Actual source code: baijfact.c
petsc-3.7.1 2016-05-15
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      2: /*
      3:     Factorization code for BAIJ format.
      4: */
      5: #include <../src/mat/impls/baij/seq/baij.h>
      6: #include <petsc/private/kernels/blockinvert.h>

     10: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_2(Mat B,Mat A,const MatFactorInfo *info)
     11: {
     12:   Mat            C     =B;
     13:   Mat_SeqBAIJ    *a    =(Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)C->data;
     14:   IS             isrow = b->row,isicol = b->icol;
     16:   const PetscInt *r,*ic;
     17:   PetscInt       i,j,k,nz,nzL,row,*pj;
     18:   const PetscInt n=a->mbs,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,bs2=a->bs2;
     19:   const PetscInt *ajtmp,*bjtmp,*bdiag=b->diag;
     20:   MatScalar      *rtmp,*pc,*mwork,*pv;
     21:   MatScalar      *aa=a->a,*v;
     22:   PetscInt       flg;
     23:   PetscReal      shift = info->shiftamount;
     24:   PetscBool      allowzeropivot,zeropivotdetected;

     27:   ISGetIndices(isrow,&r);
     28:   ISGetIndices(isicol,&ic);
     29:   allowzeropivot = PetscNot(A->erroriffailure);

     31:   /* generate work space needed by the factorization */
     32:   PetscMalloc2(bs2*n,&rtmp,bs2,&mwork);
     33:   PetscMemzero(rtmp,bs2*n*sizeof(MatScalar));

     35:   for (i=0; i<n; i++) {
     36:     /* zero rtmp */
     37:     /* L part */
     38:     nz    = bi[i+1] - bi[i];
     39:     bjtmp = bj + bi[i];
     40:     for  (j=0; j<nz; j++) {
     41:       PetscMemzero(rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
     42:     }

     44:     /* U part */
     45:     nz    = bdiag[i] - bdiag[i+1];
     46:     bjtmp = bj + bdiag[i+1]+1;
     47:     for  (j=0; j<nz; j++) {
     48:       PetscMemzero(rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
     49:     }

     51:     /* load in initial (unfactored row) */
     52:     nz    = ai[r[i]+1] - ai[r[i]];
     53:     ajtmp = aj + ai[r[i]];
     54:     v     = aa + bs2*ai[r[i]];
     55:     for (j=0; j<nz; j++) {
     56:       PetscMemcpy(rtmp+bs2*ic[ajtmp[j]],v+bs2*j,bs2*sizeof(MatScalar));
     57:     }

     59:     /* elimination */
     60:     bjtmp = bj + bi[i];
     61:     nzL   = bi[i+1] - bi[i];
     62:     for (k=0; k < nzL; k++) {
     63:       row = bjtmp[k];
     64:       pc  = rtmp + bs2*row;
     65:       for (flg=0,j=0; j<bs2; j++) {
     66:         if (pc[j] != (PetscScalar)0.0) {
     67:           flg = 1;
     68:           break;
     69:         }
     70:       }
     71:       if (flg) {
     72:         pv = b->a + bs2*bdiag[row];
     73:         /* PetscKernel_A_gets_A_times_B(bs,pc,pv,mwork); *pc = *pc * (*pv); */
     74:         PetscKernel_A_gets_A_times_B_2(pc,pv,mwork);

     76:         pj = b->j + bdiag[row+1]+1; /* begining of U(row,:) */
     77:         pv = b->a + bs2*(bdiag[row+1]+1);
     78:         nz = bdiag[row] - bdiag[row+1] - 1; /* num of entries inU(row,:), excluding diag */
     79:         for (j=0; j<nz; j++) {
     80:           /* PetscKernel_A_gets_A_minus_B_times_C(bs,rtmp+bs2*pj[j],pc,pv+bs2*j); */
     81:           /* rtmp+bs2*pj[j] = rtmp+bs2*pj[j] - (*pc)*(pv+bs2*j) */
     82:           v    = rtmp + 4*pj[j];
     83:           PetscKernel_A_gets_A_minus_B_times_C_2(v,pc,pv);
     84:           pv  += 4;
     85:         }
     86:         PetscLogFlops(16*nz+12); /* flops = 2*bs^3*nz + 2*bs^3 - bs2) */
     87:       }
     88:     }

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

     99:     /* Mark diagonal and invert diagonal for simplier triangular solves */
    100:     pv   = b->a + bs2*bdiag[i];
    101:     pj   = b->j + bdiag[i];
    102:     PetscMemcpy(pv,rtmp+bs2*pj[0],bs2*sizeof(MatScalar));
    103:
    104:     PetscKernel_A_gets_inverse_A_2(pv,shift,allowzeropivot,&zeropivotdetected);
    105:     if (zeropivotdetected) B->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;

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

    116:   PetscFree2(rtmp,mwork);
    117:   ISRestoreIndices(isicol,&ic);
    118:   ISRestoreIndices(isrow,&r);

    120:   C->ops->solve          = MatSolve_SeqBAIJ_2;
    121:   C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_2;
    122:   C->assembled           = PETSC_TRUE;

    124:   PetscLogFlops(1.333333333333*2*2*2*n); /* from inverting diagonal blocks */
    125:   return(0);
    126: }

    130: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_2_NaturalOrdering(Mat B,Mat A,const MatFactorInfo *info)
    131: {
    132:   Mat            C =B;
    133:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)C->data;
    135:   PetscInt       i,j,k,nz,nzL,row,*pj;
    136:   const PetscInt n=a->mbs,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,bs2=a->bs2;
    137:   const PetscInt *ajtmp,*bjtmp,*bdiag=b->diag;
    138:   MatScalar      *rtmp,*pc,*mwork,*pv;
    139:   MatScalar      *aa=a->a,*v;
    140:   PetscInt       flg;
    141:   PetscReal      shift = info->shiftamount;
    142:   PetscBool      allowzeropivot,zeropivotdetected;

    145:   allowzeropivot = PetscNot(A->erroriffailure);

    147:   /* generate work space needed by the factorization */
    148:   PetscMalloc2(bs2*n,&rtmp,bs2,&mwork);
    149:   PetscMemzero(rtmp,bs2*n*sizeof(MatScalar));

    151:   for (i=0; i<n; i++) {
    152:     /* zero rtmp */
    153:     /* L part */
    154:     nz    = bi[i+1] - bi[i];
    155:     bjtmp = bj + bi[i];
    156:     for  (j=0; j<nz; j++) {
    157:       PetscMemzero(rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
    158:     }

    160:     /* U part */
    161:     nz    = bdiag[i] - bdiag[i+1];
    162:     bjtmp = bj + bdiag[i+1]+1;
    163:     for  (j=0; j<nz; j++) {
    164:       PetscMemzero(rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
    165:     }

    167:     /* load in initial (unfactored row) */
    168:     nz    = ai[i+1] - ai[i];
    169:     ajtmp = aj + ai[i];
    170:     v     = aa + bs2*ai[i];
    171:     for (j=0; j<nz; j++) {
    172:       PetscMemcpy(rtmp+bs2*ajtmp[j],v+bs2*j,bs2*sizeof(MatScalar));
    173:     }

    175:     /* elimination */
    176:     bjtmp = bj + bi[i];
    177:     nzL   = bi[i+1] - bi[i];
    178:     for (k=0; k < nzL; k++) {
    179:       row = bjtmp[k];
    180:       pc  = rtmp + bs2*row;
    181:       for (flg=0,j=0; j<bs2; j++) {
    182:         if (pc[j]!=(PetscScalar)0.0) {
    183:           flg = 1;
    184:           break;
    185:         }
    186:       }
    187:       if (flg) {
    188:         pv = b->a + bs2*bdiag[row];
    189:         /* PetscKernel_A_gets_A_times_B(bs,pc,pv,mwork); *pc = *pc * (*pv); */
    190:         PetscKernel_A_gets_A_times_B_2(pc,pv,mwork);

    192:         pj = b->j + bdiag[row+1]+1; /* beginning of U(row,:) */
    193:         pv = b->a + bs2*(bdiag[row+1]+1);
    194:         nz = bdiag[row]-bdiag[row+1] - 1; /* num of entries in U(row,:) excluding diag */
    195:         for (j=0; j<nz; j++) {
    196:           /* PetscKernel_A_gets_A_minus_B_times_C(bs,rtmp+bs2*pj[j],pc,pv+bs2*j); */
    197:           /* rtmp+bs2*pj[j] = rtmp+bs2*pj[j] - (*pc)*(pv+bs2*j) */
    198:           v    = rtmp + 4*pj[j];
    199:           PetscKernel_A_gets_A_minus_B_times_C_2(v,pc,pv);
    200:           pv  += 4;
    201:         }
    202:         PetscLogFlops(16*nz+12); /* 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:     PetscMemcpy(pv,rtmp+bs2*pj[0],bs2*sizeof(MatScalar));
    219:
    220:     PetscKernel_A_gets_inverse_A_2(pv,shift,allowzeropivot,&zeropivotdetected);
    221:     if (zeropivotdetected) B->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;

    223:     /* U part */
    224:     /*
    225:     pv = b->a + bs2*bi[2*n-i];
    226:     pj = b->j + bi[2*n-i];
    227:     nz = bi[2*n-i+1] - bi[2*n-i] - 1;
    228:     */
    229:     pv = b->a + bs2*(bdiag[i+1]+1);
    230:     pj = b->j + bdiag[i+1]+1;
    231:     nz = bdiag[i] - bdiag[i+1] - 1;
    232:     for (j=0; j<nz; j++) {
    233:       PetscMemcpy(pv+bs2*j,rtmp+bs2*pj[j],bs2*sizeof(MatScalar));
    234:     }
    235:   }
    236:   PetscFree2(rtmp,mwork);

    238:   C->ops->solve          = MatSolve_SeqBAIJ_2_NaturalOrdering;
    239:   C->ops->forwardsolve   = MatForwardSolve_SeqBAIJ_2_NaturalOrdering;
    240:   C->ops->backwardsolve  = MatBackwardSolve_SeqBAIJ_2_NaturalOrdering;
    241:   C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_2_NaturalOrdering;
    242:   C->assembled           = PETSC_TRUE;

    244:   PetscLogFlops(1.333333333333*2*2*2*n); /* from inverting diagonal blocks */
    245:   return(0);
    246: }

    250: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_2_inplace(Mat B,Mat A,const MatFactorInfo *info)
    251: {
    252:   Mat            C     = B;
    253:   Mat_SeqBAIJ    *a    = (Mat_SeqBAIJ*)A->data,*b = (Mat_SeqBAIJ*)C->data;
    254:   IS             isrow = b->row,isicol = b->icol;
    256:   const PetscInt *r,*ic;
    257:   PetscInt       i,j,n = a->mbs,*bi = b->i,*bj = b->j;
    258:   PetscInt       *ajtmpold,*ajtmp,nz,row;
    259:   PetscInt       *diag_offset=b->diag,idx,*ai=a->i,*aj=a->j,*pj;
    260:   MatScalar      *pv,*v,*rtmp,m1,m2,m3,m4,*pc,*w,*x,x1,x2,x3,x4;
    261:   MatScalar      p1,p2,p3,p4;
    262:   MatScalar      *ba   = b->a,*aa = a->a;
    263:   PetscReal      shift = info->shiftamount;
    264:   PetscBool      allowzeropivot,zeropivotdetected;

    267:   allowzeropivot = PetscNot(A->erroriffailure);
    268:   ISGetIndices(isrow,&r);
    269:   ISGetIndices(isicol,&ic);
    270:   PetscMalloc1(4*(n+1),&rtmp);

    272:   for (i=0; i<n; i++) {
    273:     nz    = bi[i+1] - bi[i];
    274:     ajtmp = bj + bi[i];
    275:     for  (j=0; j<nz; j++) {
    276:       x = rtmp+4*ajtmp[j]; x[0] = x[1] = x[2] = x[3] = 0.0;
    277:     }
    278:     /* load in initial (unfactored row) */
    279:     idx      = r[i];
    280:     nz       = ai[idx+1] - ai[idx];
    281:     ajtmpold = aj + ai[idx];
    282:     v        = aa + 4*ai[idx];
    283:     for (j=0; j<nz; j++) {
    284:       x    = rtmp+4*ic[ajtmpold[j]];
    285:       x[0] = v[0]; x[1] = v[1]; x[2] = v[2]; x[3] = v[3];
    286:       v   += 4;
    287:     }
    288:     row = *ajtmp++;
    289:     while (row < i) {
    290:       pc = rtmp + 4*row;
    291:       p1 = pc[0]; p2 = pc[1]; p3 = pc[2]; p4 = pc[3];
    292:       if (p1 != (PetscScalar)0.0 || p2 != (PetscScalar)0.0 || p3 != (PetscScalar)0.0 || p4 != (PetscScalar)0.0) {
    293:         pv    = ba + 4*diag_offset[row];
    294:         pj    = bj + diag_offset[row] + 1;
    295:         x1    = pv[0]; x2 = pv[1]; x3 = pv[2]; x4 = pv[3];
    296:         pc[0] = m1 = p1*x1 + p3*x2;
    297:         pc[1] = m2 = p2*x1 + p4*x2;
    298:         pc[2] = m3 = p1*x3 + p3*x4;
    299:         pc[3] = m4 = p2*x3 + p4*x4;
    300:         nz    = bi[row+1] - diag_offset[row] - 1;
    301:         pv   += 4;
    302:         for (j=0; j<nz; j++) {
    303:           x1    = pv[0]; x2 = pv[1]; x3 = pv[2]; x4 = pv[3];
    304:           x     = rtmp + 4*pj[j];
    305:           x[0] -= m1*x1 + m3*x2;
    306:           x[1] -= m2*x1 + m4*x2;
    307:           x[2] -= m1*x3 + m3*x4;
    308:           x[3] -= m2*x3 + m4*x4;
    309:           pv   += 4;
    310:         }
    311:         PetscLogFlops(16.0*nz+12.0);
    312:       }
    313:       row = *ajtmp++;
    314:     }
    315:     /* finished row so stick it into b->a */
    316:     pv = ba + 4*bi[i];
    317:     pj = bj + bi[i];
    318:     nz = bi[i+1] - bi[i];
    319:     for (j=0; j<nz; j++) {
    320:       x     = rtmp+4*pj[j];
    321:       pv[0] = x[0]; pv[1] = x[1]; pv[2] = x[2]; pv[3] = x[3];
    322:       pv   += 4;
    323:     }
    324:     /* invert diagonal block */
    325:     w    = ba + 4*diag_offset[i];
    326:     PetscKernel_A_gets_inverse_A_2(w,shift,allowzeropivot,&zeropivotdetected);
    327:     if (zeropivotdetected) C->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    328:   }

    330:   PetscFree(rtmp);
    331:   ISRestoreIndices(isicol,&ic);
    332:   ISRestoreIndices(isrow,&r);

    334:   C->ops->solve          = MatSolve_SeqBAIJ_2_inplace;
    335:   C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_2_inplace;
    336:   C->assembled           = PETSC_TRUE;

    338:   PetscLogFlops(1.333333333333*8*b->mbs); /* from inverting diagonal blocks */
    339:   return(0);
    340: }
    341: /*
    342:       Version for when blocks are 2 by 2 Using natural ordering
    343: */
    346: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_2_NaturalOrdering_inplace(Mat C,Mat A,const MatFactorInfo *info)
    347: {
    348:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data,*b = (Mat_SeqBAIJ*)C->data;
    350:   PetscInt       i,j,n = a->mbs,*bi = b->i,*bj = b->j;
    351:   PetscInt       *ajtmpold,*ajtmp,nz,row;
    352:   PetscInt       *diag_offset = b->diag,*ai=a->i,*aj=a->j,*pj;
    353:   MatScalar      *pv,*v,*rtmp,*pc,*w,*x;
    354:   MatScalar      p1,p2,p3,p4,m1,m2,m3,m4,x1,x2,x3,x4;
    355:   MatScalar      *ba   = b->a,*aa = a->a;
    356:   PetscReal      shift = info->shiftamount;
    357:   PetscBool      allowzeropivot,zeropivotdetected;

    360:   allowzeropivot = PetscNot(A->erroriffailure);
    361:   PetscMalloc1(4*(n+1),&rtmp);
    362:   for (i=0; i<n; i++) {
    363:     nz    = bi[i+1] - bi[i];
    364:     ajtmp = bj + bi[i];
    365:     for  (j=0; j<nz; j++) {
    366:       x    = rtmp+4*ajtmp[j];
    367:       x[0] = x[1]  = x[2]  = x[3]  = 0.0;
    368:     }
    369:     /* load in initial (unfactored row) */
    370:     nz       = ai[i+1] - ai[i];
    371:     ajtmpold = aj + ai[i];
    372:     v        = aa + 4*ai[i];
    373:     for (j=0; j<nz; j++) {
    374:       x    = rtmp+4*ajtmpold[j];
    375:       x[0] = v[0];  x[1]  = v[1];  x[2]  = v[2];  x[3]  = v[3];
    376:       v   += 4;
    377:     }
    378:     row = *ajtmp++;
    379:     while (row < i) {
    380:       pc = rtmp + 4*row;
    381:       p1 = pc[0];  p2  = pc[1];  p3  = pc[2];  p4  = pc[3];
    382:       if (p1 != (PetscScalar)0.0 || p2 != (PetscScalar)0.0 || p3 != (PetscScalar)0.0 || p4 != (PetscScalar)0.0) {
    383:         pv    = ba + 4*diag_offset[row];
    384:         pj    = bj + diag_offset[row] + 1;
    385:         x1    = pv[0];  x2  = pv[1];  x3  = pv[2];  x4  = pv[3];
    386:         pc[0] = m1 = p1*x1 + p3*x2;
    387:         pc[1] = m2 = p2*x1 + p4*x2;
    388:         pc[2] = m3 = p1*x3 + p3*x4;
    389:         pc[3] = m4 = p2*x3 + p4*x4;
    390:         nz    = bi[row+1] - diag_offset[row] - 1;
    391:         pv   += 4;
    392:         for (j=0; j<nz; j++) {
    393:           x1    = pv[0];  x2  = pv[1];   x3 = pv[2];  x4  = pv[3];
    394:           x     = rtmp + 4*pj[j];
    395:           x[0] -= m1*x1 + m3*x2;
    396:           x[1] -= m2*x1 + m4*x2;
    397:           x[2] -= m1*x3 + m3*x4;
    398:           x[3] -= m2*x3 + m4*x4;
    399:           pv   += 4;
    400:         }
    401:         PetscLogFlops(16.0*nz+12.0);
    402:       }
    403:       row = *ajtmp++;
    404:     }
    405:     /* finished row so stick it into b->a */
    406:     pv = ba + 4*bi[i];
    407:     pj = bj + bi[i];
    408:     nz = bi[i+1] - bi[i];
    409:     for (j=0; j<nz; j++) {
    410:       x     = rtmp+4*pj[j];
    411:       pv[0] = x[0];  pv[1]  = x[1];  pv[2]  = x[2];  pv[3]  = x[3];
    412:       /*
    413:       printf(" col %d:",pj[j]);
    414:       PetscInt j1;
    415:       for (j1=0; j1<4; j1++) printf(" %g,",*(pv+j1));
    416:       printf("\n");
    417:       */
    418:       pv += 4;
    419:     }
    420:     /* invert diagonal block */
    421:     w = ba + 4*diag_offset[i];
    422:     PetscKernel_A_gets_inverse_A_2(w,shift, allowzeropivot,&zeropivotdetected);
    423:     if (zeropivotdetected) C->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    424:   }

    426:   PetscFree(rtmp);

    428:   C->ops->solve          = MatSolve_SeqBAIJ_2_NaturalOrdering_inplace;
    429:   C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_2_NaturalOrdering_inplace;
    430:   C->assembled           = PETSC_TRUE;

    432:   PetscLogFlops(1.333333333333*8*b->mbs); /* from inverting diagonal blocks */
    433:   return(0);
    434: }

    436: /* ----------------------------------------------------------- */
    437: /*
    438:      Version for when blocks are 1 by 1.
    439: */
    442: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_1(Mat B,Mat A,const MatFactorInfo *info)
    443: {
    444:   Mat             C     =B;
    445:   Mat_SeqBAIJ     *a    =(Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)C->data;
    446:   IS              isrow = b->row,isicol = b->icol;
    447:   PetscErrorCode  ierr;
    448:   const PetscInt  *r,*ic,*ics;
    449:   const PetscInt  n=a->mbs,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bdiag=b->diag;
    450:   PetscInt        i,j,k,nz,nzL,row,*pj;
    451:   const PetscInt  *ajtmp,*bjtmp;
    452:   MatScalar       *rtmp,*pc,multiplier,*pv;
    453:   const MatScalar *aa=a->a,*v;
    454:   PetscBool       row_identity,col_identity;
    455:   FactorShiftCtx  sctx;
    456:   const PetscInt  *ddiag;
    457:   PetscReal       rs;
    458:   MatScalar       d;

    461:   /* MatPivotSetUp(): initialize shift context sctx */
    462:   PetscMemzero(&sctx,sizeof(FactorShiftCtx));

    464:   if (info->shifttype == (PetscReal) MAT_SHIFT_POSITIVE_DEFINITE) { /* set sctx.shift_top=max{rs} */
    465:     ddiag          = a->diag;
    466:     sctx.shift_top = info->zeropivot;
    467:     for (i=0; i<n; i++) {
    468:       /* calculate sum(|aij|)-RealPart(aii), amt of shift needed for this row */
    469:       d  = (aa)[ddiag[i]];
    470:       rs = -PetscAbsScalar(d) - PetscRealPart(d);
    471:       v  = aa+ai[i];
    472:       nz = ai[i+1] - ai[i];
    473:       for (j=0; j<nz; j++) rs += PetscAbsScalar(v[j]);
    474:       if (rs>sctx.shift_top) sctx.shift_top = rs;
    475:     }
    476:     sctx.shift_top *= 1.1;
    477:     sctx.nshift_max = 5;
    478:     sctx.shift_lo   = 0.;
    479:     sctx.shift_hi   = 1.;
    480:   }

    482:   ISGetIndices(isrow,&r);
    483:   ISGetIndices(isicol,&ic);
    484:   PetscMalloc1(n+1,&rtmp);
    485:   ics  = ic;

    487:   do {
    488:     sctx.newshift = PETSC_FALSE;
    489:     for (i=0; i<n; i++) {
    490:       /* zero rtmp */
    491:       /* L part */
    492:       nz    = bi[i+1] - bi[i];
    493:       bjtmp = bj + bi[i];
    494:       for  (j=0; j<nz; j++) rtmp[bjtmp[j]] = 0.0;

    496:       /* U part */
    497:       nz    = bdiag[i]-bdiag[i+1];
    498:       bjtmp = bj + bdiag[i+1]+1;
    499:       for  (j=0; j<nz; j++) rtmp[bjtmp[j]] = 0.0;

    501:       /* load in initial (unfactored row) */
    502:       nz    = ai[r[i]+1] - ai[r[i]];
    503:       ajtmp = aj + ai[r[i]];
    504:       v     = aa + ai[r[i]];
    505:       for (j=0; j<nz; j++) rtmp[ics[ajtmp[j]]] = v[j];

    507:       /* ZeropivotApply() */
    508:       rtmp[i] += sctx.shift_amount;  /* shift the diagonal of the matrix */

    510:       /* elimination */
    511:       bjtmp = bj + bi[i];
    512:       row   = *bjtmp++;
    513:       nzL   = bi[i+1] - bi[i];
    514:       for (k=0; k < nzL; k++) {
    515:         pc = rtmp + row;
    516:         if (*pc != (PetscScalar)0.0) {
    517:           pv         = b->a + bdiag[row];
    518:           multiplier = *pc * (*pv);
    519:           *pc        = multiplier;

    521:           pj = b->j + bdiag[row+1]+1; /* beginning of U(row,:) */
    522:           pv = b->a + bdiag[row+1]+1;
    523:           nz = bdiag[row]-bdiag[row+1]-1; /* num of entries in U(row,:) excluding diag */
    524:           for (j=0; j<nz; j++) rtmp[pj[j]] -= multiplier * pv[j];
    525:           PetscLogFlops(2.0*nz);
    526:         }
    527:         row = *bjtmp++;
    528:       }

    530:       /* finished row so stick it into b->a */
    531:       rs = 0.0;
    532:       /* L part */
    533:       pv = b->a + bi[i];
    534:       pj = b->j + bi[i];
    535:       nz = bi[i+1] - bi[i];
    536:       for (j=0; j<nz; j++) {
    537:         pv[j] = rtmp[pj[j]]; rs += PetscAbsScalar(pv[j]);
    538:       }

    540:       /* U part */
    541:       pv = b->a + bdiag[i+1]+1;
    542:       pj = b->j + bdiag[i+1]+1;
    543:       nz = bdiag[i] - bdiag[i+1]-1;
    544:       for (j=0; j<nz; j++) {
    545:         pv[j] = rtmp[pj[j]]; rs += PetscAbsScalar(pv[j]);
    546:       }

    548:       sctx.rs = rs;
    549:       sctx.pv = rtmp[i];
    550:       MatPivotCheck(B,A,info,&sctx,i);
    551:       if (sctx.newshift) break; /* break for-loop */
    552:       rtmp[i] = sctx.pv; /* sctx.pv might be updated in the case of MAT_SHIFT_INBLOCKS */

    554:       /* Mark diagonal and invert diagonal for simplier triangular solves */
    555:       pv  = b->a + bdiag[i];
    556:       *pv = (PetscScalar)1.0/rtmp[i];

    558:     } /* endof for (i=0; i<n; i++) { */

    560:     /* MatPivotRefine() */
    561:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE && !sctx.newshift && sctx.shift_fraction>0 && sctx.nshift<sctx.nshift_max) {
    562:       /*
    563:        * if no shift in this attempt & shifting & started shifting & can refine,
    564:        * then try lower shift
    565:        */
    566:       sctx.shift_hi       = sctx.shift_fraction;
    567:       sctx.shift_fraction = (sctx.shift_hi+sctx.shift_lo)/2.;
    568:       sctx.shift_amount   = sctx.shift_fraction * sctx.shift_top;
    569:       sctx.newshift       = PETSC_TRUE;
    570:       sctx.nshift++;
    571:     }
    572:   } while (sctx.newshift);

    574:   PetscFree(rtmp);
    575:   ISRestoreIndices(isicol,&ic);
    576:   ISRestoreIndices(isrow,&r);

    578:   ISIdentity(isrow,&row_identity);
    579:   ISIdentity(isicol,&col_identity);
    580:   if (row_identity && col_identity) {
    581:     C->ops->solve          = MatSolve_SeqBAIJ_1_NaturalOrdering;
    582:     C->ops->forwardsolve   = MatForwardSolve_SeqBAIJ_1_NaturalOrdering;
    583:     C->ops->backwardsolve  = MatBackwardSolve_SeqBAIJ_1_NaturalOrdering;
    584:     C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_1_NaturalOrdering;
    585:   } else {
    586:     C->ops->solve          = MatSolve_SeqBAIJ_1;
    587:     C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_1;
    588:   }
    589:   C->assembled = PETSC_TRUE;
    590:   PetscLogFlops(C->cmap->n);

    592:   /* MatShiftView(A,info,&sctx) */
    593:   if (sctx.nshift) {
    594:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) {
    595:       PetscInfo4(A,"number of shift_pd tries %D, shift_amount %g, diagonal shifted up by %e fraction top_value %e\n",sctx.nshift,(double)sctx.shift_amount,(double)sctx.shift_fraction,(double)sctx.shift_top);
    596:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_NONZERO) {
    597:       PetscInfo2(A,"number of shift_nz tries %D, shift_amount %g\n",sctx.nshift,(double)sctx.shift_amount);
    598:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_INBLOCKS) {
    599:       PetscInfo2(A,"number of shift_inblocks applied %D, each shift_amount %g\n",sctx.nshift,(double)info->shiftamount);
    600:     }
    601:   }
    602:   return(0);
    603: }

    607: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_1_inplace(Mat C,Mat A,const MatFactorInfo *info)
    608: {
    609:   Mat_SeqBAIJ    *a    = (Mat_SeqBAIJ*)A->data,*b = (Mat_SeqBAIJ*)C->data;
    610:   IS             isrow = b->row,isicol = b->icol;
    612:   const PetscInt *r,*ic;
    613:   PetscInt       i,j,n = a->mbs,*bi = b->i,*bj = b->j;
    614:   PetscInt       *ajtmpold,*ajtmp,nz,row,*ai = a->i,*aj = a->j;
    615:   PetscInt       *diag_offset = b->diag,diag,*pj;
    616:   MatScalar      *pv,*v,*rtmp,multiplier,*pc;
    617:   MatScalar      *ba = b->a,*aa = a->a;
    618:   PetscBool      row_identity, col_identity;

    621:   ISGetIndices(isrow,&r);
    622:   ISGetIndices(isicol,&ic);
    623:   PetscMalloc1(n+1,&rtmp);

    625:   for (i=0; i<n; i++) {
    626:     nz    = bi[i+1] - bi[i];
    627:     ajtmp = bj + bi[i];
    628:     for  (j=0; j<nz; j++) rtmp[ajtmp[j]] = 0.0;

    630:     /* load in initial (unfactored row) */
    631:     nz       = ai[r[i]+1] - ai[r[i]];
    632:     ajtmpold = aj + ai[r[i]];
    633:     v        = aa + ai[r[i]];
    634:     for (j=0; j<nz; j++) rtmp[ic[ajtmpold[j]]] =  v[j];

    636:     row = *ajtmp++;
    637:     while (row < i) {
    638:       pc = rtmp + row;
    639:       if (*pc != 0.0) {
    640:         pv         = ba + diag_offset[row];
    641:         pj         = bj + diag_offset[row] + 1;
    642:         multiplier = *pc * *pv++;
    643:         *pc        = multiplier;
    644:         nz         = bi[row+1] - diag_offset[row] - 1;
    645:         for (j=0; j<nz; j++) rtmp[pj[j]] -= multiplier * pv[j];
    646:         PetscLogFlops(1.0+2.0*nz);
    647:       }
    648:       row = *ajtmp++;
    649:     }
    650:     /* finished row so stick it into b->a */
    651:     pv = ba + bi[i];
    652:     pj = bj + bi[i];
    653:     nz = bi[i+1] - bi[i];
    654:     for (j=0; j<nz; j++) pv[j] = rtmp[pj[j]];
    655:     diag = diag_offset[i] - bi[i];
    656:     /* check pivot entry for current row */
    657:     if (pv[diag] == 0.0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot: row in original ordering %D in permuted ordering %D",r[i],i);
    658:     pv[diag] = 1.0/pv[diag];
    659:   }

    661:   PetscFree(rtmp);
    662:   ISRestoreIndices(isicol,&ic);
    663:   ISRestoreIndices(isrow,&r);
    664:   ISIdentity(isrow,&row_identity);
    665:   ISIdentity(isicol,&col_identity);
    666:   if (row_identity && col_identity) {
    667:     C->ops->solve          = MatSolve_SeqBAIJ_1_NaturalOrdering_inplace;
    668:     C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_1_NaturalOrdering_inplace;
    669:   } else {
    670:     C->ops->solve          = MatSolve_SeqBAIJ_1_inplace;
    671:     C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_1_inplace;
    672:   }
    673:   C->assembled = PETSC_TRUE;
    674:   PetscLogFlops(C->cmap->n);
    675:   return(0);
    676: }

    680: PETSC_INTERN PetscErrorCode MatGetFactor_seqbaij_petsc(Mat A,const MatFactorType ftype,Mat *B)
    681: {
    682:   PetscInt       n = A->rmap->n;

    686: #if defined(PETSC_USE_COMPLEX)
    687:   if (A->hermitian && (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Hermitian Factor is not supported");
    688: #endif
    689:   MatCreate(PetscObjectComm((PetscObject)A),B);
    690:   MatSetSizes(*B,n,n,n,n);
    691:   if (ftype == MAT_FACTOR_LU || ftype == MAT_FACTOR_ILU || ftype == MAT_FACTOR_ILUDT) {
    692:     MatSetType(*B,MATSEQBAIJ);

    694:     (*B)->ops->lufactorsymbolic  = MatLUFactorSymbolic_SeqBAIJ;
    695:     (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqBAIJ;
    696:   } else if (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC) {
    697:     MatSetType(*B,MATSEQSBAIJ);
    698:     MatSeqSBAIJSetPreallocation(*B,A->rmap->bs,MAT_SKIP_ALLOCATION,NULL);

    700:     (*B)->ops->iccfactorsymbolic      = MatICCFactorSymbolic_SeqBAIJ;
    701:     (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqBAIJ;
    702:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Factor type not supported");
    703:   (*B)->factortype = ftype;

    705:   PetscFree((*B)->solvertype);
    706:   PetscStrallocpy(MATSOLVERPETSC,&(*B)->solvertype);
    707:   return(0);
    708: }

    710: /* ----------------------------------------------------------- */
    713: PetscErrorCode MatLUFactor_SeqBAIJ(Mat A,IS row,IS col,const MatFactorInfo *info)
    714: {
    716:   Mat            C;

    719:   MatGetFactor(A,MATSOLVERPETSC,MAT_FACTOR_LU,&C);
    720:   MatLUFactorSymbolic(C,A,row,col,info);
    721:   MatLUFactorNumeric(C,A,info);

    723:   A->ops->solve          = C->ops->solve;
    724:   A->ops->solvetranspose = C->ops->solvetranspose;

    726:   MatHeaderMerge(A,&C);
    727:   PetscLogObjectParent((PetscObject)A,(PetscObject)((Mat_SeqBAIJ*)(A->data))->icol);
    728:   return(0);
    729: }

    731: #include <../src/mat/impls/sbaij/seq/sbaij.h>
    734: PetscErrorCode MatCholeskyFactorNumeric_SeqBAIJ_N(Mat C,Mat A,const MatFactorInfo *info)
    735: {
    737:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ*)A->data;
    738:   Mat_SeqSBAIJ   *b=(Mat_SeqSBAIJ*)C->data;
    739:   IS             ip=b->row;
    740:   const PetscInt *rip;
    741:   PetscInt       i,j,mbs=a->mbs,bs=A->rmap->bs,*bi=b->i,*bj=b->j,*bcol;
    742:   PetscInt       *ai=a->i,*aj=a->j;
    743:   PetscInt       k,jmin,jmax,*jl,*il,col,nexti,ili,nz;
    744:   MatScalar      *rtmp,*ba=b->a,*bval,*aa=a->a,dk,uikdi;
    745:   PetscReal      rs;
    746:   FactorShiftCtx sctx;

    749:   if (bs > 1) { /* convert A to a SBAIJ matrix and apply Cholesky factorization from it */
    750:     if (!a->sbaijMat) {
    751:       MatConvert(A,MATSEQSBAIJ,MAT_INITIAL_MATRIX,&a->sbaijMat);
    752:     }
    753:     (a->sbaijMat)->ops->choleskyfactornumeric(C,a->sbaijMat,info);
    754:     MatDestroy(&a->sbaijMat);
    755:     return(0);
    756:   }

    758:   /* MatPivotSetUp(): initialize shift context sctx */
    759:   PetscMemzero(&sctx,sizeof(FactorShiftCtx));

    761:   ISGetIndices(ip,&rip);
    762:   PetscMalloc3(mbs,&rtmp,mbs,&il,mbs,&jl);

    764:   sctx.shift_amount = 0.;
    765:   sctx.nshift       = 0;
    766:   do {
    767:     sctx.newshift = PETSC_FALSE;
    768:     for (i=0; i<mbs; i++) {
    769:       rtmp[i] = 0.0; jl[i] = mbs; il[0] = 0;
    770:     }

    772:     for (k = 0; k<mbs; k++) {
    773:       bval = ba + bi[k];
    774:       /* initialize k-th row by the perm[k]-th row of A */
    775:       jmin = ai[rip[k]]; jmax = ai[rip[k]+1];
    776:       for (j = jmin; j < jmax; j++) {
    777:         col = rip[aj[j]];
    778:         if (col >= k) { /* only take upper triangular entry */
    779:           rtmp[col] = aa[j];
    780:           *bval++   = 0.0; /* for in-place factorization */
    781:         }
    782:       }

    784:       /* shift the diagonal of the matrix */
    785:       if (sctx.nshift) rtmp[k] += sctx.shift_amount;

    787:       /* modify k-th row by adding in those rows i with U(i,k)!=0 */
    788:       dk = rtmp[k];
    789:       i  = jl[k]; /* first row to be added to k_th row  */

    791:       while (i < k) {
    792:         nexti = jl[i]; /* next row to be added to k_th row */

    794:         /* compute multiplier, update diag(k) and U(i,k) */
    795:         ili     = il[i]; /* index of first nonzero element in U(i,k:bms-1) */
    796:         uikdi   = -ba[ili]*ba[bi[i]]; /* diagonal(k) */
    797:         dk     += uikdi*ba[ili];
    798:         ba[ili] = uikdi; /* -U(i,k) */

    800:         /* add multiple of row i to k-th row */
    801:         jmin = ili + 1; jmax = bi[i+1];
    802:         if (jmin < jmax) {
    803:           for (j=jmin; j<jmax; j++) rtmp[bj[j]] += uikdi*ba[j];
    804:           /* update il and jl for row i */
    805:           il[i] = jmin;
    806:           j     = bj[jmin]; jl[i] = jl[j]; jl[j] = i;
    807:         }
    808:         i = nexti;
    809:       }

    811:       /* shift the diagonals when zero pivot is detected */
    812:       /* compute rs=sum of abs(off-diagonal) */
    813:       rs   = 0.0;
    814:       jmin = bi[k]+1;
    815:       nz   = bi[k+1] - jmin;
    816:       if (nz) {
    817:         bcol = bj + jmin;
    818:         while (nz--) {
    819:           rs += PetscAbsScalar(rtmp[*bcol]);
    820:           bcol++;
    821:         }
    822:       }

    824:       sctx.rs = rs;
    825:       sctx.pv = dk;
    826:       MatPivotCheck(C,A,info,&sctx,k);
    827:       if (sctx.newshift) break;
    828:       dk = sctx.pv;

    830:       /* copy data into U(k,:) */
    831:       ba[bi[k]] = 1.0/dk; /* U(k,k) */
    832:       jmin      = bi[k]+1; jmax = bi[k+1];
    833:       if (jmin < jmax) {
    834:         for (j=jmin; j<jmax; j++) {
    835:           col = bj[j]; ba[j] = rtmp[col]; rtmp[col] = 0.0;
    836:         }
    837:         /* add the k-th row into il and jl */
    838:         il[k] = jmin;
    839:         i     = bj[jmin]; jl[k] = jl[i]; jl[i] = k;
    840:       }
    841:     }
    842:   } while (sctx.newshift);
    843:   PetscFree3(rtmp,il,jl);

    845:   ISRestoreIndices(ip,&rip);

    847:   C->assembled    = PETSC_TRUE;
    848:   C->preallocated = PETSC_TRUE;

    850:   PetscLogFlops(C->rmap->N);
    851:   if (sctx.nshift) {
    852:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) {
    853:       PetscInfo2(A,"number of shiftpd tries %D, shift_amount %g\n",sctx.nshift,(double)sctx.shift_amount);
    854:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_NONZERO) {
    855:       PetscInfo2(A,"number of shiftnz tries %D, shift_amount %g\n",sctx.nshift,(double)sctx.shift_amount);
    856:     }
    857:   }
    858:   return(0);
    859: }

    863: PetscErrorCode MatCholeskyFactorNumeric_SeqBAIJ_N_NaturalOrdering(Mat C,Mat A,const MatFactorInfo *info)
    864: {
    865:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ*)A->data;
    866:   Mat_SeqSBAIJ   *b=(Mat_SeqSBAIJ*)C->data;
    868:   PetscInt       i,j,am=a->mbs;
    869:   PetscInt       *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
    870:   PetscInt       k,jmin,*jl,*il,nexti,ili,*acol,*bcol,nz;
    871:   MatScalar      *rtmp,*ba=b->a,*aa=a->a,dk,uikdi,*aval,*bval;
    872:   PetscReal      rs;
    873:   FactorShiftCtx sctx;

    876:   /* MatPivotSetUp(): initialize shift context sctx */
    877:   PetscMemzero(&sctx,sizeof(FactorShiftCtx));

    879:   PetscMalloc3(am,&rtmp,am,&il,am,&jl);

    881:   do {
    882:     sctx.newshift = PETSC_FALSE;
    883:     for (i=0; i<am; i++) {
    884:       rtmp[i] = 0.0; jl[i] = am; il[0] = 0;
    885:     }

    887:     for (k = 0; k<am; k++) {
    888:       /* initialize k-th row with elements nonzero in row perm(k) of A */
    889:       nz   = ai[k+1] - ai[k];
    890:       acol = aj + ai[k];
    891:       aval = aa + ai[k];
    892:       bval = ba + bi[k];
    893:       while (nz--) {
    894:         if (*acol < k) { /* skip lower triangular entries */
    895:           acol++; aval++;
    896:         } else {
    897:           rtmp[*acol++] = *aval++;
    898:           *bval++       = 0.0; /* for in-place factorization */
    899:         }
    900:       }

    902:       /* shift the diagonal of the matrix */
    903:       if (sctx.nshift) rtmp[k] += sctx.shift_amount;

    905:       /* modify k-th row by adding in those rows i with U(i,k)!=0 */
    906:       dk = rtmp[k];
    907:       i  = jl[k]; /* first row to be added to k_th row  */

    909:       while (i < k) {
    910:         nexti = jl[i]; /* next row to be added to k_th row */
    911:         /* compute multiplier, update D(k) and U(i,k) */
    912:         ili     = il[i]; /* index of first nonzero element in U(i,k:bms-1) */
    913:         uikdi   = -ba[ili]*ba[bi[i]];
    914:         dk     += uikdi*ba[ili];
    915:         ba[ili] = uikdi; /* -U(i,k) */

    917:         /* add multiple of row i to k-th row ... */
    918:         jmin = ili + 1;
    919:         nz   = bi[i+1] - jmin;
    920:         if (nz > 0) {
    921:           bcol = bj + jmin;
    922:           bval = ba + jmin;
    923:           while (nz--) rtmp[*bcol++] += uikdi*(*bval++);
    924:           /* update il and jl for i-th row */
    925:           il[i] = jmin;
    926:           j     = bj[jmin]; jl[i] = jl[j]; jl[j] = i;
    927:         }
    928:         i = nexti;
    929:       }

    931:       /* shift the diagonals when zero pivot is detected */
    932:       /* compute rs=sum of abs(off-diagonal) */
    933:       rs   = 0.0;
    934:       jmin = bi[k]+1;
    935:       nz   = bi[k+1] - jmin;
    936:       if (nz) {
    937:         bcol = bj + jmin;
    938:         while (nz--) {
    939:           rs += PetscAbsScalar(rtmp[*bcol]);
    940:           bcol++;
    941:         }
    942:       }

    944:       sctx.rs = rs;
    945:       sctx.pv = dk;
    946:       MatPivotCheck(C,A,info,&sctx,k);
    947:       if (sctx.newshift) break;    /* sctx.shift_amount is updated */
    948:       dk = sctx.pv;

    950:       /* copy data into U(k,:) */
    951:       ba[bi[k]] = 1.0/dk;
    952:       jmin      = bi[k]+1;
    953:       nz        = bi[k+1] - jmin;
    954:       if (nz) {
    955:         bcol = bj + jmin;
    956:         bval = ba + jmin;
    957:         while (nz--) {
    958:           *bval++       = rtmp[*bcol];
    959:           rtmp[*bcol++] = 0.0;
    960:         }
    961:         /* add k-th row into il and jl */
    962:         il[k] = jmin;
    963:         i     = bj[jmin]; jl[k] = jl[i]; jl[i] = k;
    964:       }
    965:     }
    966:   } while (sctx.newshift);
    967:   PetscFree3(rtmp,il,jl);

    969:   C->ops->solve          = MatSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
    970:   C->ops->solvetranspose = MatSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
    971:   C->assembled           = PETSC_TRUE;
    972:   C->preallocated        = PETSC_TRUE;

    974:   PetscLogFlops(C->rmap->N);
    975:   if (sctx.nshift) {
    976:     if (info->shifttype == (PetscReal)MAT_SHIFT_NONZERO) {
    977:       PetscInfo2(A,"number of shiftnz tries %D, shift_amount %g\n",sctx.nshift,(double)sctx.shift_amount);
    978:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) {
    979:       PetscInfo2(A,"number of shiftpd tries %D, shift_amount %g\n",sctx.nshift,(double)sctx.shift_amount);
    980:     }
    981:   }
    982:   return(0);
    983: }

    985: #include <petscbt.h>
    986: #include <../src/mat/utils/freespace.h>
    989: PetscErrorCode MatICCFactorSymbolic_SeqBAIJ(Mat fact,Mat A,IS perm,const MatFactorInfo *info)
    990: {
    991:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data;
    992:   Mat_SeqSBAIJ       *b;
    993:   Mat                B;
    994:   PetscErrorCode     ierr;
    995:   PetscBool          perm_identity,missing;
    996:   PetscInt           reallocs=0,i,*ai=a->i,*aj=a->j,am=a->mbs,bs=A->rmap->bs,*ui;
    997:   const PetscInt     *rip;
    998:   PetscInt           jmin,jmax,nzk,k,j,*jl,prow,*il,nextprow;
    999:   PetscInt           nlnk,*lnk,*lnk_lvl=NULL,ncols,ncols_upper,*cols,*cols_lvl,*uj,**uj_ptr,**uj_lvl_ptr;
    1000:   PetscReal          fill          =info->fill,levels=info->levels;
    1001:   PetscFreeSpaceList free_space    =NULL,current_space=NULL;
    1002:   PetscFreeSpaceList free_space_lvl=NULL,current_space_lvl=NULL;
    1003:   PetscBT            lnkbt;

    1006:   MatMissingDiagonal(A,&missing,&i);
    1007:   if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry %D",i);

    1009:   if (bs > 1) {
    1010:     if (!a->sbaijMat) {
    1011:       MatConvert(A,MATSEQSBAIJ,MAT_INITIAL_MATRIX,&a->sbaijMat);
    1012:     }
    1013:     (fact)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqSBAIJ;  /* undue the change made in MatGetFactor_seqbaij_petsc */

    1015:     MatICCFactorSymbolic(fact,a->sbaijMat,perm,info);
    1016:     return(0);
    1017:   }

    1019:   ISIdentity(perm,&perm_identity);
    1020:   ISGetIndices(perm,&rip);

    1022:   /* special case that simply copies fill pattern */
    1023:   if (!levels && perm_identity) {
    1024:     PetscMalloc1(am+1,&ui);
    1025:     for (i=0; i<am; i++) ui[i] = ai[i+1] - a->diag[i]; /* ui: rowlengths - changes when !perm_identity */
    1026:     B    = fact;
    1027:     MatSeqSBAIJSetPreallocation(B,1,0,ui);

    1030:     b  = (Mat_SeqSBAIJ*)B->data;
    1031:     uj = b->j;
    1032:     for (i=0; i<am; i++) {
    1033:       aj = a->j + a->diag[i];
    1034:       for (j=0; j<ui[i]; j++) *uj++ = *aj++;
    1035:       b->ilen[i] = ui[i];
    1036:     }
    1037:     PetscFree(ui);

    1039:     B->factortype = MAT_FACTOR_NONE;

    1041:     MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    1042:     MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    1043:     B->factortype = MAT_FACTOR_ICC;

    1045:     B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqBAIJ_N_NaturalOrdering;
    1046:     return(0);
    1047:   }

    1049:   /* initialization */
    1050:   PetscMalloc1(am+1,&ui);
    1051:   ui[0] = 0;
    1052:   PetscMalloc1(2*am+1,&cols_lvl);

    1054:   /* jl: linked list for storing indices of the pivot rows
    1055:      il: il[i] points to the 1st nonzero entry of U(i,k:am-1) */
    1056:   PetscMalloc4(am,&uj_ptr,am,&uj_lvl_ptr,am,&il,am,&jl);
    1057:   for (i=0; i<am; i++) {
    1058:     jl[i] = am; il[i] = 0;
    1059:   }

    1061:   /* create and initialize a linked list for storing column indices of the active row k */
    1062:   nlnk = am + 1;
    1063:   PetscIncompleteLLCreate(am,am,nlnk,lnk,lnk_lvl,lnkbt);

    1065:   /* initial FreeSpace size is fill*(ai[am]+am)/2 */
    1066:   PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am]/2,am/2)),&free_space);

    1068:   current_space = free_space;

    1070:   PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am]/2,am/2)),&free_space_lvl);
    1071:   current_space_lvl = free_space_lvl;

    1073:   for (k=0; k<am; k++) {  /* for each active row k */
    1074:     /* initialize lnk by the column indices of row rip[k] of A */
    1075:     nzk         = 0;
    1076:     ncols       = ai[rip[k]+1] - ai[rip[k]];
    1077:     ncols_upper = 0;
    1078:     cols        = cols_lvl + am;
    1079:     for (j=0; j<ncols; j++) {
    1080:       i = rip[*(aj + ai[rip[k]] + j)];
    1081:       if (i >= k) { /* only take upper triangular entry */
    1082:         cols[ncols_upper]     = i;
    1083:         cols_lvl[ncols_upper] = -1;  /* initialize level for nonzero entries */
    1084:         ncols_upper++;
    1085:       }
    1086:     }
    1087:     PetscIncompleteLLAdd(ncols_upper,cols,levels,cols_lvl,am,nlnk,lnk,lnk_lvl,lnkbt);
    1088:     nzk += nlnk;

    1090:     /* update lnk by computing fill-in for each pivot row to be merged in */
    1091:     prow = jl[k]; /* 1st pivot row */

    1093:     while (prow < k) {
    1094:       nextprow = jl[prow];

    1096:       /* merge prow into k-th row */
    1097:       jmin  = il[prow] + 1; /* index of the 2nd nzero entry in U(prow,k:am-1) */
    1098:       jmax  = ui[prow+1];
    1099:       ncols = jmax-jmin;
    1100:       i     = jmin - ui[prow];
    1101:       cols  = uj_ptr[prow] + i; /* points to the 2nd nzero entry in U(prow,k:am-1) */
    1102:       for (j=0; j<ncols; j++) cols_lvl[j] = *(uj_lvl_ptr[prow] + i + j);
    1103:       PetscIncompleteLLAddSorted(ncols,cols,levels,cols_lvl,am,nlnk,lnk,lnk_lvl,lnkbt);
    1104:       nzk += nlnk;

    1106:       /* update il and jl for prow */
    1107:       if (jmin < jmax) {
    1108:         il[prow] = jmin;

    1110:         j = *cols; jl[prow] = jl[j]; jl[j] = prow;
    1111:       }
    1112:       prow = nextprow;
    1113:     }

    1115:     /* if free space is not available, make more free space */
    1116:     if (current_space->local_remaining<nzk) {
    1117:       i    = am - k + 1; /* num of unfactored rows */
    1118:       i    = PetscMin(PetscIntMultTruncate(i,nzk), PetscIntMultTruncate(i,i-1)); /* i*nzk, i*(i-1): estimated and max additional space needed */
    1119:       PetscFreeSpaceGet(i,&current_space);
    1120:       PetscFreeSpaceGet(i,&current_space_lvl);
    1121:       reallocs++;
    1122:     }

    1124:     /* copy data into free_space and free_space_lvl, then initialize lnk */
    1125:     PetscIncompleteLLClean(am,am,nzk,lnk,lnk_lvl,current_space->array,current_space_lvl->array,lnkbt);

    1127:     /* add the k-th row into il and jl */
    1128:     if (nzk-1 > 0) {
    1129:       i     = current_space->array[1]; /* col value of the first nonzero element in U(k, k+1:am-1) */
    1130:       jl[k] = jl[i]; jl[i] = k;
    1131:       il[k] = ui[k] + 1;
    1132:     }
    1133:     uj_ptr[k]     = current_space->array;
    1134:     uj_lvl_ptr[k] = current_space_lvl->array;

    1136:     current_space->array           += nzk;
    1137:     current_space->local_used      += nzk;
    1138:     current_space->local_remaining -= nzk;

    1140:     current_space_lvl->array           += nzk;
    1141:     current_space_lvl->local_used      += nzk;
    1142:     current_space_lvl->local_remaining -= nzk;

    1144:     ui[k+1] = ui[k] + nzk;
    1145:   }

    1147:   ISRestoreIndices(perm,&rip);
    1148:   PetscFree4(uj_ptr,uj_lvl_ptr,il,jl);
    1149:   PetscFree(cols_lvl);

    1151:   /* copy free_space into uj and free free_space; set uj in new datastructure; */
    1152:   PetscMalloc1(ui[am]+1,&uj);
    1153:   PetscFreeSpaceContiguous(&free_space,uj);
    1154:   PetscIncompleteLLDestroy(lnk,lnkbt);
    1155:   PetscFreeSpaceDestroy(free_space_lvl);

    1157:   /* put together the new matrix in MATSEQSBAIJ format */
    1158:   B    = fact;
    1159:   MatSeqSBAIJSetPreallocation(B,1,MAT_SKIP_ALLOCATION,NULL);

    1161:   b                = (Mat_SeqSBAIJ*)B->data;
    1162:   b->singlemalloc  = PETSC_FALSE;
    1163:   b->free_a        = PETSC_TRUE;
    1164:   b->free_ij       = PETSC_TRUE;

    1166:   PetscMalloc1(ui[am]+1,&b->a);

    1168:   b->j             = uj;
    1169:   b->i             = ui;
    1170:   b->diag          = 0;
    1171:   b->ilen          = 0;
    1172:   b->imax          = 0;
    1173:   b->row           = perm;
    1174:   b->pivotinblocks = PETSC_FALSE; /* need to get from MatFactorInfo */

    1176:   PetscObjectReference((PetscObject)perm);

    1178:   b->icol = perm;

    1180:   PetscObjectReference((PetscObject)perm);
    1181:   PetscMalloc1(am+1,&b->solve_work);
    1182:   PetscLogObjectMemory((PetscObject)B,(ui[am]-am)*(sizeof(PetscInt)+sizeof(MatScalar)));

    1184:   b->maxnz = b->nz = ui[am];

    1186:   B->info.factor_mallocs   = reallocs;
    1187:   B->info.fill_ratio_given = fill;
    1188:   if (ai[am] != 0.) {
    1189:     /* nonzeros in lower triangular part of A (includign diagonals)= (ai[am]+am)/2 */
    1190:     B->info.fill_ratio_needed = ((PetscReal)2*ui[am])/(ai[am]+am);
    1191:   } else {
    1192:     B->info.fill_ratio_needed = 0.0;
    1193:   }
    1194: #if defined(PETSC_USE_INFO)
    1195:   if (ai[am] != 0) {
    1196:     PetscReal af = B->info.fill_ratio_needed;
    1197:     PetscInfo3(A,"Reallocs %D Fill ratio:given %g needed %g\n",reallocs,(double)fill,(double)af);
    1198:     PetscInfo1(A,"Run with -pc_factor_fill %g or use \n",(double)af);
    1199:     PetscInfo1(A,"PCFactorSetFill(pc,%g) for best performance.\n",(double)af);
    1200:   } else {
    1201:     PetscInfo(A,"Empty matrix.\n");
    1202:   }
    1203: #endif
    1204:   if (perm_identity) {
    1205:     B->ops->solve                 = MatSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
    1206:     B->ops->solvetranspose        = MatSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
    1207:     B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqBAIJ_N_NaturalOrdering;
    1208:   } else {
    1209:     (fact)->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqBAIJ_N;
    1210:   }
    1211:   return(0);
    1212: }

    1216: PetscErrorCode MatCholeskyFactorSymbolic_SeqBAIJ(Mat fact,Mat A,IS perm,const MatFactorInfo *info)
    1217: {
    1218:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data;
    1219:   Mat_SeqSBAIJ       *b;
    1220:   Mat                B;
    1221:   PetscErrorCode     ierr;
    1222:   PetscBool          perm_identity,missing;
    1223:   PetscReal          fill = info->fill;
    1224:   const PetscInt     *rip;
    1225:   PetscInt           i,mbs=a->mbs,bs=A->rmap->bs,*ai=a->i,*aj=a->j,reallocs=0,prow;
    1226:   PetscInt           *jl,jmin,jmax,nzk,*ui,k,j,*il,nextprow;
    1227:   PetscInt           nlnk,*lnk,ncols,ncols_upper,*cols,*uj,**ui_ptr,*uj_ptr;
    1228:   PetscFreeSpaceList free_space=NULL,current_space=NULL;
    1229:   PetscBT            lnkbt;

    1232:   if (bs > 1) { /* convert to seqsbaij */
    1233:     if (!a->sbaijMat) {
    1234:       MatConvert(A,MATSEQSBAIJ,MAT_INITIAL_MATRIX,&a->sbaijMat);
    1235:     }
    1236:     (fact)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqSBAIJ; /* undue the change made in MatGetFactor_seqbaij_petsc */

    1238:     MatCholeskyFactorSymbolic(fact,a->sbaijMat,perm,info);
    1239:     return(0);
    1240:   }

    1242:   MatMissingDiagonal(A,&missing,&i);
    1243:   if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry %D",i);

    1245:   /* check whether perm is the identity mapping */
    1246:   ISIdentity(perm,&perm_identity);
    1247:   if (!perm_identity) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix reordering is not supported");
    1248:   ISGetIndices(perm,&rip);

    1250:   /* initialization */
    1251:   PetscMalloc1(mbs+1,&ui);
    1252:   ui[0] = 0;

    1254:   /* jl: linked list for storing indices of the pivot rows
    1255:      il: il[i] points to the 1st nonzero entry of U(i,k:mbs-1) */
    1256:   PetscMalloc4(mbs,&ui_ptr,mbs,&il,mbs,&jl,mbs,&cols);
    1257:   for (i=0; i<mbs; i++) {
    1258:     jl[i] = mbs; il[i] = 0;
    1259:   }

    1261:   /* create and initialize a linked list for storing column indices of the active row k */
    1262:   nlnk = mbs + 1;
    1263:   PetscLLCreate(mbs,mbs,nlnk,lnk,lnkbt);

    1265:   /* initial FreeSpace size is fill* (ai[mbs]+mbs)/2 */
    1266:   PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[mbs]/2,mbs/2)),&free_space);

    1268:   current_space = free_space;

    1270:   for (k=0; k<mbs; k++) {  /* for each active row k */
    1271:     /* initialize lnk by the column indices of row rip[k] of A */
    1272:     nzk         = 0;
    1273:     ncols       = ai[rip[k]+1] - ai[rip[k]];
    1274:     ncols_upper = 0;
    1275:     for (j=0; j<ncols; j++) {
    1276:       i = rip[*(aj + ai[rip[k]] + j)];
    1277:       if (i >= k) { /* only take upper triangular entry */
    1278:         cols[ncols_upper] = i;
    1279:         ncols_upper++;
    1280:       }
    1281:     }
    1282:     PetscLLAdd(ncols_upper,cols,mbs,nlnk,lnk,lnkbt);
    1283:     nzk += nlnk;

    1285:     /* update lnk by computing fill-in for each pivot row to be merged in */
    1286:     prow = jl[k]; /* 1st pivot row */

    1288:     while (prow < k) {
    1289:       nextprow = jl[prow];
    1290:       /* merge prow into k-th row */
    1291:       jmin   = il[prow] + 1; /* index of the 2nd nzero entry in U(prow,k:mbs-1) */
    1292:       jmax   = ui[prow+1];
    1293:       ncols  = jmax-jmin;
    1294:       uj_ptr = ui_ptr[prow] + jmin - ui[prow]; /* points to the 2nd nzero entry in U(prow,k:mbs-1) */
    1295:       PetscLLAddSorted(ncols,uj_ptr,mbs,nlnk,lnk,lnkbt);
    1296:       nzk   += nlnk;

    1298:       /* update il and jl for prow */
    1299:       if (jmin < jmax) {
    1300:         il[prow] = jmin;
    1301:         j        = *uj_ptr;
    1302:         jl[prow] = jl[j];
    1303:         jl[j]    = prow;
    1304:       }
    1305:       prow = nextprow;
    1306:     }

    1308:     /* if free space is not available, make more free space */
    1309:     if (current_space->local_remaining<nzk) {
    1310:       i    = mbs - k + 1; /* num of unfactored rows */
    1311:       i    = PetscMin(PetscIntMultTruncate(i,nzk), PetscIntMultTruncate(i,i-1)); /* i*nzk, i*(i-1): estimated and max additional space needed */
    1312:       PetscFreeSpaceGet(i,&current_space);
    1313:       reallocs++;
    1314:     }

    1316:     /* copy data into free space, then initialize lnk */
    1317:     PetscLLClean(mbs,mbs,nzk,lnk,current_space->array,lnkbt);

    1319:     /* add the k-th row into il and jl */
    1320:     if (nzk-1 > 0) {
    1321:       i     = current_space->array[1]; /* col value of the first nonzero element in U(k, k+1:mbs-1) */
    1322:       jl[k] = jl[i]; jl[i] = k;
    1323:       il[k] = ui[k] + 1;
    1324:     }
    1325:     ui_ptr[k]                       = current_space->array;
    1326:     current_space->array           += nzk;
    1327:     current_space->local_used      += nzk;
    1328:     current_space->local_remaining -= nzk;

    1330:     ui[k+1] = ui[k] + nzk;
    1331:   }

    1333:   ISRestoreIndices(perm,&rip);
    1334:   PetscFree4(ui_ptr,il,jl,cols);

    1336:   /* copy free_space into uj and free free_space; set uj in new datastructure; */
    1337:   PetscMalloc1(ui[mbs]+1,&uj);
    1338:   PetscFreeSpaceContiguous(&free_space,uj);
    1339:   PetscLLDestroy(lnk,lnkbt);

    1341:   /* put together the new matrix in MATSEQSBAIJ format */
    1342:   B    = fact;
    1343:   MatSeqSBAIJSetPreallocation(B,bs,MAT_SKIP_ALLOCATION,NULL);

    1345:   b               = (Mat_SeqSBAIJ*)B->data;
    1346:   b->singlemalloc = PETSC_FALSE;
    1347:   b->free_a       = PETSC_TRUE;
    1348:   b->free_ij      = PETSC_TRUE;

    1350:   PetscMalloc1(ui[mbs]+1,&b->a);

    1352:   b->j             = uj;
    1353:   b->i             = ui;
    1354:   b->diag          = 0;
    1355:   b->ilen          = 0;
    1356:   b->imax          = 0;
    1357:   b->row           = perm;
    1358:   b->pivotinblocks = PETSC_FALSE; /* need to get from MatFactorInfo */

    1360:   PetscObjectReference((PetscObject)perm);
    1361:   b->icol  = perm;
    1362:   PetscObjectReference((PetscObject)perm);
    1363:   PetscMalloc1(mbs+1,&b->solve_work);
    1364:   PetscLogObjectMemory((PetscObject)B,(ui[mbs]-mbs)*(sizeof(PetscInt)+sizeof(MatScalar)));
    1365:   b->maxnz = b->nz = ui[mbs];

    1367:   B->info.factor_mallocs   = reallocs;
    1368:   B->info.fill_ratio_given = fill;
    1369:   if (ai[mbs] != 0.) {
    1370:     /* nonzeros in lower triangular part of A = (ai[mbs]+mbs)/2 */
    1371:     B->info.fill_ratio_needed = ((PetscReal)2*ui[mbs])/(ai[mbs]+mbs);
    1372:   } else {
    1373:     B->info.fill_ratio_needed = 0.0;
    1374:   }
    1375: #if defined(PETSC_USE_INFO)
    1376:   if (ai[mbs] != 0.) {
    1377:     PetscReal af = B->info.fill_ratio_needed;
    1378:     PetscInfo3(A,"Reallocs %D Fill ratio:given %g needed %g\n",reallocs,(double)fill,(double)af);
    1379:     PetscInfo1(A,"Run with -pc_factor_fill %g or use \n",(double)af);
    1380:     PetscInfo1(A,"PCFactorSetFill(pc,%g) for best performance.\n",(double)af);
    1381:   } else {
    1382:     PetscInfo(A,"Empty matrix.\n");
    1383:   }
    1384: #endif
    1385:   if (perm_identity) {
    1386:     B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqBAIJ_N_NaturalOrdering;
    1387:   } else {
    1388:     B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqBAIJ_N;
    1389:   }
    1390:   return(0);
    1391: }

    1395: PetscErrorCode MatSolve_SeqBAIJ_N_NaturalOrdering(Mat A,Vec bb,Vec xx)
    1396: {
    1397:   Mat_SeqBAIJ       *a=(Mat_SeqBAIJ*)A->data;
    1398:   PetscErrorCode    ierr;
    1399:   const PetscInt    *ai=a->i,*aj=a->j,*adiag=a->diag,*vi;
    1400:   PetscInt          i,k,n=a->mbs;
    1401:   PetscInt          nz,bs=A->rmap->bs,bs2=a->bs2;
    1402:   const MatScalar   *aa=a->a,*v;
    1403:   PetscScalar       *x,*s,*t,*ls;
    1404:   const PetscScalar *b;

    1407:   VecGetArrayRead(bb,&b);
    1408:   VecGetArray(xx,&x);
    1409:   t    = a->solve_work;

    1411:   /* forward solve the lower triangular */
    1412:   PetscMemcpy(t,b,bs*sizeof(PetscScalar)); /* copy 1st block of b to t */

    1414:   for (i=1; i<n; i++) {
    1415:     v    = aa + bs2*ai[i];
    1416:     vi   = aj + ai[i];
    1417:     nz   = ai[i+1] - ai[i];
    1418:     s    = t + bs*i;
    1419:     PetscMemcpy(s,b+bs*i,bs*sizeof(PetscScalar)); /* copy i_th block of b to t */
    1420:     for (k=0;k<nz;k++) {
    1421:       PetscKernel_v_gets_v_minus_A_times_w(bs,s,v,t+bs*vi[k]);
    1422:       v += bs2;
    1423:     }
    1424:   }

    1426:   /* backward solve the upper triangular */
    1427:   ls = a->solve_work + A->cmap->n;
    1428:   for (i=n-1; i>=0; i--) {
    1429:     v    = aa + bs2*(adiag[i+1]+1);
    1430:     vi   = aj + adiag[i+1]+1;
    1431:     nz   = adiag[i] - adiag[i+1]-1;
    1432:     PetscMemcpy(ls,t+i*bs,bs*sizeof(PetscScalar));
    1433:     for (k=0; k<nz; k++) {
    1434:       PetscKernel_v_gets_v_minus_A_times_w(bs,ls,v,t+bs*vi[k]);
    1435:       v += bs2;
    1436:     }
    1437:     PetscKernel_w_gets_A_times_v(bs,ls,aa+bs2*adiag[i],t+i*bs); /* *inv(diagonal[i]) */
    1438:     PetscMemcpy(x+i*bs,t+i*bs,bs*sizeof(PetscScalar));
    1439:   }

    1441:   VecRestoreArrayRead(bb,&b);
    1442:   VecRestoreArray(xx,&x);
    1443:   PetscLogFlops(2.0*(a->bs2)*(a->nz) - A->rmap->bs*A->cmap->n);
    1444:   return(0);
    1445: }

    1449: PetscErrorCode MatSolve_SeqBAIJ_N(Mat A,Vec bb,Vec xx)
    1450: {
    1451:   Mat_SeqBAIJ        *a   =(Mat_SeqBAIJ*)A->data;
    1452:   IS                 iscol=a->col,isrow=a->row;
    1453:   PetscErrorCode     ierr;
    1454:   const PetscInt     *r,*c,*rout,*cout,*ai=a->i,*aj=a->j,*adiag=a->diag,*vi;
    1455:   PetscInt           i,m,n=a->mbs;
    1456:   PetscInt           nz,bs=A->rmap->bs,bs2=a->bs2;
    1457:   const MatScalar    *aa=a->a,*v;
    1458:   PetscScalar        *x,*s,*t,*ls;
    1459:   const PetscScalar  *b;

    1462:   VecGetArrayRead(bb,&b);
    1463:   VecGetArray(xx,&x);
    1464:   t    = a->solve_work;

    1466:   ISGetIndices(isrow,&rout); r = rout;
    1467:   ISGetIndices(iscol,&cout); c = cout;

    1469:   /* forward solve the lower triangular */
    1470:   PetscMemcpy(t,b+bs*r[0],bs*sizeof(PetscScalar));
    1471:   for (i=1; i<n; i++) {
    1472:     v    = aa + bs2*ai[i];
    1473:     vi   = aj + ai[i];
    1474:     nz   = ai[i+1] - ai[i];
    1475:     s    = t + bs*i;
    1476:     PetscMemcpy(s,b+bs*r[i],bs*sizeof(PetscScalar));
    1477:     for (m=0; m<nz; m++) {
    1478:       PetscKernel_v_gets_v_minus_A_times_w(bs,s,v,t+bs*vi[m]);
    1479:       v += bs2;
    1480:     }
    1481:   }

    1483:   /* backward solve the upper triangular */
    1484:   ls = a->solve_work + A->cmap->n;
    1485:   for (i=n-1; i>=0; i--) {
    1486:     v    = aa + bs2*(adiag[i+1]+1);
    1487:     vi   = aj + adiag[i+1]+1;
    1488:     nz   = adiag[i] - adiag[i+1] - 1;
    1489:     PetscMemcpy(ls,t+i*bs,bs*sizeof(PetscScalar));
    1490:     for (m=0; m<nz; m++) {
    1491:       PetscKernel_v_gets_v_minus_A_times_w(bs,ls,v,t+bs*vi[m]);
    1492:       v += bs2;
    1493:     }
    1494:     PetscKernel_w_gets_A_times_v(bs,ls,v,t+i*bs); /* *inv(diagonal[i]) */
    1495:     PetscMemcpy(x + bs*c[i],t+i*bs,bs*sizeof(PetscScalar));
    1496:   }
    1497:   ISRestoreIndices(isrow,&rout);
    1498:   ISRestoreIndices(iscol,&cout);
    1499:   VecRestoreArrayRead(bb,&b);
    1500:   VecRestoreArray(xx,&x);
    1501:   PetscLogFlops(2.0*(a->bs2)*(a->nz) - A->rmap->bs*A->cmap->n);
    1502:   return(0);
    1503: }

    1507: /*
    1508:     For each block in an block array saves the largest absolute value in the block into another array
    1509: */
    1510: static PetscErrorCode MatBlockAbs_private(PetscInt nbs,PetscInt bs2,PetscScalar *blockarray,PetscReal *absarray)
    1511: {
    1513:   PetscInt       i,j;

    1516:   PetscMemzero(absarray,(nbs+1)*sizeof(PetscReal));
    1517:   for (i=0; i<nbs; i++) {
    1518:     for (j=0; j<bs2; j++) {
    1519:       if (absarray[i] < PetscAbsScalar(blockarray[i*nbs+j])) absarray[i] = PetscAbsScalar(blockarray[i*nbs+j]);
    1520:     }
    1521:   }
    1522:   return(0);
    1523: }

    1527: /*
    1528:      This needs to be renamed and called by the regular MatILUFactor_SeqBAIJ when drop tolerance is used
    1529: */
    1530: PetscErrorCode MatILUDTFactor_SeqBAIJ(Mat A,IS isrow,IS iscol,const MatFactorInfo *info,Mat *fact)
    1531: {
    1532:   Mat            B = *fact;
    1533:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ*)A->data,*b;
    1534:   IS             isicol;
    1536:   const PetscInt *r,*ic;
    1537:   PetscInt       i,mbs=a->mbs,bs=A->rmap->bs,bs2=a->bs2,*ai=a->i,*aj=a->j,*ajtmp,*adiag;
    1538:   PetscInt       *bi,*bj,*bdiag;

    1540:   PetscInt  row,nzi,nzi_bl,nzi_bu,*im,dtcount,nzi_al,nzi_au;
    1541:   PetscInt  nlnk,*lnk;
    1542:   PetscBT   lnkbt;
    1543:   PetscBool row_identity,icol_identity;
    1544:   MatScalar *aatmp,*pv,*batmp,*ba,*rtmp,*pc,*multiplier,*vtmp;
    1545:   PetscInt  j,nz,*pj,*bjtmp,k,ncut,*jtmp;

    1547:   PetscReal dt=info->dt;          /* shift=info->shiftamount; */
    1548:   PetscInt  nnz_max;
    1549:   PetscBool missing;
    1550:   PetscReal *vtmp_abs;
    1551:   MatScalar *v_work;
    1552:   PetscInt  *v_pivots;
    1553:   PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;

    1556:   /* ------- symbolic factorization, can be reused ---------*/
    1557:   MatMissingDiagonal(A,&missing,&i);
    1558:   if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry %D",i);
    1559:   adiag=a->diag;

    1561:   ISInvertPermutation(iscol,PETSC_DECIDE,&isicol);

    1563:   /* bdiag is location of diagonal in factor */
    1564:   PetscMalloc1(mbs+1,&bdiag);

    1566:   /* allocate row pointers bi */
    1567:   PetscMalloc1(2*mbs+2,&bi);

    1569:   /* allocate bj and ba; max num of nonzero entries is (ai[n]+2*n*dtcount+2) */
    1570:   dtcount = (PetscInt)info->dtcount;
    1571:   if (dtcount > mbs-1) dtcount = mbs-1;
    1572:   nnz_max = ai[mbs]+2*mbs*dtcount +2;
    1573:   /* printf("MatILUDTFactor_SeqBAIJ, bs %d, ai[mbs] %d, nnz_max  %d, dtcount %d\n",bs,ai[mbs],nnz_max,dtcount); */
    1574:   PetscMalloc1(nnz_max,&bj);
    1575:   nnz_max = nnz_max*bs2;
    1576:   PetscMalloc1(nnz_max,&ba);

    1578:   /* put together the new matrix */
    1579:   MatSeqBAIJSetPreallocation_SeqBAIJ(B,bs,MAT_SKIP_ALLOCATION,NULL);
    1580:   PetscLogObjectParent((PetscObject)B,(PetscObject)isicol);

    1582:   b               = (Mat_SeqBAIJ*)(B)->data;
    1583:   b->free_a       = PETSC_TRUE;
    1584:   b->free_ij      = PETSC_TRUE;
    1585:   b->singlemalloc = PETSC_FALSE;

    1587:   b->a    = ba;
    1588:   b->j    = bj;
    1589:   b->i    = bi;
    1590:   b->diag = bdiag;
    1591:   b->ilen = 0;
    1592:   b->imax = 0;
    1593:   b->row  = isrow;
    1594:   b->col  = iscol;

    1596:   PetscObjectReference((PetscObject)isrow);
    1597:   PetscObjectReference((PetscObject)iscol);

    1599:   b->icol  = isicol;
    1600:   PetscMalloc1(bs*(mbs+1),&b->solve_work);
    1601:   PetscLogObjectMemory((PetscObject)B,nnz_max*(sizeof(PetscInt)+sizeof(MatScalar)));
    1602:   b->maxnz = nnz_max/bs2;

    1604:   (B)->factortype            = MAT_FACTOR_ILUDT;
    1605:   (B)->info.factor_mallocs   = 0;
    1606:   (B)->info.fill_ratio_given = ((PetscReal)nnz_max)/((PetscReal)(ai[mbs]*bs2));
    1607:   /* ------- end of symbolic factorization ---------*/
    1608:   ISGetIndices(isrow,&r);
    1609:   ISGetIndices(isicol,&ic);

    1611:   /* linked list for storing column indices of the active row */
    1612:   nlnk = mbs + 1;
    1613:   PetscLLCreate(mbs,mbs,nlnk,lnk,lnkbt);

    1615:   /* im: used by PetscLLAddSortedLU(); jtmp: working array for column indices of active row */
    1616:   PetscMalloc2(mbs,&im,mbs,&jtmp);
    1617:   /* rtmp, vtmp: working arrays for sparse and contiguous row entries of active row */
    1618:   PetscMalloc2(mbs*bs2,&rtmp,mbs*bs2,&vtmp);
    1619:   PetscMalloc1(mbs+1,&vtmp_abs);
    1620:   PetscMalloc3(bs,&v_work,bs2,&multiplier,bs,&v_pivots);

    1622:   allowzeropivot = PetscNot(A->erroriffailure);
    1623:   bi[0]       = 0;
    1624:   bdiag[0]    = (nnz_max/bs2)-1; /* location of diagonal in factor B */
    1625:   bi[2*mbs+1] = bdiag[0]+1; /* endof bj and ba array */
    1626:   for (i=0; i<mbs; i++) {
    1627:     /* copy initial fill into linked list */
    1628:     nzi = ai[r[i]+1] - ai[r[i]];
    1629:     if (!nzi) 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);
    1630:     nzi_al = adiag[r[i]] - ai[r[i]];
    1631:     nzi_au = ai[r[i]+1] - adiag[r[i]] -1;

    1633:     /* load in initial unfactored row */
    1634:     ajtmp = aj + ai[r[i]];
    1635:     PetscLLAddPerm(nzi,ajtmp,ic,mbs,nlnk,lnk,lnkbt);
    1636:     PetscMemzero(rtmp,mbs*bs2*sizeof(PetscScalar));
    1637:     aatmp = a->a + bs2*ai[r[i]];
    1638:     for (j=0; j<nzi; j++) {
    1639:       PetscMemcpy(rtmp+bs2*ic[ajtmp[j]],aatmp+bs2*j,bs2*sizeof(MatScalar));
    1640:     }

    1642:     /* add pivot rows into linked list */
    1643:     row = lnk[mbs];
    1644:     while (row < i) {
    1645:       nzi_bl = bi[row+1] - bi[row] + 1;
    1646:       bjtmp  = bj + bdiag[row+1]+1; /* points to 1st column next to the diagonal in U */
    1647:       PetscLLAddSortedLU(bjtmp,row,nlnk,lnk,lnkbt,i,nzi_bl,im);
    1648:       nzi   += nlnk;
    1649:       row    = lnk[row];
    1650:     }

    1652:     /* copy data from lnk into jtmp, then initialize lnk */
    1653:     PetscLLClean(mbs,mbs,nzi,lnk,jtmp,lnkbt);

    1655:     /* numerical factorization */
    1656:     bjtmp = jtmp;
    1657:     row   = *bjtmp++; /* 1st pivot row */

    1659:     while  (row < i) {
    1660:       pc = rtmp + bs2*row;
    1661:       pv = ba + bs2*bdiag[row]; /* inv(diag) of the pivot row */
    1662:       PetscKernel_A_gets_A_times_B(bs,pc,pv,multiplier); /* pc= multiplier = pc*inv(diag[row]) */
    1663:       MatBlockAbs_private(1,bs2,pc,vtmp_abs);
    1664:       if (vtmp_abs[0] > dt) { /* apply tolerance dropping rule */
    1665:         pj = bj + bdiag[row+1] + 1;         /* point to 1st entry of U(row,:) */
    1666:         pv = ba + bs2*(bdiag[row+1] + 1);
    1667:         nz = bdiag[row] - bdiag[row+1] - 1;         /* num of entries in U(row,:), excluding diagonal */
    1668:         for (j=0; j<nz; j++) {
    1669:           PetscKernel_A_gets_A_minus_B_times_C(bs,rtmp+bs2*pj[j],pc,pv+bs2*j);
    1670:         }
    1671:         /* PetscLogFlops(bslog*(nz+1.0)-bs); */
    1672:       }
    1673:       row = *bjtmp++;
    1674:     }

    1676:     /* copy sparse rtmp into contiguous vtmp; separate L and U part */
    1677:     nzi_bl = 0; j = 0;
    1678:     while (jtmp[j] < i) { /* L-part. Note: jtmp is sorted */
    1679:       PetscMemcpy(vtmp+bs2*j,rtmp+bs2*jtmp[j],bs2*sizeof(MatScalar));
    1680:       nzi_bl++; j++;
    1681:     }
    1682:     nzi_bu = nzi - nzi_bl -1;

    1684:     while (j < nzi) { /* U-part */
    1685:       PetscMemcpy(vtmp+bs2*j,rtmp+bs2*jtmp[j],bs2*sizeof(MatScalar));
    1686:       j++;
    1687:     }

    1689:     MatBlockAbs_private(nzi,bs2,vtmp,vtmp_abs);
    1690:
    1691:     bjtmp = bj + bi[i];
    1692:     batmp = ba + bs2*bi[i];
    1693:     /* apply level dropping rule to L part */
    1694:     ncut = nzi_al + dtcount;
    1695:     if (ncut < nzi_bl) {
    1696:       PetscSortSplitReal(ncut,nzi_bl,vtmp_abs,jtmp);
    1697:       PetscSortIntWithScalarArray(ncut,jtmp,vtmp);
    1698:     } else {
    1699:       ncut = nzi_bl;
    1700:     }
    1701:     for (j=0; j<ncut; j++) {
    1702:       bjtmp[j] = jtmp[j];
    1703:       PetscMemcpy(batmp+bs2*j,rtmp+bs2*bjtmp[j],bs2*sizeof(MatScalar));
    1704:     }
    1705:     bi[i+1] = bi[i] + ncut;
    1706:     nzi     = ncut + 1;

    1708:     /* apply level dropping rule to U part */
    1709:     ncut = nzi_au + dtcount;
    1710:     if (ncut < nzi_bu) {
    1711:       PetscSortSplitReal(ncut,nzi_bu,vtmp_abs+nzi_bl+1,jtmp+nzi_bl+1);
    1712:       PetscSortIntWithScalarArray(ncut,jtmp+nzi_bl+1,vtmp+nzi_bl+1);
    1713:     } else {
    1714:       ncut = nzi_bu;
    1715:     }
    1716:     nzi += ncut;

    1718:     /* mark bdiagonal */
    1719:     bdiag[i+1]    = bdiag[i] - (ncut + 1);
    1720:     bi[2*mbs - i] = bi[2*mbs - i +1] - (ncut + 1);

    1722:     bjtmp  = bj + bdiag[i];
    1723:     batmp  = ba + bs2*bdiag[i];
    1724:     PetscMemcpy(batmp,rtmp+bs2*i,bs2*sizeof(MatScalar));
    1725:     *bjtmp = i;
    1726:
    1727:     bjtmp = bj + bdiag[i+1]+1;
    1728:     batmp = ba + (bdiag[i+1]+1)*bs2;

    1730:     for (k=0; k<ncut; k++) {
    1731:       bjtmp[k] = jtmp[nzi_bl+1+k];
    1732:       PetscMemcpy(batmp+bs2*k,rtmp+bs2*bjtmp[k],bs2*sizeof(MatScalar));
    1733:     }

    1735:     im[i] = nzi; /* used by PetscLLAddSortedLU() */

    1737:     /* invert diagonal block for simplier triangular solves - add shift??? */
    1738:     batmp = ba + bs2*bdiag[i];

    1740:     PetscKernel_A_gets_inverse_A(bs,batmp,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
    1741:     if (zeropivotdetected) B->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1742:   } /* for (i=0; i<mbs; i++) */
    1743:   PetscFree3(v_work,multiplier,v_pivots);

    1745:   /* printf("end of L %d, beginning of U %d\n",bi[mbs],bdiag[mbs]); */
    1746:   if (bi[mbs] >= bdiag[mbs]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"end of L array %d cannot >= the beginning of U array %d",bi[mbs],bdiag[mbs]);

    1748:   ISRestoreIndices(isrow,&r);
    1749:   ISRestoreIndices(isicol,&ic);

    1751:   PetscLLDestroy(lnk,lnkbt);

    1753:   PetscFree2(im,jtmp);
    1754:   PetscFree2(rtmp,vtmp);

    1756:   PetscLogFlops(bs2*B->cmap->n);
    1757:   b->maxnz = b->nz = bi[mbs] + bdiag[0] - bdiag[mbs];

    1759:   ISIdentity(isrow,&row_identity);
    1760:   ISIdentity(isicol,&icol_identity);
    1761:   if (row_identity && icol_identity) {
    1762:     B->ops->solve = MatSolve_SeqBAIJ_N_NaturalOrdering;
    1763:   } else {
    1764:     B->ops->solve = MatSolve_SeqBAIJ_N;
    1765:   }

    1767:   B->ops->solveadd          = 0;
    1768:   B->ops->solvetranspose    = 0;
    1769:   B->ops->solvetransposeadd = 0;
    1770:   B->ops->matsolve          = 0;
    1771:   B->assembled              = PETSC_TRUE;
    1772:   B->preallocated           = PETSC_TRUE;
    1773:   return(0);
    1774: }