Actual source code: mpisbaij.c

  1: #define PETSCMAT_DLL

 3:  #include ../src/mat/impls/baij/mpi/mpibaij.h
 4:  #include mpisbaij.h
 5:  #include ../src/mat/impls/sbaij/seq/sbaij.h

  7: EXTERN PetscErrorCode MatSetUpMultiply_MPISBAIJ(Mat);
  8: EXTERN PetscErrorCode MatSetUpMultiply_MPISBAIJ_2comm(Mat);
  9: EXTERN PetscErrorCode DisAssemble_MPISBAIJ(Mat);
 10: EXTERN PetscErrorCode MatIncreaseOverlap_MPISBAIJ(Mat,PetscInt,IS[],PetscInt);
 11: EXTERN PetscErrorCode MatGetValues_SeqSBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],PetscScalar []);
 12: EXTERN PetscErrorCode MatGetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],PetscScalar []);
 13: EXTERN PetscErrorCode MatSetValues_SeqSBAIJ(Mat,PetscInt,const PetscInt [],PetscInt,const PetscInt [],const PetscScalar [],InsertMode);
 14: EXTERN PetscErrorCode MatSetValuesBlocked_SeqSBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
 15: EXTERN PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
 16: EXTERN PetscErrorCode MatGetRow_SeqSBAIJ(Mat,PetscInt,PetscInt*,PetscInt**,PetscScalar**);
 17: EXTERN PetscErrorCode MatRestoreRow_SeqSBAIJ(Mat,PetscInt,PetscInt*,PetscInt**,PetscScalar**);
 18: EXTERN PetscErrorCode MatZeroRows_SeqSBAIJ(Mat,IS,PetscScalar*);
 19: EXTERN PetscErrorCode MatZeroRows_SeqBAIJ(Mat,IS,PetscScalar *);
 20: EXTERN PetscErrorCode MatGetRowMaxAbs_MPISBAIJ(Mat,Vec,PetscInt[]);
 21: EXTERN PetscErrorCode MatRelax_MPISBAIJ(Mat,Vec,PetscReal,MatSORType,PetscReal,PetscInt,PetscInt,Vec);

 26: PetscErrorCode  MatStoreValues_MPISBAIJ(Mat mat)
 27: {
 28:   Mat_MPISBAIJ   *aij = (Mat_MPISBAIJ *)mat->data;

 32:   MatStoreValues(aij->A);
 33:   MatStoreValues(aij->B);
 34:   return(0);
 35: }

 41: PetscErrorCode  MatRetrieveValues_MPISBAIJ(Mat mat)
 42: {
 43:   Mat_MPISBAIJ   *aij = (Mat_MPISBAIJ *)mat->data;

 47:   MatRetrieveValues(aij->A);
 48:   MatRetrieveValues(aij->B);
 49:   return(0);
 50: }


 54: #define CHUNKSIZE  10

 56: #define  MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv) \
 57: { \
 58:  \
 59:     brow = row/bs;  \
 60:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
 61:     rmax = aimax[brow]; nrow = ailen[brow]; \
 62:       bcol = col/bs; \
 63:       ridx = row % bs; cidx = col % bs; \
 64:       low = 0; high = nrow; \
 65:       while (high-low > 3) { \
 66:         t = (low+high)/2; \
 67:         if (rp[t] > bcol) high = t; \
 68:         else              low  = t; \
 69:       } \
 70:       for (_i=low; _i<high; _i++) { \
 71:         if (rp[_i] > bcol) break; \
 72:         if (rp[_i] == bcol) { \
 73:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
 74:           if (addv == ADD_VALUES) *bap += value;  \
 75:           else                    *bap  = value;  \
 76:           goto a_noinsert; \
 77:         } \
 78:       } \
 79:       if (a->nonew == 1) goto a_noinsert; \
 80:       if (a->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
 81:       MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,aimax,a->nonew,MatScalar); \
 82:       N = nrow++ - 1;  \
 83:       /* shift up all the later entries in this row */ \
 84:       for (ii=N; ii>=_i; ii--) { \
 85:         rp[ii+1] = rp[ii]; \
 86:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
 87:       } \
 88:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  \
 89:       rp[_i]                      = bcol;  \
 90:       ap[bs2*_i + bs*cidx + ridx] = value;  \
 91:       a_noinsert:; \
 92:     ailen[brow] = nrow; \
 93: } 

 95: #define  MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv) \
 96: { \
 97:     brow = row/bs;  \
 98:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
 99:     rmax = bimax[brow]; nrow = bilen[brow]; \
100:       bcol = col/bs; \
101:       ridx = row % bs; cidx = col % bs; \
102:       low = 0; high = nrow; \
103:       while (high-low > 3) { \
104:         t = (low+high)/2; \
105:         if (rp[t] > bcol) high = t; \
106:         else              low  = t; \
107:       } \
108:       for (_i=low; _i<high; _i++) { \
109:         if (rp[_i] > bcol) break; \
110:         if (rp[_i] == bcol) { \
111:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
112:           if (addv == ADD_VALUES) *bap += value;  \
113:           else                    *bap  = value;  \
114:           goto b_noinsert; \
115:         } \
116:       } \
117:       if (b->nonew == 1) goto b_noinsert; \
118:       if (b->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
119:       MatSeqXAIJReallocateAIJ(B,b->mbs,bs2,nrow,brow,bcol,rmax,ba,bi,bj,rp,ap,bimax,b->nonew,MatScalar); \
120:       N = nrow++ - 1;  \
121:       /* shift up all the later entries in this row */ \
122:       for (ii=N; ii>=_i; ii--) { \
123:         rp[ii+1] = rp[ii]; \
124:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
125:       } \
126:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  \
127:       rp[_i]                      = bcol;  \
128:       ap[bs2*_i + bs*cidx + ridx] = value;  \
129:       b_noinsert:; \
130:     bilen[brow] = nrow; \
131: } 

133: /* Only add/insert a(i,j) with i<=j (blocks). 
134:    Any a(i,j) with i>j input by user is ingored. 
135: */
138: PetscErrorCode MatSetValues_MPISBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
139: {
140:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
141:   MatScalar      value;
142:   PetscTruth     roworiented = baij->roworiented;
144:   PetscInt       i,j,row,col;
145:   PetscInt       rstart_orig=mat->rmap->rstart;
146:   PetscInt       rend_orig=mat->rmap->rend,cstart_orig=mat->cmap->rstart;
147:   PetscInt       cend_orig=mat->cmap->rend,bs=mat->rmap->bs;

149:   /* Some Variables required in the macro */
150:   Mat            A = baij->A;
151:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)(A)->data;
152:   PetscInt       *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
153:   MatScalar      *aa=a->a;

155:   Mat            B = baij->B;
156:   Mat_SeqBAIJ   *b = (Mat_SeqBAIJ*)(B)->data;
157:   PetscInt      *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
158:   MatScalar     *ba=b->a;

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

164:   /* for stash */
165:   PetscInt      n_loc, *in_loc = PETSC_NULL;
166:   MatScalar     *v_loc = PETSC_NULL;


170:   if (!baij->donotstash){
171:     if (n > baij->n_loc) {
172:       PetscFree(baij->in_loc);
173:       PetscFree(baij->v_loc);
174:       PetscMalloc(n*sizeof(PetscInt),&baij->in_loc);
175:       PetscMalloc(n*sizeof(MatScalar),&baij->v_loc);
176:       baij->n_loc = n;
177:     }
178:     in_loc = baij->in_loc;
179:     v_loc  = baij->v_loc;
180:   }

182:   for (i=0; i<m; i++) {
183:     if (im[i] < 0) continue;
184: #if defined(PETSC_USE_DEBUG)
185:     if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
186: #endif
187:     if (im[i] >= rstart_orig && im[i] < rend_orig) { /* this processor entry */
188:       row = im[i] - rstart_orig;              /* local row index */
189:       for (j=0; j<n; j++) {
190:         if (im[i]/bs > in[j]/bs){
191:           if (a->ignore_ltriangular){
192:             continue;    /* ignore lower triangular blocks */
193:           } else {
194:             SETERRQ(PETSC_ERR_USER,"Lower triangular value cannot be set for sbaij format. Ignoring these values, run with -mat_ignore_lower_triangular or call MatSetOption(mat,MAT_IGNORE_LOWER_TRIANGULAR,PETSC_TRUE)");
195:           }
196:         }
197:         if (in[j] >= cstart_orig && in[j] < cend_orig){  /* diag entry (A) */
198:           col = in[j] - cstart_orig;          /* local col index */
199:           brow = row/bs; bcol = col/bs;
200:           if (brow > bcol) continue;  /* ignore lower triangular blocks of A */
201:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
202:           MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv);
203:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
204:         } else if (in[j] < 0) continue;
205: #if defined(PETSC_USE_DEBUG)
206:         else if (in[j] >= mat->cmap->N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap->N-1);}
207: #endif
208:         else {  /* off-diag entry (B) */
209:           if (mat->was_assembled) {
210:             if (!baij->colmap) {
211:               CreateColmap_MPIBAIJ_Private(mat);
212:             }
213: #if defined (PETSC_USE_CTABLE)
214:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
215:             col  = col - 1;
216: #else
217:             col = baij->colmap[in[j]/bs] - 1;
218: #endif
219:             if (col < 0 && !((Mat_SeqSBAIJ*)(baij->A->data))->nonew) {
220:               DisAssemble_MPISBAIJ(mat);
221:               col =  in[j];
222:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
223:               B = baij->B;
224:               b = (Mat_SeqBAIJ*)(B)->data;
225:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
226:               ba=b->a;
227:             } else col += in[j]%bs;
228:           } else col = in[j];
229:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
230:           MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv);
231:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
232:         }
233:       }
234:     } else {  /* off processor entry */
235:       if (!baij->donotstash) {
236:         n_loc = 0;
237:         for (j=0; j<n; j++){
238:           if (im[i]/bs > in[j]/bs) continue; /* ignore lower triangular blocks */
239:           in_loc[n_loc] = in[j];
240:           if (roworiented) {
241:             v_loc[n_loc] = v[i*n+j];
242:           } else {
243:             v_loc[n_loc] = v[j*m+i];
244:           }
245:           n_loc++;
246:         }
247:         MatStashValuesRow_Private(&mat->stash,im[i],n_loc,in_loc,v_loc);
248:       }
249:     }
250:   }
251:   return(0);
252: }

256: PetscErrorCode MatSetValuesBlocked_MPISBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
257: {
258:   Mat_MPISBAIJ    *baij = (Mat_MPISBAIJ*)mat->data;
259:   const MatScalar *value;
260:   MatScalar       *barray=baij->barray;
261:   PetscTruth      roworiented = baij->roworiented;
262:   PetscErrorCode  ierr;
263:   PetscInt        i,j,ii,jj,row,col,rstart=baij->rstartbs;
264:   PetscInt        rend=baij->rendbs,cstart=baij->rstartbs,stepval;
265:   PetscInt        cend=baij->rendbs,bs=mat->rmap->bs,bs2=baij->bs2;

268:   if(!barray) {
269:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
270:     baij->barray = barray;
271:   }

273:   if (roworiented) {
274:     stepval = (n-1)*bs;
275:   } else {
276:     stepval = (m-1)*bs;
277:   }
278:   for (i=0; i<m; i++) {
279:     if (im[i] < 0) continue;
280: #if defined(PETSC_USE_DEBUG)
281:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
282: #endif
283:     if (im[i] >= rstart && im[i] < rend) {
284:       row = im[i] - rstart;
285:       for (j=0; j<n; j++) {
286:         /* If NumCol = 1 then a copy is not required */
287:         if ((roworiented) && (n == 1)) {
288:           barray = (MatScalar*) v + i*bs2;
289:         } else if((!roworiented) && (m == 1)) {
290:           barray = (MatScalar*) v + j*bs2;
291:         } else { /* Here a copy is required */
292:           if (roworiented) {
293:             value = v + i*(stepval+bs)*bs + j*bs;
294:           } else {
295:             value = v + j*(stepval+bs)*bs + i*bs;
296:           }
297:           for (ii=0; ii<bs; ii++,value+=stepval) {
298:             for (jj=0; jj<bs; jj++) {
299:               *barray++  = *value++;
300:             }
301:           }
302:           barray -=bs2;
303:         }
304: 
305:         if (in[j] >= cstart && in[j] < cend){
306:           col  = in[j] - cstart;
307:           MatSetValuesBlocked_SeqSBAIJ(baij->A,1,&row,1,&col,barray,addv);
308:         }
309:         else if (in[j] < 0) continue;
310: #if defined(PETSC_USE_DEBUG)
311:         else if (in[j] >= baij->Nbs) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);}
312: #endif
313:         else {
314:           if (mat->was_assembled) {
315:             if (!baij->colmap) {
316:               CreateColmap_MPIBAIJ_Private(mat);
317:             }

319: #if defined(PETSC_USE_DEBUG)
320: #if defined (PETSC_USE_CTABLE)
321:             { PetscInt data;
322:               PetscTableFind(baij->colmap,in[j]+1,&data);
323:               if ((data - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
324:             }
325: #else
326:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
327: #endif
328: #endif
329: #if defined (PETSC_USE_CTABLE)
330:             PetscTableFind(baij->colmap,in[j]+1,&col);
331:             col  = (col - 1)/bs;
332: #else
333:             col = (baij->colmap[in[j]] - 1)/bs;
334: #endif
335:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
336:               DisAssemble_MPISBAIJ(mat);
337:               col =  in[j];
338:             }
339:           }
340:           else col = in[j];
341:           MatSetValuesBlocked_SeqBAIJ(baij->B,1,&row,1,&col,barray,addv);
342:         }
343:       }
344:     } else {
345:       if (!baij->donotstash) {
346:         if (roworiented) {
347:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
348:         } else {
349:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
350:         }
351:       }
352:     }
353:   }
354:   return(0);
355: }

359: PetscErrorCode MatGetValues_MPISBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
360: {
361:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
363:   PetscInt       bs=mat->rmap->bs,i,j,bsrstart = mat->rmap->rstart,bsrend = mat->rmap->rend;
364:   PetscInt       bscstart = mat->cmap->rstart,bscend = mat->cmap->rend,row,col,data;

367:   for (i=0; i<m; i++) {
368:     if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]); */
369:     if (idxm[i] >= mat->rmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap->N-1);
370:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
371:       row = idxm[i] - bsrstart;
372:       for (j=0; j<n; j++) {
373:         if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column %D",idxn[j]); */
374:         if (idxn[j] >= mat->cmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap->N-1);
375:         if (idxn[j] >= bscstart && idxn[j] < bscend){
376:           col = idxn[j] - bscstart;
377:           MatGetValues_SeqSBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
378:         } else {
379:           if (!baij->colmap) {
380:             CreateColmap_MPIBAIJ_Private(mat);
381:           }
382: #if defined (PETSC_USE_CTABLE)
383:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
384:           data --;
385: #else
386:           data = baij->colmap[idxn[j]/bs]-1;
387: #endif
388:           if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
389:           else {
390:             col  = data + idxn[j]%bs;
391:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
392:           }
393:         }
394:       }
395:     } else {
396:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
397:     }
398:   }
399:  return(0);
400: }

404: PetscErrorCode MatNorm_MPISBAIJ(Mat mat,NormType type,PetscReal *norm)
405: {
406:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
408:   PetscReal      sum[2],*lnorm2;

411:   if (baij->size == 1) {
412:      MatNorm(baij->A,type,norm);
413:   } else {
414:     if (type == NORM_FROBENIUS) {
415:       PetscMalloc(2*sizeof(PetscReal),&lnorm2);
416:        MatNorm(baij->A,type,lnorm2);
417:       *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2++;            /* squar power of norm(A) */
418:        MatNorm(baij->B,type,lnorm2);
419:       *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2--;             /* squar power of norm(B) */
420:       MPI_Allreduce(lnorm2,&sum,2,MPIU_REAL,MPI_SUM,((PetscObject)mat)->comm);
421:       *norm = sqrt(sum[0] + 2*sum[1]);
422:       PetscFree(lnorm2);
423:     } else if (type == NORM_INFINITY || type == NORM_1) { /* max row/column sum */
424:       Mat_SeqSBAIJ *amat=(Mat_SeqSBAIJ*)baij->A->data;
425:       Mat_SeqBAIJ  *bmat=(Mat_SeqBAIJ*)baij->B->data;
426:       PetscReal    *rsum,*rsum2,vabs;
427:       PetscInt     *jj,*garray=baij->garray,rstart=baij->rstartbs,nz;
428:       PetscInt     brow,bcol,col,bs=baij->A->rmap->bs,row,grow,gcol,mbs=amat->mbs;
429:       MatScalar    *v;

431:       PetscMalloc((2*mat->cmap->N+1)*sizeof(PetscReal),&rsum);
432:       rsum2 = rsum + mat->cmap->N;
433:       PetscMemzero(rsum,mat->cmap->N*sizeof(PetscReal));
434:       /* Amat */
435:       v = amat->a; jj = amat->j;
436:       for (brow=0; brow<mbs; brow++) {
437:         grow = bs*(rstart + brow);
438:         nz = amat->i[brow+1] - amat->i[brow];
439:         for (bcol=0; bcol<nz; bcol++){
440:           gcol = bs*(rstart + *jj); jj++;
441:           for (col=0; col<bs; col++){
442:             for (row=0; row<bs; row++){
443:               vabs = PetscAbsScalar(*v); v++;
444:               rsum[gcol+col] += vabs;
445:               /* non-diagonal block */
446:               if (bcol > 0 && vabs > 0.0) rsum[grow+row] += vabs;
447:             }
448:           }
449:         }
450:       }
451:       /* Bmat */
452:       v = bmat->a; jj = bmat->j;
453:       for (brow=0; brow<mbs; brow++) {
454:         grow = bs*(rstart + brow);
455:         nz = bmat->i[brow+1] - bmat->i[brow];
456:         for (bcol=0; bcol<nz; bcol++){
457:           gcol = bs*garray[*jj]; jj++;
458:           for (col=0; col<bs; col++){
459:             for (row=0; row<bs; row++){
460:               vabs = PetscAbsScalar(*v); v++;
461:               rsum[gcol+col] += vabs;
462:               rsum[grow+row] += vabs;
463:             }
464:           }
465:         }
466:       }
467:       MPI_Allreduce(rsum,rsum2,mat->cmap->N,MPIU_REAL,MPI_SUM,((PetscObject)mat)->comm);
468:       *norm = 0.0;
469:       for (col=0; col<mat->cmap->N; col++) {
470:         if (rsum2[col] > *norm) *norm = rsum2[col];
471:       }
472:       PetscFree(rsum);
473:     } else {
474:       SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
475:     }
476:   }
477:   return(0);
478: }

482: PetscErrorCode MatAssemblyBegin_MPISBAIJ(Mat mat,MatAssemblyType mode)
483: {
484:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
486:   PetscInt       nstash,reallocs;
487:   InsertMode     addv;

490:   if (baij->donotstash) {
491:     return(0);
492:   }

494:   /* make sure all processors are either in INSERTMODE or ADDMODE */
495:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,((PetscObject)mat)->comm);
496:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
497:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
498:   }
499:   mat->insertmode = addv; /* in case this processor had no cache */

501:   MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
502:   MatStashScatterBegin_Private(mat,&mat->bstash,baij->rangebs);
503:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
504:   PetscInfo2(mat,"Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
505:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
506:   PetscInfo2(mat,"Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
507:   return(0);
508: }

512: PetscErrorCode MatAssemblyEnd_MPISBAIJ(Mat mat,MatAssemblyType mode)
513: {
514:   Mat_MPISBAIJ   *baij=(Mat_MPISBAIJ*)mat->data;
515:   Mat_SeqSBAIJ   *a=(Mat_SeqSBAIJ*)baij->A->data;
517:   PetscInt       i,j,rstart,ncols,flg,bs2=baij->bs2;
518:   PetscInt       *row,*col;
519:   PetscTruth     other_disassembled;
520:   PetscMPIInt    n;
521:   PetscTruth     r1,r2,r3;
522:   MatScalar      *val;
523:   InsertMode     addv = mat->insertmode;

525:   /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */

528:   if (!baij->donotstash) {
529:     while (1) {
530:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
531:       if (!flg) break;

533:       for (i=0; i<n;) {
534:         /* Now identify the consecutive vals belonging to the same row */
535:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
536:         if (j < n) ncols = j-i;
537:         else       ncols = n-i;
538:         /* Now assemble all these values with a single function call */
539:         MatSetValues_MPISBAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
540:         i = j;
541:       }
542:     }
543:     MatStashScatterEnd_Private(&mat->stash);
544:     /* Now process the block-stash. Since the values are stashed column-oriented,
545:        set the roworiented flag to column oriented, and after MatSetValues() 
546:        restore the original flags */
547:     r1 = baij->roworiented;
548:     r2 = a->roworiented;
549:     r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;
550:     baij->roworiented = PETSC_FALSE;
551:     a->roworiented    = PETSC_FALSE;
552:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented    = PETSC_FALSE; /* b->roworinted */
553:     while (1) {
554:       MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
555:       if (!flg) break;
556: 
557:       for (i=0; i<n;) {
558:         /* Now identify the consecutive vals belonging to the same row */
559:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
560:         if (j < n) ncols = j-i;
561:         else       ncols = n-i;
562:         MatSetValuesBlocked_MPISBAIJ(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
563:         i = j;
564:       }
565:     }
566:     MatStashScatterEnd_Private(&mat->bstash);
567:     baij->roworiented = r1;
568:     a->roworiented    = r2;
569:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented    = r3; /* b->roworinted */
570:   }

572:   MatAssemblyBegin(baij->A,mode);
573:   MatAssemblyEnd(baij->A,mode);

575:   /* determine if any processor has disassembled, if so we must 
576:      also disassemble ourselfs, in order that we may reassemble. */
577:   /*
578:      if nonzero structure of submatrix B cannot change then we know that
579:      no processor disassembled thus we can skip this stuff
580:   */
581:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
582:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,((PetscObject)mat)->comm);
583:     if (mat->was_assembled && !other_disassembled) {
584:       DisAssemble_MPISBAIJ(mat);
585:     }
586:   }

588:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
589:     MatSetUpMultiply_MPISBAIJ(mat); /* setup Mvctx and sMvctx */
590:   }
591:   ((Mat_SeqBAIJ*)baij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
592:   MatAssemblyBegin(baij->B,mode);
593:   MatAssemblyEnd(baij->B,mode);
594: 
595:   PetscFree(baij->rowvalues);
596:   baij->rowvalues = 0;

598:   return(0);
599: }

604: static PetscErrorCode MatView_MPISBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
605: {
606:   Mat_MPISBAIJ      *baij = (Mat_MPISBAIJ*)mat->data;
607:   PetscErrorCode    ierr;
608:   PetscInt          bs = mat->rmap->bs;
609:   PetscMPIInt       size = baij->size,rank = baij->rank;
610:   PetscTruth        iascii,isdraw;
611:   PetscViewer       sviewer;
612:   PetscViewerFormat format;

615:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
616:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
617:   if (iascii) {
618:     PetscViewerGetFormat(viewer,&format);
619:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
620:       MatInfo info;
621:       MPI_Comm_rank(((PetscObject)mat)->comm,&rank);
622:       MatGetInfo(mat,MAT_LOCAL,&info);
623:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",
624:               rank,mat->rmap->N,(PetscInt)info.nz_used*bs,(PetscInt)info.nz_allocated*bs,
625:               mat->rmap->bs,(PetscInt)info.memory);
626:       MatGetInfo(baij->A,MAT_LOCAL,&info);
627:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
628:       MatGetInfo(baij->B,MAT_LOCAL,&info);
629:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
630:       PetscViewerFlush(viewer);
631:       PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
632:       VecScatterView(baij->Mvctx,viewer);
633:       return(0);
634:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
635:       PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
636:       return(0);
637:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
638:       return(0);
639:     }
640:   }

642:   if (isdraw) {
643:     PetscDraw  draw;
644:     PetscTruth isnull;
645:     PetscViewerDrawGetDraw(viewer,0,&draw);
646:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
647:   }

649:   if (size == 1) {
650:     PetscObjectSetName((PetscObject)baij->A,((PetscObject)mat)->name);
651:     MatView(baij->A,viewer);
652:   } else {
653:     /* assemble the entire matrix onto first processor. */
654:     Mat          A;
655:     Mat_SeqSBAIJ *Aloc;
656:     Mat_SeqBAIJ  *Bloc;
657:     PetscInt     M = mat->rmap->N,N = mat->cmap->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
658:     MatScalar    *a;

660:     /* Should this be the same type as mat? */
661:     MatCreate(((PetscObject)mat)->comm,&A);
662:     if (!rank) {
663:       MatSetSizes(A,M,N,M,N);
664:     } else {
665:       MatSetSizes(A,0,0,M,N);
666:     }
667:     MatSetType(A,MATMPISBAIJ);
668:     MatMPISBAIJSetPreallocation(A,mat->rmap->bs,0,PETSC_NULL,0,PETSC_NULL);
669:     PetscLogObjectParent(mat,A);

671:     /* copy over the A part */
672:     Aloc  = (Mat_SeqSBAIJ*)baij->A->data;
673:     ai    = Aloc->i; aj = Aloc->j; a = Aloc->a;
674:     PetscMalloc(bs*sizeof(PetscInt),&rvals);

676:     for (i=0; i<mbs; i++) {
677:       rvals[0] = bs*(baij->rstartbs + i);
678:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
679:       for (j=ai[i]; j<ai[i+1]; j++) {
680:         col = (baij->cstartbs+aj[j])*bs;
681:         for (k=0; k<bs; k++) {
682:           MatSetValues_MPISBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
683:           col++; a += bs;
684:         }
685:       }
686:     }
687:     /* copy over the B part */
688:     Bloc = (Mat_SeqBAIJ*)baij->B->data;
689:     ai = Bloc->i; aj = Bloc->j; a = Bloc->a;
690:     for (i=0; i<mbs; i++) {
691: 
692:       rvals[0] = bs*(baij->rstartbs + i);
693:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
694:       for (j=ai[i]; j<ai[i+1]; j++) {
695:         col = baij->garray[aj[j]]*bs;
696:         for (k=0; k<bs; k++) {
697:           MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
698:           col++; a += bs;
699:         }
700:       }
701:     }
702:     PetscFree(rvals);
703:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
704:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
705:     /* 
706:        Everyone has to call to draw the matrix since the graphics waits are
707:        synchronized across all processors that share the PetscDraw object
708:     */
709:     PetscViewerGetSingleton(viewer,&sviewer);
710:     if (!rank) {
711:       PetscObjectSetName((PetscObject)((Mat_MPISBAIJ*)(A->data))->A,((PetscObject)mat)->name);
712:       MatView(((Mat_MPISBAIJ*)(A->data))->A,sviewer);
713:     }
714:     PetscViewerRestoreSingleton(viewer,&sviewer);
715:     MatDestroy(A);
716:   }
717:   return(0);
718: }

722: PetscErrorCode MatView_MPISBAIJ(Mat mat,PetscViewer viewer)
723: {
725:   PetscTruth     iascii,isdraw,issocket,isbinary;

728:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
729:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
730:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
731:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
732:   if (iascii || isdraw || issocket || isbinary) {
733:     MatView_MPISBAIJ_ASCIIorDraworSocket(mat,viewer);
734:   } else {
735:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPISBAIJ matrices",((PetscObject)viewer)->type_name);
736:   }
737:   return(0);
738: }

742: PetscErrorCode MatDestroy_MPISBAIJ(Mat mat)
743: {
744:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;

748: #if defined(PETSC_USE_LOG)
749:   PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap->N,mat->cmap->N);
750: #endif
751:   MatStashDestroy_Private(&mat->stash);
752:   MatStashDestroy_Private(&mat->bstash);
753:   MatDestroy(baij->A);
754:   MatDestroy(baij->B);
755: #if defined (PETSC_USE_CTABLE)
756:   if (baij->colmap) {PetscTableDestroy(baij->colmap);}
757: #else
758:   PetscFree(baij->colmap);
759: #endif
760:   PetscFree(baij->garray);
761:   if (baij->lvec)   {VecDestroy(baij->lvec);}
762:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
763:   if (baij->slvec0) {
764:     VecDestroy(baij->slvec0);
765:     VecDestroy(baij->slvec0b);
766:   }
767:   if (baij->slvec1) {
768:     VecDestroy(baij->slvec1);
769:     VecDestroy(baij->slvec1a);
770:     VecDestroy(baij->slvec1b);
771:   }
772:   if (baij->sMvctx)  {VecScatterDestroy(baij->sMvctx);}
773:   PetscFree(baij->rowvalues);
774:   PetscFree(baij->barray);
775:   PetscFree(baij->hd);
776: #if defined(PETSC_USE_MAT_SINGLE)
777:   PetscFree(baij->setvaluescopy);
778: #endif
779:   PetscFree(baij->in_loc);
780:   PetscFree(baij->v_loc);
781:   PetscFree(baij->rangebs);
782:   PetscFree(baij);

784:   PetscObjectChangeTypeName((PetscObject)mat,0);
785:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
786:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
787:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
788:   PetscObjectComposeFunction((PetscObject)mat,"MatMPISBAIJSetPreallocation_C","",PETSC_NULL);
789:   return(0);
790: }

794: PetscErrorCode MatMult_MPISBAIJ(Mat A,Vec xx,Vec yy)
795: {
796:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
798:   PetscInt       nt,mbs=a->mbs,bs=A->rmap->bs;
799:   PetscScalar    *x,*from,zero=0.0;
800: 
802:   VecGetLocalSize(xx,&nt);
803:   if (nt != A->cmap->n) {
804:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
805:   }

807:   /* diagonal part */
808:   (*a->A->ops->mult)(a->A,xx,a->slvec1a);
809:   VecSet(a->slvec1b,zero);

811:   /* subdiagonal part */
812:   (*a->B->ops->multtranspose)(a->B,xx,a->slvec0b);

814:   /* copy x into the vec slvec0 */
815:   VecGetArray(a->slvec0,&from);
816:   VecGetArray(xx,&x);

818:   PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
819:   VecRestoreArray(a->slvec0,&from);
820:   VecRestoreArray(xx,&x);
821: 
822:   VecScatterBegin(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
823:   VecScatterEnd(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
824:   /* supperdiagonal part */
825:   (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,yy);
826:   return(0);
827: }

831: PetscErrorCode MatMult_MPISBAIJ_2comm(Mat A,Vec xx,Vec yy)
832: {
833:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
835:   PetscInt       nt;

838:   VecGetLocalSize(xx,&nt);
839:   if (nt != A->cmap->n) {
840:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
841:   }
842:   VecGetLocalSize(yy,&nt);
843:   if (nt != A->rmap->N) {
844:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
845:   }

847:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
848:   /* do diagonal part */
849:   (*a->A->ops->mult)(a->A,xx,yy);
850:   /* do supperdiagonal part */
851:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
852:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
853:   /* do subdiagonal part */
854:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
855:   VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
856:   VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);

858:   return(0);
859: }

863: PetscErrorCode MatMultAdd_MPISBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
864: {
865:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
867:   PetscInt       mbs=a->mbs,bs=A->rmap->bs;
868:   PetscScalar    *x,*from,zero=0.0;
869: 
871:   /*
872:   PetscSynchronizedPrintf(((PetscObject)A)->comm," MatMultAdd is called ...\n");
873:   PetscSynchronizedFlush(((PetscObject)A)->comm);
874:   */
875:   /* diagonal part */
876:   (*a->A->ops->multadd)(a->A,xx,yy,a->slvec1a);
877:   VecSet(a->slvec1b,zero);

879:   /* subdiagonal part */
880:   (*a->B->ops->multtranspose)(a->B,xx,a->slvec0b);

882:   /* copy x into the vec slvec0 */
883:   VecGetArray(a->slvec0,&from);
884:   VecGetArray(xx,&x);
885:   PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
886:   VecRestoreArray(a->slvec0,&from);
887: 
888:   VecScatterBegin(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
889:   VecRestoreArray(xx,&x);
890:   VecScatterEnd(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
891: 
892:   /* supperdiagonal part */
893:   (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,zz);
894: 
895:   return(0);
896: }

900: PetscErrorCode MatMultAdd_MPISBAIJ_2comm(Mat A,Vec xx,Vec yy,Vec zz)
901: {
902:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

906:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
907:   /* do diagonal part */
908:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
909:   /* do supperdiagonal part */
910:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
911:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);

913:   /* do subdiagonal part */
914:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
915:   VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
916:   VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);

918:   return(0);
919: }

921: /*
922:   This only works correctly for square matrices where the subblock A->A is the 
923:    diagonal block
924: */
927: PetscErrorCode MatGetDiagonal_MPISBAIJ(Mat A,Vec v)
928: {
929:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

933:   /* if (a->rmap->N != a->cmap->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block"); */
934:   MatGetDiagonal(a->A,v);
935:   return(0);
936: }

940: PetscErrorCode MatScale_MPISBAIJ(Mat A,PetscScalar aa)
941: {
942:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

946:   MatScale(a->A,aa);
947:   MatScale(a->B,aa);
948:   return(0);
949: }

953: PetscErrorCode MatGetRow_MPISBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
954: {
955:   Mat_MPISBAIJ   *mat = (Mat_MPISBAIJ*)matin->data;
956:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
958:   PetscInt       bs = matin->rmap->bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
959:   PetscInt       nztot,nzA,nzB,lrow,brstart = matin->rmap->rstart,brend = matin->rmap->rend;
960:   PetscInt       *cmap,*idx_p,cstart = mat->rstartbs;

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

966:   if (!mat->rowvalues && (idx || v)) {
967:     /*
968:         allocate enough space to hold information from the longest row.
969:     */
970:     Mat_SeqSBAIJ *Aa = (Mat_SeqSBAIJ*)mat->A->data;
971:     Mat_SeqBAIJ  *Ba = (Mat_SeqBAIJ*)mat->B->data;
972:     PetscInt     max = 1,mbs = mat->mbs,tmp;
973:     for (i=0; i<mbs; i++) {
974:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i]; /* row length */
975:       if (max < tmp) { max = tmp; }
976:     }
977:     PetscMalloc(max*bs2*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
978:     mat->rowindices = (PetscInt*)(mat->rowvalues + max*bs2);
979:   }
980: 
981:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
982:   lrow = row - brstart;  /* local row index */

984:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
985:   if (!v)   {pvA = 0; pvB = 0;}
986:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
987:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
988:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
989:   nztot = nzA + nzB;

991:   cmap  = mat->garray;
992:   if (v  || idx) {
993:     if (nztot) {
994:       /* Sort by increasing column numbers, assuming A and B already sorted */
995:       PetscInt imark = -1;
996:       if (v) {
997:         *v = v_p = mat->rowvalues;
998:         for (i=0; i<nzB; i++) {
999:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1000:           else break;
1001:         }
1002:         imark = i;
1003:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1004:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1005:       }
1006:       if (idx) {
1007:         *idx = idx_p = mat->rowindices;
1008:         if (imark > -1) {
1009:           for (i=0; i<imark; i++) {
1010:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1011:           }
1012:         } else {
1013:           for (i=0; i<nzB; i++) {
1014:             if (cmap[cworkB[i]/bs] < cstart)
1015:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1016:             else break;
1017:           }
1018:           imark = i;
1019:         }
1020:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1021:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1022:       }
1023:     } else {
1024:       if (idx) *idx = 0;
1025:       if (v)   *v   = 0;
1026:     }
1027:   }
1028:   *nz = nztot;
1029:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1030:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1031:   return(0);
1032: }

1036: PetscErrorCode MatRestoreRow_MPISBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1037: {
1038:   Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;

1041:   if (!baij->getrowactive) {
1042:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1043:   }
1044:   baij->getrowactive = PETSC_FALSE;
1045:   return(0);
1046: }

1050: PetscErrorCode MatGetRowUpperTriangular_MPISBAIJ(Mat A)
1051: {
1052:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
1053:   Mat_SeqSBAIJ   *aA = (Mat_SeqSBAIJ*)a->A->data;

1056:   aA->getrow_utriangular = PETSC_TRUE;
1057:   return(0);
1058: }
1061: PetscErrorCode MatRestoreRowUpperTriangular_MPISBAIJ(Mat A)
1062: {
1063:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
1064:   Mat_SeqSBAIJ   *aA = (Mat_SeqSBAIJ*)a->A->data;

1067:   aA->getrow_utriangular = PETSC_FALSE;
1068:   return(0);
1069: }

1073: PetscErrorCode MatRealPart_MPISBAIJ(Mat A)
1074: {
1075:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

1079:   MatRealPart(a->A);
1080:   MatRealPart(a->B);
1081:   return(0);
1082: }

1086: PetscErrorCode MatImaginaryPart_MPISBAIJ(Mat A)
1087: {
1088:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

1092:   MatImaginaryPart(a->A);
1093:   MatImaginaryPart(a->B);
1094:   return(0);
1095: }

1099: PetscErrorCode MatZeroEntries_MPISBAIJ(Mat A)
1100: {
1101:   Mat_MPISBAIJ   *l = (Mat_MPISBAIJ*)A->data;

1105:   MatZeroEntries(l->A);
1106:   MatZeroEntries(l->B);
1107:   return(0);
1108: }

1112: PetscErrorCode MatGetInfo_MPISBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1113: {
1114:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)matin->data;
1115:   Mat            A = a->A,B = a->B;
1117:   PetscReal      isend[5],irecv[5];

1120:   info->block_size     = (PetscReal)matin->rmap->bs;
1121:   MatGetInfo(A,MAT_LOCAL,info);
1122:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1123:   isend[3] = info->memory;  isend[4] = info->mallocs;
1124:   MatGetInfo(B,MAT_LOCAL,info);
1125:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1126:   isend[3] += info->memory;  isend[4] += info->mallocs;
1127:   if (flag == MAT_LOCAL) {
1128:     info->nz_used      = isend[0];
1129:     info->nz_allocated = isend[1];
1130:     info->nz_unneeded  = isend[2];
1131:     info->memory       = isend[3];
1132:     info->mallocs      = isend[4];
1133:   } else if (flag == MAT_GLOBAL_MAX) {
1134:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,((PetscObject)matin)->comm);
1135:     info->nz_used      = irecv[0];
1136:     info->nz_allocated = irecv[1];
1137:     info->nz_unneeded  = irecv[2];
1138:     info->memory       = irecv[3];
1139:     info->mallocs      = irecv[4];
1140:   } else if (flag == MAT_GLOBAL_SUM) {
1141:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,((PetscObject)matin)->comm);
1142:     info->nz_used      = irecv[0];
1143:     info->nz_allocated = irecv[1];
1144:     info->nz_unneeded  = irecv[2];
1145:     info->memory       = irecv[3];
1146:     info->mallocs      = irecv[4];
1147:   } else {
1148:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1149:   }
1150:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1151:   info->fill_ratio_needed = 0;
1152:   info->factor_mallocs    = 0;
1153:   return(0);
1154: }

1158: PetscErrorCode MatSetOption_MPISBAIJ(Mat A,MatOption op,PetscTruth flg)
1159: {
1160:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
1161:   Mat_SeqSBAIJ   *aA = (Mat_SeqSBAIJ*)a->A->data;

1165:   switch (op) {
1166:   case MAT_NEW_NONZERO_LOCATIONS:
1167:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1168:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1169:   case MAT_KEEP_ZEROED_ROWS:
1170:   case MAT_NEW_NONZERO_LOCATION_ERR:
1171:     MatSetOption(a->A,op,flg);
1172:     MatSetOption(a->B,op,flg);
1173:     break;
1174:   case MAT_ROW_ORIENTED:
1175:     a->roworiented = flg;
1176:     MatSetOption(a->A,op,flg);
1177:     MatSetOption(a->B,op,flg);
1178:     break;
1179:   case MAT_NEW_DIAGONALS:
1180:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1181:     break;
1182:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1183:     a->donotstash = flg;
1184:     break;
1185:   case MAT_USE_HASH_TABLE:
1186:     a->ht_flag = flg;
1187:     break;
1188:   case MAT_HERMITIAN:
1189:     if (flg) SETERRQ(PETSC_ERR_SUP,"Matrix must be symmetric");
1190:   case MAT_SYMMETRIC:
1191:   case MAT_STRUCTURALLY_SYMMETRIC:
1192:   case MAT_SYMMETRY_ETERNAL:
1193:     if (!flg) SETERRQ(PETSC_ERR_SUP,"Matrix must be symmetric");
1194:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1195:     break;
1196:   case MAT_IGNORE_LOWER_TRIANGULAR:
1197:     aA->ignore_ltriangular = flg;
1198:     break;
1199:   case MAT_ERROR_LOWER_TRIANGULAR:
1200:     aA->ignore_ltriangular = flg;
1201:     break;
1202:   case MAT_GETROW_UPPERTRIANGULAR:
1203:     aA->getrow_utriangular = flg;
1204:     break;
1205:   default:
1206:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
1207:   }
1208:   return(0);
1209: }

1213: PetscErrorCode MatTranspose_MPISBAIJ(Mat A,MatReuse reuse,Mat *B)
1214: {
1217:   if (MAT_INITIAL_MATRIX || *B != A) {
1218:     MatDuplicate(A,MAT_COPY_VALUES,B);
1219:   }
1220:   return(0);
1221: }

1225: PetscErrorCode MatDiagonalScale_MPISBAIJ(Mat mat,Vec ll,Vec rr)
1226: {
1227:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
1228:   Mat            a=baij->A, b=baij->B;
1230:   PetscInt       nv,m,n;
1231:   PetscTruth     flg;

1234:   if (ll != rr){
1235:     VecEqual(ll,rr,&flg);
1236:     if (!flg)
1237:       SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"For symmetric format, left and right scaling vectors must be same\n");
1238:   }
1239:   if (!ll) return(0);

1241:   MatGetLocalSize(mat,&m,&n);
1242:   if (m != n) SETERRQ2(PETSC_ERR_ARG_SIZ,"For symmetric format, local size %d %d must be same",m,n);
1243: 
1244:   VecGetLocalSize(rr,&nv);
1245:   if (nv!=n) SETERRQ(PETSC_ERR_ARG_SIZ,"Left and right vector non-conforming local size");

1247:   VecScatterBegin(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1248: 
1249:   /* left diagonalscale the off-diagonal part */
1250:   (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1251: 
1252:   /* scale the diagonal part */
1253:   (*a->ops->diagonalscale)(a,ll,rr);

1255:   /* right diagonalscale the off-diagonal part */
1256:   VecScatterEnd(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1257:   (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1258:   return(0);
1259: }

1263: PetscErrorCode MatSetUnfactored_MPISBAIJ(Mat A)
1264: {
1265:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

1269:   MatSetUnfactored(a->A);
1270:   return(0);
1271: }

1273: static PetscErrorCode MatDuplicate_MPISBAIJ(Mat,MatDuplicateOption,Mat *);

1277: PetscErrorCode MatEqual_MPISBAIJ(Mat A,Mat B,PetscTruth *flag)
1278: {
1279:   Mat_MPISBAIJ   *matB = (Mat_MPISBAIJ*)B->data,*matA = (Mat_MPISBAIJ*)A->data;
1280:   Mat            a,b,c,d;
1281:   PetscTruth     flg;

1285:   a = matA->A; b = matA->B;
1286:   c = matB->A; d = matB->B;

1288:   MatEqual(a,c,&flg);
1289:   if (flg) {
1290:     MatEqual(b,d,&flg);
1291:   }
1292:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,((PetscObject)A)->comm);
1293:   return(0);
1294: }

1298: PetscErrorCode MatCopy_MPISBAIJ(Mat A,Mat B,MatStructure str)
1299: {
1301:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ *)A->data;
1302:   Mat_MPISBAIJ   *b = (Mat_MPISBAIJ *)B->data;

1305:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1306:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1307:     MatGetRowUpperTriangular(A);
1308:     MatCopy_Basic(A,B,str);
1309:     MatRestoreRowUpperTriangular(A);
1310:   } else {
1311:     MatCopy(a->A,b->A,str);
1312:     MatCopy(a->B,b->B,str);
1313:   }
1314:   return(0);
1315: }

1319: PetscErrorCode MatSetUpPreallocation_MPISBAIJ(Mat A)
1320: {

1324:   MatMPISBAIJSetPreallocation(A,-PetscMax(A->rmap->bs,1),PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1325:   return(0);
1326: }

1328:  #include petscblaslapack.h
1331: PetscErrorCode MatAXPY_MPISBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1332: {
1334:   Mat_MPISBAIJ   *xx=(Mat_MPISBAIJ *)X->data,*yy=(Mat_MPISBAIJ *)Y->data;
1335:   PetscBLASInt   bnz,one=1;
1336:   Mat_SeqSBAIJ   *xa,*ya;
1337:   Mat_SeqBAIJ    *xb,*yb;

1340:   if (str == SAME_NONZERO_PATTERN) {
1341:     PetscScalar alpha = a;
1342:     xa = (Mat_SeqSBAIJ *)xx->A->data;
1343:     ya = (Mat_SeqSBAIJ *)yy->A->data;
1344:     bnz = PetscBLASIntCast(xa->nz);
1345:     BLASaxpy_(&bnz,&alpha,xa->a,&one,ya->a,&one);
1346:     xb = (Mat_SeqBAIJ *)xx->B->data;
1347:     yb = (Mat_SeqBAIJ *)yy->B->data;
1348:     bnz = PetscBLASIntCast(xb->nz);
1349:     BLASaxpy_(&bnz,&alpha,xb->a,&one,yb->a,&one);
1350:   } else {
1351:     MatGetRowUpperTriangular(X);
1352:     MatAXPY_Basic(Y,a,X,str);
1353:     MatRestoreRowUpperTriangular(X);
1354:   }
1355:   return(0);
1356: }

1360: PetscErrorCode MatGetSubMatrices_MPISBAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1361: {
1363:   PetscInt       i;
1364:   PetscTruth     flg;

1367:   for (i=0; i<n; i++) {
1368:     ISEqual(irow[i],icol[i],&flg);
1369:     if (!flg) {
1370:       SETERRQ(PETSC_ERR_SUP,"Can only get symmetric submatrix for MPISBAIJ matrices");
1371:     }
1372:   }
1373:   MatGetSubMatrices_MPIBAIJ(A,n,irow,icol,scall,B);
1374:   return(0);
1375: }
1376: 

1378: /* -------------------------------------------------------------------*/
1379: static struct _MatOps MatOps_Values = {
1380:        MatSetValues_MPISBAIJ,
1381:        MatGetRow_MPISBAIJ,
1382:        MatRestoreRow_MPISBAIJ,
1383:        MatMult_MPISBAIJ,
1384: /* 4*/ MatMultAdd_MPISBAIJ,
1385:        MatMult_MPISBAIJ,       /* transpose versions are same as non-transpose */
1386:        MatMultAdd_MPISBAIJ,
1387:        0,
1388:        0,
1389:        0,
1390: /*10*/ 0,
1391:        0,
1392:        0,
1393:        MatRelax_MPISBAIJ,
1394:        MatTranspose_MPISBAIJ,
1395: /*15*/ MatGetInfo_MPISBAIJ,
1396:        MatEqual_MPISBAIJ,
1397:        MatGetDiagonal_MPISBAIJ,
1398:        MatDiagonalScale_MPISBAIJ,
1399:        MatNorm_MPISBAIJ,
1400: /*20*/ MatAssemblyBegin_MPISBAIJ,
1401:        MatAssemblyEnd_MPISBAIJ,
1402:        0,
1403:        MatSetOption_MPISBAIJ,
1404:        MatZeroEntries_MPISBAIJ,
1405: /*25*/ 0,
1406:        0,
1407:        0,
1408:        0,
1409:        0,
1410: /*30*/ MatSetUpPreallocation_MPISBAIJ,
1411:        0,
1412:        0,
1413:        0,
1414:        0,
1415: /*35*/ MatDuplicate_MPISBAIJ,
1416:        0,
1417:        0,
1418:        0,
1419:        0,
1420: /*40*/ MatAXPY_MPISBAIJ,
1421:        MatGetSubMatrices_MPISBAIJ,
1422:        MatIncreaseOverlap_MPISBAIJ,
1423:        MatGetValues_MPISBAIJ,
1424:        MatCopy_MPISBAIJ,
1425: /*45*/ 0,
1426:        MatScale_MPISBAIJ,
1427:        0,
1428:        0,
1429:        0,
1430: /*50*/ 0,
1431:        0,
1432:        0,
1433:        0,
1434:        0,
1435: /*55*/ 0,
1436:        0,
1437:        MatSetUnfactored_MPISBAIJ,
1438:        0,
1439:        MatSetValuesBlocked_MPISBAIJ,
1440: /*60*/ 0,
1441:        0,
1442:        0,
1443:        0,
1444:        0,
1445: /*65*/ 0,
1446:        0,
1447:        0,
1448:        0,
1449:        0,
1450: /*70*/ MatGetRowMaxAbs_MPISBAIJ,
1451:        0,
1452:        0,
1453:        0,
1454:        0,
1455: /*75*/ 0,
1456:        0,
1457:        0,
1458:        0,
1459:        0,
1460: /*80*/ 0,
1461:        0,
1462:        0,
1463:        0,
1464:        MatLoad_MPISBAIJ,
1465: /*85*/ 0,
1466:        0,
1467:        0,
1468:        0,
1469:        0,
1470: /*90*/ 0,
1471:        0,
1472:        0,
1473:        0,
1474:        0,
1475: /*95*/ 0,
1476:        0,
1477:        0,
1478:        0,
1479:        0,
1480: /*100*/0,
1481:        0,
1482:        0,
1483:        0,
1484:        0,
1485: /*105*/0,
1486:        MatRealPart_MPISBAIJ,
1487:        MatImaginaryPart_MPISBAIJ,
1488:        MatGetRowUpperTriangular_MPISBAIJ,
1489:        MatRestoreRowUpperTriangular_MPISBAIJ
1490: };


1496: PetscErrorCode  MatGetDiagonalBlock_MPISBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
1497: {
1499:   *a      = ((Mat_MPISBAIJ *)A->data)->A;
1500:   *iscopy = PETSC_FALSE;
1501:   return(0);
1502: }

1508: PetscErrorCode  MatMPISBAIJSetPreallocation_MPISBAIJ(Mat B,PetscInt bs,PetscInt d_nz,PetscInt *d_nnz,PetscInt o_nz,PetscInt *o_nnz)
1509: {
1510:   Mat_MPISBAIJ   *b;
1512:   PetscInt       i,mbs,Mbs,newbs = PetscAbs(bs);

1515:   if (bs < 0){
1516:     PetscOptionsBegin(((PetscObject)B)->comm,((PetscObject)B)->prefix,"Options for MPISBAIJ matrix","Mat");
1517:       PetscOptionsInt("-mat_block_size","Set the blocksize used to store the matrix","MatMPIBAIJSetPreallocation",newbs,&newbs,PETSC_NULL);
1518:     PetscOptionsEnd();
1519:     bs   = PetscAbs(bs);
1520:   }
1521:   if ((d_nnz || o_nnz) && newbs != bs) {
1522:     SETERRQ(PETSC_ERR_ARG_WRONG,"Cannot change blocksize from command line if setting d_nnz or o_nnz");
1523:   }
1524:   bs = newbs;

1526:   if (d_nz == PETSC_DECIDE || d_nz == PETSC_DEFAULT) d_nz = 3;
1527:   if (o_nz == PETSC_DECIDE || o_nz == PETSC_DEFAULT) o_nz = 1;
1528:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
1529:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);

1531:   B->rmap->bs = B->cmap->bs = bs;
1532:   PetscMapSetUp(B->rmap);
1533:   PetscMapSetUp(B->cmap);

1535:   if (d_nnz) {
1536:     for (i=0; i<B->rmap->n/bs; i++) {
1537:       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]);
1538:     }
1539:   }
1540:   if (o_nnz) {
1541:     for (i=0; i<B->rmap->n/bs; i++) {
1542:       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]);
1543:     }
1544:   }
1545:   B->preallocated = PETSC_TRUE;

1547:   b   = (Mat_MPISBAIJ*)B->data;
1548:   mbs = B->rmap->n/bs;
1549:   Mbs = B->rmap->N/bs;
1550:   if (mbs*bs != B->rmap->n) {
1551:     SETERRQ2(PETSC_ERR_ARG_SIZ,"No of local rows %D must be divisible by blocksize %D",B->rmap->N,bs);
1552:   }

1554:   B->rmap->bs  = bs;
1555:   b->bs2 = bs*bs;
1556:   b->mbs = mbs;
1557:   b->nbs = mbs;
1558:   b->Mbs = Mbs;
1559:   b->Nbs = Mbs;

1561:   for (i=0; i<=b->size; i++) {
1562:     b->rangebs[i] = B->rmap->range[i]/bs;
1563:   }
1564:   b->rstartbs = B->rmap->rstart/bs;
1565:   b->rendbs   = B->rmap->rend/bs;
1566: 
1567:   b->cstartbs = B->cmap->rstart/bs;
1568:   b->cendbs   = B->cmap->rend/bs;
1569: 
1570:   MatCreate(PETSC_COMM_SELF,&b->A);
1571:   MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
1572:   MatSetType(b->A,MATSEQSBAIJ);
1573:   MatSeqSBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
1574:   PetscLogObjectParent(B,b->A);

1576:   MatCreate(PETSC_COMM_SELF,&b->B);
1577:   MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
1578:   MatSetType(b->B,MATSEQBAIJ);
1579:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
1580:   PetscLogObjectParent(B,b->B);

1582:   /* build cache for off array entries formed */
1583:   MatStashCreate_Private(((PetscObject)B)->comm,bs,&B->bstash);

1585:   return(0);
1586: }

1590: #if defined(PETSC_HAVE_MUMPS)
1592: #endif
1593: #if defined(PETSC_HAVE_SPOOLES)
1595: #endif
1596: #if defined(PETSC_HAVE_PASTIX)
1598: #endif

1601: /*MC
1602:    MATMPISBAIJ - MATMPISBAIJ = "mpisbaij" - A matrix type to be used for distributed symmetric sparse block matrices, 
1603:    based on block compressed sparse row format.  Only the upper triangular portion of the matrix is stored.

1605:    Options Database Keys:
1606: . -mat_type mpisbaij - sets the matrix type to "mpisbaij" during a call to MatSetFromOptions()

1608:   Level: beginner

1610: .seealso: MatCreateMPISBAIJ
1611: M*/

1616: PetscErrorCode  MatCreate_MPISBAIJ(Mat B)
1617: {
1618:   Mat_MPISBAIJ   *b;
1620:   PetscTruth     flg;


1624:   PetscNewLog(B,Mat_MPISBAIJ,&b);
1625:   B->data = (void*)b;
1626:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));

1628:   B->ops->destroy    = MatDestroy_MPISBAIJ;
1629:   B->ops->view       = MatView_MPISBAIJ;
1630:   B->mapping         = 0;
1631:   B->assembled       = PETSC_FALSE;

1633:   B->insertmode = NOT_SET_VALUES;
1634:   MPI_Comm_rank(((PetscObject)B)->comm,&b->rank);
1635:   MPI_Comm_size(((PetscObject)B)->comm,&b->size);

1637:   /* build local table of row and column ownerships */
1638:   PetscMalloc((b->size+2)*sizeof(PetscInt),&b->rangebs);

1640:   /* build cache for off array entries formed */
1641:   MatStashCreate_Private(((PetscObject)B)->comm,1,&B->stash);
1642:   b->donotstash  = PETSC_FALSE;
1643:   b->colmap      = PETSC_NULL;
1644:   b->garray      = PETSC_NULL;
1645:   b->roworiented = PETSC_TRUE;

1647:   /* stuff used in block assembly */
1648:   b->barray       = 0;

1650:   /* stuff used for matrix vector multiply */
1651:   b->lvec         = 0;
1652:   b->Mvctx        = 0;
1653:   b->slvec0       = 0;
1654:   b->slvec0b      = 0;
1655:   b->slvec1       = 0;
1656:   b->slvec1a      = 0;
1657:   b->slvec1b      = 0;
1658:   b->sMvctx       = 0;

1660:   /* stuff for MatGetRow() */
1661:   b->rowindices   = 0;
1662:   b->rowvalues    = 0;
1663:   b->getrowactive = PETSC_FALSE;

1665:   /* hash table stuff */
1666:   b->ht           = 0;
1667:   b->hd           = 0;
1668:   b->ht_size      = 0;
1669:   b->ht_flag      = PETSC_FALSE;
1670:   b->ht_fact      = 0;
1671:   b->ht_total_ct  = 0;
1672:   b->ht_insert_ct = 0;

1674:   b->in_loc       = 0;
1675:   b->v_loc        = 0;
1676:   b->n_loc        = 0;
1677:   PetscOptionsBegin(((PetscObject)B)->comm,PETSC_NULL,"Options for loading MPISBAIJ matrix 1","Mat");
1678:     PetscOptionsTruth("-mat_use_hash_table","Use hash table to save memory in constructing matrix","MatSetOption",PETSC_FALSE,&flg,PETSC_NULL);
1679:     if (flg) {
1680:       PetscReal fact = 1.39;
1681:       MatSetOption(B,MAT_USE_HASH_TABLE,PETSC_TRUE);
1682:       PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,PETSC_NULL);
1683:       if (fact <= 1.0) fact = 1.39;
1684:       MatMPIBAIJSetHashTableFactor(B,fact);
1685:       PetscInfo1(B,"Hash table Factor used %5.2f\n",fact);
1686:     }
1687:   PetscOptionsEnd();

1689: #if defined(PETSC_HAVE_PASTIX)
1690:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpisbaij_pastix_C",
1691:                                            "MatGetFactor_mpisbaij_pastix",
1692:                                            MatGetFactor_mpisbaij_pastix);
1693: #endif
1694: #if defined(PETSC_HAVE_MUMPS)
1695:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpisbaij_mumps_C",
1696:                                      "MatGetFactor_mpisbaij_mumps",
1697:                                      MatGetFactor_mpisbaij_mumps);
1698: #endif
1699: #if defined(PETSC_HAVE_SPOOLES)
1700:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpisbaij_spooles_C",
1701:                                      "MatGetFactor_mpisbaij_spooles",
1702:                                      MatGetFactor_mpisbaij_spooles);
1703: #endif
1704:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
1705:                                      "MatStoreValues_MPISBAIJ",
1706:                                      MatStoreValues_MPISBAIJ);
1707:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
1708:                                      "MatRetrieveValues_MPISBAIJ",
1709:                                      MatRetrieveValues_MPISBAIJ);
1710:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
1711:                                      "MatGetDiagonalBlock_MPISBAIJ",
1712:                                      MatGetDiagonalBlock_MPISBAIJ);
1713:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPISBAIJSetPreallocation_C",
1714:                                      "MatMPISBAIJSetPreallocation_MPISBAIJ",
1715:                                      MatMPISBAIJSetPreallocation_MPISBAIJ);
1716:   B->symmetric                  = PETSC_TRUE;
1717:   B->structurally_symmetric     = PETSC_TRUE;
1718:   B->symmetric_set              = PETSC_TRUE;
1719:   B->structurally_symmetric_set = PETSC_TRUE;
1720:   PetscObjectChangeTypeName((PetscObject)B,MATMPISBAIJ);
1721:   return(0);
1722: }

1725: /*MC
1726:    MATSBAIJ - MATSBAIJ = "sbaij" - A matrix type to be used for symmetric block sparse matrices.

1728:    This matrix type is identical to MATSEQSBAIJ when constructed with a single process communicator,
1729:    and MATMPISBAIJ otherwise.

1731:    Options Database Keys:
1732: . -mat_type sbaij - sets the matrix type to "sbaij" during a call to MatSetFromOptions()

1734:   Level: beginner

1736: .seealso: MatCreateMPISBAIJ,MATSEQSBAIJ,MATMPISBAIJ
1737: M*/

1742: PetscErrorCode  MatCreate_SBAIJ(Mat A)
1743: {
1745:   PetscMPIInt    size;

1748:   MPI_Comm_size(((PetscObject)A)->comm,&size);
1749:   if (size == 1) {
1750:     MatSetType(A,MATSEQSBAIJ);
1751:   } else {
1752:     MatSetType(A,MATMPISBAIJ);
1753:   }
1754:   return(0);
1755: }

1760: /*@C
1761:    MatMPISBAIJSetPreallocation - For good matrix assembly performance
1762:    the user should preallocate the matrix storage by setting the parameters 
1763:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
1764:    performance can be increased by more than a factor of 50.

1766:    Collective on Mat

1768:    Input Parameters:
1769: +  A - the matrix 
1770: .  bs   - size of blockk
1771: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
1772:            submatrix  (same for all local rows)
1773: .  d_nnz - array containing the number of block nonzeros in the various block rows 
1774:            in the upper triangular and diagonal part of the in diagonal portion of the local
1775:            (possibly different for each block row) or PETSC_NULL.  You must leave room 
1776:            for the diagonal entry even if it is zero.
1777: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
1778:            submatrix (same for all local rows).
1779: -  o_nnz - array containing the number of nonzeros in the various block rows of the
1780:            off-diagonal portion of the local submatrix (possibly different for
1781:            each block row) or PETSC_NULL.


1784:    Options Database Keys:
1785: .   -mat_no_unroll - uses code that does not unroll the loops in the 
1786:                      block calculations (much slower)
1787: .   -mat_block_size - size of the blocks to use

1789:    Notes:

1791:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
1792:    than it must be used on all processors that share the object for that argument.

1794:    If the *_nnz parameter is given then the *_nz parameter is ignored

1796:    Storage Information:
1797:    For a square global matrix we define each processor's diagonal portion 
1798:    to be its local rows and the corresponding columns (a square submatrix);  
1799:    each processor's off-diagonal portion encompasses the remainder of the
1800:    local matrix (a rectangular submatrix). 

1802:    The user can specify preallocated storage for the diagonal part of
1803:    the local submatrix with either d_nz or d_nnz (not both).  Set 
1804:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
1805:    memory allocation.  Likewise, specify preallocated storage for the
1806:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

1808:    You can call MatGetInfo() to get information on how effective the preallocation was;
1809:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
1810:    You can also run with the option -info and look for messages with the string 
1811:    malloc in them to see if additional memory allocation was needed.

1813:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1814:    the figure below we depict these three local rows and all columns (0-11).

1816: .vb
1817:            0 1 2 3 4 5 6 7 8 9 10 11
1818:           -------------------
1819:    row 3  |  o o o d d d o o o o o o
1820:    row 4  |  o o o d d d o o o o o o
1821:    row 5  |  o o o d d d o o o o o o
1822:           -------------------
1823: .ve
1824:   
1825:    Thus, any entries in the d locations are stored in the d (diagonal) 
1826:    submatrix, and any entries in the o locations are stored in the
1827:    o (off-diagonal) submatrix.  Note that the d matrix is stored in
1828:    MatSeqSBAIJ format and the o submatrix in MATSEQBAIJ format.

1830:    Now d_nz should indicate the number of block nonzeros per row in the upper triangular
1831:    plus the diagonal part of the d matrix,
1832:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
1833:    In general, for PDE problems in which most nonzeros are near the diagonal,
1834:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
1835:    or you will get TERRIBLE performance; see the users' manual chapter on
1836:    matrices.

1838:    Level: intermediate

1840: .keywords: matrix, block, aij, compressed row, sparse, parallel

1842: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
1843: @*/
1844: PetscErrorCode  MatMPISBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
1845: {
1846:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

1849:   PetscObjectQueryFunction((PetscObject)B,"MatMPISBAIJSetPreallocation_C",(void (**)(void))&f);
1850:   if (f) {
1851:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
1852:   }
1853:   return(0);
1854: }

1858: /*@C
1859:    MatCreateMPISBAIJ - Creates a sparse parallel matrix in symmetric block AIJ format
1860:    (block compressed row).  For good matrix assembly performance
1861:    the user should preallocate the matrix storage by setting the parameters 
1862:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
1863:    performance can be increased by more than a factor of 50.

1865:    Collective on MPI_Comm

1867:    Input Parameters:
1868: +  comm - MPI communicator
1869: .  bs   - size of blockk
1870: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1871:            This value should be the same as the local size used in creating the 
1872:            y vector for the matrix-vector product y = Ax.
1873: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1874:            This value should be the same as the local size used in creating the 
1875:            x vector for the matrix-vector product y = Ax.
1876: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
1877: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
1878: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
1879:            submatrix  (same for all local rows)
1880: .  d_nnz - array containing the number of block nonzeros in the various block rows 
1881:            in the upper triangular portion of the in diagonal portion of the local 
1882:            (possibly different for each block block row) or PETSC_NULL.  
1883:            You must leave room for the diagonal entry even if it is zero.
1884: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
1885:            submatrix (same for all local rows).
1886: -  o_nnz - array containing the number of nonzeros in the various block rows of the
1887:            off-diagonal portion of the local submatrix (possibly different for
1888:            each block row) or PETSC_NULL.

1890:    Output Parameter:
1891: .  A - the matrix 

1893:    Options Database Keys:
1894: .   -mat_no_unroll - uses code that does not unroll the loops in the 
1895:                      block calculations (much slower)
1896: .   -mat_block_size - size of the blocks to use
1897: .   -mat_mpi - use the parallel matrix data structures even on one processor 
1898:                (defaults to using SeqBAIJ format on one processor)

1900:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
1901:    MatXXXXSetPreallocation() paradgm instead of this routine directly. This is definitely
1902:    true if you plan to use the external direct solvers such as SuperLU, MUMPS or Spooles.
1903:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

1905:    Notes:
1906:    The number of rows and columns must be divisible by blocksize.
1907:    This matrix type does not support complex Hermitian operation.

1909:    The user MUST specify either the local or global matrix dimensions
1910:    (possibly both).

1912:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
1913:    than it must be used on all processors that share the object for that argument.

1915:    If the *_nnz parameter is given then the *_nz parameter is ignored

1917:    Storage Information:
1918:    For a square global matrix we define each processor's diagonal portion 
1919:    to be its local rows and the corresponding columns (a square submatrix);  
1920:    each processor's off-diagonal portion encompasses the remainder of the
1921:    local matrix (a rectangular submatrix). 

1923:    The user can specify preallocated storage for the diagonal part of
1924:    the local submatrix with either d_nz or d_nnz (not both).  Set 
1925:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
1926:    memory allocation.  Likewise, specify preallocated storage for the
1927:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

1929:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1930:    the figure below we depict these three local rows and all columns (0-11).

1932: .vb
1933:            0 1 2 3 4 5 6 7 8 9 10 11
1934:           -------------------
1935:    row 3  |  o o o d d d o o o o o o
1936:    row 4  |  o o o d d d o o o o o o
1937:    row 5  |  o o o d d d o o o o o o
1938:           -------------------
1939: .ve
1940:   
1941:    Thus, any entries in the d locations are stored in the d (diagonal) 
1942:    submatrix, and any entries in the o locations are stored in the
1943:    o (off-diagonal) submatrix.  Note that the d matrix is stored in
1944:    MatSeqSBAIJ format and the o submatrix in MATSEQBAIJ format.

1946:    Now d_nz should indicate the number of block nonzeros per row in the upper triangular
1947:    plus the diagonal part of the d matrix,
1948:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
1949:    In general, for PDE problems in which most nonzeros are near the diagonal,
1950:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
1951:    or you will get TERRIBLE performance; see the users' manual chapter on
1952:    matrices.

1954:    Level: intermediate

1956: .keywords: matrix, block, aij, compressed row, sparse, parallel

1958: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
1959: @*/

1961: PetscErrorCode  MatCreateMPISBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
1962: {
1964:   PetscMPIInt    size;

1967:   MatCreate(comm,A);
1968:   MatSetSizes(*A,m,n,M,N);
1969:   MPI_Comm_size(comm,&size);
1970:   if (size > 1) {
1971:     MatSetType(*A,MATMPISBAIJ);
1972:     MatMPISBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
1973:   } else {
1974:     MatSetType(*A,MATSEQSBAIJ);
1975:     MatSeqSBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
1976:   }
1977:   return(0);
1978: }


1983: static PetscErrorCode MatDuplicate_MPISBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
1984: {
1985:   Mat            mat;
1986:   Mat_MPISBAIJ   *a,*oldmat = (Mat_MPISBAIJ*)matin->data;
1988:   PetscInt       len=0,nt,bs=matin->rmap->bs,mbs=oldmat->mbs;
1989:   PetscScalar    *array;

1992:   *newmat       = 0;
1993:   MatCreate(((PetscObject)matin)->comm,&mat);
1994:   MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
1995:   MatSetType(mat,((PetscObject)matin)->type_name);
1996:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
1997:   PetscMapCopy(((PetscObject)matin)->comm,matin->rmap,mat->rmap);
1998:   PetscMapCopy(((PetscObject)matin)->comm,matin->cmap,mat->cmap);
1999: 
2000:   mat->factor       = matin->factor;
2001:   mat->preallocated = PETSC_TRUE;
2002:   mat->assembled    = PETSC_TRUE;
2003:   mat->insertmode   = NOT_SET_VALUES;

2005:   a = (Mat_MPISBAIJ*)mat->data;
2006:   a->bs2   = oldmat->bs2;
2007:   a->mbs   = oldmat->mbs;
2008:   a->nbs   = oldmat->nbs;
2009:   a->Mbs   = oldmat->Mbs;
2010:   a->Nbs   = oldmat->Nbs;


2013:   a->size         = oldmat->size;
2014:   a->rank         = oldmat->rank;
2015:   a->donotstash   = oldmat->donotstash;
2016:   a->roworiented  = oldmat->roworiented;
2017:   a->rowindices   = 0;
2018:   a->rowvalues    = 0;
2019:   a->getrowactive = PETSC_FALSE;
2020:   a->barray       = 0;
2021:   a->rstartbs    = oldmat->rstartbs;
2022:   a->rendbs      = oldmat->rendbs;
2023:   a->cstartbs    = oldmat->cstartbs;
2024:   a->cendbs      = oldmat->cendbs;

2026:   /* hash table stuff */
2027:   a->ht           = 0;
2028:   a->hd           = 0;
2029:   a->ht_size      = 0;
2030:   a->ht_flag      = oldmat->ht_flag;
2031:   a->ht_fact      = oldmat->ht_fact;
2032:   a->ht_total_ct  = 0;
2033:   a->ht_insert_ct = 0;
2034: 
2035:   PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+2)*sizeof(PetscInt));
2036:   MatStashCreate_Private(((PetscObject)matin)->comm,1,&mat->stash);
2037:   MatStashCreate_Private(((PetscObject)matin)->comm,matin->rmap->bs,&mat->bstash);
2038:   if (oldmat->colmap) {
2039: #if defined (PETSC_USE_CTABLE)
2040:     PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2041: #else
2042:     PetscMalloc((a->Nbs)*sizeof(PetscInt),&a->colmap);
2043:     PetscLogObjectMemory(mat,(a->Nbs)*sizeof(PetscInt));
2044:     PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
2045: #endif
2046:   } else a->colmap = 0;

2048:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2049:     PetscMalloc(len*sizeof(PetscInt),&a->garray);
2050:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2051:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
2052:   } else a->garray = 0;
2053: 
2054:    VecDuplicate(oldmat->lvec,&a->lvec);
2055:   PetscLogObjectParent(mat,a->lvec);
2056:    VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2057:   PetscLogObjectParent(mat,a->Mvctx);

2059:    VecDuplicate(oldmat->slvec0,&a->slvec0);
2060:   PetscLogObjectParent(mat,a->slvec0);
2061:    VecDuplicate(oldmat->slvec1,&a->slvec1);
2062:   PetscLogObjectParent(mat,a->slvec1);

2064:   VecGetLocalSize(a->slvec1,&nt);
2065:   VecGetArray(a->slvec1,&array);
2066:   VecCreateSeqWithArray(PETSC_COMM_SELF,bs*mbs,array,&a->slvec1a);
2067:   VecCreateSeqWithArray(PETSC_COMM_SELF,nt-bs*mbs,array+bs*mbs,&a->slvec1b);
2068:   VecRestoreArray(a->slvec1,&array);
2069:   VecGetArray(a->slvec0,&array);
2070:   VecCreateSeqWithArray(PETSC_COMM_SELF,nt-bs*mbs,array+bs*mbs,&a->slvec0b);
2071:   VecRestoreArray(a->slvec0,&array);
2072:   PetscLogObjectParent(mat,a->slvec0);
2073:   PetscLogObjectParent(mat,a->slvec1);
2074:   PetscLogObjectParent(mat,a->slvec0b);
2075:   PetscLogObjectParent(mat,a->slvec1a);
2076:   PetscLogObjectParent(mat,a->slvec1b);

2078:   /*  VecScatterCopy(oldmat->sMvctx,&a->sMvctx); - not written yet, replaced by the lazy trick: */
2079:   PetscObjectReference((PetscObject)oldmat->sMvctx);
2080:   a->sMvctx = oldmat->sMvctx;
2081:   PetscLogObjectParent(mat,a->sMvctx);

2083:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2084:   PetscLogObjectParent(mat,a->A);
2085:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2086:   PetscLogObjectParent(mat,a->B);
2087:   PetscFListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
2088:   *newmat = mat;
2089:   return(0);
2090: }

2092:  #include petscsys.h

2096: PetscErrorCode MatLoad_MPISBAIJ(PetscViewer viewer, const MatType type,Mat *newmat)
2097: {
2098:   Mat            A;
2100:   PetscInt       i,nz,j,rstart,rend;
2101:   PetscScalar    *vals,*buf;
2102:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2103:   MPI_Status     status;
2104:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag,*sndcounts = 0,*browners,maxnz,*rowners,*locrowlens,mmbs;
2105:   PetscInt       header[4],*rowlengths = 0,M,N,m,*cols;
2106:   PetscInt       *procsnz = 0,jj,*mycols,*ibuf;
2107:   PetscInt       bs=1,Mbs,mbs,extra_rows;
2108:   PetscInt       *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
2109:   PetscInt       dcount,kmax,k,nzcount,tmp;
2110:   int            fd;
2111: 
2113:   PetscOptionsBegin(comm,PETSC_NULL,"Options for loading MPISBAIJ matrix 2","Mat");
2114:     PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
2115:   PetscOptionsEnd();

2117:   MPI_Comm_size(comm,&size);
2118:   MPI_Comm_rank(comm,&rank);
2119:   if (!rank) {
2120:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2121:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2122:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2123:     if (header[3] < 0) {
2124:       SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as MPISBAIJ");
2125:     }
2126:   }

2128:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2129:   M = header[1]; N = header[2];

2131:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2133:   /* 
2134:      This code adds extra rows to make sure the number of rows is 
2135:      divisible by the blocksize
2136:   */
2137:   Mbs        = M/bs;
2138:   extra_rows = bs - M + bs*(Mbs);
2139:   if (extra_rows == bs) extra_rows = 0;
2140:   else                  Mbs++;
2141:   if (extra_rows &&!rank) {
2142:     PetscInfo(viewer,"Padding loaded matrix to match blocksize\n");
2143:   }

2145:   /* determine ownership of all rows */
2146:   mbs        = Mbs/size + ((Mbs % size) > rank);
2147:   m          = mbs*bs;
2148:   PetscMalloc(2*(size+2)*sizeof(PetscMPIInt),&rowners);
2149:   browners   = rowners + size + 1;
2150:   mmbs       = PetscMPIIntCast(mbs);
2151:   MPI_Allgather(&mmbs,1,MPI_INT,rowners+1,1,MPI_INT,comm);
2152:   rowners[0] = 0;
2153:   for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
2154:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2155:   rstart = rowners[rank];
2156:   rend   = rowners[rank+1];
2157: 
2158:   /* distribute row lengths to all processors */
2159:   PetscMalloc((rend-rstart)*bs*sizeof(PetscMPIInt),&locrowlens);
2160:   if (!rank) {
2161:     PetscMalloc((M+extra_rows)*sizeof(PetscInt),&rowlengths);
2162:     PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2163:     for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2164:     PetscMalloc(size*sizeof(PetscMPIInt),&sndcounts);
2165:     for (i=0; i<size; i++) sndcounts[i] = browners[i+1] - browners[i];
2166:     MPI_Scatterv(rowlengths,sndcounts,browners,MPIU_INT,locrowlens,(rend-rstart)*bs,MPIU_INT,0,comm);
2167:     PetscFree(sndcounts);
2168:   } else {
2169:     MPI_Scatterv(0,0,0,MPIU_INT,locrowlens,(rend-rstart)*bs,MPIU_INT,0,comm);
2170:   }
2171: 
2172:   if (!rank) {   /* procs[0] */
2173:     /* calculate the number of nonzeros on each processor */
2174:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2175:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2176:     for (i=0; i<size; i++) {
2177:       for (j=rowners[i]*bs; j< rowners[i+1]*bs; j++) {
2178:         procsnz[i] += rowlengths[j];
2179:       }
2180:     }
2181:     PetscFree(rowlengths);
2182: 
2183:     /* determine max buffer needed and allocate it */
2184:     maxnz = 0;
2185:     for (i=0; i<size; i++) {
2186:       maxnz = PetscMax(maxnz,procsnz[i]);
2187:     }
2188:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2190:     /* read in my part of the matrix column indices  */
2191:     nz     = procsnz[0];
2192:     PetscMalloc(nz*sizeof(PetscInt),&ibuf);
2193:     mycols = ibuf;
2194:     if (size == 1)  nz -= extra_rows;
2195:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2196:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2198:     /* read in every ones (except the last) and ship off */
2199:     for (i=1; i<size-1; i++) {
2200:       nz   = procsnz[i];
2201:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2202:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2203:     }
2204:     /* read in the stuff for the last proc */
2205:     if (size != 1) {
2206:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2207:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2208:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2209:       MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
2210:     }
2211:     PetscFree(cols);
2212:   } else {  /* procs[i], i>0 */
2213:     /* determine buffer space needed for message */
2214:     nz = 0;
2215:     for (i=0; i<m; i++) {
2216:       nz += locrowlens[i];
2217:     }
2218:     PetscMalloc(nz*sizeof(PetscInt),&ibuf);
2219:     mycols = ibuf;
2220:     /* receive message of column indices*/
2221:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2222:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2223:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2224:   }

2226:   /* loop over local rows, determining number of off diagonal entries */
2227:   PetscMalloc(2*(rend-rstart+1)*sizeof(PetscInt),&dlens);
2228:   odlens   = dlens + (rend-rstart);
2229:   PetscMalloc(3*Mbs*sizeof(PetscInt),&mask);
2230:   PetscMemzero(mask,3*Mbs*sizeof(PetscInt));
2231:   masked1  = mask    + Mbs;
2232:   masked2  = masked1 + Mbs;
2233:   rowcount = 0; nzcount = 0;
2234:   for (i=0; i<mbs; i++) {
2235:     dcount  = 0;
2236:     odcount = 0;
2237:     for (j=0; j<bs; j++) {
2238:       kmax = locrowlens[rowcount];
2239:       for (k=0; k<kmax; k++) {
2240:         tmp = mycols[nzcount++]/bs; /* block col. index */
2241:         if (!mask[tmp]) {
2242:           mask[tmp] = 1;
2243:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp; /* entry in off-diag portion */
2244:           else masked1[dcount++] = tmp; /* entry in diag portion */
2245:         }
2246:       }
2247:       rowcount++;
2248:     }
2249: 
2250:     dlens[i]  = dcount;  /* d_nzz[i] */
2251:     odlens[i] = odcount; /* o_nzz[i] */

2253:     /* zero out the mask elements we set */
2254:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2255:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2256:   }
2257: 
2258:   /* create our matrix */
2259:   MatCreate(comm,&A);
2260:   MatSetSizes(A,m,m,PETSC_DETERMINE,PETSC_DETERMINE);
2261:   MatSetType(A,type);
2262:   MatMPISBAIJSetPreallocation(A,bs,0,dlens,0,odlens);
2263: 
2264:   if (!rank) {
2265:     PetscMalloc(maxnz*sizeof(PetscScalar),&buf);
2266:     /* read in my part of the matrix numerical values  */
2267:     nz = procsnz[0];
2268:     vals = buf;
2269:     mycols = ibuf;
2270:     if (size == 1)  nz -= extra_rows;
2271:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2272:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2274:     /* insert into matrix */
2275:     jj      = rstart*bs;
2276:     for (i=0; i<m; i++) {
2277:       MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2278:       mycols += locrowlens[i];
2279:       vals   += locrowlens[i];
2280:       jj++;
2281:     }

2283:     /* read in other processors (except the last one) and ship out */
2284:     for (i=1; i<size-1; i++) {
2285:       nz   = procsnz[i];
2286:       vals = buf;
2287:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2288:       MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)A)->tag,comm);
2289:     }
2290:     /* the last proc */
2291:     if (size != 1){
2292:       nz   = procsnz[i] - extra_rows;
2293:       vals = buf;
2294:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2295:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2296:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,((PetscObject)A)->tag,comm);
2297:     }
2298:     PetscFree(procsnz);

2300:   } else {
2301:     /* receive numeric values */
2302:     PetscMalloc(nz*sizeof(PetscScalar),&buf);

2304:     /* receive message of values*/
2305:     vals   = buf;
2306:     mycols = ibuf;
2307:     MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)A)->tag,comm,&status);
2308:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2309:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2311:     /* insert into matrix */
2312:     jj      = rstart*bs;
2313:     for (i=0; i<m; i++) {
2314:       MatSetValues_MPISBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2315:       mycols += locrowlens[i];
2316:       vals   += locrowlens[i];
2317:       jj++;
2318:     }
2319:   }

2321:   PetscFree(locrowlens);
2322:   PetscFree(buf);
2323:   PetscFree(ibuf);
2324:   PetscFree(rowners);
2325:   PetscFree(dlens);
2326:   PetscFree(mask);
2327:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2328:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2329:   *newmat = A;
2330:   return(0);
2331: }

2335: /*XXXXX@
2336:    MatMPISBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2338:    Input Parameters:
2339: .  mat  - the matrix
2340: .  fact - factor

2342:    Collective on Mat

2344:    Level: advanced

2346:   Notes:
2347:    This can also be set by the command line option: -mat_use_hash_table fact

2349: .keywords: matrix, hashtable, factor, HT

2351: .seealso: MatSetOption()
2352: @XXXXX*/


2357: PetscErrorCode MatGetRowMaxAbs_MPISBAIJ(Mat A,Vec v,PetscInt idx[])
2358: {
2359:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
2360:   Mat_SeqBAIJ    *b = (Mat_SeqBAIJ*)(a->B)->data;
2361:   PetscReal      atmp;
2362:   PetscReal      *work,*svalues,*rvalues;
2364:   PetscInt       i,bs,mbs,*bi,*bj,brow,j,ncols,krow,kcol,col,row,Mbs,bcol;
2365:   PetscMPIInt    rank,size;
2366:   PetscInt       *rowners_bs,dest,count,source;
2367:   PetscScalar    *va;
2368:   MatScalar      *ba;
2369:   MPI_Status     stat;

2372:   if (idx) SETERRQ(PETSC_ERR_SUP,"Send email to petsc-maint@mcs.anl.gov");
2373:   MatGetRowMaxAbs(a->A,v,PETSC_NULL);
2374:   VecGetArray(v,&va);

2376:   MPI_Comm_size(((PetscObject)A)->comm,&size);
2377:   MPI_Comm_rank(((PetscObject)A)->comm,&rank);

2379:   bs   = A->rmap->bs;
2380:   mbs  = a->mbs;
2381:   Mbs  = a->Mbs;
2382:   ba   = b->a;
2383:   bi   = b->i;
2384:   bj   = b->j;

2386:   /* find ownerships */
2387:   rowners_bs = A->rmap->range;

2389:   /* each proc creates an array to be distributed */
2390:   PetscMalloc(bs*Mbs*sizeof(PetscReal),&work);
2391:   PetscMemzero(work,bs*Mbs*sizeof(PetscReal));

2393:   /* row_max for B */
2394:   if (rank != size-1){
2395:     for (i=0; i<mbs; i++) {
2396:       ncols = bi[1] - bi[0]; bi++;
2397:       brow  = bs*i;
2398:       for (j=0; j<ncols; j++){
2399:         bcol = bs*(*bj);
2400:         for (kcol=0; kcol<bs; kcol++){
2401:           col = bcol + kcol;                 /* local col index */
2402:           col += rowners_bs[rank+1];      /* global col index */
2403:           for (krow=0; krow<bs; krow++){
2404:             atmp = PetscAbsScalar(*ba); ba++;
2405:             row = brow + krow;    /* local row index */
2406:             if (PetscRealPart(va[row]) < atmp) va[row] = atmp;
2407:             if (work[col] < atmp) work[col] = atmp;
2408:           }
2409:         }
2410:         bj++;
2411:       }
2412:     }

2414:     /* send values to its owners */
2415:     for (dest=rank+1; dest<size; dest++){
2416:       svalues = work + rowners_bs[dest];
2417:       count   = rowners_bs[dest+1]-rowners_bs[dest];
2418:       MPI_Send(svalues,count,MPIU_REAL,dest,rank,((PetscObject)A)->comm);
2419:     }
2420:   }
2421: 
2422:   /* receive values */
2423:   if (rank){
2424:     rvalues = work;
2425:     count   = rowners_bs[rank+1]-rowners_bs[rank];
2426:     for (source=0; source<rank; source++){
2427:       MPI_Recv(rvalues,count,MPIU_REAL,MPI_ANY_SOURCE,MPI_ANY_TAG,((PetscObject)A)->comm,&stat);
2428:       /* process values */
2429:       for (i=0; i<count; i++){
2430:         if (PetscRealPart(va[i]) < rvalues[i]) va[i] = rvalues[i];
2431:       }
2432:     }
2433:   }

2435:   VecRestoreArray(v,&va);
2436:   PetscFree(work);
2437:   return(0);
2438: }

2442: PetscErrorCode MatRelax_MPISBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
2443: {
2444:   Mat_MPISBAIJ   *mat = (Mat_MPISBAIJ*)matin->data;
2446:   PetscInt       mbs=mat->mbs,bs=matin->rmap->bs;
2447:   PetscScalar    *x,*b,*ptr,zero=0.0;
2448:   Vec            bb1;
2449: 
2451:   if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
2452:   if (bs > 1)
2453:     SETERRQ(PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");

2455:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
2456:     if ( flag & SOR_ZERO_INITIAL_GUESS ) {
2457:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
2458:       its--;
2459:     }

2461:     VecDuplicate(bb,&bb1);
2462:     while (its--){
2463: 
2464:       /* lower triangular part: slvec0b = - B^T*xx */
2465:       (*mat->B->ops->multtranspose)(mat->B,xx,mat->slvec0b);
2466: 
2467:       /* copy xx into slvec0a */
2468:       VecGetArray(mat->slvec0,&ptr);
2469:       VecGetArray(xx,&x);
2470:       PetscMemcpy(ptr,x,bs*mbs*sizeof(MatScalar));
2471:       VecRestoreArray(mat->slvec0,&ptr);

2473:       VecScale(mat->slvec0,-1.0);

2475:       /* copy bb into slvec1a */
2476:       VecGetArray(mat->slvec1,&ptr);
2477:       VecGetArray(bb,&b);
2478:       PetscMemcpy(ptr,b,bs*mbs*sizeof(MatScalar));
2479:       VecRestoreArray(mat->slvec1,&ptr);

2481:       /* set slvec1b = 0 */
2482:       VecSet(mat->slvec1b,zero);

2484:       VecScatterBegin(mat->sMvctx,mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD);
2485:       VecRestoreArray(xx,&x);
2486:       VecRestoreArray(bb,&b);
2487:       VecScatterEnd(mat->sMvctx,mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD);

2489:       /* upper triangular part: bb1 = bb1 - B*x */
2490:       (*mat->B->ops->multadd)(mat->B,mat->slvec1b,mat->slvec1a,bb1);
2491: 
2492:       /* local diagonal sweep */
2493:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
2494:     }
2495:     VecDestroy(bb1);
2496:   } else {
2497:     SETERRQ(PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2498:   }
2499:   return(0);
2500: }

2504: PetscErrorCode MatRelax_MPISBAIJ_2comm(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
2505: {
2506:   Mat_MPISBAIJ   *mat = (Mat_MPISBAIJ*)matin->data;
2508:   Vec            lvec1,bb1;
2509: 
2511:   if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
2512:   if (matin->rmap->bs > 1)
2513:     SETERRQ(PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");

2515:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
2516:     if ( flag & SOR_ZERO_INITIAL_GUESS ) {
2517:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
2518:       its--;
2519:     }

2521:     VecDuplicate(mat->lvec,&lvec1);
2522:     VecDuplicate(bb,&bb1);
2523:     while (its--){
2524:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2525: 
2526:       /* lower diagonal part: bb1 = bb - B^T*xx */
2527:       (*mat->B->ops->multtranspose)(mat->B,xx,lvec1);
2528:       VecScale(lvec1,-1.0);

2530:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2531:       VecCopy(bb,bb1);
2532:       VecScatterBegin(mat->Mvctx,lvec1,bb1,ADD_VALUES,SCATTER_REVERSE);

2534:       /* upper diagonal part: bb1 = bb1 - B*x */
2535:       VecScale(mat->lvec,-1.0);
2536:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb1,bb1);

2538:       VecScatterEnd(mat->Mvctx,lvec1,bb1,ADD_VALUES,SCATTER_REVERSE);
2539: 
2540:       /* diagonal sweep */
2541:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
2542:     }
2543:     VecDestroy(lvec1);
2544:     VecDestroy(bb1);
2545:   } else {
2546:     SETERRQ(PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2547:   }
2548:   return(0);
2549: }