Actual source code: mpibaij.c

  1: /*$Id: mpibaij.c,v 1.220 2001/04/09 15:13:50 bsmith Exp $*/

  3: #include "src/mat/impls/baij/mpi/mpibaij.h"   /*I  "petscmat.h"  I*/
  4: #include "src/vec/vecimpl.h"

  6: EXTERN int MatSetUpMultiply_MPIBAIJ(Mat);
  7: EXTERN int DisAssemble_MPIBAIJ(Mat);
  8: EXTERN int MatIncreaseOverlap_MPIBAIJ(Mat,int,IS *,int);
  9: EXTERN int MatGetSubMatrices_MPIBAIJ(Mat,int,IS *,IS *,MatReuse,Mat **);
 10: EXTERN int MatGetValues_SeqBAIJ(Mat,int,int *,int,int *,Scalar *);
 11: EXTERN int MatSetValues_SeqBAIJ(Mat,int,int *,int,int *,Scalar *,InsertMode);
 12: EXTERN int MatSetValuesBlocked_SeqBAIJ(Mat,int,int*,int,int*,Scalar*,InsertMode);
 13: EXTERN int MatGetRow_SeqBAIJ(Mat,int,int*,int**,Scalar**);
 14: EXTERN int MatRestoreRow_SeqBAIJ(Mat,int,int*,int**,Scalar**);
 15: EXTERN int MatPrintHelp_SeqBAIJ(Mat);
 16: EXTERN int MatZeroRows_SeqBAIJ(Mat,IS,Scalar*);

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

 39: int MatGetRowMax_MPIBAIJ(Mat A,Vec v)
 40: {
 41:   Mat_MPIBAIJ  *a = (Mat_MPIBAIJ*)A->data;
 42:   int          ierr,i;
 43:   Scalar       *va,*vb;
 44:   Vec          vtmp;

 47: 
 48:   MatGetRowMax(a->A,v);
 49:   VecGetArray(v,&va);

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

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

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

 66: EXTERN_C_BEGIN
 67: int MatStoreValues_MPIBAIJ(Mat mat)
 68: {
 69:   Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
 70:   int         ierr;

 73:   MatStoreValues(aij->A);
 74:   MatStoreValues(aij->B);
 75:   return(0);
 76: }
 77: EXTERN_C_END

 79: EXTERN_C_BEGIN
 80: int MatRetrieveValues_MPIBAIJ(Mat mat)
 81: {
 82:   Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
 83:   int         ierr;

 86:   MatRetrieveValues(aij->A);
 87:   MatRetrieveValues(aij->B);
 88:   return(0);
 89: }
 90: EXTERN_C_END

 92: /* 
 93:      Local utility routine that creates a mapping from the global column 
 94:    number to the local number in the off-diagonal part of the local 
 95:    storage of the matrix.  This is done in a non scable way since the 
 96:    length of colmap equals the global matrix length. 
 97: */
 98: static int CreateColmap_MPIBAIJ_Private(Mat mat)
 99: {
100:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
101:   Mat_SeqBAIJ *B = (Mat_SeqBAIJ*)baij->B->data;
102:   int         nbs = B->nbs,i,bs=B->bs,ierr;

105: #if defined (PETSC_USE_CTABLE)
106:   PetscTableCreate(baij->nbs,&baij->colmap);
107:   for (i=0; i<nbs; i++){
108:     PetscTableAdd(baij->colmap,baij->garray[i]+1,i*bs+1);
109:   }
110: #else
111:   PetscMalloc((baij->Nbs+1)*sizeof(int),&baij->colmap);
112:   PetscLogObjectMemory(mat,baij->Nbs*sizeof(int));
113:   PetscMemzero(baij->colmap,baij->Nbs*sizeof(int));
114:   for (i=0; i<nbs; i++) baij->colmap[baij->garray[i]] = i*bs+1;
115: #endif
116:   return(0);
117: }

119: #define CHUNKSIZE  10

121: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv) 
122: { 
123:  
124:     brow = row/bs;  
125:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; 
126:     rmax = aimax[brow]; nrow = ailen[brow]; 
127:       bcol = col/bs; 
128:       ridx = row % bs; cidx = col % bs; 
129:       low = 0; high = nrow; 
130:       while (high-low > 3) { 
131:         t = (low+high)/2; 
132:         if (rp[t] > bcol) high = t; 
133:         else              low  = t; 
134:       } 
135:       for (_i=low; _i<high; _i++) { 
136:         if (rp[_i] > bcol) break; 
137:         if (rp[_i] == bcol) { 
138:           bap  = ap +  bs2*_i + bs*cidx + ridx; 
139:           if (addv == ADD_VALUES) *bap += value;  
140:           else                    *bap  = value;  
141:           goto a_noinsert; 
142:         } 
143:       } 
144:       if (a->nonew == 1) goto a_noinsert; 
145:       else if (a->nonew == -1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero into matrix"); 
146:       if (nrow >= rmax) { 
147:         /* there is no extra room in row, therefore enlarge */ 
148:         int       new_nz = ai[a->mbs] + CHUNKSIZE,len,*new_i,*new_j; 
149:         MatScalar *new_a; 
150:  
151:         if (a->nonew == -2) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix"); 
152:  
153:         /* malloc new storage space */ 
154:         len     = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(a->mbs+1)*sizeof(int); 
155:         PetscMalloc(len,&new_a); 
156:         new_j   = (int*)(new_a + bs2*new_nz); 
157:         new_i   = new_j + new_nz; 
158:  
159:         /* copy over old data into new slots */ 
160:         for (ii=0; ii<brow+1; ii++) {new_i[ii] = ai[ii];} 
161:         for (ii=brow+1; ii<a->mbs+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;} 
162:         PetscMemcpy(new_j,aj,(ai[brow]+nrow)*sizeof(int)); 
163:         len = (new_nz - CHUNKSIZE - ai[brow] - nrow); 
164:         PetscMemcpy(new_j+ai[brow]+nrow+CHUNKSIZE,aj+ai[brow]+nrow,len*sizeof(int)); 
165:         PetscMemcpy(new_a,aa,(ai[brow]+nrow)*bs2*sizeof(MatScalar)); 
166:         PetscMemzero(new_a+bs2*(ai[brow]+nrow),bs2*CHUNKSIZE*sizeof(Scalar)); 
167:         PetscMemcpy(new_a+bs2*(ai[brow]+nrow+CHUNKSIZE), 
168:                     aa+bs2*(ai[brow]+nrow),bs2*len*sizeof(MatScalar));  
169:         /* free up old matrix storage */ 
170:         PetscFree(a->a);  
171:         if (!a->singlemalloc) { 
172:           PetscFree(a->i); 
173:           PetscFree(a->j);
174:         } 
175:         aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;  
176:         a->singlemalloc = PETSC_TRUE; 
177:  
178:         rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; 
179:         rmax = aimax[brow] = aimax[brow] + CHUNKSIZE; 
180:         PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar))); 
181:         a->maxnz += bs2*CHUNKSIZE; 
182:         a->reallocs++; 
183:         a->nz++; 
184:       } 
185:       N = nrow++ - 1;  
186:       /* shift up all the later entries in this row */ 
187:       for (ii=N; ii>=_i; ii--) { 
188:         rp[ii+1] = rp[ii]; 
189:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); 
190:       } 
191:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  
192:       rp[_i]                      = bcol;  
193:       ap[bs2*_i + bs*cidx + ridx] = value;  
194:       a_noinsert:; 
195:     ailen[brow] = nrow; 
196: } 

198: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv) 
199: { 
200:     brow = row/bs;  
201:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; 
202:     rmax = bimax[brow]; nrow = bilen[brow]; 
203:       bcol = col/bs; 
204:       ridx = row % bs; cidx = col % bs; 
205:       low = 0; high = nrow; 
206:       while (high-low > 3) { 
207:         t = (low+high)/2; 
208:         if (rp[t] > bcol) high = t; 
209:         else              low  = t; 
210:       } 
211:       for (_i=low; _i<high; _i++) { 
212:         if (rp[_i] > bcol) break; 
213:         if (rp[_i] == bcol) { 
214:           bap  = ap +  bs2*_i + bs*cidx + ridx; 
215:           if (addv == ADD_VALUES) *bap += value;  
216:           else                    *bap  = value;  
217:           goto b_noinsert; 
218:         } 
219:       } 
220:       if (b->nonew == 1) goto b_noinsert; 
221:       else if (b->nonew == -1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero into matrix"); 
222:       if (nrow >= rmax) { 
223:         /* there is no extra room in row, therefore enlarge */ 
224:         int       new_nz = bi[b->mbs] + CHUNKSIZE,len,*new_i,*new_j; 
225:         MatScalar *new_a; 
226:  
227:         if (b->nonew == -2) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix"); 
228:  
229:         /* malloc new storage space */ 
230:         len     = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(b->mbs+1)*sizeof(int); 
231:         ierr    = PetscMalloc(len,&new_a); 
232:         new_j   = (int*)(new_a + bs2*new_nz); 
233:         new_i   = new_j + new_nz; 
234:  
235:         /* copy over old data into new slots */ 
236:         for (ii=0; ii<brow+1; ii++) {new_i[ii] = bi[ii];} 
237:         for (ii=brow+1; ii<b->mbs+1; ii++) {new_i[ii] = bi[ii]+CHUNKSIZE;} 
238:         PetscMemcpy(new_j,bj,(bi[brow]+nrow)*sizeof(int)); 
239:         len  = (new_nz - CHUNKSIZE - bi[brow] - nrow); 
240:         PetscMemcpy(new_j+bi[brow]+nrow+CHUNKSIZE,bj+bi[brow]+nrow,len*sizeof(int)); 
241:         PetscMemcpy(new_a,ba,(bi[brow]+nrow)*bs2*sizeof(MatScalar)); 
242:         PetscMemzero(new_a+bs2*(bi[brow]+nrow),bs2*CHUNKSIZE*sizeof(MatScalar)); 
243:         PetscMemcpy(new_a+bs2*(bi[brow]+nrow+CHUNKSIZE), 
244:                     ba+bs2*(bi[brow]+nrow),bs2*len*sizeof(MatScalar));  
245:         /* free up old matrix storage */ 
246:         PetscFree(b->a);  
247:         if (!b->singlemalloc) { 
248:           PetscFree(b->i); 
249:           PetscFree(b->j); 
250:         } 
251:         ba = b->a = new_a; bi = b->i = new_i; bj = b->j = new_j;  
252:         b->singlemalloc = PETSC_TRUE; 
253:  
254:         rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; 
255:         rmax = bimax[brow] = bimax[brow] + CHUNKSIZE; 
256:         PetscLogObjectMemory(B,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar))); 
257:         b->maxnz += bs2*CHUNKSIZE; 
258:         b->reallocs++; 
259:         b->nz++; 
260:       } 
261:       N = nrow++ - 1;  
262:       /* shift up all the later entries in this row */ 
263:       for (ii=N; ii>=_i; ii--) { 
264:         rp[ii+1] = rp[ii]; 
265:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); 
266:       } 
267:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  
268:       rp[_i]                      = bcol;  
269:       ap[bs2*_i + bs*cidx + ridx] = value;  
270:       b_noinsert:; 
271:     bilen[brow] = nrow; 
272: } 

274: #if defined(PETSC_USE_MAT_SINGLE)
275: int MatSetValues_MPIBAIJ(Mat mat,int m,int *im,int n,int *in,Scalar *v,InsertMode addv)
276: {
277:   Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
278:   int         ierr,i,N = m*n;
279:   MatScalar   *vsingle;

282:   if (N > b->setvalueslen) {
283:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
284:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
285:     b->setvalueslen  = N;
286:   }
287:   vsingle = b->setvaluescopy;

289:   for (i=0; i<N; i++) {
290:     vsingle[i] = v[i];
291:   }
292:   MatSetValues_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
293:   return(0);
294: }

296: int MatSetValuesBlocked_MPIBAIJ(Mat mat,int m,int *im,int n,int *in,Scalar *v,InsertMode addv)
297: {
298:   Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
299:   int         ierr,i,N = m*n*b->bs2;
300:   MatScalar   *vsingle;

303:   if (N > b->setvalueslen) {
304:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
305:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
306:     b->setvalueslen  = N;
307:   }
308:   vsingle = b->setvaluescopy;
309:   for (i=0; i<N; i++) {
310:     vsingle[i] = v[i];
311:   }
312:   MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
313:   return(0);
314: }

316: int MatSetValues_MPIBAIJ_HT(Mat mat,int m,int *im,int n,int *in,Scalar *v,InsertMode addv)
317: {
318:   Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
319:   int         ierr,i,N = m*n;
320:   MatScalar   *vsingle;

323:   if (N > b->setvalueslen) {
324:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
325:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
326:     b->setvalueslen  = N;
327:   }
328:   vsingle = b->setvaluescopy;
329:   for (i=0; i<N; i++) {
330:     vsingle[i] = v[i];
331:   }
332:   MatSetValues_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
333:   return(0);
334: }

336: int MatSetValuesBlocked_MPIBAIJ_HT(Mat mat,int m,int *im,int n,int *in,Scalar *v,InsertMode addv)
337: {
338:   Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
339:   int         ierr,i,N = m*n*b->bs2;
340:   MatScalar   *vsingle;

343:   if (N > b->setvalueslen) {
344:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
345:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
346:     b->setvalueslen  = N;
347:   }
348:   vsingle = b->setvaluescopy;
349:   for (i=0; i<N; i++) {
350:     vsingle[i] = v[i];
351:   }
352:   MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
353:   return(0);
354: }
355: #endif

357: int MatSetValues_MPIBAIJ_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
358: {
359:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
360:   MatScalar   value;
361:   PetscTruth  roworiented = baij->roworiented;
362:   int         ierr,i,j,row,col;
363:   int         rstart_orig=baij->rstart_bs;
364:   int         rend_orig=baij->rend_bs,cstart_orig=baij->cstart_bs;
365:   int         cend_orig=baij->cend_bs,bs=baij->bs;

367:   /* Some Variables required in the macro */
368:   Mat         A = baij->A;
369:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)(A)->data;
370:   int         *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
371:   MatScalar   *aa=a->a;

373:   Mat         B = baij->B;
374:   Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(B)->data;
375:   int         *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
376:   MatScalar   *ba=b->a;

378:   int         *rp,ii,nrow,_i,rmax,N,brow,bcol;
379:   int         low,high,t,ridx,cidx,bs2=a->bs2;
380:   MatScalar   *ap,*bap;

383:   for (i=0; i<m; i++) {
384:     if (im[i] < 0) continue;
385: #if defined(PETSC_USE_BOPT_g)
386:     if (im[i] >= mat->M) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
387: #endif
388:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
389:       row = im[i] - rstart_orig;
390:       for (j=0; j<n; j++) {
391:         if (in[j] >= cstart_orig && in[j] < cend_orig){
392:           col = in[j] - cstart_orig;
393:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
394:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv);
395:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
396:         } else if (in[j] < 0) continue;
397: #if defined(PETSC_USE_BOPT_g)
398:         else if (in[j] >= mat->N) {SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Column too large");}
399: #endif
400:         else {
401:           if (mat->was_assembled) {
402:             if (!baij->colmap) {
403:               CreateColmap_MPIBAIJ_Private(mat);
404:             }
405: #if defined (PETSC_USE_CTABLE)
406:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
407:             col  = col - 1 + in[j]%bs;
408: #else
409:             col = baij->colmap[in[j]/bs] - 1 + in[j]%bs;
410: #endif
411:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
412:               DisAssemble_MPIBAIJ(mat);
413:               col =  in[j];
414:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
415:               B = baij->B;
416:               b = (Mat_SeqBAIJ*)(B)->data;
417:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
418:               ba=b->a;
419:             }
420:           } else col = in[j];
421:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
422:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv);
423:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
424:         }
425:       }
426:     } else {
427:       if (!baij->donotstash) {
428:         if (roworiented) {
429:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
430:         } else {
431:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
432:         }
433:       }
434:     }
435:   }
436:   return(0);
437: }

439: int MatSetValuesBlocked_MPIBAIJ_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
440: {
441:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
442:   MatScalar   *value,*barray=baij->barray;
443:   PetscTruth  roworiented = baij->roworiented;
444:   int         ierr,i,j,ii,jj,row,col,rstart=baij->rstart;
445:   int         rend=baij->rend,cstart=baij->cstart,stepval;
446:   int         cend=baij->cend,bs=baij->bs,bs2=baij->bs2;
447: 
449:   if(!barray) {
450:     ierr         = PetscMalloc(bs2*sizeof(MatScalar),&barray);
451:     baij->barray = barray;
452:   }

454:   if (roworiented) {
455:     stepval = (n-1)*bs;
456:   } else {
457:     stepval = (m-1)*bs;
458:   }
459:   for (i=0; i<m; i++) {
460:     if (im[i] < 0) continue;
461: #if defined(PETSC_USE_BOPT_g)
462:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %d max %d",im[i],baij->Mbs);
463: #endif
464:     if (im[i] >= rstart && im[i] < rend) {
465:       row = im[i] - rstart;
466:       for (j=0; j<n; j++) {
467:         /* If NumCol = 1 then a copy is not required */
468:         if ((roworiented) && (n == 1)) {
469:           barray = v + i*bs2;
470:         } else if((!roworiented) && (m == 1)) {
471:           barray = v + j*bs2;
472:         } else { /* Here a copy is required */
473:           if (roworiented) {
474:             value = v + i*(stepval+bs)*bs + j*bs;
475:           } else {
476:             value = v + j*(stepval+bs)*bs + i*bs;
477:           }
478:           for (ii=0; ii<bs; ii++,value+=stepval) {
479:             for (jj=0; jj<bs; jj++) {
480:               *barray++  = *value++;
481:             }
482:           }
483:           barray -=bs2;
484:         }
485: 
486:         if (in[j] >= cstart && in[j] < cend){
487:           col  = in[j] - cstart;
488:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->A,1,&row,1,&col,barray,addv);
489:         }
490:         else if (in[j] < 0) continue;
491: #if defined(PETSC_USE_BOPT_g)
492:         else if (in[j] >= baij->Nbs) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %d max %d",in[j],baij->Nbs);}
493: #endif
494:         else {
495:           if (mat->was_assembled) {
496:             if (!baij->colmap) {
497:               CreateColmap_MPIBAIJ_Private(mat);
498:             }

500: #if defined(PETSC_USE_BOPT_g)
501: #if defined (PETSC_USE_CTABLE)
502:             { int data;
503:               PetscTableFind(baij->colmap,in[j]+1,&data);
504:               if ((data - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
505:             }
506: #else
507:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
508: #endif
509: #endif
510: #if defined (PETSC_USE_CTABLE)
511:             PetscTableFind(baij->colmap,in[j]+1,&col);
512:             col  = (col - 1)/bs;
513: #else
514:             col = (baij->colmap[in[j]] - 1)/bs;
515: #endif
516:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
517:               DisAssemble_MPIBAIJ(mat);
518:               col =  in[j];
519:             }
520:           }
521:           else col = in[j];
522:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->B,1,&row,1,&col,barray,addv);
523:         }
524:       }
525:     } else {
526:       if (!baij->donotstash) {
527:         if (roworiented) {
528:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
529:         } else {
530:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
531:         }
532:       }
533:     }
534:   }
535:   return(0);
536: }

538: #define HASH_KEY 0.6180339887
539: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(int)((size)*(tmp-(int)tmp)))
540: /* #define HASH(size,key) ((int)((size)*fmod(((key)*HASH_KEY),1))) */
541: /* #define HASH(size,key,tmp) ((int)((size)*fmod(((key)*HASH_KEY),1))) */
542: int MatSetValues_MPIBAIJ_HT_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
543: {
544:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
545:   PetscTruth  roworiented = baij->roworiented;
546:   int         ierr,i,j,row,col;
547:   int         rstart_orig=baij->rstart_bs;
548:   int         rend_orig=baij->rend_bs,Nbs=baij->Nbs;
549:   int         h1,key,size=baij->ht_size,bs=baij->bs,*HT=baij->ht,idx;
550:   PetscReal   tmp;
551:   MatScalar   **HD = baij->hd,value;
552: #if defined(PETSC_USE_BOPT_g)
553:   int         total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
554: #endif


558:   for (i=0; i<m; i++) {
559: #if defined(PETSC_USE_BOPT_g)
560:     if (im[i] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
561:     if (im[i] >= mat->M) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
562: #endif
563:       row = im[i];
564:     if (row >= rstart_orig && row < rend_orig) {
565:       for (j=0; j<n; j++) {
566:         col = in[j];
567:         if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
568:         /* Look up into the Hash Table */
569:         key = (row/bs)*Nbs+(col/bs)+1;
570:         h1  = HASH(size,key,tmp);

572: 
573:         idx = h1;
574: #if defined(PETSC_USE_BOPT_g)
575:         insert_ct++;
576:         total_ct++;
577:         if (HT[idx] != key) {
578:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
579:           if (idx == size) {
580:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
581:             if (idx == h1) {
582:               SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"(row,col) has no entry in the hash table");
583:             }
584:           }
585:         }
586: #else
587:         if (HT[idx] != key) {
588:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
589:           if (idx == size) {
590:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
591:             if (idx == h1) {
592:               SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"(row,col) has no entry in the hash table");
593:             }
594:           }
595:         }
596: #endif
597:         /* A HASH table entry is found, so insert the values at the correct address */
598:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
599:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
600:       }
601:     } else {
602:       if (!baij->donotstash) {
603:         if (roworiented) {
604:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
605:         } else {
606:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
607:         }
608:       }
609:     }
610:   }
611: #if defined(PETSC_USE_BOPT_g)
612:   baij->ht_total_ct = total_ct;
613:   baij->ht_insert_ct = insert_ct;
614: #endif
615:   return(0);
616: }

618: int MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
619: {
620:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
621:   PetscTruth  roworiented = baij->roworiented;
622:   int         ierr,i,j,ii,jj,row,col;
623:   int         rstart=baij->rstart ;
624:   int         rend=baij->rend,stepval,bs=baij->bs,bs2=baij->bs2;
625:   int         h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
626:   PetscReal   tmp;
627:   MatScalar   **HD = baij->hd,*baij_a;
628:   MatScalar   *v_t,*value;
629: #if defined(PETSC_USE_BOPT_g)
630:   int         total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
631: #endif
632: 

635:   if (roworiented) {
636:     stepval = (n-1)*bs;
637:   } else {
638:     stepval = (m-1)*bs;
639:   }
640:   for (i=0; i<m; i++) {
641: #if defined(PETSC_USE_BOPT_g)
642:     if (im[i] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
643:     if (im[i] >= baij->Mbs) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
644: #endif
645:     row   = im[i];
646:     v_t   = v + i*bs2;
647:     if (row >= rstart && row < rend) {
648:       for (j=0; j<n; j++) {
649:         col = in[j];

651:         /* Look up into the Hash Table */
652:         key = row*Nbs+col+1;
653:         h1  = HASH(size,key,tmp);
654: 
655:         idx = h1;
656: #if defined(PETSC_USE_BOPT_g)
657:         total_ct++;
658:         insert_ct++;
659:        if (HT[idx] != key) {
660:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
661:           if (idx == size) {
662:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
663:             if (idx == h1) {
664:               SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"(row,col) has no entry in the hash table");
665:             }
666:           }
667:         }
668: #else  
669:         if (HT[idx] != key) {
670:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
671:           if (idx == size) {
672:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
673:             if (idx == h1) {
674:               SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"(row,col) has no entry in the hash table");
675:             }
676:           }
677:         }
678: #endif
679:         baij_a = HD[idx];
680:         if (roworiented) {
681:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
682:           /* value = v + (i*(stepval+bs)+j)*bs; */
683:           value = v_t;
684:           v_t  += bs;
685:           if (addv == ADD_VALUES) {
686:             for (ii=0; ii<bs; ii++,value+=stepval) {
687:               for (jj=ii; jj<bs2; jj+=bs) {
688:                 baij_a[jj]  += *value++;
689:               }
690:             }
691:           } else {
692:             for (ii=0; ii<bs; ii++,value+=stepval) {
693:               for (jj=ii; jj<bs2; jj+=bs) {
694:                 baij_a[jj]  = *value++;
695:               }
696:             }
697:           }
698:         } else {
699:           value = v + j*(stepval+bs)*bs + i*bs;
700:           if (addv == ADD_VALUES) {
701:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
702:               for (jj=0; jj<bs; jj++) {
703:                 baij_a[jj]  += *value++;
704:               }
705:             }
706:           } else {
707:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
708:               for (jj=0; jj<bs; jj++) {
709:                 baij_a[jj]  = *value++;
710:               }
711:             }
712:           }
713:         }
714:       }
715:     } else {
716:       if (!baij->donotstash) {
717:         if (roworiented) {
718:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
719:         } else {
720:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
721:         }
722:       }
723:     }
724:   }
725: #if defined(PETSC_USE_BOPT_g)
726:   baij->ht_total_ct = total_ct;
727:   baij->ht_insert_ct = insert_ct;
728: #endif
729:   return(0);
730: }

732: int MatGetValues_MPIBAIJ(Mat mat,int m,int *idxm,int n,int *idxn,Scalar *v)
733: {
734:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
735:   int        bs=baij->bs,ierr,i,j,bsrstart = baij->rstart*bs,bsrend = baij->rend*bs;
736:   int        bscstart = baij->cstart*bs,bscend = baij->cend*bs,row,col,data;

739:   for (i=0; i<m; i++) {
740:     if (idxm[i] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
741:     if (idxm[i] >= mat->M) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
742:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
743:       row = idxm[i] - bsrstart;
744:       for (j=0; j<n; j++) {
745:         if (idxn[j] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column");
746:         if (idxn[j] >= mat->N) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
747:         if (idxn[j] >= bscstart && idxn[j] < bscend){
748:           col = idxn[j] - bscstart;
749:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
750:         } else {
751:           if (!baij->colmap) {
752:             CreateColmap_MPIBAIJ_Private(mat);
753:           }
754: #if defined (PETSC_USE_CTABLE)
755:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
756:           data --;
757: #else
758:           data = baij->colmap[idxn[j]/bs]-1;
759: #endif
760:           if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
761:           else {
762:             col  = data + idxn[j]%bs;
763:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
764:           }
765:         }
766:       }
767:     } else {
768:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
769:     }
770:   }
771:  return(0);
772: }

774: int MatNorm_MPIBAIJ(Mat mat,NormType type,PetscReal *norm)
775: {
776:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
777:   Mat_SeqBAIJ *amat = (Mat_SeqBAIJ*)baij->A->data,*bmat = (Mat_SeqBAIJ*)baij->B->data;
778:   int        ierr,i,bs2=baij->bs2;
779:   PetscReal  sum = 0.0;
780:   MatScalar  *v;

783:   if (baij->size == 1) {
784:      MatNorm(baij->A,type,norm);
785:   } else {
786:     if (type == NORM_FROBENIUS) {
787:       v = amat->a;
788:       for (i=0; i<amat->nz*bs2; i++) {
789: #if defined(PETSC_USE_COMPLEX)
790:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
791: #else
792:         sum += (*v)*(*v); v++;
793: #endif
794:       }
795:       v = bmat->a;
796:       for (i=0; i<bmat->nz*bs2; i++) {
797: #if defined(PETSC_USE_COMPLEX)
798:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
799: #else
800:         sum += (*v)*(*v); v++;
801: #endif
802:       }
803:       MPI_Allreduce(&sum,norm,1,MPI_DOUBLE,MPI_SUM,mat->comm);
804:       *norm = sqrt(*norm);
805:     } else {
806:       SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
807:     }
808:   }
809:   return(0);
810: }


813: /*
814:   Creates the hash table, and sets the table 
815:   This table is created only once. 
816:   If new entried need to be added to the matrix
817:   then the hash table has to be destroyed and
818:   recreated.
819: */
820: int MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
821: {
822:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
823:   Mat         A = baij->A,B=baij->B;
824:   Mat_SeqBAIJ *a=(Mat_SeqBAIJ *)A->data,*b=(Mat_SeqBAIJ *)B->data;
825:   int         i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
826:   int         size,bs2=baij->bs2,rstart=baij->rstart,ierr;
827:   int         cstart=baij->cstart,*garray=baij->garray,row,col,Nbs=baij->Nbs;
828:   int         *HT,key;
829:   MatScalar   **HD;
830:   PetscReal   tmp;
831: #if defined(PETSC_USE_BOPT_g)
832:   int         ct=0,max=0;
833: #endif

836:   baij->ht_size=(int)(factor*nz);
837:   size = baij->ht_size;

839:   if (baij->ht) {
840:     return(0);
841:   }
842: 
843:   /* Allocate Memory for Hash Table */
844:   ierr     = PetscMalloc((size)*(sizeof(int)+sizeof(MatScalar*))+1,&baij->hd);
845:   baij->ht = (int*)(baij->hd + size);
846:   HD       = baij->hd;
847:   HT       = baij->ht;


850:   PetscMemzero(HD,size*(sizeof(int)+sizeof(Scalar*)));
851: 

853:   /* Loop Over A */
854:   for (i=0; i<a->mbs; i++) {
855:     for (j=ai[i]; j<ai[i+1]; j++) {
856:       row = i+rstart;
857:       col = aj[j]+cstart;
858: 
859:       key = row*Nbs + col + 1;
860:       h1  = HASH(size,key,tmp);
861:       for (k=0; k<size; k++){
862:         if (HT[(h1+k)%size] == 0.0) {
863:           HT[(h1+k)%size] = key;
864:           HD[(h1+k)%size] = a->a + j*bs2;
865:           break;
866: #if defined(PETSC_USE_BOPT_g)
867:         } else {
868:           ct++;
869: #endif
870:         }
871:       }
872: #if defined(PETSC_USE_BOPT_g)
873:       if (k> max) max = k;
874: #endif
875:     }
876:   }
877:   /* Loop Over B */
878:   for (i=0; i<b->mbs; i++) {
879:     for (j=bi[i]; j<bi[i+1]; j++) {
880:       row = i+rstart;
881:       col = garray[bj[j]];
882:       key = row*Nbs + col + 1;
883:       h1  = HASH(size,key,tmp);
884:       for (k=0; k<size; k++){
885:         if (HT[(h1+k)%size] == 0.0) {
886:           HT[(h1+k)%size] = key;
887:           HD[(h1+k)%size] = b->a + j*bs2;
888:           break;
889: #if defined(PETSC_USE_BOPT_g)
890:         } else {
891:           ct++;
892: #endif
893:         }
894:       }
895: #if defined(PETSC_USE_BOPT_g)
896:       if (k> max) max = k;
897: #endif
898:     }
899:   }
900: 
901:   /* Print Summary */
902: #if defined(PETSC_USE_BOPT_g)
903:   for (i=0,j=0; i<size; i++) {
904:     if (HT[i]) {j++;}
905:   }
906:   PetscLogInfo(0,"MatCreateHashTable_MPIBAIJ_Private: Average Search = %5.2f,max search = %dn",(j== 0)? 0.0:((PetscReal)(ct+j))/j,max);
907: #endif
908:   return(0);
909: }

911: int MatAssemblyBegin_MPIBAIJ(Mat mat,MatAssemblyType mode)
912: {
913:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
914:   int         ierr,nstash,reallocs;
915:   InsertMode  addv;

918:   if (baij->donotstash) {
919:     return(0);
920:   }

922:   /* make sure all processors are either in INSERTMODE or ADDMODE */
923:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
924:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
925:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
926:   }
927:   mat->insertmode = addv; /* in case this processor had no cache */

929:   MatStashScatterBegin_Private(&mat->stash,baij->rowners_bs);
930:   MatStashScatterBegin_Private(&mat->bstash,baij->rowners);
931:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
932:   PetscLogInfo(0,"MatAssemblyBegin_MPIBAIJ:Stash has %d entries,uses %d mallocs.n",nstash,reallocs);
933:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
934:   PetscLogInfo(0,"MatAssemblyBegin_MPIBAIJ:Block-Stash has %d entries, uses %d mallocs.n",nstash,reallocs);
935:   return(0);
936: }

938: int MatAssemblyEnd_MPIBAIJ(Mat mat,MatAssemblyType mode)
939: {
940:   Mat_MPIBAIJ *baij=(Mat_MPIBAIJ*)mat->data;
941:   Mat_SeqBAIJ *a=(Mat_SeqBAIJ*)baij->A->data,*b=(Mat_SeqBAIJ*)baij->B->data;
942:   int         i,j,rstart,ncols,n,ierr,flg,bs2=baij->bs2;
943:   int         *row,*col,other_disassembled;
944:   PetscTruth  r1,r2,r3;
945:   MatScalar   *val;
946:   InsertMode  addv = mat->insertmode;

949:   if (!baij->donotstash) {
950:     while (1) {
951:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
952:       if (!flg) break;

954:       for (i=0; i<n;) {
955:         /* Now identify the consecutive vals belonging to the same row */
956:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
957:         if (j < n) ncols = j-i;
958:         else       ncols = n-i;
959:         /* Now assemble all these values with a single function call */
960:         MatSetValues_MPIBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i,addv);
961:         i = j;
962:       }
963:     }
964:     MatStashScatterEnd_Private(&mat->stash);
965:     /* Now process the block-stash. Since the values are stashed column-oriented,
966:        set the roworiented flag to column oriented, and after MatSetValues() 
967:        restore the original flags */
968:     r1 = baij->roworiented;
969:     r2 = a->roworiented;
970:     r3 = b->roworiented;
971:     baij->roworiented = PETSC_FALSE;
972:     a->roworiented    = PETSC_FALSE;
973:     b->roworiented    = PETSC_FALSE;
974:     while (1) {
975:       MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
976:       if (!flg) break;
977: 
978:       for (i=0; i<n;) {
979:         /* Now identify the consecutive vals belonging to the same row */
980:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
981:         if (j < n) ncols = j-i;
982:         else       ncols = n-i;
983:         MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
984:         i = j;
985:       }
986:     }
987:     MatStashScatterEnd_Private(&mat->bstash);
988:     baij->roworiented = r1;
989:     a->roworiented    = r2;
990:     b->roworiented    = r3;
991:   }

993:   MatAssemblyBegin(baij->A,mode);
994:   MatAssemblyEnd(baij->A,mode);

996:   /* determine if any processor has disassembled, if so we must 
997:      also disassemble ourselfs, in order that we may reassemble. */
998:   /*
999:      if nonzero structure of submatrix B cannot change then we know that
1000:      no processor disassembled thus we can skip this stuff
1001:   */
1002:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
1003:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
1004:     if (mat->was_assembled && !other_disassembled) {
1005:       DisAssemble_MPIBAIJ(mat);
1006:     }
1007:   }

1009:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
1010:     MatSetUpMultiply_MPIBAIJ(mat);
1011:   }
1012:   MatAssemblyBegin(baij->B,mode);
1013:   MatAssemblyEnd(baij->B,mode);
1014: 
1015: #if defined(PETSC_USE_BOPT_g)
1016:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
1017:     PetscLogInfo(0,"MatAssemblyEnd_MPIBAIJ:Average Hash Table Search in MatSetValues = %5.2fn",((double)baij->ht_total_ct)/baij->ht_insert_ct);
1018:     baij->ht_total_ct  = 0;
1019:     baij->ht_insert_ct = 0;
1020:   }
1021: #endif
1022:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
1023:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);
1024:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
1025:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
1026:   }

1028:   if (baij->rowvalues) {
1029:     PetscFree(baij->rowvalues);
1030:     baij->rowvalues = 0;
1031:   }
1032:   return(0);
1033: }

1035: static int MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1036: {
1037:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
1038:   int               ierr,bs = baij->bs,size = baij->size,rank = baij->rank;
1039:   PetscTruth        isascii,isdraw;
1040:   PetscViewer       sviewer;
1041:   PetscViewerFormat format;

1044:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
1045:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1046:   if (isascii) {
1047:     PetscViewerGetFormat(viewer,&format);
1048:     if (format == PETSC_VIEWER_ASCII_INFO_LONG) {
1049:       MatInfo info;
1050:       MPI_Comm_rank(mat->comm,&rank);
1051:       MatGetInfo(mat,MAT_LOCAL,&info);
1052:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d bs %d mem %dn",
1053:               rank,mat->m,(int)info.nz_used*bs,(int)info.nz_allocated*bs,
1054:               baij->bs,(int)info.memory);
1055:       MatGetInfo(baij->A,MAT_LOCAL,&info);
1056:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %d n",rank,(int)info.nz_used*bs);
1057:       MatGetInfo(baij->B,MAT_LOCAL,&info);
1058:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %d n",rank,(int)info.nz_used*bs);
1059:       PetscViewerFlush(viewer);
1060:       VecScatterView(baij->Mvctx,viewer);
1061:       return(0);
1062:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1063:       PetscViewerASCIIPrintf(viewer,"  block size is %dn",bs);
1064:       return(0);
1065:     }
1066:   }

1068:   if (isdraw) {
1069:     PetscDraw       draw;
1070:     PetscTruth isnull;
1071:     PetscViewerDrawGetDraw(viewer,0,&draw);
1072:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1073:   }

1075:   if (size == 1) {
1076:     MatView(baij->A,viewer);
1077:   } else {
1078:     /* assemble the entire matrix onto first processor. */
1079:     Mat         A;
1080:     Mat_SeqBAIJ *Aloc;
1081:     int         M = mat->M,N = mat->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
1082:     MatScalar   *a;

1084:     if (!rank) {
1085:       MatCreateMPIBAIJ(mat->comm,baij->bs,M,N,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);
1086:     } else {
1087:       MatCreateMPIBAIJ(mat->comm,baij->bs,0,0,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);
1088:     }
1089:     PetscLogObjectParent(mat,A);

1091:     /* copy over the A part */
1092:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
1093:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1094:     PetscMalloc(bs*sizeof(int),&rvals);

1096:     for (i=0; i<mbs; i++) {
1097:       rvals[0] = bs*(baij->rstart + i);
1098:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1099:       for (j=ai[i]; j<ai[i+1]; j++) {
1100:         col = (baij->cstart+aj[j])*bs;
1101:         for (k=0; k<bs; k++) {
1102:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1103:           col++; a += bs;
1104:         }
1105:       }
1106:     }
1107:     /* copy over the B part */
1108:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
1109:     ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1110:     for (i=0; i<mbs; i++) {
1111:       rvals[0] = bs*(baij->rstart + i);
1112:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1113:       for (j=ai[i]; j<ai[i+1]; j++) {
1114:         col = baij->garray[aj[j]]*bs;
1115:         for (k=0; k<bs; k++) {
1116:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1117:           col++; a += bs;
1118:         }
1119:       }
1120:     }
1121:     PetscFree(rvals);
1122:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1123:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1124:     /* 
1125:        Everyone has to call to draw the matrix since the graphics waits are
1126:        synchronized across all processors that share the PetscDraw object
1127:     */
1128:     PetscViewerGetSingleton(viewer,&sviewer);
1129:     if (!rank) {
1130:       MatView(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1131:     }
1132:     PetscViewerRestoreSingleton(viewer,&sviewer);
1133:     MatDestroy(A);
1134:   }
1135:   return(0);
1136: }

1138: int MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1139: {
1140:   int        ierr;
1141:   PetscTruth isascii,isdraw,issocket,isbinary;

1144:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
1145:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1146:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1147:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1148:   if (isascii || isdraw || issocket || isbinary) {
1149:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1150:   } else {
1151:     SETERRQ1(1,"Viewer type %s not supported by MPIBAIJ matrices",((PetscObject)viewer)->type_name);
1152:   }
1153:   return(0);
1154: }

1156: int MatDestroy_MPIBAIJ(Mat mat)
1157: {
1158:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1159:   int         ierr;

1162: #if defined(PETSC_USE_LOG)
1163:   PetscLogObjectState((PetscObject)mat,"Rows=%d,Cols=%d",mat->M,mat->N);
1164: #endif
1165:   MatStashDestroy_Private(&mat->stash);
1166:   MatStashDestroy_Private(&mat->bstash);
1167:   PetscFree(baij->rowners);
1168:   MatDestroy(baij->A);
1169:   MatDestroy(baij->B);
1170: #if defined (PETSC_USE_CTABLE)
1171:   if (baij->colmap) {PetscTableDelete(baij->colmap);}
1172: #else
1173:   if (baij->colmap) {PetscFree(baij->colmap);}
1174: #endif
1175:   if (baij->garray) {PetscFree(baij->garray);}
1176:   if (baij->lvec)   {VecDestroy(baij->lvec);}
1177:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
1178:   if (baij->rowvalues) {PetscFree(baij->rowvalues);}
1179:   if (baij->barray) {PetscFree(baij->barray);}
1180:   if (baij->hd) {PetscFree(baij->hd);}
1181: #if defined(PETSC_USE_MAT_SINGLE)
1182:   if (baij->setvaluescopy) {PetscFree(baij->setvaluescopy);}
1183: #endif
1184:   PetscFree(baij);
1185:   return(0);
1186: }

1188: int MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1189: {
1190:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1191:   int         ierr,nt;

1194:   VecGetLocalSize(xx,&nt);
1195:   if (nt != A->n) {
1196:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1197:   }
1198:   VecGetLocalSize(yy,&nt);
1199:   if (nt != A->m) {
1200:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1201:   }
1202:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1203:   (*a->A->ops->mult)(a->A,xx,yy);
1204:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1205:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1206:   VecScatterPostRecvs(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1207:   return(0);
1208: }

1210: int MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1211: {
1212:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1213:   int        ierr;

1216:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1217:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1218:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1219:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1220:   return(0);
1221: }

1223: int MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1224: {
1225:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1226:   int         ierr;

1229:   /* do nondiagonal part */
1230:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1231:   /* send it on its way */
1232:   VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1233:   /* do local part */
1234:   (*a->A->ops->multtranspose)(a->A,xx,yy);
1235:   /* receive remote parts: note this assumes the values are not actually */
1236:   /* inserted in yy until the next line, which is true for my implementation*/
1237:   /* but is not perhaps always true. */
1238:   VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1239:   return(0);
1240: }

1242: int MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1243: {
1244:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1245:   int         ierr;

1248:   /* do nondiagonal part */
1249:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1250:   /* send it on its way */
1251:   VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1252:   /* do local part */
1253:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1254:   /* receive remote parts: note this assumes the values are not actually */
1255:   /* inserted in yy until the next line, which is true for my implementation*/
1256:   /* but is not perhaps always true. */
1257:   VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1258:   return(0);
1259: }

1261: /*
1262:   This only works correctly for square matrices where the subblock A->A is the 
1263:    diagonal block
1264: */
1265: int MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1266: {
1267:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1268:   int         ierr;

1271:   if (A->M != A->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1272:   MatGetDiagonal(a->A,v);
1273:   return(0);
1274: }

1276: int MatScale_MPIBAIJ(Scalar *aa,Mat A)
1277: {
1278:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1279:   int         ierr;

1282:   MatScale(aa,a->A);
1283:   MatScale(aa,a->B);
1284:   return(0);
1285: }

1287: int MatGetOwnershipRange_MPIBAIJ(Mat matin,int *m,int *n)
1288: {
1289:   Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)matin->data;

1292:   if (m) *m = mat->rstart_bs;
1293:   if (n) *n = mat->rend_bs;
1294:   return(0);
1295: }

1297: int MatGetRow_MPIBAIJ(Mat matin,int row,int *nz,int **idx,Scalar **v)
1298: {
1299:   Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)matin->data;
1300:   Scalar     *vworkA,*vworkB,**pvA,**pvB,*v_p;
1301:   int        bs = mat->bs,bs2 = mat->bs2,i,ierr,*cworkA,*cworkB,**pcA,**pcB;
1302:   int        nztot,nzA,nzB,lrow,brstart = mat->rstart*bs,brend = mat->rend*bs;
1303:   int        *cmap,*idx_p,cstart = mat->cstart;

1306:   if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1307:   mat->getrowactive = PETSC_TRUE;

1309:   if (!mat->rowvalues && (idx || v)) {
1310:     /*
1311:         allocate enough space to hold information from the longest row.
1312:     */
1313:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1314:     int     max = 1,mbs = mat->mbs,tmp;
1315:     for (i=0; i<mbs; i++) {
1316:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1317:       if (max < tmp) { max = tmp; }
1318:     }
1319:     PetscMalloc(max*bs2*(sizeof(int)+sizeof(Scalar)),&mat->rowvalues);
1320:     mat->rowindices = (int*)(mat->rowvalues + max*bs2);
1321:   }
1322: 
1323:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1324:   lrow = row - brstart;

1326:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1327:   if (!v)   {pvA = 0; pvB = 0;}
1328:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1329:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1330:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1331:   nztot = nzA + nzB;

1333:   cmap  = mat->garray;
1334:   if (v  || idx) {
1335:     if (nztot) {
1336:       /* Sort by increasing column numbers, assuming A and B already sorted */
1337:       int imark = -1;
1338:       if (v) {
1339:         *v = v_p = mat->rowvalues;
1340:         for (i=0; i<nzB; i++) {
1341:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1342:           else break;
1343:         }
1344:         imark = i;
1345:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1346:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1347:       }
1348:       if (idx) {
1349:         *idx = idx_p = mat->rowindices;
1350:         if (imark > -1) {
1351:           for (i=0; i<imark; i++) {
1352:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1353:           }
1354:         } else {
1355:           for (i=0; i<nzB; i++) {
1356:             if (cmap[cworkB[i]/bs] < cstart)
1357:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1358:             else break;
1359:           }
1360:           imark = i;
1361:         }
1362:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1363:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1364:       }
1365:     } else {
1366:       if (idx) *idx = 0;
1367:       if (v)   *v   = 0;
1368:     }
1369:   }
1370:   *nz = nztot;
1371:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1372:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1373:   return(0);
1374: }

1376: int MatRestoreRow_MPIBAIJ(Mat mat,int row,int *nz,int **idx,Scalar **v)
1377: {
1378:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1381:   if (baij->getrowactive == PETSC_FALSE) {
1382:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1383:   }
1384:   baij->getrowactive = PETSC_FALSE;
1385:   return(0);
1386: }

1388: int MatGetBlockSize_MPIBAIJ(Mat mat,int *bs)
1389: {
1390:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1393:   *bs = baij->bs;
1394:   return(0);
1395: }

1397: int MatZeroEntries_MPIBAIJ(Mat A)
1398: {
1399:   Mat_MPIBAIJ *l = (Mat_MPIBAIJ*)A->data;
1400:   int         ierr;

1403:   MatZeroEntries(l->A);
1404:   MatZeroEntries(l->B);
1405:   return(0);
1406: }

1408: int MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1409: {
1410:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)matin->data;
1411:   Mat         A = a->A,B = a->B;
1412:   int         ierr;
1413:   PetscReal   isend[5],irecv[5];

1416:   info->block_size     = (double)a->bs;
1417:   MatGetInfo(A,MAT_LOCAL,info);
1418:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1419:   isend[3] = info->memory;  isend[4] = info->mallocs;
1420:   MatGetInfo(B,MAT_LOCAL,info);
1421:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1422:   isend[3] += info->memory;  isend[4] += info->mallocs;
1423:   if (flag == MAT_LOCAL) {
1424:     info->nz_used      = isend[0];
1425:     info->nz_allocated = isend[1];
1426:     info->nz_unneeded  = isend[2];
1427:     info->memory       = isend[3];
1428:     info->mallocs      = isend[4];
1429:   } else if (flag == MAT_GLOBAL_MAX) {
1430:     MPI_Allreduce(isend,irecv,5,MPI_DOUBLE,MPI_MAX,matin->comm);
1431:     info->nz_used      = irecv[0];
1432:     info->nz_allocated = irecv[1];
1433:     info->nz_unneeded  = irecv[2];
1434:     info->memory       = irecv[3];
1435:     info->mallocs      = irecv[4];
1436:   } else if (flag == MAT_GLOBAL_SUM) {
1437:     MPI_Allreduce(isend,irecv,5,MPI_DOUBLE,MPI_SUM,matin->comm);
1438:     info->nz_used      = irecv[0];
1439:     info->nz_allocated = irecv[1];
1440:     info->nz_unneeded  = irecv[2];
1441:     info->memory       = irecv[3];
1442:     info->mallocs      = irecv[4];
1443:   } else {
1444:     SETERRQ1(1,"Unknown MatInfoType argument %d",flag);
1445:   }
1446:   info->rows_global       = (double)A->M;
1447:   info->columns_global    = (double)A->N;
1448:   info->rows_local        = (double)A->m;
1449:   info->columns_local     = (double)A->N;
1450:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1451:   info->fill_ratio_needed = 0;
1452:   info->factor_mallocs    = 0;
1453:   return(0);
1454: }

1456: int MatSetOption_MPIBAIJ(Mat A,MatOption op)
1457: {
1458:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1459:   int         ierr;

1462:   if (op == MAT_NO_NEW_NONZERO_LOCATIONS ||
1463:       op == MAT_YES_NEW_NONZERO_LOCATIONS ||
1464:       op == MAT_COLUMNS_UNSORTED ||
1465:       op == MAT_COLUMNS_SORTED ||
1466:       op == MAT_NEW_NONZERO_ALLOCATION_ERR ||
1467:       op == MAT_KEEP_ZEROED_ROWS ||
1468:       op == MAT_NEW_NONZERO_LOCATION_ERR) {
1469:         MatSetOption(a->A,op);
1470:         MatSetOption(a->B,op);
1471:   } else if (op == MAT_ROW_ORIENTED) {
1472:         a->roworiented = PETSC_TRUE;
1473:         MatSetOption(a->A,op);
1474:         MatSetOption(a->B,op);
1475:   } else if (op == MAT_ROWS_SORTED ||
1476:              op == MAT_ROWS_UNSORTED ||
1477:              op == MAT_YES_NEW_DIAGONALS ||
1478:              op == MAT_USE_HASH_TABLE) {
1479:     PetscLogInfo(A,"Info:MatSetOption_MPIBAIJ:Option ignoredn");
1480:   } else if (op == MAT_COLUMN_ORIENTED) {
1481:     a->roworiented = PETSC_FALSE;
1482:     MatSetOption(a->A,op);
1483:     MatSetOption(a->B,op);
1484:   } else if (op == MAT_IGNORE_OFF_PROC_ENTRIES) {
1485:     a->donotstash = PETSC_TRUE;
1486:   } else if (op == MAT_NO_NEW_DIAGONALS) {
1487:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1488:   } else if (op == MAT_USE_HASH_TABLE) {
1489:     a->ht_flag = PETSC_TRUE;
1490:   } else {
1491:     SETERRQ(PETSC_ERR_SUP,"unknown option");
1492:   }
1493:   return(0);
1494: }

1496: int MatTranspose_MPIBAIJ(Mat A,Mat *matout)
1497: {
1498:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)A->data;
1499:   Mat_SeqBAIJ *Aloc;
1500:   Mat         B;
1501:   int         ierr,M=A->M,N=A->N,*ai,*aj,i,*rvals,j,k,col;
1502:   int         bs=baij->bs,mbs=baij->mbs;
1503:   MatScalar   *a;
1504: 
1506:   if (!matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1507:   MatCreateMPIBAIJ(A->comm,baij->bs,A->n,A->m,N,M,0,PETSC_NULL,0,PETSC_NULL,&B);
1508: 
1509:   /* copy over the A part */
1510:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1511:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1512:   PetscMalloc(bs*sizeof(int),&rvals);
1513: 
1514:   for (i=0; i<mbs; i++) {
1515:     rvals[0] = bs*(baij->rstart + i);
1516:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1517:     for (j=ai[i]; j<ai[i+1]; j++) {
1518:       col = (baij->cstart+aj[j])*bs;
1519:       for (k=0; k<bs; k++) {
1520:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1521:         col++; a += bs;
1522:       }
1523:     }
1524:   }
1525:   /* copy over the B part */
1526:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1527:   ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1528:   for (i=0; i<mbs; i++) {
1529:     rvals[0] = bs*(baij->rstart + i);
1530:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1531:     for (j=ai[i]; j<ai[i+1]; j++) {
1532:       col = baij->garray[aj[j]]*bs;
1533:       for (k=0; k<bs; k++) {
1534:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1535:         col++; a += bs;
1536:       }
1537:     }
1538:   }
1539:   PetscFree(rvals);
1540:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1541:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1542: 
1543:   if (matout) {
1544:     *matout = B;
1545:   } else {
1546:     MatHeaderCopy(A,B);
1547:   }
1548:   return(0);
1549: }

1551: int MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1552: {
1553:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1554:   Mat         a = baij->A,b = baij->B;
1555:   int         ierr,s1,s2,s3;

1558:   MatGetLocalSize(mat,&s2,&s3);
1559:   if (rr) {
1560:     VecGetLocalSize(rr,&s1);
1561:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1562:     /* Overlap communication with computation. */
1563:     VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1564:   }
1565:   if (ll) {
1566:     VecGetLocalSize(ll,&s1);
1567:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1568:     (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1569:   }
1570:   /* scale  the diagonal block */
1571:   (*a->ops->diagonalscale)(a,ll,rr);

1573:   if (rr) {
1574:     /* Do a scatter end and then right scale the off-diagonal block */
1575:     VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1576:     (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1577:   }
1578: 
1579:   return(0);
1580: }

1582: int MatZeroRows_MPIBAIJ(Mat A,IS is,Scalar *diag)
1583: {
1584:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;
1585:   int            i,ierr,N,*rows,*owners = l->rowners,size = l->size;
1586:   int            *procs,*nprocs,j,idx,nsends,*work,row;
1587:   int            nmax,*svalues,*starts,*owner,nrecvs,rank = l->rank;
1588:   int            *rvalues,tag = A->tag,count,base,slen,n,*source;
1589:   int            *lens,imdex,*lrows,*values,bs=l->bs,rstart_bs=l->rstart_bs;
1590:   MPI_Comm       comm = A->comm;
1591:   MPI_Request    *send_waits,*recv_waits;
1592:   MPI_Status     recv_status,*send_status;
1593:   IS             istmp;
1594:   PetscTruth     found;
1595: 
1597:   ISGetLocalSize(is,&N);
1598:   ISGetIndices(is,&rows);
1599: 
1600:   /*  first count number of contributors to each processor */
1601:   ierr  = PetscMalloc(2*size*sizeof(int),&nprocs);
1602:   ierr  = PetscMemzero(nprocs,2*size*sizeof(int));
1603:   procs = nprocs + size;
1604:   ierr  = PetscMalloc((N+1)*sizeof(int),&owner); /* see note*/
1605:   for (i=0; i<N; i++) {
1606:     idx   = rows[i];
1607:     found = PETSC_FALSE;
1608:     for (j=0; j<size; j++) {
1609:       if (idx >= owners[j]*bs && idx < owners[j+1]*bs) {
1610:         nprocs[j]++; procs[j] = 1; owner[i] = j; found = PETSC_TRUE; break;
1611:       }
1612:     }
1613:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1614:   }
1615:   nsends = 0;  for (i=0; i<size; i++) { nsends += procs[i];}
1616: 
1617:   /* inform other processors of number of messages and max length*/
1618:   ierr   = PetscMalloc(2*size*sizeof(int),&work);
1619:   ierr   = MPI_Allreduce(nprocs,work,2*size,MPI_INT,PetscMaxSum_Op,comm);
1620:   nmax   = work[rank];
1621:   nrecvs = work[size+rank];
1622:   ierr   = PetscFree(work);
1623: 
1624:   /* post receives:   */
1625:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(int),&rvalues);
1626:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1627:   for (i=0; i<nrecvs; i++) {
1628:     MPI_Irecv(rvalues+nmax*i,nmax,MPI_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1629:   }
1630: 
1631:   /* do sends:
1632:      1) starts[i] gives the starting index in svalues for stuff going to 
1633:      the ith processor
1634:   */
1635:   PetscMalloc((N+1)*sizeof(int),&svalues);
1636:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1637:   PetscMalloc((size+1)*sizeof(int),&starts);
1638:   starts[0]  = 0;
1639:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[i-1];}
1640:   for (i=0; i<N; i++) {
1641:     svalues[starts[owner[i]]++] = rows[i];
1642:   }
1643:   ISRestoreIndices(is,&rows);
1644: 
1645:   starts[0] = 0;
1646:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[i-1];}
1647:   count = 0;
1648:   for (i=0; i<size; i++) {
1649:     if (procs[i]) {
1650:       MPI_Isend(svalues+starts[i],nprocs[i],MPI_INT,i,tag,comm,send_waits+count++);
1651:     }
1652:   }
1653:   PetscFree(starts);

1655:   base = owners[rank]*bs;
1656: 
1657:   /*  wait on receives */
1658:   ierr   = PetscMalloc(2*(nrecvs+1)*sizeof(int),&lens);
1659:   source = lens + nrecvs;
1660:   count  = nrecvs; slen = 0;
1661:   while (count) {
1662:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1663:     /* unpack receives into our local space */
1664:     MPI_Get_count(&recv_status,MPI_INT,&n);
1665:     source[imdex]  = recv_status.MPI_SOURCE;
1666:     lens[imdex]    = n;
1667:     slen          += n;
1668:     count--;
1669:   }
1670:   PetscFree(recv_waits);
1671: 
1672:   /* move the data into the send scatter */
1673:   PetscMalloc((slen+1)*sizeof(int),&lrows);
1674:   count = 0;
1675:   for (i=0; i<nrecvs; i++) {
1676:     values = rvalues + i*nmax;
1677:     for (j=0; j<lens[i]; j++) {
1678:       lrows[count++] = values[j] - base;
1679:     }
1680:   }
1681:   PetscFree(rvalues);
1682:   PetscFree(lens);
1683:   PetscFree(owner);
1684:   PetscFree(nprocs);
1685: 
1686:   /* actually zap the local rows */
1687:   ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);
1688:   PetscLogObjectParent(A,istmp);

1690:   /*
1691:         Zero the required rows. If the "diagonal block" of the matrix
1692:      is square and the user wishes to set the diagonal we use seperate
1693:      code so that MatSetValues() is not called for each diagonal allocating
1694:      new memory, thus calling lots of mallocs and slowing things down.

1696:        Contributed by: Mathew Knepley
1697:   */
1698:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1699:   MatZeroRows_SeqBAIJ(l->B,istmp,0);
1700:   if (diag && (l->A->M == l->A->N)) {
1701:     MatZeroRows_SeqBAIJ(l->A,istmp,diag);
1702:   } else if (diag) {
1703:     MatZeroRows_SeqBAIJ(l->A,istmp,0);
1704:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) {
1705:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options n
1706: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1707:     }
1708:     for (i=0; i<slen; i++) {
1709:       row  = lrows[i] + rstart_bs;
1710:       MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);
1711:     }
1712:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1713:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1714:   } else {
1715:     MatZeroRows_SeqBAIJ(l->A,istmp,0);
1716:   }

1718:   ISDestroy(istmp);
1719:   PetscFree(lrows);

1721:   /* wait on sends */
1722:   if (nsends) {
1723:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1724:     MPI_Waitall(nsends,send_waits,send_status);
1725:     PetscFree(send_status);
1726:   }
1727:   PetscFree(send_waits);
1728:   PetscFree(svalues);

1730:   return(0);
1731: }

1733: int MatPrintHelp_MPIBAIJ(Mat A)
1734: {
1735:   Mat_MPIBAIJ *a   = (Mat_MPIBAIJ*)A->data;
1736:   MPI_Comm    comm = A->comm;
1737:   static int  called = 0;
1738:   int         ierr;

1741:   if (!a->rank) {
1742:     MatPrintHelp_SeqBAIJ(a->A);
1743:   }
1744:   if (called) {return(0);} else called = 1;
1745:   (*PetscHelpPrintf)(comm," Options for MATMPIBAIJ matrix format (the defaults):n");
1746:   (*PetscHelpPrintf)(comm,"  -mat_use_hash_table <factor>: Use hashtable for efficient matrix assemblyn");
1747:   return(0);
1748: }

1750: int MatSetUnfactored_MPIBAIJ(Mat A)
1751: {
1752:   Mat_MPIBAIJ *a   = (Mat_MPIBAIJ*)A->data;
1753:   int         ierr;

1756:   MatSetUnfactored(a->A);
1757:   return(0);
1758: }

1760: static int MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat *);

1762: int MatEqual_MPIBAIJ(Mat A,Mat B,PetscTruth *flag)
1763: {
1764:   Mat_MPIBAIJ *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1765:   Mat         a,b,c,d;
1766:   PetscTruth  flg;
1767:   int         ierr;

1770:   PetscTypeCompare((PetscObject)B,MATMPIBAIJ,&flg);
1771:   if (!flg) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type");
1772:   a = matA->A; b = matA->B;
1773:   c = matB->A; d = matB->B;

1775:   MatEqual(a,c,&flg);
1776:   if (flg == PETSC_TRUE) {
1777:     MatEqual(b,d,&flg);
1778:   }
1779:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1780:   return(0);
1781: }


1784: int MatSetUpPreallocation_MPIBAIJ(Mat A)
1785: {
1786:   int        ierr;

1789:    MatMPIBAIJSetPreallocation(A,1,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1790:   return(0);
1791: }

1793: /* -------------------------------------------------------------------*/
1794: static struct _MatOps MatOps_Values = {
1795:   MatSetValues_MPIBAIJ,
1796:   MatGetRow_MPIBAIJ,
1797:   MatRestoreRow_MPIBAIJ,
1798:   MatMult_MPIBAIJ,
1799:   MatMultAdd_MPIBAIJ,
1800:   MatMultTranspose_MPIBAIJ,
1801:   MatMultTransposeAdd_MPIBAIJ,
1802:   0,
1803:   0,
1804:   0,
1805:   0,
1806:   0,
1807:   0,
1808:   0,
1809:   MatTranspose_MPIBAIJ,
1810:   MatGetInfo_MPIBAIJ,
1811:   MatEqual_MPIBAIJ,
1812:   MatGetDiagonal_MPIBAIJ,
1813:   MatDiagonalScale_MPIBAIJ,
1814:   MatNorm_MPIBAIJ,
1815:   MatAssemblyBegin_MPIBAIJ,
1816:   MatAssemblyEnd_MPIBAIJ,
1817:   0,
1818:   MatSetOption_MPIBAIJ,
1819:   MatZeroEntries_MPIBAIJ,
1820:   MatZeroRows_MPIBAIJ,
1821:   0,
1822:   0,
1823:   0,
1824:   0,
1825:   MatSetUpPreallocation_MPIBAIJ,
1826:   0,
1827:   MatGetOwnershipRange_MPIBAIJ,
1828:   0,
1829:   0,
1830:   0,
1831:   0,
1832:   MatDuplicate_MPIBAIJ,
1833:   0,
1834:   0,
1835:   0,
1836:   0,
1837:   0,
1838:   MatGetSubMatrices_MPIBAIJ,
1839:   MatIncreaseOverlap_MPIBAIJ,
1840:   MatGetValues_MPIBAIJ,
1841:   0,
1842:   MatPrintHelp_MPIBAIJ,
1843:   MatScale_MPIBAIJ,
1844:   0,
1845:   0,
1846:   0,
1847:   MatGetBlockSize_MPIBAIJ,
1848:   0,
1849:   0,
1850:   0,
1851:   0,
1852:   0,
1853:   0,
1854:   MatSetUnfactored_MPIBAIJ,
1855:   0,
1856:   MatSetValuesBlocked_MPIBAIJ,
1857:   0,
1858:   MatDestroy_MPIBAIJ,
1859:   MatView_MPIBAIJ,
1860:   MatGetMaps_Petsc,
1861:   0,
1862:   0,
1863:   0,
1864:   0,
1865:   0,
1866:   0,
1867:   MatGetRowMax_MPIBAIJ};


1870: EXTERN_C_BEGIN
1871: int MatGetDiagonalBlock_MPIBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
1872: {
1874:   *a      = ((Mat_MPIBAIJ *)A->data)->A;
1875:   *iscopy = PETSC_FALSE;
1876:   return(0);
1877: }
1878: EXTERN_C_END

1880: EXTERN_C_BEGIN
1881: int MatCreate_MPIBAIJ(Mat B)
1882: {
1883:   Mat_MPIBAIJ  *b;
1884:   int          ierr;
1885:   PetscTruth   flg;


1889:   PetscNew(Mat_MPIBAIJ,&b);
1890:   B->data = (void*)b;

1892:   ierr    = PetscMemzero(b,sizeof(Mat_MPIBAIJ));
1893:   ierr    = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
1894:   B->mapping    = 0;
1895:   B->factor     = 0;
1896:   B->assembled  = PETSC_FALSE;

1898:   B->insertmode = NOT_SET_VALUES;
1899:   MPI_Comm_rank(B->comm,&b->rank);
1900:   MPI_Comm_size(B->comm,&b->size);

1902:   /* build local table of row and column ownerships */
1903:   ierr          = PetscMalloc(3*(b->size+2)*sizeof(int),&b->rowners);
1904:   PetscLogObjectMemory(B,3*(b->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPIBAIJ));
1905:   b->cowners    = b->rowners + b->size + 2;
1906:   b->rowners_bs = b->cowners + b->size + 2;

1908:   /* build cache for off array entries formed */
1909:   MatStashCreate_Private(B->comm,1,&B->stash);
1910:   b->donotstash  = PETSC_FALSE;
1911:   b->colmap      = PETSC_NULL;
1912:   b->garray      = PETSC_NULL;
1913:   b->roworiented = PETSC_TRUE;

1915: #if defined(PETSC_USE_MAT_SINGLE)
1916:   /* stuff for MatSetValues_XXX in single precision */
1917:   b->setvalueslen     = 0;
1918:   b->setvaluescopy    = PETSC_NULL;
1919: #endif

1921:   /* stuff used in block assembly */
1922:   b->barray       = 0;

1924:   /* stuff used for matrix vector multiply */
1925:   b->lvec         = 0;
1926:   b->Mvctx        = 0;

1928:   /* stuff for MatGetRow() */
1929:   b->rowindices   = 0;
1930:   b->rowvalues    = 0;
1931:   b->getrowactive = PETSC_FALSE;

1933:   /* hash table stuff */
1934:   b->ht           = 0;
1935:   b->hd           = 0;
1936:   b->ht_size      = 0;
1937:   b->ht_flag      = PETSC_FALSE;
1938:   b->ht_fact      = 0;
1939:   b->ht_total_ct  = 0;
1940:   b->ht_insert_ct = 0;

1942:   PetscOptionsHasName(PETSC_NULL,"-mat_use_hash_table",&flg);
1943:   if (flg) {
1944:     double fact = 1.39;
1945:     MatSetOption(B,MAT_USE_HASH_TABLE);
1946:     PetscOptionsGetDouble(PETSC_NULL,"-mat_use_hash_table",&fact,PETSC_NULL);
1947:     if (fact <= 1.0) fact = 1.39;
1948:     MatMPIBAIJSetHashTableFactor(B,fact);
1949:     PetscLogInfo(0,"MatCreateMPIBAIJ:Hash table Factor used %5.2fn",fact);
1950:   }
1951:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
1952:                                      "MatStoreValues_MPIBAIJ",
1953:                                      MatStoreValues_MPIBAIJ);
1954:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
1955:                                      "MatRetrieveValues_MPIBAIJ",
1956:                                      MatRetrieveValues_MPIBAIJ);
1957:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
1958:                                      "MatGetDiagonalBlock_MPIBAIJ",
1959:                                      MatGetDiagonalBlock_MPIBAIJ);
1960:   return(0);
1961: }
1962: EXTERN_C_END

1964: /*@C
1965:    MatMPIBAIJSetPreallocation - Creates a sparse parallel matrix in block AIJ format
1966:    (block compressed row).  For good matrix assembly performance
1967:    the user should preallocate the matrix storage by setting the parameters 
1968:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
1969:    performance can be increased by more than a factor of 50.

1971:    Collective on Mat

1973:    Input Parameters:
1974: +  A - the matrix 
1975: .  bs   - size of blockk
1976: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
1977:            submatrix  (same for all local rows)
1978: .  d_nnz - array containing the number of block nonzeros in the various block rows 
1979:            of the in diagonal portion of the local (possibly different for each block
1980:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
1981: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
1982:            submatrix (same for all local rows).
1983: -  o_nnz - array containing the number of nonzeros in the various block rows of the
1984:            off-diagonal portion of the local submatrix (possibly different for
1985:            each block row) or PETSC_NULL.

1987:    Output Parameter:


1990:    Options Database Keys:
1991: .   -mat_no_unroll - uses code that does not unroll the loops in the 
1992:                      block calculations (much slower)
1993: .   -mat_block_size - size of the blocks to use

1995:    Notes:
1996:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
1997:    than it must be used on all processors that share the object for that argument.

1999:    Storage Information:
2000:    For a square global matrix we define each processor's diagonal portion 
2001:    to be its local rows and the corresponding columns (a square submatrix);  
2002:    each processor's off-diagonal portion encompasses the remainder of the
2003:    local matrix (a rectangular submatrix). 

2005:    The user can specify preallocated storage for the diagonal part of
2006:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2007:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2008:    memory allocation.  Likewise, specify preallocated storage for the
2009:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2011:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2012:    the figure below we depict these three local rows and all columns (0-11).

2014: .vb
2015:            0 1 2 3 4 5 6 7 8 9 10 11
2016:           -------------------
2017:    row 3  |  o o o d d d o o o o o o
2018:    row 4  |  o o o d d d o o o o o o
2019:    row 5  |  o o o d d d o o o o o o
2020:           -------------------
2021: .ve
2022:   
2023:    Thus, any entries in the d locations are stored in the d (diagonal) 
2024:    submatrix, and any entries in the o locations are stored in the
2025:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2026:    stored simply in the MATSEQBAIJ format for compressed row storage.

2028:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2029:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2030:    In general, for PDE problems in which most nonzeros are near the diagonal,
2031:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2032:    or you will get TERRIBLE performance; see the users' manual chapter on
2033:    matrices.

2035:    Level: intermediate

2037: .keywords: matrix, block, aij, compressed row, sparse, parallel

2039: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2040: @*/
2041: int MatMPIBAIJSetPreallocation(Mat B,int bs,int d_nz,int *d_nnz,int o_nz,int *o_nnz)
2042: {
2043:   Mat_MPIBAIJ  *b;
2044:   int          ierr,i;
2045:   PetscTruth   flg2;

2048:   PetscTypeCompare((PetscObject)B,MATMPIBAIJ,&flg2);
2049:   if (!flg2) return(0);

2051:   B->preallocated = PETSC_TRUE;
2052:   PetscOptionsGetInt(PETSC_NULL,"-mat_block_size",&bs,PETSC_NULL);

2054:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
2055:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2056:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2057:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %d",d_nz);
2058:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %d",o_nz);
2059:   if (d_nnz) {
2060:   for (i=0; i<B->m/bs; i++) {
2061:       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]);
2062:     }
2063:   }
2064:   if (o_nnz) {
2065:     for (i=0; i<B->m/bs; i++) {
2066:       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]);
2067:     }
2068:   }
2069: 
2070:   PetscSplitOwnershipBlock(B->comm,bs,&B->m,&B->M);
2071:   PetscSplitOwnershipBlock(B->comm,bs,&B->n,&B->N);
2072:   MapCreateMPI(B->comm,B->m,B->M,&B->rmap);
2073:   MapCreateMPI(B->comm,B->n,B->N,&B->cmap);

2075:   b = (Mat_MPIBAIJ*)B->data;
2076:   b->bs  = bs;
2077:   b->bs2 = bs*bs;
2078:   b->mbs = B->m/bs;
2079:   b->nbs = B->n/bs;
2080:   b->Mbs = B->M/bs;
2081:   b->Nbs = B->N/bs;

2083:   MPI_Allgather(&b->mbs,1,MPI_INT,b->rowners+1,1,MPI_INT,B->comm);
2084:   b->rowners[0]    = 0;
2085:   for (i=2; i<=b->size; i++) {
2086:     b->rowners[i] += b->rowners[i-1];
2087:   }
2088:   b->rstart    = b->rowners[b->rank];
2089:   b->rend      = b->rowners[b->rank+1];

2091:   MPI_Allgather(&b->nbs,1,MPI_INT,b->cowners+1,1,MPI_INT,B->comm);
2092:   b->cowners[0] = 0;
2093:   for (i=2; i<=b->size; i++) {
2094:     b->cowners[i] += b->cowners[i-1];
2095:   }
2096:   b->cstart    = b->cowners[b->rank];
2097:   b->cend      = b->cowners[b->rank+1];

2099:   for (i=0; i<=b->size; i++) {
2100:     b->rowners_bs[i] = b->rowners[i]*bs;
2101:   }
2102:   b->rstart_bs = b->rstart*bs;
2103:   b->rend_bs   = b->rend*bs;
2104:   b->cstart_bs = b->cstart*bs;
2105:   b->cend_bs   = b->cend*bs;

2107:   MatCreateSeqBAIJ(PETSC_COMM_SELF,bs,B->m,B->n,d_nz,d_nnz,&b->A);
2108:   PetscLogObjectParent(B,b->A);
2109:   MatCreateSeqBAIJ(PETSC_COMM_SELF,bs,B->m,B->N,o_nz,o_nnz,&b->B);
2110:   PetscLogObjectParent(B,b->B);
2111:   MatStashCreate_Private(B->comm,bs,&B->bstash);

2113:   return(0);
2114: }

2116: /*@C
2117:    MatCreateMPIBAIJ - Creates a sparse parallel matrix in block AIJ format
2118:    (block compressed row).  For good matrix assembly performance
2119:    the user should preallocate the matrix storage by setting the parameters 
2120:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2121:    performance can be increased by more than a factor of 50.

2123:    Collective on MPI_Comm

2125:    Input Parameters:
2126: +  comm - MPI communicator
2127: .  bs   - size of blockk
2128: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2129:            This value should be the same as the local size used in creating the 
2130:            y vector for the matrix-vector product y = Ax.
2131: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
2132:            This value should be the same as the local size used in creating the 
2133:            x vector for the matrix-vector product y = Ax.
2134: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2135: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2136: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local 
2137:            submatrix  (same for all local rows)
2138: .  d_nnz - array containing the number of nonzero blocks in the various block rows 
2139:            of the in diagonal portion of the local (possibly different for each block
2140:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2141: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
2142:            submatrix (same for all local rows).
2143: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
2144:            off-diagonal portion of the local submatrix (possibly different for
2145:            each block row) or PETSC_NULL.

2147:    Output Parameter:
2148: .  A - the matrix 

2150:    Options Database Keys:
2151: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2152:                      block calculations (much slower)
2153: .   -mat_block_size - size of the blocks to use

2155:    Notes:
2156:    A nonzero block is any block that as 1 or more nonzeros in it

2158:    The user MUST specify either the local or global matrix dimensions
2159:    (possibly both).

2161:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2162:    than it must be used on all processors that share the object for that argument.

2164:    Storage Information:
2165:    For a square global matrix we define each processor's diagonal portion 
2166:    to be its local rows and the corresponding columns (a square submatrix);  
2167:    each processor's off-diagonal portion encompasses the remainder of the
2168:    local matrix (a rectangular submatrix). 

2170:    The user can specify preallocated storage for the diagonal part of
2171:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2172:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2173:    memory allocation.  Likewise, specify preallocated storage for the
2174:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2176:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2177:    the figure below we depict these three local rows and all columns (0-11).

2179: .vb
2180:            0 1 2 3 4 5 6 7 8 9 10 11
2181:           -------------------
2182:    row 3  |  o o o d d d o o o o o o
2183:    row 4  |  o o o d d d o o o o o o
2184:    row 5  |  o o o d d d o o o o o o
2185:           -------------------
2186: .ve
2187:   
2188:    Thus, any entries in the d locations are stored in the d (diagonal) 
2189:    submatrix, and any entries in the o locations are stored in the
2190:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2191:    stored simply in the MATSEQBAIJ format for compressed row storage.

2193:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2194:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2195:    In general, for PDE problems in which most nonzeros are near the diagonal,
2196:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2197:    or you will get TERRIBLE performance; see the users' manual chapter on
2198:    matrices.

2200:    Level: intermediate

2202: .keywords: matrix, block, aij, compressed row, sparse, parallel

2204: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2205: @*/
2206: int MatCreateMPIBAIJ(MPI_Comm comm,int bs,int m,int n,int M,int N,int d_nz,int *d_nnz,int o_nz,int *o_nnz,Mat *A)
2207: {
2208:   int ierr,size;

2211:   MatCreate(comm,m,n,M,N,A);
2212:   MPI_Comm_size(comm,&size);
2213:   if (size > 1) {
2214:     MatSetType(*A,MATMPIBAIJ);
2215:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2216:   } else {
2217:     MatSetType(*A,MATSEQBAIJ);
2218:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2219:   }
2220:   return(0);
2221: }

2223: static int MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2224: {
2225:   Mat         mat;
2226:   Mat_MPIBAIJ *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
2227:   int         ierr,len=0;

2230:   *newmat       = 0;
2231:   MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);
2232:   MatSetType(mat,MATMPIBAIJ);
2233:   mat->preallocated = PETSC_TRUE;
2234:   mat->assembled    = PETSC_TRUE;
2235:   a      = (Mat_MPIBAIJ*)mat->data;
2236:   a->bs  = oldmat->bs;
2237:   a->bs2 = oldmat->bs2;
2238:   a->mbs = oldmat->mbs;
2239:   a->nbs = oldmat->nbs;
2240:   a->Mbs = oldmat->Mbs;
2241:   a->Nbs = oldmat->Nbs;
2242: 
2243:   a->rstart       = oldmat->rstart;
2244:   a->rend         = oldmat->rend;
2245:   a->cstart       = oldmat->cstart;
2246:   a->cend         = oldmat->cend;
2247:   a->size         = oldmat->size;
2248:   a->rank         = oldmat->rank;
2249:   a->donotstash   = oldmat->donotstash;
2250:   a->roworiented  = oldmat->roworiented;
2251:   a->rowindices   = 0;
2252:   a->rowvalues    = 0;
2253:   a->getrowactive = PETSC_FALSE;
2254:   a->barray       = 0;
2255:   a->rstart_bs    = oldmat->rstart_bs;
2256:   a->rend_bs      = oldmat->rend_bs;
2257:   a->cstart_bs    = oldmat->cstart_bs;
2258:   a->cend_bs      = oldmat->cend_bs;

2260:   /* hash table stuff */
2261:   a->ht           = 0;
2262:   a->hd           = 0;
2263:   a->ht_size      = 0;
2264:   a->ht_flag      = oldmat->ht_flag;
2265:   a->ht_fact      = oldmat->ht_fact;
2266:   a->ht_total_ct  = 0;
2267:   a->ht_insert_ct = 0;

2269:   PetscMemcpy(a->rowners,oldmat->rowners,3*(a->size+2)*sizeof(int));
2270:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2271:   MatStashCreate_Private(matin->comm,oldmat->bs,&mat->bstash);
2272:   if (oldmat->colmap) {
2273: #if defined (PETSC_USE_CTABLE)
2274:   PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2275: #else
2276:   PetscMalloc((a->Nbs)*sizeof(int),&a->colmap);
2277:   PetscLogObjectMemory(mat,(a->Nbs)*sizeof(int));
2278:   PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(int));
2279: #endif
2280:   } else a->colmap = 0;
2281:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2282:     PetscMalloc(len*sizeof(int),&a->garray);
2283:     PetscLogObjectMemory(mat,len*sizeof(int));
2284:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(int));
2285:   } else a->garray = 0;
2286: 
2287:    VecDuplicate(oldmat->lvec,&a->lvec);
2288:   PetscLogObjectParent(mat,a->lvec);
2289:    VecScatterCopy(oldmat->Mvctx,&a->Mvctx);

2291:   PetscLogObjectParent(mat,a->Mvctx);
2292:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2293:   PetscLogObjectParent(mat,a->A);
2294:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2295:   PetscLogObjectParent(mat,a->B);
2296:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2297:   *newmat = mat;
2298:   return(0);
2299: }

2301: #include "petscsys.h"

2303: EXTERN_C_BEGIN
2304: int MatLoad_MPIBAIJ(PetscViewer viewer,MatType type,Mat *newmat)
2305: {
2306:   Mat          A;
2307:   int          i,nz,ierr,j,rstart,rend,fd;
2308:   Scalar       *vals,*buf;
2309:   MPI_Comm     comm = ((PetscObject)viewer)->comm;
2310:   MPI_Status   status;
2311:   int          header[4],rank,size,*rowlengths = 0,M,N,m,*rowners,*browners,maxnz,*cols;
2312:   int          *locrowlens,*sndcounts = 0,*procsnz = 0,jj,*mycols,*ibuf;
2313:   int          tag = ((PetscObject)viewer)->tag,bs=1,Mbs,mbs,extra_rows;
2314:   int          *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
2315:   int          dcount,kmax,k,nzcount,tmp;
2316: 
2318:   PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);

2320:   MPI_Comm_size(comm,&size);
2321:   MPI_Comm_rank(comm,&rank);
2322:   if (!rank) {
2323:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2324:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2325:     if (header[0] != MAT_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2326:     if (header[3] < 0) {
2327:       SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as MPIBAIJ");
2328:     }
2329:   }

2331:   MPI_Bcast(header+1,3,MPI_INT,0,comm);
2332:   M = header[1]; N = header[2];

2334:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2336:   /* 
2337:      This code adds extra rows to make sure the number of rows is 
2338:      divisible by the blocksize
2339:   */
2340:   Mbs        = M/bs;
2341:   extra_rows = bs - M + bs*(Mbs);
2342:   if (extra_rows == bs) extra_rows = 0;
2343:   else                  Mbs++;
2344:   if (extra_rows &&!rank) {
2345:     PetscLogInfo(0,"MatLoad_MPIBAIJ:Padding loaded matrix to match blocksizen");
2346:   }

2348:   /* determine ownership of all rows */
2349:   mbs        = Mbs/size + ((Mbs % size) > rank);
2350:   m          = mbs*bs;
2351:   ierr       = PetscMalloc(2*(size+2)*sizeof(int),&rowners);
2352:   browners   = rowners + size + 1;
2353:   ierr       = MPI_Allgather(&mbs,1,MPI_INT,rowners+1,1,MPI_INT,comm);
2354:   rowners[0] = 0;
2355:   for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
2356:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2357:   rstart = rowners[rank];
2358:   rend   = rowners[rank+1];

2360:   /* distribute row lengths to all processors */
2361:   PetscMalloc((rend-rstart)*bs*sizeof(int),&locrowlens);
2362:   if (!rank) {
2363:     PetscMalloc((M+extra_rows)*sizeof(int),&rowlengths);
2364:     PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2365:     for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2366:     PetscMalloc(size*sizeof(int),&sndcounts);
2367:     for (i=0; i<size; i++) sndcounts[i] = browners[i+1] - browners[i];
2368:     MPI_Scatterv(rowlengths,sndcounts,browners,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
2369:     PetscFree(sndcounts);
2370:   } else {
2371:     MPI_Scatterv(0,0,0,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
2372:   }

2374:   if (!rank) {
2375:     /* calculate the number of nonzeros on each processor */
2376:     PetscMalloc(size*sizeof(int),&procsnz);
2377:     PetscMemzero(procsnz,size*sizeof(int));
2378:     for (i=0; i<size; i++) {
2379:       for (j=rowners[i]*bs; j< rowners[i+1]*bs; j++) {
2380:         procsnz[i] += rowlengths[j];
2381:       }
2382:     }
2383:     PetscFree(rowlengths);
2384: 
2385:     /* determine max buffer needed and allocate it */
2386:     maxnz = 0;
2387:     for (i=0; i<size; i++) {
2388:       maxnz = PetscMax(maxnz,procsnz[i]);
2389:     }
2390:     PetscMalloc(maxnz*sizeof(int),&cols);

2392:     /* read in my part of the matrix column indices  */
2393:     nz     = procsnz[0];
2394:     ierr   = PetscMalloc(nz*sizeof(int),&ibuf);
2395:     mycols = ibuf;
2396:     if (size == 1)  nz -= extra_rows;
2397:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2398:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2400:     /* read in every ones (except the last) and ship off */
2401:     for (i=1; i<size-1; i++) {
2402:       nz   = procsnz[i];
2403:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2404:       MPI_Send(cols,nz,MPI_INT,i,tag,comm);
2405:     }
2406:     /* read in the stuff for the last proc */
2407:     if (size != 1) {
2408:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2409:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2410:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2411:       MPI_Send(cols,nz+extra_rows,MPI_INT,size-1,tag,comm);
2412:     }
2413:     PetscFree(cols);
2414:   } else {
2415:     /* determine buffer space needed for message */
2416:     nz = 0;
2417:     for (i=0; i<m; i++) {
2418:       nz += locrowlens[i];
2419:     }
2420:     ierr   = PetscMalloc(nz*sizeof(int),&ibuf);
2421:     mycols = ibuf;
2422:     /* receive message of column indices*/
2423:     MPI_Recv(mycols,nz,MPI_INT,0,tag,comm,&status);
2424:     MPI_Get_count(&status,MPI_INT,&maxnz);
2425:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2426:   }
2427: 
2428:   /* loop over local rows, determining number of off diagonal entries */
2429:   ierr     = PetscMalloc(2*(rend-rstart+1)*sizeof(int),&dlens);
2430:   odlens   = dlens + (rend-rstart);
2431:   ierr     = PetscMalloc(3*Mbs*sizeof(int),&mask);
2432:   ierr     = PetscMemzero(mask,3*Mbs*sizeof(int));
2433:   masked1  = mask    + Mbs;
2434:   masked2  = masked1 + Mbs;
2435:   rowcount = 0; nzcount = 0;
2436:   for (i=0; i<mbs; i++) {
2437:     dcount  = 0;
2438:     odcount = 0;
2439:     for (j=0; j<bs; j++) {
2440:       kmax = locrowlens[rowcount];
2441:       for (k=0; k<kmax; k++) {
2442:         tmp = mycols[nzcount++]/bs;
2443:         if (!mask[tmp]) {
2444:           mask[tmp] = 1;
2445:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
2446:           else masked1[dcount++] = tmp;
2447:         }
2448:       }
2449:       rowcount++;
2450:     }
2451: 
2452:     dlens[i]  = dcount;
2453:     odlens[i] = odcount;

2455:     /* zero out the mask elements we set */
2456:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2457:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2458:   }

2460:   /* create our matrix */
2461:   MatCreateMPIBAIJ(comm,bs,m,m,M+extra_rows,N+extra_rows,0,dlens,0,odlens,newmat);
2462:   A = *newmat;
2463:   MatSetOption(A,MAT_COLUMNS_SORTED);
2464: 
2465:   if (!rank) {
2466:     PetscMalloc(maxnz*sizeof(Scalar),&buf);
2467:     /* read in my part of the matrix numerical values  */
2468:     nz = procsnz[0];
2469:     vals = buf;
2470:     mycols = ibuf;
2471:     if (size == 1)  nz -= extra_rows;
2472:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2473:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2475:     /* insert into matrix */
2476:     jj      = rstart*bs;
2477:     for (i=0; i<m; i++) {
2478:       MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2479:       mycols += locrowlens[i];
2480:       vals   += locrowlens[i];
2481:       jj++;
2482:     }
2483:     /* read in other processors (except the last one) and ship out */
2484:     for (i=1; i<size-1; i++) {
2485:       nz   = procsnz[i];
2486:       vals = buf;
2487:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2488:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2489:     }
2490:     /* the last proc */
2491:     if (size != 1){
2492:       nz   = procsnz[i] - extra_rows;
2493:       vals = buf;
2494:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2495:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2496:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2497:     }
2498:     PetscFree(procsnz);
2499:   } else {
2500:     /* receive numeric values */
2501:     PetscMalloc(nz*sizeof(Scalar),&buf);

2503:     /* receive message of values*/
2504:     vals   = buf;
2505:     mycols = ibuf;
2506:     ierr   = MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2507:     ierr   = MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2508:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2510:     /* insert into matrix */
2511:     jj      = rstart*bs;
2512:     for (i=0; i<m; i++) {
2513:       ierr    = MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2514:       mycols += locrowlens[i];
2515:       vals   += locrowlens[i];
2516:       jj++;
2517:     }
2518:   }
2519:   PetscFree(locrowlens);
2520:   PetscFree(buf);
2521:   PetscFree(ibuf);
2522:   PetscFree(rowners);
2523:   PetscFree(dlens);
2524:   PetscFree(mask);
2525:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2526:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2527:   return(0);
2528: }
2529: EXTERN_C_END

2531: /*@
2532:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2534:    Input Parameters:
2535: .  mat  - the matrix
2536: .  fact - factor

2538:    Collective on Mat

2540:    Level: advanced

2542:   Notes:
2543:    This can also be set by the command line option: -mat_use_hash_table fact

2545: .keywords: matrix, hashtable, factor, HT

2547: .seealso: MatSetOption()
2548: @*/
2549: int MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2550: {
2551:   Mat_MPIBAIJ *baij;
2552:   int         ierr;
2553:   PetscTruth  flg;

2557:   PetscTypeCompare((PetscObject)mat,MATMPIBAIJ,&flg);
2558:   if (!flg) {
2559:     SETERRQ(PETSC_ERR_ARG_WRONG,"Incorrect matrix type. Use MPIBAIJ only.");
2560:   }
2561:   baij = (Mat_MPIBAIJ*)mat->data;
2562:   baij->ht_fact = fact;
2563:   return(0);
2564: }

2566: int MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,int **colmap)
2567: {
2568:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2570:   *Ad     = a->A;
2571:   *Ao     = a->B;
2572:   *colmap = a->garray;
2573:   return(0);
2574: }