Actual source code: mpibaij.c

  1: /*$Id: mpibaij.c,v 1.234 2001/09/25 22:56:49 balay Exp $*/

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

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

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

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

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

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

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

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

 67: EXTERN_C_BEGIN
 70: int MatStoreValues_MPIBAIJ(Mat mat)
 71: {
 72:   Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
 73:   int         ierr;

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

 82: EXTERN_C_BEGIN
 85: int MatRetrieveValues_MPIBAIJ(Mat mat)
 86: {
 87:   Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
 88:   int         ierr;

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

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

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

126: #define CHUNKSIZE  10

128: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv) \
129: { \
130:  \
131:     brow = row/bs;  \
132:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
133:     rmax = aimax[brow]; nrow = ailen[brow]; \
134:       bcol = col/bs; \
135:       ridx = row % bs; cidx = col % bs; \
136:       low = 0; high = nrow; \
137:       while (high-low > 3) { \
138:         t = (low+high)/2; \
139:         if (rp[t] > bcol) high = t; \
140:         else              low  = t; \
141:       } \
142:       for (_i=low; _i<high; _i++) { \
143:         if (rp[_i] > bcol) break; \
144:         if (rp[_i] == bcol) { \
145:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
146:           if (addv == ADD_VALUES) *bap += value;  \
147:           else                    *bap  = value;  \
148:           goto a_noinsert; \
149:         } \
150:       } \
151:       if (a->nonew == 1) goto a_noinsert; \
152:       else if (a->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%d, %d) into matrix", row, col); \
153:       if (nrow >= rmax) { \
154:         /* there is no extra room in row, therefore enlarge */ \
155:         int       new_nz = ai[a->mbs] + CHUNKSIZE,len,*new_i,*new_j; \
156:         MatScalar *new_a; \
157:  \
158:         if (a->nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%d, %d) in the matrix", row, col); \
159:  \
160:         /* malloc new storage space */ \
161:         len     = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(a->mbs+1)*sizeof(int); \
162:         PetscMalloc(len,&new_a); \
163:         new_j   = (int*)(new_a + bs2*new_nz); \
164:         new_i   = new_j + new_nz; \
165:  \
166:         /* copy over old data into new slots */ \
167:         for (ii=0; ii<brow+1; ii++) {new_i[ii] = ai[ii];} \
168:         for (ii=brow+1; ii<a->mbs+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;} \
169:         PetscMemcpy(new_j,aj,(ai[brow]+nrow)*sizeof(int)); \
170:         len = (new_nz - CHUNKSIZE - ai[brow] - nrow); \
171:         PetscMemcpy(new_j+ai[brow]+nrow+CHUNKSIZE,aj+ai[brow]+nrow,len*sizeof(int)); \
172:         PetscMemcpy(new_a,aa,(ai[brow]+nrow)*bs2*sizeof(MatScalar)); \
173:         PetscMemzero(new_a+bs2*(ai[brow]+nrow),bs2*CHUNKSIZE*sizeof(PetscScalar)); \
174:         PetscMemcpy(new_a+bs2*(ai[brow]+nrow+CHUNKSIZE), \
175:                     aa+bs2*(ai[brow]+nrow),bs2*len*sizeof(MatScalar));  \
176:         /* free up old matrix storage */ \
177:         PetscFree(a->a);  \
178:         if (!a->singlemalloc) { \
179:           PetscFree(a->i); \
180:           PetscFree(a->j);\
181:         } \
182:         aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;  \
183:         a->singlemalloc = PETSC_TRUE; \
184:  \
185:         rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
186:         rmax = aimax[brow] = aimax[brow] + CHUNKSIZE; \
187:         PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar))); \
188:         a->maxnz += bs2*CHUNKSIZE; \
189:         a->reallocs++; \
190:         a->nz++; \
191:       } \
192:       N = nrow++ - 1;  \
193:       /* shift up all the later entries in this row */ \
194:       for (ii=N; ii>=_i; ii--) { \
195:         rp[ii+1] = rp[ii]; \
196:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
197:       } \
198:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  \
199:       rp[_i]                      = bcol;  \
200:       ap[bs2*_i + bs*cidx + ridx] = value;  \
201:       a_noinsert:; \
202:     ailen[brow] = nrow; \
203: } 

205: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv) \
206: { \
207:     brow = row/bs;  \
208:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
209:     rmax = bimax[brow]; nrow = bilen[brow]; \
210:       bcol = col/bs; \
211:       ridx = row % bs; cidx = col % bs; \
212:       low = 0; high = nrow; \
213:       while (high-low > 3) { \
214:         t = (low+high)/2; \
215:         if (rp[t] > bcol) high = t; \
216:         else              low  = t; \
217:       } \
218:       for (_i=low; _i<high; _i++) { \
219:         if (rp[_i] > bcol) break; \
220:         if (rp[_i] == bcol) { \
221:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
222:           if (addv == ADD_VALUES) *bap += value;  \
223:           else                    *bap  = value;  \
224:           goto b_noinsert; \
225:         } \
226:       } \
227:       if (b->nonew == 1) goto b_noinsert; \
228:       else if (b->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%d, %d) into matrix", row, col); \
229:       if (nrow >= rmax) { \
230:         /* there is no extra room in row, therefore enlarge */ \
231:         int       new_nz = bi[b->mbs] + CHUNKSIZE,len,*new_i,*new_j; \
232:         MatScalar *new_a; \
233:  \
234:         if (b->nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%d, %d) in the matrix", row, col); \
235:  \
236:         /* malloc new storage space */ \
237:         len     = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(b->mbs+1)*sizeof(int); \
238:         PetscMalloc(len,&new_a); \
239:         new_j   = (int*)(new_a + bs2*new_nz); \
240:         new_i   = new_j + new_nz; \
241:  \
242:         /* copy over old data into new slots */ \
243:         for (ii=0; ii<brow+1; ii++) {new_i[ii] = bi[ii];} \
244:         for (ii=brow+1; ii<b->mbs+1; ii++) {new_i[ii] = bi[ii]+CHUNKSIZE;} \
245:         PetscMemcpy(new_j,bj,(bi[brow]+nrow)*sizeof(int)); \
246:         len  = (new_nz - CHUNKSIZE - bi[brow] - nrow); \
247:         PetscMemcpy(new_j+bi[brow]+nrow+CHUNKSIZE,bj+bi[brow]+nrow,len*sizeof(int)); \
248:         PetscMemcpy(new_a,ba,(bi[brow]+nrow)*bs2*sizeof(MatScalar)); \
249:         PetscMemzero(new_a+bs2*(bi[brow]+nrow),bs2*CHUNKSIZE*sizeof(MatScalar)); \
250:         PetscMemcpy(new_a+bs2*(bi[brow]+nrow+CHUNKSIZE), \
251:                     ba+bs2*(bi[brow]+nrow),bs2*len*sizeof(MatScalar));  \
252:         /* free up old matrix storage */ \
253:         PetscFree(b->a);  \
254:         if (!b->singlemalloc) { \
255:           PetscFree(b->i); \
256:           PetscFree(b->j); \
257:         } \
258:         ba = b->a = new_a; bi = b->i = new_i; bj = b->j = new_j;  \
259:         b->singlemalloc = PETSC_TRUE; \
260:  \
261:         rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
262:         rmax = bimax[brow] = bimax[brow] + CHUNKSIZE; \
263:         PetscLogObjectMemory(B,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar))); \
264:         b->maxnz += bs2*CHUNKSIZE; \
265:         b->reallocs++; \
266:         b->nz++; \
267:       } \
268:       N = nrow++ - 1;  \
269:       /* shift up all the later entries in this row */ \
270:       for (ii=N; ii>=_i; ii--) { \
271:         rp[ii+1] = rp[ii]; \
272:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
273:       } \
274:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  \
275:       rp[_i]                      = bcol;  \
276:       ap[bs2*_i + bs*cidx + ridx] = value;  \
277:       b_noinsert:; \
278:     bilen[brow] = nrow; \
279: } 

281: #if defined(PETSC_USE_MAT_SINGLE)
284: int MatSetValues_MPIBAIJ(Mat mat,int m,const int im[],int n,const int in[],const PetscScalar v[],InsertMode addv)
285: {
286:   Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
287:   int         ierr,i,N = m*n;
288:   MatScalar   *vsingle;

291:   if (N > b->setvalueslen) {
292:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
293:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
294:     b->setvalueslen  = N;
295:   }
296:   vsingle = b->setvaluescopy;

298:   for (i=0; i<N; i++) {
299:     vsingle[i] = v[i];
300:   }
301:   MatSetValues_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
302:   return(0);
303: }

307: int MatSetValuesBlocked_MPIBAIJ(Mat mat,int m,const int im[],int n,const int in[],const PetscScalar v[],InsertMode addv)
308: {
309:   Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
310:   int         ierr,i,N = m*n*b->bs2;
311:   MatScalar   *vsingle;

314:   if (N > b->setvalueslen) {
315:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
316:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
317:     b->setvalueslen  = N;
318:   }
319:   vsingle = b->setvaluescopy;
320:   for (i=0; i<N; i++) {
321:     vsingle[i] = v[i];
322:   }
323:   MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
324:   return(0);
325: }

329: int MatSetValues_MPIBAIJ_HT(Mat mat,int m,const int im[],int n,const int in[],const PetscScalar v[],InsertMode addv)
330: {
331:   Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
332:   int         ierr,i,N = m*n;
333:   MatScalar   *vsingle;

336:   if (N > b->setvalueslen) {
337:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
338:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
339:     b->setvalueslen  = N;
340:   }
341:   vsingle = b->setvaluescopy;
342:   for (i=0; i<N; i++) {
343:     vsingle[i] = v[i];
344:   }
345:   MatSetValues_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
346:   return(0);
347: }

351: int MatSetValuesBlocked_MPIBAIJ_HT(Mat mat,int m,const int im[],int n,const int in[],const PetscScalar v[],InsertMode addv)
352: {
353:   Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
354:   int         ierr,i,N = m*n*b->bs2;
355:   MatScalar   *vsingle;

358:   if (N > b->setvalueslen) {
359:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
360:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
361:     b->setvalueslen  = N;
362:   }
363:   vsingle = b->setvaluescopy;
364:   for (i=0; i<N; i++) {
365:     vsingle[i] = v[i];
366:   }
367:   MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
368:   return(0);
369: }
370: #endif

374: int MatSetValues_MPIBAIJ_MatScalar(Mat mat,int m,const int im[],int n,const int in[],const MatScalar v[],InsertMode addv)
375: {
376:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
377:   MatScalar   value;
378:   PetscTruth  roworiented = baij->roworiented;
379:   int         ierr,i,j,row,col;
380:   int         rstart_orig=baij->rstart_bs;
381:   int         rend_orig=baij->rend_bs,cstart_orig=baij->cstart_bs;
382:   int         cend_orig=baij->cend_bs,bs=baij->bs;

384:   /* Some Variables required in the macro */
385:   Mat         A = baij->A;
386:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)(A)->data;
387:   int         *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
388:   MatScalar   *aa=a->a;

390:   Mat         B = baij->B;
391:   Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(B)->data;
392:   int         *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
393:   MatScalar   *ba=b->a;

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

400:   for (i=0; i<m; i++) {
401:     if (im[i] < 0) continue;
402: #if defined(PETSC_USE_BOPT_g)
403:     if (im[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",im[i],mat->M-1);
404: #endif
405:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
406:       row = im[i] - rstart_orig;
407:       for (j=0; j<n; j++) {
408:         if (in[j] >= cstart_orig && in[j] < cend_orig){
409:           col = in[j] - cstart_orig;
410:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
411:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv);
412:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
413:         } else if (in[j] < 0) continue;
414: #if defined(PETSC_USE_BOPT_g)
415:         else if (in[j] >= mat->N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[i],mat->N-1);}
416: #endif
417:         else {
418:           if (mat->was_assembled) {
419:             if (!baij->colmap) {
420:               CreateColmap_MPIBAIJ_Private(mat);
421:             }
422: #if defined (PETSC_USE_CTABLE)
423:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
424:             col  = col - 1;
425: #else
426:             col = baij->colmap[in[j]/bs] - 1;
427: #endif
428:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
429:               DisAssemble_MPIBAIJ(mat);
430:               col =  in[j];
431:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
432:               B = baij->B;
433:               b = (Mat_SeqBAIJ*)(B)->data;
434:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
435:               ba=b->a;
436:             } else col += in[j]%bs;
437:           } else col = in[j];
438:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
439:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv);
440:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
441:         }
442:       }
443:     } else {
444:       if (!baij->donotstash) {
445:         if (roworiented) {
446:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
447:         } else {
448:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
449:         }
450:       }
451:     }
452:   }
453:   return(0);
454: }

458: int MatSetValuesBlocked_MPIBAIJ_MatScalar(Mat mat,int m,const int im[],int n,const int in[],const MatScalar v[],InsertMode addv)
459: {
460:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
461:   const MatScalar *value;
462:   MatScalar       *barray=baij->barray;
463:   PetscTruth      roworiented = baij->roworiented;
464:   int             ierr,i,j,ii,jj,row,col,rstart=baij->rstart;
465:   int             rend=baij->rend,cstart=baij->cstart,stepval;
466:   int             cend=baij->cend,bs=baij->bs,bs2=baij->bs2;
467: 
469:   if(!barray) {
470:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
471:     baij->barray = barray;
472:   }

474:   if (roworiented) {
475:     stepval = (n-1)*bs;
476:   } else {
477:     stepval = (m-1)*bs;
478:   }
479:   for (i=0; i<m; i++) {
480:     if (im[i] < 0) continue;
481: #if defined(PETSC_USE_BOPT_g)
482:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %d max %d",im[i],baij->Mbs-1);
483: #endif
484:     if (im[i] >= rstart && im[i] < rend) {
485:       row = im[i] - rstart;
486:       for (j=0; j<n; j++) {
487:         /* If NumCol = 1 then a copy is not required */
488:         if ((roworiented) && (n == 1)) {
489:           barray = (MatScalar*)v + i*bs2;
490:         } else if((!roworiented) && (m == 1)) {
491:           barray = (MatScalar*)v + j*bs2;
492:         } else { /* Here a copy is required */
493:           if (roworiented) {
494:             value = v + i*(stepval+bs)*bs + j*bs;
495:           } else {
496:             value = v + j*(stepval+bs)*bs + i*bs;
497:           }
498:           for (ii=0; ii<bs; ii++,value+=stepval) {
499:             for (jj=0; jj<bs; jj++) {
500:               *barray++  = *value++;
501:             }
502:           }
503:           barray -=bs2;
504:         }
505: 
506:         if (in[j] >= cstart && in[j] < cend){
507:           col  = in[j] - cstart;
508:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->A,1,&row,1,&col,barray,addv);
509:         }
510:         else if (in[j] < 0) continue;
511: #if defined(PETSC_USE_BOPT_g)
512:         else if (in[j] >= baij->Nbs) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %d max %d",in[j],baij->Nbs-1);}
513: #endif
514:         else {
515:           if (mat->was_assembled) {
516:             if (!baij->colmap) {
517:               CreateColmap_MPIBAIJ_Private(mat);
518:             }

520: #if defined(PETSC_USE_BOPT_g)
521: #if defined (PETSC_USE_CTABLE)
522:             { int data;
523:               PetscTableFind(baij->colmap,in[j]+1,&data);
524:               if ((data - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
525:             }
526: #else
527:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
528: #endif
529: #endif
530: #if defined (PETSC_USE_CTABLE)
531:             PetscTableFind(baij->colmap,in[j]+1,&col);
532:             col  = (col - 1)/bs;
533: #else
534:             col = (baij->colmap[in[j]] - 1)/bs;
535: #endif
536:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
537:               DisAssemble_MPIBAIJ(mat);
538:               col =  in[j];
539:             }
540:           }
541:           else col = in[j];
542:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->B,1,&row,1,&col,barray,addv);
543:         }
544:       }
545:     } else {
546:       if (!baij->donotstash) {
547:         if (roworiented) {
548:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
549:         } else {
550:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
551:         }
552:       }
553:     }
554:   }
555:   return(0);
556: }

558: #define HASH_KEY 0.6180339887
559: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(int)((size)*(tmp-(int)tmp)))
560: /* #define HASH(size,key) ((int)((size)*fmod(((key)*HASH_KEY),1))) */
561: /* #define HASH(size,key,tmp) ((int)((size)*fmod(((key)*HASH_KEY),1))) */
564: int MatSetValues_MPIBAIJ_HT_MatScalar(Mat mat,int m,const int im[],int n,const int in[],const MatScalar v[],InsertMode addv)
565: {
566:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
567:   PetscTruth  roworiented = baij->roworiented;
568:   int         ierr,i,j,row,col;
569:   int         rstart_orig=baij->rstart_bs;
570:   int         rend_orig=baij->rend_bs,Nbs=baij->Nbs;
571:   int         h1,key,size=baij->ht_size,bs=baij->bs,*HT=baij->ht,idx;
572:   PetscReal   tmp;
573:   MatScalar   **HD = baij->hd,value;
574: #if defined(PETSC_USE_BOPT_g)
575:   int         total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
576: #endif


580:   for (i=0; i<m; i++) {
581: #if defined(PETSC_USE_BOPT_g)
582:     if (im[i] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
583:     if (im[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",im[i],mat->M-1);
584: #endif
585:       row = im[i];
586:     if (row >= rstart_orig && row < rend_orig) {
587:       for (j=0; j<n; j++) {
588:         col = in[j];
589:         if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
590:         /* Look up into the Hash Table */
591:         key = (row/bs)*Nbs+(col/bs)+1;
592:         h1  = HASH(size,key,tmp);

594: 
595:         idx = h1;
596: #if defined(PETSC_USE_BOPT_g)
597:         insert_ct++;
598:         total_ct++;
599:         if (HT[idx] != key) {
600:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
601:           if (idx == size) {
602:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
603:             if (idx == h1) {
604:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%d,%d) has no entry in the hash table", row, col);
605:             }
606:           }
607:         }
608: #else
609:         if (HT[idx] != key) {
610:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
611:           if (idx == size) {
612:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
613:             if (idx == h1) {
614:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%d,%d) has no entry in the hash table", row, col);
615:             }
616:           }
617:         }
618: #endif
619:         /* A HASH table entry is found, so insert the values at the correct address */
620:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
621:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
622:       }
623:     } else {
624:       if (!baij->donotstash) {
625:         if (roworiented) {
626:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
627:         } else {
628:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
629:         }
630:       }
631:     }
632:   }
633: #if defined(PETSC_USE_BOPT_g)
634:   baij->ht_total_ct = total_ct;
635:   baij->ht_insert_ct = insert_ct;
636: #endif
637:   return(0);
638: }

642: int MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(Mat mat,int m,const int im[],int n,const int in[],const MatScalar v[],InsertMode addv)
643: {
644:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
645:   PetscTruth      roworiented = baij->roworiented;
646:   int             ierr,i,j,ii,jj,row,col;
647:   int             rstart=baij->rstart ;
648:   int             rend=baij->rend,stepval,bs=baij->bs,bs2=baij->bs2;
649:   int             h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
650:   PetscReal       tmp;
651:   MatScalar       **HD = baij->hd,*baij_a;
652:   const MatScalar *v_t,*value;
653: #if defined(PETSC_USE_BOPT_g)
654:   int             total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
655: #endif
656: 

659:   if (roworiented) {
660:     stepval = (n-1)*bs;
661:   } else {
662:     stepval = (m-1)*bs;
663:   }
664:   for (i=0; i<m; i++) {
665: #if defined(PETSC_USE_BOPT_g)
666:     if (im[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",im[i]);
667:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",im[i],baij->Mbs-1);
668: #endif
669:     row   = im[i];
670:     v_t   = v + i*bs2;
671:     if (row >= rstart && row < rend) {
672:       for (j=0; j<n; j++) {
673:         col = in[j];

675:         /* Look up into the Hash Table */
676:         key = row*Nbs+col+1;
677:         h1  = HASH(size,key,tmp);
678: 
679:         idx = h1;
680: #if defined(PETSC_USE_BOPT_g)
681:         total_ct++;
682:         insert_ct++;
683:        if (HT[idx] != key) {
684:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
685:           if (idx == size) {
686:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
687:             if (idx == h1) {
688:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%d,%d) has no entry in the hash table", row, col);
689:             }
690:           }
691:         }
692: #else  
693:         if (HT[idx] != key) {
694:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
695:           if (idx == size) {
696:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
697:             if (idx == h1) {
698:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%d,%d) has no entry in the hash table", row, col);
699:             }
700:           }
701:         }
702: #endif
703:         baij_a = HD[idx];
704:         if (roworiented) {
705:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
706:           /* value = v + (i*(stepval+bs)+j)*bs; */
707:           value = v_t;
708:           v_t  += bs;
709:           if (addv == ADD_VALUES) {
710:             for (ii=0; ii<bs; ii++,value+=stepval) {
711:               for (jj=ii; jj<bs2; jj+=bs) {
712:                 baij_a[jj]  += *value++;
713:               }
714:             }
715:           } else {
716:             for (ii=0; ii<bs; ii++,value+=stepval) {
717:               for (jj=ii; jj<bs2; jj+=bs) {
718:                 baij_a[jj]  = *value++;
719:               }
720:             }
721:           }
722:         } else {
723:           value = v + j*(stepval+bs)*bs + i*bs;
724:           if (addv == ADD_VALUES) {
725:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
726:               for (jj=0; jj<bs; jj++) {
727:                 baij_a[jj]  += *value++;
728:               }
729:             }
730:           } else {
731:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
732:               for (jj=0; jj<bs; jj++) {
733:                 baij_a[jj]  = *value++;
734:               }
735:             }
736:           }
737:         }
738:       }
739:     } else {
740:       if (!baij->donotstash) {
741:         if (roworiented) {
742:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
743:         } else {
744:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
745:         }
746:       }
747:     }
748:   }
749: #if defined(PETSC_USE_BOPT_g)
750:   baij->ht_total_ct = total_ct;
751:   baij->ht_insert_ct = insert_ct;
752: #endif
753:   return(0);
754: }

758: int MatGetValues_MPIBAIJ(Mat mat,int m,const int idxm[],int n,const int idxn[],PetscScalar v[])
759: {
760:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
761:   int        bs=baij->bs,ierr,i,j,bsrstart = baij->rstart*bs,bsrend = baij->rend*bs;
762:   int        bscstart = baij->cstart*bs,bscend = baij->cend*bs,row,col,data;

765:   for (i=0; i<m; i++) {
766:     if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",idxm[i]);
767:     if (idxm[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",idxm[i],mat->M-1);
768:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
769:       row = idxm[i] - bsrstart;
770:       for (j=0; j<n; j++) {
771:         if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",idxn[j]);
772:         if (idxn[j] >= mat->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",idxn[j],mat->N-1);
773:         if (idxn[j] >= bscstart && idxn[j] < bscend){
774:           col = idxn[j] - bscstart;
775:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
776:         } else {
777:           if (!baij->colmap) {
778:             CreateColmap_MPIBAIJ_Private(mat);
779:           }
780: #if defined (PETSC_USE_CTABLE)
781:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
782:           data --;
783: #else
784:           data = baij->colmap[idxn[j]/bs]-1;
785: #endif
786:           if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
787:           else {
788:             col  = data + idxn[j]%bs;
789:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
790:           }
791:         }
792:       }
793:     } else {
794:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
795:     }
796:   }
797:  return(0);
798: }

802: int MatNorm_MPIBAIJ(Mat mat,NormType type,PetscReal *nrm)
803: {
804:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
805:   Mat_SeqBAIJ *amat = (Mat_SeqBAIJ*)baij->A->data,*bmat = (Mat_SeqBAIJ*)baij->B->data;
806:   int        ierr,i,bs2=baij->bs2;
807:   PetscReal  sum = 0.0;
808:   MatScalar  *v;

811:   if (baij->size == 1) {
812:      MatNorm(baij->A,type,nrm);
813:   } else {
814:     if (type == NORM_FROBENIUS) {
815:       v = amat->a;
816:       for (i=0; i<amat->nz*bs2; i++) {
817: #if defined(PETSC_USE_COMPLEX)
818:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
819: #else
820:         sum += (*v)*(*v); v++;
821: #endif
822:       }
823:       v = bmat->a;
824:       for (i=0; i<bmat->nz*bs2; i++) {
825: #if defined(PETSC_USE_COMPLEX)
826:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
827: #else
828:         sum += (*v)*(*v); v++;
829: #endif
830:       }
831:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPI_SUM,mat->comm);
832:       *nrm = sqrt(*nrm);
833:     } else {
834:       SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
835:     }
836:   }
837:   return(0);
838: }


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

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

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


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

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

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

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

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

961:   MatStashScatterBegin_Private(&mat->stash,baij->rowners_bs);
962:   MatStashScatterBegin_Private(&mat->bstash,baij->rowners);
963:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
964:   PetscLogInfo(0,"MatAssemblyBegin_MPIBAIJ:Stash has %d entries,uses %d mallocs.\n",nstash,reallocs);
965:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
966:   PetscLogInfo(0,"MatAssemblyBegin_MPIBAIJ:Block-Stash has %d entries, uses %d mallocs.\n",nstash,reallocs);
967:   return(0);
968: }

972: int MatAssemblyEnd_MPIBAIJ(Mat mat,MatAssemblyType mode)
973: {
974:   Mat_MPIBAIJ *baij=(Mat_MPIBAIJ*)mat->data;
975:   Mat_SeqBAIJ *a=(Mat_SeqBAIJ*)baij->A->data,*b=(Mat_SeqBAIJ*)baij->B->data;
976:   int         i,j,rstart,ncols,n,ierr,flg,bs2=baij->bs2;
977:   int         *row,*col,other_disassembled;
978:   PetscTruth  r1,r2,r3;
979:   MatScalar   *val;
980:   InsertMode  addv = mat->insertmode;

983:   if (!baij->donotstash) {
984:     while (1) {
985:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
986:       if (!flg) break;

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

1027:   MatAssemblyBegin(baij->A,mode);
1028:   MatAssemblyEnd(baij->A,mode);

1030:   /* determine if any processor has disassembled, if so we must 
1031:      also disassemble ourselfs, in order that we may reassemble. */
1032:   /*
1033:      if nonzero structure of submatrix B cannot change then we know that
1034:      no processor disassembled thus we can skip this stuff
1035:   */
1036:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
1037:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
1038:     if (mat->was_assembled && !other_disassembled) {
1039:       DisAssemble_MPIBAIJ(mat);
1040:     }
1041:   }

1043:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
1044:     MatSetUpMultiply_MPIBAIJ(mat);
1045:   }
1046:   MatAssemblyBegin(baij->B,mode);
1047:   MatAssemblyEnd(baij->B,mode);
1048: 
1049: #if defined(PETSC_USE_BOPT_g)
1050:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
1051:     PetscLogInfo(0,"MatAssemblyEnd_MPIBAIJ:Average Hash Table Search in MatSetValues = %5.2f\n",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
1052:     baij->ht_total_ct  = 0;
1053:     baij->ht_insert_ct = 0;
1054:   }
1055: #endif
1056:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
1057:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);
1058:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
1059:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
1060:   }

1062:   if (baij->rowvalues) {
1063:     PetscFree(baij->rowvalues);
1064:     baij->rowvalues = 0;
1065:   }
1066:   return(0);
1067: }

1071: static int MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1072: {
1073:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
1074:   int               ierr,bs = baij->bs,size = baij->size,rank = baij->rank;
1075:   PetscTruth        isascii,isdraw;
1076:   PetscViewer       sviewer;
1077:   PetscViewerFormat format;

1080:   /* printf(" MatView_MPIBAIJ_ASCIIorDraworSocket is called ...\n"); */
1081:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
1082:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1083:   if (isascii) {
1084:     PetscViewerGetFormat(viewer,&format);
1085:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1086:       MatInfo info;
1087:       MPI_Comm_rank(mat->comm,&rank);
1088:       MatGetInfo(mat,MAT_LOCAL,&info);
1089:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d bs %d mem %d\n",
1090:               rank,mat->m,(int)info.nz_used*bs,(int)info.nz_allocated*bs,
1091:               baij->bs,(int)info.memory);
1092:       MatGetInfo(baij->A,MAT_LOCAL,&info);
1093:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %d \n",rank,(int)info.nz_used*bs);
1094:       MatGetInfo(baij->B,MAT_LOCAL,&info);
1095:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %d \n",rank,(int)info.nz_used*bs);
1096:       PetscViewerFlush(viewer);
1097:       VecScatterView(baij->Mvctx,viewer);
1098:       return(0);
1099:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1100:       PetscViewerASCIIPrintf(viewer,"  block size is %d\n",bs);
1101:       return(0);
1102:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1103:       return(0);
1104:     }
1105:   }

1107:   if (isdraw) {
1108:     PetscDraw       draw;
1109:     PetscTruth isnull;
1110:     PetscViewerDrawGetDraw(viewer,0,&draw);
1111:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1112:   }

1114:   if (size == 1) {
1115:     PetscObjectSetName((PetscObject)baij->A,mat->name);
1116:     MatView(baij->A,viewer);
1117:   } else {
1118:     /* assemble the entire matrix onto first processor. */
1119:     Mat         A;
1120:     Mat_SeqBAIJ *Aloc;
1121:     int         M = mat->M,N = mat->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
1122:     MatScalar   *a;

1124:     /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1125:     /* Perhaps this should be the type of mat? */
1126:     if (!rank) {
1127:       MatCreate(mat->comm,M,N,M,N,&A);
1128:     } else {
1129:       MatCreate(mat->comm,0,0,M,N,&A);
1130:     }
1131:     MatSetType(A,MATMPIBAIJ);
1132:     MatMPIBAIJSetPreallocation(A,baij->bs,0,PETSC_NULL,0,PETSC_NULL);
1133:     PetscLogObjectParent(mat,A);

1135:     /* copy over the A part */
1136:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
1137:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1138:     PetscMalloc(bs*sizeof(int),&rvals);

1140:     for (i=0; i<mbs; i++) {
1141:       rvals[0] = bs*(baij->rstart + i);
1142:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1143:       for (j=ai[i]; j<ai[i+1]; j++) {
1144:         col = (baij->cstart+aj[j])*bs;
1145:         for (k=0; k<bs; k++) {
1146:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1147:           col++; a += bs;
1148:         }
1149:       }
1150:     }
1151:     /* copy over the B part */
1152:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
1153:     ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1154:     for (i=0; i<mbs; i++) {
1155:       rvals[0] = bs*(baij->rstart + i);
1156:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1157:       for (j=ai[i]; j<ai[i+1]; j++) {
1158:         col = baij->garray[aj[j]]*bs;
1159:         for (k=0; k<bs; k++) {
1160:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1161:           col++; a += bs;
1162:         }
1163:       }
1164:     }
1165:     PetscFree(rvals);
1166:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1167:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1168:     /* 
1169:        Everyone has to call to draw the matrix since the graphics waits are
1170:        synchronized across all processors that share the PetscDraw object
1171:     */
1172:     PetscViewerGetSingleton(viewer,&sviewer);
1173:     if (!rank) {
1174:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,mat->name);
1175:       MatView(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1176:     }
1177:     PetscViewerRestoreSingleton(viewer,&sviewer);
1178:     MatDestroy(A);
1179:   }
1180:   return(0);
1181: }

1185: int MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1186: {
1187:   int        ierr;
1188:   PetscTruth isascii,isdraw,issocket,isbinary;

1191:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
1192:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1193:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1194:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1195:   if (isascii || isdraw || issocket || isbinary) {
1196:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1197:   } else {
1198:     SETERRQ1(1,"Viewer type %s not supported by MPIBAIJ matrices",((PetscObject)viewer)->type_name);
1199:   }
1200:   return(0);
1201: }

1205: int MatDestroy_MPIBAIJ(Mat mat)
1206: {
1207:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1208:   int         ierr;

1211: #if defined(PETSC_USE_LOG)
1212:   PetscLogObjectState((PetscObject)mat,"Rows=%d,Cols=%d",mat->M,mat->N);
1213: #endif
1214:   MatStashDestroy_Private(&mat->stash);
1215:   MatStashDestroy_Private(&mat->bstash);
1216:   PetscFree(baij->rowners);
1217:   MatDestroy(baij->A);
1218:   MatDestroy(baij->B);
1219: #if defined (PETSC_USE_CTABLE)
1220:   if (baij->colmap) {PetscTableDelete(baij->colmap);}
1221: #else
1222:   if (baij->colmap) {PetscFree(baij->colmap);}
1223: #endif
1224:   if (baij->garray) {PetscFree(baij->garray);}
1225:   if (baij->lvec)   {VecDestroy(baij->lvec);}
1226:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
1227:   if (baij->rowvalues) {PetscFree(baij->rowvalues);}
1228:   if (baij->barray) {PetscFree(baij->barray);}
1229:   if (baij->hd) {PetscFree(baij->hd);}
1230: #if defined(PETSC_USE_MAT_SINGLE)
1231:   if (baij->setvaluescopy) {PetscFree(baij->setvaluescopy);}
1232: #endif
1233:   PetscFree(baij);
1234:   return(0);
1235: }

1239: int MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1240: {
1241:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1242:   int         ierr,nt;

1245:   VecGetLocalSize(xx,&nt);
1246:   if (nt != A->n) {
1247:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1248:   }
1249:   VecGetLocalSize(yy,&nt);
1250:   if (nt != A->m) {
1251:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1252:   }
1253:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1254:   (*a->A->ops->mult)(a->A,xx,yy);
1255:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1256:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1257:   VecScatterPostRecvs(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1258:   return(0);
1259: }

1263: int MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1264: {
1265:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1266:   int        ierr;

1269:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1270:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1271:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1272:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1273:   return(0);
1274: }

1278: int MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1279: {
1280:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1281:   int         ierr;

1284:   /* do nondiagonal part */
1285:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1286:   /* send it on its way */
1287:   VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1288:   /* do local part */
1289:   (*a->A->ops->multtranspose)(a->A,xx,yy);
1290:   /* receive remote parts: note this assumes the values are not actually */
1291:   /* inserted in yy until the next line, which is true for my implementation*/
1292:   /* but is not perhaps always true. */
1293:   VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1294:   return(0);
1295: }

1299: int MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1300: {
1301:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1302:   int         ierr;

1305:   /* do nondiagonal part */
1306:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1307:   /* send it on its way */
1308:   VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1309:   /* do local part */
1310:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1311:   /* receive remote parts: note this assumes the values are not actually */
1312:   /* inserted in yy until the next line, which is true for my implementation*/
1313:   /* but is not perhaps always true. */
1314:   VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1315:   return(0);
1316: }

1318: /*
1319:   This only works correctly for square matrices where the subblock A->A is the 
1320:    diagonal block
1321: */
1324: int MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1325: {
1326:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1327:   int         ierr;

1330:   if (A->M != A->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1331:   MatGetDiagonal(a->A,v);
1332:   return(0);
1333: }

1337: int MatScale_MPIBAIJ(const PetscScalar *aa,Mat A)
1338: {
1339:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1340:   int         ierr;

1343:   MatScale(aa,a->A);
1344:   MatScale(aa,a->B);
1345:   return(0);
1346: }

1350: int MatGetRow_MPIBAIJ(Mat matin,int row,int *nz,int **idx,PetscScalar **v)
1351: {
1352:   Mat_MPIBAIJ  *mat = (Mat_MPIBAIJ*)matin->data;
1353:   PetscScalar  *vworkA,*vworkB,**pvA,**pvB,*v_p;
1354:   int          bs = mat->bs,bs2 = mat->bs2,i,ierr,*cworkA,*cworkB,**pcA,**pcB;
1355:   int          nztot,nzA,nzB,lrow,brstart = mat->rstart*bs,brend = mat->rend*bs;
1356:   int          *cmap,*idx_p,cstart = mat->cstart;

1359:   if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1360:   mat->getrowactive = PETSC_TRUE;

1362:   if (!mat->rowvalues && (idx || v)) {
1363:     /*
1364:         allocate enough space to hold information from the longest row.
1365:     */
1366:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1367:     int     max = 1,mbs = mat->mbs,tmp;
1368:     for (i=0; i<mbs; i++) {
1369:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1370:       if (max < tmp) { max = tmp; }
1371:     }
1372:     PetscMalloc(max*bs2*(sizeof(int)+sizeof(PetscScalar)),&mat->rowvalues);
1373:     mat->rowindices = (int*)(mat->rowvalues + max*bs2);
1374:   }
1375: 
1376:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1377:   lrow = row - brstart;

1379:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1380:   if (!v)   {pvA = 0; pvB = 0;}
1381:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1382:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1383:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1384:   nztot = nzA + nzB;

1386:   cmap  = mat->garray;
1387:   if (v  || idx) {
1388:     if (nztot) {
1389:       /* Sort by increasing column numbers, assuming A and B already sorted */
1390:       int imark = -1;
1391:       if (v) {
1392:         *v = v_p = mat->rowvalues;
1393:         for (i=0; i<nzB; i++) {
1394:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1395:           else break;
1396:         }
1397:         imark = i;
1398:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1399:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1400:       }
1401:       if (idx) {
1402:         *idx = idx_p = mat->rowindices;
1403:         if (imark > -1) {
1404:           for (i=0; i<imark; i++) {
1405:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1406:           }
1407:         } else {
1408:           for (i=0; i<nzB; i++) {
1409:             if (cmap[cworkB[i]/bs] < cstart)
1410:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1411:             else break;
1412:           }
1413:           imark = i;
1414:         }
1415:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1416:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1417:       }
1418:     } else {
1419:       if (idx) *idx = 0;
1420:       if (v)   *v   = 0;
1421:     }
1422:   }
1423:   *nz = nztot;
1424:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1425:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1426:   return(0);
1427: }

1431: int MatRestoreRow_MPIBAIJ(Mat mat,int row,int *nz,int **idx,PetscScalar **v)
1432: {
1433:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1436:   if (baij->getrowactive == PETSC_FALSE) {
1437:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1438:   }
1439:   baij->getrowactive = PETSC_FALSE;
1440:   return(0);
1441: }

1445: int MatGetBlockSize_MPIBAIJ(Mat mat,int *bs)
1446: {
1447:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1450:   *bs = baij->bs;
1451:   return(0);
1452: }

1456: int MatZeroEntries_MPIBAIJ(Mat A)
1457: {
1458:   Mat_MPIBAIJ *l = (Mat_MPIBAIJ*)A->data;
1459:   int         ierr;

1462:   MatZeroEntries(l->A);
1463:   MatZeroEntries(l->B);
1464:   return(0);
1465: }

1469: int MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1470: {
1471:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)matin->data;
1472:   Mat         A = a->A,B = a->B;
1473:   int         ierr;
1474:   PetscReal   isend[5],irecv[5];

1477:   info->block_size     = (PetscReal)a->bs;
1478:   MatGetInfo(A,MAT_LOCAL,info);
1479:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1480:   isend[3] = info->memory;  isend[4] = info->mallocs;
1481:   MatGetInfo(B,MAT_LOCAL,info);
1482:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1483:   isend[3] += info->memory;  isend[4] += info->mallocs;
1484:   if (flag == MAT_LOCAL) {
1485:     info->nz_used      = isend[0];
1486:     info->nz_allocated = isend[1];
1487:     info->nz_unneeded  = isend[2];
1488:     info->memory       = isend[3];
1489:     info->mallocs      = isend[4];
1490:   } else if (flag == MAT_GLOBAL_MAX) {
1491:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1492:     info->nz_used      = irecv[0];
1493:     info->nz_allocated = irecv[1];
1494:     info->nz_unneeded  = irecv[2];
1495:     info->memory       = irecv[3];
1496:     info->mallocs      = irecv[4];
1497:   } else if (flag == MAT_GLOBAL_SUM) {
1498:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1499:     info->nz_used      = irecv[0];
1500:     info->nz_allocated = irecv[1];
1501:     info->nz_unneeded  = irecv[2];
1502:     info->memory       = irecv[3];
1503:     info->mallocs      = irecv[4];
1504:   } else {
1505:     SETERRQ1(1,"Unknown MatInfoType argument %d",flag);
1506:   }
1507:   info->rows_global       = (PetscReal)A->M;
1508:   info->columns_global    = (PetscReal)A->N;
1509:   info->rows_local        = (PetscReal)A->m;
1510:   info->columns_local     = (PetscReal)A->N;
1511:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1512:   info->fill_ratio_needed = 0;
1513:   info->factor_mallocs    = 0;
1514:   return(0);
1515: }

1519: int MatSetOption_MPIBAIJ(Mat A,MatOption op)
1520: {
1521:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1522:   int         ierr;

1525:   switch (op) {
1526:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1527:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1528:   case MAT_COLUMNS_UNSORTED:
1529:   case MAT_COLUMNS_SORTED:
1530:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1531:   case MAT_KEEP_ZEROED_ROWS:
1532:   case MAT_NEW_NONZERO_LOCATION_ERR:
1533:     MatSetOption(a->A,op);
1534:     MatSetOption(a->B,op);
1535:     break;
1536:   case MAT_ROW_ORIENTED:
1537:     a->roworiented = PETSC_TRUE;
1538:     MatSetOption(a->A,op);
1539:     MatSetOption(a->B,op);
1540:     break;
1541:   case MAT_ROWS_SORTED:
1542:   case MAT_ROWS_UNSORTED:
1543:   case MAT_YES_NEW_DIAGONALS:
1544:     PetscLogInfo(A,"Info:MatSetOption_MPIBAIJ:Option ignored\n");
1545:     break;
1546:   case MAT_COLUMN_ORIENTED:
1547:     a->roworiented = PETSC_FALSE;
1548:     MatSetOption(a->A,op);
1549:     MatSetOption(a->B,op);
1550:     break;
1551:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1552:     a->donotstash = PETSC_TRUE;
1553:     break;
1554:   case MAT_NO_NEW_DIAGONALS:
1555:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1556:   case MAT_USE_HASH_TABLE:
1557:     a->ht_flag = PETSC_TRUE;
1558:     break;
1559:   case MAT_SYMMETRIC:
1560:   case MAT_STRUCTURALLY_SYMMETRIC:
1561:   case MAT_NOT_SYMMETRIC:
1562:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1563:   case MAT_HERMITIAN:
1564:   case MAT_NOT_HERMITIAN:
1565:   case MAT_SYMMETRY_ETERNAL:
1566:   case MAT_NOT_SYMMETRY_ETERNAL:
1567:     break;
1568:   default:
1569:     SETERRQ(PETSC_ERR_SUP,"unknown option");
1570:   }
1571:   return(0);
1572: }

1576: int MatTranspose_MPIBAIJ(Mat A,Mat *matout)
1577: {
1578:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)A->data;
1579:   Mat_SeqBAIJ *Aloc;
1580:   Mat         B;
1581:   int         ierr,M=A->M,N=A->N,*ai,*aj,i,*rvals,j,k,col;
1582:   int         bs=baij->bs,mbs=baij->mbs;
1583:   MatScalar   *a;
1584: 
1586:   if (!matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1587:   MatCreate(A->comm,A->n,A->m,N,M,&B);
1588:   MatSetType(B,A->type_name);
1589:   MatMPIBAIJSetPreallocation(B,baij->bs,0,PETSC_NULL,0,PETSC_NULL);
1590: 
1591:   /* copy over the A part */
1592:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1593:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1594:   PetscMalloc(bs*sizeof(int),&rvals);
1595: 
1596:   for (i=0; i<mbs; i++) {
1597:     rvals[0] = bs*(baij->rstart + i);
1598:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1599:     for (j=ai[i]; j<ai[i+1]; j++) {
1600:       col = (baij->cstart+aj[j])*bs;
1601:       for (k=0; k<bs; k++) {
1602:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1603:         col++; a += bs;
1604:       }
1605:     }
1606:   }
1607:   /* copy over the B part */
1608:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1609:   ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1610:   for (i=0; i<mbs; i++) {
1611:     rvals[0] = bs*(baij->rstart + i);
1612:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1613:     for (j=ai[i]; j<ai[i+1]; j++) {
1614:       col = baij->garray[aj[j]]*bs;
1615:       for (k=0; k<bs; k++) {
1616:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1617:         col++; a += bs;
1618:       }
1619:     }
1620:   }
1621:   PetscFree(rvals);
1622:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1623:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1624: 
1625:   if (matout) {
1626:     *matout = B;
1627:   } else {
1628:     MatHeaderCopy(A,B);
1629:   }
1630:   return(0);
1631: }

1635: int MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1636: {
1637:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1638:   Mat         a = baij->A,b = baij->B;
1639:   int         ierr,s1,s2,s3;

1642:   MatGetLocalSize(mat,&s2,&s3);
1643:   if (rr) {
1644:     VecGetLocalSize(rr,&s1);
1645:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1646:     /* Overlap communication with computation. */
1647:     VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1648:   }
1649:   if (ll) {
1650:     VecGetLocalSize(ll,&s1);
1651:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1652:     (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1653:   }
1654:   /* scale  the diagonal block */
1655:   (*a->ops->diagonalscale)(a,ll,rr);

1657:   if (rr) {
1658:     /* Do a scatter end and then right scale the off-diagonal block */
1659:     VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1660:     (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1661:   }
1662: 
1663:   return(0);
1664: }

1668: int MatZeroRows_MPIBAIJ(Mat A,IS is,const PetscScalar *diag)
1669: {
1670:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;
1671:   int            i,ierr,N,*rows,*owners = l->rowners,size = l->size;
1672:   int            *nprocs,j,idx,nsends,row;
1673:   int            nmax,*svalues,*starts,*owner,nrecvs,rank = l->rank;
1674:   int            *rvalues,tag = A->tag,count,base,slen,n,*source;
1675:   int            *lens,imdex,*lrows,*values,bs=l->bs,rstart_bs=l->rstart_bs;
1676:   MPI_Comm       comm = A->comm;
1677:   MPI_Request    *send_waits,*recv_waits;
1678:   MPI_Status     recv_status,*send_status;
1679:   IS             istmp;
1680:   PetscTruth     found;
1681: 
1683:   ISGetLocalSize(is,&N);
1684:   ISGetIndices(is,&rows);
1685: 
1686:   /*  first count number of contributors to each processor */
1687:   PetscMalloc(2*size*sizeof(int),&nprocs);
1688:   PetscMemzero(nprocs,2*size*sizeof(int));
1689:   PetscMalloc((N+1)*sizeof(int),&owner); /* see note*/
1690:   for (i=0; i<N; i++) {
1691:     idx   = rows[i];
1692:     found = PETSC_FALSE;
1693:     for (j=0; j<size; j++) {
1694:       if (idx >= owners[j]*bs && idx < owners[j+1]*bs) {
1695:         nprocs[2*j]++; nprocs[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break;
1696:       }
1697:     }
1698:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1699:   }
1700:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
1701: 
1702:   /* inform other processors of number of messages and max length*/
1703:   PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
1704: 
1705:   /* post receives:   */
1706:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(int),&rvalues);
1707:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1708:   for (i=0; i<nrecvs; i++) {
1709:     MPI_Irecv(rvalues+nmax*i,nmax,MPI_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1710:   }
1711: 
1712:   /* do sends:
1713:      1) starts[i] gives the starting index in svalues for stuff going to 
1714:      the ith processor
1715:   */
1716:   PetscMalloc((N+1)*sizeof(int),&svalues);
1717:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1718:   PetscMalloc((size+1)*sizeof(int),&starts);
1719:   starts[0]  = 0;
1720:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1721:   for (i=0; i<N; i++) {
1722:     svalues[starts[owner[i]]++] = rows[i];
1723:   }
1724:   ISRestoreIndices(is,&rows);
1725: 
1726:   starts[0] = 0;
1727:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1728:   count = 0;
1729:   for (i=0; i<size; i++) {
1730:     if (nprocs[2*i+1]) {
1731:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPI_INT,i,tag,comm,send_waits+count++);
1732:     }
1733:   }
1734:   PetscFree(starts);

1736:   base = owners[rank]*bs;
1737: 
1738:   /*  wait on receives */
1739:   PetscMalloc(2*(nrecvs+1)*sizeof(int),&lens);
1740:   source = lens + nrecvs;
1741:   count  = nrecvs; slen = 0;
1742:   while (count) {
1743:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1744:     /* unpack receives into our local space */
1745:     MPI_Get_count(&recv_status,MPI_INT,&n);
1746:     source[imdex]  = recv_status.MPI_SOURCE;
1747:     lens[imdex]    = n;
1748:     slen          += n;
1749:     count--;
1750:   }
1751:   PetscFree(recv_waits);
1752: 
1753:   /* move the data into the send scatter */
1754:   PetscMalloc((slen+1)*sizeof(int),&lrows);
1755:   count = 0;
1756:   for (i=0; i<nrecvs; i++) {
1757:     values = rvalues + i*nmax;
1758:     for (j=0; j<lens[i]; j++) {
1759:       lrows[count++] = values[j] - base;
1760:     }
1761:   }
1762:   PetscFree(rvalues);
1763:   PetscFree(lens);
1764:   PetscFree(owner);
1765:   PetscFree(nprocs);
1766: 
1767:   /* actually zap the local rows */
1768:   ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);
1769:   PetscLogObjectParent(A,istmp);

1771:   /*
1772:         Zero the required rows. If the "diagonal block" of the matrix
1773:      is square and the user wishes to set the diagonal we use seperate
1774:      code so that MatSetValues() is not called for each diagonal allocating
1775:      new memory, thus calling lots of mallocs and slowing things down.

1777:        Contributed by: Mathew Knepley
1778:   */
1779:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1780:   MatZeroRows_SeqBAIJ(l->B,istmp,0);
1781:   if (diag && (l->A->M == l->A->N)) {
1782:     MatZeroRows_SeqBAIJ(l->A,istmp,diag);
1783:   } else if (diag) {
1784:     MatZeroRows_SeqBAIJ(l->A,istmp,0);
1785:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) {
1786:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1787: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1788:     }
1789:     for (i=0; i<slen; i++) {
1790:       row  = lrows[i] + rstart_bs;
1791:       MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);
1792:     }
1793:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1794:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1795:   } else {
1796:     MatZeroRows_SeqBAIJ(l->A,istmp,0);
1797:   }

1799:   ISDestroy(istmp);
1800:   PetscFree(lrows);

1802:   /* wait on sends */
1803:   if (nsends) {
1804:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1805:     MPI_Waitall(nsends,send_waits,send_status);
1806:     PetscFree(send_status);
1807:   }
1808:   PetscFree(send_waits);
1809:   PetscFree(svalues);

1811:   return(0);
1812: }

1816: int MatPrintHelp_MPIBAIJ(Mat A)
1817: {
1818:   Mat_MPIBAIJ *a   = (Mat_MPIBAIJ*)A->data;
1819:   MPI_Comm    comm = A->comm;
1820:   static int  called = 0;
1821:   int         ierr;

1824:   if (!a->rank) {
1825:     MatPrintHelp_SeqBAIJ(a->A);
1826:   }
1827:   if (called) {return(0);} else called = 1;
1828:   (*PetscHelpPrintf)(comm," Options for MATMPIBAIJ matrix format (the defaults):\n");
1829:   (*PetscHelpPrintf)(comm,"  -mat_use_hash_table <factor>: Use hashtable for efficient matrix assembly\n");
1830:   return(0);
1831: }

1835: int MatSetUnfactored_MPIBAIJ(Mat A)
1836: {
1837:   Mat_MPIBAIJ *a   = (Mat_MPIBAIJ*)A->data;
1838:   int         ierr;

1841:   MatSetUnfactored(a->A);
1842:   return(0);
1843: }

1845: static int MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat *);

1849: int MatEqual_MPIBAIJ(Mat A,Mat B,PetscTruth *flag)
1850: {
1851:   Mat_MPIBAIJ *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1852:   Mat         a,b,c,d;
1853:   PetscTruth  flg;
1854:   int         ierr;

1857:   a = matA->A; b = matA->B;
1858:   c = matB->A; d = matB->B;

1860:   MatEqual(a,c,&flg);
1861:   if (flg == PETSC_TRUE) {
1862:     MatEqual(b,d,&flg);
1863:   }
1864:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1865:   return(0);
1866: }


1871: int MatSetUpPreallocation_MPIBAIJ(Mat A)
1872: {
1873:   int        ierr;

1876:    MatMPIBAIJSetPreallocation(A,1,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1877:   return(0);
1878: }

1880: /* -------------------------------------------------------------------*/
1881: static struct _MatOps MatOps_Values = {
1882:        MatSetValues_MPIBAIJ,
1883:        MatGetRow_MPIBAIJ,
1884:        MatRestoreRow_MPIBAIJ,
1885:        MatMult_MPIBAIJ,
1886: /* 4*/ MatMultAdd_MPIBAIJ,
1887:        MatMultTranspose_MPIBAIJ,
1888:        MatMultTransposeAdd_MPIBAIJ,
1889:        0,
1890:        0,
1891:        0,
1892: /*10*/ 0,
1893:        0,
1894:        0,
1895:        0,
1896:        MatTranspose_MPIBAIJ,
1897: /*15*/ MatGetInfo_MPIBAIJ,
1898:        MatEqual_MPIBAIJ,
1899:        MatGetDiagonal_MPIBAIJ,
1900:        MatDiagonalScale_MPIBAIJ,
1901:        MatNorm_MPIBAIJ,
1902: /*20*/ MatAssemblyBegin_MPIBAIJ,
1903:        MatAssemblyEnd_MPIBAIJ,
1904:        0,
1905:        MatSetOption_MPIBAIJ,
1906:        MatZeroEntries_MPIBAIJ,
1907: /*25*/ MatZeroRows_MPIBAIJ,
1908:        0,
1909:        0,
1910:        0,
1911:        0,
1912: /*30*/ MatSetUpPreallocation_MPIBAIJ,
1913:        0,
1914:        0,
1915:        0,
1916:        0,
1917: /*35*/ MatDuplicate_MPIBAIJ,
1918:        0,
1919:        0,
1920:        0,
1921:        0,
1922: /*40*/ 0,
1923:        MatGetSubMatrices_MPIBAIJ,
1924:        MatIncreaseOverlap_MPIBAIJ,
1925:        MatGetValues_MPIBAIJ,
1926:        0,
1927: /*45*/ MatPrintHelp_MPIBAIJ,
1928:        MatScale_MPIBAIJ,
1929:        0,
1930:        0,
1931:        0,
1932: /*50*/ MatGetBlockSize_MPIBAIJ,
1933:        0,
1934:        0,
1935:        0,
1936:        0,
1937: /*55*/ 0,
1938:        0,
1939:        MatSetUnfactored_MPIBAIJ,
1940:        0,
1941:        MatSetValuesBlocked_MPIBAIJ,
1942: /*60*/ 0,
1943:        MatDestroy_MPIBAIJ,
1944:        MatView_MPIBAIJ,
1945:        MatGetPetscMaps_Petsc,
1946:        0,
1947: /*65*/ 0,
1948:        0,
1949:        0,
1950:        0,
1951:        0,
1952: /*70*/ MatGetRowMax_MPIBAIJ,
1953:        0,
1954:        0,
1955:        0,
1956:        0,
1957: /*75*/ 0,
1958:        0,
1959:        0,
1960:        0,
1961:        0,
1962: /*80*/ 0,
1963:        0,
1964:        0,
1965:        0,
1966: /*85*/ MatLoad_MPIBAIJ
1967: };


1970: EXTERN_C_BEGIN
1973: int MatGetDiagonalBlock_MPIBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
1974: {
1976:   *a      = ((Mat_MPIBAIJ *)A->data)->A;
1977:   *iscopy = PETSC_FALSE;
1978:   return(0);
1979: }
1980: EXTERN_C_END

1982: EXTERN_C_BEGIN
1983: extern int MatConvert_MPIBAIJ_MPISBAIJ(Mat,const MatType,Mat*);
1984: EXTERN_C_END

1986: EXTERN_C_BEGIN
1989: int MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,int bs,int d_nz,int *d_nnz,int o_nz,int *o_nnz)
1990: {
1991:   Mat_MPIBAIJ  *b;
1992:   int          ierr,i;

1995:   B->preallocated = PETSC_TRUE;
1996:   PetscOptionsGetInt(PETSC_NULL,"-mat_block_size",&bs,PETSC_NULL);

1998:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
1999:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2000:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2001:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %d",d_nz);
2002:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %d",o_nz);
2003:   if (d_nnz) {
2004:   for (i=0; i<B->m/bs; i++) {
2005:       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]);
2006:     }
2007:   }
2008:   if (o_nnz) {
2009:     for (i=0; i<B->m/bs; i++) {
2010:       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]);
2011:     }
2012:   }
2013: 
2014:   PetscSplitOwnershipBlock(B->comm,bs,&B->m,&B->M);
2015:   PetscSplitOwnershipBlock(B->comm,bs,&B->n,&B->N);
2016:   PetscMapCreateMPI(B->comm,B->m,B->M,&B->rmap);
2017:   PetscMapCreateMPI(B->comm,B->n,B->N,&B->cmap);

2019:   b = (Mat_MPIBAIJ*)B->data;
2020:   b->bs  = bs;
2021:   b->bs2 = bs*bs;
2022:   b->mbs = B->m/bs;
2023:   b->nbs = B->n/bs;
2024:   b->Mbs = B->M/bs;
2025:   b->Nbs = B->N/bs;

2027:   MPI_Allgather(&b->mbs,1,MPI_INT,b->rowners+1,1,MPI_INT,B->comm);
2028:   b->rowners[0]    = 0;
2029:   for (i=2; i<=b->size; i++) {
2030:     b->rowners[i] += b->rowners[i-1];
2031:   }
2032:   b->rstart    = b->rowners[b->rank];
2033:   b->rend      = b->rowners[b->rank+1];

2035:   MPI_Allgather(&b->nbs,1,MPI_INT,b->cowners+1,1,MPI_INT,B->comm);
2036:   b->cowners[0] = 0;
2037:   for (i=2; i<=b->size; i++) {
2038:     b->cowners[i] += b->cowners[i-1];
2039:   }
2040:   b->cstart    = b->cowners[b->rank];
2041:   b->cend      = b->cowners[b->rank+1];

2043:   for (i=0; i<=b->size; i++) {
2044:     b->rowners_bs[i] = b->rowners[i]*bs;
2045:   }
2046:   b->rstart_bs = b->rstart*bs;
2047:   b->rend_bs   = b->rend*bs;
2048:   b->cstart_bs = b->cstart*bs;
2049:   b->cend_bs   = b->cend*bs;

2051:   MatCreate(PETSC_COMM_SELF,B->m,B->n,B->m,B->n,&b->A);
2052:   MatSetType(b->A,MATSEQBAIJ);
2053:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
2054:   PetscLogObjectParent(B,b->A);
2055:   MatCreate(PETSC_COMM_SELF,B->m,B->N,B->m,B->N,&b->B);
2056:   MatSetType(b->B,MATSEQBAIJ);
2057:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
2058:   PetscLogObjectParent(B,b->B);

2060:   MatStashCreate_Private(B->comm,bs,&B->bstash);

2062:   return(0);
2063: }
2064: EXTERN_C_END

2066: EXTERN_C_BEGIN
2067: EXTERN int MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2068: EXTERN int MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);
2069: EXTERN_C_END

2071: /*MC
2072:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

2074:    Options Database Keys:
2075: . -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()

2077:   Level: beginner

2079: .seealso: MatCreateMPIBAIJ
2080: M*/

2082: EXTERN_C_BEGIN
2085: int MatCreate_MPIBAIJ(Mat B)
2086: {
2087:   Mat_MPIBAIJ  *b;
2088:   int          ierr;
2089:   PetscTruth   flg;

2092:   PetscNew(Mat_MPIBAIJ,&b);
2093:   B->data = (void*)b;

2095:   PetscMemzero(b,sizeof(Mat_MPIBAIJ));
2096:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2097:   B->mapping    = 0;
2098:   B->factor     = 0;
2099:   B->assembled  = PETSC_FALSE;

2101:   B->insertmode = NOT_SET_VALUES;
2102:   MPI_Comm_rank(B->comm,&b->rank);
2103:   MPI_Comm_size(B->comm,&b->size);

2105:   /* build local table of row and column ownerships */
2106:   PetscMalloc(3*(b->size+2)*sizeof(int),&b->rowners);
2107:   PetscLogObjectMemory(B,3*(b->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPIBAIJ));
2108:   b->cowners    = b->rowners + b->size + 2;
2109:   b->rowners_bs = b->cowners + b->size + 2;

2111:   /* build cache for off array entries formed */
2112:   MatStashCreate_Private(B->comm,1,&B->stash);
2113:   b->donotstash  = PETSC_FALSE;
2114:   b->colmap      = PETSC_NULL;
2115:   b->garray      = PETSC_NULL;
2116:   b->roworiented = PETSC_TRUE;

2118: #if defined(PETSC_USE_MAT_SINGLE)
2119:   /* stuff for MatSetValues_XXX in single precision */
2120:   b->setvalueslen     = 0;
2121:   b->setvaluescopy    = PETSC_NULL;
2122: #endif

2124:   /* stuff used in block assembly */
2125:   b->barray       = 0;

2127:   /* stuff used for matrix vector multiply */
2128:   b->lvec         = 0;
2129:   b->Mvctx        = 0;

2131:   /* stuff for MatGetRow() */
2132:   b->rowindices   = 0;
2133:   b->rowvalues    = 0;
2134:   b->getrowactive = PETSC_FALSE;

2136:   /* hash table stuff */
2137:   b->ht           = 0;
2138:   b->hd           = 0;
2139:   b->ht_size      = 0;
2140:   b->ht_flag      = PETSC_FALSE;
2141:   b->ht_fact      = 0;
2142:   b->ht_total_ct  = 0;
2143:   b->ht_insert_ct = 0;

2145:   PetscOptionsHasName(PETSC_NULL,"-mat_use_hash_table",&flg);
2146:   if (flg) {
2147:     PetscReal fact = 1.39;
2148:     MatSetOption(B,MAT_USE_HASH_TABLE);
2149:     PetscOptionsGetReal(PETSC_NULL,"-mat_use_hash_table",&fact,PETSC_NULL);
2150:     if (fact <= 1.0) fact = 1.39;
2151:     MatMPIBAIJSetHashTableFactor(B,fact);
2152:     PetscLogInfo(0,"MatCreateMPIBAIJ:Hash table Factor used %5.2f\n",fact);
2153:   }
2154:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2155:                                      "MatStoreValues_MPIBAIJ",
2156:                                      MatStoreValues_MPIBAIJ);
2157:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2158:                                      "MatRetrieveValues_MPIBAIJ",
2159:                                      MatRetrieveValues_MPIBAIJ);
2160:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
2161:                                      "MatGetDiagonalBlock_MPIBAIJ",
2162:                                      MatGetDiagonalBlock_MPIBAIJ);
2163:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocation_C",
2164:                                      "MatMPIBAIJSetPreallocation_MPIBAIJ",
2165:                                      MatMPIBAIJSetPreallocation_MPIBAIJ);
2166:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
2167:                                      "MatDiagonalScaleLocal_MPIBAIJ",
2168:                                      MatDiagonalScaleLocal_MPIBAIJ);
2169:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetHashTableFactor_C",
2170:                                      "MatSetHashTableFactor_MPIBAIJ",
2171:                                      MatSetHashTableFactor_MPIBAIJ);
2172:   return(0);
2173: }
2174: EXTERN_C_END

2176: /*MC
2177:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

2179:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
2180:    and MATMPIBAIJ otherwise.

2182:    Options Database Keys:
2183: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

2185:   Level: beginner

2187: .seealso: MatCreateMPIBAIJ,MATSEQBAIJ,MATMPIBAIJ
2188: M*/

2190: EXTERN_C_BEGIN
2193: int MatCreate_BAIJ(Mat A) {
2194:   int ierr,size;

2197:   PetscObjectChangeTypeName((PetscObject)A,MATBAIJ);
2198:   MPI_Comm_size(A->comm,&size);
2199:   if (size == 1) {
2200:     MatSetType(A,MATSEQBAIJ);
2201:   } else {
2202:     MatSetType(A,MATMPIBAIJ);
2203:   }
2204:   return(0);
2205: }
2206: EXTERN_C_END

2210: /*@C
2211:    MatMPIBAIJSetPreallocation - Creates a sparse parallel matrix in block AIJ format
2212:    (block compressed row).  For good matrix assembly performance
2213:    the user should preallocate the matrix storage by setting the parameters 
2214:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2215:    performance can be increased by more than a factor of 50.

2217:    Collective on Mat

2219:    Input Parameters:
2220: +  A - the matrix 
2221: .  bs   - size of blockk
2222: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
2223:            submatrix  (same for all local rows)
2224: .  d_nnz - array containing the number of block nonzeros in the various block rows 
2225:            of the in diagonal portion of the local (possibly different for each block
2226:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2227: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
2228:            submatrix (same for all local rows).
2229: -  o_nnz - array containing the number of nonzeros in the various block rows of the
2230:            off-diagonal portion of the local submatrix (possibly different for
2231:            each block row) or PETSC_NULL.

2233:    Output Parameter:


2236:    Options Database Keys:
2237: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2238:                      block calculations (much slower)
2239: .   -mat_block_size - size of the blocks to use

2241:    Notes:
2242:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2243:    than it must be used on all processors that share the object for that argument.

2245:    Storage Information:
2246:    For a square global matrix we define each processor's diagonal portion 
2247:    to be its local rows and the corresponding columns (a square submatrix);  
2248:    each processor's off-diagonal portion encompasses the remainder of the
2249:    local matrix (a rectangular submatrix). 

2251:    The user can specify preallocated storage for the diagonal part of
2252:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2253:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2254:    memory allocation.  Likewise, specify preallocated storage for the
2255:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2257:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2258:    the figure below we depict these three local rows and all columns (0-11).

2260: .vb
2261:            0 1 2 3 4 5 6 7 8 9 10 11
2262:           -------------------
2263:    row 3  |  o o o d d d o o o o o o
2264:    row 4  |  o o o d d d o o o o o o
2265:    row 5  |  o o o d d d o o o o o o
2266:           -------------------
2267: .ve
2268:   
2269:    Thus, any entries in the d locations are stored in the d (diagonal) 
2270:    submatrix, and any entries in the o locations are stored in the
2271:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2272:    stored simply in the MATSEQBAIJ format for compressed row storage.

2274:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2275:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2276:    In general, for PDE problems in which most nonzeros are near the diagonal,
2277:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2278:    or you will get TERRIBLE performance; see the users' manual chapter on
2279:    matrices.

2281:    Level: intermediate

2283: .keywords: matrix, block, aij, compressed row, sparse, parallel

2285: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2286: @*/
2287: int MatMPIBAIJSetPreallocation(Mat B,int bs,int d_nz,const int d_nnz[],int o_nz,const int o_nnz[])
2288: {
2289:   int ierr,(*f)(Mat,int,int,const int[],int,const int[]);

2292:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",(void (**)(void))&f);
2293:   if (f) {
2294:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
2295:   }
2296:   return(0);
2297: }

2301: /*@C
2302:    MatCreateMPIBAIJ - Creates a sparse parallel matrix in block AIJ format
2303:    (block compressed row).  For good matrix assembly performance
2304:    the user should preallocate the matrix storage by setting the parameters 
2305:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2306:    performance can be increased by more than a factor of 50.

2308:    Collective on MPI_Comm

2310:    Input Parameters:
2311: +  comm - MPI communicator
2312: .  bs   - size of blockk
2313: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2314:            This value should be the same as the local size used in creating the 
2315:            y vector for the matrix-vector product y = Ax.
2316: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
2317:            This value should be the same as the local size used in creating the 
2318:            x vector for the matrix-vector product y = Ax.
2319: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2320: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2321: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local 
2322:            submatrix  (same for all local rows)
2323: .  d_nnz - array containing the number of nonzero blocks in the various block rows 
2324:            of the in diagonal portion of the local (possibly different for each block
2325:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2326: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
2327:            submatrix (same for all local rows).
2328: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
2329:            off-diagonal portion of the local submatrix (possibly different for
2330:            each block row) or PETSC_NULL.

2332:    Output Parameter:
2333: .  A - the matrix 

2335:    Options Database Keys:
2336: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2337:                      block calculations (much slower)
2338: .   -mat_block_size - size of the blocks to use

2340:    Notes:
2341:    A nonzero block is any block that as 1 or more nonzeros in it

2343:    The user MUST specify either the local or global matrix dimensions
2344:    (possibly both).

2346:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2347:    than it must be used on all processors that share the object for that argument.

2349:    Storage Information:
2350:    For a square global matrix we define each processor's diagonal portion 
2351:    to be its local rows and the corresponding columns (a square submatrix);  
2352:    each processor's off-diagonal portion encompasses the remainder of the
2353:    local matrix (a rectangular submatrix). 

2355:    The user can specify preallocated storage for the diagonal part of
2356:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2357:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2358:    memory allocation.  Likewise, specify preallocated storage for the
2359:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2361:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2362:    the figure below we depict these three local rows and all columns (0-11).

2364: .vb
2365:            0 1 2 3 4 5 6 7 8 9 10 11
2366:           -------------------
2367:    row 3  |  o o o d d d o o o o o o
2368:    row 4  |  o o o d d d o o o o o o
2369:    row 5  |  o o o d d d o o o o o o
2370:           -------------------
2371: .ve
2372:   
2373:    Thus, any entries in the d locations are stored in the d (diagonal) 
2374:    submatrix, and any entries in the o locations are stored in the
2375:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2376:    stored simply in the MATSEQBAIJ format for compressed row storage.

2378:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2379:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2380:    In general, for PDE problems in which most nonzeros are near the diagonal,
2381:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2382:    or you will get TERRIBLE performance; see the users' manual chapter on
2383:    matrices.

2385:    Level: intermediate

2387: .keywords: matrix, block, aij, compressed row, sparse, parallel

2389: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2390: @*/
2391: int MatCreateMPIBAIJ(MPI_Comm comm,int bs,int m,int n,int M,int N,int d_nz,const int d_nnz[],int o_nz,const int o_nnz[],Mat *A)
2392: {
2393:   int ierr,size;

2396:   MatCreate(comm,m,n,M,N,A);
2397:   MPI_Comm_size(comm,&size);
2398:   if (size > 1) {
2399:     MatSetType(*A,MATMPIBAIJ);
2400:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2401:   } else {
2402:     MatSetType(*A,MATSEQBAIJ);
2403:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2404:   }
2405:   return(0);
2406: }

2410: static int MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2411: {
2412:   Mat         mat;
2413:   Mat_MPIBAIJ *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
2414:   int         ierr,len=0;

2417:   *newmat       = 0;
2418:   MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);
2419:   MatSetType(mat,matin->type_name);

2421:   PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));
2422:   mat->factor       = matin->factor;
2423:   mat->preallocated = PETSC_TRUE;
2424:   mat->assembled    = PETSC_TRUE;
2425:   mat->insertmode   = NOT_SET_VALUES;

2427:   a      = (Mat_MPIBAIJ*)mat->data;
2428:   a->bs  = oldmat->bs;
2429:   a->bs2 = oldmat->bs2;
2430:   a->mbs = oldmat->mbs;
2431:   a->nbs = oldmat->nbs;
2432:   a->Mbs = oldmat->Mbs;
2433:   a->Nbs = oldmat->Nbs;
2434: 
2435:   a->rstart       = oldmat->rstart;
2436:   a->rend         = oldmat->rend;
2437:   a->cstart       = oldmat->cstart;
2438:   a->cend         = oldmat->cend;
2439:   a->size         = oldmat->size;
2440:   a->rank         = oldmat->rank;
2441:   a->donotstash   = oldmat->donotstash;
2442:   a->roworiented  = oldmat->roworiented;
2443:   a->rowindices   = 0;
2444:   a->rowvalues    = 0;
2445:   a->getrowactive = PETSC_FALSE;
2446:   a->barray       = 0;
2447:   a->rstart_bs    = oldmat->rstart_bs;
2448:   a->rend_bs      = oldmat->rend_bs;
2449:   a->cstart_bs    = oldmat->cstart_bs;
2450:   a->cend_bs      = oldmat->cend_bs;

2452:   /* hash table stuff */
2453:   a->ht           = 0;
2454:   a->hd           = 0;
2455:   a->ht_size      = 0;
2456:   a->ht_flag      = oldmat->ht_flag;
2457:   a->ht_fact      = oldmat->ht_fact;
2458:   a->ht_total_ct  = 0;
2459:   a->ht_insert_ct = 0;

2461:   PetscMemcpy(a->rowners,oldmat->rowners,3*(a->size+2)*sizeof(int));
2462:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2463:   MatStashCreate_Private(matin->comm,oldmat->bs,&mat->bstash);
2464:   if (oldmat->colmap) {
2465: #if defined (PETSC_USE_CTABLE)
2466:   PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2467: #else
2468:   PetscMalloc((a->Nbs)*sizeof(int),&a->colmap);
2469:   PetscLogObjectMemory(mat,(a->Nbs)*sizeof(int));
2470:   PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(int));
2471: #endif
2472:   } else a->colmap = 0;

2474:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2475:     PetscMalloc(len*sizeof(int),&a->garray);
2476:     PetscLogObjectMemory(mat,len*sizeof(int));
2477:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(int));
2478:   } else a->garray = 0;
2479: 
2480:    VecDuplicate(oldmat->lvec,&a->lvec);
2481:   PetscLogObjectParent(mat,a->lvec);
2482:    VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2483:   PetscLogObjectParent(mat,a->Mvctx);

2485:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2486:   PetscLogObjectParent(mat,a->A);
2487:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2488:   PetscLogObjectParent(mat,a->B);
2489:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2490:   *newmat = mat;

2492:   return(0);
2493: }

2495:  #include petscsys.h

2499: int MatLoad_MPIBAIJ(PetscViewer viewer,const MatType type,Mat *newmat)
2500: {
2501:   Mat          A;
2502:   int          i,nz,ierr,j,rstart,rend,fd;
2503:   PetscScalar  *vals,*buf;
2504:   MPI_Comm     comm = ((PetscObject)viewer)->comm;
2505:   MPI_Status   status;
2506:   int          header[4],rank,size,*rowlengths = 0,M,N,m,*rowners,*browners,maxnz,*cols;
2507:   int          *locrowlens,*sndcounts = 0,*procsnz = 0,jj,*mycols,*ibuf;
2508:   int          tag = ((PetscObject)viewer)->tag,bs=1,Mbs,mbs,extra_rows;
2509:   int          *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
2510:   int          dcount,kmax,k,nzcount,tmp;
2511: 
2513:   PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);

2515:   MPI_Comm_size(comm,&size);
2516:   MPI_Comm_rank(comm,&rank);
2517:   if (!rank) {
2518:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2519:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2520:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2521:     if (header[3] < 0) {
2522:       SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as MPIBAIJ");
2523:     }
2524:   }

2526:   MPI_Bcast(header+1,3,MPI_INT,0,comm);
2527:   M = header[1]; N = header[2];

2529:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2531:   /* 
2532:      This code adds extra rows to make sure the number of rows is 
2533:      divisible by the blocksize
2534:   */
2535:   Mbs        = M/bs;
2536:   extra_rows = bs - M + bs*(Mbs);
2537:   if (extra_rows == bs) extra_rows = 0;
2538:   else                  Mbs++;
2539:   if (extra_rows &&!rank) {
2540:     PetscLogInfo(0,"MatLoad_MPIBAIJ:Padding loaded matrix to match blocksize\n");
2541:   }

2543:   /* determine ownership of all rows */
2544:   mbs        = Mbs/size + ((Mbs % size) > rank);
2545:   m          = mbs*bs;
2546:   PetscMalloc(2*(size+2)*sizeof(int),&rowners);
2547:   browners   = rowners + size + 1;
2548:   MPI_Allgather(&mbs,1,MPI_INT,rowners+1,1,MPI_INT,comm);
2549:   rowners[0] = 0;
2550:   for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
2551:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2552:   rstart = rowners[rank];
2553:   rend   = rowners[rank+1];

2555:   /* distribute row lengths to all processors */
2556:   PetscMalloc((rend-rstart)*bs*sizeof(int),&locrowlens);
2557:   if (!rank) {
2558:     PetscMalloc((M+extra_rows)*sizeof(int),&rowlengths);
2559:     PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2560:     for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2561:     PetscMalloc(size*sizeof(int),&sndcounts);
2562:     for (i=0; i<size; i++) sndcounts[i] = browners[i+1] - browners[i];
2563:     MPI_Scatterv(rowlengths,sndcounts,browners,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
2564:     PetscFree(sndcounts);
2565:   } else {
2566:     MPI_Scatterv(0,0,0,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
2567:   }

2569:   if (!rank) {
2570:     /* calculate the number of nonzeros on each processor */
2571:     PetscMalloc(size*sizeof(int),&procsnz);
2572:     PetscMemzero(procsnz,size*sizeof(int));
2573:     for (i=0; i<size; i++) {
2574:       for (j=rowners[i]*bs; j< rowners[i+1]*bs; j++) {
2575:         procsnz[i] += rowlengths[j];
2576:       }
2577:     }
2578:     PetscFree(rowlengths);
2579: 
2580:     /* determine max buffer needed and allocate it */
2581:     maxnz = 0;
2582:     for (i=0; i<size; i++) {
2583:       maxnz = PetscMax(maxnz,procsnz[i]);
2584:     }
2585:     PetscMalloc(maxnz*sizeof(int),&cols);

2587:     /* read in my part of the matrix column indices  */
2588:     nz     = procsnz[0];
2589:     PetscMalloc(nz*sizeof(int),&ibuf);
2590:     mycols = ibuf;
2591:     if (size == 1)  nz -= extra_rows;
2592:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2593:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2595:     /* read in every ones (except the last) and ship off */
2596:     for (i=1; i<size-1; i++) {
2597:       nz   = procsnz[i];
2598:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2599:       MPI_Send(cols,nz,MPI_INT,i,tag,comm);
2600:     }
2601:     /* read in the stuff for the last proc */
2602:     if (size != 1) {
2603:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2604:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2605:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2606:       MPI_Send(cols,nz+extra_rows,MPI_INT,size-1,tag,comm);
2607:     }
2608:     PetscFree(cols);
2609:   } else {
2610:     /* determine buffer space needed for message */
2611:     nz = 0;
2612:     for (i=0; i<m; i++) {
2613:       nz += locrowlens[i];
2614:     }
2615:     PetscMalloc(nz*sizeof(int),&ibuf);
2616:     mycols = ibuf;
2617:     /* receive message of column indices*/
2618:     MPI_Recv(mycols,nz,MPI_INT,0,tag,comm,&status);
2619:     MPI_Get_count(&status,MPI_INT,&maxnz);
2620:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2621:   }
2622: 
2623:   /* loop over local rows, determining number of off diagonal entries */
2624:   PetscMalloc(2*(rend-rstart+1)*sizeof(int),&dlens);
2625:   odlens   = dlens + (rend-rstart);
2626:   PetscMalloc(3*Mbs*sizeof(int),&mask);
2627:   PetscMemzero(mask,3*Mbs*sizeof(int));
2628:   masked1  = mask    + Mbs;
2629:   masked2  = masked1 + Mbs;
2630:   rowcount = 0; nzcount = 0;
2631:   for (i=0; i<mbs; i++) {
2632:     dcount  = 0;
2633:     odcount = 0;
2634:     for (j=0; j<bs; j++) {
2635:       kmax = locrowlens[rowcount];
2636:       for (k=0; k<kmax; k++) {
2637:         tmp = mycols[nzcount++]/bs;
2638:         if (!mask[tmp]) {
2639:           mask[tmp] = 1;
2640:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
2641:           else masked1[dcount++] = tmp;
2642:         }
2643:       }
2644:       rowcount++;
2645:     }
2646: 
2647:     dlens[i]  = dcount;
2648:     odlens[i] = odcount;

2650:     /* zero out the mask elements we set */
2651:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2652:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2653:   }

2655:   /* create our matrix */
2656:   MatCreate(comm,m,m,M+extra_rows,N+extra_rows,&A);
2657:   MatSetType(A,type);CHKERRQ(ierr)
2658:   MatMPIBAIJSetPreallocation(A,bs,0,dlens,0,odlens);

2660:   /* Why doesn't this called using MatSetOption(A,MAT_COLUMNS_SORTED); */
2661:   MatSetOption(A,MAT_COLUMNS_SORTED);
2662: 
2663:   if (!rank) {
2664:     PetscMalloc(maxnz*sizeof(PetscScalar),&buf);
2665:     /* read in my part of the matrix numerical values  */
2666:     nz = procsnz[0];
2667:     vals = buf;
2668:     mycols = ibuf;
2669:     if (size == 1)  nz -= extra_rows;
2670:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2671:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2673:     /* insert into matrix */
2674:     jj      = rstart*bs;
2675:     for (i=0; i<m; i++) {
2676:       MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2677:       mycols += locrowlens[i];
2678:       vals   += locrowlens[i];
2679:       jj++;
2680:     }
2681:     /* read in other processors (except the last one) and ship out */
2682:     for (i=1; i<size-1; i++) {
2683:       nz   = procsnz[i];
2684:       vals = buf;
2685:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2686:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2687:     }
2688:     /* the last proc */
2689:     if (size != 1){
2690:       nz   = procsnz[i] - extra_rows;
2691:       vals = buf;
2692:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2693:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2694:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2695:     }
2696:     PetscFree(procsnz);
2697:   } else {
2698:     /* receive numeric values */
2699:     PetscMalloc(nz*sizeof(PetscScalar),&buf);

2701:     /* receive message of values*/
2702:     vals   = buf;
2703:     mycols = ibuf;
2704:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2705:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2706:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2708:     /* insert into matrix */
2709:     jj      = rstart*bs;
2710:     for (i=0; i<m; i++) {
2711:       MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2712:       mycols += locrowlens[i];
2713:       vals   += locrowlens[i];
2714:       jj++;
2715:     }
2716:   }
2717:   PetscFree(locrowlens);
2718:   PetscFree(buf);
2719:   PetscFree(ibuf);
2720:   PetscFree(rowners);
2721:   PetscFree(dlens);
2722:   PetscFree(mask);
2723:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2724:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

2726:   *newmat = A;
2727:   return(0);
2728: }

2732: /*@
2733:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2735:    Input Parameters:
2736: .  mat  - the matrix
2737: .  fact - factor

2739:    Collective on Mat

2741:    Level: advanced

2743:   Notes:
2744:    This can also be set by the command line option: -mat_use_hash_table fact

2746: .keywords: matrix, hashtable, factor, HT

2748: .seealso: MatSetOption()
2749: @*/
2750: int MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2751: {
2752:   int ierr,(*f)(Mat,PetscReal);

2755:   PetscObjectQueryFunction((PetscObject)mat,"MatSetHashTableFactor_C",(void (**)(void))&f);
2756:   if (f) {
2757:     (*f)(mat,fact);
2758:   }
2759:   return(0);
2760: }

2764: int MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
2765: {
2766:   Mat_MPIBAIJ *baij;

2769:   baij = (Mat_MPIBAIJ*)mat->data;
2770:   baij->ht_fact = fact;
2771:   return(0);
2772: }

2776: int MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,int *colmap[])
2777: {
2778:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2780:   *Ad     = a->A;
2781:   *Ao     = a->B;
2782:   *colmap = a->garray;
2783:   return(0);
2784: }