Actual source code: klu.c
petsc-3.7.1 2016-05-15
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      2: /*
      3:    Provides an interface to the KLUv1.2 sparse solver

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

     10: */
     11: #include <../src/mat/impls/aij/seq/aij.h>

     13: #if defined(PETSC_USE_64BIT_INDICES)
     14: #define klu_K_defaults                klu_l_defaults
     15: #define klu_K_analyze                 klu_l_analyze
     16: #define klu_K_analyze_given           klu_l_analyze_given
     17: #define klu_K_free_symbolic           klu_l_free_symbolic
     18: #define klu_K_free_numeric            klu_l_free_numeric
     19: #define klu_K_common                  klu_l_common
     20: #define klu_K_symbolic                klu_l_symbolic
     21: #define klu_K_numeric                 klu_l_numeric
     22: #if defined(PETSC_USE_COMPLEX)
     23: #define klu_K_factor                  klu_zl_factor
     24: #define klu_K_solve                   klu_zl_solve
     25: #define klu_K_tsolve                  klu_zl_tsolve
     26: #define klu_K_refactor                klu_zl_refactor
     27: #define klu_K_sort                    klu_zl_sort
     28: #define klu_K_flops                   klu_zl_flops
     29: #define klu_K_rgrowth                 klu_zl_rgrowth
     30: #define klu_K_condest                 klu_zl_condest
     31: #define klu_K_rcond                   klu_zl_rcond
     32: #define klu_K_scale                   klu_zl_scale
     33: #else
     34: #define klu_K_factor                  klu_l_factor
     35: #define klu_K_solve                   klu_l_solve
     36: #define klu_K_tsolve                  klu_l_tsolve
     37: #define klu_K_refactor                klu_l_refactor
     38: #define klu_K_sort                    klu_l_sort
     39: #define klu_K_flops                   klu_l_flops
     40: #define klu_K_rgrowth                 klu_l_rgrowth
     41: #define klu_K_condest                 klu_l_condest
     42: #define klu_K_rcond                   klu_l_rcond
     43: #define klu_K_scale                   klu_l_scale
     44: #endif
     45: #else
     46: #define klu_K_defaults                klu_defaults
     47: #define klu_K_analyze                 klu_analyze
     48: #define klu_K_analyze_given           klu_analyze_given
     49: #define klu_K_free_symbolic           klu_free_symbolic
     50: #define klu_K_free_numeric            klu_free_numeric
     51: #define klu_K_common                  klu_common
     52: #define klu_K_symbolic                klu_symbolic
     53: #define klu_K_numeric                 klu_numeric
     54: #if defined(PETSC_USE_COMPLEX)
     55: #define klu_K_factor                  klu_z_factor
     56: #define klu_K_solve                   klu_z_solve
     57: #define klu_K_tsolve                  klu_z_tsolve
     58: #define klu_K_refactor                klu_z_refactor
     59: #define klu_K_sort                    klu_z_sort
     60: #define klu_K_flops                   klu_z_flops
     61: #define klu_K_rgrowth                 klu_z_rgrowth
     62: #define klu_K_condest                 klu_z_condest
     63: #define klu_K_rcond                   klu_z_rcond
     64: #define klu_K_scale                   klu_z_scale
     65: #else
     66: #define klu_K_factor                  klu_factor
     67: #define klu_K_solve                   klu_solve
     68: #define klu_K_tsolve                  klu_tsolve
     69: #define klu_K_refactor                klu_refactor
     70: #define klu_K_sort                    klu_sort
     71: #define klu_K_flops                   klu_flops
     72: #define klu_K_rgrowth                 klu_rgrowth
     73: #define klu_K_condest                 klu_condest
     74: #define klu_K_rcond                   klu_rcond
     75: #define klu_K_scale                   klu_scale
     76: #endif
     77: #endif

     80: #define SuiteSparse_long long long
     81: #define SuiteSparse_long_max LONG_LONG_MAX
     82: #define SuiteSparse_long_id "%lld"

     84: EXTERN_C_BEGIN
     85: #include <klu.h>
     86: EXTERN_C_END

     88: static const char *KluOrderingTypes[] = {"AMD","COLAMD","PETSC"};
     89: static const char *scale[] ={"NONE","SUM","MAX"};

     91: typedef struct {
     92:   klu_K_common   Common;
     93:   klu_K_symbolic *Symbolic;
     94:   klu_K_numeric  *Numeric;
     95:   PetscInt     *perm_c,*perm_r;
     96:   MatStructure flg;
     97:   PetscBool    PetscMatOrdering;

     99:   /* Flag to clean up KLU objects during Destroy */
    100:   PetscBool CleanUpKLU;
    101: } Mat_KLU;

    105: static PetscErrorCode MatDestroy_KLU(Mat A)
    106: {
    108:   Mat_KLU    *lu=(Mat_KLU*)A->spptr;

    111:   if (lu && lu->CleanUpKLU) {
    112:     klu_K_free_symbolic(&lu->Symbolic,&lu->Common);
    113:     klu_K_free_numeric(&lu->Numeric,&lu->Common);
    114:     PetscFree2(lu->perm_r,lu->perm_c);
    115:   }
    116:   PetscFree(A->spptr);
    117:   MatDestroy_SeqAIJ(A);
    118:   return(0);
    119: }

    123: static PetscErrorCode MatSolveTranspose_KLU(Mat A,Vec b,Vec x)
    124: {
    125:   Mat_KLU       *lu = (Mat_KLU*)A->spptr;
    126:   PetscScalar    *xa;
    128:   PetscInt       status;

    131:   /* KLU uses a column major format, solve Ax = b by klu_*_solve */
    132:   /* ----------------------------------*/
    133:   VecCopy(b,x); /* klu_solve stores the solution in rhs */
    134:   VecGetArray(x,&xa);
    135:   status = klu_K_solve(lu->Symbolic,lu->Numeric,A->rmap->n,1,(PetscReal*)xa,&lu->Common);
    136:   if (status != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Solve failed");
    137:   VecRestoreArray(x,&xa);
    138:   return(0);
    139: }

    143: static PetscErrorCode MatSolve_KLU(Mat A,Vec b,Vec x)
    144: {
    145:   Mat_KLU       *lu = (Mat_KLU*)A->spptr;
    146:   PetscScalar    *xa;
    148:   PetscInt       status;

    151:   /* KLU uses a column major format, solve A^Tx = b by klu_*_tsolve */
    152:   /* ----------------------------------*/
    153:   VecCopy(b,x); /* klu_solve stores the solution in rhs */
    154:   VecGetArray(x,&xa);
    155: #if defined(PETSC_USE_COMPLEX)
    156:   PetscInt conj_solve=1;
    157:   status = klu_K_tsolve(lu->Symbolic,lu->Numeric,A->rmap->n,1,(PetscReal*)xa,conj_solve,&lu->Common); /* conjugate solve */
    158: #else
    159:   status = klu_K_tsolve(lu->Symbolic,lu->Numeric,A->rmap->n,1,xa,&lu->Common);
    160: #endif
    161:   if (status != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Solve failed");
    162:   VecRestoreArray(x,&xa);
    163:   return(0);
    164: }

    168: static PetscErrorCode MatLUFactorNumeric_KLU(Mat F,Mat A,const MatFactorInfo *info)
    169: {
    170:   Mat_KLU        *lu = (Mat_KLU*)(F)->spptr;
    171:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    172:   PetscInt       *ai = a->i,*aj=a->j;
    173:   PetscScalar    *av = a->a;

    176:   /* numeric factorization of A' */
    177:   /* ----------------------------*/

    179:   if (lu->flg == SAME_NONZERO_PATTERN && lu->Numeric) {
    180:     klu_K_free_numeric(&lu->Numeric,&lu->Common);
    181:   }
    182:   lu->Numeric = klu_K_factor(ai,aj,(PetscReal*)av,lu->Symbolic,&lu->Common);
    183:   if(!lu->Numeric) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Numeric factorization failed");

    185:   lu->flg                = SAME_NONZERO_PATTERN;
    186:   lu->CleanUpKLU         = PETSC_TRUE;
    187:   F->ops->solve          = MatSolve_KLU;
    188:   F->ops->solvetranspose = MatSolveTranspose_KLU;
    189:   return(0);
    190: }

    194: static PetscErrorCode MatLUFactorSymbolic_KLU(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
    195: {
    196:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    197:   Mat_KLU       *lu = (Mat_KLU*)(F->spptr);
    199:   PetscInt       i,*ai = a->i,*aj = a->j,m=A->rmap->n,n=A->cmap->n;
    200:   const PetscInt *ra,*ca;

    203:   if (lu->PetscMatOrdering) {
    204:     ISGetIndices(r,&ra);
    205:     ISGetIndices(c,&ca);
    206:     PetscMalloc2(m,&lu->perm_r,n,&lu->perm_c);
    207:     /* we cannot simply memcpy on 64 bit archs */
    208:     for (i = 0; i < m; i++) lu->perm_r[i] = ra[i];
    209:     for (i = 0; i < n; i++) lu->perm_c[i] = ca[i];
    210:     ISRestoreIndices(r,&ra);
    211:     ISRestoreIndices(c,&ca);
    212:   }

    214:   /* symbolic factorization of A' */
    215:   /* ---------------------------------------------------------------------- */
    216:   if (lu->PetscMatOrdering) { /* use Petsc ordering */
    217:     lu->Symbolic = klu_K_analyze_given(n,ai,aj,lu->perm_c,lu->perm_r,&lu->Common);
    218:   } else { /* use klu internal ordering */
    219:     lu->Symbolic = klu_K_analyze(n,ai,aj,&lu->Common);
    220:   }
    221:   if (!lu->Symbolic) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Symbolic Factorization failed");

    223:   lu->flg                   = DIFFERENT_NONZERO_PATTERN;
    224:   lu->CleanUpKLU            = PETSC_TRUE;
    225:   (F)->ops->lufactornumeric = MatLUFactorNumeric_KLU;
    226:   return(0);
    227: }

    231: static PetscErrorCode MatFactorInfo_KLU(Mat A,PetscViewer viewer)
    232: {
    233:   Mat_KLU       *lu= (Mat_KLU*)A->spptr;
    234:   klu_K_numeric *Numeric=(klu_K_numeric*)lu->Numeric;

    238:   /* check if matrix is KLU type */
    239:   if (A->ops->solve != MatSolve_KLU) return(0);

    241:   PetscViewerASCIIPrintf(viewer,"KLU stats:\n");
    242:   PetscViewerASCIIPrintf(viewer,"  Number of diagonal blocks: %d\n",Numeric->nblocks);
    243:   PetscViewerASCIIPrintf(viewer,"  Total nonzeros=%d\n",Numeric->lnz+Numeric->unz);

    245:   PetscViewerASCIIPrintf(viewer,"KLU runtime parameters:\n");

    247:   /* Control parameters used by numeric factorization */
    248:   PetscViewerASCIIPrintf(viewer,"  Partial pivoting tolerance: %g\n",lu->Common.tol);
    249:   /* BTF preordering */
    250:   PetscViewerASCIIPrintf(viewer,"  BTF preordering enabled: %d\n",lu->Common.btf);
    251:   /* mat ordering */
    252:   if (!lu->PetscMatOrdering) {
    253:     PetscViewerASCIIPrintf(viewer,"  Ordering: %s (not using the PETSc ordering)\n",KluOrderingTypes[(int)lu->Common.ordering]);
    254:   } else {
    255:     PetscViewerASCIIPrintf(viewer,"  Using PETSc ordering\n");
    256:   }
    257:   /* matrix row scaling */
    258:   PetscViewerASCIIPrintf(viewer, "  Matrix row scaling: %s\n",scale[(int)lu->Common.scale]);
    259:   return(0);
    260: }

    264: static PetscErrorCode MatView_KLU(Mat A,PetscViewer viewer)
    265: {
    266:   PetscErrorCode    ierr;
    267:   PetscBool         iascii;
    268:   PetscViewerFormat format;

    271:   MatView_SeqAIJ(A,viewer);

    273:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    274:   if (iascii) {
    275:     PetscViewerGetFormat(viewer,&format);
    276:     if (format == PETSC_VIEWER_ASCII_INFO) {
    277:       MatFactorInfo_KLU(A,viewer);
    278:     }
    279:   }
    280:   return(0);
    281: }

    285: PetscErrorCode MatFactorGetSolverPackage_seqaij_klu(Mat A,const MatSolverPackage *type)
    286: {
    288:   *type = MATSOLVERKLU;
    289:   return(0);
    290: }

    293: /*MC
    294:   MATSOLVERKLU = "klu" - A matrix type providing direct solvers (LU) for sequential matrices
    295:   via the external package KLU.

    297:   ./configure --download-suitesparse to install PETSc to use KLU

    299:   Use -pc_type lu -pc_factor_mat_solver_package klu to us this direct solver

    301:   Consult KLU documentation for more information on the options database keys below.

    303:   Options Database Keys:
    304: + -mat_klu_pivot_tol <0.001>                  - Partial pivoting tolerance
    305: . -mat_klu_use_btf <1>                        - Use BTF preordering
    306: . -mat_klu_ordering <AMD>                     - KLU reordering scheme to reduce fill-in (choose one of) AMD COLAMD PETSC
    307: - -mat_klu_row_scale <NONE>                   - Matrix row scaling (choose one of) NONE SUM MAX

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

    311:    Level: beginner

    313: .seealso: PCLU, MATSOLVERUMFPACK, MATSOLVERCHOLMOD, PCFactorSetMatSolverPackage(), MatSolverPackage
    314: M*/

    318: PETSC_INTERN PetscErrorCode MatGetFactor_seqaij_klu(Mat A,MatFactorType ftype,Mat *F)
    319: {
    320:   Mat            B;
    321:   Mat_KLU       *lu;
    323:   PetscInt       m=A->rmap->n,n=A->cmap->n,idx,status;
    324:   PetscBool      flg;

    327:   /* Create the factorization matrix F */
    328:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    329:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,m,n);
    330:   MatSetType(B,((PetscObject)A)->type_name);
    331:   MatSeqAIJSetPreallocation(B,0,NULL);
    332:   PetscNewLog(B,&lu);

    334:   B->spptr                 = lu;
    335:   B->ops->lufactorsymbolic = MatLUFactorSymbolic_KLU;
    336:   B->ops->destroy          = MatDestroy_KLU;
    337:   B->ops->view             = MatView_KLU;

    339:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_seqaij_klu);

    341:   B->factortype   = MAT_FACTOR_LU;
    342:   B->assembled    = PETSC_TRUE;           /* required by -ksp_view */
    343:   B->preallocated = PETSC_TRUE;

    345:   PetscFree(B->solvertype);
    346:   PetscStrallocpy(MATSOLVERKLU,&B->solvertype);

    348:   /* initializations */
    349:   /* ------------------------------------------------*/
    350:   /* get the default control parameters */
    351:   status = klu_K_defaults(&lu->Common);
    352:   if(status <= 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Initialization failed");

    354:   lu->Common.scale = 0; /* No row scaling */

    356:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"KLU Options","Mat");
    357:   /* Partial pivoting tolerance */
    358:   PetscOptionsReal("-mat_klu_pivot_tol","Partial pivoting tolerance","None",lu->Common.tol,&lu->Common.tol,NULL);
    359:   /* BTF pre-ordering */
    360:   PetscOptionsInt("-mat_klu_use_btf","Enable BTF preordering","None",lu->Common.btf,&lu->Common.btf,NULL);
    361:   /* Matrix reordering */
    362:   PetscOptionsEList("-mat_klu_ordering","Internal ordering method","None",KluOrderingTypes,sizeof(KluOrderingTypes)/sizeof(KluOrderingTypes[0]),KluOrderingTypes[0],&idx,&flg);
    363:   if (flg) {
    364:     if ((int)idx == 2) lu->PetscMatOrdering = PETSC_TRUE;   /* use Petsc mat ordering (note: size is for the transpose, and PETSc r = Klu perm_c) */
    365:     else lu->Common.ordering = (int)idx;
    366:   }
    367:   /* Matrix row scaling */
    368:   PetscOptionsEList("-mat_klu_row_scale","Matrix row scaling","None",scale,3,scale[0],&idx,&flg);
    369:   PetscOptionsEnd();
    370:   *F = B;
    371:   return(0);
    372: }