Actual source code: baijfact2.c
petsc-3.7.1 2016-05-15
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      2: /*
      3:     Factorization code for BAIJ format.
      4: */

      6: #include <../src/mat/impls/baij/seq/baij.h>
      7: #include <petsc/private/kernels/blockinvert.h>
      8: #include <petscbt.h>
      9: #include <../src/mat/utils/freespace.h>

     11: /* ----------------------------------------------------------------*/
     12: extern PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat,Mat,MatDuplicateOption,PetscBool);

     16: /*
     17:    This is not much faster than MatLUFactorNumeric_SeqBAIJ_N() but the solve is faster at least sometimes
     18: */
     19: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_15_NaturalOrdering(Mat B,Mat A,const MatFactorInfo *info)
     20: {
     21:   Mat             C =B;
     22:   Mat_SeqBAIJ     *a=(Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)C->data;
     23:   PetscErrorCode  ierr;
     24:   PetscInt        i,j,k,ipvt[15];
     25:   const PetscInt  n=a->mbs,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,*ajtmp,*bjtmp,*bdiag=b->diag,*pj;
     26:   PetscInt        nz,nzL,row;
     27:   MatScalar       *rtmp,*pc,*mwork,*pv,*vv,work[225];
     28:   const MatScalar *v,*aa=a->a;
     29:   PetscInt        bs2 = a->bs2,bs=A->rmap->bs,flg;
     30:   PetscInt        sol_ver;
     31:   PetscBool       allowzeropivot,zeropivotdetected;

     34:   allowzeropivot = PetscNot(A->erroriffailure);
     35:   PetscOptionsGetInt(NULL,((PetscObject)A)->prefix,"-sol_ver",&sol_ver,NULL);

     37:   /* generate work space needed by the factorization */
     38:   PetscMalloc2(bs2*n,&rtmp,bs2,&mwork);
     39:   PetscMemzero(rtmp,bs2*n*sizeof(MatScalar));

     41:   for (i=0; i<n; i++) {
     42:     /* zero rtmp */
     43:     /* L part */
     44:     nz    = bi[i+1] - bi[i];
     45:     bjtmp = bj + bi[i];
     46:     for  (j=0; j<nz; j++) {
     47:       PetscMemzero(rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
     48:     }

     50:     /* U part */
     51:     nz    = bdiag[i] - bdiag[i+1];
     52:     bjtmp = bj + bdiag[i+1]+1;
     53:     for  (j=0; j<nz; j++) {
     54:       PetscMemzero(rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
     55:     }

     57:     /* load in initial (unfactored row) */
     58:     nz    = ai[i+1] - ai[i];
     59:     ajtmp = aj + ai[i];
     60:     v     = aa + bs2*ai[i];
     61:     for (j=0; j<nz; j++) {
     62:       PetscMemcpy(rtmp+bs2*ajtmp[j],v+bs2*j,bs2*sizeof(MatScalar));
     63:     }

     65:     /* elimination */
     66:     bjtmp = bj + bi[i];
     67:     nzL   = bi[i+1] - bi[i];
     68:     for (k=0; k < nzL; k++) {
     69:       row = bjtmp[k];
     70:       pc  = rtmp + bs2*row;
     71:       for (flg=0,j=0; j<bs2; j++) {
     72:         if (pc[j]!=0.0) {
     73:           flg = 1;
     74:           break;
     75:         }
     76:       }
     77:       if (flg) {
     78:         pv = b->a + bs2*bdiag[row];
     79:         PetscKernel_A_gets_A_times_B(bs,pc,pv,mwork);
     80:         /*PetscKernel_A_gets_A_times_B_15(pc,pv,mwork);*/
     81:         pj = b->j + bdiag[row+1]+1; /* begining of U(row,:) */
     82:         pv = b->a + bs2*(bdiag[row+1]+1);
     83:         nz = bdiag[row] - bdiag[row+1] - 1; /* num of entries inU(row,:), excluding diag */
     84:         for (j=0; j<nz; j++) {
     85:           vv = rtmp + bs2*pj[j];
     86:           PetscKernel_A_gets_A_minus_B_times_C(bs,vv,pc,pv);
     87:           /* PetscKernel_A_gets_A_minus_B_times_C_15(vv,pc,pv); */
     88:           pv += bs2;
     89:         }
     90:         PetscLogFlops(2*bs2*bs*(nz+1)-bs2); /* flops = 2*bs^3*nz + 2*bs^3 - bs2) */
     91:       }
     92:     }

     94:     /* finished row so stick it into b->a */
     95:     /* L part */
     96:     pv = b->a + bs2*bi[i];
     97:     pj = b->j + bi[i];
     98:     nz = bi[i+1] - bi[i];
     99:     for (j=0; j<nz; j++) {
    100:       PetscMemcpy(pv+bs2*j,rtmp+bs2*pj[j],bs2*sizeof(MatScalar));
    101:     }

    103:     /* Mark diagonal and invert diagonal for simplier triangular solves */
    104:     pv   = b->a + bs2*bdiag[i];
    105:     pj   = b->j + bdiag[i];
    106:     PetscMemcpy(pv,rtmp+bs2*pj[0],bs2*sizeof(MatScalar));
    107:     PetscKernel_A_gets_inverse_A_15(pv,ipvt,work,info->shiftamount,allowzeropivot,&zeropivotdetected);
    108:     if (zeropivotdetected) C->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;

    110:     /* U part */
    111:     pv = b->a + bs2*(bdiag[i+1]+1);
    112:     pj = b->j + bdiag[i+1]+1;
    113:     nz = bdiag[i] - bdiag[i+1] - 1;
    114:     for (j=0; j<nz; j++) {
    115:       PetscMemcpy(pv+bs2*j,rtmp+bs2*pj[j],bs2*sizeof(MatScalar));
    116:     }
    117:   }

    119:   PetscFree2(rtmp,mwork);

    121:   C->ops->solve          = MatSolve_SeqBAIJ_15_NaturalOrdering_ver1;
    122:   C->ops->solvetranspose = MatSolve_SeqBAIJ_N_NaturalOrdering;
    123:   C->assembled           = PETSC_TRUE;

    125:   PetscLogFlops(1.333333333333*bs*bs2*b->mbs); /* from inverting diagonal blocks */
    126:   return(0);
    127: }

    131: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_N(Mat B,Mat A,const MatFactorInfo *info)
    132: {
    133:   Mat            C     =B;
    134:   Mat_SeqBAIJ    *a    =(Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)C->data;
    135:   IS             isrow = b->row,isicol = b->icol;
    137:   const PetscInt *r,*ic;
    138:   PetscInt       i,j,k,n=a->mbs,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
    139:   PetscInt       *ajtmp,*bjtmp,nz,nzL,row,*bdiag=b->diag,*pj;
    140:   MatScalar      *rtmp,*pc,*mwork,*v,*pv,*aa=a->a;
    141:   PetscInt       bs=A->rmap->bs,bs2 = a->bs2,*v_pivots,flg;
    142:   MatScalar      *v_work;
    143:   PetscBool      col_identity,row_identity,both_identity;
    144:   PetscBool      allowzeropivot,zeropivotdetected;

    147:   ISGetIndices(isrow,&r);
    148:   ISGetIndices(isicol,&ic);
    149:   allowzeropivot = PetscNot(A->erroriffailure);

    151:   PetscMalloc1(bs2*n,&rtmp);
    152:   PetscMemzero(rtmp,bs2*n*sizeof(MatScalar));

    154:   /* generate work space needed by dense LU factorization */
    155:   PetscMalloc3(bs,&v_work,bs2,&mwork,bs,&v_pivots);

    157:   for (i=0; i<n; i++) {
    158:     /* zero rtmp */
    159:     /* L part */
    160:     nz    = bi[i+1] - bi[i];
    161:     bjtmp = bj + bi[i];
    162:     for  (j=0; j<nz; j++) {
    163:       PetscMemzero(rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
    164:     }

    166:     /* U part */
    167:     nz    = bdiag[i] - bdiag[i+1];
    168:     bjtmp = bj + bdiag[i+1]+1;
    169:     for  (j=0; j<nz; j++) {
    170:       PetscMemzero(rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
    171:     }

    173:     /* load in initial (unfactored row) */
    174:     nz    = ai[r[i]+1] - ai[r[i]];
    175:     ajtmp = aj + ai[r[i]];
    176:     v     = aa + bs2*ai[r[i]];
    177:     for (j=0; j<nz; j++) {
    178:       PetscMemcpy(rtmp+bs2*ic[ajtmp[j]],v+bs2*j,bs2*sizeof(MatScalar));
    179:     }

    181:     /* elimination */
    182:     bjtmp = bj + bi[i];
    183:     nzL   = bi[i+1] - bi[i];
    184:     for (k=0; k < nzL; k++) {
    185:       row = bjtmp[k];
    186:       pc  = rtmp + bs2*row;
    187:       for (flg=0,j=0; j<bs2; j++) {
    188:         if (pc[j]!=0.0) {
    189:           flg = 1;
    190:           break;
    191:         }
    192:       }
    193:       if (flg) {
    194:         pv = b->a + bs2*bdiag[row];
    195:         PetscKernel_A_gets_A_times_B(bs,pc,pv,mwork); /* *pc = *pc * (*pv); */
    196:         pj = b->j + bdiag[row+1]+1;         /* begining of U(row,:) */
    197:         pv = b->a + bs2*(bdiag[row+1]+1);
    198:         nz = bdiag[row] - bdiag[row+1] - 1;         /* num of entries inU(row,:), excluding diag */
    199:         for (j=0; j<nz; j++) {
    200:           PetscKernel_A_gets_A_minus_B_times_C(bs,rtmp+bs2*pj[j],pc,pv+bs2*j);
    201:         }
    202:         PetscLogFlops(2*bs2*bs*(nz+1)-bs2); /* flops = 2*bs^3*nz + 2*bs^3 - bs2) */
    203:       }
    204:     }

    206:     /* finished row so stick it into b->a */
    207:     /* L part */
    208:     pv = b->a + bs2*bi[i];
    209:     pj = b->j + bi[i];
    210:     nz = bi[i+1] - bi[i];
    211:     for (j=0; j<nz; j++) {
    212:       PetscMemcpy(pv+bs2*j,rtmp+bs2*pj[j],bs2*sizeof(MatScalar));
    213:     }

    215:     /* Mark diagonal and invert diagonal for simplier triangular solves */
    216:     pv = b->a + bs2*bdiag[i];
    217:     pj = b->j + bdiag[i];
    218:     /* if (*pj != i)SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_SUP,"row %d != *pj %d",i,*pj); */
    219:     PetscMemcpy(pv,rtmp+bs2*pj[0],bs2*sizeof(MatScalar));

    221:     PetscKernel_A_gets_inverse_A(bs,pv,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
    222:     if (zeropivotdetected) B->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;

    224:     /* U part */
    225:     pv = b->a + bs2*(bdiag[i+1]+1);
    226:     pj = b->j + bdiag[i+1]+1;
    227:     nz = bdiag[i] - bdiag[i+1] - 1;
    228:     for (j=0; j<nz; j++) {
    229:       PetscMemcpy(pv+bs2*j,rtmp+bs2*pj[j],bs2*sizeof(MatScalar));
    230:     }
    231:   }

    233:   PetscFree(rtmp);
    234:   PetscFree3(v_work,mwork,v_pivots);
    235:   ISRestoreIndices(isicol,&ic);
    236:   ISRestoreIndices(isrow,&r);

    238:   ISIdentity(isrow,&row_identity);
    239:   ISIdentity(isicol,&col_identity);

    241:   both_identity = (PetscBool) (row_identity && col_identity);
    242:   if (both_identity) {
    243:     C->ops->solve = MatSolve_SeqBAIJ_N_NaturalOrdering;
    244:   } else {
    245:     C->ops->solve = MatSolve_SeqBAIJ_N;
    246:   }
    247:   C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_N;

    249:   C->assembled = PETSC_TRUE;

    251:   PetscLogFlops(1.333333333333*bs*bs2*b->mbs); /* from inverting diagonal blocks */
    252:   return(0);
    253: }

    255: /*
    256:    ilu(0) with natural ordering under new data structure.
    257:    See MatILUFactorSymbolic_SeqAIJ_ilu0() for detailed description
    258:    because this code is almost identical to MatILUFactorSymbolic_SeqAIJ_ilu0_inplace().
    259: */

    263: PetscErrorCode MatILUFactorSymbolic_SeqBAIJ_ilu0(Mat fact,Mat A,IS isrow,IS iscol,const MatFactorInfo *info)
    264: {

    266:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data,*b;
    268:   PetscInt       n=a->mbs,*ai=a->i,*aj,*adiag=a->diag,bs2 = a->bs2;
    269:   PetscInt       i,j,nz,*bi,*bj,*bdiag,bi_temp;

    272:   MatDuplicateNoCreate_SeqBAIJ(fact,A,MAT_DO_NOT_COPY_VALUES,PETSC_FALSE);
    273:   b    = (Mat_SeqBAIJ*)(fact)->data;

    275:   /* allocate matrix arrays for new data structure */
    276:   PetscMalloc3(bs2*ai[n]+1,&b->a,ai[n]+1,&b->j,n+1,&b->i);
    277:   PetscLogObjectMemory((PetscObject)fact,ai[n]*(bs2*sizeof(PetscScalar)+sizeof(PetscInt))+(n+1)*sizeof(PetscInt));

    279:   b->singlemalloc    = PETSC_TRUE;
    280:   b->free_a          = PETSC_TRUE;
    281:   b->free_ij         = PETSC_TRUE;
    282:   fact->preallocated = PETSC_TRUE;
    283:   fact->assembled    = PETSC_TRUE;
    284:   if (!b->diag) {
    285:     PetscMalloc1(n+1,&b->diag);
    286:     PetscLogObjectMemory((PetscObject)fact,(n+1)*sizeof(PetscInt));
    287:   }
    288:   bdiag = b->diag;

    290:   if (n > 0) {
    291:     PetscMemzero(b->a,bs2*ai[n]*sizeof(MatScalar));
    292:   }

    294:   /* set bi and bj with new data structure */
    295:   bi = b->i;
    296:   bj = b->j;

    298:   /* L part */
    299:   bi[0] = 0;
    300:   for (i=0; i<n; i++) {
    301:     nz      = adiag[i] - ai[i];
    302:     bi[i+1] = bi[i] + nz;
    303:     aj      = a->j + ai[i];
    304:     for (j=0; j<nz; j++) {
    305:       *bj = aj[j]; bj++;
    306:     }
    307:   }

    309:   /* U part */
    310:   bi_temp  = bi[n];
    311:   bdiag[n] = bi[n]-1;
    312:   for (i=n-1; i>=0; i--) {
    313:     nz      = ai[i+1] - adiag[i] - 1;
    314:     bi_temp = bi_temp + nz + 1;
    315:     aj      = a->j + adiag[i] + 1;
    316:     for (j=0; j<nz; j++) {
    317:       *bj = aj[j]; bj++;
    318:     }
    319:     /* diag[i] */
    320:     *bj      = i; bj++;
    321:     bdiag[i] = bi_temp - 1;
    322:   }
    323:   return(0);
    324: }

    328: PetscErrorCode MatILUFactorSymbolic_SeqBAIJ(Mat fact,Mat A,IS isrow,IS iscol,const MatFactorInfo *info)
    329: {
    330:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data,*b;
    331:   IS                 isicol;
    332:   PetscErrorCode     ierr;
    333:   const PetscInt     *r,*ic;
    334:   PetscInt           n=a->mbs,*ai=a->i,*aj=a->j,d;
    335:   PetscInt           *bi,*cols,nnz,*cols_lvl;
    336:   PetscInt           *bdiag,prow,fm,nzbd,reallocs=0,dcount=0;
    337:   PetscInt           i,levels,diagonal_fill;
    338:   PetscBool          col_identity,row_identity,both_identity;
    339:   PetscReal          f;
    340:   PetscInt           nlnk,*lnk,*lnk_lvl=NULL;
    341:   PetscBT            lnkbt;
    342:   PetscInt           nzi,*bj,**bj_ptr,**bjlvl_ptr;
    343:   PetscFreeSpaceList free_space    =NULL,current_space=NULL;
    344:   PetscFreeSpaceList free_space_lvl=NULL,current_space_lvl=NULL;
    345:   PetscBool          missing;
    346:   PetscInt           bs=A->rmap->bs,bs2=a->bs2;

    349:   if (A->rmap->n != A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Must be square matrix, rows %D columns %D",A->rmap->n,A->cmap->n);
    350:   if (bs>1) {  /* check shifttype */
    351:     if (info->shifttype == MAT_SHIFT_NONZERO || info->shifttype == MAT_SHIFT_POSITIVE_DEFINITE) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only MAT_SHIFT_NONE and MAT_SHIFT_INBLOCKS are supported for BAIJ matrix");
    352:   }

    354:   MatMissingDiagonal(A,&missing,&d);
    355:   if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry %D",d);

    357:   f             = info->fill;
    358:   levels        = (PetscInt)info->levels;
    359:   diagonal_fill = (PetscInt)info->diagonal_fill;

    361:   ISInvertPermutation(iscol,PETSC_DECIDE,&isicol);

    363:   ISIdentity(isrow,&row_identity);
    364:   ISIdentity(iscol,&col_identity);

    366:   both_identity = (PetscBool) (row_identity && col_identity);

    368:   if (!levels && both_identity) {
    369:     /* special case: ilu(0) with natural ordering */
    370:     MatILUFactorSymbolic_SeqBAIJ_ilu0(fact,A,isrow,iscol,info);
    371:     MatSeqBAIJSetNumericFactorization(fact,both_identity);

    373:     fact->factortype               = MAT_FACTOR_ILU;
    374:     (fact)->info.factor_mallocs    = 0;
    375:     (fact)->info.fill_ratio_given  = info->fill;
    376:     (fact)->info.fill_ratio_needed = 1.0;

    378:     b                = (Mat_SeqBAIJ*)(fact)->data;
    379:     b->row           = isrow;
    380:     b->col           = iscol;
    381:     b->icol          = isicol;
    382:     PetscObjectReference((PetscObject)isrow);
    383:     PetscObjectReference((PetscObject)iscol);
    384:     b->pivotinblocks = (info->pivotinblocks) ? PETSC_TRUE : PETSC_FALSE;

    386:     PetscMalloc1((n+1)*bs,&b->solve_work);
    387:     return(0);
    388:   }

    390:   ISGetIndices(isrow,&r);
    391:   ISGetIndices(isicol,&ic);

    393:   /* get new row pointers */
    394:   PetscMalloc1(n+1,&bi);
    395:   bi[0] = 0;
    396:   /* bdiag is location of diagonal in factor */
    397:   PetscMalloc1(n+1,&bdiag);
    398:   bdiag[0] = 0;

    400:   PetscMalloc2(n,&bj_ptr,n,&bjlvl_ptr);

    402:   /* create a linked list for storing column indices of the active row */
    403:   nlnk = n + 1;
    404:   PetscIncompleteLLCreate(n,n,nlnk,lnk,lnk_lvl,lnkbt);

    406:   /* initial FreeSpace size is f*(ai[n]+1) */
    407:   PetscFreeSpaceGet(PetscRealIntMultTruncate(f,ai[n]+1),&free_space);
    408:   current_space     = free_space;
    409:   PetscFreeSpaceGet(PetscRealIntMultTruncate(f,ai[n]+1),&free_space_lvl);
    410:   current_space_lvl = free_space_lvl;

    412:   for (i=0; i<n; i++) {
    413:     nzi = 0;
    414:     /* copy current row into linked list */
    415:     nnz = ai[r[i]+1] - ai[r[i]];
    416:     if (!nnz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Empty row in matrix: row in original ordering %D in permuted ordering %D",r[i],i);
    417:     cols   = aj + ai[r[i]];
    418:     lnk[i] = -1; /* marker to indicate if diagonal exists */
    419:     PetscIncompleteLLInit(nnz,cols,n,ic,nlnk,lnk,lnk_lvl,lnkbt);
    420:     nzi   += nlnk;

    422:     /* make sure diagonal entry is included */
    423:     if (diagonal_fill && lnk[i] == -1) {
    424:       fm = n;
    425:       while (lnk[fm] < i) fm = lnk[fm];
    426:       lnk[i]     = lnk[fm]; /* insert diagonal into linked list */
    427:       lnk[fm]    = i;
    428:       lnk_lvl[i] = 0;
    429:       nzi++; dcount++;
    430:     }

    432:     /* add pivot rows into the active row */
    433:     nzbd = 0;
    434:     prow = lnk[n];
    435:     while (prow < i) {
    436:       nnz      = bdiag[prow];
    437:       cols     = bj_ptr[prow] + nnz + 1;
    438:       cols_lvl = bjlvl_ptr[prow] + nnz + 1;
    439:       nnz      = bi[prow+1] - bi[prow] - nnz - 1;

    441:       PetscILULLAddSorted(nnz,cols,levels,cols_lvl,prow,nlnk,lnk,lnk_lvl,lnkbt,prow);
    442:       nzi += nlnk;
    443:       prow = lnk[prow];
    444:       nzbd++;
    445:     }
    446:     bdiag[i] = nzbd;
    447:     bi[i+1]  = bi[i] + nzi;

    449:     /* if free space is not available, make more free space */
    450:     if (current_space->local_remaining<nzi) {
    451:       nnz  = PetscIntMultTruncate(2,PetscIntMultTruncate(nzi,(n - i))); /* estimated and max additional space needed */
    452:       PetscFreeSpaceGet(nnz,&current_space);
    453:       PetscFreeSpaceGet(nnz,&current_space_lvl);
    454:       reallocs++;
    455:     }

    457:     /* copy data into free_space and free_space_lvl, then initialize lnk */
    458:     PetscIncompleteLLClean(n,n,nzi,lnk,lnk_lvl,current_space->array,current_space_lvl->array,lnkbt);

    460:     bj_ptr[i]    = current_space->array;
    461:     bjlvl_ptr[i] = current_space_lvl->array;

    463:     /* make sure the active row i has diagonal entry */
    464:     if (*(bj_ptr[i]+bdiag[i]) != i) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Row %D has missing diagonal in factored matrix\ntry running with -pc_factor_nonzeros_along_diagonal or -pc_factor_diagonal_fill",i);

    466:     current_space->array           += nzi;
    467:     current_space->local_used      += nzi;
    468:     current_space->local_remaining -= nzi;

    470:     current_space_lvl->array           += nzi;
    471:     current_space_lvl->local_used      += nzi;
    472:     current_space_lvl->local_remaining -= nzi;
    473:   }

    475:   ISRestoreIndices(isrow,&r);
    476:   ISRestoreIndices(isicol,&ic);

    478:   /* copy free_space into bj and free free_space; set bi, bj, bdiag in new datastructure; */
    479:   PetscMalloc1(bi[n]+1,&bj);
    480:   PetscFreeSpaceContiguous_LU(&free_space,bj,n,bi,bdiag);

    482:   PetscIncompleteLLDestroy(lnk,lnkbt);
    483:   PetscFreeSpaceDestroy(free_space_lvl);
    484:   PetscFree2(bj_ptr,bjlvl_ptr);

    486: #if defined(PETSC_USE_INFO)
    487:   {
    488:     PetscReal af = ((PetscReal)(bdiag[0]+1))/((PetscReal)ai[n]);
    489:     PetscInfo3(A,"Reallocs %D Fill ratio:given %g needed %g\n",reallocs,(double)f,(double)af);
    490:     PetscInfo1(A,"Run with -[sub_]pc_factor_fill %g or use \n",(double)af);
    491:     PetscInfo1(A,"PCFactorSetFill([sub]pc,%g);\n",(double)af);
    492:     PetscInfo(A,"for best performance.\n");
    493:     if (diagonal_fill) {
    494:       PetscInfo1(A,"Detected and replaced %D missing diagonals\n",dcount);
    495:     }
    496:   }
    497: #endif

    499:   /* put together the new matrix */
    500:   MatSeqBAIJSetPreallocation_SeqBAIJ(fact,bs,MAT_SKIP_ALLOCATION,NULL);
    501:   PetscLogObjectParent((PetscObject)fact,(PetscObject)isicol);

    503:   b               = (Mat_SeqBAIJ*)(fact)->data;
    504:   b->free_a       = PETSC_TRUE;
    505:   b->free_ij      = PETSC_TRUE;
    506:   b->singlemalloc = PETSC_FALSE;

    508:   PetscMalloc1(bs2*(bdiag[0]+1),&b->a);

    510:   b->j          = bj;
    511:   b->i          = bi;
    512:   b->diag       = bdiag;
    513:   b->free_diag  = PETSC_TRUE;
    514:   b->ilen       = 0;
    515:   b->imax       = 0;
    516:   b->row        = isrow;
    517:   b->col        = iscol;
    518:   PetscObjectReference((PetscObject)isrow);
    519:   PetscObjectReference((PetscObject)iscol);
    520:   b->icol       = isicol;

    522:   PetscMalloc1(bs*n+bs,&b->solve_work);
    523:   /* In b structure:  Free imax, ilen, old a, old j.
    524:      Allocate bdiag, solve_work, new a, new j */
    525:   PetscLogObjectMemory((PetscObject)fact,(bdiag[0]+1) * (sizeof(PetscInt)+bs2*sizeof(PetscScalar)));
    526:   b->maxnz = b->nz = bdiag[0]+1;

    528:   fact->info.factor_mallocs    = reallocs;
    529:   fact->info.fill_ratio_given  = f;
    530:   fact->info.fill_ratio_needed = ((PetscReal)(bdiag[0]+1))/((PetscReal)ai[n]);

    532:   MatSeqBAIJSetNumericFactorization(fact,both_identity);
    533:   return(0);
    534: }

    536: /*
    537:      This code is virtually identical to MatILUFactorSymbolic_SeqAIJ
    538:    except that the data structure of Mat_SeqAIJ is slightly different.
    539:    Not a good example of code reuse.
    540: */
    543: PetscErrorCode MatILUFactorSymbolic_SeqBAIJ_inplace(Mat fact,Mat A,IS isrow,IS iscol,const MatFactorInfo *info)
    544: {
    545:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data,*b;
    546:   IS             isicol;
    548:   const PetscInt *r,*ic,*ai = a->i,*aj = a->j,*xi;
    549:   PetscInt       prow,n = a->mbs,*ainew,*ajnew,jmax,*fill,nz,*im,*ajfill,*flev,*xitmp;
    550:   PetscInt       *dloc,idx,row,m,fm,nzf,nzi,reallocate = 0,dcount = 0;
    551:   PetscInt       incrlev,nnz,i,bs = A->rmap->bs,bs2 = a->bs2,levels,diagonal_fill,dd;
    552:   PetscBool      col_identity,row_identity,both_identity,flg;
    553:   PetscReal      f;

    556:   MatMissingDiagonal_SeqBAIJ(A,&flg,&dd);
    557:   if (flg) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix A is missing diagonal entry in row %D",dd);

    559:   f             = info->fill;
    560:   levels        = (PetscInt)info->levels;
    561:   diagonal_fill = (PetscInt)info->diagonal_fill;

    563:   ISInvertPermutation(iscol,PETSC_DECIDE,&isicol);

    565:   ISIdentity(isrow,&row_identity);
    566:   ISIdentity(iscol,&col_identity);
    567:   both_identity = (PetscBool) (row_identity && col_identity);

    569:   if (!levels && both_identity) {  /* special case copy the nonzero structure */
    570:     MatDuplicateNoCreate_SeqBAIJ(fact,A,MAT_DO_NOT_COPY_VALUES,PETSC_TRUE);
    571:     MatSeqBAIJSetNumericFactorization_inplace(fact,both_identity);

    573:     fact->factortype = MAT_FACTOR_ILU;
    574:     b                = (Mat_SeqBAIJ*)fact->data;
    575:     b->row           = isrow;
    576:     b->col           = iscol;
    577:     PetscObjectReference((PetscObject)isrow);
    578:     PetscObjectReference((PetscObject)iscol);
    579:     b->icol          = isicol;
    580:     b->pivotinblocks = (info->pivotinblocks) ? PETSC_TRUE : PETSC_FALSE;

    582:     PetscMalloc1((n+1)*bs,&b->solve_work);
    583:     return(0);
    584:   }

    586:   /* general case perform the symbolic factorization */
    587:   ISGetIndices(isrow,&r);
    588:   ISGetIndices(isicol,&ic);

    590:   /* get new row pointers */
    591:   PetscMalloc1(n+1,&ainew);
    592:   ainew[0] = 0;
    593:   /* don't know how many column pointers are needed so estimate */
    594:   jmax = (PetscInt)(f*ai[n] + 1);
    595:   PetscMalloc1(jmax,&ajnew);
    596:   /* ajfill is level of fill for each fill entry */
    597:   PetscMalloc1(jmax,&ajfill);
    598:   /* fill is a linked list of nonzeros in active row */
    599:   PetscMalloc1(n+1,&fill);
    600:   /* im is level for each filled value */
    601:   PetscMalloc1(n+1,&im);
    602:   /* dloc is location of diagonal in factor */
    603:   PetscMalloc1(n+1,&dloc);
    604:   dloc[0] = 0;
    605:   for (prow=0; prow<n; prow++) {

    607:     /* copy prow into linked list */
    608:     nzf = nz = ai[r[prow]+1] - ai[r[prow]];
    609:     if (!nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Empty row in matrix: row in original ordering %D in permuted ordering %D",r[prow],prow);
    610:     xi         = aj + ai[r[prow]];
    611:     fill[n]    = n;
    612:     fill[prow] = -1;   /* marker for diagonal entry */
    613:     while (nz--) {
    614:       fm  = n;
    615:       idx = ic[*xi++];
    616:       do {
    617:         m  = fm;
    618:         fm = fill[m];
    619:       } while (fm < idx);
    620:       fill[m]   = idx;
    621:       fill[idx] = fm;
    622:       im[idx]   = 0;
    623:     }

    625:     /* make sure diagonal entry is included */
    626:     if (diagonal_fill && fill[prow] == -1) {
    627:       fm = n;
    628:       while (fill[fm] < prow) fm = fill[fm];
    629:       fill[prow] = fill[fm];    /* insert diagonal into linked list */
    630:       fill[fm]   = prow;
    631:       im[prow]   = 0;
    632:       nzf++;
    633:       dcount++;
    634:     }

    636:     nzi = 0;
    637:     row = fill[n];
    638:     while (row < prow) {
    639:       incrlev = im[row] + 1;
    640:       nz      = dloc[row];
    641:       xi      = ajnew  + ainew[row] + nz + 1;
    642:       flev    = ajfill + ainew[row] + nz + 1;
    643:       nnz     = ainew[row+1] - ainew[row] - nz - 1;
    644:       fm      = row;
    645:       while (nnz-- > 0) {
    646:         idx = *xi++;
    647:         if (*flev + incrlev > levels) {
    648:           flev++;
    649:           continue;
    650:         }
    651:         do {
    652:           m  = fm;
    653:           fm = fill[m];
    654:         } while (fm < idx);
    655:         if (fm != idx) {
    656:           im[idx]   = *flev + incrlev;
    657:           fill[m]   = idx;
    658:           fill[idx] = fm;
    659:           fm        = idx;
    660:           nzf++;
    661:         } else if (im[idx] > *flev + incrlev) im[idx] = *flev+incrlev;
    662:         flev++;
    663:       }
    664:       row = fill[row];
    665:       nzi++;
    666:     }
    667:     /* copy new filled row into permanent storage */
    668:     ainew[prow+1] = ainew[prow] + nzf;
    669:     if (ainew[prow+1] > jmax) {

    671:       /* estimate how much additional space we will need */
    672:       /* use the strategy suggested by David Hysom <hysom@perch-t.icase.edu> */
    673:       /* just double the memory each time */
    674:       PetscInt maxadd = jmax;
    675:       /* maxadd = (int)(((f*ai[n]+1)*(n-prow+5))/n); */
    676:       if (maxadd < nzf) maxadd = (n-prow)*(nzf+1);
    677:       jmax += maxadd;

    679:       /* allocate a longer ajnew and ajfill */
    680:       PetscMalloc1(jmax,&xitmp);
    681:       PetscMemcpy(xitmp,ajnew,ainew[prow]*sizeof(PetscInt));
    682:       PetscFree(ajnew);
    683:       ajnew  = xitmp;
    684:       PetscMalloc1(jmax,&xitmp);
    685:       PetscMemcpy(xitmp,ajfill,ainew[prow]*sizeof(PetscInt));
    686:       PetscFree(ajfill);
    687:       ajfill = xitmp;
    688:       reallocate++;   /* count how many reallocations are needed */
    689:     }
    690:     xitmp      = ajnew + ainew[prow];
    691:     flev       = ajfill + ainew[prow];
    692:     dloc[prow] = nzi;
    693:     fm         = fill[n];
    694:     while (nzf--) {
    695:       *xitmp++ = fm;
    696:       *flev++  = im[fm];
    697:       fm       = fill[fm];
    698:     }
    699:     /* make sure row has diagonal entry */
    700:     if (ajnew[ainew[prow]+dloc[prow]] != prow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Row %D has missing diagonal in factored matrix\n\
    701:                                                         try running with -pc_factor_nonzeros_along_diagonal or -pc_factor_diagonal_fill",prow);
    702:   }
    703:   PetscFree(ajfill);
    704:   ISRestoreIndices(isrow,&r);
    705:   ISRestoreIndices(isicol,&ic);
    706:   PetscFree(fill);
    707:   PetscFree(im);

    709: #if defined(PETSC_USE_INFO)
    710:   {
    711:     PetscReal af = ((PetscReal)ainew[n])/((PetscReal)ai[n]);
    712:     PetscInfo3(A,"Reallocs %D Fill ratio:given %g needed %g\n",reallocate,(double)f,(double)af);
    713:     PetscInfo1(A,"Run with -pc_factor_fill %g or use \n",(double)af);
    714:     PetscInfo1(A,"PCFactorSetFill(pc,%g);\n",(double)af);
    715:     PetscInfo(A,"for best performance.\n");
    716:     if (diagonal_fill) {
    717:       PetscInfo1(A,"Detected and replaced %D missing diagonals\n",dcount);
    718:     }
    719:   }
    720: #endif

    722:   /* put together the new matrix */
    723:   MatSeqBAIJSetPreallocation_SeqBAIJ(fact,bs,MAT_SKIP_ALLOCATION,NULL);
    724:   PetscLogObjectParent((PetscObject)fact,(PetscObject)isicol);
    725:   b    = (Mat_SeqBAIJ*)fact->data;

    727:   b->free_a       = PETSC_TRUE;
    728:   b->free_ij      = PETSC_TRUE;
    729:   b->singlemalloc = PETSC_FALSE;

    731:   PetscMalloc1(bs2*ainew[n],&b->a);

    733:   b->j          = ajnew;
    734:   b->i          = ainew;
    735:   for (i=0; i<n; i++) dloc[i] += ainew[i];
    736:   b->diag          = dloc;
    737:   b->free_diag     = PETSC_TRUE;
    738:   b->ilen          = 0;
    739:   b->imax          = 0;
    740:   b->row           = isrow;
    741:   b->col           = iscol;
    742:   b->pivotinblocks = (info->pivotinblocks) ? PETSC_TRUE : PETSC_FALSE;

    744:   PetscObjectReference((PetscObject)isrow);
    745:   PetscObjectReference((PetscObject)iscol);
    746:   b->icol = isicol;
    747:   PetscMalloc1(bs*n+bs,&b->solve_work);
    748:   /* In b structure:  Free imax, ilen, old a, old j.
    749:      Allocate dloc, solve_work, new a, new j */
    750:   PetscLogObjectMemory((PetscObject)fact,(ainew[n]-n)*(sizeof(PetscInt))+bs2*ainew[n]*sizeof(PetscScalar));
    751:   b->maxnz = b->nz = ainew[n];

    753:   fact->info.factor_mallocs    = reallocate;
    754:   fact->info.fill_ratio_given  = f;
    755:   fact->info.fill_ratio_needed = ((PetscReal)ainew[n])/((PetscReal)ai[prow]);

    757:   MatSeqBAIJSetNumericFactorization_inplace(fact,both_identity);
    758:   return(0);
    759: }

    763: PetscErrorCode MatSetUnfactored_SeqBAIJ_4_NaturalOrdering_SSE(Mat A)
    764: {
    765:   /* Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data; */
    766:   /* int i,*AJ=a->j,nz=a->nz; */

    769:   /* Undo Column scaling */
    770:   /*    while (nz--) { */
    771:   /*      AJ[i] = AJ[i]/4; */
    772:   /*    } */
    773:   /* This should really invoke a push/pop logic, but we don't have that yet. */
    774:   A->ops->setunfactored = NULL;
    775:   return(0);
    776: }

    780: PetscErrorCode MatSetUnfactored_SeqBAIJ_4_NaturalOrdering_SSE_usj(Mat A)
    781: {
    782:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
    783:   PetscInt       *AJ=a->j,nz=a->nz;
    784:   unsigned short *aj=(unsigned short*)AJ;

    787:   /* Is this really necessary? */
    788:   while (nz--) {
    789:     AJ[nz] = (int)((unsigned int)aj[nz]); /* First extend, then convert to signed. */
    790:   }
    791:   A->ops->setunfactored = NULL;
    792:   return(0);
    793: }