Date: 2022-08-02T03:13:48+00:00

Actual source code: klu.c

      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(a,b,c,d)           klu_l_analyze((SuiteSparse_long)a,(SuiteSparse_long*)b,(SuiteSparse_long*)c,d)
     16: #define klu_K_analyze_given(a,b,c,d,e,f) klu_l_analyze_given((SuiteSparse_long)a,(SuiteSparse_long*)b,(SuiteSparse_long*)c,(SuiteSparse_long*)d,(SuiteSparse_long*)e,f)
     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(a,b,c,d,e)       klu_zl_factor((SuiteSparse_long*)a,(SuiteSparse_long*)b,c,d,e);
     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(a,b,c,d,e)       klu_l_factor((SuiteSparse_long*)a,(SuiteSparse_long*)b,c,d,e);
     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

     79: EXTERN_C_BEGIN
     80: #include <klu.h>
     81: EXTERN_C_END

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

     86: typedef struct {
     87:   klu_K_common   Common;
     88:   klu_K_symbolic *Symbolic;
     89:   klu_K_numeric  *Numeric;
     90:   PetscInt       *perm_c,*perm_r;
     91:   MatStructure   flg;
     92:   PetscBool      PetscMatOrdering;
     93:   PetscBool      CleanUpKLU;
     94: } Mat_KLU;

     96: static PetscErrorCode MatDestroy_KLU(Mat A)
     97: {
     98:   Mat_KLU    *lu=(Mat_KLU*)A->data;

    100:   if (lu->CleanUpKLU) {
    101:     klu_K_free_symbolic(&lu->Symbolic,&lu->Common);
    102:     klu_K_free_numeric(&lu->Numeric,&lu->Common);
    103:     PetscFree2(lu->perm_r,lu->perm_c);
    104:   }
    105:   PetscFree(A->data);
    106:   return 0;
    107: }

    109: static PetscErrorCode MatSolveTranspose_KLU(Mat A,Vec b,Vec x)
    110: {
    111:   Mat_KLU     *lu = (Mat_KLU*)A->data;
    112:   PetscScalar *xa;
    113:   PetscInt     status;

    115:   /* KLU uses a column major format, solve Ax = b by klu_*_solve */
    116:   /* ----------------------------------*/
    117:   VecCopy(b,x); /* klu_solve stores the solution in rhs */
    118:   VecGetArray(x,&xa);
    119:   status = klu_K_solve(lu->Symbolic,lu->Numeric,A->rmap->n,1,(PetscReal*)xa,&lu->Common);
    121:   VecRestoreArray(x,&xa);
    122:   return 0;
    123: }

    125: static PetscErrorCode MatSolve_KLU(Mat A,Vec b,Vec x)
    126: {
    127:   Mat_KLU     *lu = (Mat_KLU*)A->data;
    128:   PetscScalar *xa;
    129:   PetscInt     status;

    131:   /* KLU uses a column major format, solve A^Tx = b by klu_*_tsolve */
    132:   /* ----------------------------------*/
    133:   VecCopy(b,x); /* klu_solve stores the solution in rhs */
    134:   VecGetArray(x,&xa);
    135: #if defined(PETSC_USE_COMPLEX)
    136:   PetscInt conj_solve=1;
    137:   status = klu_K_tsolve(lu->Symbolic,lu->Numeric,A->rmap->n,1,(PetscReal*)xa,conj_solve,&lu->Common); /* conjugate solve */
    138: #else
    139:   status = klu_K_tsolve(lu->Symbolic,lu->Numeric,A->rmap->n,1,xa,&lu->Common);
    140: #endif
    142:   VecRestoreArray(x,&xa);
    143:   return 0;
    144: }

    146: static PetscErrorCode MatLUFactorNumeric_KLU(Mat F,Mat A,const MatFactorInfo *info)
    147: {
    148:   Mat_KLU        *lu = (Mat_KLU*)(F)->data;
    149:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    150:   PetscInt       *ai = a->i,*aj=a->j;
    151:   PetscScalar    *av = a->a;

    153:   /* numeric factorization of A' */
    154:   /* ----------------------------*/

    156:   if (lu->flg == SAME_NONZERO_PATTERN && lu->Numeric) {
    157:     klu_K_free_numeric(&lu->Numeric,&lu->Common);
    158:   }
    159:   lu->Numeric = klu_K_factor(ai,aj,(PetscReal*)av,lu->Symbolic,&lu->Common);

    162:   lu->flg                = SAME_NONZERO_PATTERN;
    163:   lu->CleanUpKLU         = PETSC_TRUE;
    164:   F->ops->solve          = MatSolve_KLU;
    165:   F->ops->solvetranspose = MatSolveTranspose_KLU;
    166:   return 0;
    167: }

    169: static PetscErrorCode MatLUFactorSymbolic_KLU(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
    170: {
    171:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    172:   Mat_KLU        *lu = (Mat_KLU*)(F->data);
    173:   PetscInt       i,*ai = a->i,*aj = a->j,m=A->rmap->n,n=A->cmap->n;
    174:   const PetscInt *ra,*ca;

    176:   if (lu->PetscMatOrdering) {
    177:     ISGetIndices(r,&ra);
    178:     ISGetIndices(c,&ca);
    179:     PetscMalloc2(m,&lu->perm_r,n,&lu->perm_c);
    180:     /* we cannot simply memcpy on 64 bit archs */
    181:     for (i = 0; i < m; i++) lu->perm_r[i] = ra[i];
    182:     for (i = 0; i < n; i++) lu->perm_c[i] = ca[i];
    183:     ISRestoreIndices(r,&ra);
    184:     ISRestoreIndices(c,&ca);
    185:   }

    187:   /* symbolic factorization of A' */
    188:   /* ---------------------------------------------------------------------- */
    189:   if (r) {
    190:     lu->PetscMatOrdering = PETSC_TRUE;
    191:     lu->Symbolic = klu_K_analyze_given(n,ai,aj,lu->perm_c,lu->perm_r,&lu->Common);
    192:   } else { /* use klu internal ordering */
    193:     lu->Symbolic = klu_K_analyze(n,ai,aj,&lu->Common);
    194:   }

    197:   lu->flg                   = DIFFERENT_NONZERO_PATTERN;
    198:   lu->CleanUpKLU            = PETSC_TRUE;
    199:   (F)->ops->lufactornumeric = MatLUFactorNumeric_KLU;
    200:   return 0;
    201: }

    203: static PetscErrorCode MatView_Info_KLU(Mat A,PetscViewer viewer)
    204: {
    205:   Mat_KLU       *lu= (Mat_KLU*)A->data;
    206:   klu_K_numeric *Numeric=(klu_K_numeric*)lu->Numeric;

    208:   PetscViewerASCIIPrintf(viewer,"KLU stats:\n");
    209:   PetscViewerASCIIPrintf(viewer,"  Number of diagonal blocks: %" PetscInt_FMT "\n",(PetscInt)(Numeric->nblocks));
    210:   PetscViewerASCIIPrintf(viewer,"  Total nonzeros=%" PetscInt_FMT "\n",(PetscInt)(Numeric->lnz+Numeric->unz));
    211:   PetscViewerASCIIPrintf(viewer,"KLU runtime parameters:\n");
    212:   /* Control parameters used by numeric factorization */
    213:   PetscViewerASCIIPrintf(viewer,"  Partial pivoting tolerance: %g\n",lu->Common.tol);
    214:   /* BTF preordering */
    215:   PetscViewerASCIIPrintf(viewer,"  BTF preordering enabled: %" PetscInt_FMT "\n",(PetscInt)(lu->Common.btf));
    216:   /* mat ordering */
    217:   if (!lu->PetscMatOrdering) {
    218:     PetscViewerASCIIPrintf(viewer,"  Ordering: %s (not using the PETSc ordering)\n",KluOrderingTypes[(int)lu->Common.ordering]);
    219:   }
    220:   /* matrix row scaling */
    221:   PetscViewerASCIIPrintf(viewer, "  Matrix row scaling: %s\n",scale[(int)lu->Common.scale]);
    222:   return 0;
    223: }

    225: static PetscErrorCode MatView_KLU(Mat A,PetscViewer viewer)
    226: {
    227:   PetscBool         iascii;
    228:   PetscViewerFormat format;

    230:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    231:   if (iascii) {
    232:     PetscViewerGetFormat(viewer,&format);
    233:     if (format == PETSC_VIEWER_ASCII_INFO) {
    234:       MatView_Info_KLU(A,viewer);
    235:     }
    236:   }
    237:   return 0;
    238: }

    240: PetscErrorCode MatFactorGetSolverType_seqaij_klu(Mat A,MatSolverType *type)
    241: {
    242:   *type = MATSOLVERKLU;
    243:   return 0;
    244: }

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

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

    252:   Use -pc_type lu -pc_factor_mat_solver_type klu to use this direct solver

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

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

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

    264:    Level: beginner

    266: .seealso: PCLU, MATSOLVERUMFPACK, MATSOLVERCHOLMOD, PCFactorSetMatSolverType(), MatSolverType
    267: M*/

    269: PETSC_INTERN PetscErrorCode MatGetFactor_seqaij_klu(Mat A,MatFactorType ftype,Mat *F)
    270: {
    271:   Mat            B;
    272:   Mat_KLU       *lu;
    274:   PetscInt       m=A->rmap->n,n=A->cmap->n,idx = 0,status;
    275:   PetscBool      flg;

    277:   /* Create the factorization matrix F */
    278:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    279:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,m,n);
    280:   PetscStrallocpy("klu",&((PetscObject)B)->type_name);
    281:   MatSetUp(B);

    283:   PetscNewLog(B,&lu);

    285:   B->data                  = lu;
    286:   B->ops->getinfo          = MatGetInfo_External;
    287:   B->ops->lufactorsymbolic = MatLUFactorSymbolic_KLU;
    288:   B->ops->destroy          = MatDestroy_KLU;
    289:   B->ops->view             = MatView_KLU;

    291:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_seqaij_klu);

    293:   B->factortype   = MAT_FACTOR_LU;
    294:   B->assembled    = PETSC_TRUE;           /* required by -ksp_view */
    295:   B->preallocated = PETSC_TRUE;

    297:   PetscFree(B->solvertype);
    298:   PetscStrallocpy(MATSOLVERKLU,&B->solvertype);
    299:   B->canuseordering = PETSC_TRUE;
    300:   PetscStrallocpy(MATORDERINGEXTERNAL,(char**)&B->preferredordering[MAT_FACTOR_LU]);

    302:   /* initializations */
    303:   /* ------------------------------------------------*/
    304:   /* get the default control parameters */
    305:   status = klu_K_defaults(&lu->Common);

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

    310:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"KLU Options","Mat");
    311:   /* Partial pivoting tolerance */
    312:   PetscOptionsReal("-mat_klu_pivot_tol","Partial pivoting tolerance","None",lu->Common.tol,&lu->Common.tol,NULL);
    313:   /* BTF pre-ordering */
    314:   PetscOptionsInt("-mat_klu_use_btf","Enable BTF preordering","None",(PetscInt)lu->Common.btf,(PetscInt*)&lu->Common.btf,NULL);
    315:   /* Matrix reordering */
    316:   PetscOptionsEList("-mat_klu_ordering","Internal ordering method","None",KluOrderingTypes,sizeof(KluOrderingTypes)/sizeof(KluOrderingTypes[0]),KluOrderingTypes[0],&idx,&flg);
    317:   lu->Common.ordering = (int)idx;
    318:   /* Matrix row scaling */
    319:   PetscOptionsEList("-mat_klu_row_scale","Matrix row scaling","None",scale,3,scale[0],&idx,&flg);
    320:   PetscOptionsEnd();
    321:   *F = B;
    322:   return 0;
    323: }