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 MatGetRowMaxAbs_MPIBAIJ(Mat A,Vec v,PetscInt idx[])
 40: {
 41:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
 43:   PetscInt       i,*idxb = 0;
 44:   PetscScalar    *va,*vb;
 45:   Vec            vtmp;

 48: 
 49:   MatGetRowMaxAbs(a->A,v,idx);
 50:   VecGetArray(v,&va);
 51:   if (idx) {
 52:     for (i=0; i<A->cmap.n; i++) {if (PetscAbsScalar(va[i])) idx[i] += A->cmap.rstart;}
 53:   }

 55:   VecCreateSeq(PETSC_COMM_SELF,A->rmap.n,&vtmp);
 56:   if (idx) {PetscMalloc(A->rmap.n*sizeof(PetscInt),&idxb);}
 57:   MatGetRowMaxAbs(a->B,vtmp,idxb);
 58:   VecGetArray(vtmp,&vb);

 60:   for (i=0; i<A->rmap.n; i++){
 61:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {va[i] = vb[i]; if (idx) idx[i] = A->cmap.bs*a->garray[idxb[i]/A->cmap.bs] + (idxb[i] % A->cmap.bs);}
 62:   }

 64:   VecRestoreArray(v,&va);
 65:   VecRestoreArray(vtmp,&vb);
 66:   if (idxb) {PetscFree(idxb);}
 67:   VecDestroy(vtmp);
 68:   return(0);
 69: }

 74: PetscErrorCode  MatStoreValues_MPIBAIJ(Mat mat)
 75: {
 76:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ *)mat->data;

 80:   MatStoreValues(aij->A);
 81:   MatStoreValues(aij->B);
 82:   return(0);
 83: }

 89: PetscErrorCode  MatRetrieveValues_MPIBAIJ(Mat mat)
 90: {
 91:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ *)mat->data;

 95:   MatRetrieveValues(aij->A);
 96:   MatRetrieveValues(aij->B);
 97:   return(0);
 98: }

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

117: #if defined (PETSC_USE_CTABLE)
118:   PetscTableCreate(baij->nbs,&baij->colmap);
119:   for (i=0; i<nbs; i++){
120:     PetscTableAdd(baij->colmap,baij->garray[i]+1,i*bs+1);
121:   }
122: #else
123:   PetscMalloc((baij->Nbs+1)*sizeof(PetscInt),&baij->colmap);
124:   PetscLogObjectMemory(mat,baij->Nbs*sizeof(PetscInt));
125:   PetscMemzero(baij->colmap,baij->Nbs*sizeof(PetscInt));
126:   for (i=0; i<nbs; i++) baij->colmap[baij->garray[i]] = i*bs+1;
127: #endif
128:   return(0);
129: }

131: #define CHUNKSIZE  10

133: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv) \
134: { \
135:  \
136:     brow = row/bs;  \
137:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
138:     rmax = aimax[brow]; nrow = ailen[brow]; \
139:       bcol = col/bs; \
140:       ridx = row % bs; cidx = col % bs; \
141:       low = 0; high = nrow; \
142:       while (high-low > 3) { \
143:         t = (low+high)/2; \
144:         if (rp[t] > bcol) high = t; \
145:         else              low  = t; \
146:       } \
147:       for (_i=low; _i<high; _i++) { \
148:         if (rp[_i] > bcol) break; \
149:         if (rp[_i] == bcol) { \
150:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
151:           if (addv == ADD_VALUES) *bap += value;  \
152:           else                    *bap  = value;  \
153:           goto a_noinsert; \
154:         } \
155:       } \
156:       if (a->nonew == 1) goto a_noinsert; \
157:       if (a->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
158:       MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,aimax,a->nonew,MatScalar); \
159:       N = nrow++ - 1;  \
160:       /* shift up all the later entries in this row */ \
161:       for (ii=N; ii>=_i; ii--) { \
162:         rp[ii+1] = rp[ii]; \
163:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
164:       } \
165:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  \
166:       rp[_i]                      = bcol;  \
167:       ap[bs2*_i + bs*cidx + ridx] = value;  \
168:       a_noinsert:; \
169:     ailen[brow] = nrow; \
170: } 

172: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv) \
173: { \
174:     brow = row/bs;  \
175:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
176:     rmax = bimax[brow]; nrow = bilen[brow]; \
177:       bcol = col/bs; \
178:       ridx = row % bs; cidx = col % bs; \
179:       low = 0; high = nrow; \
180:       while (high-low > 3) { \
181:         t = (low+high)/2; \
182:         if (rp[t] > bcol) high = t; \
183:         else              low  = t; \
184:       } \
185:       for (_i=low; _i<high; _i++) { \
186:         if (rp[_i] > bcol) break; \
187:         if (rp[_i] == bcol) { \
188:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
189:           if (addv == ADD_VALUES) *bap += value;  \
190:           else                    *bap  = value;  \
191:           goto b_noinsert; \
192:         } \
193:       } \
194:       if (b->nonew == 1) goto b_noinsert; \
195:       if (b->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
196:       MatSeqXAIJReallocateAIJ(B,b->mbs,bs2,nrow,brow,bcol,rmax,ba,bi,bj,rp,ap,bimax,b->nonew,MatScalar); \
197:       CHKMEMQ;\
198:       N = nrow++ - 1;  \
199:       /* shift up all the later entries in this row */ \
200:       for (ii=N; ii>=_i; ii--) { \
201:         rp[ii+1] = rp[ii]; \
202:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
203:       } \
204:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  \
205:       rp[_i]                      = bcol;  \
206:       ap[bs2*_i + bs*cidx + ridx] = value;  \
207:       b_noinsert:; \
208:     bilen[brow] = nrow; \
209: } 

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

222:   if (N > b->setvalueslen) {
223:     PetscFree(b->setvaluescopy);
224:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
225:     b->setvalueslen  = N;
226:   }
227:   vsingle = b->setvaluescopy;

229:   for (i=0; i<N; i++) {
230:     vsingle[i] = v[i];
231:   }
232:   MatSetValues_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
233:   return(0);
234: }

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

246:   if (N > b->setvalueslen) {
247:     PetscFree(b->setvaluescopy);
248:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
249:     b->setvalueslen  = N;
250:   }
251:   vsingle = b->setvaluescopy;
252:   for (i=0; i<N; i++) {
253:     vsingle[i] = v[i];
254:   }
255:   MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
256:   return(0);
257: }

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

269:   if (N > b->setvalueslen) {
270:     PetscFree(b->setvaluescopy);
271:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
272:     b->setvalueslen  = N;
273:   }
274:   vsingle = b->setvaluescopy;
275:   for (i=0; i<N; i++) {
276:     vsingle[i] = v[i];
277:   }
278:   MatSetValues_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
279:   return(0);
280: }

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

292:   if (N > b->setvalueslen) {
293:     PetscFree(b->setvaluescopy);
294:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
295:     b->setvalueslen  = N;
296:   }
297:   vsingle = b->setvaluescopy;
298:   for (i=0; i<N; i++) {
299:     vsingle[i] = v[i];
300:   }
301:   MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
302:   return(0);
303: }
304: #endif

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

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

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

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

335:   for (i=0; i<m; i++) {
336:     if (im[i] < 0) continue;
337: #if defined(PETSC_USE_DEBUG)
338:     if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
339: #endif
340:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
341:       row = im[i] - rstart_orig;
342:       for (j=0; j<n; j++) {
343:         if (in[j] >= cstart_orig && in[j] < cend_orig){
344:           col = in[j] - cstart_orig;
345:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
346:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv);
347:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
348:         } else if (in[j] < 0) continue;
349: #if defined(PETSC_USE_DEBUG)
350:         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);}
351: #endif
352:         else {
353:           if (mat->was_assembled) {
354:             if (!baij->colmap) {
355:               CreateColmap_MPIBAIJ_Private(mat);
356:             }
357: #if defined (PETSC_USE_CTABLE)
358:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
359:             col  = col - 1;
360: #else
361:             col = baij->colmap[in[j]/bs] - 1;
362: #endif
363:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
364:               DisAssemble_MPIBAIJ(mat);
365:               col =  in[j];
366:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
367:               B = baij->B;
368:               b = (Mat_SeqBAIJ*)(B)->data;
369:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
370:               ba=b->a;
371:             } else col += in[j]%bs;
372:           } else col = in[j];
373:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
374:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv);
375:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
376:         }
377:       }
378:     } else {
379:       if (!baij->donotstash) {
380:         if (roworiented) {
381:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
382:         } else {
383:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
384:         }
385:       }
386:     }
387:   }
388:   return(0);
389: }

393: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
394: {
395:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
396:   const MatScalar *value;
397:   MatScalar       *barray=baij->barray;
398:   PetscTruth      roworiented = baij->roworiented;
399:   PetscErrorCode  ierr;
400:   PetscInt        i,j,ii,jj,row,col,rstart=baij->rstartbs;
401:   PetscInt        rend=baij->rendbs,cstart=baij->cstartbs,stepval;
402:   PetscInt        cend=baij->cendbs,bs=mat->rmap.bs,bs2=baij->bs2;
403: 
405:   if(!barray) {
406:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
407:     baij->barray = barray;
408:   }

410:   if (roworiented) {
411:     stepval = (n-1)*bs;
412:   } else {
413:     stepval = (m-1)*bs;
414:   }
415:   for (i=0; i<m; i++) {
416:     if (im[i] < 0) continue;
417: #if defined(PETSC_USE_DEBUG)
418:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
419: #endif
420:     if (im[i] >= rstart && im[i] < rend) {
421:       row = im[i] - rstart;
422:       for (j=0; j<n; j++) {
423:         /* If NumCol = 1 then a copy is not required */
424:         if ((roworiented) && (n == 1)) {
425:           barray = (MatScalar*)v + i*bs2;
426:         } else if((!roworiented) && (m == 1)) {
427:           barray = (MatScalar*)v + j*bs2;
428:         } else { /* Here a copy is required */
429:           if (roworiented) {
430:             value = v + i*(stepval+bs)*bs + j*bs;
431:           } else {
432:             value = v + j*(stepval+bs)*bs + i*bs;
433:           }
434:           for (ii=0; ii<bs; ii++,value+=stepval) {
435:             for (jj=0; jj<bs; jj++) {
436:               *barray++  = *value++;
437:             }
438:           }
439:           barray -=bs2;
440:         }
441: 
442:         if (in[j] >= cstart && in[j] < cend){
443:           col  = in[j] - cstart;
444:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->A,1,&row,1,&col,barray,addv);
445:         }
446:         else if (in[j] < 0) continue;
447: #if defined(PETSC_USE_DEBUG)
448:         else if (in[j] >= baij->Nbs) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);}
449: #endif
450:         else {
451:           if (mat->was_assembled) {
452:             if (!baij->colmap) {
453:               CreateColmap_MPIBAIJ_Private(mat);
454:             }

456: #if defined(PETSC_USE_DEBUG)
457: #if defined (PETSC_USE_CTABLE)
458:             { PetscInt data;
459:               PetscTableFind(baij->colmap,in[j]+1,&data);
460:               if ((data - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
461:             }
462: #else
463:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
464: #endif
465: #endif
466: #if defined (PETSC_USE_CTABLE)
467:             PetscTableFind(baij->colmap,in[j]+1,&col);
468:             col  = (col - 1)/bs;
469: #else
470:             col = (baij->colmap[in[j]] - 1)/bs;
471: #endif
472:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
473:               DisAssemble_MPIBAIJ(mat);
474:               col =  in[j];
475:             }
476:           }
477:           else col = in[j];
478:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->B,1,&row,1,&col,barray,addv);
479:         }
480:       }
481:     } else {
482:       if (!baij->donotstash) {
483:         if (roworiented) {
484:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
485:         } else {
486:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
487:         }
488:       }
489:     }
490:   }
491:   return(0);
492: }

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


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

531: 
532:         idx = h1;
533: #if defined(PETSC_USE_DEBUG)
534:         insert_ct++;
535:         total_ct++;
536:         if (HT[idx] != key) {
537:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
538:           if (idx == size) {
539:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
540:             if (idx == h1) {
541:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
542:             }
543:           }
544:         }
545: #else
546:         if (HT[idx] != key) {
547:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
548:           if (idx == size) {
549:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
550:             if (idx == h1) {
551:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
552:             }
553:           }
554:         }
555: #endif
556:         /* A HASH table entry is found, so insert the values at the correct address */
557:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
558:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
559:       }
560:     } else {
561:       if (!baij->donotstash) {
562:         if (roworiented) {
563:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
564:         } else {
565:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
566:         }
567:       }
568:     }
569:   }
570: #if defined(PETSC_USE_DEBUG)
571:   baij->ht_total_ct = total_ct;
572:   baij->ht_insert_ct = insert_ct;
573: #endif
574:   return(0);
575: }

579: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
580: {
581:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
582:   PetscTruth      roworiented = baij->roworiented;
583:   PetscErrorCode  ierr;
584:   PetscInt        i,j,ii,jj,row,col;
585:   PetscInt        rstart=baij->rstartbs;
586:   PetscInt        rend=mat->rmap.rend,stepval,bs=mat->rmap.bs,bs2=baij->bs2,nbs2=n*bs2;
587:   PetscInt        h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
588:   PetscReal       tmp;
589:   MatScalar       **HD = baij->hd,*baij_a;
590:   const MatScalar *v_t,*value;
591: #if defined(PETSC_USE_DEBUG)
592:   PetscInt        total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
593: #endif
594: 

597:   if (roworiented) {
598:     stepval = (n-1)*bs;
599:   } else {
600:     stepval = (m-1)*bs;
601:   }
602:   for (i=0; i<m; i++) {
603: #if defined(PETSC_USE_DEBUG)
604:     if (im[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",im[i]);
605:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],baij->Mbs-1);
606: #endif
607:     row   = im[i];
608:     v_t   = v + i*nbs2;
609:     if (row >= rstart && row < rend) {
610:       for (j=0; j<n; j++) {
611:         col = in[j];

613:         /* Look up into the Hash Table */
614:         key = row*Nbs+col+1;
615:         h1  = HASH(size,key,tmp);
616: 
617:         idx = h1;
618: #if defined(PETSC_USE_DEBUG)
619:         total_ct++;
620:         insert_ct++;
621:        if (HT[idx] != key) {
622:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
623:           if (idx == size) {
624:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
625:             if (idx == h1) {
626:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
627:             }
628:           }
629:         }
630: #else  
631:         if (HT[idx] != key) {
632:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
633:           if (idx == size) {
634:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
635:             if (idx == h1) {
636:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
637:             }
638:           }
639:         }
640: #endif
641:         baij_a = HD[idx];
642:         if (roworiented) {
643:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
644:           /* value = v + (i*(stepval+bs)+j)*bs; */
645:           value = v_t;
646:           v_t  += bs;
647:           if (addv == ADD_VALUES) {
648:             for (ii=0; ii<bs; ii++,value+=stepval) {
649:               for (jj=ii; jj<bs2; jj+=bs) {
650:                 baij_a[jj]  += *value++;
651:               }
652:             }
653:           } else {
654:             for (ii=0; ii<bs; ii++,value+=stepval) {
655:               for (jj=ii; jj<bs2; jj+=bs) {
656:                 baij_a[jj]  = *value++;
657:               }
658:             }
659:           }
660:         } else {
661:           value = v + j*(stepval+bs)*bs + i*bs;
662:           if (addv == ADD_VALUES) {
663:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
664:               for (jj=0; jj<bs; jj++) {
665:                 baij_a[jj]  += *value++;
666:               }
667:             }
668:           } else {
669:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
670:               for (jj=0; jj<bs; jj++) {
671:                 baij_a[jj]  = *value++;
672:               }
673:             }
674:           }
675:         }
676:       }
677:     } else {
678:       if (!baij->donotstash) {
679:         if (roworiented) {
680:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
681:         } else {
682:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
683:         }
684:       }
685:     }
686:   }
687: #if defined(PETSC_USE_DEBUG)
688:   baij->ht_total_ct = total_ct;
689:   baij->ht_insert_ct = insert_ct;
690: #endif
691:   return(0);
692: }

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

704:   for (i=0; i<m; i++) {
705:     if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);
706:     if (idxm[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap.N-1);
707:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
708:       row = idxm[i] - bsrstart;
709:       for (j=0; j<n; j++) {
710:         if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]);
711:         if (idxn[j] >= mat->cmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap.N-1);
712:         if (idxn[j] >= bscstart && idxn[j] < bscend){
713:           col = idxn[j] - bscstart;
714:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
715:         } else {
716:           if (!baij->colmap) {
717:             CreateColmap_MPIBAIJ_Private(mat);
718:           }
719: #if defined (PETSC_USE_CTABLE)
720:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
721:           data --;
722: #else
723:           data = baij->colmap[idxn[j]/bs]-1;
724: #endif
725:           if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
726:           else {
727:             col  = data + idxn[j]%bs;
728:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
729:           }
730:         }
731:       }
732:     } else {
733:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
734:     }
735:   }
736:  return(0);
737: }

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

751:   if (baij->size == 1) {
752:      MatNorm(baij->A,type,nrm);
753:   } else {
754:     if (type == NORM_FROBENIUS) {
755:       v = amat->a;
756:       nz = amat->nz*bs2;
757:       for (i=0; i<nz; i++) {
758: #if defined(PETSC_USE_COMPLEX)
759:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
760: #else
761:         sum += (*v)*(*v); v++;
762: #endif
763:       }
764:       v = bmat->a;
765:       nz = bmat->nz*bs2;
766:       for (i=0; i<nz; i++) {
767: #if defined(PETSC_USE_COMPLEX)
768:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
769: #else
770:         sum += (*v)*(*v); v++;
771: #endif
772:       }
773:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPI_SUM,mat->comm);
774:       *nrm = sqrt(*nrm);
775:     } else if (type == NORM_1) { /* max column sum */
776:       PetscReal *tmp,*tmp2;
777:       PetscInt  *jj,*garray=baij->garray,cstart=baij->rstartbs;
778:       PetscMalloc((2*mat->cmap.N+1)*sizeof(PetscReal),&tmp);
779:       tmp2 = tmp + mat->cmap.N;
780:       PetscMemzero(tmp,mat->cmap.N*sizeof(PetscReal));
781:       v = amat->a; jj = amat->j;
782:       for (i=0; i<amat->nz; i++) {
783:         for (j=0; j<bs; j++){
784:           col = bs*(cstart + *jj) + j; /* column index */
785:           for (row=0; row<bs; row++){
786:             tmp[col] += PetscAbsScalar(*v);  v++;
787:           }
788:         }
789:         jj++;
790:       }
791:       v = bmat->a; jj = bmat->j;
792:       for (i=0; i<bmat->nz; i++) {
793:         for (j=0; j<bs; j++){
794:           col = bs*garray[*jj] + j;
795:           for (row=0; row<bs; row++){
796:             tmp[col] += PetscAbsScalar(*v); v++;
797:           }
798:         }
799:         jj++;
800:       }
801:       MPI_Allreduce(tmp,tmp2,mat->cmap.N,MPIU_REAL,MPI_SUM,mat->comm);
802:       *nrm = 0.0;
803:       for (j=0; j<mat->cmap.N; j++) {
804:         if (tmp2[j] > *nrm) *nrm = tmp2[j];
805:       }
806:       PetscFree(tmp);
807:     } else if (type == NORM_INFINITY) { /* max row sum */
808:       PetscReal *sums;
809:       PetscMalloc(bs*sizeof(PetscReal),&sums);CHKERRQ(ierr)
810:       sum = 0.0;
811:       for (j=0; j<amat->mbs; j++) {
812:         for (row=0; row<bs; row++) sums[row] = 0.0;
813:         v = amat->a + bs2*amat->i[j];
814:         nz = amat->i[j+1]-amat->i[j];
815:         for (i=0; i<nz; i++) {
816:           for (col=0; col<bs; col++){
817:             for (row=0; row<bs; row++){
818:               sums[row] += PetscAbsScalar(*v); v++;
819:             }
820:           }
821:         }
822:         v = bmat->a + bs2*bmat->i[j];
823:         nz = bmat->i[j+1]-bmat->i[j];
824:         for (i=0; i<nz; i++) {
825:           for (col=0; col<bs; col++){
826:             for (row=0; row<bs; row++){
827:               sums[row] += PetscAbsScalar(*v); v++;
828:             }
829:           }
830:         }
831:         for (row=0; row<bs; row++){
832:           if (sums[row] > sum) sum = sums[row];
833:         }
834:       }
835:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPI_MAX,mat->comm);
836:       PetscFree(sums);
837:     } else {
838:       SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
839:     }
840:   }
841:   return(0);
842: }

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

870:   baij->ht_size=(PetscInt)(factor*nz);
871:   size = baij->ht_size;

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


884:   PetscMemzero(HD,size*(sizeof(PetscInt)+sizeof(PetscScalar*)));
885: 

887:   /* Loop Over A */
888:   for (i=0; i<a->mbs; i++) {
889:     for (j=ai[i]; j<ai[i+1]; j++) {
890:       row = i+rstart;
891:       col = aj[j]+cstart;
892: 
893:       key = row*Nbs + col + 1;
894:       h1  = HASH(size,key,tmp);
895:       for (k=0; k<size; k++){
896:         if (!HT[(h1+k)%size]) {
897:           HT[(h1+k)%size] = key;
898:           HD[(h1+k)%size] = a->a + j*bs2;
899:           break;
900: #if defined(PETSC_USE_INFO)
901:         } else {
902:           ct++;
903: #endif
904:         }
905:       }
906: #if defined(PETSC_USE_INFO)
907:       if (k> max) max = k;
908: #endif
909:     }
910:   }
911:   /* Loop Over B */
912:   for (i=0; i<b->mbs; i++) {
913:     for (j=bi[i]; j<bi[i+1]; j++) {
914:       row = i+rstart;
915:       col = garray[bj[j]];
916:       key = row*Nbs + col + 1;
917:       h1  = HASH(size,key,tmp);
918:       for (k=0; k<size; k++){
919:         if (!HT[(h1+k)%size]) {
920:           HT[(h1+k)%size] = key;
921:           HD[(h1+k)%size] = b->a + j*bs2;
922:           break;
923: #if defined(PETSC_USE_INFO)
924:         } else {
925:           ct++;
926: #endif
927:         }
928:       }
929: #if defined(PETSC_USE_INFO)
930:       if (k> max) max = k;
931: #endif
932:     }
933:   }
934: 
935:   /* Print Summary */
936: #if defined(PETSC_USE_INFO)
937:   for (i=0,j=0; i<size; i++) {
938:     if (HT[i]) {j++;}
939:   }
940:   PetscInfo2(0,"Average Search = %5.2f,max search = %D\n",(!j)? 0.0:((PetscReal)(ct+j))/j,max);
941: #endif
942:   return(0);
943: }

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

955:   if (baij->donotstash) {
956:     return(0);
957:   }

959:   /* make sure all processors are either in INSERTMODE or ADDMODE */
960:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
961:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
962:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
963:   }
964:   mat->insertmode = addv; /* in case this processor had no cache */

966:   MatStashScatterBegin_Private(&mat->stash,mat->rmap.range);
967:   MatStashScatterBegin_Private(&mat->bstash,baij->rangebs);
968:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
969:   PetscInfo2(0,"Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
970:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
971:   PetscInfo2(0,"Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
972:   return(0);
973: }

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

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

996:       for (i=0; i<n;) {
997:         /* Now identify the consecutive vals belonging to the same row */
998:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
999:         if (j < n) ncols = j-i;
1000:         else       ncols = n-i;
1001:         /* Now assemble all these values with a single function call */
1002:         MatSetValues_MPIBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i,addv);
1003:         i = j;
1004:       }
1005:     }
1006:     MatStashScatterEnd_Private(&mat->stash);
1007:     /* Now process the block-stash. Since the values are stashed column-oriented,
1008:        set the roworiented flag to column oriented, and after MatSetValues() 
1009:        restore the original flags */
1010:     r1 = baij->roworiented;
1011:     r2 = a->roworiented;
1012:     r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;
1013:     baij->roworiented = PETSC_FALSE;
1014:     a->roworiented    = PETSC_FALSE;
1015:     (((Mat_SeqBAIJ*)baij->B->data))->roworiented    = PETSC_FALSE; /* b->roworiented */
1016:     while (1) {
1017:       MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
1018:       if (!flg) break;
1019: 
1020:       for (i=0; i<n;) {
1021:         /* Now identify the consecutive vals belonging to the same row */
1022:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
1023:         if (j < n) ncols = j-i;
1024:         else       ncols = n-i;
1025:         MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
1026:         i = j;
1027:       }
1028:     }
1029:     MatStashScatterEnd_Private(&mat->bstash);
1030:     baij->roworiented = r1;
1031:     a->roworiented    = r2;
1032:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented    = r3; /* b->roworiented */
1033:   }
1034: 
1035:   MatAssemblyBegin(baij->A,mode);
1036:   MatAssemblyEnd(baij->A,mode);

1038:   /* determine if any processor has disassembled, if so we must 
1039:      also disassemble ourselfs, in order that we may reassemble. */
1040:   /*
1041:      if nonzero structure of submatrix B cannot change then we know that
1042:      no processor disassembled thus we can skip this stuff
1043:   */
1044:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
1045:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
1046:     if (mat->was_assembled && !other_disassembled) {
1047:       DisAssemble_MPIBAIJ(mat);
1048:     }
1049:   }

1051:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
1052:     MatSetUpMultiply_MPIBAIJ(mat);
1053:   }
1054:   ((Mat_SeqBAIJ*)baij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
1055:   MatAssemblyBegin(baij->B,mode);
1056:   MatAssemblyEnd(baij->B,mode);
1057: 
1058: #if defined(PETSC_USE_INFO)
1059:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
1060:     PetscInfo1(0,"Average Hash Table Search in MatSetValues = %5.2f\n",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
1061:     baij->ht_total_ct  = 0;
1062:     baij->ht_insert_ct = 0;
1063:   }
1064: #endif
1065:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
1066:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);
1067:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
1068:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
1069:   }

1071:   PetscFree(baij->rowvalues);
1072:   baij->rowvalues = 0;
1073:   return(0);
1074: }

1078: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1079: {
1080:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
1081:   PetscErrorCode    ierr;
1082:   PetscMPIInt       size = baij->size,rank = baij->rank;
1083:   PetscInt          bs = mat->rmap.bs;
1084:   PetscTruth        iascii,isdraw;
1085:   PetscViewer       sviewer;
1086:   PetscViewerFormat format;

1089:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1090:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1091:   if (iascii) {
1092:     PetscViewerGetFormat(viewer,&format);
1093:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1094:       MatInfo info;
1095:       MPI_Comm_rank(mat->comm,&rank);
1096:       MatGetInfo(mat,MAT_LOCAL,&info);
1097:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",
1098:               rank,mat->rmap.N,(PetscInt)info.nz_used*bs,(PetscInt)info.nz_allocated*bs,
1099:               mat->rmap.bs,(PetscInt)info.memory);
1100:       MatGetInfo(baij->A,MAT_LOCAL,&info);
1101:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
1102:       MatGetInfo(baij->B,MAT_LOCAL,&info);
1103:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
1104:       PetscViewerFlush(viewer);
1105:       PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
1106:       VecScatterView(baij->Mvctx,viewer);
1107:       return(0);
1108:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1109:       PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
1110:       return(0);
1111:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1112:       return(0);
1113:     }
1114:   }

1116:   if (isdraw) {
1117:     PetscDraw       draw;
1118:     PetscTruth isnull;
1119:     PetscViewerDrawGetDraw(viewer,0,&draw);
1120:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1121:   }

1123:   if (size == 1) {
1124:     PetscObjectSetName((PetscObject)baij->A,mat->name);
1125:     MatView(baij->A,viewer);
1126:   } else {
1127:     /* assemble the entire matrix onto first processor. */
1128:     Mat         A;
1129:     Mat_SeqBAIJ *Aloc;
1130:     PetscInt    M = mat->rmap.N,N = mat->cmap.N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
1131:     MatScalar   *a;

1133:     /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1134:     /* Perhaps this should be the type of mat? */
1135:     MatCreate(mat->comm,&A);
1136:     if (!rank) {
1137:       MatSetSizes(A,M,N,M,N);
1138:     } else {
1139:       MatSetSizes(A,0,0,M,N);
1140:     }
1141:     MatSetType(A,MATMPIBAIJ);
1142:     MatMPIBAIJSetPreallocation(A,mat->rmap.bs,0,PETSC_NULL,0,PETSC_NULL);
1143:     PetscLogObjectParent(mat,A);

1145:     /* copy over the A part */
1146:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
1147:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1148:     PetscMalloc(bs*sizeof(PetscInt),&rvals);

1150:     for (i=0; i<mbs; i++) {
1151:       rvals[0] = bs*(baij->rstartbs + i);
1152:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1153:       for (j=ai[i]; j<ai[i+1]; j++) {
1154:         col = (baij->cstartbs+aj[j])*bs;
1155:         for (k=0; k<bs; k++) {
1156:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1157:           col++; a += bs;
1158:         }
1159:       }
1160:     }
1161:     /* copy over the B part */
1162:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
1163:     ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1164:     for (i=0; i<mbs; i++) {
1165:       rvals[0] = bs*(baij->rstartbs + i);
1166:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1167:       for (j=ai[i]; j<ai[i+1]; j++) {
1168:         col = baij->garray[aj[j]]*bs;
1169:         for (k=0; k<bs; k++) {
1170:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1171:           col++; a += bs;
1172:         }
1173:       }
1174:     }
1175:     PetscFree(rvals);
1176:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1177:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1178:     /* 
1179:        Everyone has to call to draw the matrix since the graphics waits are
1180:        synchronized across all processors that share the PetscDraw object
1181:     */
1182:     PetscViewerGetSingleton(viewer,&sviewer);
1183:     if (!rank) {
1184:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,mat->name);
1185:       MatView(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1186:     }
1187:     PetscViewerRestoreSingleton(viewer,&sviewer);
1188:     MatDestroy(A);
1189:   }
1190:   return(0);
1191: }

1195: PetscErrorCode MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1196: {
1198:   PetscTruth     iascii,isdraw,issocket,isbinary;

1201:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1202:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1203:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1204:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1205:   if (iascii || isdraw || issocket || isbinary) {
1206:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1207:   } else {
1208:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIBAIJ matrices",((PetscObject)viewer)->type_name);
1209:   }
1210:   return(0);
1211: }

1215: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
1216: {
1217:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;

1221: #if defined(PETSC_USE_LOG)
1222:   PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap.N,mat->cmap.N);
1223: #endif
1224:   MatStashDestroy_Private(&mat->stash);
1225:   MatStashDestroy_Private(&mat->bstash);
1226:   MatDestroy(baij->A);
1227:   MatDestroy(baij->B);
1228: #if defined (PETSC_USE_CTABLE)
1229:   if (baij->colmap) {PetscTableDestroy(baij->colmap);}
1230: #else
1231:   PetscFree(baij->colmap);
1232: #endif
1233:   PetscFree(baij->garray);
1234:   if (baij->lvec)   {VecDestroy(baij->lvec);}
1235:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
1236:   PetscFree(baij->rowvalues);
1237:   PetscFree(baij->barray);
1238:   PetscFree(baij->hd);
1239: #if defined(PETSC_USE_MAT_SINGLE)
1240:   PetscFree(baij->setvaluescopy);
1241: #endif
1242:   PetscFree(baij->rangebs);
1243:   PetscFree(baij);

1245:   PetscObjectChangeTypeName((PetscObject)mat,0);
1246:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
1247:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
1248:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
1249:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C","",PETSC_NULL);
1250:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C","",PETSC_NULL);
1251:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
1252:   PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C","",PETSC_NULL);
1253:   return(0);
1254: }

1258: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1259: {
1260:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1262:   PetscInt       nt;

1265:   VecGetLocalSize(xx,&nt);
1266:   if (nt != A->cmap.n) {
1267:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1268:   }
1269:   VecGetLocalSize(yy,&nt);
1270:   if (nt != A->rmap.n) {
1271:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1272:   }
1273:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1274:   (*a->A->ops->mult)(a->A,xx,yy);
1275:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1276:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1277:   return(0);
1278: }

1282: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1283: {
1284:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1288:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1289:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1290:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1291:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1292:   return(0);
1293: }

1297: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1298: {
1299:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1301:   PetscTruth     merged;

1304:   VecScatterGetMerged(a->Mvctx,&merged);
1305:   /* do nondiagonal part */
1306:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1307:   if (!merged) {
1308:     /* send it on its way */
1309:     VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1310:     /* do local part */
1311:     (*a->A->ops->multtranspose)(a->A,xx,yy);
1312:     /* receive remote parts: note this assumes the values are not actually */
1313:     /* inserted in yy until the next line */
1314:     VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1315:   } else {
1316:     /* do local part */
1317:     (*a->A->ops->multtranspose)(a->A,xx,yy);
1318:     /* send it on its way */
1319:     VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1320:     /* values actually were received in the Begin() but we need to call this nop */
1321:     VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1322:   }
1323:   return(0);
1324: }

1328: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1329: {
1330:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1334:   /* do nondiagonal part */
1335:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1336:   /* send it on its way */
1337:   VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1338:   /* do local part */
1339:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1340:   /* receive remote parts: note this assumes the values are not actually */
1341:   /* inserted in yy until the next line, which is true for my implementation*/
1342:   /* but is not perhaps always true. */
1343:   VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1344:   return(0);
1345: }

1347: /*
1348:   This only works correctly for square matrices where the subblock A->A is the 
1349:    diagonal block
1350: */
1353: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1354: {
1355:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1359:   if (A->rmap.N != A->cmap.N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1360:   MatGetDiagonal(a->A,v);
1361:   return(0);
1362: }

1366: PetscErrorCode MatScale_MPIBAIJ(Mat A,PetscScalar aa)
1367: {
1368:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1372:   MatScale(a->A,aa);
1373:   MatScale(a->B,aa);
1374:   return(0);
1375: }

1379: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1380: {
1381:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
1382:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1384:   PetscInt       bs = matin->rmap.bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
1385:   PetscInt       nztot,nzA,nzB,lrow,brstart = matin->rmap.rstart,brend = matin->rmap.rend;
1386:   PetscInt       *cmap,*idx_p,cstart = mat->cstartbs;

1389:   if (mat->getrowactive) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1390:   mat->getrowactive = PETSC_TRUE;

1392:   if (!mat->rowvalues && (idx || v)) {
1393:     /*
1394:         allocate enough space to hold information from the longest row.
1395:     */
1396:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1397:     PetscInt     max = 1,mbs = mat->mbs,tmp;
1398:     for (i=0; i<mbs; i++) {
1399:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1400:       if (max < tmp) { max = tmp; }
1401:     }
1402:     PetscMalloc(max*bs2*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1403:     mat->rowindices = (PetscInt*)(mat->rowvalues + max*bs2);
1404:   }
1405: 
1406:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1407:   lrow = row - brstart;

1409:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1410:   if (!v)   {pvA = 0; pvB = 0;}
1411:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1412:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1413:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1414:   nztot = nzA + nzB;

1416:   cmap  = mat->garray;
1417:   if (v  || idx) {
1418:     if (nztot) {
1419:       /* Sort by increasing column numbers, assuming A and B already sorted */
1420:       PetscInt imark = -1;
1421:       if (v) {
1422:         *v = v_p = mat->rowvalues;
1423:         for (i=0; i<nzB; i++) {
1424:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1425:           else break;
1426:         }
1427:         imark = i;
1428:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1429:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1430:       }
1431:       if (idx) {
1432:         *idx = idx_p = mat->rowindices;
1433:         if (imark > -1) {
1434:           for (i=0; i<imark; i++) {
1435:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1436:           }
1437:         } else {
1438:           for (i=0; i<nzB; i++) {
1439:             if (cmap[cworkB[i]/bs] < cstart)
1440:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1441:             else break;
1442:           }
1443:           imark = i;
1444:         }
1445:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1446:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1447:       }
1448:     } else {
1449:       if (idx) *idx = 0;
1450:       if (v)   *v   = 0;
1451:     }
1452:   }
1453:   *nz = nztot;
1454:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1455:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1456:   return(0);
1457: }

1461: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1462: {
1463:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1466:   if (!baij->getrowactive) {
1467:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1468:   }
1469:   baij->getrowactive = PETSC_FALSE;
1470:   return(0);
1471: }

1475: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1476: {
1477:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;

1481:   MatZeroEntries(l->A);
1482:   MatZeroEntries(l->B);
1483:   return(0);
1484: }

1488: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1489: {
1490:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)matin->data;
1491:   Mat            A = a->A,B = a->B;
1493:   PetscReal      isend[5],irecv[5];

1496:   info->block_size     = (PetscReal)matin->rmap.bs;
1497:   MatGetInfo(A,MAT_LOCAL,info);
1498:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1499:   isend[3] = info->memory;  isend[4] = info->mallocs;
1500:   MatGetInfo(B,MAT_LOCAL,info);
1501:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1502:   isend[3] += info->memory;  isend[4] += info->mallocs;
1503:   if (flag == MAT_LOCAL) {
1504:     info->nz_used      = isend[0];
1505:     info->nz_allocated = isend[1];
1506:     info->nz_unneeded  = isend[2];
1507:     info->memory       = isend[3];
1508:     info->mallocs      = isend[4];
1509:   } else if (flag == MAT_GLOBAL_MAX) {
1510:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1511:     info->nz_used      = irecv[0];
1512:     info->nz_allocated = irecv[1];
1513:     info->nz_unneeded  = irecv[2];
1514:     info->memory       = irecv[3];
1515:     info->mallocs      = irecv[4];
1516:   } else if (flag == MAT_GLOBAL_SUM) {
1517:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1518:     info->nz_used      = irecv[0];
1519:     info->nz_allocated = irecv[1];
1520:     info->nz_unneeded  = irecv[2];
1521:     info->memory       = irecv[3];
1522:     info->mallocs      = irecv[4];
1523:   } else {
1524:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1525:   }
1526:   info->rows_global       = (PetscReal)A->rmap.N;
1527:   info->columns_global    = (PetscReal)A->cmap.N;
1528:   info->rows_local        = (PetscReal)A->rmap.N;
1529:   info->columns_local     = (PetscReal)A->cmap.N;
1530:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1531:   info->fill_ratio_needed = 0;
1532:   info->factor_mallocs    = 0;
1533:   return(0);
1534: }

1538: PetscErrorCode MatSetOption_MPIBAIJ(Mat A,MatOption op)
1539: {
1540:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1544:   switch (op) {
1545:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1546:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1547:   case MAT_COLUMNS_UNSORTED:
1548:   case MAT_COLUMNS_SORTED:
1549:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1550:   case MAT_KEEP_ZEROED_ROWS:
1551:   case MAT_NEW_NONZERO_LOCATION_ERR:
1552:     MatSetOption(a->A,op);
1553:     MatSetOption(a->B,op);
1554:     break;
1555:   case MAT_ROW_ORIENTED:
1556:     a->roworiented = PETSC_TRUE;
1557:     MatSetOption(a->A,op);
1558:     MatSetOption(a->B,op);
1559:     break;
1560:   case MAT_ROWS_SORTED:
1561:   case MAT_ROWS_UNSORTED:
1562:   case MAT_YES_NEW_DIAGONALS:
1563:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1564:     break;
1565:   case MAT_COLUMN_ORIENTED:
1566:     a->roworiented = PETSC_FALSE;
1567:     MatSetOption(a->A,op);
1568:     MatSetOption(a->B,op);
1569:     break;
1570:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1571:     a->donotstash = PETSC_TRUE;
1572:     break;
1573:   case MAT_NO_NEW_DIAGONALS:
1574:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1575:   case MAT_USE_HASH_TABLE:
1576:     a->ht_flag = PETSC_TRUE;
1577:     break;
1578:   case MAT_SYMMETRIC:
1579:   case MAT_STRUCTURALLY_SYMMETRIC:
1580:   case MAT_HERMITIAN:
1581:   case MAT_SYMMETRY_ETERNAL:
1582:     MatSetOption(a->A,op);
1583:     break;
1584:   case MAT_NOT_SYMMETRIC:
1585:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1586:   case MAT_NOT_HERMITIAN:
1587:   case MAT_NOT_SYMMETRY_ETERNAL:
1588:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1589:     break;
1590:   default:
1591:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
1592:   }
1593:   return(0);
1594: }

1598: PetscErrorCode MatTranspose_MPIBAIJ(Mat A,Mat *matout)
1599: {
1600:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)A->data;
1601:   Mat_SeqBAIJ    *Aloc;
1602:   Mat            B;
1604:   PetscInt       M=A->rmap.N,N=A->cmap.N,*ai,*aj,i,*rvals,j,k,col;
1605:   PetscInt       bs=A->rmap.bs,mbs=baij->mbs;
1606:   MatScalar      *a;
1607: 
1609:   if (!matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1610:   MatCreate(A->comm,&B);
1611:   MatSetSizes(B,A->cmap.n,A->rmap.n,N,M);
1612:   MatSetType(B,A->type_name);
1613:   MatMPIBAIJSetPreallocation(B,A->rmap.bs,0,PETSC_NULL,0,PETSC_NULL);
1614: 
1615:   /* copy over the A part */
1616:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1617:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1618:   PetscMalloc(bs*sizeof(PetscInt),&rvals);
1619: 
1620:   for (i=0; i<mbs; i++) {
1621:     rvals[0] = bs*(baij->rstartbs + i);
1622:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1623:     for (j=ai[i]; j<ai[i+1]; j++) {
1624:       col = (baij->cstartbs+aj[j])*bs;
1625:       for (k=0; k<bs; k++) {
1626:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1627:         col++; a += bs;
1628:       }
1629:     }
1630:   }
1631:   /* copy over the B part */
1632:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1633:   ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1634:   for (i=0; i<mbs; i++) {
1635:     rvals[0] = bs*(baij->rstartbs + i);
1636:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1637:     for (j=ai[i]; j<ai[i+1]; j++) {
1638:       col = baij->garray[aj[j]]*bs;
1639:       for (k=0; k<bs; k++) {
1640:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1641:         col++; a += bs;
1642:       }
1643:     }
1644:   }
1645:   PetscFree(rvals);
1646:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1647:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1648: 
1649:   if (matout) {
1650:     *matout = B;
1651:   } else {
1652:     MatHeaderCopy(A,B);
1653:   }
1654:   return(0);
1655: }

1659: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1660: {
1661:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
1662:   Mat            a = baij->A,b = baij->B;
1664:   PetscInt       s1,s2,s3;

1667:   MatGetLocalSize(mat,&s2,&s3);
1668:   if (rr) {
1669:     VecGetLocalSize(rr,&s1);
1670:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1671:     /* Overlap communication with computation. */
1672:     VecScatterBegin(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1673:   }
1674:   if (ll) {
1675:     VecGetLocalSize(ll,&s1);
1676:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1677:     (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1678:   }
1679:   /* scale  the diagonal block */
1680:   (*a->ops->diagonalscale)(a,ll,rr);

1682:   if (rr) {
1683:     /* Do a scatter end and then right scale the off-diagonal block */
1684:     VecScatterEnd(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1685:     (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1686:   }
1687: 
1688:   return(0);
1689: }

1693: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1694: {
1695:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;
1697:   PetscMPIInt    imdex,size = l->size,n,rank = l->rank;
1698:   PetscInt       i,*owners = A->rmap.range;
1699:   PetscInt       *nprocs,j,idx,nsends,row;
1700:   PetscInt       nmax,*svalues,*starts,*owner,nrecvs;
1701:   PetscInt       *rvalues,tag = A->tag,count,base,slen,*source,lastidx = -1;
1702:   PetscInt       *lens,*lrows,*values,rstart_bs=A->rmap.rstart;
1703:   MPI_Comm       comm = A->comm;
1704:   MPI_Request    *send_waits,*recv_waits;
1705:   MPI_Status     recv_status,*send_status;
1706: #if defined(PETSC_DEBUG)
1707:   PetscTruth     found = PETSC_FALSE;
1708: #endif
1709: 
1711:   /*  first count number of contributors to each processor */
1712:   PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
1713:   PetscMemzero(nprocs,2*size*sizeof(PetscInt));
1714:   PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
1715:   j = 0;
1716:   for (i=0; i<N; i++) {
1717:     if (lastidx > (idx = rows[i])) j = 0;
1718:     lastidx = idx;
1719:     for (; j<size; j++) {
1720:       if (idx >= owners[j] && idx < owners[j+1]) {
1721:         nprocs[2*j]++;
1722:         nprocs[2*j+1] = 1;
1723:         owner[i] = j;
1724: #if defined(PETSC_DEBUG)
1725:         found = PETSC_TRUE;
1726: #endif
1727:         break;
1728:       }
1729:     }
1730: #if defined(PETSC_DEBUG)
1731:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1732:     found = PETSC_FALSE;
1733: #endif
1734:   }
1735:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
1736: 
1737:   /* inform other processors of number of messages and max length*/
1738:   PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
1739: 
1740:   /* post receives:   */
1741:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
1742:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1743:   for (i=0; i<nrecvs; i++) {
1744:     MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1745:   }
1746: 
1747:   /* do sends:
1748:      1) starts[i] gives the starting index in svalues for stuff going to 
1749:      the ith processor
1750:   */
1751:   PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
1752:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1753:   PetscMalloc((size+1)*sizeof(PetscInt),&starts);
1754:   starts[0]  = 0;
1755:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1756:   for (i=0; i<N; i++) {
1757:     svalues[starts[owner[i]]++] = rows[i];
1758:   }
1759: 
1760:   starts[0] = 0;
1761:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1762:   count = 0;
1763:   for (i=0; i<size; i++) {
1764:     if (nprocs[2*i+1]) {
1765:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
1766:     }
1767:   }
1768:   PetscFree(starts);

1770:   base = owners[rank];
1771: 
1772:   /*  wait on receives */
1773:   PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
1774:   source = lens + nrecvs;
1775:   count  = nrecvs; slen = 0;
1776:   while (count) {
1777:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1778:     /* unpack receives into our local space */
1779:     MPI_Get_count(&recv_status,MPIU_INT,&n);
1780:     source[imdex]  = recv_status.MPI_SOURCE;
1781:     lens[imdex]    = n;
1782:     slen          += n;
1783:     count--;
1784:   }
1785:   PetscFree(recv_waits);
1786: 
1787:   /* move the data into the send scatter */
1788:   PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
1789:   count = 0;
1790:   for (i=0; i<nrecvs; i++) {
1791:     values = rvalues + i*nmax;
1792:     for (j=0; j<lens[i]; j++) {
1793:       lrows[count++] = values[j] - base;
1794:     }
1795:   }
1796:   PetscFree(rvalues);
1797:   PetscFree(lens);
1798:   PetscFree(owner);
1799:   PetscFree(nprocs);
1800: 
1801:   /* actually zap the local rows */
1802:   /*
1803:         Zero the required rows. If the "diagonal block" of the matrix
1804:      is square and the user wishes to set the diagonal we use separate
1805:      code so that MatSetValues() is not called for each diagonal allocating
1806:      new memory, thus calling lots of mallocs and slowing things down.

1808:        Contributed by: Matthew Knepley
1809:   */
1810:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1811:   MatZeroRows_SeqBAIJ(l->B,slen,lrows,0.0);
1812:   if ((diag != 0.0) && (l->A->rmap.N == l->A->cmap.N)) {
1813:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,diag);
1814:   } else if (diag != 0.0) {
1815:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0);
1816:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) {
1817:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1818: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1819:     }
1820:     for (i=0; i<slen; i++) {
1821:       row  = lrows[i] + rstart_bs;
1822:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
1823:     }
1824:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1825:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1826:   } else {
1827:     MatZeroRows_SeqBAIJ(l->A,slen,lrows,0.0);
1828:   }

1830:   PetscFree(lrows);

1832:   /* wait on sends */
1833:   if (nsends) {
1834:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1835:     MPI_Waitall(nsends,send_waits,send_status);
1836:     PetscFree(send_status);
1837:   }
1838:   PetscFree(send_waits);
1839:   PetscFree(svalues);

1841:   return(0);
1842: }

1846: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1847: {
1848:   Mat_MPIBAIJ    *a   = (Mat_MPIBAIJ*)A->data;

1852:   MatSetUnfactored(a->A);
1853:   return(0);
1854: }

1856: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat *);

1860: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscTruth *flag)
1861: {
1862:   Mat_MPIBAIJ    *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1863:   Mat            a,b,c,d;
1864:   PetscTruth     flg;

1868:   a = matA->A; b = matA->B;
1869:   c = matB->A; d = matB->B;

1871:   MatEqual(a,c,&flg);
1872:   if (flg) {
1873:     MatEqual(b,d,&flg);
1874:   }
1875:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1876:   return(0);
1877: }

1881: PetscErrorCode MatCopy_MPIBAIJ(Mat A,Mat B,MatStructure str)
1882: {
1884:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ *)A->data;
1885:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ *)B->data;

1888:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1889:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1890:     MatCopy_Basic(A,B,str);
1891:   } else {
1892:     MatCopy(a->A,b->A,str);
1893:     MatCopy(a->B,b->B,str);
1894:   }
1895:   return(0);
1896: }

1900: PetscErrorCode MatSetUpPreallocation_MPIBAIJ(Mat A)
1901: {

1905:    MatMPIBAIJSetPreallocation(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1906:   return(0);
1907: }

1909:  #include petscblaslapack.h
1912: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1913: {
1915:   Mat_MPIBAIJ    *xx=(Mat_MPIBAIJ *)X->data,*yy=(Mat_MPIBAIJ *)Y->data;
1916:   PetscBLASInt   bnz,one=1;
1917:   Mat_SeqBAIJ    *x,*y;

1920:   if (str == SAME_NONZERO_PATTERN) {
1921:     PetscScalar alpha = a;
1922:     x = (Mat_SeqBAIJ *)xx->A->data;
1923:     y = (Mat_SeqBAIJ *)yy->A->data;
1924:     bnz = (PetscBLASInt)x->nz;
1925:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1926:     x = (Mat_SeqBAIJ *)xx->B->data;
1927:     y = (Mat_SeqBAIJ *)yy->B->data;
1928:     bnz = (PetscBLASInt)x->nz;
1929:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1930:   } else {
1931:     MatAXPY_Basic(Y,a,X,str);
1932:   }
1933:   return(0);
1934: }

1938: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1939: {
1940:   Mat_MPIBAIJ   *a = (Mat_MPIBAIJ*)A->data;

1944:   MatRealPart(a->A);
1945:   MatRealPart(a->B);
1946:   return(0);
1947: }

1951: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1952: {
1953:   Mat_MPIBAIJ   *a = (Mat_MPIBAIJ*)A->data;

1957:   MatImaginaryPart(a->A);
1958:   MatImaginaryPart(a->B);
1959:   return(0);
1960: }

1962: /* -------------------------------------------------------------------*/
1963: static struct _MatOps MatOps_Values = {
1964:        MatSetValues_MPIBAIJ,
1965:        MatGetRow_MPIBAIJ,
1966:        MatRestoreRow_MPIBAIJ,
1967:        MatMult_MPIBAIJ,
1968: /* 4*/ MatMultAdd_MPIBAIJ,
1969:        MatMultTranspose_MPIBAIJ,
1970:        MatMultTransposeAdd_MPIBAIJ,
1971:        0,
1972:        0,
1973:        0,
1974: /*10*/ 0,
1975:        0,
1976:        0,
1977:        0,
1978:        MatTranspose_MPIBAIJ,
1979: /*15*/ MatGetInfo_MPIBAIJ,
1980:        MatEqual_MPIBAIJ,
1981:        MatGetDiagonal_MPIBAIJ,
1982:        MatDiagonalScale_MPIBAIJ,
1983:        MatNorm_MPIBAIJ,
1984: /*20*/ MatAssemblyBegin_MPIBAIJ,
1985:        MatAssemblyEnd_MPIBAIJ,
1986:        0,
1987:        MatSetOption_MPIBAIJ,
1988:        MatZeroEntries_MPIBAIJ,
1989: /*25*/ MatZeroRows_MPIBAIJ,
1990:        0,
1991:        0,
1992:        0,
1993:        0,
1994: /*30*/ MatSetUpPreallocation_MPIBAIJ,
1995:        0,
1996:        0,
1997:        0,
1998:        0,
1999: /*35*/ MatDuplicate_MPIBAIJ,
2000:        0,
2001:        0,
2002:        0,
2003:        0,
2004: /*40*/ MatAXPY_MPIBAIJ,
2005:        MatGetSubMatrices_MPIBAIJ,
2006:        MatIncreaseOverlap_MPIBAIJ,
2007:        MatGetValues_MPIBAIJ,
2008:        MatCopy_MPIBAIJ,
2009: /*45*/ 0,
2010:        MatScale_MPIBAIJ,
2011:        0,
2012:        0,
2013:        0,
2014: /*50*/ 0,
2015:        0,
2016:        0,
2017:        0,
2018:        0,
2019: /*55*/ 0,
2020:        0,
2021:        MatSetUnfactored_MPIBAIJ,
2022:        0,
2023:        MatSetValuesBlocked_MPIBAIJ,
2024: /*60*/ 0,
2025:        MatDestroy_MPIBAIJ,
2026:        MatView_MPIBAIJ,
2027:        0,
2028:        0,
2029: /*65*/ 0,
2030:        0,
2031:        0,
2032:        0,
2033:        0,
2034: /*70*/ MatGetRowMaxAbs_MPIBAIJ,
2035:        0,
2036:        0,
2037:        0,
2038:        0,
2039: /*75*/ 0,
2040:        0,
2041:        0,
2042:        0,
2043:        0,
2044: /*80*/ 0,
2045:        0,
2046:        0,
2047:        0,
2048:        MatLoad_MPIBAIJ,
2049: /*85*/ 0,
2050:        0,
2051:        0,
2052:        0,
2053:        0,
2054: /*90*/ 0,
2055:        0,
2056:        0,
2057:        0,
2058:        0,
2059: /*95*/ 0,
2060:        0,
2061:        0,
2062:        0,
2063:        0,
2064: /*100*/0,
2065:        0,
2066:        0,
2067:        0,
2068:        0,
2069: /*105*/0,
2070:        MatRealPart_MPIBAIJ,
2071:        MatImaginaryPart_MPIBAIJ};


2077: PetscErrorCode  MatGetDiagonalBlock_MPIBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
2078: {
2080:   *a      = ((Mat_MPIBAIJ *)A->data)->A;
2081:   *iscopy = PETSC_FALSE;
2082:   return(0);
2083: }


2092: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
2093: {
2094:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)B->data;
2095:   PetscInt       m = B->rmap.n/bs,cstart = baij->cstartbs, cend = baij->cendbs,j,nnz,i,d;
2096:   PetscInt       *d_nnz,*o_nnz,nnz_max = 0,rstart = baij->rstartbs,ii;
2097:   const PetscInt *JJ;
2098:   PetscScalar    *values;

2102: #if defined(PETSC_OPT_g)
2103:   if (Ii[0]) SETERRQ1(PETSC_ERR_ARG_RANGE,"Ii[0] must be 0 it is %D",Ii[0]);
2104: #endif
2105:   PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
2106:   o_nnz = d_nnz + m;

2108:   for (i=0; i<m; i++) {
2109:     nnz     = Ii[i+1]- Ii[i];
2110:     JJ      = J + Ii[i];
2111:     nnz_max = PetscMax(nnz_max,nnz);
2112: #if defined(PETSC_OPT_g)
2113:     if (nnz < 0) SETERRQ1(PETSC_ERR_ARG_RANGE,"Local row %D has a negative %D number of columns",i,nnz);
2114: #endif
2115:     for (j=0; j<nnz; j++) {
2116:       if (*JJ >= cstart) break;
2117:       JJ++;
2118:     }
2119:     d = 0;
2120:     for (; j<nnz; j++) {
2121:       if (*JJ++ >= cend) break;
2122:       d++;
2123:     }
2124:     d_nnz[i] = d;
2125:     o_nnz[i] = nnz - d;
2126:   }
2127:   MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
2128:   PetscFree(d_nnz);

2130:   if (v) values = (PetscScalar*)v;
2131:   else {
2132:     PetscMalloc(bs*bs*(nnz_max+1)*sizeof(PetscScalar),&values);
2133:     PetscMemzero(values,bs*bs*nnz_max*sizeof(PetscScalar));
2134:   }

2136:   MatSetOption(B,MAT_COLUMNS_SORTED);
2137:   for (i=0; i<m; i++) {
2138:     ii   = i + rstart;
2139:     nnz  = Ii[i+1]- Ii[i];
2140:     MatSetValuesBlocked_MPIBAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
2141:   }
2142:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2143:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2144:   MatSetOption(B,MAT_COLUMNS_UNSORTED);

2146:   if (!v) {
2147:     PetscFree(values);
2148:   }
2149:   return(0);
2150: }

2154: /*@C
2155:    MatMPIBAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
2156:    (the default parallel PETSc format).  

2158:    Collective on MPI_Comm

2160:    Input Parameters:
2161: +  A - the matrix 
2162: .  i - the indices into j for the start of each local row (starts with zero)
2163: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
2164: -  v - optional values in the matrix

2166:    Level: developer

2168: .keywords: matrix, aij, compressed row, sparse, parallel

2170: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ
2171: @*/
2172: PetscErrorCode  MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2173: {
2174:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]);

2177:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",(void (**)(void))&f);
2178:   if (f) {
2179:     (*f)(B,bs,i,j,v);
2180:   }
2181:   return(0);
2182: }

2187: PetscErrorCode  MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,PetscInt *d_nnz,PetscInt o_nz,PetscInt *o_nnz)
2188: {
2189:   Mat_MPIBAIJ    *b;
2191:   PetscInt       i;

2194:   B->preallocated = PETSC_TRUE;
2195:   PetscOptionsBegin(B->comm,B->prefix,"Options for MPIBAIJ matrix","Mat");
2196:     PetscOptionsInt("-mat_block_size","Set the blocksize used to store the matrix","MatMPIBAIJSetPreallocation",bs,&bs,PETSC_NULL);
2197:   PetscOptionsEnd();

2199:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
2200:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2201:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2202:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
2203:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);
2204: 
2205:   B->rmap.bs  = bs;
2206:   B->cmap.bs  = bs;
2207:   PetscMapSetUp(&B->rmap);
2208:   PetscMapSetUp(&B->cmap);

2210:   if (d_nnz) {
2211:     for (i=0; i<B->rmap.n/bs; i++) {
2212:       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]);
2213:     }
2214:   }
2215:   if (o_nnz) {
2216:     for (i=0; i<B->rmap.n/bs; i++) {
2217:       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]);
2218:     }
2219:   }

2221:   b = (Mat_MPIBAIJ*)B->data;
2222:   b->bs2 = bs*bs;
2223:   b->mbs = B->rmap.n/bs;
2224:   b->nbs = B->cmap.n/bs;
2225:   b->Mbs = B->rmap.N/bs;
2226:   b->Nbs = B->cmap.N/bs;

2228:   for (i=0; i<=b->size; i++) {
2229:     b->rangebs[i] = B->rmap.range[i]/bs;
2230:   }
2231:   b->rstartbs = B->rmap.rstart/bs;
2232:   b->rendbs   = B->rmap.rend/bs;
2233:   b->cstartbs = B->cmap.rstart/bs;
2234:   b->cendbs   = B->cmap.rend/bs;

2236:   MatCreate(PETSC_COMM_SELF,&b->A);
2237:   MatSetSizes(b->A,B->rmap.n,B->cmap.n,B->rmap.n,B->cmap.n);
2238:   MatSetType(b->A,MATSEQBAIJ);
2239:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
2240:   PetscLogObjectParent(B,b->A);
2241:   MatCreate(PETSC_COMM_SELF,&b->B);
2242:   MatSetSizes(b->B,B->rmap.n,B->cmap.N,B->rmap.n,B->cmap.N);
2243:   MatSetType(b->B,MATSEQBAIJ);
2244:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
2245:   PetscLogObjectParent(B,b->B);

2247:   MatStashCreate_Private(B->comm,bs,&B->bstash);

2249:   return(0);
2250: }

2254: EXTERN PetscErrorCode  MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2255: EXTERN PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);

2258: /*MC
2259:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

2261:    Options Database Keys:
2262: + -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()
2263: . -mat_block_size <bs> - set the blocksize used to store the matrix
2264: - -mat_use_hash_table <fact>

2266:   Level: beginner

2268: .seealso: MatCreateMPIBAIJ
2269: M*/

2274: PetscErrorCode  MatCreate_MPIBAIJ(Mat B)
2275: {
2276:   Mat_MPIBAIJ    *b;
2278:   PetscTruth     flg;

2281:   PetscNew(Mat_MPIBAIJ,&b);
2282:   B->data = (void*)b;


2285:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2286:   B->mapping    = 0;
2287:   B->factor     = 0;
2288:   B->assembled  = PETSC_FALSE;

2290:   B->insertmode = NOT_SET_VALUES;
2291:   MPI_Comm_rank(B->comm,&b->rank);
2292:   MPI_Comm_size(B->comm,&b->size);

2294:   /* build local table of row and column ownerships */
2295:   PetscMalloc((b->size+1)*sizeof(PetscInt),&b->rangebs);

2297:   /* build cache for off array entries formed */
2298:   MatStashCreate_Private(B->comm,1,&B->stash);
2299:   b->donotstash  = PETSC_FALSE;
2300:   b->colmap      = PETSC_NULL;
2301:   b->garray      = PETSC_NULL;
2302:   b->roworiented = PETSC_TRUE;

2304: #if defined(PETSC_USE_MAT_SINGLE)
2305:   /* stuff for MatSetValues_XXX in single precision */
2306:   b->setvalueslen     = 0;
2307:   b->setvaluescopy    = PETSC_NULL;
2308: #endif

2310:   /* stuff used in block assembly */
2311:   b->barray       = 0;

2313:   /* stuff used for matrix vector multiply */
2314:   b->lvec         = 0;
2315:   b->Mvctx        = 0;

2317:   /* stuff for MatGetRow() */
2318:   b->rowindices   = 0;
2319:   b->rowvalues    = 0;
2320:   b->getrowactive = PETSC_FALSE;

2322:   /* hash table stuff */
2323:   b->ht           = 0;
2324:   b->hd           = 0;
2325:   b->ht_size      = 0;
2326:   b->ht_flag      = PETSC_FALSE;
2327:   b->ht_fact      = 0;
2328:   b->ht_total_ct  = 0;
2329:   b->ht_insert_ct = 0;

2331:   PetscOptionsBegin(B->comm,PETSC_NULL,"Options for loading MPIBAIJ matrix 1","Mat");
2332:     PetscOptionsTruth("-mat_use_hash_table","Use hash table to save memory in constructing matrix","MatSetOption",PETSC_FALSE,&flg,PETSC_NULL);
2333:     if (flg) {
2334:       PetscReal fact = 1.39;
2335:       MatSetOption(B,MAT_USE_HASH_TABLE);
2336:       PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,PETSC_NULL);
2337:       if (fact <= 1.0) fact = 1.39;
2338:       MatMPIBAIJSetHashTableFactor(B,fact);
2339:       PetscInfo1(0,"Hash table Factor used %5.2f\n",fact);
2340:     }
2341:   PetscOptionsEnd();

2343:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2344:                                      "MatStoreValues_MPIBAIJ",
2345:                                      MatStoreValues_MPIBAIJ);
2346:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2347:                                      "MatRetrieveValues_MPIBAIJ",
2348:                                      MatRetrieveValues_MPIBAIJ);
2349:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
2350:                                      "MatGetDiagonalBlock_MPIBAIJ",
2351:                                      MatGetDiagonalBlock_MPIBAIJ);
2352:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocation_C",
2353:                                      "MatMPIBAIJSetPreallocation_MPIBAIJ",
2354:                                      MatMPIBAIJSetPreallocation_MPIBAIJ);
2355:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",
2356:                                      "MatMPIBAIJSetPreallocationCSR_MPIBAIJ",
2357:                                      MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
2358:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
2359:                                      "MatDiagonalScaleLocal_MPIBAIJ",
2360:                                      MatDiagonalScaleLocal_MPIBAIJ);
2361:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetHashTableFactor_C",
2362:                                      "MatSetHashTableFactor_MPIBAIJ",
2363:                                      MatSetHashTableFactor_MPIBAIJ);
2364:   PetscObjectChangeTypeName((PetscObject)B,MATMPIBAIJ);
2365:   return(0);
2366: }

2369: /*MC
2370:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

2372:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
2373:    and MATMPIBAIJ otherwise.

2375:    Options Database Keys:
2376: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

2378:   Level: beginner

2380: .seealso: MatCreateMPIBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2381: M*/

2386: PetscErrorCode  MatCreate_BAIJ(Mat A)
2387: {
2389:   PetscMPIInt    size;

2392:   MPI_Comm_size(A->comm,&size);
2393:   if (size == 1) {
2394:     MatSetType(A,MATSEQBAIJ);
2395:   } else {
2396:     MatSetType(A,MATMPIBAIJ);
2397:   }
2398:   return(0);
2399: }

2404: /*@C
2405:    MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
2406:    (block compressed row).  For good matrix assembly performance
2407:    the user should preallocate the matrix storage by setting the parameters 
2408:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2409:    performance can be increased by more than a factor of 50.

2411:    Collective on Mat

2413:    Input Parameters:
2414: +  A - the matrix 
2415: .  bs   - size of blockk
2416: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
2417:            submatrix  (same for all local rows)
2418: .  d_nnz - array containing the number of block nonzeros in the various block rows 
2419:            of the in diagonal portion of the local (possibly different for each block
2420:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2421: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
2422:            submatrix (same for all local rows).
2423: -  o_nnz - array containing the number of nonzeros in the various block rows of the
2424:            off-diagonal portion of the local submatrix (possibly different for
2425:            each block row) or PETSC_NULL.

2427:    If the *_nnz parameter is given then the *_nz parameter is ignored

2429:    Options Database Keys:
2430: +   -mat_block_size - size of the blocks to use
2431: -   -mat_use_hash_table <fact>

2433:    Notes:
2434:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2435:    than it must be used on all processors that share the object for that argument.

2437:    Storage Information:
2438:    For a square global matrix we define each processor's diagonal portion 
2439:    to be its local rows and the corresponding columns (a square submatrix);  
2440:    each processor's off-diagonal portion encompasses the remainder of the
2441:    local matrix (a rectangular submatrix). 

2443:    The user can specify preallocated storage for the diagonal part of
2444:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2445:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2446:    memory allocation.  Likewise, specify preallocated storage for the
2447:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2449:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2450:    the figure below we depict these three local rows and all columns (0-11).

2452: .vb
2453:            0 1 2 3 4 5 6 7 8 9 10 11
2454:           -------------------
2455:    row 3  |  o o o d d d o o o o o o
2456:    row 4  |  o o o d d d o o o o o o
2457:    row 5  |  o o o d d d o o o o o o
2458:           -------------------
2459: .ve
2460:   
2461:    Thus, any entries in the d locations are stored in the d (diagonal) 
2462:    submatrix, and any entries in the o locations are stored in the
2463:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2464:    stored simply in the MATSEQBAIJ format for compressed row storage.

2466:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2467:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2468:    In general, for PDE problems in which most nonzeros are near the diagonal,
2469:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2470:    or you will get TERRIBLE performance; see the users' manual chapter on
2471:    matrices.

2473:    Level: intermediate

2475: .keywords: matrix, block, aij, compressed row, sparse, parallel

2477: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocationCSR()
2478: @*/
2479: PetscErrorCode  MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2480: {
2481:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

2484:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",(void (**)(void))&f);
2485:   if (f) {
2486:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
2487:   }
2488:   return(0);
2489: }

2493: /*@C
2494:    MatCreateMPIBAIJ - Creates a sparse parallel matrix in block AIJ format
2495:    (block compressed row).  For good matrix assembly performance
2496:    the user should preallocate the matrix storage by setting the parameters 
2497:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2498:    performance can be increased by more than a factor of 50.

2500:    Collective on MPI_Comm

2502:    Input Parameters:
2503: +  comm - MPI communicator
2504: .  bs   - size of blockk
2505: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2506:            This value should be the same as the local size used in creating the 
2507:            y vector for the matrix-vector product y = Ax.
2508: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
2509:            This value should be the same as the local size used in creating the 
2510:            x vector for the matrix-vector product y = Ax.
2511: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2512: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2513: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local 
2514:            submatrix  (same for all local rows)
2515: .  d_nnz - array containing the number of nonzero blocks in the various block rows 
2516:            of the in diagonal portion of the local (possibly different for each block
2517:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2518: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
2519:            submatrix (same for all local rows).
2520: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
2521:            off-diagonal portion of the local submatrix (possibly different for
2522:            each block row) or PETSC_NULL.

2524:    Output Parameter:
2525: .  A - the matrix 

2527:    Options Database Keys:
2528: +   -mat_block_size - size of the blocks to use
2529: -   -mat_use_hash_table <fact>

2531:    Notes:
2532:    If the *_nnz parameter is given then the *_nz parameter is ignored

2534:    A nonzero block is any block that as 1 or more nonzeros in it

2536:    The user MUST specify either the local or global matrix dimensions
2537:    (possibly both).

2539:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2540:    than it must be used on all processors that share the object for that argument.

2542:    Storage Information:
2543:    For a square global matrix we define each processor's diagonal portion 
2544:    to be its local rows and the corresponding columns (a square submatrix);  
2545:    each processor's off-diagonal portion encompasses the remainder of the
2546:    local matrix (a rectangular submatrix). 

2548:    The user can specify preallocated storage for the diagonal part of
2549:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2550:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2551:    memory allocation.  Likewise, specify preallocated storage for the
2552:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2554:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2555:    the figure below we depict these three local rows and all columns (0-11).

2557: .vb
2558:            0 1 2 3 4 5 6 7 8 9 10 11
2559:           -------------------
2560:    row 3  |  o o o d d d o o o o o o
2561:    row 4  |  o o o d d d o o o o o o
2562:    row 5  |  o o o d d d o o o o o o
2563:           -------------------
2564: .ve
2565:   
2566:    Thus, any entries in the d locations are stored in the d (diagonal) 
2567:    submatrix, and any entries in the o locations are stored in the
2568:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2569:    stored simply in the MATSEQBAIJ format for compressed row storage.

2571:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2572:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2573:    In general, for PDE problems in which most nonzeros are near the diagonal,
2574:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2575:    or you will get TERRIBLE performance; see the users' manual chapter on
2576:    matrices.

2578:    Level: intermediate

2580: .keywords: matrix, block, aij, compressed row, sparse, parallel

2582: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
2583: @*/
2584: 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)
2585: {
2587:   PetscMPIInt    size;

2590:   MatCreate(comm,A);
2591:   MatSetSizes(*A,m,n,M,N);
2592:   MPI_Comm_size(comm,&size);
2593:   if (size > 1) {
2594:     MatSetType(*A,MATMPIBAIJ);
2595:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2596:   } else {
2597:     MatSetType(*A,MATSEQBAIJ);
2598:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2599:   }
2600:   return(0);
2601: }

2605: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2606: {
2607:   Mat            mat;
2608:   Mat_MPIBAIJ    *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
2610:   PetscInt       len=0;

2613:   *newmat       = 0;
2614:   MatCreate(matin->comm,&mat);
2615:   MatSetSizes(mat,matin->rmap.n,matin->cmap.n,matin->rmap.N,matin->cmap.N);
2616:   MatSetType(mat,matin->type_name);
2617:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));

2619:   mat->factor       = matin->factor;
2620:   mat->preallocated = PETSC_TRUE;
2621:   mat->assembled    = PETSC_TRUE;
2622:   mat->insertmode   = NOT_SET_VALUES;

2624:   a      = (Mat_MPIBAIJ*)mat->data;
2625:   mat->rmap.bs  = matin->rmap.bs;
2626:   a->bs2   = oldmat->bs2;
2627:   a->mbs   = oldmat->mbs;
2628:   a->nbs   = oldmat->nbs;
2629:   a->Mbs   = oldmat->Mbs;
2630:   a->Nbs   = oldmat->Nbs;
2631: 
2632:   PetscMapCopy(matin->comm,&matin->rmap,&mat->rmap);
2633:   PetscMapCopy(matin->comm,&matin->cmap,&mat->cmap);

2635:   a->size         = oldmat->size;
2636:   a->rank         = oldmat->rank;
2637:   a->donotstash   = oldmat->donotstash;
2638:   a->roworiented  = oldmat->roworiented;
2639:   a->rowindices   = 0;
2640:   a->rowvalues    = 0;
2641:   a->getrowactive = PETSC_FALSE;
2642:   a->barray       = 0;
2643:   a->rstartbs     = oldmat->rstartbs;
2644:   a->rendbs       = oldmat->rendbs;
2645:   a->cstartbs     = oldmat->cstartbs;
2646:   a->cendbs       = oldmat->cendbs;

2648:   /* hash table stuff */
2649:   a->ht           = 0;
2650:   a->hd           = 0;
2651:   a->ht_size      = 0;
2652:   a->ht_flag      = oldmat->ht_flag;
2653:   a->ht_fact      = oldmat->ht_fact;
2654:   a->ht_total_ct  = 0;
2655:   a->ht_insert_ct = 0;

2657:   PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+1)*sizeof(PetscInt));
2658:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2659:   MatStashCreate_Private(matin->comm,matin->rmap.bs,&mat->bstash);
2660:   if (oldmat->colmap) {
2661: #if defined (PETSC_USE_CTABLE)
2662:   PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2663: #else
2664:   PetscMalloc((a->Nbs)*sizeof(PetscInt),&a->colmap);
2665:   PetscLogObjectMemory(mat,(a->Nbs)*sizeof(PetscInt));
2666:   PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
2667: #endif
2668:   } else a->colmap = 0;

2670:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2671:     PetscMalloc(len*sizeof(PetscInt),&a->garray);
2672:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2673:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
2674:   } else a->garray = 0;
2675: 
2676:   VecDuplicate(oldmat->lvec,&a->lvec);
2677:   PetscLogObjectParent(mat,a->lvec);
2678:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2679:   PetscLogObjectParent(mat,a->Mvctx);

2681:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2682:   PetscLogObjectParent(mat,a->A);
2683:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2684:   PetscLogObjectParent(mat,a->B);
2685:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2686:   *newmat = mat;

2688:   return(0);
2689: }

2691:  #include petscsys.h

2695: PetscErrorCode MatLoad_MPIBAIJ(PetscViewer viewer, MatType type,Mat *newmat)
2696: {
2697:   Mat            A;
2699:   int            fd;
2700:   PetscInt       i,nz,j,rstart,rend;
2701:   PetscScalar    *vals,*buf;
2702:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2703:   MPI_Status     status;
2704:   PetscMPIInt    rank,size,maxnz;
2705:   PetscInt       header[4],*rowlengths = 0,M,N,m,*rowners,*cols;
2706:   PetscInt       *locrowlens = PETSC_NULL,*procsnz = PETSC_NULL,*browners = PETSC_NULL;
2707:   PetscInt       jj,*mycols,*ibuf,bs=1,Mbs,mbs,extra_rows,mmax;
2708:   PetscMPIInt    tag = ((PetscObject)viewer)->tag;
2709:   PetscInt       *dlens = PETSC_NULL,*odlens = PETSC_NULL,*mask = PETSC_NULL,*masked1 = PETSC_NULL,*masked2 = PETSC_NULL,rowcount,odcount;
2710:   PetscInt       dcount,kmax,k,nzcount,tmp,mend;

2713:   PetscOptionsBegin(comm,PETSC_NULL,"Options for loading MPIBAIJ matrix 2","Mat");
2714:     PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
2715:   PetscOptionsEnd();

2717:   MPI_Comm_size(comm,&size);
2718:   MPI_Comm_rank(comm,&rank);
2719:   if (!rank) {
2720:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2721:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2722:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2723:   }

2725:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2726:   M = header[1]; N = header[2];

2728:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2730:   /* 
2731:      This code adds extra rows to make sure the number of rows is 
2732:      divisible by the blocksize
2733:   */
2734:   Mbs        = M/bs;
2735:   extra_rows = bs - M + bs*Mbs;
2736:   if (extra_rows == bs) extra_rows = 0;
2737:   else                  Mbs++;
2738:   if (extra_rows && !rank) {
2739:     PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2740:   }

2742:   /* determine ownership of all rows */
2743:   mbs        = Mbs/size + ((Mbs % size) > rank);
2744:   m          = mbs*bs;
2745:   PetscMalloc2(size+1,PetscInt,&rowners,size+1,PetscInt,&browners);
2746:   MPI_Allgather(&mbs,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);

2748:   /* process 0 needs enough room for process with most rows */
2749:   if (!rank) {
2750:     mmax = rowners[1];
2751:     for (i=2; i<size; i++) {
2752:       mmax = PetscMax(mmax,rowners[i]);
2753:     }
2754:     mmax*=bs;
2755:   } else mmax = m;

2757:   rowners[0] = 0;
2758:   for (i=2; i<=size; i++)  rowners[i] += rowners[i-1];
2759:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2760:   rstart = rowners[rank];
2761:   rend   = rowners[rank+1];

2763:   /* distribute row lengths to all processors */
2764:   PetscMalloc((mmax+1)*sizeof(PetscInt),&locrowlens);
2765:   if (!rank) {
2766:     mend = m;
2767:     if (size == 1) mend = mend - extra_rows;
2768:     PetscBinaryRead(fd,locrowlens,mend,PETSC_INT);
2769:     for (j=mend; j<m; j++) locrowlens[j] = 1;
2770:     PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2771:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2772:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2773:     for (j=0; j<m; j++) {
2774:       procsnz[0] += locrowlens[j];
2775:     }
2776:     for (i=1; i<size; i++) {
2777:       mend = browners[i+1] - browners[i];
2778:       if (i == size-1) mend = mend - extra_rows;
2779:       PetscBinaryRead(fd,rowlengths,mend,PETSC_INT);
2780:       for (j=mend; j<browners[i+1] - browners[i]; j++) rowlengths[j] = 1;
2781:       /* calculate the number of nonzeros on each processor */
2782:       for (j=0; j<browners[i+1]-browners[i]; j++) {
2783:         procsnz[i] += rowlengths[j];
2784:       }
2785:       MPI_Send(rowlengths,browners[i+1]-browners[i],MPIU_INT,i,tag,comm);
2786:     }
2787:     PetscFree(rowlengths);
2788:   } else {
2789:     MPI_Recv(locrowlens,m,MPIU_INT,0,tag,comm,&status);
2790:   }

2792:   if (!rank) {
2793:     /* determine max buffer needed and allocate it */
2794:     maxnz = procsnz[0];
2795:     for (i=1; i<size; i++) {
2796:       maxnz = PetscMax(maxnz,procsnz[i]);
2797:     }
2798:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2800:     /* read in my part of the matrix column indices  */
2801:     nz     = procsnz[0];
2802:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
2803:     mycols = ibuf;
2804:     if (size == 1)  nz -= extra_rows;
2805:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2806:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2808:     /* read in every ones (except the last) and ship off */
2809:     for (i=1; i<size-1; i++) {
2810:       nz   = procsnz[i];
2811:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2812:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2813:     }
2814:     /* read in the stuff for the last proc */
2815:     if (size != 1) {
2816:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2817:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2818:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2819:       MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
2820:     }
2821:     PetscFree(cols);
2822:   } else {
2823:     /* determine buffer space needed for message */
2824:     nz = 0;
2825:     for (i=0; i<m; i++) {
2826:       nz += locrowlens[i];
2827:     }
2828:     PetscMalloc((nz+1)*sizeof(PetscInt),&ibuf);
2829:     mycols = ibuf;
2830:     /* receive message of column indices*/
2831:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2832:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2833:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2834:   }
2835: 
2836:   /* loop over local rows, determining number of off diagonal entries */
2837:   PetscMalloc2(rend-rstart,PetscInt,&dlens,rend-rstart,PetscInt,&odlens);
2838:   PetscMalloc3(Mbs,PetscInt,&mask,Mbs,PetscInt,&masked1,Mbs,PetscInt,&masked2);
2839:   PetscMemzero(mask,Mbs*sizeof(PetscInt));
2840:   PetscMemzero(masked1,Mbs*sizeof(PetscInt));
2841:   PetscMemzero(masked2,Mbs*sizeof(PetscInt));
2842:   rowcount = 0; nzcount = 0;
2843:   for (i=0; i<mbs; i++) {
2844:     dcount  = 0;
2845:     odcount = 0;
2846:     for (j=0; j<bs; j++) {
2847:       kmax = locrowlens[rowcount];
2848:       for (k=0; k<kmax; k++) {
2849:         tmp = mycols[nzcount++]/bs;
2850:         if (!mask[tmp]) {
2851:           mask[tmp] = 1;
2852:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
2853:           else masked1[dcount++] = tmp;
2854:         }
2855:       }
2856:       rowcount++;
2857:     }
2858: 
2859:     dlens[i]  = dcount;
2860:     odlens[i] = odcount;

2862:     /* zero out the mask elements we set */
2863:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2864:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2865:   }

2867:   /* create our matrix */
2868:   MatCreate(comm,&A);
2869:   MatSetSizes(A,m,m,M+extra_rows,N+extra_rows);
2870:   MatSetType(A,type);CHKERRQ(ierr)
2871:   MatMPIBAIJSetPreallocation(A,bs,0,dlens,0,odlens);

2873:   /* Why doesn't this called using MatSetOption(A,MAT_COLUMNS_SORTED); */
2874:   MatSetOption(A,MAT_COLUMNS_SORTED);
2875: 
2876:   if (!rank) {
2877:     PetscMalloc((maxnz+1)*sizeof(PetscScalar),&buf);
2878:     /* read in my part of the matrix numerical values  */
2879:     nz = procsnz[0];
2880:     vals = buf;
2881:     mycols = ibuf;
2882:     if (size == 1)  nz -= extra_rows;
2883:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2884:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2886:     /* insert into matrix */
2887:     jj      = rstart*bs;
2888:     for (i=0; i<m; i++) {
2889:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2890:       mycols += locrowlens[i];
2891:       vals   += locrowlens[i];
2892:       jj++;
2893:     }
2894:     /* read in other processors (except the last one) and ship out */
2895:     for (i=1; i<size-1; i++) {
2896:       nz   = procsnz[i];
2897:       vals = buf;
2898:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2899:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2900:     }
2901:     /* the last proc */
2902:     if (size != 1){
2903:       nz   = procsnz[i] - extra_rows;
2904:       vals = buf;
2905:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2906:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2907:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2908:     }
2909:     PetscFree(procsnz);
2910:   } else {
2911:     /* receive numeric values */
2912:     PetscMalloc((nz+1)*sizeof(PetscScalar),&buf);

2914:     /* receive message of values*/
2915:     vals   = buf;
2916:     mycols = ibuf;
2917:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2918:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2919:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2921:     /* insert into matrix */
2922:     jj      = rstart*bs;
2923:     for (i=0; i<m; i++) {
2924:       MatSetValues_MPIBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2925:       mycols += locrowlens[i];
2926:       vals   += locrowlens[i];
2927:       jj++;
2928:     }
2929:   }
2930:   PetscFree(locrowlens);
2931:   PetscFree(buf);
2932:   PetscFree(ibuf);
2933:   PetscFree2(rowners,browners);
2934:   PetscFree2(dlens,odlens);
2935:   PetscFree3(mask,masked1,masked2);
2936:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2937:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

2939:   *newmat = A;
2940:   return(0);
2941: }

2945: /*@
2946:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2948:    Input Parameters:
2949: .  mat  - the matrix
2950: .  fact - factor

2952:    Collective on Mat

2954:    Level: advanced

2956:   Notes:
2957:    This can also be set by the command line option: -mat_use_hash_table <fact>

2959: .keywords: matrix, hashtable, factor, HT

2961: .seealso: MatSetOption()
2962: @*/
2963: PetscErrorCode  MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2964: {
2965:   PetscErrorCode ierr,(*f)(Mat,PetscReal);

2968:   PetscObjectQueryFunction((PetscObject)mat,"MatSetHashTableFactor_C",(void (**)(void))&f);
2969:   if (f) {
2970:     (*f)(mat,fact);
2971:   }
2972:   return(0);
2973: }

2978: PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
2979: {
2980:   Mat_MPIBAIJ *baij;

2983:   baij = (Mat_MPIBAIJ*)mat->data;
2984:   baij->ht_fact = fact;
2985:   return(0);
2986: }

2991: PetscErrorCode  MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
2992: {
2993:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2995:   *Ad     = a->A;
2996:   *Ao     = a->B;
2997:   *colmap = a->garray;
2998:   return(0);
2999: }