
2652:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2653:   MatStashCreate_Private(matin->comm,matin->rmap.bs,&mat->bstash);
2654:   if (oldmat->colmap) {
2655: #if defined (PETSC_USE_CTABLE)
2656:   PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2657: #else
2658:   PetscMalloc((a->Nbs)*sizeof(PetscInt),&a->colmap);
2659:   PetscLogObjectMemory(mat,(a->Nbs)*sizeof(PetscInt));
2660:   PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
2661: #endif
2662:   } else a->colmap = 0;

2664:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2665:     PetscMalloc(len*sizeof(PetscInt),&a->garray);
2666:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2667:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
2668:   } else a->garray = 0;
2669: 
2670:   VecDuplicate(oldmat->lvec,&a->lvec);
2671:   PetscLogObjectParent(mat,a->lvec);
2672:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2673:   PetscLogObjectParent(mat,a->Mvctx);

2675:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2676:   PetscLogObjectParent(mat,a->A);
2677:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2678:   PetscLogObjectParent(mat,a->B);
2679:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2680:   *newmat = mat;

2682:   return(0);
2683: }

2685:  #include petscsys.h

2689: PetscErrorCode MatLoad_MPIBAIJ(PetscViewer viewer, MatType type,Mat *newmat)
2690: {
2691:   Mat            A;
2693:   int            fd;
2694:   PetscInt       i,nz,j,rstart,rend;
2695:   PetscScalar    *vals,*buf;
2696:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2697:   MPI_Status     status;
2698:   PetscMPIInt    rank,size,maxnz;
2699:   PetscInt       header[4],*rowlengths = 0,M,N,m,*rowners,*cols;
2700:   PetscInt       *locrowlens = PETSC_NULL,*procsnz = PETSC_NULL,*browners = PETSC_NULL;
2701:   PetscInt       jj,*mycols,*ibuf,bs=1,Mbs,mbs,extra_rows,mmax;
2702:   PetscMPIInt    tag = ((PetscObject)viewer)->tag;
2703:   PetscInt       *dlens = PETSC_NULL,*odlens = PETSC_NULL,*mask = PETSC_NULL,*masked1 = PETSC_NULL,*masked2 = PETSC_NULL,rowcount,odcount;
2704:   PetscInt       dcount,kmax,k,nzcount,tmp,mend;

2707:   PetscOptionsBegin(comm,PETSC_NULL,"Options for loading MPIBAIJ matrix","Mat");
2708:     PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
2709:   PetscOptionsEnd();

2711:   MPI_Comm_size(comm,&size);
2712:   MPI_Comm_rank(comm,&rank);
2713:   if (!rank) {
2714:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2715:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2716:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2717:   }

2719:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2720:   M = header[1]; N = header[2];

2722:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2724:   /* 
2725:      This code adds extra rows to make sure the number of rows is 
2726:      divisible by the blocksize
2727:   */
2728:   Mbs        = M/bs;
2729:   extra_rows = bs - M + bs*Mbs;
2730:   if (extra_rows == bs) extra_rows = 0;
2731:   else                  Mbs++;
2732:   if (extra_rows && !rank) {
2733:     PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2734:   }

2736:   /* determine ownership of all rows */
2737:   mbs        = Mbs/size + ((Mbs % size) > rank);
2738:   m          = mbs*bs;
2739:   PetscMalloc2(size+1,PetscInt,&rowners,size+1,PetscInt,&browners);
2740:   MPI_Allgather(&mbs,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);

2742:   /* process 0 needs enough room for process with most rows */
2743:   if (!rank) {
2744:     mmax = rowners[1];
2745:     for (i=2; i<size; i++) {
2746:       mmax = PetscMax(mmax,rowners[i]);
2747:     }
2748:     mmax*=bs;
2749:   } else mmax = m;

2751:   rowners[0] = 0;
2752:   for (i=2; i<=size; i++)  rowners[i] += rowners[i-1];
2753:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2754:   rstart = rowners[rank];
2755:   rend   = rowners[rank+1];

2757:   /* distribute row lengths to all processors */
2758:   PetscMalloc((mmax+1)*sizeof(PetscInt),&locrowlens);
2759:   if (!rank) {
2760:     mend = m;
2761:     if (size == 1) mend = mend - extra_rows;
2762:     PetscBinaryRead(fd,locrowlens,mend,PETSC_INT);
2763:     for (j=mend; j<m; j++) locrowlens[j] = 1;
2764:     PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2765:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2766:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2767:     for (j=0; j<m; j++) {
2768:       procsnz[0] += locrowlens[j];
2769:     }
2770:     for (i=1; i<size; i++) {
2771:       mend = browners[i+1] - browners[i];
2772:       if (i == size-1) mend = mend - extra_rows;
2773:       PetscBinaryRead(fd,rowlengths,mend,PETSC_INT);
2774:       for (j=mend; j<browners[i+1] - browners[i]; j++) rowlengths[j] = 1;
2775:       /* calculate the number of nonzeros on each processor */
2776:       for (j=0; j<browners[i+1]-browners[i]; j++) {
2777:         procsnz[i] += rowlengths[j];
2778:       }
2779:       MPI_Send(rowlengths,browners[i+1]-browners[i],MPIU_INT,i,tag,comm);
2780:     }
2781:     PetscFree(rowlengths);
2782:   } else {
2783:     MPI_Recv(locrowlens,m,MPIU_INT,0,tag,comm,&status);
2784:   }

2786:   if (!rank) {
2787:     /* determine max buffer needed and allocate it */
2788:     maxnz = procsnz[0];
2789:     for (i=1; i<size; i++) {
2790:       maxnz = PetscMax(maxnz,procsnz[i]);
2791:     }
2792:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2794:     /* read in my part of the matrix column indices  */
2795:     nz     = procsnz[0];
2796:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
2797:     mycols = ibuf;
2798:     if (size == 1)  nz -= extra_rows;
2799:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2800:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2802:     /* read in every ones (except the last) and ship off */
2803:     for (i=1; i<size-1; i++) {
2804:       nz   = procsnz[i];
2805:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2806:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2807:     }
2808:     /* read in the stuff for the last proc */
2809:     if (size != 1) {
2810:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2811:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2812:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2813:       MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
2814:     }
2815:     PetscFree(cols);
2816:   } else {
2817:     /* determine buffer space needed for message */
2818:     nz = 0;
2819:     for (i=0; i<m; i++) {
2820:       nz += locrowlens[i];
2821:     }
2822:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
2823:     mycols = ibuf;
2824:     /* receive message of column indices*/
2825:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2826:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2827:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2828:   }
2829: 
2830:   /* loop over local rows, determining number of off diagonal entries */
2831:   PetscMalloc2(rend-rstart,PetscInt,&dlens,rend-rstart,PetscInt,&odlens);
2832:   PetscMalloc3(Mbs,PetscInt,&mask,Mbs,PetscInt,&masked1,Mbs,PetscInt,&masked2);
2833:   PetscMemzero(mask,Mbs*sizeof(PetscInt));
2834:   PetscMemzero(masked1,Mbs*sizeof(PetscInt));
2835:   PetscMemzero(masked2,Mbs*sizeof(PetscInt));
2836:   rowcount = 0; nzcount = 0;
2837:   for (i=0; i<mbs; i++) {
2838:     dcount  = 0;
2839:     odcount = 0;
2840:     for (j=0; j<bs; j++) {
2841:       kmax = locrowlens[rowcount];
2842:       for (k=0; k<kmax; k++) {
2843:         tmp = mycols[nzcount++]/bs;
2844:         if (!mask[tmp]) {
2845:           mask[tmp] = 1;
2846:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
2847:           else masked1[dcount++] = tmp;
2848:         }
2849:       }
2850:       rowcount++;
2851:     }
2852: 
2853:     dlens[i]  = dcount;
2854:     odlens[i] = odcount;

2856:     /* zero out the mask elements we set */
2857:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2858:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2859:   }

2861:   /* create our matrix */
2862:   MatCreate(comm,&A);
2863:   MatSetSizes(A,m,m,M+extra_rows,N+extra_rows);
2864:   MatSetType(A,type);CHKERRQ(ierr)
2865:   MatMPIBAIJSetPreallocation(A,bs,0,dlens,0,odlens);

2867:   /* Why doesn't this called using MatSetOption(A,MAT_COLUMNS_SORTED); */
2868:   MatSetOption(A,MAT_COLUMNS_SORTED);
2869: 
2870:   if (!rank) {
2871:     PetscMalloc((maxnz+1)*sizeof(PetscScalar),&buf);
2872:     /* read in my part of the matrix numerical values  */
2873:     nz = procsnz[0];
2874:     vals = buf;
2875:     mycols = ibuf;
2876:     if (size == 1)  nz -= extra_rows;
2877:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2878:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2880:     /* insert into matrix */
2881:     jj      = rstart*bs;
2882:     for (i=0; i<m; i++) {
2883:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2884:       mycols += locrowlens[i];
2885:       vals   += locrowlens[i];
2886:       jj++;
2887:     }
2888:     /* read in other processors (except the last one) and ship out */
2889:     for (i=1; i<size-1; i++) {
2890:       nz   = procsnz[i];
2891:       vals = buf;
2892:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2893:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2894:     }
2895:     /* the last proc */
2896:     if (size != 1){
2897:       nz   = procsnz[i] - extra_rows;
2898:       vals = buf;
2899:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2900:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2901:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2902:     }
2903:     PetscFree(procsnz);
2904:   } else {
2905:     /* receive numeric values */
2906:     PetscMalloc((nz+1)*sizeof(PetscScalar),&buf);

2908:     /* receive message of values*/
2909:     vals   = buf;
2910:     mycols = ibuf;
2911:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2912:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2913:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2915:     /* insert into matrix */
2916:     jj      = rstart*bs;
2917:     for (i=0; i<m; i++) {
2918:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2919:       mycols += locrowlens[i];
2920:       vals   += locrowlens[i];
2921:       jj++;
2922:     }
2923:   }
2924:   PetscFree(locrowlens);
2925:   PetscFree(buf);
2926:   PetscFree(ibuf);
2927:   PetscFree2(rowners,browners);
2928:   PetscFree2(dlens,odlens);
2929:   PetscFree3(mask,masked1,masked2);
2930:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2931:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

2933:   *newmat = A;
2934:   return(0);
2935: }

2939: /*@
2940:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2942:    Input Parameters:
2943: .  mat  - the matrix
2944: .  fact - factor

2946:    Collective on Mat

2948:    Level: advanced

2950:   Notes:
2951:    This can also be set by the command line option: -mat_use_hash_table <fact>

2953: .keywords: matrix, hashtable, factor, HT

2955: .seealso: MatSetOption()
2956: @*/
2957: PetscErrorCode  MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2958: {
2959:   PetscErrorCode ierr,(*f)(Mat,PetscReal);

2962:   PetscObjectQueryFunction((PetscObject)mat,"MatSetHashTableFactor_C",(void (**)(void))&f);
2963:   if (f) {
2964:     (*f)(mat,fact);
2965:   }
2966:   return(0);
2967: }

2972: PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
2973: {
2974:   Mat_MPIBAIJ *baij;

2977:   baij = (Mat_MPIBAIJ*)mat->data;
2978:   baij->ht_fact = fact;
2979:   return(0);
2980: }

2985: PetscErrorCode  MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
2986: {
2987:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2989:   *Ad     = a->A;
2990:   *Ao     = a->B;
2991:   *colmap = a->garray;
2992:   return(0);
2993: }