Date: 2013-07-02T14:43:33+00:00

Actual source code: sbaijcholmod.c
petsc-3.4.2 2013-07-02

      2: /*
      3:    Provides an interface to the CHOLMOD 1.7.1 sparse solver

      5:    When build with PETSC_USE_64BIT_INDICES this will use UF_Long as the
      6:    integer type in UMFPACK, otherwise it will use int. This means
      7:    all integers in this file as simply declared as PetscInt. Also it means
      8:    that UMFPACK UL_Long version MUST be built with 64 bit integers when used.

     10: */

     12: #include <../src/mat/impls/sbaij/seq/sbaij.h>
     13: #include <../src/mat/impls/sbaij/seq/cholmod/cholmodimpl.h>

     15: /*
     16:    This is a terrible hack, but it allows the error handler to retain a context.
     17:    Note that this hack really cannot be made both reentrant and concurrent.
     18: */
     19: static Mat static_F;

     23: static void CholmodErrorHandler(int status,const char *file,int line,const char *message)
     24: {

     27:   if (status > CHOLMOD_OK) {
     28:     PetscInfo4(static_F,"CHOLMOD warning %d at %s:%d: %s",status,file,line,message);
     29:   } else if (status == CHOLMOD_OK) { /* Documentation says this can happen, but why? */
     30:     PetscInfo3(static_F,"CHOLMOD OK at %s:%d: %s",file,line,message);
     31:   } else {
     32:     PetscErrorPrintf("CHOLMOD error %d at %s:%d: %s\n",status,file,line,message);
     33:   }
     34:   PetscFunctionReturnVoid();
     35: }

     39: PetscErrorCode  CholmodStart(Mat F)
     40: {
     42:   Mat_CHOLMOD    *chol=(Mat_CHOLMOD*)F->spptr;
     43:   cholmod_common *c;
     44:   PetscBool      flg;

     47:   if (chol->common) return(0);
     48:   PetscMalloc(sizeof(*chol->common),&chol->common);
     49:   !cholmod_X_start(chol->common);

     51:   c                = chol->common;
     52:   c->error_handler = CholmodErrorHandler;

     54: #define CHOLMOD_OPTION_DOUBLE(name,help) do {                            \
     55:     PetscReal tmp = (PetscReal)c->name;                                  \
     56:     PetscOptionsReal("-mat_cholmod_" #name,help,"None",tmp,&tmp,0); \
     57:     c->name = (double)tmp;                                               \
     58: } while (0)

     60: #define CHOLMOD_OPTION_INT(name,help) do {                               \
     61:     PetscInt tmp = (PetscInt)c->name;                                    \
     62:     PetscOptionsInt("-mat_cholmod_" #name,help,"None",tmp,&tmp,0); \
     63:     c->name = (int)tmp;                                                  \
     64: } while (0)

     66: #define CHOLMOD_OPTION_SIZE_T(name,help) do {                            \
     67:     PetscInt tmp = (PetscInt)c->name;                                    \
     68:     PetscOptionsInt("-mat_cholmod_" #name,help,"None",tmp,&tmp,0); \
     69:     if (tmp < 0) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_OUTOFRANGE,"value must be positive"); \
     70:     c->name = (size_t)tmp;                                               \
     71: } while (0)

     73: #define CHOLMOD_OPTION_TRUTH(name,help) do {                             \
     74:     PetscBool tmp = (PetscBool) !!c->name;                              \
     75:     PetscOptionsBool("-mat_cholmod_" #name,help,"None",tmp,&tmp,0); \
     76:     c->name = (int)tmp;                                                  \
     77: } while (0)

     79:   PetscOptionsBegin(PetscObjectComm((PetscObject)F),((PetscObject)F)->prefix,"CHOLMOD Options","Mat");
     80:   /* CHOLMOD handles first-time packing and refactor-packing separately, but we usually want them to be the same. */
     81:   chol->pack = (PetscBool)c->final_pack;

     83:   PetscOptionsBool("-mat_cholmod_pack","Pack factors after factorization [disable for frequent repeat factorization]","None",chol->pack,&chol->pack,0);
     84:   c->final_pack = (int)chol->pack;

     86:   CHOLMOD_OPTION_DOUBLE(dbound,"Minimum absolute value of diagonal entries of D");
     87:   CHOLMOD_OPTION_DOUBLE(grow0,"Global growth ratio when factors are modified");
     88:   CHOLMOD_OPTION_DOUBLE(grow1,"Column growth ratio when factors are modified");
     89:   CHOLMOD_OPTION_SIZE_T(grow2,"Affine column growth constant when factors are modified");
     90:   CHOLMOD_OPTION_SIZE_T(maxrank,"Max rank of update, larger values are faster but use more memory [2,4,8]");
     91:   {
     92:     static const char *const list[] = {"SIMPLICIAL","AUTO","SUPERNODAL","MatCholmodFactorType","MAT_CHOLMOD_FACTOR_",0};
     93:     PetscEnum                choice = (PetscEnum)c->supernodal;

     95:     PetscOptionsEnum("-mat_cholmod_factor","Factorization method","None",list,(PetscEnum)c->supernodal,&choice,0);
     96:     c->supernodal = (int)choice;
     97:   }
     98:   if (c->supernodal) CHOLMOD_OPTION_DOUBLE(supernodal_switch,"flop/nnz_L threshold for switching to supernodal factorization");
     99:   CHOLMOD_OPTION_TRUTH(final_asis,"Leave factors \"as is\"");
    100:   CHOLMOD_OPTION_TRUTH(final_pack,"Pack the columns when finished (use FALSE if the factors will be updated later)");
    101:   if (!c->final_asis) {
    102:     CHOLMOD_OPTION_TRUTH(final_super,"Leave supernodal factors instead of converting to simplicial");
    103:     CHOLMOD_OPTION_TRUTH(final_ll,"Turn LDL' factorization into LL'");
    104:     CHOLMOD_OPTION_TRUTH(final_monotonic,"Ensure columns are monotonic when done");
    105:     CHOLMOD_OPTION_TRUTH(final_resymbol,"Remove numerically zero values resulting from relaxed supernodal amalgamation");
    106:   }
    107:   {
    108:     PetscReal tmp[] = {(PetscReal)c->zrelax[0],(PetscReal)c->zrelax[1],(PetscReal)c->zrelax[2]};
    109:     PetscInt  n     = 3;
    110:     PetscOptionsRealArray("-mat_cholmod_zrelax","3 real supernodal relaxed amalgamation parameters","None",tmp,&n,&flg);
    111:     if (flg && n != 3) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_OUTOFRANGE,"must provide exactly 3 parameters to -mat_cholmod_zrelax");
    112:     if (flg) while (n--) c->zrelax[n] = (double)tmp[n];
    113:   }
    114:   {
    115:     PetscInt n,tmp[] = {(PetscInt)c->nrelax[0],(PetscInt)c->nrelax[1],(PetscInt)c->nrelax[2]};
    116:     PetscOptionsIntArray("-mat_cholmod_nrelax","3 size_t supernodal relaxed amalgamation parameters","None",tmp,&n,&flg);
    117:     if (flg && n != 3) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_OUTOFRANGE,"must provide exactly 3 parameters to -mat_cholmod_nrelax");
    118:     if (flg) while (n--) c->nrelax[n] = (size_t)tmp[n];
    119:   }
    120:   CHOLMOD_OPTION_TRUTH(prefer_upper,"Work with upper triangular form [faster when using fill-reducing ordering, slower in natural ordering]");
    121:   CHOLMOD_OPTION_TRUTH(default_nesdis,"Use NESDIS instead of METIS for nested dissection");
    122:   CHOLMOD_OPTION_INT(print,"Verbosity level");
    123:   PetscOptionsEnd();
    124:   return(0);
    125: }

    129: static PetscErrorCode MatWrapCholmod_seqsbaij(Mat A,PetscBool values,cholmod_sparse *C,PetscBool  *aijalloc)
    130: {
    131:   Mat_SeqSBAIJ   *sbaij = (Mat_SeqSBAIJ*)A->data;

    135:   PetscMemzero(C,sizeof(*C));
    136:   /* CHOLMOD uses column alignment, SBAIJ stores the upper factor, so we pass it on as a lower factor, swapping the meaning of row and column */
    137:   C->nrow   = (size_t)A->cmap->n;
    138:   C->ncol   = (size_t)A->rmap->n;
    139:   C->nzmax  = (size_t)sbaij->maxnz;
    140:   C->p      = sbaij->i;
    141:   C->i      = sbaij->j;
    142:   C->x      = sbaij->a;
    143:   C->stype  = -1;
    144:   C->itype  = CHOLMOD_INT_TYPE;
    145:   C->xtype  = CHOLMOD_SCALAR_TYPE;
    146:   C->dtype  = CHOLMOD_DOUBLE;
    147:   C->sorted = 1;
    148:   C->packed = 1;
    149:   *aijalloc = PETSC_FALSE;
    150:   return(0);
    151: }

    155: static PetscErrorCode VecWrapCholmod(Vec X,cholmod_dense *Y)
    156: {
    158:   PetscScalar    *x;
    159:   PetscInt       n;

    162:   PetscMemzero(Y,sizeof(*Y));
    163:   VecGetArray(X,&x);
    164:   VecGetSize(X,&n);

    166:   Y->x     = (double*)x;
    167:   Y->nrow  = n;
    168:   Y->ncol  = 1;
    169:   Y->nzmax = n;
    170:   Y->d     = n;
    171:   Y->x     = (double*)x;
    172:   Y->xtype = CHOLMOD_SCALAR_TYPE;
    173:   Y->dtype = CHOLMOD_DOUBLE;
    174:   return(0);
    175: }

    179: PetscErrorCode  MatDestroy_CHOLMOD(Mat F)
    180: {
    182:   Mat_CHOLMOD    *chol=(Mat_CHOLMOD*)F->spptr;

    185:   if (chol) {
    186:     !cholmod_X_free_factor(&chol->factor,chol->common);
    187:     !cholmod_X_finish(chol->common);
    188:     PetscFree(chol->common);
    189:     PetscFree(chol->matrix);
    190:     (*chol->Destroy)(F);
    191:   }
    192:   PetscFree(F->spptr);
    193:   return(0);
    194: }

    196: static PetscErrorCode MatSolve_CHOLMOD(Mat,Vec,Vec);

    198: static const char *const CholmodOrderingMethods[] = {"User","AMD","METIS","NESDIS(default)","Natural","NESDIS(small=20000)","NESDIS(small=4,no constrained)","NESDIS()"};

    202: static PetscErrorCode MatFactorInfo_CHOLMOD(Mat F,PetscViewer viewer)
    203: {
    204:   Mat_CHOLMOD          *chol = (Mat_CHOLMOD*)F->spptr;
    205:   const cholmod_common *c    = chol->common;
    206:   PetscErrorCode       ierr;
    207:   PetscInt             i;

    210:   if (F->ops->solve != MatSolve_CHOLMOD) return(0);
    211:   PetscViewerASCIIPrintf(viewer,"CHOLMOD run parameters:\n");
    212:   PetscViewerASCIIPushTab(viewer);
    213:   PetscViewerASCIIPrintf(viewer,"Pack factors after symbolic factorization: %s\n",chol->pack ? "TRUE" : "FALSE");
    214:   PetscViewerASCIIPrintf(viewer,"Common.dbound            %g  (Smallest absolute value of diagonal entries of D)\n",c->dbound);
    215:   PetscViewerASCIIPrintf(viewer,"Common.grow0             %g\n",c->grow0);
    216:   PetscViewerASCIIPrintf(viewer,"Common.grow1             %g\n",c->grow1);
    217:   PetscViewerASCIIPrintf(viewer,"Common.grow2             %u\n",(unsigned)c->grow2);
    218:   PetscViewerASCIIPrintf(viewer,"Common.maxrank           %u\n",(unsigned)c->maxrank);
    219:   PetscViewerASCIIPrintf(viewer,"Common.supernodal_switch %g\n",c->supernodal_switch);
    220:   PetscViewerASCIIPrintf(viewer,"Common.supernodal        %d\n",c->supernodal);
    221:   PetscViewerASCIIPrintf(viewer,"Common.final_asis        %d\n",c->final_asis);
    222:   PetscViewerASCIIPrintf(viewer,"Common.final_super       %d\n",c->final_super);
    223:   PetscViewerASCIIPrintf(viewer,"Common.final_ll          %d\n",c->final_ll);
    224:   PetscViewerASCIIPrintf(viewer,"Common.final_pack        %d\n",c->final_pack);
    225:   PetscViewerASCIIPrintf(viewer,"Common.final_monotonic   %d\n",c->final_monotonic);
    226:   PetscViewerASCIIPrintf(viewer,"Common.final_resymbol    %d\n",c->final_resymbol);
    227:   PetscViewerASCIIPrintf(viewer,"Common.zrelax            [%g,%g,%g]\n",c->zrelax[0],c->zrelax[1],c->zrelax[2]);
    228:   PetscViewerASCIIPrintf(viewer,"Common.nrelax            [%u,%u,%u]\n",(unsigned)c->nrelax[0],(unsigned)c->nrelax[1],(unsigned)c->nrelax[2]);
    229:   PetscViewerASCIIPrintf(viewer,"Common.prefer_upper      %d\n",c->prefer_upper);
    230:   PetscViewerASCIIPrintf(viewer,"Common.print             %d\n",c->print);
    231:   for (i=0; i<c->nmethods; i++) {
    232:     PetscViewerASCIIPrintf(viewer,"Ordering method %D%s:\n",i,i==c->selected ? " [SELECTED]" : "");
    233:     PetscViewerASCIIPrintf(viewer,"  lnz %g, fl %g, prune_dense %g, prune_dense2 %g\n",
    234:                                   c->method[i].lnz,c->method[i].fl,c->method[i].prune_dense,c->method[i].prune_dense2);
    235:   }
    236:   PetscViewerASCIIPrintf(viewer,"Common.postorder         %d\n",c->postorder);
    237:   PetscViewerASCIIPrintf(viewer,"Common.default_nesdis    %d (use NESDIS instead of METIS for nested dissection)\n",c->default_nesdis);
    238:   /* Statistics */
    239:   PetscViewerASCIIPrintf(viewer,"Common.fl                %g (flop count from most recent analysis)\n",c->fl);
    240:   PetscViewerASCIIPrintf(viewer,"Common.lnz               %g (fundamental nz in L)\n",c->lnz);
    241:   PetscViewerASCIIPrintf(viewer,"Common.anz               %g\n",c->anz);
    242:   PetscViewerASCIIPrintf(viewer,"Common.modfl             %g (flop count from most recent update)\n",c->modfl);
    243:   PetscViewerASCIIPrintf(viewer,"Common.malloc_count      %g (number of live objects)\n",(double)c->malloc_count);
    244:   PetscViewerASCIIPrintf(viewer,"Common.memory_usage      %g (peak memory usage in bytes)\n",(double)c->memory_usage);
    245:   PetscViewerASCIIPrintf(viewer,"Common.memory_inuse      %g (current memory usage in bytes)\n",(double)c->memory_inuse);
    246:   PetscViewerASCIIPrintf(viewer,"Common.nrealloc_col      %g (number of column reallocations)\n",c->nrealloc_col);
    247:   PetscViewerASCIIPrintf(viewer,"Common.nrealloc_factor   %g (number of factor reallocations due to column reallocations)\n",c->nrealloc_factor);
    248:   PetscViewerASCIIPrintf(viewer,"Common.ndbounds_hit      %g (number of times diagonal was modified by dbound)\n",c->ndbounds_hit);
    249:   PetscViewerASCIIPrintf(viewer,"Common.rowfacfl          %g (number of flops in last call to cholmod_rowfac)\n",c->rowfacfl);
    250:   PetscViewerASCIIPrintf(viewer,"Common.aatfl             %g (number of flops to compute A(:,f)*A(:,f)')\n",c->aatfl);
    251:   PetscViewerASCIIPopTab(viewer);
    252:   return(0);
    253: }

    257: PetscErrorCode  MatView_CHOLMOD(Mat F,PetscViewer viewer)
    258: {
    259:   PetscErrorCode    ierr;
    260:   PetscBool         iascii;
    261:   PetscViewerFormat format;

    264:   MatView_SeqSBAIJ(F,viewer);
    265:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    266:   if (iascii) {
    267:     PetscViewerGetFormat(viewer,&format);
    268:     if (format == PETSC_VIEWER_ASCII_INFO) {
    269:       MatFactorInfo_CHOLMOD(F,viewer);
    270:     }
    271:   }
    272:   return(0);
    273: }

    277: static PetscErrorCode MatSolve_CHOLMOD(Mat F,Vec B,Vec X)
    278: {
    279:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->spptr;
    280:   cholmod_dense  cholB,*cholX;
    281:   PetscScalar    *x;

    285:   VecWrapCholmod(B,&cholB);
    286:   static_F = F;
    287:   cholX    = cholmod_X_solve(CHOLMOD_A,chol->factor,&cholB,chol->common);
    288:   if (!cholX) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"CHOLMOD failed");
    289:   VecGetArray(X,&x);
    290:   PetscMemcpy(x,cholX->x,cholX->nrow*sizeof(*x));
    291:   !cholmod_X_free_dense(&cholX,chol->common);
    292:   VecRestoreArray(X,&x);
    293:   return(0);
    294: }

    298: static PetscErrorCode MatCholeskyFactorNumeric_CHOLMOD(Mat F,Mat A,const MatFactorInfo *info)
    299: {
    300:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->spptr;
    301:   cholmod_sparse cholA;
    302:   PetscBool      aijalloc;

    306:   (*chol->Wrap)(A,PETSC_TRUE,&cholA,&aijalloc);
    307:   static_F = F;
    308:   !cholmod_X_factorize(&cholA,chol->factor,chol->common);
    309:   if (ierr) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD factorization failed with status %d",chol->common->status);
    310:   if (chol->common->status == CHOLMOD_NOT_POSDEF) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_MAT_CH_ZRPVT,"CHOLMOD detected that the matrix is not positive definite, failure at column %u",(unsigned)chol->factor->minor);

    312:   if (aijalloc) {PetscFree3(cholA.p,cholA.i,cholA.x);}

    314:   F->ops->solve          = MatSolve_CHOLMOD;
    315:   F->ops->solvetranspose = MatSolve_CHOLMOD;
    316:   return(0);
    317: }

    321: PetscErrorCode  MatCholeskyFactorSymbolic_CHOLMOD(Mat F,Mat A,IS perm,const MatFactorInfo *info)
    322: {
    323:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->spptr;
    325:   cholmod_sparse cholA;
    326:   PetscBool      aijalloc;
    327:   PetscInt       *fset = 0;
    328:   size_t         fsize = 0;

    331:   (*chol->Wrap)(A,PETSC_FALSE,&cholA,&aijalloc);
    332:   static_F = F;
    333:   if (chol->factor) {
    334:     !cholmod_X_resymbol(&cholA,fset,fsize,(int)chol->pack,chol->factor,chol->common);
    335:     if (ierr) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
    336:   } else if (perm) {
    337:     const PetscInt *ip;
    338:     ISGetIndices(perm,&ip);
    339:     chol->factor = cholmod_X_analyze_p(&cholA,(PetscInt*)ip,fset,fsize,chol->common);
    340:     if (!chol->factor) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
    341:     ISRestoreIndices(perm,&ip);
    342:   } else {
    343:     chol->factor = cholmod_X_analyze(&cholA,chol->common);
    344:     if (!chol->factor) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
    345:   }

    347:   if (aijalloc) {PetscFree3(cholA.p,cholA.i,cholA.x);}

    349:   F->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_CHOLMOD;
    350:   return(0);
    351: }

    355: PetscErrorCode MatFactorGetSolverPackage_seqsbaij_cholmod(Mat A,const MatSolverPackage *type)
    356: {
    358:   *type = MATSOLVERCHOLMOD;
    359:   return(0);
    360: }

    362: /*MC
    363:   MATSOLVERCHOLMOD = "cholmod" - A matrix type providing direct solvers (Cholesky) for sequential matrices
    364:   via the external package CHOLMOD.

    366:   ./configure --download-cholmod to install PETSc to use CHOLMOD

    368:   Consult CHOLMOD documentation for more information about the Common parameters
    369:   which correspond to the options database keys below.

    371:   Options Database Keys:
    372: + -mat_cholmod_dbound <0>          - Minimum absolute value of diagonal entries of D (None)
    373: . -mat_cholmod_grow0 <1.2>         - Global growth ratio when factors are modified (None)
    374: . -mat_cholmod_grow1 <1.2>         - Column growth ratio when factors are modified (None)
    375: . -mat_cholmod_grow2 <5>           - Affine column growth constant when factors are modified (None)
    376: . -mat_cholmod_maxrank <8>         - Max rank of update, larger values are faster but use more memory [2,4,8] (None)
    377: . -mat_cholmod_factor <AUTO>       - (choose one of) SIMPLICIAL AUTO SUPERNODAL
    378: . -mat_cholmod_supernodal_switch <40> - flop/nnz_L threshold for switching to supernodal factorization (None)
    379: . -mat_cholmod_final_asis <TRUE>   - Leave factors "as is" (None)
    380: . -mat_cholmod_final_pack <TRUE>   - Pack the columns when finished (use FALSE if the factors will be updated later) (None)
    381: . -mat_cholmod_zrelax <0.8>        - 3 real supernodal relaxed amalgamation parameters (None)
    382: . -mat_cholmod_nrelax <4>          - 3 size_t supernodal relaxed amalgamation parameters (None)
    383: . -mat_cholmod_prefer_upper <TRUE> - Work with upper triangular form (faster when using fill-reducing ordering, slower in natural ordering) (None)
    384: - -mat_cholmod_print <3>           - Verbosity level (None)

    386:    Level: beginner

    388: .seealso: PCCHOLESKY, PCFactorSetMatSolverPackage(), MatSolverPackage
    389: M*/

    393: PETSC_EXTERN PetscErrorCode MatGetFactor_seqsbaij_cholmod(Mat A,MatFactorType ftype,Mat *F)
    394: {
    395:   Mat            B;
    396:   Mat_CHOLMOD    *chol;
    398:   PetscInt       m=A->rmap->n,n=A->cmap->n,bs;

    401:   if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_SUP,"CHOLMOD cannot do %s factorization with SBAIJ, only %s",
    402:                                              MatFactorTypes[ftype],MatFactorTypes[MAT_FACTOR_CHOLESKY]);
    403:   MatGetBlockSize(A,&bs);
    404:   if (bs != 1) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"CHOLMOD only supports block size=1, given %D",bs);
    405:   /* Create the factorization matrix F */
    406:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    407:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,m,n);
    408:   MatSetType(B,((PetscObject)A)->type_name);
    409:   MatSeqSBAIJSetPreallocation(B,1,0,NULL);
    410:   PetscNewLog(B,Mat_CHOLMOD,&chol);

    412:   chol->Wrap    = MatWrapCholmod_seqsbaij;
    413:   chol->Destroy = MatDestroy_SeqSBAIJ;
    414:   B->spptr      = chol;

    416:   B->ops->view                   = MatView_CHOLMOD;
    417:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_CHOLMOD;
    418:   B->ops->destroy                = MatDestroy_CHOLMOD;
    419:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_seqsbaij_cholmod);
    420:   B->factortype                  = MAT_FACTOR_CHOLESKY;
    421:   B->assembled                   = PETSC_TRUE; /* required by -ksp_view */
    422:   B->preallocated                = PETSC_TRUE;

    424:   CholmodStart(B);
    425:   *F   = B;
    426:   return(0);
    427: }