Actual source code: klu.c
petsc-3.13.6 2020-09-29
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","PETSC"};
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: {
99: Mat_KLU *lu=(Mat_KLU*)A->data;
102: if (lu->CleanUpKLU) {
103: klu_K_free_symbolic(&lu->Symbolic,&lu->Common);
104: klu_K_free_numeric(&lu->Numeric,&lu->Common);
105: PetscFree2(lu->perm_r,lu->perm_c);
106: }
107: PetscFree(A->data);
108: return(0);
109: }
111: static PetscErrorCode MatSolveTranspose_KLU(Mat A,Vec b,Vec x)
112: {
113: Mat_KLU *lu = (Mat_KLU*)A->data;
114: PetscScalar *xa;
116: PetscInt status;
119: /* KLU uses a column major format, solve Ax = b by klu_*_solve */
120: /* ----------------------------------*/
121: VecCopy(b,x); /* klu_solve stores the solution in rhs */
122: VecGetArray(x,&xa);
123: status = klu_K_solve(lu->Symbolic,lu->Numeric,A->rmap->n,1,(PetscReal*)xa,&lu->Common);
124: if (status != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Solve failed");
125: VecRestoreArray(x,&xa);
126: return(0);
127: }
129: static PetscErrorCode MatSolve_KLU(Mat A,Vec b,Vec x)
130: {
131: Mat_KLU *lu = (Mat_KLU*)A->data;
132: PetscScalar *xa;
134: PetscInt status;
137: /* KLU uses a column major format, solve A^Tx = b by klu_*_tsolve */
138: /* ----------------------------------*/
139: VecCopy(b,x); /* klu_solve stores the solution in rhs */
140: VecGetArray(x,&xa);
141: #if defined(PETSC_USE_COMPLEX)
142: PetscInt conj_solve=1;
143: status = klu_K_tsolve(lu->Symbolic,lu->Numeric,A->rmap->n,1,(PetscReal*)xa,conj_solve,&lu->Common); /* conjugate solve */
144: #else
145: status = klu_K_tsolve(lu->Symbolic,lu->Numeric,A->rmap->n,1,xa,&lu->Common);
146: #endif
147: if (status != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Solve failed");
148: VecRestoreArray(x,&xa);
149: return(0);
150: }
152: static PetscErrorCode MatLUFactorNumeric_KLU(Mat F,Mat A,const MatFactorInfo *info)
153: {
154: Mat_KLU *lu = (Mat_KLU*)(F)->data;
155: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
156: PetscInt *ai = a->i,*aj=a->j;
157: PetscScalar *av = a->a;
160: /* numeric factorization of A' */
161: /* ----------------------------*/
163: if (lu->flg == SAME_NONZERO_PATTERN && lu->Numeric) {
164: klu_K_free_numeric(&lu->Numeric,&lu->Common);
165: }
166: lu->Numeric = klu_K_factor(ai,aj,(PetscReal*)av,lu->Symbolic,&lu->Common);
167: if(!lu->Numeric) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Numeric factorization failed");
169: lu->flg = SAME_NONZERO_PATTERN;
170: lu->CleanUpKLU = PETSC_TRUE;
171: F->ops->solve = MatSolve_KLU;
172: F->ops->solvetranspose = MatSolveTranspose_KLU;
173: return(0);
174: }
176: static PetscErrorCode MatLUFactorSymbolic_KLU(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
177: {
178: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
179: Mat_KLU *lu = (Mat_KLU*)(F->data);
181: PetscInt i,*ai = a->i,*aj = a->j,m=A->rmap->n,n=A->cmap->n;
182: const PetscInt *ra,*ca;
185: if (lu->PetscMatOrdering) {
186: ISGetIndices(r,&ra);
187: ISGetIndices(c,&ca);
188: PetscMalloc2(m,&lu->perm_r,n,&lu->perm_c);
189: /* we cannot simply memcpy on 64 bit archs */
190: for (i = 0; i < m; i++) lu->perm_r[i] = ra[i];
191: for (i = 0; i < n; i++) lu->perm_c[i] = ca[i];
192: ISRestoreIndices(r,&ra);
193: ISRestoreIndices(c,&ca);
194: }
196: /* symbolic factorization of A' */
197: /* ---------------------------------------------------------------------- */
198: if (lu->PetscMatOrdering) { /* use Petsc ordering */
199: lu->Symbolic = klu_K_analyze_given(n,ai,aj,lu->perm_c,lu->perm_r,&lu->Common);
200: } else { /* use klu internal ordering */
201: lu->Symbolic = klu_K_analyze(n,ai,aj,&lu->Common);
202: }
203: if (!lu->Symbolic) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Symbolic Factorization failed");
205: lu->flg = DIFFERENT_NONZERO_PATTERN;
206: lu->CleanUpKLU = PETSC_TRUE;
207: (F)->ops->lufactornumeric = MatLUFactorNumeric_KLU;
208: return(0);
209: }
211: static PetscErrorCode MatView_Info_KLU(Mat A,PetscViewer viewer)
212: {
213: Mat_KLU *lu= (Mat_KLU*)A->data;
214: klu_K_numeric *Numeric=(klu_K_numeric*)lu->Numeric;
218: /* check if matrix is KLU type */
219: if (A->ops->solve != MatSolve_KLU) return(0);
221: PetscViewerASCIIPrintf(viewer,"KLU stats:\n");
222: PetscViewerASCIIPrintf(viewer," Number of diagonal blocks: %d\n",Numeric->nblocks);
223: PetscViewerASCIIPrintf(viewer," Total nonzeros=%d\n",Numeric->lnz+Numeric->unz);
225: PetscViewerASCIIPrintf(viewer,"KLU runtime parameters:\n");
227: /* Control parameters used by numeric factorization */
228: PetscViewerASCIIPrintf(viewer," Partial pivoting tolerance: %g\n",lu->Common.tol);
229: /* BTF preordering */
230: PetscViewerASCIIPrintf(viewer," BTF preordering enabled: %d\n",lu->Common.btf);
231: /* mat ordering */
232: if (!lu->PetscMatOrdering) {
233: PetscViewerASCIIPrintf(viewer," Ordering: %s (not using the PETSc ordering)\n",KluOrderingTypes[(int)lu->Common.ordering]);
234: } else {
235: PetscViewerASCIIPrintf(viewer," Using PETSc ordering\n");
236: }
237: /* matrix row scaling */
238: PetscViewerASCIIPrintf(viewer, " Matrix row scaling: %s\n",scale[(int)lu->Common.scale]);
239: return(0);
240: }
242: static PetscErrorCode MatView_KLU(Mat A,PetscViewer viewer)
243: {
244: PetscErrorCode ierr;
245: PetscBool iascii;
246: PetscViewerFormat format;
249: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
250: if (iascii) {
251: PetscViewerGetFormat(viewer,&format);
252: if (format == PETSC_VIEWER_ASCII_INFO) {
253: MatView_Info_KLU(A,viewer);
254: }
255: }
256: return(0);
257: }
259: PetscErrorCode MatFactorGetSolverType_seqaij_klu(Mat A,MatSolverType *type)
260: {
262: *type = MATSOLVERKLU;
263: return(0);
264: }
267: /*MC
268: MATSOLVERKLU = "klu" - A matrix type providing direct solvers (LU) for sequential matrices
269: via the external package KLU.
271: ./configure --download-suitesparse to install PETSc to use KLU
273: Use -pc_type lu -pc_factor_mat_solver_type klu to use this direct solver
275: Consult KLU documentation for more information on the options database keys below.
277: Options Database Keys:
278: + -mat_klu_pivot_tol <0.001> - Partial pivoting tolerance
279: . -mat_klu_use_btf <1> - Use BTF preordering
280: . -mat_klu_ordering <AMD> - KLU reordering scheme to reduce fill-in (choose one of) AMD COLAMD PETSC
281: - -mat_klu_row_scale <NONE> - Matrix row scaling (choose one of) NONE SUM MAX
283: Note: KLU is part of SuiteSparse http://faculty.cse.tamu.edu/davis/suitesparse.html
285: Level: beginner
287: .seealso: PCLU, MATSOLVERUMFPACK, MATSOLVERCHOLMOD, PCFactorSetMatSolverType(), MatSolverType
288: M*/
290: PETSC_INTERN PetscErrorCode MatGetFactor_seqaij_klu(Mat A,MatFactorType ftype,Mat *F)
291: {
292: Mat B;
293: Mat_KLU *lu;
295: PetscInt m=A->rmap->n,n=A->cmap->n,idx,status;
296: PetscBool flg;
299: /* Create the factorization matrix F */
300: MatCreate(PetscObjectComm((PetscObject)A),&B);
301: MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,m,n);
302: PetscStrallocpy("klu",&((PetscObject)B)->type_name);
303: MatSetUp(B);
305: PetscNewLog(B,&lu);
307: B->data = lu;
308: B->ops->getinfo = MatGetInfo_External;
309: B->ops->lufactorsymbolic = MatLUFactorSymbolic_KLU;
310: B->ops->destroy = MatDestroy_KLU;
311: B->ops->view = MatView_KLU;
313: PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_seqaij_klu);
315: B->factortype = MAT_FACTOR_LU;
316: B->assembled = PETSC_TRUE; /* required by -ksp_view */
317: B->preallocated = PETSC_TRUE;
319: PetscFree(B->solvertype);
320: PetscStrallocpy(MATSOLVERKLU,&B->solvertype);
322: /* initializations */
323: /* ------------------------------------------------*/
324: /* get the default control parameters */
325: status = klu_K_defaults(&lu->Common);
326: if(status <= 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Initialization failed");
328: lu->Common.scale = 0; /* No row scaling */
330: PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"KLU Options","Mat");
331: /* Partial pivoting tolerance */
332: PetscOptionsReal("-mat_klu_pivot_tol","Partial pivoting tolerance","None",lu->Common.tol,&lu->Common.tol,NULL);
333: /* BTF pre-ordering */
334: PetscOptionsInt("-mat_klu_use_btf","Enable BTF preordering","None",(PetscInt)lu->Common.btf,(PetscInt*)&lu->Common.btf,NULL);
335: /* Matrix reordering */
336: PetscOptionsEList("-mat_klu_ordering","Internal ordering method","None",KluOrderingTypes,sizeof(KluOrderingTypes)/sizeof(KluOrderingTypes[0]),KluOrderingTypes[0],&idx,&flg);
337: if (flg) {
338: if ((int)idx == 2) lu->PetscMatOrdering = PETSC_TRUE; /* use Petsc mat ordering (note: size is for the transpose, and PETSc r = Klu perm_c) */
339: else lu->Common.ordering = (int)idx;
340: }
341: /* Matrix row scaling */
342: PetscOptionsEList("-mat_klu_row_scale","Matrix row scaling","None",scale,3,scale[0],&idx,&flg);
343: PetscOptionsEnd();
344: *F = B;
345: return(0);
346: }