Actual source code: sbaijcholmod.c
petsc-3.13.6 2020-09-29
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      2: /*
      3:    Provides an interface to the CHOLMOD sparse solver available through SuiteSparse version 4.2.1

      5:    When built with PETSC_USE_64BIT_INDICES this will use Suitesparse_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 one cannot use 64BIT_INDICES on 32bit machines [as Suitesparse_long is 32bit only]

     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;

     21: static void CholmodErrorHandler(int status,const char *file,int line,const char *message)
     22: {

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

     36: PetscErrorCode  CholmodStart(Mat F)
     37: {
     39:   Mat_CHOLMOD    *chol=(Mat_CHOLMOD*)F->data;
     40:   cholmod_common *c;
     41:   PetscBool      flg;

     44:   if (chol->common) return(0);
     45:   PetscMalloc1(1,&chol->common);
     46:   !cholmod_X_start(chol->common);

     48:   c                = chol->common;
     49:   c->error_handler = CholmodErrorHandler;

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

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

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

     70: #define CHOLMOD_OPTION_BOOL(name,help) do {                             \
     71:     PetscBool tmp = (PetscBool) !!c->name;                              \
     72:     PetscOptionsBool("-mat_cholmod_" #name,help,"None",tmp,&tmp,NULL); \
     73:     c->name = (int)tmp;                                                  \
     74: } while (0)

     76:   PetscOptionsBegin(PetscObjectComm((PetscObject)F),((PetscObject)F)->prefix,"CHOLMOD Options","Mat");
     77:   CHOLMOD_OPTION_INT(nmethods,"Number of different ordering methods to try");

     79: #if defined(PETSC_USE_SUITESPARSE_GPU)
     80:   c->useGPU = 1;
     81:   CHOLMOD_OPTION_INT(useGPU,"Use GPU for BLAS 1, otherwise 0");
     82:   CHOLMOD_OPTION_SIZE_T(maxGpuMemBytes,"Maximum memory to allocate on the GPU");
     83:   CHOLMOD_OPTION_DOUBLE(maxGpuMemFraction,"Fraction of available GPU memory to allocate");
     84: #endif

     86:   /* CHOLMOD handles first-time packing and refactor-packing separately, but we usually want them to be the same. */
     87:   chol->pack = (PetscBool)c->final_pack;
     88:   PetscOptionsBool("-mat_cholmod_pack","Pack factors after factorization [disable for frequent repeat factorization]","None",chol->pack,&chol->pack,NULL);
     89:   c->final_pack = (int)chol->pack;

     91:   CHOLMOD_OPTION_DOUBLE(dbound,"Minimum absolute value of diagonal entries of D");
     92:   CHOLMOD_OPTION_DOUBLE(grow0,"Global growth ratio when factors are modified");
     93:   CHOLMOD_OPTION_DOUBLE(grow1,"Column growth ratio when factors are modified");
     94:   CHOLMOD_OPTION_SIZE_T(grow2,"Affine column growth constant when factors are modified");
     95:   CHOLMOD_OPTION_SIZE_T(maxrank,"Max rank of update, larger values are faster but use more memory [2,4,8]");
     96:   {
     97:     static const char *const list[] = {"SIMPLICIAL","AUTO","SUPERNODAL","MatCholmodFactorType","MAT_CHOLMOD_FACTOR_",0};
     98:     PetscOptionsEnum("-mat_cholmod_factor","Factorization method","None",list,(PetscEnum)c->supernodal,(PetscEnum*)&c->supernodal,NULL);
     99:   }
    100:   if (c->supernodal) CHOLMOD_OPTION_DOUBLE(supernodal_switch,"flop/nnz_L threshold for switching to supernodal factorization");
    101:   CHOLMOD_OPTION_BOOL(final_asis,"Leave factors \"as is\"");
    102:   CHOLMOD_OPTION_BOOL(final_pack,"Pack the columns when finished (use FALSE if the factors will be updated later)");
    103:   if (!c->final_asis) {
    104:     CHOLMOD_OPTION_BOOL(final_super,"Leave supernodal factors instead of converting to simplicial");
    105:     CHOLMOD_OPTION_BOOL(final_ll,"Turn LDL' factorization into LL'");
    106:     CHOLMOD_OPTION_BOOL(final_monotonic,"Ensure columns are monotonic when done");
    107:     CHOLMOD_OPTION_BOOL(final_resymbol,"Remove numerically zero values resulting from relaxed supernodal amalgamation");
    108:   }
    109:   {
    110:     PetscReal tmp[] = {(PetscReal)c->zrelax[0],(PetscReal)c->zrelax[1],(PetscReal)c->zrelax[2]};
    111:     PetscInt  n     = 3;
    112:     PetscOptionsRealArray("-mat_cholmod_zrelax","3 real supernodal relaxed amalgamation parameters","None",tmp,&n,&flg);
    113:     if (flg && n != 3) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_OUTOFRANGE,"must provide exactly 3 parameters to -mat_cholmod_zrelax");
    114:     if (flg) while (n--) c->zrelax[n] = (double)tmp[n];
    115:   }
    116:   {
    117:     PetscInt n,tmp[] = {(PetscInt)c->nrelax[0],(PetscInt)c->nrelax[1],(PetscInt)c->nrelax[2]};
    118:     PetscOptionsIntArray("-mat_cholmod_nrelax","3 size_t supernodal relaxed amalgamation parameters","None",tmp,&n,&flg);
    119:     if (flg && n != 3) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_OUTOFRANGE,"must provide exactly 3 parameters to -mat_cholmod_nrelax");
    120:     if (flg) while (n--) c->nrelax[n] = (size_t)tmp[n];
    121:   }
    122:   CHOLMOD_OPTION_BOOL(prefer_upper,"Work with upper triangular form [faster when using fill-reducing ordering, slower in natural ordering]");
    123:   CHOLMOD_OPTION_BOOL(default_nesdis,"Use NESDIS instead of METIS for nested dissection");
    124:   CHOLMOD_OPTION_INT(print,"Verbosity level");
    125:   PetscOptionsEnd();
    126:   return(0);
    127: }

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

    136:   PetscMemzero(C,sizeof(*C));
    137:   /* 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 */
    138:   C->nrow   = (size_t)A->cmap->n;
    139:   C->ncol   = (size_t)A->rmap->n;
    140:   C->nzmax  = (size_t)sbaij->maxnz;
    141:   C->p      = sbaij->i;
    142:   C->i      = sbaij->j;
    143:   if (values) {
    144: #if defined(PETSC_USE_COMPLEX)
    145:     /* we need to pass CHOLMOD the conjugate matrix */
    146:     PetscScalar *v;
    147:     PetscInt    i;

    149:     PetscMalloc1(sbaij->maxnz,&v);
    150:     for (i = 0; i < sbaij->maxnz; i++) v[i] = PetscConj(sbaij->a[i]);
    151:     C->x = v;
    152:     vallocin = PETSC_TRUE;
    153: #else
    154:     C->x = sbaij->a;
    155: #endif
    156:   }
    157:   C->stype  = -1;
    158:   C->itype  = CHOLMOD_INT_TYPE;
    159:   C->xtype  = values ? CHOLMOD_SCALAR_TYPE : CHOLMOD_PATTERN;
    160:   C->dtype  = CHOLMOD_DOUBLE;
    161:   C->sorted = 1;
    162:   C->packed = 1;
    163:   *aijalloc = PETSC_FALSE;
    164:   *valloc   = vallocin;
    165:   return(0);
    166: }

    168: #define GET_ARRAY_READ 0
    169: #define GET_ARRAY_WRITE 1

    171: static PetscErrorCode VecWrapCholmod(Vec X,PetscInt rw,cholmod_dense *Y)
    172: {
    174:   PetscScalar    *x;
    175:   PetscInt       n;

    178:   PetscMemzero(Y,sizeof(*Y));
    179:   switch (rw) {
    180:   case GET_ARRAY_READ:
    181:     VecGetArrayRead(X,(const PetscScalar**)&x);
    182:     break;
    183:   case GET_ARRAY_WRITE:
    184:     VecGetArrayWrite(X,&x);
    185:     break;
    186:   default:
    187:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Case %D not handled",rw);
    188:     break;
    189:   }
    190:   VecGetSize(X,&n);

    192:   Y->x     = x;
    193:   Y->nrow  = n;
    194:   Y->ncol  = 1;
    195:   Y->nzmax = n;
    196:   Y->d     = n;
    197:   Y->xtype = CHOLMOD_SCALAR_TYPE;
    198:   Y->dtype = CHOLMOD_DOUBLE;
    199:   return(0);
    200: }

    202: static PetscErrorCode VecUnWrapCholmod(Vec X,PetscInt rw,cholmod_dense *Y)
    203: {
    204:   PetscErrorCode    ierr;

    207:   switch (rw) {
    208:   case GET_ARRAY_READ:
    209:     VecRestoreArrayRead(X,(const PetscScalar**)&Y->x);
    210:     break;
    211:   case GET_ARRAY_WRITE:
    212:     VecRestoreArrayWrite(X,(PetscScalar**)&Y->x);
    213:     break;
    214:   default:
    215:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Case %D not handled",rw);
    216:     break;
    217:   }
    218:   return(0);
    219: }

    221: static PetscErrorCode MatDenseWrapCholmod(Mat X,PetscInt rw,cholmod_dense *Y)
    222: {
    224:   PetscScalar    *x;
    225:   PetscInt       m,n,lda;

    228:   PetscMemzero(Y,sizeof(*Y));
    229:   switch (rw) {
    230:   case GET_ARRAY_READ:
    231:     MatDenseGetArrayRead(X,(const PetscScalar**)&x);
    232:     break;
    233:   case GET_ARRAY_WRITE:
    234:     /* we don't have MatDenseGetArrayWrite */
    235:     MatDenseGetArray(X,&x);
    236:     break;
    237:   default:
    238:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Case %D not handled",rw);
    239:     break;
    240:   }
    241:   MatDenseGetLDA(X,&lda);
    242:   MatGetLocalSize(X,&m,&n);

    244:   Y->x     = x;
    245:   Y->nrow  = m;
    246:   Y->ncol  = n;
    247:   Y->nzmax = lda*n;
    248:   Y->d     = lda;
    249:   Y->xtype = CHOLMOD_SCALAR_TYPE;
    250:   Y->dtype = CHOLMOD_DOUBLE;
    251:   return(0);
    252: }

    254: static PetscErrorCode MatDenseUnWrapCholmod(Mat X,PetscInt rw,cholmod_dense *Y)
    255: {
    256:   PetscErrorCode    ierr;

    259:   switch (rw) {
    260:   case GET_ARRAY_READ:
    261:     MatDenseRestoreArrayRead(X,(const PetscScalar**)&Y->x);
    262:     break;
    263:   case GET_ARRAY_WRITE:
    264:     /* we don't have MatDenseRestoreArrayWrite */
    265:     MatDenseRestoreArray(X,(PetscScalar**)&Y->x);
    266:     break;
    267:   default:
    268:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Case %D not handled",rw);
    269:     break;
    270:   }
    271:   return(0);
    272: }

    274: PETSC_INTERN PetscErrorCode  MatDestroy_CHOLMOD(Mat F)
    275: {
    277:   Mat_CHOLMOD    *chol=(Mat_CHOLMOD*)F->data;

    280:   !cholmod_X_free_factor(&chol->factor,chol->common);
    281:   !cholmod_X_finish(chol->common);
    282:   PetscFree(chol->common);
    283:   PetscFree(chol->matrix);
    284:   PetscObjectComposeFunction((PetscObject)F,"MatFactorGetSolverType_C",NULL);
    285:   PetscFree(F->data);
    286:   return(0);
    287: }

    289: static PetscErrorCode MatSolve_CHOLMOD(Mat,Vec,Vec);
    290: static PetscErrorCode MatMatSolve_CHOLMOD(Mat,Mat,Mat);

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

    294: static PetscErrorCode MatView_Info_CHOLMOD(Mat F,PetscViewer viewer)
    295: {
    296:   Mat_CHOLMOD          *chol = (Mat_CHOLMOD*)F->data;
    297:   const cholmod_common *c    = chol->common;
    298:   PetscErrorCode       ierr;
    299:   PetscInt             i;

    302:   if (F->ops->solve != MatSolve_CHOLMOD) return(0);
    303:   PetscViewerASCIIPrintf(viewer,"CHOLMOD run parameters:\n");
    304:   PetscViewerASCIIPushTab(viewer);
    305:   PetscViewerASCIIPrintf(viewer,"Pack factors after symbolic factorization: %s\n",chol->pack ? "TRUE" : "FALSE");
    306:   PetscViewerASCIIPrintf(viewer,"Common.dbound            %g  (Smallest absolute value of diagonal entries of D)\n",c->dbound);
    307:   PetscViewerASCIIPrintf(viewer,"Common.grow0             %g\n",c->grow0);
    308:   PetscViewerASCIIPrintf(viewer,"Common.grow1             %g\n",c->grow1);
    309:   PetscViewerASCIIPrintf(viewer,"Common.grow2             %u\n",(unsigned)c->grow2);
    310:   PetscViewerASCIIPrintf(viewer,"Common.maxrank           %u\n",(unsigned)c->maxrank);
    311:   PetscViewerASCIIPrintf(viewer,"Common.supernodal_switch %g\n",c->supernodal_switch);
    312:   PetscViewerASCIIPrintf(viewer,"Common.supernodal        %d\n",c->supernodal);
    313:   PetscViewerASCIIPrintf(viewer,"Common.final_asis        %d\n",c->final_asis);
    314:   PetscViewerASCIIPrintf(viewer,"Common.final_super       %d\n",c->final_super);
    315:   PetscViewerASCIIPrintf(viewer,"Common.final_ll          %d\n",c->final_ll);
    316:   PetscViewerASCIIPrintf(viewer,"Common.final_pack        %d\n",c->final_pack);
    317:   PetscViewerASCIIPrintf(viewer,"Common.final_monotonic   %d\n",c->final_monotonic);
    318:   PetscViewerASCIIPrintf(viewer,"Common.final_resymbol    %d\n",c->final_resymbol);
    319:   PetscViewerASCIIPrintf(viewer,"Common.zrelax            [%g,%g,%g]\n",c->zrelax[0],c->zrelax[1],c->zrelax[2]);
    320:   PetscViewerASCIIPrintf(viewer,"Common.nrelax            [%u,%u,%u]\n",(unsigned)c->nrelax[0],(unsigned)c->nrelax[1],(unsigned)c->nrelax[2]);
    321:   PetscViewerASCIIPrintf(viewer,"Common.prefer_upper      %d\n",c->prefer_upper);
    322:   PetscViewerASCIIPrintf(viewer,"Common.print             %d\n",c->print);
    323:   for (i=0; i<c->nmethods; i++) {
    324:     PetscViewerASCIIPrintf(viewer,"Ordering method %D%s:\n",i,i==c->selected ? " [SELECTED]" : "");
    325:     PetscViewerASCIIPrintf(viewer,"  lnz %g, fl %g, prune_dense %g, prune_dense2 %g\n",
    326:                                   c->method[i].lnz,c->method[i].fl,c->method[i].prune_dense,c->method[i].prune_dense2);
    327:   }
    328:   PetscViewerASCIIPrintf(viewer,"Common.postorder         %d\n",c->postorder);
    329:   PetscViewerASCIIPrintf(viewer,"Common.default_nesdis    %d (use NESDIS instead of METIS for nested dissection)\n",c->default_nesdis);
    330:   /* Statistics */
    331:   PetscViewerASCIIPrintf(viewer,"Common.fl                %g (flop count from most recent analysis)\n",c->fl);
    332:   PetscViewerASCIIPrintf(viewer,"Common.lnz               %g (fundamental nz in L)\n",c->lnz);
    333:   PetscViewerASCIIPrintf(viewer,"Common.anz               %g\n",c->anz);
    334:   PetscViewerASCIIPrintf(viewer,"Common.modfl             %g (flop count from most recent update)\n",c->modfl);
    335:   PetscViewerASCIIPrintf(viewer,"Common.malloc_count      %g (number of live objects)\n",(double)c->malloc_count);
    336:   PetscViewerASCIIPrintf(viewer,"Common.memory_usage      %g (peak memory usage in bytes)\n",(double)c->memory_usage);
    337:   PetscViewerASCIIPrintf(viewer,"Common.memory_inuse      %g (current memory usage in bytes)\n",(double)c->memory_inuse);
    338:   PetscViewerASCIIPrintf(viewer,"Common.nrealloc_col      %g (number of column reallocations)\n",c->nrealloc_col);
    339:   PetscViewerASCIIPrintf(viewer,"Common.nrealloc_factor   %g (number of factor reallocations due to column reallocations)\n",c->nrealloc_factor);
    340:   PetscViewerASCIIPrintf(viewer,"Common.ndbounds_hit      %g (number of times diagonal was modified by dbound)\n",c->ndbounds_hit);
    341:   PetscViewerASCIIPrintf(viewer,"Common.rowfacfl          %g (number of flops in last call to cholmod_rowfac)\n",c->rowfacfl);
    342:   PetscViewerASCIIPrintf(viewer,"Common.aatfl             %g (number of flops to compute A(:,f)*A(:,f)')\n",c->aatfl);
    343: #if defined(PETSC_USE_SUITESPARSE_GPU)
    344:   PetscViewerASCIIPrintf(viewer,"Common.useGPU            %d\n",c->useGPU);
    345: #endif
    346:   PetscViewerASCIIPopTab(viewer);
    347:   return(0);
    348: }

    350: PETSC_INTERN PetscErrorCode  MatView_CHOLMOD(Mat F,PetscViewer viewer)
    351: {
    352:   PetscErrorCode    ierr;
    353:   PetscBool         iascii;
    354:   PetscViewerFormat format;

    357:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    358:   if (iascii) {
    359:     PetscViewerGetFormat(viewer,&format);
    360:     if (format == PETSC_VIEWER_ASCII_INFO) {
    361:       MatView_Info_CHOLMOD(F,viewer);
    362:     }
    363:   }
    364:   return(0);
    365: }

    367: static PetscErrorCode MatSolve_CHOLMOD(Mat F,Vec B,Vec X)
    368: {
    369:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->data;
    370:   cholmod_dense  cholB,cholX,*X_handle,*Y_handle = NULL,*E_handle = NULL;

    374:   static_F = F;
    375:   VecWrapCholmod(B,GET_ARRAY_READ,&cholB);
    376:   VecWrapCholmod(X,GET_ARRAY_WRITE,&cholX);
    377:   X_handle = &cholX;
    378:   !cholmod_X_solve2(CHOLMOD_A,chol->factor,&cholB,NULL,&X_handle,NULL,&Y_handle,&E_handle,chol->common);
    379:   !cholmod_X_free_dense(&Y_handle,chol->common);
    380:   !cholmod_X_free_dense(&E_handle,chol->common);
    381:   VecUnWrapCholmod(B,GET_ARRAY_READ,&cholB);
    382:   VecUnWrapCholmod(X,GET_ARRAY_WRITE,&cholX);
    383:   return(0);
    384: }

    386: static PetscErrorCode MatMatSolve_CHOLMOD(Mat F,Mat B,Mat X)
    387: {
    388:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->data;
    389:   cholmod_dense  cholB,cholX,*X_handle,*Y_handle = NULL,*E_handle = NULL;

    393:   static_F = F;
    394:   MatDenseWrapCholmod(B,GET_ARRAY_READ,&cholB);
    395:   MatDenseWrapCholmod(X,GET_ARRAY_WRITE,&cholX);
    396:   X_handle = &cholX;
    397:   !cholmod_X_solve2(CHOLMOD_A,chol->factor,&cholB,NULL,&X_handle,NULL,&Y_handle,&E_handle,chol->common);
    398:   !cholmod_X_free_dense(&Y_handle,chol->common);
    399:   !cholmod_X_free_dense(&E_handle,chol->common);
    400:   MatDenseUnWrapCholmod(B,GET_ARRAY_READ,&cholB);
    401:   MatDenseUnWrapCholmod(X,GET_ARRAY_WRITE,&cholX);
    402:   return(0);
    403: }

    405: static PetscErrorCode MatCholeskyFactorNumeric_CHOLMOD(Mat F,Mat A,const MatFactorInfo *info)
    406: {
    407:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->data;
    408:   cholmod_sparse cholA;
    409:   PetscBool      aijalloc,valloc;

    413:   (*chol->Wrap)(A,PETSC_TRUE,&cholA,&aijalloc,&valloc);
    414:   static_F = F;
    415:   !cholmod_X_factorize(&cholA,chol->factor,chol->common);
    416:   if (ierr) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD factorization failed with status %d",chol->common->status);
    417:   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);

    419:   if (aijalloc) {PetscFree2(cholA.p,cholA.i);}
    420:   if (valloc) {PetscFree(cholA.x);}
    421: #if defined(PETSC_USE_SUITESPARSE_GPU)
    422:   PetscLogGpuTimeAdd(chol->common->CHOLMOD_GPU_GEMM_TIME + chol->common->CHOLMOD_GPU_SYRK_TIME + chol->common->CHOLMOD_GPU_TRSM_TIME + chol->common->CHOLMOD_GPU_POTRF_TIME);
    423: #endif

    425:   F->ops->solve             = MatSolve_CHOLMOD;
    426:   F->ops->solvetranspose    = MatSolve_CHOLMOD;
    427:   F->ops->matsolve          = MatMatSolve_CHOLMOD;
    428:   F->ops->matsolvetranspose = MatMatSolve_CHOLMOD;
    429:   return(0);
    430: }

    432: PETSC_INTERN PetscErrorCode  MatCholeskyFactorSymbolic_CHOLMOD(Mat F,Mat A,IS perm,const MatFactorInfo *info)
    433: {
    434:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->data;
    436:   cholmod_sparse cholA;
    437:   PetscBool      aijalloc,valloc;
    438:   PetscInt       *fset = 0;
    439:   size_t         fsize = 0;

    442:   (*chol->Wrap)(A,PETSC_FALSE,&cholA,&aijalloc,&valloc);
    443:   static_F = F;
    444:   if (chol->factor) {
    445:     !cholmod_X_resymbol(&cholA,fset,fsize,(int)chol->pack,chol->factor,chol->common);
    446:     if (ierr) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
    447:   } else if (perm) {
    448:     const PetscInt *ip;
    449:     ISGetIndices(perm,&ip);
    450:     chol->factor = cholmod_X_analyze_p(&cholA,(PetscInt*)ip,fset,fsize,chol->common);
    451:     if (!chol->factor) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
    452:     ISRestoreIndices(perm,&ip);
    453:   } else {
    454:     chol->factor = cholmod_X_analyze(&cholA,chol->common);
    455:     if (!chol->factor) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
    456:   }

    458:   if (aijalloc) {PetscFree2(cholA.p,cholA.i);}
    459:   if (valloc) {PetscFree(cholA.x);}

    461:   F->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_CHOLMOD;
    462:   return(0);
    463: }

    465: static PetscErrorCode MatFactorGetSolverType_seqsbaij_cholmod(Mat A,MatSolverType *type)
    466: {
    468:   *type = MATSOLVERCHOLMOD;
    469:   return(0);
    470: }

    472: PETSC_INTERN PetscErrorCode MatGetInfo_CHOLMOD(Mat F,MatInfoType flag,MatInfo *info)
    473: {
    474:   Mat_CHOLMOD *chol = (Mat_CHOLMOD*)F->data;

    477:   info->block_size        = 1.0;
    478:   info->nz_allocated      = chol->common->lnz;
    479:   info->nz_used           = chol->common->lnz;
    480:   info->nz_unneeded       = 0.0;
    481:   info->assemblies        = 0.0;
    482:   info->mallocs           = 0.0;
    483:   info->memory            = chol->common->memory_inuse;
    484:   info->fill_ratio_given  = 0;
    485:   info->fill_ratio_needed = 0;
    486:   info->factor_mallocs    = chol->common->malloc_count;
    487:   return(0);
    488: }

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

    494:   Use ./configure --download-suitesparse to install PETSc to use CHOLMOD

    496:   Use -pc_type cholesky -pc_factor_mat_solver_type cholmod to use this direct solver

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

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

    516:    Level: beginner

    518:    Note: CHOLMOD is part of SuiteSparse http://faculty.cse.tamu.edu/davis/suitesparse.html

    520: .seealso: PCCHOLESKY, PCFactorSetMatSolverType(), MatSolverType
    521: M*/

    523: PETSC_INTERN PetscErrorCode MatGetFactor_seqsbaij_cholmod(Mat A,MatFactorType ftype,Mat *F)
    524: {
    525:   Mat            B;
    526:   Mat_CHOLMOD    *chol;
    528:   PetscInt       m=A->rmap->n,n=A->cmap->n,bs;
    529:   const char     *prefix;

    532:   MatGetBlockSize(A,&bs);
    533:   if (bs != 1) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"CHOLMOD only supports block size=1, given %D",bs);
    534: #if defined(PETSC_USE_COMPLEX)
    535:   if (!A->hermitian) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Only for hermitian matrices");
    536: #endif
    537:   /* Create the factorization matrix F */
    538:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    539:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,m,n);
    540:   PetscStrallocpy("cholmod",&((PetscObject)B)->type_name);
    541:   MatGetOptionsPrefix(A,&prefix);
    542:   MatSetOptionsPrefix(B,prefix);
    543:   MatSetUp(B);
    544:   PetscNewLog(B,&chol);

    546:   chol->Wrap    = MatWrapCholmod_seqsbaij;
    547:   B->data       = chol;

    549:   B->ops->getinfo                = MatGetInfo_CHOLMOD;
    550:   B->ops->view                   = MatView_CHOLMOD;
    551:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_CHOLMOD;
    552:   B->ops->destroy                = MatDestroy_CHOLMOD;
    553:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_seqsbaij_cholmod);
    554:   B->factortype                  = MAT_FACTOR_CHOLESKY;
    555:   B->assembled                   = PETSC_TRUE;
    556:   B->preallocated                = PETSC_TRUE;

    558:   CholmodStart(B);

    560:   PetscFree(B->solvertype);
    561:   PetscStrallocpy(MATSOLVERCHOLMOD,&B->solvertype);

    563:   *F   = B;
    564:   return(0);
    565: }