Actual source code: klu.c

petsc-3.6.1 2015-08-06
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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) {
184:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Numeric factorization failed");
185:   }

187:   lu->flg                = SAME_NONZERO_PATTERN;
188:   lu->CleanUpKLU         = PETSC_TRUE;
189:   F->ops->solve          = MatSolve_KLU;
190:   F->ops->solvetranspose = MatSolveTranspose_KLU;
191:   return(0);
192: }

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

205:   if (lu->PetscMatOrdering) {
206:     ISGetIndices(r,&ra);
207:     ISGetIndices(c,&ca);
208:     PetscMalloc2(m,&lu->perm_r,n,&lu->perm_c);
209:     /* we cannot simply memcpy on 64 bit archs */
210:     for (i = 0; i < m; i++) lu->perm_r[i] = ra[i];
211:     for (i = 0; i < n; i++) lu->perm_c[i] = ca[i];
212:     ISRestoreIndices(r,&ra);
213:     ISRestoreIndices(c,&ca);
214:   }

216:   /* symbolic factorization of A' */
217:   /* ---------------------------------------------------------------------- */
218:   if (lu->PetscMatOrdering) { /* use Petsc ordering */
219:     lu->Symbolic = klu_K_analyze_given(n,ai,aj,lu->perm_c,lu->perm_r,&lu->Common);
220:   } else { /* use klu internal ordering */
221:     lu->Symbolic = klu_K_analyze(n,ai,aj,&lu->Common);
222:   }
223:   if (!lu->Symbolic) {
224:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Symbolic Factorization failed");
225:   }

227:   lu->flg                   = DIFFERENT_NONZERO_PATTERN;
228:   lu->CleanUpKLU            = PETSC_TRUE;
229:   (F)->ops->lufactornumeric = MatLUFactorNumeric_KLU;
230:   return(0);
231: }

235: static PetscErrorCode MatFactorInfo_KLU(Mat A,PetscViewer viewer)
236: {
237:   Mat_KLU       *lu= (Mat_KLU*)A->spptr;
238:   klu_K_numeric *Numeric=(klu_K_numeric*)lu->Numeric;

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

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

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

251:   /* Control parameters used by numeric factorization */
252:   PetscViewerASCIIPrintf(viewer,"  Partial pivoting tolerance: %g\n",lu->Common.tol);
253:   /* BTF preordering */
254:   PetscViewerASCIIPrintf(viewer,"  BTF preordering enabled: %d\n",lu->Common.btf);
255:   /* mat ordering */
256:   if (!lu->PetscMatOrdering) {
257:     PetscViewerASCIIPrintf(viewer,"  Ordering: %s (not using the PETSc ordering)\n",KluOrderingTypes[(int)lu->Common.ordering]);
258:   } else {
259:     PetscViewerASCIIPrintf(viewer,"  Using PETSc ordering\n");
260:   }
261:   /* matrix row scaling */
262:   PetscViewerASCIIPrintf(viewer, "  Matrix row scaling: %s\n",scale[(int)lu->Common.scale]);
263:   return(0);
264: }

268: static PetscErrorCode MatView_KLU(Mat A,PetscViewer viewer)
269: {
270:   PetscErrorCode    ierr;
271:   PetscBool         iascii;
272:   PetscViewerFormat format;

275:   MatView_SeqAIJ(A,viewer);

277:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
278:   if (iascii) {
279:     PetscViewerGetFormat(viewer,&format);
280:     if (format == PETSC_VIEWER_ASCII_INFO) {
281:       MatFactorInfo_KLU(A,viewer);
282:     }
283:   }
284:   return(0);
285: }

289: PetscErrorCode MatFactorGetSolverPackage_seqaij_klu(Mat A,const MatSolverPackage *type)
290: {
292:   *type = MATSOLVERKLU;
293:   return(0);
294: }


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

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

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

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

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

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

315:    Level: beginner

317: .seealso: PCLU, MATSOLVERUMFPACK, MATSOLVERCHOLMOD, PCFactorSetMatSolverPackage(), MatSolverPackage
318: M*/

322: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_klu(Mat A,MatFactorType ftype,Mat *F)
323: {
324:   Mat            B;
325:   Mat_KLU       *lu;
327:   PetscInt       m=A->rmap->n,n=A->cmap->n,idx,status;
328:   PetscBool      flg;

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

338:   B->spptr                 = lu;
339:   B->ops->lufactorsymbolic = MatLUFactorSymbolic_KLU;
340:   B->ops->destroy          = MatDestroy_KLU;
341:   B->ops->view             = MatView_KLU;

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

345:   B->factortype   = MAT_FACTOR_LU;
346:   B->assembled    = PETSC_TRUE;           /* required by -ksp_view */
347:   B->preallocated = PETSC_TRUE;

349:   /* initializations */
350:   /* ------------------------------------------------*/
351:   /* get the default control parameters */
352:   status = klu_K_defaults(&lu->Common);
353:   if(status <= 0) {
354:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Initialization failed");
355:   }
356:   lu->Common.scale = 0; /* No row scaling */

358:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"KLU Options","Mat");
359:   /* Partial pivoting tolerance */
360:   PetscOptionsReal("-mat_klu_pivot_tol","Partial pivoting tolerance","None",lu->Common.tol,&lu->Common.tol,NULL);
361:   /* BTF pre-ordering */
362:   PetscOptionsInt("-mat_klu_use_btf","Enable BTF preordering","None",lu->Common.btf,&lu->Common.btf,NULL);
363:   /* Matrix reordering */
364:   PetscOptionsEList("-mat_klu_ordering","Internal ordering method","None",KluOrderingTypes,sizeof(KluOrderingTypes)/sizeof(KluOrderingTypes[0]),KluOrderingTypes[0],&idx,&flg);
365:   if (flg) {
366:     if ((int)idx == 2) lu->PetscMatOrdering = PETSC_TRUE;   /* use Petsc mat ordering (note: size is for the transpose, and PETSc r = Klu perm_c) */
367:     else lu->Common.ordering = (int)idx;
368:   }
369:   /* Matrix row scaling */
370:   PetscOptionsEList("-mat_klu_row_scale","Matrix row scaling","None",scale,3,scale[0],&idx,&flg);
371:   PetscOptionsEnd();
372:   *F = B;
373:   return(0);
374: }