Actual source code: matmatmult.c
petsc-3.7.1 2016-05-15
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      2: /*
      3:   Defines matrix-matrix product routines for pairs of SeqAIJ matrices
      4:           C = A * B
      5: */

      7: #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/
      8: #include <../src/mat/utils/freespace.h>
      9: #include <../src/mat/utils/petscheap.h>
     10: #include <petscbt.h>
     11: #include <petsc/private/isimpl.h>
     12: #include <../src/mat/impls/dense/seq/dense.h>

     14: static PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat,Mat,PetscReal,Mat*);

     18: PETSC_INTERN PetscErrorCode MatMatMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
     19: {
     21:   const char     *algTypes[6] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed"};
     22:   PetscInt       alg=0; /* set default algorithm */

     25:   if (scall == MAT_INITIAL_MATRIX) {
     26:     PetscObjectOptionsBegin((PetscObject)A);
     27:     PetscOptionsEList("-matmatmult_via","Algorithmic approach","MatMatMult",algTypes,6,algTypes[0],&alg,NULL);
     28:     PetscOptionsEnd();
     29:     PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
     30:     switch (alg) {
     31:     case 1:
     32:       MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(A,B,fill,C);
     33:       break;
     34:     case 2:
     35:       MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(A,B,fill,C);
     36:       break;
     37:     case 3:
     38:       MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(A,B,fill,C);
     39:       break;
     40:     case 4:
     41:       MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(A,B,fill,C);
     42:       break;
     43:     case 5:
     44:       MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(A,B,fill,C);
     45:       break;
     46:     default:
     47:       MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);
     48:      break;
     49:     }
     50:     PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
     51:   }

     53:   PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
     54:   (*(*C)->ops->matmultnumeric)(A,B,*C);
     55:   PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
     56:   return(0);
     57: }

     61: static PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat A,Mat B,PetscReal fill,Mat *C)
     62: {
     63:   PetscErrorCode     ierr;
     64:   Mat_SeqAIJ         *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
     65:   PetscInt           *ai=a->i,*bi=b->i,*ci,*cj;
     66:   PetscInt           am =A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
     67:   PetscReal          afill;
     68:   PetscInt           i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax;
     69:   PetscTable         ta;
     70:   PetscBT            lnkbt;
     71:   PetscFreeSpaceList free_space=NULL,current_space=NULL;

     74:   /* Get ci and cj */
     75:   /*---------------*/
     76:   /* Allocate ci array, arrays for fill computation and */
     77:   /* free space for accumulating nonzero column info */
     78:   PetscMalloc1(am+2,&ci);
     79:   ci[0] = 0;

     81:   /* create and initialize a linked list */
     82:   PetscTableCreate(bn,bn,&ta);
     83:   MatRowMergeMax_SeqAIJ(b,bm,ta);
     84:   PetscTableGetCount(ta,&Crmax);
     85:   PetscTableDestroy(&ta);

     87:   PetscLLCondensedCreate(Crmax,bn,&lnk,&lnkbt);

     89:   /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
     90:   PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);

     92:   current_space = free_space;

     94:   /* Determine ci and cj */
     95:   for (i=0; i<am; i++) {
     96:     anzi = ai[i+1] - ai[i];
     97:     aj   = a->j + ai[i];
     98:     for (j=0; j<anzi; j++) {
     99:       brow = aj[j];
    100:       bnzj = bi[brow+1] - bi[brow];
    101:       bj   = b->j + bi[brow];
    102:       /* add non-zero cols of B into the sorted linked list lnk */
    103:       PetscLLCondensedAddSorted(bnzj,bj,lnk,lnkbt);
    104:     }
    105:     cnzi = lnk[0];

    107:     /* If free space is not available, make more free space */
    108:     /* Double the amount of total space in the list */
    109:     if (current_space->local_remaining<cnzi) {
    110:       PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),&current_space);
    111:       ndouble++;
    112:     }

    114:     /* Copy data into free space, then initialize lnk */
    115:     PetscLLCondensedClean(bn,cnzi,current_space->array,lnk,lnkbt);

    117:     current_space->array           += cnzi;
    118:     current_space->local_used      += cnzi;
    119:     current_space->local_remaining -= cnzi;

    121:     ci[i+1] = ci[i] + cnzi;
    122:   }

    124:   /* Column indices are in the list of free space */
    125:   /* Allocate space for cj, initialize cj, and */
    126:   /* destroy list of free space and other temporary array(s) */
    127:   PetscMalloc1(ci[am]+1,&cj);
    128:   PetscFreeSpaceContiguous(&free_space,cj);
    129:   PetscLLCondensedDestroy(lnk,lnkbt);

    131:   /* put together the new symbolic matrix */
    132:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);
    133:   MatSetBlockSizesFromMats(*C,A,B);

    135:   /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
    136:   /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
    137:   c                         = (Mat_SeqAIJ*)((*C)->data);
    138:   c->free_a                 = PETSC_FALSE;
    139:   c->free_ij                = PETSC_TRUE;
    140:   c->nonew                  = 0;
    141:   (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; /* fast, needs non-scalable O(bn) array 'abdense' */

    143:   /* set MatInfo */
    144:   afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
    145:   if (afill < 1.0) afill = 1.0;
    146:   c->maxnz                     = ci[am];
    147:   c->nz                        = ci[am];
    148:   (*C)->info.mallocs           = ndouble;
    149:   (*C)->info.fill_ratio_given  = fill;
    150:   (*C)->info.fill_ratio_needed = afill;

    152: #if defined(PETSC_USE_INFO)
    153:   if (ci[am]) {
    154:     PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);
    155:     PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
    156:   } else {
    157:     PetscInfo((*C),"Empty matrix product\n");
    158:   }
    159: #endif
    160:   return(0);
    161: }

    165: PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)
    166: {
    168:   PetscLogDouble flops=0.0;
    169:   Mat_SeqAIJ     *a   = (Mat_SeqAIJ*)A->data;
    170:   Mat_SeqAIJ     *b   = (Mat_SeqAIJ*)B->data;
    171:   Mat_SeqAIJ     *c   = (Mat_SeqAIJ*)C->data;
    172:   PetscInt       *ai  =a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j;
    173:   PetscInt       am   =A->rmap->n,cm=C->rmap->n;
    174:   PetscInt       i,j,k,anzi,bnzi,cnzi,brow;
    175:   PetscScalar    *aa=a->a,*ba=b->a,*baj,*ca,valtmp;
    176:   PetscScalar    *ab_dense;

    179:   if (!c->a) { /* first call of MatMatMultNumeric_SeqAIJ_SeqAIJ, allocate ca and matmult_abdense */
    180:     PetscMalloc1(ci[cm]+1,&ca);
    181:     c->a      = ca;
    182:     c->free_a = PETSC_TRUE;
    183:   } else {
    184:     ca        = c->a;
    185:   }
    186:   if (!c->matmult_abdense) {
    187:     PetscCalloc1(B->cmap->N,&ab_dense);
    188:     c->matmult_abdense = ab_dense;
    189:   } else {
    190:     ab_dense = c->matmult_abdense;
    191:   }

    193:   /* clean old values in C */
    194:   PetscMemzero(ca,ci[cm]*sizeof(MatScalar));
    195:   /* Traverse A row-wise. */
    196:   /* Build the ith row in C by summing over nonzero columns in A, */
    197:   /* the rows of B corresponding to nonzeros of A. */
    198:   for (i=0; i<am; i++) {
    199:     anzi = ai[i+1] - ai[i];
    200:     for (j=0; j<anzi; j++) {
    201:       brow = aj[j];
    202:       bnzi = bi[brow+1] - bi[brow];
    203:       bjj  = bj + bi[brow];
    204:       baj  = ba + bi[brow];
    205:       /* perform dense axpy */
    206:       valtmp = aa[j];
    207:       for (k=0; k<bnzi; k++) {
    208:         ab_dense[bjj[k]] += valtmp*baj[k];
    209:       }
    210:       flops += 2*bnzi;
    211:     }
    212:     aj += anzi; aa += anzi;

    214:     cnzi = ci[i+1] - ci[i];
    215:     for (k=0; k<cnzi; k++) {
    216:       ca[k]          += ab_dense[cj[k]];
    217:       ab_dense[cj[k]] = 0.0; /* zero ab_dense */
    218:     }
    219:     flops += cnzi;
    220:     cj    += cnzi; ca += cnzi;
    221:   }
    222:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    223:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
    224:   PetscLogFlops(flops);
    225:   return(0);
    226: }

    230: PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,Mat C)
    231: {
    233:   PetscLogDouble flops=0.0;
    234:   Mat_SeqAIJ     *a   = (Mat_SeqAIJ*)A->data;
    235:   Mat_SeqAIJ     *b   = (Mat_SeqAIJ*)B->data;
    236:   Mat_SeqAIJ     *c   = (Mat_SeqAIJ*)C->data;
    237:   PetscInt       *ai  = a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j;
    238:   PetscInt       am   = A->rmap->N,cm=C->rmap->N;
    239:   PetscInt       i,j,k,anzi,bnzi,cnzi,brow;
    240:   PetscScalar    *aa=a->a,*ba=b->a,*baj,*ca=c->a,valtmp;
    241:   PetscInt       nextb;

    244:   /* clean old values in C */
    245:   PetscMemzero(ca,ci[cm]*sizeof(MatScalar));
    246:   /* Traverse A row-wise. */
    247:   /* Build the ith row in C by summing over nonzero columns in A, */
    248:   /* the rows of B corresponding to nonzeros of A. */
    249:   for (i=0; i<am; i++) {
    250:     anzi = ai[i+1] - ai[i];
    251:     cnzi = ci[i+1] - ci[i];
    252:     for (j=0; j<anzi; j++) {
    253:       brow = aj[j];
    254:       bnzi = bi[brow+1] - bi[brow];
    255:       bjj  = bj + bi[brow];
    256:       baj  = ba + bi[brow];
    257:       /* perform sparse axpy */
    258:       valtmp = aa[j];
    259:       nextb  = 0;
    260:       for (k=0; nextb<bnzi; k++) {
    261:         if (cj[k] == bjj[nextb]) { /* ccol == bcol */
    262:           ca[k] += valtmp*baj[nextb++];
    263:         }
    264:       }
    265:       flops += 2*bnzi;
    266:     }
    267:     aj += anzi; aa += anzi;
    268:     cj += cnzi; ca += cnzi;
    269:   }

    271:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    272:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
    273:   PetscLogFlops(flops);
    274:   return(0);
    275: }

    279: PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat A,Mat B,PetscReal fill,Mat *C)
    280: {
    281:   PetscErrorCode     ierr;
    282:   Mat_SeqAIJ         *a  = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
    283:   PetscInt           *ai = a->i,*bi=b->i,*ci,*cj;
    284:   PetscInt           am  = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
    285:   MatScalar          *ca;
    286:   PetscReal          afill;
    287:   PetscInt           i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax;
    288:   PetscTable         ta;
    289:   PetscFreeSpaceList free_space=NULL,current_space=NULL;

    292:   /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_fast() */
    293:   /*-----------------------------------------------------------------------------------------*/
    294:   /* Allocate arrays for fill computation and free space for accumulating nonzero column */
    295:   PetscMalloc1(am+2,&ci);
    296:   ci[0] = 0;

    298:   /* create and initialize a linked list */
    299:   PetscTableCreate(bn,bn,&ta);
    300:   MatRowMergeMax_SeqAIJ(b,bm,ta);
    301:   PetscTableGetCount(ta,&Crmax);
    302:   PetscTableDestroy(&ta);

    304:   PetscLLCondensedCreate_fast(Crmax,&lnk);

    306:   /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
    307:   PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);
    308:   current_space = free_space;

    310:   /* Determine ci and cj */
    311:   for (i=0; i<am; i++) {
    312:     anzi = ai[i+1] - ai[i];
    313:     aj   = a->j + ai[i];
    314:     for (j=0; j<anzi; j++) {
    315:       brow = aj[j];
    316:       bnzj = bi[brow+1] - bi[brow];
    317:       bj   = b->j + bi[brow];
    318:       /* add non-zero cols of B into the sorted linked list lnk */
    319:       PetscLLCondensedAddSorted_fast(bnzj,bj,lnk);
    320:     }
    321:     cnzi = lnk[1];

    323:     /* If free space is not available, make more free space */
    324:     /* Double the amount of total space in the list */
    325:     if (current_space->local_remaining<cnzi) {
    326:       PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),&current_space);
    327:       ndouble++;
    328:     }

    330:     /* Copy data into free space, then initialize lnk */
    331:     PetscLLCondensedClean_fast(cnzi,current_space->array,lnk);

    333:     current_space->array           += cnzi;
    334:     current_space->local_used      += cnzi;
    335:     current_space->local_remaining -= cnzi;

    337:     ci[i+1] = ci[i] + cnzi;
    338:   }

    340:   /* Column indices are in the list of free space */
    341:   /* Allocate space for cj, initialize cj, and */
    342:   /* destroy list of free space and other temporary array(s) */
    343:   PetscMalloc1(ci[am]+1,&cj);
    344:   PetscFreeSpaceContiguous(&free_space,cj);
    345:   PetscLLCondensedDestroy_fast(lnk);

    347:   /* Allocate space for ca */
    348:   PetscMalloc1(ci[am]+1,&ca);
    349:   PetscMemzero(ca,(ci[am]+1)*sizeof(MatScalar));

    351:   /* put together the new symbolic matrix */
    352:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,C);
    353:   MatSetBlockSizesFromMats(*C,A,B);

    355:   /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
    356:   /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
    357:   c          = (Mat_SeqAIJ*)((*C)->data);
    358:   c->free_a  = PETSC_TRUE;
    359:   c->free_ij = PETSC_TRUE;
    360:   c->nonew   = 0;

    362:   (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable; /* slower, less memory */

    364:   /* set MatInfo */
    365:   afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
    366:   if (afill < 1.0) afill = 1.0;
    367:   c->maxnz                     = ci[am];
    368:   c->nz                        = ci[am];
    369:   (*C)->info.mallocs           = ndouble;
    370:   (*C)->info.fill_ratio_given  = fill;
    371:   (*C)->info.fill_ratio_needed = afill;

    373: #if defined(PETSC_USE_INFO)
    374:   if (ci[am]) {
    375:     PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);
    376:     PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
    377:   } else {
    378:     PetscInfo((*C),"Empty matrix product\n");
    379:   }
    380: #endif
    381:   return(0);
    382: }

    387: PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,PetscReal fill,Mat *C)
    388: {
    389:   PetscErrorCode     ierr;
    390:   Mat_SeqAIJ         *a  = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
    391:   PetscInt           *ai = a->i,*bi=b->i,*ci,*cj;
    392:   PetscInt           am  = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
    393:   MatScalar          *ca;
    394:   PetscReal          afill;
    395:   PetscInt           i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax;
    396:   PetscTable         ta;
    397:   PetscFreeSpaceList free_space=NULL,current_space=NULL;

    400:   /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_Scalalbe() */
    401:   /*---------------------------------------------------------------------------------------------*/
    402:   /* Allocate arrays for fill computation and free space for accumulating nonzero column */
    403:   PetscMalloc1(am+2,&ci);
    404:   ci[0] = 0;

    406:   /* create and initialize a linked list */
    407:   PetscTableCreate(bn,bn,&ta);
    408:   MatRowMergeMax_SeqAIJ(b,bm,ta);
    409:   PetscTableGetCount(ta,&Crmax);
    410:   PetscTableDestroy(&ta);
    411:   PetscLLCondensedCreate_Scalable(Crmax,&lnk);

    413:   /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
    414:   PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);
    415:   current_space = free_space;

    417:   /* Determine ci and cj */
    418:   for (i=0; i<am; i++) {
    419:     anzi = ai[i+1] - ai[i];
    420:     aj   = a->j + ai[i];
    421:     for (j=0; j<anzi; j++) {
    422:       brow = aj[j];
    423:       bnzj = bi[brow+1] - bi[brow];
    424:       bj   = b->j + bi[brow];
    425:       /* add non-zero cols of B into the sorted linked list lnk */
    426:       PetscLLCondensedAddSorted_Scalable(bnzj,bj,lnk);
    427:     }
    428:     cnzi = lnk[0];

    430:     /* If free space is not available, make more free space */
    431:     /* Double the amount of total space in the list */
    432:     if (current_space->local_remaining<cnzi) {
    433:       PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),&current_space);
    434:       ndouble++;
    435:     }

    437:     /* Copy data into free space, then initialize lnk */
    438:     PetscLLCondensedClean_Scalable(cnzi,current_space->array,lnk);

    440:     current_space->array           += cnzi;
    441:     current_space->local_used      += cnzi;
    442:     current_space->local_remaining -= cnzi;

    444:     ci[i+1] = ci[i] + cnzi;
    445:   }

    447:   /* Column indices are in the list of free space */
    448:   /* Allocate space for cj, initialize cj, and */
    449:   /* destroy list of free space and other temporary array(s) */
    450:   PetscMalloc1(ci[am]+1,&cj);
    451:   PetscFreeSpaceContiguous(&free_space,cj);
    452:   PetscLLCondensedDestroy_Scalable(lnk);

    454:   /* Allocate space for ca */
    455:   /*-----------------------*/
    456:   PetscMalloc1(ci[am]+1,&ca);
    457:   PetscMemzero(ca,(ci[am]+1)*sizeof(MatScalar));

    459:   /* put together the new symbolic matrix */
    460:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,C);
    461:   MatSetBlockSizesFromMats(*C,A,B);

    463:   /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
    464:   /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
    465:   c          = (Mat_SeqAIJ*)((*C)->data);
    466:   c->free_a  = PETSC_TRUE;
    467:   c->free_ij = PETSC_TRUE;
    468:   c->nonew   = 0;

    470:   (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable; /* slower, less memory */

    472:   /* set MatInfo */
    473:   afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
    474:   if (afill < 1.0) afill = 1.0;
    475:   c->maxnz                     = ci[am];
    476:   c->nz                        = ci[am];
    477:   (*C)->info.mallocs           = ndouble;
    478:   (*C)->info.fill_ratio_given  = fill;
    479:   (*C)->info.fill_ratio_needed = afill;

    481: #if defined(PETSC_USE_INFO)
    482:   if (ci[am]) {
    483:     PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);
    484:     PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
    485:   } else {
    486:     PetscInfo((*C),"Empty matrix product\n");
    487:   }
    488: #endif
    489:   return(0);
    490: }

    494: PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat A,Mat B,PetscReal fill,Mat *C)
    495: {
    496:   PetscErrorCode     ierr;
    497:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
    498:   const PetscInt     *ai=a->i,*bi=b->i,*aj=a->j,*bj=b->j;
    499:   PetscInt           *ci,*cj,*bb;
    500:   PetscInt           am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
    501:   PetscReal          afill;
    502:   PetscInt           i,j,col,ndouble = 0;
    503:   PetscFreeSpaceList free_space=NULL,current_space=NULL;
    504:   PetscHeap          h;

    507:   /* Get ci and cj - by merging sorted rows using a heap */
    508:   /*---------------------------------------------------------------------------------------------*/
    509:   /* Allocate arrays for fill computation and free space for accumulating nonzero column */
    510:   PetscMalloc1(am+2,&ci);
    511:   ci[0] = 0;

    513:   /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
    514:   PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);
    515:   current_space = free_space;

    517:   PetscHeapCreate(a->rmax,&h);
    518:   PetscMalloc1(a->rmax,&bb);

    520:   /* Determine ci and cj */
    521:   for (i=0; i<am; i++) {
    522:     const PetscInt anzi  = ai[i+1] - ai[i]; /* number of nonzeros in this row of A, this is the number of rows of B that we merge */
    523:     const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */
    524:     ci[i+1] = ci[i];
    525:     /* Populate the min heap */
    526:     for (j=0; j<anzi; j++) {
    527:       bb[j] = bi[acol[j]];         /* bb points at the start of the row */
    528:       if (bb[j] < bi[acol[j]+1]) { /* Add if row is nonempty */
    529:         PetscHeapAdd(h,j,bj[bb[j]++]);
    530:       }
    531:     }
    532:     /* Pick off the min element, adding it to free space */
    533:     PetscHeapPop(h,&j,&col);
    534:     while (j >= 0) {
    535:       if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */
    536:         PetscFreeSpaceGet(PetscMin(PetscIntMultTruncate(2,current_space->total_array_size),16 << 20),&current_space);
    537:         ndouble++;
    538:       }
    539:       *(current_space->array++) = col;
    540:       current_space->local_used++;
    541:       current_space->local_remaining--;
    542:       ci[i+1]++;

    544:       /* stash if anything else remains in this row of B */
    545:       if (bb[j] < bi[acol[j]+1]) {PetscHeapStash(h,j,bj[bb[j]++]);}
    546:       while (1) {               /* pop and stash any other rows of B that also had an entry in this column */
    547:         PetscInt j2,col2;
    548:         PetscHeapPeek(h,&j2,&col2);
    549:         if (col2 != col) break;
    550:         PetscHeapPop(h,&j2,&col2);
    551:         if (bb[j2] < bi[acol[j2]+1]) {PetscHeapStash(h,j2,bj[bb[j2]++]);}
    552:       }
    553:       /* Put any stashed elements back into the min heap */
    554:       PetscHeapUnstash(h);
    555:       PetscHeapPop(h,&j,&col);
    556:     }
    557:   }
    558:   PetscFree(bb);
    559:   PetscHeapDestroy(&h);

    561:   /* Column indices are in the list of free space */
    562:   /* Allocate space for cj, initialize cj, and */
    563:   /* destroy list of free space and other temporary array(s) */
    564:   PetscMalloc1(ci[am],&cj);
    565:   PetscFreeSpaceContiguous(&free_space,cj);

    567:   /* put together the new symbolic matrix */
    568:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);
    569:   MatSetBlockSizesFromMats(*C,A,B);

    571:   /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
    572:   /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
    573:   c          = (Mat_SeqAIJ*)((*C)->data);
    574:   c->free_a  = PETSC_TRUE;
    575:   c->free_ij = PETSC_TRUE;
    576:   c->nonew   = 0;

    578:   (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ;

    580:   /* set MatInfo */
    581:   afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
    582:   if (afill < 1.0) afill = 1.0;
    583:   c->maxnz                     = ci[am];
    584:   c->nz                        = ci[am];
    585:   (*C)->info.mallocs           = ndouble;
    586:   (*C)->info.fill_ratio_given  = fill;
    587:   (*C)->info.fill_ratio_needed = afill;

    589: #if defined(PETSC_USE_INFO)
    590:   if (ci[am]) {
    591:     PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);
    592:     PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
    593:   } else {
    594:     PetscInfo((*C),"Empty matrix product\n");
    595:   }
    596: #endif
    597:   return(0);
    598: }

    602: PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat A,Mat B,PetscReal fill,Mat *C)
    603: {
    604:   PetscErrorCode     ierr;
    605:   Mat_SeqAIJ         *a  = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
    606:   const PetscInt     *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j;
    607:   PetscInt           *ci,*cj,*bb;
    608:   PetscInt           am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
    609:   PetscReal          afill;
    610:   PetscInt           i,j,col,ndouble = 0;
    611:   PetscFreeSpaceList free_space=NULL,current_space=NULL;
    612:   PetscHeap          h;
    613:   PetscBT            bt;

    616:   /* Get ci and cj - using a heap for the sorted rows, but use BT so that each index is only added once */
    617:   /*---------------------------------------------------------------------------------------------*/
    618:   /* Allocate arrays for fill computation and free space for accumulating nonzero column */
    619:   PetscMalloc1(am+2,&ci);
    620:   ci[0] = 0;

    622:   /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
    623:   PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);

    625:   current_space = free_space;

    627:   PetscHeapCreate(a->rmax,&h);
    628:   PetscMalloc1(a->rmax,&bb);
    629:   PetscBTCreate(bn,&bt);

    631:   /* Determine ci and cj */
    632:   for (i=0; i<am; i++) {
    633:     const PetscInt anzi  = ai[i+1] - ai[i]; /* number of nonzeros in this row of A, this is the number of rows of B that we merge */
    634:     const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */
    635:     const PetscInt *fptr = current_space->array; /* Save beginning of the row so we can clear the BT later */
    636:     ci[i+1] = ci[i];
    637:     /* Populate the min heap */
    638:     for (j=0; j<anzi; j++) {
    639:       PetscInt brow = acol[j];
    640:       for (bb[j] = bi[brow]; bb[j] < bi[brow+1]; bb[j]++) {
    641:         PetscInt bcol = bj[bb[j]];
    642:         if (!PetscBTLookupSet(bt,bcol)) { /* new entry */
    643:           PetscHeapAdd(h,j,bcol);
    644:           bb[j]++;
    645:           break;
    646:         }
    647:       }
    648:     }
    649:     /* Pick off the min element, adding it to free space */
    650:     PetscHeapPop(h,&j,&col);
    651:     while (j >= 0) {
    652:       if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */
    653:         fptr = NULL;                      /* need PetscBTMemzero */
    654:         PetscFreeSpaceGet(PetscMin(PetscIntMultTruncate(2,current_space->total_array_size),16 << 20),&current_space);
    655:         ndouble++;
    656:       }
    657:       *(current_space->array++) = col;
    658:       current_space->local_used++;
    659:       current_space->local_remaining--;
    660:       ci[i+1]++;

    662:       /* stash if anything else remains in this row of B */
    663:       for (; bb[j] < bi[acol[j]+1]; bb[j]++) {
    664:         PetscInt bcol = bj[bb[j]];
    665:         if (!PetscBTLookupSet(bt,bcol)) { /* new entry */
    666:           PetscHeapAdd(h,j,bcol);
    667:           bb[j]++;
    668:           break;
    669:         }
    670:       }
    671:       PetscHeapPop(h,&j,&col);
    672:     }
    673:     if (fptr) {                 /* Clear the bits for this row */
    674:       for (; fptr<current_space->array; fptr++) {PetscBTClear(bt,*fptr);}
    675:     } else {                    /* We reallocated so we don't remember (easily) how to clear only the bits we changed */
    676:       PetscBTMemzero(bn,bt);
    677:     }
    678:   }
    679:   PetscFree(bb);
    680:   PetscHeapDestroy(&h);
    681:   PetscBTDestroy(&bt);

    683:   /* Column indices are in the list of free space */
    684:   /* Allocate space for cj, initialize cj, and */
    685:   /* destroy list of free space and other temporary array(s) */
    686:   PetscMalloc1(ci[am],&cj);
    687:   PetscFreeSpaceContiguous(&free_space,cj);

    689:   /* put together the new symbolic matrix */
    690:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);
    691:   MatSetBlockSizesFromMats(*C,A,B);

    693:   /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
    694:   /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
    695:   c          = (Mat_SeqAIJ*)((*C)->data);
    696:   c->free_a  = PETSC_TRUE;
    697:   c->free_ij = PETSC_TRUE;
    698:   c->nonew   = 0;

    700:   (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ;

    702:   /* set MatInfo */
    703:   afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
    704:   if (afill < 1.0) afill = 1.0;
    705:   c->maxnz                     = ci[am];
    706:   c->nz                        = ci[am];
    707:   (*C)->info.mallocs           = ndouble;
    708:   (*C)->info.fill_ratio_given  = fill;
    709:   (*C)->info.fill_ratio_needed = afill;

    711: #if defined(PETSC_USE_INFO)
    712:   if (ci[am]) {
    713:     PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);
    714:     PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
    715:   } else {
    716:     PetscInfo((*C),"Empty matrix product\n");
    717:   }
    718: #endif
    719:   return(0);
    720: }

    724: /* concatenate unique entries and then sort */
    725: PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
    726: {
    727:   PetscErrorCode     ierr;
    728:   Mat_SeqAIJ         *a  = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
    729:   const PetscInt     *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j;
    730:   PetscInt           *ci,*cj;
    731:   PetscInt           am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
    732:   PetscReal          afill;
    733:   PetscInt           i,j,ndouble = 0;
    734:   PetscSegBuffer     seg,segrow;
    735:   char               *seen;

    738:   PetscMalloc1(am+1,&ci);
    739:   ci[0] = 0;

    741:   /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
    742:   PetscSegBufferCreate(sizeof(PetscInt),(PetscInt)(fill*(ai[am]+bi[bm])),&seg);
    743:   PetscSegBufferCreate(sizeof(PetscInt),100,&segrow);
    744:   PetscMalloc1(bn,&seen);
    745:   PetscMemzero(seen,bn*sizeof(char));

    747:   /* Determine ci and cj */
    748:   for (i=0; i<am; i++) {
    749:     const PetscInt anzi  = ai[i+1] - ai[i]; /* number of nonzeros in this row of A, this is the number of rows of B that we merge */
    750:     const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */
    751:     PetscInt packlen = 0,*PETSC_RESTRICT crow;
    752:     /* Pack segrow */
    753:     for (j=0; j<anzi; j++) {
    754:       PetscInt brow = acol[j],bjstart = bi[brow],bjend = bi[brow+1],k;
    755:       for (k=bjstart; k<bjend; k++) {
    756:         PetscInt bcol = bj[k];
    757:         if (!seen[bcol]) { /* new entry */
    758:           PetscInt *PETSC_RESTRICT slot;
    759:           PetscSegBufferGetInts(segrow,1,&slot);
    760:           *slot = bcol;
    761:           seen[bcol] = 1;
    762:           packlen++;
    763:         }
    764:       }
    765:     }
    766:     PetscSegBufferGetInts(seg,packlen,&crow);
    767:     PetscSegBufferExtractTo(segrow,crow);
    768:     PetscSortInt(packlen,crow);
    769:     ci[i+1] = ci[i] + packlen;
    770:     for (j=0; j<packlen; j++) seen[crow[j]] = 0;
    771:   }
    772:   PetscSegBufferDestroy(&segrow);
    773:   PetscFree(seen);

    775:   /* Column indices are in the segmented buffer */
    776:   PetscSegBufferExtractAlloc(seg,&cj);
    777:   PetscSegBufferDestroy(&seg);

    779:   /* put together the new symbolic matrix */
    780:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);
    781:   MatSetBlockSizesFromMats(*C,A,B);

    783:   /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
    784:   /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
    785:   c          = (Mat_SeqAIJ*)((*C)->data);
    786:   c->free_a  = PETSC_TRUE;
    787:   c->free_ij = PETSC_TRUE;
    788:   c->nonew   = 0;

    790:   (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ;

    792:   /* set MatInfo */
    793:   afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
    794:   if (afill < 1.0) afill = 1.0;
    795:   c->maxnz                     = ci[am];
    796:   c->nz                        = ci[am];
    797:   (*C)->info.mallocs           = ndouble;
    798:   (*C)->info.fill_ratio_given  = fill;
    799:   (*C)->info.fill_ratio_needed = afill;

    801: #if defined(PETSC_USE_INFO)
    802:   if (ci[am]) {
    803:     PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);
    804:     PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
    805:   } else {
    806:     PetscInfo((*C),"Empty matrix product\n");
    807:   }
    808: #endif
    809:   return(0);
    810: }

    812: /* This routine is not used. Should be removed! */
    815: PetscErrorCode MatMatTransposeMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
    816: {

    820:   if (scall == MAT_INITIAL_MATRIX) {
    821:     PetscLogEventBegin(MAT_MatTransposeMultSymbolic,A,B,0,0);
    822:     MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);
    823:     PetscLogEventEnd(MAT_MatTransposeMultSymbolic,A,B,0,0);
    824:   }
    825:   PetscLogEventBegin(MAT_MatTransposeMultNumeric,A,B,0,0);
    826:   MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(A,B,*C);
    827:   PetscLogEventEnd(MAT_MatTransposeMultNumeric,A,B,0,0);
    828:   return(0);
    829: }

    833: PetscErrorCode MatDestroy_SeqAIJ_MatMatMultTrans(Mat A)
    834: {
    835:   PetscErrorCode      ierr;
    836:   Mat_SeqAIJ          *a=(Mat_SeqAIJ*)A->data;
    837:   Mat_MatMatTransMult *abt=a->abt;

    840:   (abt->destroy)(A);
    841:   MatTransposeColoringDestroy(&abt->matcoloring);
    842:   MatDestroy(&abt->Bt_den);
    843:   MatDestroy(&abt->ABt_den);
    844:   PetscFree(abt);
    845:   return(0);
    846: }

    850: PetscErrorCode MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
    851: {
    852:   PetscErrorCode      ierr;
    853:   Mat                 Bt;
    854:   PetscInt            *bti,*btj;
    855:   Mat_MatMatTransMult *abt;
    856:   Mat_SeqAIJ          *c;

    859:   /* create symbolic Bt */
    860:   MatGetSymbolicTranspose_SeqAIJ(B,&bti,&btj);
    861:   MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,B->cmap->n,B->rmap->n,bti,btj,NULL,&Bt);
    862:   MatSetBlockSizes(Bt,PetscAbs(A->cmap->bs),PetscAbs(B->cmap->bs));

    864:   /* get symbolic C=A*Bt */
    865:   MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,Bt,fill,C);

    867:   /* create a supporting struct for reuse intermidiate dense matrices with matcoloring */
    868:   PetscNew(&abt);
    869:   c      = (Mat_SeqAIJ*)(*C)->data;
    870:   c->abt = abt;

    872:   abt->usecoloring = PETSC_FALSE;
    873:   abt->destroy     = (*C)->ops->destroy;
    874:   (*C)->ops->destroy     = MatDestroy_SeqAIJ_MatMatMultTrans;

    876:   PetscOptionsGetBool(((PetscObject)A)->options,NULL,"-matmattransmult_color",&abt->usecoloring,NULL);
    877:   if (abt->usecoloring) {
    878:     /* Create MatTransposeColoring from symbolic C=A*B^T */
    879:     MatTransposeColoring matcoloring;
    880:     MatColoring          coloring;
    881:     ISColoring           iscoloring;
    882:     Mat                  Bt_dense,C_dense;
    883:     Mat_SeqAIJ           *c=(Mat_SeqAIJ*)(*C)->data;
    884:     /* inode causes memory problem, don't know why */
    885:     if (c->inode.use) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MAT_USE_INODES is not supported. Use '-mat_no_inode'");

    887:     MatColoringCreate(*C,&coloring);
    888:     MatColoringSetDistance(coloring,2);
    889:     MatColoringSetType(coloring,MATCOLORINGSL);
    890:     MatColoringSetFromOptions(coloring);
    891:     MatColoringApply(coloring,&iscoloring);
    892:     MatColoringDestroy(&coloring);
    893:     MatTransposeColoringCreate(*C,iscoloring,&matcoloring);

    895:     abt->matcoloring = matcoloring;

    897:     ISColoringDestroy(&iscoloring);

    899:     /* Create Bt_dense and C_dense = A*Bt_dense */
    900:     MatCreate(PETSC_COMM_SELF,&Bt_dense);
    901:     MatSetSizes(Bt_dense,A->cmap->n,matcoloring->ncolors,A->cmap->n,matcoloring->ncolors);
    902:     MatSetType(Bt_dense,MATSEQDENSE);
    903:     MatSeqDenseSetPreallocation(Bt_dense,NULL);

    905:     Bt_dense->assembled = PETSC_TRUE;
    906:     abt->Bt_den   = Bt_dense;

    908:     MatCreate(PETSC_COMM_SELF,&C_dense);
    909:     MatSetSizes(C_dense,A->rmap->n,matcoloring->ncolors,A->rmap->n,matcoloring->ncolors);
    910:     MatSetType(C_dense,MATSEQDENSE);
    911:     MatSeqDenseSetPreallocation(C_dense,NULL);

    913:     Bt_dense->assembled = PETSC_TRUE;
    914:     abt->ABt_den  = C_dense;

    916: #if defined(PETSC_USE_INFO)
    917:     {
    918:       Mat_SeqAIJ *c = (Mat_SeqAIJ*)(*C)->data;
    919:       PetscInfo7(*C,"Use coloring of C=A*B^T; B^T: %D %D, Bt_dense: %D,%D; Cnz %D / (cm*ncolors %D) = %g\n",B->cmap->n,B->rmap->n,Bt_dense->rmap->n,Bt_dense->cmap->n,c->nz,A->rmap->n*matcoloring->ncolors,(PetscReal)(c->nz)/(A->rmap->n*matcoloring->ncolors));
    920:     }
    921: #endif
    922:   }
    923:   /* clean up */
    924:   MatDestroy(&Bt);
    925:   MatRestoreSymbolicTranspose_SeqAIJ(B,&bti,&btj);
    926:   return(0);
    927: }

    931: PetscErrorCode MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)
    932: {
    933:   PetscErrorCode      ierr;
    934:   Mat_SeqAIJ          *a   =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data;
    935:   PetscInt            *ai  =a->i,*aj=a->j,*bi=b->i,*bj=b->j,anzi,bnzj,nexta,nextb,*acol,*bcol,brow;
    936:   PetscInt            cm   =C->rmap->n,*ci=c->i,*cj=c->j,i,j,cnzi,*ccol;
    937:   PetscLogDouble      flops=0.0;
    938:   MatScalar           *aa  =a->a,*aval,*ba=b->a,*bval,*ca,*cval;
    939:   Mat_MatMatTransMult *abt = c->abt;

    942:   /* clear old values in C */
    943:   if (!c->a) {
    944:     PetscMalloc1(ci[cm]+1,&ca);
    945:     c->a      = ca;
    946:     c->free_a = PETSC_TRUE;
    947:   } else {
    948:     ca =  c->a;
    949:   }
    950:   PetscMemzero(ca,ci[cm]*sizeof(MatScalar));

    952:   if (abt->usecoloring) {
    953:     MatTransposeColoring matcoloring = abt->matcoloring;
    954:     Mat                  Bt_dense,C_dense = abt->ABt_den;

    956:     /* Get Bt_dense by Apply MatTransposeColoring to B */
    957:     Bt_dense = abt->Bt_den;
    958:     MatTransColoringApplySpToDen(matcoloring,B,Bt_dense);

    960:     /* C_dense = A*Bt_dense */
    961:     MatMatMultNumeric_SeqAIJ_SeqDense(A,Bt_dense,C_dense);

    963:     /* Recover C from C_dense */
    964:     MatTransColoringApplyDenToSp(matcoloring,C_dense,C);
    965:     return(0);
    966:   }

    968:   for (i=0; i<cm; i++) {
    969:     anzi = ai[i+1] - ai[i];
    970:     acol = aj + ai[i];
    971:     aval = aa + ai[i];
    972:     cnzi = ci[i+1] - ci[i];
    973:     ccol = cj + ci[i];
    974:     cval = ca + ci[i];
    975:     for (j=0; j<cnzi; j++) {
    976:       brow = ccol[j];
    977:       bnzj = bi[brow+1] - bi[brow];
    978:       bcol = bj + bi[brow];
    979:       bval = ba + bi[brow];

    981:       /* perform sparse inner-product c(i,j)=A[i,:]*B[j,:]^T */
    982:       nexta = 0; nextb = 0;
    983:       while (nexta<anzi && nextb<bnzj) {
    984:         while (nexta < anzi && acol[nexta] < bcol[nextb]) nexta++;
    985:         if (nexta == anzi) break;
    986:         while (nextb < bnzj && acol[nexta] > bcol[nextb]) nextb++;
    987:         if (nextb == bnzj) break;
    988:         if (acol[nexta] == bcol[nextb]) {
    989:           cval[j] += aval[nexta]*bval[nextb];
    990:           nexta++; nextb++;
    991:           flops += 2;
    992:         }
    993:       }
    994:     }
    995:   }
    996:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    997:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
    998:   PetscLogFlops(flops);
    999:   return(0);
    1000: }

    1004: PetscErrorCode MatTransposeMatMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
    1005: {

    1009:   if (scall == MAT_INITIAL_MATRIX) {
    1010:     PetscLogEventBegin(MAT_TransposeMatMultSymbolic,A,B,0,0);
    1011:     MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);
    1012:     PetscLogEventEnd(MAT_TransposeMatMultSymbolic,A,B,0,0);
    1013:   }
    1014:   PetscLogEventBegin(MAT_TransposeMatMultNumeric,A,B,0,0);
    1015:   MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(A,B,*C);
    1016:   PetscLogEventEnd(MAT_TransposeMatMultNumeric,A,B,0,0);
    1017:   return(0);
    1018: }

    1022: PetscErrorCode MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
    1023: {
    1025:   Mat            At;
    1026:   PetscInt       *ati,*atj;

    1029:   /* create symbolic At */
    1030:   MatGetSymbolicTranspose_SeqAIJ(A,&ati,&atj);
    1031:   MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,A->cmap->n,A->rmap->n,ati,atj,NULL,&At);
    1032:   MatSetBlockSizes(At,PetscAbs(A->cmap->bs),PetscAbs(B->cmap->bs));

    1034:   /* get symbolic C=At*B */
    1035:   MatMatMultSymbolic_SeqAIJ_SeqAIJ(At,B,fill,C);

    1037:   /* clean up */
    1038:   MatDestroy(&At);
    1039:   MatRestoreSymbolicTranspose_SeqAIJ(A,&ati,&atj);
    1040:   return(0);
    1041: }

    1045: PetscErrorCode MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)
    1046: {
    1048:   Mat_SeqAIJ     *a   =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data;
    1049:   PetscInt       am   =A->rmap->n,anzi,*ai=a->i,*aj=a->j,*bi=b->i,*bj,bnzi,nextb;
    1050:   PetscInt       cm   =C->rmap->n,*ci=c->i,*cj=c->j,crow,*cjj,i,j,k;
    1051:   PetscLogDouble flops=0.0;
    1052:   MatScalar      *aa  =a->a,*ba,*ca,*caj;

    1055:   if (!c->a) {
    1056:     PetscMalloc1(ci[cm]+1,&ca);

    1058:     c->a      = ca;
    1059:     c->free_a = PETSC_TRUE;
    1060:   } else {
    1061:     ca = c->a;
    1062:   }
    1063:   /* clear old values in C */
    1064:   PetscMemzero(ca,ci[cm]*sizeof(MatScalar));

    1066:   /* compute A^T*B using outer product (A^T)[:,i]*B[i,:] */
    1067:   for (i=0; i<am; i++) {
    1068:     bj   = b->j + bi[i];
    1069:     ba   = b->a + bi[i];
    1070:     bnzi = bi[i+1] - bi[i];
    1071:     anzi = ai[i+1] - ai[i];
    1072:     for (j=0; j<anzi; j++) {
    1073:       nextb = 0;
    1074:       crow  = *aj++;
    1075:       cjj   = cj + ci[crow];
    1076:       caj   = ca + ci[crow];
    1077:       /* perform sparse axpy operation.  Note cjj includes bj. */
    1078:       for (k=0; nextb<bnzi; k++) {
    1079:         if (cjj[k] == *(bj+nextb)) { /* ccol == bcol */
    1080:           caj[k] += (*aa)*(*(ba+nextb));
    1081:           nextb++;
    1082:         }
    1083:       }
    1084:       flops += 2*bnzi;
    1085:       aa++;
    1086:     }
    1087:   }

    1089:   /* Assemble the final matrix and clean up */
    1090:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    1091:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
    1092:   PetscLogFlops(flops);
    1093:   return(0);
    1094: }

    1098: PETSC_INTERN PetscErrorCode MatMatMult_SeqAIJ_SeqDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
    1099: {

    1103:   if (scall == MAT_INITIAL_MATRIX) {
    1104:     PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
    1105:     MatMatMultSymbolic_SeqAIJ_SeqDense(A,B,fill,C);
    1106:     PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
    1107:   }
    1108:   PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
    1109:   MatMatMultNumeric_SeqAIJ_SeqDense(A,B,*C);
    1110:   PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
    1111:   return(0);
    1112: }

    1116: PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqDense(Mat A,Mat B,PetscReal fill,Mat *C)
    1117: {

    1121:   MatMatMultSymbolic_SeqDense_SeqDense(A,B,0.0,C);

    1123:   (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqDense;
    1124:   return(0);
    1125: }

    1129: PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqDense(Mat A,Mat B,Mat C)
    1130: {
    1131:   Mat_SeqAIJ        *a=(Mat_SeqAIJ*)A->data;
    1132:   Mat_SeqDense      *bd = (Mat_SeqDense*)B->data;
    1133:   PetscErrorCode    ierr;
    1134:   PetscScalar       *c,*b,r1,r2,r3,r4,*c1,*c2,*c3,*c4,aatmp;
    1135:   const PetscScalar *aa,*b1,*b2,*b3,*b4;
    1136:   const PetscInt    *aj;
    1137:   PetscInt          cm=C->rmap->n,cn=B->cmap->n,bm=bd->lda,am=A->rmap->n;
    1138:   PetscInt          am4=4*am,bm4=4*bm,col,i,j,n,ajtmp;

    1141:   if (!cm || !cn) return(0);
    1142:   if (B->rmap->n != A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number columns in A %D not equal rows in B %D\n",A->cmap->n,B->rmap->n);
    1143:   if (A->rmap->n != C->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number rows in C %D not equal rows in A %D\n",C->rmap->n,A->rmap->n);
    1144:   if (B->cmap->n != C->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number columns in B %D not equal columns in C %D\n",B->cmap->n,C->cmap->n);
    1145:   b = bd->v;
    1146:   MatDenseGetArray(C,&c);
    1147:   b1 = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm;
    1148:   c1 = c; c2 = c1 + am; c3 = c2 + am; c4 = c3 + am;
    1149:   for (col=0; col<cn-4; col += 4) {  /* over columns of C */
    1150:     for (i=0; i<am; i++) {        /* over rows of C in those columns */
    1151:       r1 = r2 = r3 = r4 = 0.0;
    1152:       n  = a->i[i+1] - a->i[i];
    1153:       aj = a->j + a->i[i];
    1154:       aa = a->a + a->i[i];
    1155:       for (j=0; j<n; j++) {
    1156:         aatmp = aa[j]; ajtmp = aj[j];
    1157:         r1 += aatmp*b1[ajtmp];
    1158:         r2 += aatmp*b2[ajtmp];
    1159:         r3 += aatmp*b3[ajtmp];
    1160:         r4 += aatmp*b4[ajtmp];
    1161:       }
    1162:       c1[i] = r1;
    1163:       c2[i] = r2;
    1164:       c3[i] = r3;
    1165:       c4[i] = r4;
    1166:     }
    1167:     b1 += bm4; b2 += bm4; b3 += bm4; b4 += bm4;
    1168:     c1 += am4; c2 += am4; c3 += am4; c4 += am4;
    1169:   }
    1170:   for (; col<cn; col++) {   /* over extra columns of C */
    1171:     for (i=0; i<am; i++) {  /* over rows of C in those columns */
    1172:       r1 = 0.0;
    1173:       n  = a->i[i+1] - a->i[i];
    1174:       aj = a->j + a->i[i];
    1175:       aa = a->a + a->i[i];
    1176:       for (j=0; j<n; j++) {
    1177:         r1 += aa[j]*b1[aj[j]];
    1178:       }
    1179:       c1[i] = r1;
    1180:     }
    1181:     b1 += bm;
    1182:     c1 += am;
    1183:   }
    1184:   PetscLogFlops(cn*(2.0*a->nz));
    1185:   MatDenseRestoreArray(C,&c);
    1186:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    1187:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
    1188:   return(0);
    1189: }

    1191: /*
    1192:    Note very similar to MatMult_SeqAIJ(), should generate both codes from same base
    1193: */
    1196: PetscErrorCode MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat A,Mat B,Mat C)
    1197: {
    1198:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;
    1199:   Mat_SeqDense   *bd = (Mat_SeqDense*)B->data;
    1201:   PetscScalar    *b,*c,r1,r2,r3,r4,*b1,*b2,*b3,*b4;
    1202:   MatScalar      *aa;
    1203:   PetscInt       cm  = C->rmap->n, cn=B->cmap->n, bm=bd->lda, col, i,j,n,*aj, am = A->rmap->n,*ii,arm;
    1204:   PetscInt       am2 = 2*am, am3 = 3*am,  bm4 = 4*bm,colam,*ridx;

    1207:   if (!cm || !cn) return(0);
    1208:   b = bd->v;
    1209:   MatDenseGetArray(C,&c);
    1210:   b1   = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm;

    1212:   if (a->compressedrow.use) { /* use compressed row format */
    1213:     for (col=0; col<cn-4; col += 4) {  /* over columns of C */
    1214:       colam = col*am;
    1215:       arm   = a->compressedrow.nrows;
    1216:       ii    = a->compressedrow.i;
    1217:       ridx  = a->compressedrow.rindex;
    1218:       for (i=0; i<arm; i++) {        /* over rows of C in those columns */
    1219:         r1 = r2 = r3 = r4 = 0.0;
    1220:         n  = ii[i+1] - ii[i];
    1221:         aj = a->j + ii[i];
    1222:         aa = a->a + ii[i];
    1223:         for (j=0; j<n; j++) {
    1224:           r1 += (*aa)*b1[*aj];
    1225:           r2 += (*aa)*b2[*aj];
    1226:           r3 += (*aa)*b3[*aj];
    1227:           r4 += (*aa++)*b4[*aj++];
    1228:         }
    1229:         c[colam       + ridx[i]] += r1;
    1230:         c[colam + am  + ridx[i]] += r2;
    1231:         c[colam + am2 + ridx[i]] += r3;
    1232:         c[colam + am3 + ridx[i]] += r4;
    1233:       }
    1234:       b1 += bm4;
    1235:       b2 += bm4;
    1236:       b3 += bm4;
    1237:       b4 += bm4;
    1238:     }
    1239:     for (; col<cn; col++) {     /* over extra columns of C */
    1240:       colam = col*am;
    1241:       arm   = a->compressedrow.nrows;
    1242:       ii    = a->compressedrow.i;
    1243:       ridx  = a->compressedrow.rindex;
    1244:       for (i=0; i<arm; i++) {  /* over rows of C in those columns */
    1245:         r1 = 0.0;
    1246:         n  = ii[i+1] - ii[i];
    1247:         aj = a->j + ii[i];
    1248:         aa = a->a + ii[i];

    1250:         for (j=0; j<n; j++) {
    1251:           r1 += (*aa++)*b1[*aj++];
    1252:         }
    1253:         c[colam + ridx[i]] += r1;
    1254:       }
    1255:       b1 += bm;
    1256:     }
    1257:   } else {
    1258:     for (col=0; col<cn-4; col += 4) {  /* over columns of C */
    1259:       colam = col*am;
    1260:       for (i=0; i<am; i++) {        /* over rows of C in those columns */
    1261:         r1 = r2 = r3 = r4 = 0.0;
    1262:         n  = a->i[i+1] - a->i[i];
    1263:         aj = a->j + a->i[i];
    1264:         aa = a->a + a->i[i];
    1265:         for (j=0; j<n; j++) {
    1266:           r1 += (*aa)*b1[*aj];
    1267:           r2 += (*aa)*b2[*aj];
    1268:           r3 += (*aa)*b3[*aj];
    1269:           r4 += (*aa++)*b4[*aj++];
    1270:         }
    1271:         c[colam + i]       += r1;
    1272:         c[colam + am + i]  += r2;
    1273:         c[colam + am2 + i] += r3;
    1274:         c[colam + am3 + i] += r4;
    1275:       }
    1276:       b1 += bm4;
    1277:       b2 += bm4;
    1278:       b3 += bm4;
    1279:       b4 += bm4;
    1280:     }
    1281:     for (; col<cn; col++) {     /* over extra columns of C */
    1282:       colam = col*am;
    1283:       for (i=0; i<am; i++) {  /* over rows of C in those columns */
    1284:         r1 = 0.0;
    1285:         n  = a->i[i+1] - a->i[i];
    1286:         aj = a->j + a->i[i];
    1287:         aa = a->a + a->i[i];

    1289:         for (j=0; j<n; j++) {
    1290:           r1 += (*aa++)*b1[*aj++];
    1291:         }
    1292:         c[colam + i] += r1;
    1293:       }
    1294:       b1 += bm;
    1295:     }
    1296:   }
    1297:   PetscLogFlops(cn*2.0*a->nz);
    1298:   MatDenseRestoreArray(C,&c);
    1299:   return(0);
    1300: }

    1304: PetscErrorCode  MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring coloring,Mat B,Mat Btdense)
    1305: {
    1307:   Mat_SeqAIJ     *b       = (Mat_SeqAIJ*)B->data;
    1308:   Mat_SeqDense   *btdense = (Mat_SeqDense*)Btdense->data;
    1309:   PetscInt       *bi      = b->i,*bj=b->j;
    1310:   PetscInt       m        = Btdense->rmap->n,n=Btdense->cmap->n,j,k,l,col,anz,*btcol,brow,ncolumns;
    1311:   MatScalar      *btval,*btval_den,*ba=b->a;
    1312:   PetscInt       *columns=coloring->columns,*colorforcol=coloring->colorforcol,ncolors=coloring->ncolors;

    1315:   btval_den=btdense->v;
    1316:   PetscMemzero(btval_den,(m*n)*sizeof(MatScalar));
    1317:   for (k=0; k<ncolors; k++) {
    1318:     ncolumns = coloring->ncolumns[k];
    1319:     for (l=0; l<ncolumns; l++) { /* insert a row of B to a column of Btdense */
    1320:       col   = *(columns + colorforcol[k] + l);
    1321:       btcol = bj + bi[col];
    1322:       btval = ba + bi[col];
    1323:       anz   = bi[col+1] - bi[col];
    1324:       for (j=0; j<anz; j++) {
    1325:         brow            = btcol[j];
    1326:         btval_den[brow] = btval[j];
    1327:       }
    1328:     }
    1329:     btval_den += m;
    1330:   }
    1331:   return(0);
    1332: }

    1336: PetscErrorCode MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring matcoloring,Mat Cden,Mat Csp)
    1337: {
    1339:   Mat_SeqAIJ     *csp = (Mat_SeqAIJ*)Csp->data;
    1340:   PetscScalar    *ca_den,*ca_den_ptr,*ca=csp->a;
    1341:   PetscInt       k,l,m=Cden->rmap->n,ncolors=matcoloring->ncolors;
    1342:   PetscInt       brows=matcoloring->brows,*den2sp=matcoloring->den2sp;
    1343:   PetscInt       nrows,*row,*idx;
    1344:   PetscInt       *rows=matcoloring->rows,*colorforrow=matcoloring->colorforrow;

    1347:   MatDenseGetArray(Cden,&ca_den);

    1349:   if (brows > 0) {
    1350:     PetscInt *lstart,row_end,row_start;
    1351:     lstart = matcoloring->lstart;
    1352:     PetscMemzero(lstart,ncolors*sizeof(PetscInt));

    1354:     row_end = brows;
    1355:     if (row_end > m) row_end = m;
    1356:     for (row_start=0; row_start<m; row_start+=brows) { /* loop over row blocks of Csp */
    1357:       ca_den_ptr = ca_den;
    1358:       for (k=0; k<ncolors; k++) { /* loop over colors (columns of Cden) */
    1359:         nrows = matcoloring->nrows[k];
    1360:         row   = rows  + colorforrow[k];
    1361:         idx   = den2sp + colorforrow[k];
    1362:         for (l=lstart[k]; l<nrows; l++) {
    1363:           if (row[l] >= row_end) {
    1364:             lstart[k] = l;
    1365:             break;
    1366:           } else {
    1367:             ca[idx[l]] = ca_den_ptr[row[l]];
    1368:           }
    1369:         }
    1370:         ca_den_ptr += m;
    1371:       }
    1372:       row_end += brows;
    1373:       if (row_end > m) row_end = m;
    1374:     }
    1375:   } else { /* non-blocked impl: loop over columns of Csp - slow if Csp is large */
    1376:     ca_den_ptr = ca_den;
    1377:     for (k=0; k<ncolors; k++) {
    1378:       nrows = matcoloring->nrows[k];
    1379:       row   = rows  + colorforrow[k];
    1380:       idx   = den2sp + colorforrow[k];
    1381:       for (l=0; l<nrows; l++) {
    1382:         ca[idx[l]] = ca_den_ptr[row[l]];
    1383:       }
    1384:       ca_den_ptr += m;
    1385:     }
    1386:   }

    1388:   MatDenseRestoreArray(Cden,&ca_den);
    1389: #if defined(PETSC_USE_INFO)
    1390:   if (matcoloring->brows > 0) {
    1391:     PetscInfo1(Csp,"Loop over %D row blocks for den2sp\n",brows);
    1392:   } else {
    1393:     PetscInfo(Csp,"Loop over colors/columns of Cden, inefficient for large sparse matrix product \n");
    1394:   }
    1395: #endif
    1396:   return(0);
    1397: }

    1401: PetscErrorCode MatTransposeColoringCreate_SeqAIJ(Mat mat,ISColoring iscoloring,MatTransposeColoring c)
    1402: {
    1404:   PetscInt       i,n,nrows,Nbs,j,k,m,ncols,col,cm;
    1405:   const PetscInt *is,*ci,*cj,*row_idx;
    1406:   PetscInt       nis = iscoloring->n,*rowhit,bs = 1;
    1407:   IS             *isa;
    1408:   Mat_SeqAIJ     *csp = (Mat_SeqAIJ*)mat->data;
    1409:   PetscInt       *colorforrow,*rows,*rows_i,*idxhit,*spidx,*den2sp,*den2sp_i;
    1410:   PetscInt       *colorforcol,*columns,*columns_i,brows;
    1411:   PetscBool      flg;

    1414:   ISColoringGetIS(iscoloring,PETSC_IGNORE,&isa);

    1416:   /* bs >1 is not being tested yet! */
    1417:   Nbs       = mat->cmap->N/bs;
    1418:   c->M      = mat->rmap->N/bs;  /* set total rows, columns and local rows */
    1419:   c->N      = Nbs;
    1420:   c->m      = c->M;
    1421:   c->rstart = 0;
    1422:   c->brows  = 100;

    1424:   c->ncolors = nis;
    1425:   PetscMalloc3(nis,&c->ncolumns,nis,&c->nrows,nis+1,&colorforrow);
    1426:   PetscMalloc1(csp->nz+1,&rows);
    1427:   PetscMalloc1(csp->nz+1,&den2sp);

    1429:   brows = c->brows;
    1430:   PetscOptionsGetInt(NULL,NULL,"-matden2sp_brows",&brows,&flg);
    1431:   if (flg) c->brows = brows;
    1432:   if (brows > 0) {
    1433:     PetscMalloc1(nis+1,&c->lstart);
    1434:   }

    1436:   colorforrow[0] = 0;
    1437:   rows_i         = rows;
    1438:   den2sp_i       = den2sp;

    1440:   PetscMalloc1(nis+1,&colorforcol);
    1441:   PetscMalloc1(Nbs+1,&columns);

    1443:   colorforcol[0] = 0;
    1444:   columns_i      = columns;

    1446:   /* get column-wise storage of mat */
    1447:   MatGetColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);

    1449:   cm   = c->m;
    1450:   PetscMalloc1(cm+1,&rowhit);
    1451:   PetscMalloc1(cm+1,&idxhit);
    1452:   for (i=0; i<nis; i++) { /* loop over color */
    1453:     ISGetLocalSize(isa[i],&n);
    1454:     ISGetIndices(isa[i],&is);

    1456:     c->ncolumns[i] = n;
    1457:     if (n) {
    1458:       PetscMemcpy(columns_i,is,n*sizeof(PetscInt));
    1459:     }
    1460:     colorforcol[i+1] = colorforcol[i] + n;
    1461:     columns_i       += n;

    1463:     /* fast, crude version requires O(N*N) work */
    1464:     PetscMemzero(rowhit,cm*sizeof(PetscInt));

    1466:     for (j=0; j<n; j++) { /* loop over columns*/
    1467:       col     = is[j];
    1468:       row_idx = cj + ci[col];
    1469:       m       = ci[col+1] - ci[col];
    1470:       for (k=0; k<m; k++) { /* loop over columns marking them in rowhit */
    1471:         idxhit[*row_idx]   = spidx[ci[col] + k];
    1472:         rowhit[*row_idx++] = col + 1;
    1473:       }
    1474:     }
    1475:     /* count the number of hits */
    1476:     nrows = 0;
    1477:     for (j=0; j<cm; j++) {
    1478:       if (rowhit[j]) nrows++;
    1479:     }
    1480:     c->nrows[i]      = nrows;
    1481:     colorforrow[i+1] = colorforrow[i] + nrows;

    1483:     nrows = 0;
    1484:     for (j=0; j<cm; j++) { /* loop over rows */
    1485:       if (rowhit[j]) {
    1486:         rows_i[nrows]   = j;
    1487:         den2sp_i[nrows] = idxhit[j];
    1488:         nrows++;
    1489:       }
    1490:     }
    1491:     den2sp_i += nrows;

    1493:     ISRestoreIndices(isa[i],&is);
    1494:     rows_i += nrows;
    1495:   }
    1496:   MatRestoreColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    1497:   PetscFree(rowhit);
    1498:   ISColoringRestoreIS(iscoloring,&isa);
    1499:   if (csp->nz != colorforrow[nis]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"csp->nz %d != colorforrow[nis] %d",csp->nz,colorforrow[nis]);

    1501:   c->colorforrow = colorforrow;
    1502:   c->rows        = rows;
    1503:   c->den2sp      = den2sp;
    1504:   c->colorforcol = colorforcol;
    1505:   c->columns     = columns;

    1507:   PetscFree(idxhit);
    1508:   return(0);
    1509: }