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
 4:  #include src/vec/vecimpl.h

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

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

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

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

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

 57:   for (i=0; i<A->m; i++){
 58:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) va[i] = vb[i];
 59:   }

 61:   VecRestoreArray(v,&va);
 62:   VecRestoreArray(vtmp,&vb);
 63:   VecDestroy(vtmp);
 64: 
 65:   return(0);
 66: }

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

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

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

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

 98: /* 
 99:      Local utility routine that creates a mapping from the global column 
100:    number to the local number in the off-diagonal part of the local 
101:    storage of the matrix.  This is done in a non scable way since the 
102:    length of colmap equals the global matrix length. 
103: */
106: static int CreateColmap_MPIBAIJ_Private(Mat mat)
107: {
108:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
109:   Mat_SeqBAIJ *B = (Mat_SeqBAIJ*)baij->B->data;
110:   int         nbs = B->nbs,i,bs=B->bs,ierr;

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

127: #define CHUNKSIZE  10

129: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv) \
130: { \
131:  \
132:     brow = row/bs;  \
133:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
134:     rmax = aimax[brow]; nrow = ailen[brow]; \
135:       bcol = col/bs; \
136:       ridx = row % bs; cidx = col % bs; \
137:       low = 0; high = nrow; \
138:       while (high-low > 3) { \
139:         t = (low+high)/2; \
140:         if (rp[t] > bcol) high = t; \
141:         else              low  = t; \
142:       } \
143:       for (_i=low; _i<high; _i++) { \
144:         if (rp[_i] > bcol) break; \
145:         if (rp[_i] == bcol) { \
146:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
147:           if (addv == ADD_VALUES) *bap += value;  \
148:           else                    *bap  = value;  \
149:           goto a_noinsert; \
150:         } \
151:       } \
152:       if (a->nonew == 1) goto a_noinsert; \
153:       else if (a->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%d, %d) into matrix", row, col); \
154:       if (nrow >= rmax) { \
155:         /* there is no extra room in row, therefore enlarge */ \
156:         int       new_nz = ai[a->mbs] + CHUNKSIZE,len,*new_i,*new_j; \
157:         MatScalar *new_a; \
158:  \
159:         if (a->nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%d, %d) in the matrix", row, col); \
160:  \
161:         /* malloc new storage space */ \
162:         len     = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(a->mbs+1)*sizeof(int); \
163:         PetscMalloc(len,&new_a); \
164:         new_j   = (int*)(new_a + bs2*new_nz); \
165:         new_i   = new_j + new_nz; \
166:  \
167:         /* copy over old data into new slots */ \
168:         for (ii=0; ii<brow+1; ii++) {new_i[ii] = ai[ii];} \
169:         for (ii=brow+1; ii<a->mbs+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;} \
170:         PetscMemcpy(new_j,aj,(ai[brow]+nrow)*sizeof(int)); \
171:         len = (new_nz - CHUNKSIZE - ai[brow] - nrow); \
172:         PetscMemcpy(new_j+ai[brow]+nrow+CHUNKSIZE,aj+ai[brow]+nrow,len*sizeof(int)); \
173:         PetscMemcpy(new_a,aa,(ai[brow]+nrow)*bs2*sizeof(MatScalar)); \
174:         PetscMemzero(new_a+bs2*(ai[brow]+nrow),bs2*CHUNKSIZE*sizeof(PetscScalar)); \
175:         PetscMemcpy(new_a+bs2*(ai[brow]+nrow+CHUNKSIZE), \
176:                     aa+bs2*(ai[brow]+nrow),bs2*len*sizeof(MatScalar));  \
177:         /* free up old matrix storage */ \
178:         PetscFree(a->a);  \
179:         if (!a->singlemalloc) { \
180:           PetscFree(a->i); \
181:           PetscFree(a->j);\
182:         } \
183:         aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;  \
184:         a->singlemalloc = PETSC_TRUE; \
185:  \
186:         rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
187:         rmax = aimax[brow] = aimax[brow] + CHUNKSIZE; \
188:         PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar))); \
189:         a->maxnz += bs2*CHUNKSIZE; \
190:         a->reallocs++; \
191:         a->nz++; \
192:       } \
193:       N = nrow++ - 1;  \
194:       /* shift up all the later entries in this row */ \
195:       for (ii=N; ii>=_i; ii--) { \
196:         rp[ii+1] = rp[ii]; \
197:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
198:       } \
199:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  \
200:       rp[_i]                      = bcol;  \
201:       ap[bs2*_i + bs*cidx + ridx] = value;  \
202:       a_noinsert:; \
203:     ailen[brow] = nrow; \
204: } 

206: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv) \
207: { \
208:     brow = row/bs;  \
209:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
210:     rmax = bimax[brow]; nrow = bilen[brow]; \
211:       bcol = col/bs; \
212:       ridx = row % bs; cidx = col % bs; \
213:       low = 0; high = nrow; \
214:       while (high-low > 3) { \
215:         t = (low+high)/2; \
216:         if (rp[t] > bcol) high = t; \
217:         else              low  = t; \
218:       } \
219:       for (_i=low; _i<high; _i++) { \
220:         if (rp[_i] > bcol) break; \
221:         if (rp[_i] == bcol) { \
222:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
223:           if (addv == ADD_VALUES) *bap += value;  \
224:           else                    *bap  = value;  \
225:           goto b_noinsert; \
226:         } \
227:       } \
228:       if (b->nonew == 1) goto b_noinsert; \
229:       else if (b->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%d, %d) into matrix", row, col); \
230:       if (nrow >= rmax) { \
231:         /* there is no extra room in row, therefore enlarge */ \
232:         int       new_nz = bi[b->mbs] + CHUNKSIZE,len,*new_i,*new_j; \
233:         MatScalar *new_a; \
234:  \
235:         if (b->nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%d, %d) in the matrix", row, col); \
236:  \
237:         /* malloc new storage space */ \
238:         len     = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(b->mbs+1)*sizeof(int); \
239:         PetscMalloc(len,&new_a); \
240:         new_j   = (int*)(new_a + bs2*new_nz); \
241:         new_i   = new_j + new_nz; \
242:  \
243:         /* copy over old data into new slots */ \
244:         for (ii=0; ii<brow+1; ii++) {new_i[ii] = bi[ii];} \
245:         for (ii=brow+1; ii<b->mbs+1; ii++) {new_i[ii] = bi[ii]+CHUNKSIZE;} \
246:         PetscMemcpy(new_j,bj,(bi[brow]+nrow)*sizeof(int)); \
247:         len  = (new_nz - CHUNKSIZE - bi[brow] - nrow); \
248:         PetscMemcpy(new_j+bi[brow]+nrow+CHUNKSIZE,bj+bi[brow]+nrow,len*sizeof(int)); \
249:         PetscMemcpy(new_a,ba,(bi[brow]+nrow)*bs2*sizeof(MatScalar)); \
250:         PetscMemzero(new_a+bs2*(bi[brow]+nrow),bs2*CHUNKSIZE*sizeof(MatScalar)); \
251:         PetscMemcpy(new_a+bs2*(bi[brow]+nrow+CHUNKSIZE), \
252:                     ba+bs2*(bi[brow]+nrow),bs2*len*sizeof(MatScalar));  \
253:         /* free up old matrix storage */ \
254:         PetscFree(b->a);  \
255:         if (!b->singlemalloc) { \
256:           PetscFree(b->i); \
257:           PetscFree(b->j); \
258:         } \
259:         ba = b->a = new_a; bi = b->i = new_i; bj = b->j = new_j;  \
260:         b->singlemalloc = PETSC_TRUE; \
261:  \
262:         rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
263:         rmax = bimax[brow] = bimax[brow] + CHUNKSIZE; \
264:         PetscLogObjectMemory(B,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar))); \
265:         b->maxnz += bs2*CHUNKSIZE; \
266:         b->reallocs++; \
267:         b->nz++; \
268:       } \
269:       N = nrow++ - 1;  \
270:       /* shift up all the later entries in this row */ \
271:       for (ii=N; ii>=_i; ii--) { \
272:         rp[ii+1] = rp[ii]; \
273:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
274:       } \
275:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  \
276:       rp[_i]                      = bcol;  \
277:       ap[bs2*_i + bs*cidx + ridx] = value;  \
278:       b_noinsert:; \
279:     bilen[brow] = nrow; \
280: } 

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

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

299:   for (i=0; i<N; i++) {
300:     vsingle[i] = v[i];
301:   }
302:   MatSetValues_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
303:   return(0);
304: }

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

315:   if (N > b->setvalueslen) {
316:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
317:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
318:     b->setvalueslen  = N;
319:   }
320:   vsingle = b->setvaluescopy;
321:   for (i=0; i<N; i++) {
322:     vsingle[i] = v[i];
323:   }
324:   MatSetValuesBlocked_MPIBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
325:   return(0);
326: }

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

337:   if (N > b->setvalueslen) {
338:     if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
339:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
340:     b->setvalueslen  = N;
341:   }
342:   vsingle = b->setvaluescopy;
343:   for (i=0; i<N; i++) {
344:     vsingle[i] = v[i];
345:   }
346:   MatSetValues_MPIBAIJ_HT_MatScalar(mat,m,im,n,in,vsingle,addv);
347:   return(0);
348: }

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

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

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

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

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

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

401:   for (i=0; i<m; i++) {
402:     if (im[i] < 0) continue;
403: #if defined(PETSC_USE_BOPT_g)
404:     if (im[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",im[i],mat->M-1);
405: #endif
406:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
407:       row = im[i] - rstart_orig;
408:       for (j=0; j<n; j++) {
409:         if (in[j] >= cstart_orig && in[j] < cend_orig){
410:           col = in[j] - cstart_orig;
411:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
412:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv);
413:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
414:         } else if (in[j] < 0) continue;
415: #if defined(PETSC_USE_BOPT_g)
416:         else if (in[j] >= mat->N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[i],mat->N-1);}
417: #endif
418:         else {
419:           if (mat->was_assembled) {
420:             if (!baij->colmap) {
421:               CreateColmap_MPIBAIJ_Private(mat);
422:             }
423: #if defined (PETSC_USE_CTABLE)
424:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
425:             col  = col - 1;
426: #else
427:             col = baij->colmap[in[j]/bs] - 1;
428: #endif
429:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
430:               DisAssemble_MPIBAIJ(mat);
431:               col =  in[j];
432:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
433:               B = baij->B;
434:               b = (Mat_SeqBAIJ*)(B)->data;
435:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
436:               ba=b->a;
437:             } else col += in[j]%bs;
438:           } else col = in[j];
439:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
440:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv);
441:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
442:         }
443:       }
444:     } else {
445:       if (!baij->donotstash) {
446:         if (roworiented) {
447:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
448:         } else {
449:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
450:         }
451:       }
452:     }
453:   }
454:   return(0);
455: }

459: int MatSetValuesBlocked_MPIBAIJ_MatScalar(Mat mat,int m,const int im[],int n,const int in[],const MatScalar v[],InsertMode addv)
460: {
461:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
462:   const MatScalar *value;
463:   MatScalar       *barray=baij->barray;
464:   PetscTruth      roworiented = baij->roworiented;
465:   int             ierr,i,j,ii,jj,row,col,rstart=baij->rstart;
466:   int             rend=baij->rend,cstart=baij->cstart,stepval;
467:   int             cend=baij->cend,bs=baij->bs,bs2=baij->bs2;
468: 
470:   if(!barray) {
471:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
472:     baij->barray = barray;
473:   }

475:   if (roworiented) {
476:     stepval = (n-1)*bs;
477:   } else {
478:     stepval = (m-1)*bs;
479:   }
480:   for (i=0; i<m; i++) {
481:     if (im[i] < 0) continue;
482: #if defined(PETSC_USE_BOPT_g)
483:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %d max %d",im[i],baij->Mbs-1);
484: #endif
485:     if (im[i] >= rstart && im[i] < rend) {
486:       row = im[i] - rstart;
487:       for (j=0; j<n; j++) {
488:         /* If NumCol = 1 then a copy is not required */
489:         if ((roworiented) && (n == 1)) {
490:           barray = (MatScalar*)v + i*bs2;
491:         } else if((!roworiented) && (m == 1)) {
492:           barray = (MatScalar*)v + j*bs2;
493:         } else { /* Here a copy is required */
494:           if (roworiented) {
495:             value = v + i*(stepval+bs)*bs + j*bs;
496:           } else {
497:             value = v + j*(stepval+bs)*bs + i*bs;
498:           }
499:           for (ii=0; ii<bs; ii++,value+=stepval) {
500:             for (jj=0; jj<bs; jj++) {
501:               *barray++  = *value++;
502:             }
503:           }
504:           barray -=bs2;
505:         }
506: 
507:         if (in[j] >= cstart && in[j] < cend){
508:           col  = in[j] - cstart;
509:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->A,1,&row,1,&col,barray,addv);
510:         }
511:         else if (in[j] < 0) continue;
512: #if defined(PETSC_USE_BOPT_g)
513:         else if (in[j] >= baij->Nbs) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %d max %d",in[j],baij->Nbs-1);}
514: #endif
515:         else {
516:           if (mat->was_assembled) {
517:             if (!baij->colmap) {
518:               CreateColmap_MPIBAIJ_Private(mat);
519:             }

521: #if defined(PETSC_USE_BOPT_g)
522: #if defined (PETSC_USE_CTABLE)
523:             { int data;
524:               PetscTableFind(baij->colmap,in[j]+1,&data);
525:               if ((data - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
526:             }
527: #else
528:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
529: #endif
530: #endif
531: #if defined (PETSC_USE_CTABLE)
532:             PetscTableFind(baij->colmap,in[j]+1,&col);
533:             col  = (col - 1)/bs;
534: #else
535:             col = (baij->colmap[in[j]] - 1)/bs;
536: #endif
537:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
538:               DisAssemble_MPIBAIJ(mat);
539:               col =  in[j];
540:             }
541:           }
542:           else col = in[j];
543:           MatSetValuesBlocked_SeqBAIJ_MatScalar(baij->B,1,&row,1,&col,barray,addv);
544:         }
545:       }
546:     } else {
547:       if (!baij->donotstash) {
548:         if (roworiented) {
549:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
550:         } else {
551:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
552:         }
553:       }
554:     }
555:   }
556:   return(0);
557: }

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


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

595: 
596:         idx = h1;
597: #if defined(PETSC_USE_BOPT_g)
598:         insert_ct++;
599:         total_ct++;
600:         if (HT[idx] != key) {
601:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
602:           if (idx == size) {
603:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
604:             if (idx == h1) {
605:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%d,%d) has no entry in the hash table", row, col);
606:             }
607:           }
608:         }
609: #else
610:         if (HT[idx] != key) {
611:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
612:           if (idx == size) {
613:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
614:             if (idx == h1) {
615:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%d,%d) has no entry in the hash table", row, col);
616:             }
617:           }
618:         }
619: #endif
620:         /* A HASH table entry is found, so insert the values at the correct address */
621:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
622:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
623:       }
624:     } else {
625:       if (!baij->donotstash) {
626:         if (roworiented) {
627:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
628:         } else {
629:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
630:         }
631:       }
632:     }
633:   }
634: #if defined(PETSC_USE_BOPT_g)
635:   baij->ht_total_ct = total_ct;
636:   baij->ht_insert_ct = insert_ct;
637: #endif
638:   return(0);
639: }

643: int MatSetValuesBlocked_MPIBAIJ_HT_MatScalar(Mat mat,int m,const int im[],int n,const int in[],const MatScalar v[],InsertMode addv)
644: {
645:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
646:   PetscTruth      roworiented = baij->roworiented;
647:   int             ierr,i,j,ii,jj,row,col;
648:   int             rstart=baij->rstart ;
649:   int             rend=baij->rend,stepval,bs=baij->bs,bs2=baij->bs2;
650:   int             h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
651:   PetscReal       tmp;
652:   MatScalar       **HD = baij->hd,*baij_a;
653:   const MatScalar *v_t,*value;
654: #if defined(PETSC_USE_BOPT_g)
655:   int             total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
656: #endif
657: 

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

676:         /* Look up into the Hash Table */
677:         key = row*Nbs+col+1;
678:         h1  = HASH(size,key,tmp);
679: 
680:         idx = h1;
681: #if defined(PETSC_USE_BOPT_g)
682:         total_ct++;
683:         insert_ct++;
684:        if (HT[idx] != key) {
685:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++);
686:           if (idx == size) {
687:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++);
688:             if (idx == h1) {
689:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%d,%d) has no entry in the hash table", row, col);
690:             }
691:           }
692:         }
693: #else  
694:         if (HT[idx] != key) {
695:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++);
696:           if (idx == size) {
697:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++);
698:             if (idx == h1) {
699:               SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"(%d,%d) has no entry in the hash table", row, col);
700:             }
701:           }
702:         }
703: #endif
704:         baij_a = HD[idx];
705:         if (roworiented) {
706:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
707:           /* value = v + (i*(stepval+bs)+j)*bs; */
708:           value = v_t;
709:           v_t  += bs;
710:           if (addv == ADD_VALUES) {
711:             for (ii=0; ii<bs; ii++,value+=stepval) {
712:               for (jj=ii; jj<bs2; jj+=bs) {
713:                 baij_a[jj]  += *value++;
714:               }
715:             }
716:           } else {
717:             for (ii=0; ii<bs; ii++,value+=stepval) {
718:               for (jj=ii; jj<bs2; jj+=bs) {
719:                 baij_a[jj]  = *value++;
720:               }
721:             }
722:           }
723:         } else {
724:           value = v + j*(stepval+bs)*bs + i*bs;
725:           if (addv == ADD_VALUES) {
726:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
727:               for (jj=0; jj<bs; jj++) {
728:                 baij_a[jj]  += *value++;
729:               }
730:             }
731:           } else {
732:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
733:               for (jj=0; jj<bs; jj++) {
734:                 baij_a[jj]  = *value++;
735:               }
736:             }
737:           }
738:         }
739:       }
740:     } else {
741:       if (!baij->donotstash) {
742:         if (roworiented) {
743:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
744:         } else {
745:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
746:         }
747:       }
748:     }
749:   }
750: #if defined(PETSC_USE_BOPT_g)
751:   baij->ht_total_ct = total_ct;
752:   baij->ht_insert_ct = insert_ct;
753: #endif
754:   return(0);
755: }

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

766:   for (i=0; i<m; i++) {
767:     if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",idxm[i]);
768:     if (idxm[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",idxm[i],mat->M-1);
769:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
770:       row = idxm[i] - bsrstart;
771:       for (j=0; j<n; j++) {
772:         if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",idxn[j]);
773:         if (idxn[j] >= mat->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",idxn[j],mat->N-1);
774:         if (idxn[j] >= bscstart && idxn[j] < bscend){
775:           col = idxn[j] - bscstart;
776:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
777:         } else {
778:           if (!baij->colmap) {
779:             CreateColmap_MPIBAIJ_Private(mat);
780:           }
781: #if defined (PETSC_USE_CTABLE)
782:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
783:           data --;
784: #else
785:           data = baij->colmap[idxn[j]/bs]-1;
786: #endif
787:           if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
788:           else {
789:             col  = data + idxn[j]%bs;
790:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
791:           }
792:         }
793:       }
794:     } else {
795:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
796:     }
797:   }
798:  return(0);
799: }

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

812:   if (baij->size == 1) {
813:      MatNorm(baij->A,type,nrm);
814:   } else {
815:     if (type == NORM_FROBENIUS) {
816:       v = amat->a;
817:       for (i=0; i<amat->nz*bs2; i++) {
818: #if defined(PETSC_USE_COMPLEX)
819:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
820: #else
821:         sum += (*v)*(*v); v++;
822: #endif
823:       }
824:       v = bmat->a;
825:       for (i=0; i<bmat->nz*bs2; i++) {
826: #if defined(PETSC_USE_COMPLEX)
827:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
828: #else
829:         sum += (*v)*(*v); v++;
830: #endif
831:       }
832:       MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPI_SUM,mat->comm);
833:       *nrm = sqrt(*nrm);
834:     } else {
835:       SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
836:     }
837:   }
838:   return(0);
839: }


842: /*
843:   Creates the hash table, and sets the table 
844:   This table is created only once. 
845:   If new entried need to be added to the matrix
846:   then the hash table has to be destroyed and
847:   recreated.
848: */
851: int MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
852: {
853:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
854:   Mat         A = baij->A,B=baij->B;
855:   Mat_SeqBAIJ *a=(Mat_SeqBAIJ *)A->data,*b=(Mat_SeqBAIJ *)B->data;
856:   int         i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
857:   int         size,bs2=baij->bs2,rstart=baij->rstart,ierr;
858:   int         cstart=baij->cstart,*garray=baij->garray,row,col,Nbs=baij->Nbs;
859:   int         *HT,key;
860:   MatScalar   **HD;
861:   PetscReal   tmp;
862: #if defined(PETSC_USE_BOPT_g)
863:   int         ct=0,max=0;
864: #endif

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

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


881:   PetscMemzero(HD,size*(sizeof(int)+sizeof(PetscScalar*)));
882: 

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

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

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

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

962:   MatStashScatterBegin_Private(&mat->stash,baij->rowners_bs);
963:   MatStashScatterBegin_Private(&mat->bstash,baij->rowners);
964:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
965:   PetscLogInfo(0,"MatAssemblyBegin_MPIBAIJ:Stash has %d entries,uses %d mallocs.\n",nstash,reallocs);
966:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
967:   PetscLogInfo(0,"MatAssemblyBegin_MPIBAIJ:Block-Stash has %d entries, uses %d mallocs.\n",nstash,reallocs);
968:   return(0);
969: }

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

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

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

1028:   MatAssemblyBegin(baij->A,mode);
1029:   MatAssemblyEnd(baij->A,mode);

1031:   /* determine if any processor has disassembled, if so we must 
1032:      also disassemble ourselfs, in order that we may reassemble. */
1033:   /*
1034:      if nonzero structure of submatrix B cannot change then we know that
1035:      no processor disassembled thus we can skip this stuff
1036:   */
1037:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
1038:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
1039:     if (mat->was_assembled && !other_disassembled) {
1040:       DisAssemble_MPIBAIJ(mat);
1041:     }
1042:   }

1044:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
1045:     MatSetUpMultiply_MPIBAIJ(mat);
1046:   }
1047:   MatAssemblyBegin(baij->B,mode);
1048:   MatAssemblyEnd(baij->B,mode);
1049: 
1050: #if defined(PETSC_USE_BOPT_g)
1051:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
1052:     PetscLogInfo(0,"MatAssemblyEnd_MPIBAIJ:Average Hash Table Search in MatSetValues = %5.2f\n",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
1053:     baij->ht_total_ct  = 0;
1054:     baij->ht_insert_ct = 0;
1055:   }
1056: #endif
1057:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
1058:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);
1059:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
1060:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
1061:   }

1063:   if (baij->rowvalues) {
1064:     PetscFree(baij->rowvalues);
1065:     baij->rowvalues = 0;
1066:   }
1067:   return(0);
1068: }

1072: static int MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1073: {
1074:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
1075:   int               ierr,bs = baij->bs,size = baij->size,rank = baij->rank;
1076:   PetscTruth        isascii,isdraw;
1077:   PetscViewer       sviewer;
1078:   PetscViewerFormat format;

1081:   /* printf(" MatView_MPIBAIJ_ASCIIorDraworSocket is called ...\n"); */
1082:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
1083:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1084:   if (isascii) {
1085:     PetscViewerGetFormat(viewer,&format);
1086:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1087:       MatInfo info;
1088:       MPI_Comm_rank(mat->comm,&rank);
1089:       MatGetInfo(mat,MAT_LOCAL,&info);
1090:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d bs %d mem %d\n",
1091:               rank,mat->m,(int)info.nz_used*bs,(int)info.nz_allocated*bs,
1092:               baij->bs,(int)info.memory);
1093:       MatGetInfo(baij->A,MAT_LOCAL,&info);
1094:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %d \n",rank,(int)info.nz_used*bs);
1095:       MatGetInfo(baij->B,MAT_LOCAL,&info);
1096:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %d \n",rank,(int)info.nz_used*bs);
1097:       PetscViewerFlush(viewer);
1098:       VecScatterView(baij->Mvctx,viewer);
1099:       return(0);
1100:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1101:       PetscViewerASCIIPrintf(viewer,"  block size is %d\n",bs);
1102:       return(0);
1103:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1104:       return(0);
1105:     }
1106:   }

1108:   if (isdraw) {
1109:     PetscDraw       draw;
1110:     PetscTruth isnull;
1111:     PetscViewerDrawGetDraw(viewer,0,&draw);
1112:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1113:   }

1115:   if (size == 1) {
1116:     PetscObjectSetName((PetscObject)baij->A,mat->name);
1117:     MatView(baij->A,viewer);
1118:   } else {
1119:     /* assemble the entire matrix onto first processor. */
1120:     Mat         A;
1121:     Mat_SeqBAIJ *Aloc;
1122:     int         M = mat->M,N = mat->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
1123:     MatScalar   *a;

1125:     if (!rank) {
1126:       MatCreateMPIBAIJ(mat->comm,baij->bs,M,N,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);
1127:     } else {
1128:       MatCreateMPIBAIJ(mat->comm,baij->bs,0,0,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);
1129:     }
1130:     PetscLogObjectParent(mat,A);

1132:     /* copy over the A part */
1133:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
1134:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1135:     PetscMalloc(bs*sizeof(int),&rvals);

1137:     for (i=0; i<mbs; i++) {
1138:       rvals[0] = bs*(baij->rstart + i);
1139:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1140:       for (j=ai[i]; j<ai[i+1]; j++) {
1141:         col = (baij->cstart+aj[j])*bs;
1142:         for (k=0; k<bs; k++) {
1143:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1144:           col++; a += bs;
1145:         }
1146:       }
1147:     }
1148:     /* copy over the B part */
1149:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
1150:     ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1151:     for (i=0; i<mbs; i++) {
1152:       rvals[0] = bs*(baij->rstart + i);
1153:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1154:       for (j=ai[i]; j<ai[i+1]; j++) {
1155:         col = baij->garray[aj[j]]*bs;
1156:         for (k=0; k<bs; k++) {
1157:           MatSetValues_MPIBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
1158:           col++; a += bs;
1159:         }
1160:       }
1161:     }
1162:     PetscFree(rvals);
1163:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1164:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1165:     /* 
1166:        Everyone has to call to draw the matrix since the graphics waits are
1167:        synchronized across all processors that share the PetscDraw object
1168:     */
1169:     PetscViewerGetSingleton(viewer,&sviewer);
1170:     if (!rank) {
1171:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,mat->name);
1172:       MatView(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1173:     }
1174:     PetscViewerRestoreSingleton(viewer,&sviewer);
1175:     MatDestroy(A);
1176:   }
1177:   return(0);
1178: }

1182: int MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1183: {
1184:   int        ierr;
1185:   PetscTruth isascii,isdraw,issocket,isbinary;

1188:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
1189:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1190:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1191:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1192:   if (isascii || isdraw || issocket || isbinary) {
1193:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1194:   } else {
1195:     SETERRQ1(1,"Viewer type %s not supported by MPIBAIJ matrices",((PetscObject)viewer)->type_name);
1196:   }
1197:   return(0);
1198: }

1202: int MatDestroy_MPIBAIJ(Mat mat)
1203: {
1204:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1205:   int         ierr;

1208: #if defined(PETSC_USE_LOG)
1209:   PetscLogObjectState((PetscObject)mat,"Rows=%d,Cols=%d",mat->M,mat->N);
1210: #endif
1211:   MatStashDestroy_Private(&mat->stash);
1212:   MatStashDestroy_Private(&mat->bstash);
1213:   PetscFree(baij->rowners);
1214:   MatDestroy(baij->A);
1215:   MatDestroy(baij->B);
1216: #if defined (PETSC_USE_CTABLE)
1217:   if (baij->colmap) {PetscTableDelete(baij->colmap);}
1218: #else
1219:   if (baij->colmap) {PetscFree(baij->colmap);}
1220: #endif
1221:   if (baij->garray) {PetscFree(baij->garray);}
1222:   if (baij->lvec)   {VecDestroy(baij->lvec);}
1223:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
1224:   if (baij->rowvalues) {PetscFree(baij->rowvalues);}
1225:   if (baij->barray) {PetscFree(baij->barray);}
1226:   if (baij->hd) {PetscFree(baij->hd);}
1227: #if defined(PETSC_USE_MAT_SINGLE)
1228:   if (baij->setvaluescopy) {PetscFree(baij->setvaluescopy);}
1229: #endif
1230:   PetscFree(baij);
1231:   return(0);
1232: }

1236: int MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1237: {
1238:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1239:   int         ierr,nt;

1242:   VecGetLocalSize(xx,&nt);
1243:   if (nt != A->n) {
1244:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1245:   }
1246:   VecGetLocalSize(yy,&nt);
1247:   if (nt != A->m) {
1248:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1249:   }
1250:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1251:   (*a->A->ops->mult)(a->A,xx,yy);
1252:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1253:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1254:   VecScatterPostRecvs(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1255:   return(0);
1256: }

1260: int MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1261: {
1262:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1263:   int        ierr;

1266:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1267:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1268:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
1269:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1270:   return(0);
1271: }

1275: int MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1276: {
1277:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1278:   int         ierr;

1281:   /* do nondiagonal part */
1282:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1283:   /* send it on its way */
1284:   VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1285:   /* do local part */
1286:   (*a->A->ops->multtranspose)(a->A,xx,yy);
1287:   /* receive remote parts: note this assumes the values are not actually */
1288:   /* inserted in yy until the next line, which is true for my implementation*/
1289:   /* but is not perhaps always true. */
1290:   VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1291:   return(0);
1292: }

1296: int MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1297: {
1298:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1299:   int         ierr;

1302:   /* do nondiagonal part */
1303:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1304:   /* send it on its way */
1305:   VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1306:   /* do local part */
1307:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1308:   /* receive remote parts: note this assumes the values are not actually */
1309:   /* inserted in yy until the next line, which is true for my implementation*/
1310:   /* but is not perhaps always true. */
1311:   VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
1312:   return(0);
1313: }

1315: /*
1316:   This only works correctly for square matrices where the subblock A->A is the 
1317:    diagonal block
1318: */
1321: int MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1322: {
1323:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1324:   int         ierr;

1327:   if (A->M != A->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1328:   MatGetDiagonal(a->A,v);
1329:   return(0);
1330: }

1334: int MatScale_MPIBAIJ(const PetscScalar *aa,Mat A)
1335: {
1336:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1337:   int         ierr;

1340:   MatScale(aa,a->A);
1341:   MatScale(aa,a->B);
1342:   return(0);
1343: }

1347: int MatGetRow_MPIBAIJ(Mat matin,int row,int *nz,int **idx,PetscScalar **v)
1348: {
1349:   Mat_MPIBAIJ  *mat = (Mat_MPIBAIJ*)matin->data;
1350:   PetscScalar  *vworkA,*vworkB,**pvA,**pvB,*v_p;
1351:   int          bs = mat->bs,bs2 = mat->bs2,i,ierr,*cworkA,*cworkB,**pcA,**pcB;
1352:   int          nztot,nzA,nzB,lrow,brstart = mat->rstart*bs,brend = mat->rend*bs;
1353:   int          *cmap,*idx_p,cstart = mat->cstart;

1356:   if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1357:   mat->getrowactive = PETSC_TRUE;

1359:   if (!mat->rowvalues && (idx || v)) {
1360:     /*
1361:         allocate enough space to hold information from the longest row.
1362:     */
1363:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1364:     int     max = 1,mbs = mat->mbs,tmp;
1365:     for (i=0; i<mbs; i++) {
1366:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1367:       if (max < tmp) { max = tmp; }
1368:     }
1369:     PetscMalloc(max*bs2*(sizeof(int)+sizeof(PetscScalar)),&mat->rowvalues);
1370:     mat->rowindices = (int*)(mat->rowvalues + max*bs2);
1371:   }
1372: 
1373:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1374:   lrow = row - brstart;

1376:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1377:   if (!v)   {pvA = 0; pvB = 0;}
1378:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1379:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1380:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1381:   nztot = nzA + nzB;

1383:   cmap  = mat->garray;
1384:   if (v  || idx) {
1385:     if (nztot) {
1386:       /* Sort by increasing column numbers, assuming A and B already sorted */
1387:       int imark = -1;
1388:       if (v) {
1389:         *v = v_p = mat->rowvalues;
1390:         for (i=0; i<nzB; i++) {
1391:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1392:           else break;
1393:         }
1394:         imark = i;
1395:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1396:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1397:       }
1398:       if (idx) {
1399:         *idx = idx_p = mat->rowindices;
1400:         if (imark > -1) {
1401:           for (i=0; i<imark; i++) {
1402:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1403:           }
1404:         } else {
1405:           for (i=0; i<nzB; i++) {
1406:             if (cmap[cworkB[i]/bs] < cstart)
1407:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1408:             else break;
1409:           }
1410:           imark = i;
1411:         }
1412:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1413:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1414:       }
1415:     } else {
1416:       if (idx) *idx = 0;
1417:       if (v)   *v   = 0;
1418:     }
1419:   }
1420:   *nz = nztot;
1421:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1422:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1423:   return(0);
1424: }

1428: int MatRestoreRow_MPIBAIJ(Mat mat,int row,int *nz,int **idx,PetscScalar **v)
1429: {
1430:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1433:   if (baij->getrowactive == PETSC_FALSE) {
1434:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1435:   }
1436:   baij->getrowactive = PETSC_FALSE;
1437:   return(0);
1438: }

1442: int MatGetBlockSize_MPIBAIJ(Mat mat,int *bs)
1443: {
1444:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1447:   *bs = baij->bs;
1448:   return(0);
1449: }

1453: int MatZeroEntries_MPIBAIJ(Mat A)
1454: {
1455:   Mat_MPIBAIJ *l = (Mat_MPIBAIJ*)A->data;
1456:   int         ierr;

1459:   MatZeroEntries(l->A);
1460:   MatZeroEntries(l->B);
1461:   return(0);
1462: }

1466: int MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1467: {
1468:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)matin->data;
1469:   Mat         A = a->A,B = a->B;
1470:   int         ierr;
1471:   PetscReal   isend[5],irecv[5];

1474:   info->block_size     = (PetscReal)a->bs;
1475:   MatGetInfo(A,MAT_LOCAL,info);
1476:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1477:   isend[3] = info->memory;  isend[4] = info->mallocs;
1478:   MatGetInfo(B,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:   if (flag == MAT_LOCAL) {
1482:     info->nz_used      = isend[0];
1483:     info->nz_allocated = isend[1];
1484:     info->nz_unneeded  = isend[2];
1485:     info->memory       = isend[3];
1486:     info->mallocs      = isend[4];
1487:   } else if (flag == MAT_GLOBAL_MAX) {
1488:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1489:     info->nz_used      = irecv[0];
1490:     info->nz_allocated = irecv[1];
1491:     info->nz_unneeded  = irecv[2];
1492:     info->memory       = irecv[3];
1493:     info->mallocs      = irecv[4];
1494:   } else if (flag == MAT_GLOBAL_SUM) {
1495:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1496:     info->nz_used      = irecv[0];
1497:     info->nz_allocated = irecv[1];
1498:     info->nz_unneeded  = irecv[2];
1499:     info->memory       = irecv[3];
1500:     info->mallocs      = irecv[4];
1501:   } else {
1502:     SETERRQ1(1,"Unknown MatInfoType argument %d",flag);
1503:   }
1504:   info->rows_global       = (PetscReal)A->M;
1505:   info->columns_global    = (PetscReal)A->N;
1506:   info->rows_local        = (PetscReal)A->m;
1507:   info->columns_local     = (PetscReal)A->N;
1508:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1509:   info->fill_ratio_needed = 0;
1510:   info->factor_mallocs    = 0;
1511:   return(0);
1512: }

1516: int MatSetOption_MPIBAIJ(Mat A,MatOption op)
1517: {
1518:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1519:   int         ierr;

1522:   switch (op) {
1523:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1524:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1525:   case MAT_COLUMNS_UNSORTED:
1526:   case MAT_COLUMNS_SORTED:
1527:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1528:   case MAT_KEEP_ZEROED_ROWS:
1529:   case MAT_NEW_NONZERO_LOCATION_ERR:
1530:     MatSetOption(a->A,op);
1531:     MatSetOption(a->B,op);
1532:     break;
1533:   case MAT_ROW_ORIENTED:
1534:     a->roworiented = PETSC_TRUE;
1535:     MatSetOption(a->A,op);
1536:     MatSetOption(a->B,op);
1537:     break;
1538:   case MAT_ROWS_SORTED:
1539:   case MAT_ROWS_UNSORTED:
1540:   case MAT_YES_NEW_DIAGONALS:
1541:     PetscLogInfo(A,"Info:MatSetOption_MPIBAIJ:Option ignored\n");
1542:     break;
1543:   case MAT_COLUMN_ORIENTED:
1544:     a->roworiented = PETSC_FALSE;
1545:     MatSetOption(a->A,op);
1546:     MatSetOption(a->B,op);
1547:     break;
1548:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1549:     a->donotstash = PETSC_TRUE;
1550:     break;
1551:   case MAT_NO_NEW_DIAGONALS:
1552:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1553:   case MAT_USE_HASH_TABLE:
1554:     a->ht_flag = PETSC_TRUE;
1555:     break;
1556:   default:
1557:     SETERRQ(PETSC_ERR_SUP,"unknown option");
1558:   }
1559:   return(0);
1560: }

1564: int MatTranspose_MPIBAIJ(Mat A,Mat *matout)
1565: {
1566:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)A->data;
1567:   Mat_SeqBAIJ *Aloc;
1568:   Mat         B;
1569:   int         ierr,M=A->M,N=A->N,*ai,*aj,i,*rvals,j,k,col;
1570:   int         bs=baij->bs,mbs=baij->mbs;
1571:   MatScalar   *a;
1572: 
1574:   if (!matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1575:   MatCreateMPIBAIJ(A->comm,baij->bs,A->n,A->m,N,M,0,PETSC_NULL,0,PETSC_NULL,&B);
1576: 
1577:   /* copy over the A part */
1578:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1579:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1580:   PetscMalloc(bs*sizeof(int),&rvals);
1581: 
1582:   for (i=0; i<mbs; i++) {
1583:     rvals[0] = bs*(baij->rstart + i);
1584:     for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
1585:     for (j=ai[i]; j<ai[i+1]; j++) {
1586:       col = (baij->cstart+aj[j])*bs;
1587:       for (k=0; k<bs; k++) {
1588:         MatSetValues_MPIBAIJ_MatScalar(B,1,&col,bs,rvals,a,INSERT_VALUES);
1589:         col++; a += bs;
1590:       }
1591:     }
1592:   }
1593:   /* copy over the B part */
1594:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1595:   ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
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->garray[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:   PetscFree(rvals);
1608:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1609:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1610: 
1611:   if (matout) {
1612:     *matout = B;
1613:   } else {
1614:     MatHeaderCopy(A,B);
1615:   }
1616:   return(0);
1617: }

1621: int MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1622: {
1623:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1624:   Mat         a = baij->A,b = baij->B;
1625:   int         ierr,s1,s2,s3;

1628:   MatGetLocalSize(mat,&s2,&s3);
1629:   if (rr) {
1630:     VecGetLocalSize(rr,&s1);
1631:     if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1632:     /* Overlap communication with computation. */
1633:     VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1634:   }
1635:   if (ll) {
1636:     VecGetLocalSize(ll,&s1);
1637:     if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1638:     (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1639:   }
1640:   /* scale  the diagonal block */
1641:   (*a->ops->diagonalscale)(a,ll,rr);

1643:   if (rr) {
1644:     /* Do a scatter end and then right scale the off-diagonal block */
1645:     VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1646:     (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1647:   }
1648: 
1649:   return(0);
1650: }

1654: int MatZeroRows_MPIBAIJ(Mat A,IS is,const PetscScalar *diag)
1655: {
1656:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;
1657:   int            i,ierr,N,*rows,*owners = l->rowners,size = l->size;
1658:   int            *nprocs,j,idx,nsends,row;
1659:   int            nmax,*svalues,*starts,*owner,nrecvs,rank = l->rank;
1660:   int            *rvalues,tag = A->tag,count,base,slen,n,*source;
1661:   int            *lens,imdex,*lrows,*values,bs=l->bs,rstart_bs=l->rstart_bs;
1662:   MPI_Comm       comm = A->comm;
1663:   MPI_Request    *send_waits,*recv_waits;
1664:   MPI_Status     recv_status,*send_status;
1665:   IS             istmp;
1666:   PetscTruth     found;
1667: 
1669:   ISGetLocalSize(is,&N);
1670:   ISGetIndices(is,&rows);
1671: 
1672:   /*  first count number of contributors to each processor */
1673:   PetscMalloc(2*size*sizeof(int),&nprocs);
1674:   PetscMemzero(nprocs,2*size*sizeof(int));
1675:   PetscMalloc((N+1)*sizeof(int),&owner); /* see note*/
1676:   for (i=0; i<N; i++) {
1677:     idx   = rows[i];
1678:     found = PETSC_FALSE;
1679:     for (j=0; j<size; j++) {
1680:       if (idx >= owners[j]*bs && idx < owners[j+1]*bs) {
1681:         nprocs[2*j]++; nprocs[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break;
1682:       }
1683:     }
1684:     if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1685:   }
1686:   nsends = 0;  for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
1687: 
1688:   /* inform other processors of number of messages and max length*/
1689:   PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
1690: 
1691:   /* post receives:   */
1692:   PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(int),&rvalues);
1693:   PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1694:   for (i=0; i<nrecvs; i++) {
1695:     MPI_Irecv(rvalues+nmax*i,nmax,MPI_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1696:   }
1697: 
1698:   /* do sends:
1699:      1) starts[i] gives the starting index in svalues for stuff going to 
1700:      the ith processor
1701:   */
1702:   PetscMalloc((N+1)*sizeof(int),&svalues);
1703:   PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1704:   PetscMalloc((size+1)*sizeof(int),&starts);
1705:   starts[0]  = 0;
1706:   for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1707:   for (i=0; i<N; i++) {
1708:     svalues[starts[owner[i]]++] = rows[i];
1709:   }
1710:   ISRestoreIndices(is,&rows);
1711: 
1712:   starts[0] = 0;
1713:   for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
1714:   count = 0;
1715:   for (i=0; i<size; i++) {
1716:     if (nprocs[2*i+1]) {
1717:       MPI_Isend(svalues+starts[i],nprocs[2*i],MPI_INT,i,tag,comm,send_waits+count++);
1718:     }
1719:   }
1720:   PetscFree(starts);

1722:   base = owners[rank]*bs;
1723: 
1724:   /*  wait on receives */
1725:   PetscMalloc(2*(nrecvs+1)*sizeof(int),&lens);
1726:   source = lens + nrecvs;
1727:   count  = nrecvs; slen = 0;
1728:   while (count) {
1729:     MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1730:     /* unpack receives into our local space */
1731:     MPI_Get_count(&recv_status,MPI_INT,&n);
1732:     source[imdex]  = recv_status.MPI_SOURCE;
1733:     lens[imdex]    = n;
1734:     slen          += n;
1735:     count--;
1736:   }
1737:   PetscFree(recv_waits);
1738: 
1739:   /* move the data into the send scatter */
1740:   PetscMalloc((slen+1)*sizeof(int),&lrows);
1741:   count = 0;
1742:   for (i=0; i<nrecvs; i++) {
1743:     values = rvalues + i*nmax;
1744:     for (j=0; j<lens[i]; j++) {
1745:       lrows[count++] = values[j] - base;
1746:     }
1747:   }
1748:   PetscFree(rvalues);
1749:   PetscFree(lens);
1750:   PetscFree(owner);
1751:   PetscFree(nprocs);
1752: 
1753:   /* actually zap the local rows */
1754:   ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);
1755:   PetscLogObjectParent(A,istmp);

1757:   /*
1758:         Zero the required rows. If the "diagonal block" of the matrix
1759:      is square and the user wishes to set the diagonal we use seperate
1760:      code so that MatSetValues() is not called for each diagonal allocating
1761:      new memory, thus calling lots of mallocs and slowing things down.

1763:        Contributed by: Mathew Knepley
1764:   */
1765:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1766:   MatZeroRows_SeqBAIJ(l->B,istmp,0);
1767:   if (diag && (l->A->M == l->A->N)) {
1768:     MatZeroRows_SeqBAIJ(l->A,istmp,diag);
1769:   } else if (diag) {
1770:     MatZeroRows_SeqBAIJ(l->A,istmp,0);
1771:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) {
1772:       SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1773: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1774:     }
1775:     for (i=0; i<slen; i++) {
1776:       row  = lrows[i] + rstart_bs;
1777:       MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);
1778:     }
1779:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1780:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1781:   } else {
1782:     MatZeroRows_SeqBAIJ(l->A,istmp,0);
1783:   }

1785:   ISDestroy(istmp);
1786:   PetscFree(lrows);

1788:   /* wait on sends */
1789:   if (nsends) {
1790:     PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1791:     MPI_Waitall(nsends,send_waits,send_status);
1792:     PetscFree(send_status);
1793:   }
1794:   PetscFree(send_waits);
1795:   PetscFree(svalues);

1797:   return(0);
1798: }

1802: int MatPrintHelp_MPIBAIJ(Mat A)
1803: {
1804:   Mat_MPIBAIJ *a   = (Mat_MPIBAIJ*)A->data;
1805:   MPI_Comm    comm = A->comm;
1806:   static int  called = 0;
1807:   int         ierr;

1810:   if (!a->rank) {
1811:     MatPrintHelp_SeqBAIJ(a->A);
1812:   }
1813:   if (called) {return(0);} else called = 1;
1814:   (*PetscHelpPrintf)(comm," Options for MATMPIBAIJ matrix format (the defaults):\n");
1815:   (*PetscHelpPrintf)(comm,"  -mat_use_hash_table <factor>: Use hashtable for efficient matrix assembly\n");
1816:   return(0);
1817: }

1821: int MatSetUnfactored_MPIBAIJ(Mat A)
1822: {
1823:   Mat_MPIBAIJ *a   = (Mat_MPIBAIJ*)A->data;
1824:   int         ierr;

1827:   MatSetUnfactored(a->A);
1828:   return(0);
1829: }

1831: static int MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat *);

1835: int MatEqual_MPIBAIJ(Mat A,Mat B,PetscTruth *flag)
1836: {
1837:   Mat_MPIBAIJ *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1838:   Mat         a,b,c,d;
1839:   PetscTruth  flg;
1840:   int         ierr;

1843:   a = matA->A; b = matA->B;
1844:   c = matB->A; d = matB->B;

1846:   MatEqual(a,c,&flg);
1847:   if (flg == PETSC_TRUE) {
1848:     MatEqual(b,d,&flg);
1849:   }
1850:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1851:   return(0);
1852: }


1857: int MatSetUpPreallocation_MPIBAIJ(Mat A)
1858: {
1859:   int        ierr;

1862:    MatMPIBAIJSetPreallocation(A,1,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1863:   return(0);
1864: }

1866: /* -------------------------------------------------------------------*/
1867: static struct _MatOps MatOps_Values = {
1868:        MatSetValues_MPIBAIJ,
1869:        MatGetRow_MPIBAIJ,
1870:        MatRestoreRow_MPIBAIJ,
1871:        MatMult_MPIBAIJ,
1872: /* 4*/ MatMultAdd_MPIBAIJ,
1873:        MatMultTranspose_MPIBAIJ,
1874:        MatMultTransposeAdd_MPIBAIJ,
1875:        0,
1876:        0,
1877:        0,
1878: /*10*/ 0,
1879:        0,
1880:        0,
1881:        0,
1882:        MatTranspose_MPIBAIJ,
1883: /*15*/ MatGetInfo_MPIBAIJ,
1884:        MatEqual_MPIBAIJ,
1885:        MatGetDiagonal_MPIBAIJ,
1886:        MatDiagonalScale_MPIBAIJ,
1887:        MatNorm_MPIBAIJ,
1888: /*20*/ MatAssemblyBegin_MPIBAIJ,
1889:        MatAssemblyEnd_MPIBAIJ,
1890:        0,
1891:        MatSetOption_MPIBAIJ,
1892:        MatZeroEntries_MPIBAIJ,
1893: /*25*/ MatZeroRows_MPIBAIJ,
1894:        0,
1895:        0,
1896:        0,
1897:        0,
1898: /*30*/ MatSetUpPreallocation_MPIBAIJ,
1899:        0,
1900:        0,
1901:        0,
1902:        0,
1903: /*35*/ MatDuplicate_MPIBAIJ,
1904:        0,
1905:        0,
1906:        0,
1907:        0,
1908: /*40*/ 0,
1909:        MatGetSubMatrices_MPIBAIJ,
1910:        MatIncreaseOverlap_MPIBAIJ,
1911:        MatGetValues_MPIBAIJ,
1912:        0,
1913: /*45*/ MatPrintHelp_MPIBAIJ,
1914:        MatScale_MPIBAIJ,
1915:        0,
1916:        0,
1917:        0,
1918: /*50*/ MatGetBlockSize_MPIBAIJ,
1919:        0,
1920:        0,
1921:        0,
1922:        0,
1923: /*55*/ 0,
1924:        0,
1925:        MatSetUnfactored_MPIBAIJ,
1926:        0,
1927:        MatSetValuesBlocked_MPIBAIJ,
1928: /*60*/ 0,
1929:        MatDestroy_MPIBAIJ,
1930:        MatView_MPIBAIJ,
1931:        MatGetPetscMaps_Petsc,
1932:        0,
1933: /*65*/ 0,
1934:        0,
1935:        0,
1936:        0,
1937:        0,
1938: /*70*/ MatGetRowMax_MPIBAIJ,
1939:        0,
1940:        0,
1941:        0,
1942:        0,
1943: /*75*/ 0,
1944:        0,
1945:        0,
1946:        0,
1947:        0,
1948: /*80*/ 0,
1949:        0,
1950:        0,
1951:        0,
1952:        0,
1953: /*85*/ MatLoad_MPIBAIJ
1954: };


1957: EXTERN_C_BEGIN
1960: int MatGetDiagonalBlock_MPIBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
1961: {
1963:   *a      = ((Mat_MPIBAIJ *)A->data)->A;
1964:   *iscopy = PETSC_FALSE;
1965:   return(0);
1966: }
1967: EXTERN_C_END

1969: EXTERN_C_BEGIN
1972: int MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,int bs,int d_nz,int *d_nnz,int o_nz,int *o_nnz)
1973: {
1974:   Mat_MPIBAIJ  *b;
1975:   int          ierr,i;

1978:   B->preallocated = PETSC_TRUE;
1979:   PetscOptionsGetInt(PETSC_NULL,"-mat_block_size",&bs,PETSC_NULL);

1981:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
1982:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
1983:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
1984:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %d",d_nz);
1985:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %d",o_nz);
1986:   if (d_nnz) {
1987:   for (i=0; i<B->m/bs; i++) {
1988:       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]);
1989:     }
1990:   }
1991:   if (o_nnz) {
1992:     for (i=0; i<B->m/bs; i++) {
1993:       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]);
1994:     }
1995:   }
1996: 
1997:   PetscSplitOwnershipBlock(B->comm,bs,&B->m,&B->M);
1998:   PetscSplitOwnershipBlock(B->comm,bs,&B->n,&B->N);
1999:   PetscMapCreateMPI(B->comm,B->m,B->M,&B->rmap);
2000:   PetscMapCreateMPI(B->comm,B->n,B->N,&B->cmap);

2002:   b = (Mat_MPIBAIJ*)B->data;
2003:   b->bs  = bs;
2004:   b->bs2 = bs*bs;
2005:   b->mbs = B->m/bs;
2006:   b->nbs = B->n/bs;
2007:   b->Mbs = B->M/bs;
2008:   b->Nbs = B->N/bs;

2010:   MPI_Allgather(&b->mbs,1,MPI_INT,b->rowners+1,1,MPI_INT,B->comm);
2011:   b->rowners[0]    = 0;
2012:   for (i=2; i<=b->size; i++) {
2013:     b->rowners[i] += b->rowners[i-1];
2014:   }
2015:   b->rstart    = b->rowners[b->rank];
2016:   b->rend      = b->rowners[b->rank+1];

2018:   MPI_Allgather(&b->nbs,1,MPI_INT,b->cowners+1,1,MPI_INT,B->comm);
2019:   b->cowners[0] = 0;
2020:   for (i=2; i<=b->size; i++) {
2021:     b->cowners[i] += b->cowners[i-1];
2022:   }
2023:   b->cstart    = b->cowners[b->rank];
2024:   b->cend      = b->cowners[b->rank+1];

2026:   for (i=0; i<=b->size; i++) {
2027:     b->rowners_bs[i] = b->rowners[i]*bs;
2028:   }
2029:   b->rstart_bs = b->rstart*bs;
2030:   b->rend_bs   = b->rend*bs;
2031:   b->cstart_bs = b->cstart*bs;
2032:   b->cend_bs   = b->cend*bs;

2034:   MatCreateSeqBAIJ(PETSC_COMM_SELF,bs,B->m,B->n,d_nz,d_nnz,&b->A);
2035:   PetscLogObjectParent(B,b->A);
2036:   MatCreateSeqBAIJ(PETSC_COMM_SELF,bs,B->m,B->N,o_nz,o_nnz,&b->B);
2037:   PetscLogObjectParent(B,b->B);
2038:   MatStashCreate_Private(B->comm,bs,&B->bstash);

2040:   return(0);
2041: }
2042: EXTERN_C_END

2044: EXTERN_C_BEGIN
2045: EXTERN int MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2046: EXTERN int MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);
2047: EXTERN_C_END

2049: /*MC
2050:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

2052:    Options Database Keys:
2053: . -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()

2055:   Level: beginner

2057: .seealso: MatCreateMPIBAIJ
2058: M*/

2060: EXTERN_C_BEGIN
2063: int MatCreate_MPIBAIJ(Mat B)
2064: {
2065:   Mat_MPIBAIJ  *b;
2066:   int          ierr;
2067:   PetscTruth   flg;


2071:   PetscNew(Mat_MPIBAIJ,&b);
2072:   B->data = (void*)b;

2074:   PetscMemzero(b,sizeof(Mat_MPIBAIJ));
2075:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2076:   B->mapping    = 0;
2077:   B->factor     = 0;
2078:   B->assembled  = PETSC_FALSE;

2080:   B->insertmode = NOT_SET_VALUES;
2081:   MPI_Comm_rank(B->comm,&b->rank);
2082:   MPI_Comm_size(B->comm,&b->size);

2084:   /* build local table of row and column ownerships */
2085:   PetscMalloc(3*(b->size+2)*sizeof(int),&b->rowners);
2086:   PetscLogObjectMemory(B,3*(b->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPIBAIJ));
2087:   b->cowners    = b->rowners + b->size + 2;
2088:   b->rowners_bs = b->cowners + b->size + 2;

2090:   /* build cache for off array entries formed */
2091:   MatStashCreate_Private(B->comm,1,&B->stash);
2092:   b->donotstash  = PETSC_FALSE;
2093:   b->colmap      = PETSC_NULL;
2094:   b->garray      = PETSC_NULL;
2095:   b->roworiented = PETSC_TRUE;

2097: #if defined(PETSC_USE_MAT_SINGLE)
2098:   /* stuff for MatSetValues_XXX in single precision */
2099:   b->setvalueslen     = 0;
2100:   b->setvaluescopy    = PETSC_NULL;
2101: #endif

2103:   /* stuff used in block assembly */
2104:   b->barray       = 0;

2106:   /* stuff used for matrix vector multiply */
2107:   b->lvec         = 0;
2108:   b->Mvctx        = 0;

2110:   /* stuff for MatGetRow() */
2111:   b->rowindices   = 0;
2112:   b->rowvalues    = 0;
2113:   b->getrowactive = PETSC_FALSE;

2115:   /* hash table stuff */
2116:   b->ht           = 0;
2117:   b->hd           = 0;
2118:   b->ht_size      = 0;
2119:   b->ht_flag      = PETSC_FALSE;
2120:   b->ht_fact      = 0;
2121:   b->ht_total_ct  = 0;
2122:   b->ht_insert_ct = 0;

2124:   PetscOptionsHasName(PETSC_NULL,"-mat_use_hash_table",&flg);
2125:   if (flg) {
2126:     PetscReal fact = 1.39;
2127:     MatSetOption(B,MAT_USE_HASH_TABLE);
2128:     PetscOptionsGetReal(PETSC_NULL,"-mat_use_hash_table",&fact,PETSC_NULL);
2129:     if (fact <= 1.0) fact = 1.39;
2130:     MatMPIBAIJSetHashTableFactor(B,fact);
2131:     PetscLogInfo(0,"MatCreateMPIBAIJ:Hash table Factor used %5.2f\n",fact);
2132:   }
2133:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2134:                                      "MatStoreValues_MPIBAIJ",
2135:                                      MatStoreValues_MPIBAIJ);
2136:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2137:                                      "MatRetrieveValues_MPIBAIJ",
2138:                                      MatRetrieveValues_MPIBAIJ);
2139:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
2140:                                      "MatGetDiagonalBlock_MPIBAIJ",
2141:                                      MatGetDiagonalBlock_MPIBAIJ);
2142:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIBAIJSetPreallocation_C",
2143:                                      "MatMPIBAIJSetPreallocation_MPIBAIJ",
2144:                                      MatMPIBAIJSetPreallocation_MPIBAIJ);
2145:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
2146:                                      "MatDiagonalScaleLocal_MPIBAIJ",
2147:                                      MatDiagonalScaleLocal_MPIBAIJ);
2148:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetHashTableFactor_C",
2149:                                      "MatSetHashTableFactor_MPIBAIJ",
2150:                                      MatSetHashTableFactor_MPIBAIJ);
2151:   return(0);
2152: }
2153: EXTERN_C_END

2155: /*MC
2156:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

2158:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
2159:    and MATMPIBAIJ otherwise.

2161:    Options Database Keys:
2162: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

2164:   Level: beginner

2166: .seealso: MatCreateMPIBAIJ,MATSEQBAIJ,MATMPIBAIJ
2167: M*/

2169: EXTERN_C_BEGIN
2172: int MatCreate_BAIJ(Mat A) {
2173:   int ierr,size;

2176:   PetscObjectChangeTypeName((PetscObject)A,MATBAIJ);
2177:   MPI_Comm_size(A->comm,&size);
2178:   if (size == 1) {
2179:     MatSetType(A,MATSEQBAIJ);
2180:   } else {
2181:     MatSetType(A,MATMPIBAIJ);
2182:   }
2183:   return(0);
2184: }
2185: EXTERN_C_END

2189: /*@C
2190:    MatMPIBAIJSetPreallocation - Creates a sparse parallel matrix in block AIJ format
2191:    (block compressed row).  For good matrix assembly performance
2192:    the user should preallocate the matrix storage by setting the parameters 
2193:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2194:    performance can be increased by more than a factor of 50.

2196:    Collective on Mat

2198:    Input Parameters:
2199: +  A - the matrix 
2200: .  bs   - size of blockk
2201: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
2202:            submatrix  (same for all local rows)
2203: .  d_nnz - array containing the number of block nonzeros in the various block rows 
2204:            of the in diagonal portion of the local (possibly different for each block
2205:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2206: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
2207:            submatrix (same for all local rows).
2208: -  o_nnz - array containing the number of nonzeros in the various block rows of the
2209:            off-diagonal portion of the local submatrix (possibly different for
2210:            each block row) or PETSC_NULL.

2212:    Output Parameter:


2215:    Options Database Keys:
2216: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2217:                      block calculations (much slower)
2218: .   -mat_block_size - size of the blocks to use

2220:    Notes:
2221:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2222:    than it must be used on all processors that share the object for that argument.

2224:    Storage Information:
2225:    For a square global matrix we define each processor's diagonal portion 
2226:    to be its local rows and the corresponding columns (a square submatrix);  
2227:    each processor's off-diagonal portion encompasses the remainder of the
2228:    local matrix (a rectangular submatrix). 

2230:    The user can specify preallocated storage for the diagonal part of
2231:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2232:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2233:    memory allocation.  Likewise, specify preallocated storage for the
2234:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2236:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2237:    the figure below we depict these three local rows and all columns (0-11).

2239: .vb
2240:            0 1 2 3 4 5 6 7 8 9 10 11
2241:           -------------------
2242:    row 3  |  o o o d d d o o o o o o
2243:    row 4  |  o o o d d d o o o o o o
2244:    row 5  |  o o o d d d o o o o o o
2245:           -------------------
2246: .ve
2247:   
2248:    Thus, any entries in the d locations are stored in the d (diagonal) 
2249:    submatrix, and any entries in the o locations are stored in the
2250:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2251:    stored simply in the MATSEQBAIJ format for compressed row storage.

2253:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2254:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2255:    In general, for PDE problems in which most nonzeros are near the diagonal,
2256:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2257:    or you will get TERRIBLE performance; see the users' manual chapter on
2258:    matrices.

2260:    Level: intermediate

2262: .keywords: matrix, block, aij, compressed row, sparse, parallel

2264: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2265: @*/
2266: int MatMPIBAIJSetPreallocation(Mat B,int bs,int d_nz,const int d_nnz[],int o_nz,const int o_nnz[])
2267: {
2268:   int ierr,(*f)(Mat,int,int,const int[],int,const int[]);

2271:   PetscObjectQueryFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",(void (**)(void))&f);
2272:   if (f) {
2273:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
2274:   }
2275:   return(0);
2276: }

2280: /*@C
2281:    MatCreateMPIBAIJ - Creates a sparse parallel matrix in block AIJ format
2282:    (block compressed row).  For good matrix assembly performance
2283:    the user should preallocate the matrix storage by setting the parameters 
2284:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
2285:    performance can be increased by more than a factor of 50.

2287:    Collective on MPI_Comm

2289:    Input Parameters:
2290: +  comm - MPI communicator
2291: .  bs   - size of blockk
2292: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2293:            This value should be the same as the local size used in creating the 
2294:            y vector for the matrix-vector product y = Ax.
2295: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
2296:            This value should be the same as the local size used in creating the 
2297:            x vector for the matrix-vector product y = Ax.
2298: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2299: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2300: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local 
2301:            submatrix  (same for all local rows)
2302: .  d_nnz - array containing the number of nonzero blocks in the various block rows 
2303:            of the in diagonal portion of the local (possibly different for each block
2304:            row) or PETSC_NULL.  You must leave room for the diagonal entry even if it is zero.
2305: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
2306:            submatrix (same for all local rows).
2307: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
2308:            off-diagonal portion of the local submatrix (possibly different for
2309:            each block row) or PETSC_NULL.

2311:    Output Parameter:
2312: .  A - the matrix 

2314:    Options Database Keys:
2315: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2316:                      block calculations (much slower)
2317: .   -mat_block_size - size of the blocks to use

2319:    Notes:
2320:    A nonzero block is any block that as 1 or more nonzeros in it

2322:    The user MUST specify either the local or global matrix dimensions
2323:    (possibly both).

2325:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
2326:    than it must be used on all processors that share the object for that argument.

2328:    Storage Information:
2329:    For a square global matrix we define each processor's diagonal portion 
2330:    to be its local rows and the corresponding columns (a square submatrix);  
2331:    each processor's off-diagonal portion encompasses the remainder of the
2332:    local matrix (a rectangular submatrix). 

2334:    The user can specify preallocated storage for the diagonal part of
2335:    the local submatrix with either d_nz or d_nnz (not both).  Set 
2336:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
2337:    memory allocation.  Likewise, specify preallocated storage for the
2338:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

2340:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2341:    the figure below we depict these three local rows and all columns (0-11).

2343: .vb
2344:            0 1 2 3 4 5 6 7 8 9 10 11
2345:           -------------------
2346:    row 3  |  o o o d d d o o o o o o
2347:    row 4  |  o o o d d d o o o o o o
2348:    row 5  |  o o o d d d o o o o o o
2349:           -------------------
2350: .ve
2351:   
2352:    Thus, any entries in the d locations are stored in the d (diagonal) 
2353:    submatrix, and any entries in the o locations are stored in the
2354:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
2355:    stored simply in the MATSEQBAIJ format for compressed row storage.

2357:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
2358:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2359:    In general, for PDE problems in which most nonzeros are near the diagonal,
2360:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2361:    or you will get TERRIBLE performance; see the users' manual chapter on
2362:    matrices.

2364:    Level: intermediate

2366: .keywords: matrix, block, aij, compressed row, sparse, parallel

2368: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2369: @*/
2370: 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)
2371: {
2372:   int ierr,size;

2375:   MatCreate(comm,m,n,M,N,A);
2376:   MPI_Comm_size(comm,&size);
2377:   if (size > 1) {
2378:     MatSetType(*A,MATMPIBAIJ);
2379:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2380:   } else {
2381:     MatSetType(*A,MATSEQBAIJ);
2382:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2383:   }
2384:   return(0);
2385: }

2389: static int MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2390: {
2391:   Mat         mat;
2392:   Mat_MPIBAIJ *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
2393:   int         ierr,len=0;

2396:   *newmat       = 0;
2397:   MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);
2398:   MatSetType(mat,MATMPIBAIJ);
2399:   mat->preallocated = PETSC_TRUE;
2400:   mat->assembled    = PETSC_TRUE;
2401:   a      = (Mat_MPIBAIJ*)mat->data;
2402:   a->bs  = oldmat->bs;
2403:   a->bs2 = oldmat->bs2;
2404:   a->mbs = oldmat->mbs;
2405:   a->nbs = oldmat->nbs;
2406:   a->Mbs = oldmat->Mbs;
2407:   a->Nbs = oldmat->Nbs;
2408: 
2409:   a->rstart       = oldmat->rstart;
2410:   a->rend         = oldmat->rend;
2411:   a->cstart       = oldmat->cstart;
2412:   a->cend         = oldmat->cend;
2413:   a->size         = oldmat->size;
2414:   a->rank         = oldmat->rank;
2415:   a->donotstash   = oldmat->donotstash;
2416:   a->roworiented  = oldmat->roworiented;
2417:   a->rowindices   = 0;
2418:   a->rowvalues    = 0;
2419:   a->getrowactive = PETSC_FALSE;
2420:   a->barray       = 0;
2421:   a->rstart_bs    = oldmat->rstart_bs;
2422:   a->rend_bs      = oldmat->rend_bs;
2423:   a->cstart_bs    = oldmat->cstart_bs;
2424:   a->cend_bs      = oldmat->cend_bs;

2426:   /* hash table stuff */
2427:   a->ht           = 0;
2428:   a->hd           = 0;
2429:   a->ht_size      = 0;
2430:   a->ht_flag      = oldmat->ht_flag;
2431:   a->ht_fact      = oldmat->ht_fact;
2432:   a->ht_total_ct  = 0;
2433:   a->ht_insert_ct = 0;

2435:   PetscMemcpy(a->rowners,oldmat->rowners,3*(a->size+2)*sizeof(int));
2436:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2437:   MatStashCreate_Private(matin->comm,oldmat->bs,&mat->bstash);
2438:   if (oldmat->colmap) {
2439: #if defined (PETSC_USE_CTABLE)
2440:   PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2441: #else
2442:   PetscMalloc((a->Nbs)*sizeof(int),&a->colmap);
2443:   PetscLogObjectMemory(mat,(a->Nbs)*sizeof(int));
2444:   PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(int));
2445: #endif
2446:   } else a->colmap = 0;
2447:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2448:     PetscMalloc(len*sizeof(int),&a->garray);
2449:     PetscLogObjectMemory(mat,len*sizeof(int));
2450:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(int));
2451:   } else a->garray = 0;
2452: 
2453:    VecDuplicate(oldmat->lvec,&a->lvec);
2454:   PetscLogObjectParent(mat,a->lvec);
2455:    VecScatterCopy(oldmat->Mvctx,&a->Mvctx);

2457:   PetscLogObjectParent(mat,a->Mvctx);
2458:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2459:   PetscLogObjectParent(mat,a->A);
2460:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2461:   PetscLogObjectParent(mat,a->B);
2462:   PetscFListDuplicate(matin->qlist,&mat->qlist);
2463:   *newmat = mat;
2464:   return(0);
2465: }

2467:  #include petscsys.h

2471: int MatLoad_MPIBAIJ(PetscViewer viewer,MatType type,Mat *newmat)
2472: {
2473:   Mat          A;
2474:   int          i,nz,ierr,j,rstart,rend,fd;
2475:   PetscScalar  *vals,*buf;
2476:   MPI_Comm     comm = ((PetscObject)viewer)->comm;
2477:   MPI_Status   status;
2478:   int          header[4],rank,size,*rowlengths = 0,M,N,m,*rowners,*browners,maxnz,*cols;
2479:   int          *locrowlens,*sndcounts = 0,*procsnz = 0,jj,*mycols,*ibuf;
2480:   int          tag = ((PetscObject)viewer)->tag,bs=1,Mbs,mbs,extra_rows;
2481:   int          *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
2482:   int          dcount,kmax,k,nzcount,tmp;
2483: 
2485:   PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);

2487:   MPI_Comm_size(comm,&size);
2488:   MPI_Comm_rank(comm,&rank);
2489:   if (!rank) {
2490:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2491:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2492:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2493:     if (header[3] < 0) {
2494:       SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as MPIBAIJ");
2495:     }
2496:   }

2498:   MPI_Bcast(header+1,3,MPI_INT,0,comm);
2499:   M = header[1]; N = header[2];

2501:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2503:   /* 
2504:      This code adds extra rows to make sure the number of rows is 
2505:      divisible by the blocksize
2506:   */
2507:   Mbs        = M/bs;
2508:   extra_rows = bs - M + bs*(Mbs);
2509:   if (extra_rows == bs) extra_rows = 0;
2510:   else                  Mbs++;
2511:   if (extra_rows &&!rank) {
2512:     PetscLogInfo(0,"MatLoad_MPIBAIJ:Padding loaded matrix to match blocksize\n");
2513:   }

2515:   /* determine ownership of all rows */
2516:   mbs        = Mbs/size + ((Mbs % size) > rank);
2517:   m          = mbs*bs;
2518:   PetscMalloc(2*(size+2)*sizeof(int),&rowners);
2519:   browners   = rowners + size + 1;
2520:   MPI_Allgather(&mbs,1,MPI_INT,rowners+1,1,MPI_INT,comm);
2521:   rowners[0] = 0;
2522:   for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
2523:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2524:   rstart = rowners[rank];
2525:   rend   = rowners[rank+1];

2527:   /* distribute row lengths to all processors */
2528:   PetscMalloc((rend-rstart)*bs*sizeof(int),&locrowlens);
2529:   if (!rank) {
2530:     PetscMalloc((M+extra_rows)*sizeof(int),&rowlengths);
2531:     PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2532:     for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2533:     PetscMalloc(size*sizeof(int),&sndcounts);
2534:     for (i=0; i<size; i++) sndcounts[i] = browners[i+1] - browners[i];
2535:     MPI_Scatterv(rowlengths,sndcounts,browners,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
2536:     PetscFree(sndcounts);
2537:   } else {
2538:     MPI_Scatterv(0,0,0,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
2539:   }

2541:   if (!rank) {
2542:     /* calculate the number of nonzeros on each processor */
2543:     PetscMalloc(size*sizeof(int),&procsnz);
2544:     PetscMemzero(procsnz,size*sizeof(int));
2545:     for (i=0; i<size; i++) {
2546:       for (j=rowners[i]*bs; j< rowners[i+1]*bs; j++) {
2547:         procsnz[i] += rowlengths[j];
2548:       }
2549:     }
2550:     PetscFree(rowlengths);
2551: 
2552:     /* determine max buffer needed and allocate it */
2553:     maxnz = 0;
2554:     for (i=0; i<size; i++) {
2555:       maxnz = PetscMax(maxnz,procsnz[i]);
2556:     }
2557:     PetscMalloc(maxnz*sizeof(int),&cols);

2559:     /* read in my part of the matrix column indices  */
2560:     nz     = procsnz[0];
2561:     PetscMalloc(nz*sizeof(int),&ibuf);
2562:     mycols = ibuf;
2563:     if (size == 1)  nz -= extra_rows;
2564:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2565:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2567:     /* read in every ones (except the last) and ship off */
2568:     for (i=1; i<size-1; i++) {
2569:       nz   = procsnz[i];
2570:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2571:       MPI_Send(cols,nz,MPI_INT,i,tag,comm);
2572:     }
2573:     /* read in the stuff for the last proc */
2574:     if (size != 1) {
2575:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2576:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2577:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2578:       MPI_Send(cols,nz+extra_rows,MPI_INT,size-1,tag,comm);
2579:     }
2580:     PetscFree(cols);
2581:   } else {
2582:     /* determine buffer space needed for message */
2583:     nz = 0;
2584:     for (i=0; i<m; i++) {
2585:       nz += locrowlens[i];
2586:     }
2587:     PetscMalloc(nz*sizeof(int),&ibuf);
2588:     mycols = ibuf;
2589:     /* receive message of column indices*/
2590:     MPI_Recv(mycols,nz,MPI_INT,0,tag,comm,&status);
2591:     MPI_Get_count(&status,MPI_INT,&maxnz);
2592:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2593:   }
2594: 
2595:   /* loop over local rows, determining number of off diagonal entries */
2596:   PetscMalloc(2*(rend-rstart+1)*sizeof(int),&dlens);
2597:   odlens   = dlens + (rend-rstart);
2598:   PetscMalloc(3*Mbs*sizeof(int),&mask);
2599:   PetscMemzero(mask,3*Mbs*sizeof(int));
2600:   masked1  = mask    + Mbs;
2601:   masked2  = masked1 + Mbs;
2602:   rowcount = 0; nzcount = 0;
2603:   for (i=0; i<mbs; i++) {
2604:     dcount  = 0;
2605:     odcount = 0;
2606:     for (j=0; j<bs; j++) {
2607:       kmax = locrowlens[rowcount];
2608:       for (k=0; k<kmax; k++) {
2609:         tmp = mycols[nzcount++]/bs;
2610:         if (!mask[tmp]) {
2611:           mask[tmp] = 1;
2612:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
2613:           else masked1[dcount++] = tmp;
2614:         }
2615:       }
2616:       rowcount++;
2617:     }
2618: 
2619:     dlens[i]  = dcount;
2620:     odlens[i] = odcount;

2622:     /* zero out the mask elements we set */
2623:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2624:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2625:   }

2627:   /* create our matrix */
2628:   MatCreate(comm,m,m,M+extra_rows,N+extra_rows,&A);
2629:   MatSetType(A,type);CHKERRQ(ierr)
2630:   MatMPIBAIJSetPreallocation(A,bs,0,dlens,0,odlens);

2632:   /* Why doesn't this called using MatSetOption(A,MAT_COLUMNS_SORTED); */
2633:   MatSetOption(A,MAT_COLUMNS_SORTED);
2634: 
2635:   if (!rank) {
2636:     PetscMalloc(maxnz*sizeof(PetscScalar),&buf);
2637:     /* read in my part of the matrix numerical values  */
2638:     nz = procsnz[0];
2639:     vals = buf;
2640:     mycols = ibuf;
2641:     if (size == 1)  nz -= extra_rows;
2642:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2643:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2645:     /* insert into matrix */
2646:     jj      = rstart*bs;
2647:     for (i=0; i<m; i++) {
2648:       MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2649:       mycols += locrowlens[i];
2650:       vals   += locrowlens[i];
2651:       jj++;
2652:     }
2653:     /* read in other processors (except the last one) and ship out */
2654:     for (i=1; i<size-1; i++) {
2655:       nz   = procsnz[i];
2656:       vals = buf;
2657:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2658:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2659:     }
2660:     /* the last proc */
2661:     if (size != 1){
2662:       nz   = procsnz[i] - extra_rows;
2663:       vals = buf;
2664:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2665:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2666:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2667:     }
2668:     PetscFree(procsnz);
2669:   } else {
2670:     /* receive numeric values */
2671:     PetscMalloc(nz*sizeof(PetscScalar),&buf);

2673:     /* receive message of values*/
2674:     vals   = buf;
2675:     mycols = ibuf;
2676:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2677:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2678:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2680:     /* insert into matrix */
2681:     jj      = rstart*bs;
2682:     for (i=0; i<m; i++) {
2683:       MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2684:       mycols += locrowlens[i];
2685:       vals   += locrowlens[i];
2686:       jj++;
2687:     }
2688:   }
2689:   PetscFree(locrowlens);
2690:   PetscFree(buf);
2691:   PetscFree(ibuf);
2692:   PetscFree(rowners);
2693:   PetscFree(dlens);
2694:   PetscFree(mask);
2695:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2696:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

2698:   *newmat = A;
2699:   return(0);
2700: }

2704: /*@
2705:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2707:    Input Parameters:
2708: .  mat  - the matrix
2709: .  fact - factor

2711:    Collective on Mat

2713:    Level: advanced

2715:   Notes:
2716:    This can also be set by the command line option: -mat_use_hash_table fact

2718: .keywords: matrix, hashtable, factor, HT

2720: .seealso: MatSetOption()
2721: @*/
2722: int MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2723: {
2724:   int ierr,(*f)(Mat,PetscReal);

2727:   PetscObjectQueryFunction((PetscObject)mat,"MatSetHashTableFactor_C",(void (**)(void))&f);
2728:   if (f) {
2729:     (*f)(mat,fact);
2730:   }
2731:   return(0);
2732: }

2736: int MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
2737: {
2738:   Mat_MPIBAIJ *baij;

2742:   baij = (Mat_MPIBAIJ*)mat->data;
2743:   baij->ht_fact = fact;
2744:   return(0);
2745: }

2749: int MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,int *colmap[])
2750: {
2751:   Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2753:   *Ad     = a->A;
2754:   *Ao     = a->B;
2755:   *colmap = a->garray;
2756:   return(0);
2757: }