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: PetscFunctionBegin;
101: if (lu->CleanUpKLU) {
102: klu_K_free_symbolic(&lu->Symbolic, &lu->Common);
103: klu_K_free_numeric(&lu->Numeric, &lu->Common);
104: PetscCall(PetscFree2(lu->perm_r, lu->perm_c));
105: }
106: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
107: PetscCall(PetscFree(A->data));
108: PetscFunctionReturn(PETSC_SUCCESS);
109: }
111: static PetscErrorCode MatSolveTranspose_KLU(Mat A, Vec b, Vec x)
112: {
113: Mat_KLU *lu = (Mat_KLU *)A->data;
114: PetscScalar *xa;
115: PetscInt status;
117: PetscFunctionBegin;
118: /* KLU uses a column major format, solve Ax = b by klu_*_solve */
119: PetscCall(VecCopy(b, x)); /* klu_solve stores the solution in rhs */
120: PetscCall(VecGetArray(x, &xa));
121: status = klu_K_solve(lu->Symbolic, lu->Numeric, A->rmap->n, 1, (PetscReal *)xa, &lu->Common);
122: PetscCheck(status == 1, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Solve failed");
123: PetscCall(VecRestoreArray(x, &xa));
124: PetscFunctionReturn(PETSC_SUCCESS);
125: }
127: static PetscErrorCode MatSolve_KLU(Mat A, Vec b, Vec x)
128: {
129: Mat_KLU *lu = (Mat_KLU *)A->data;
130: PetscScalar *xa;
131: PetscInt status;
133: PetscFunctionBegin;
134: /* KLU uses a column major format, solve A^Tx = b by klu_*_tsolve */
135: PetscCall(VecCopy(b, x)); /* klu_solve stores the solution in rhs */
136: PetscCall(VecGetArray(x, &xa));
137: #if defined(PETSC_USE_COMPLEX)
138: PetscInt conj_solve = 1;
139: status = klu_K_tsolve(lu->Symbolic, lu->Numeric, A->rmap->n, 1, (PetscReal *)xa, conj_solve, &lu->Common); /* conjugate solve */
140: #else
141: status = klu_K_tsolve(lu->Symbolic, lu->Numeric, A->rmap->n, 1, xa, &lu->Common);
142: #endif
143: PetscCheck(status == 1, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Solve failed");
144: PetscCall(VecRestoreArray(x, &xa));
145: PetscFunctionReturn(PETSC_SUCCESS);
146: }
148: static PetscErrorCode MatLUFactorNumeric_KLU(Mat F, Mat A, const MatFactorInfo *info)
149: {
150: Mat_KLU *lu = (Mat_KLU *)(F)->data;
151: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
152: PetscInt *ai = a->i, *aj = a->j;
153: PetscScalar *av = a->a;
155: PetscFunctionBegin;
156: /* numeric factorization of A' */
158: if (lu->flg == SAME_NONZERO_PATTERN && lu->Numeric) klu_K_free_numeric(&lu->Numeric, &lu->Common);
159: lu->Numeric = klu_K_factor(ai, aj, (PetscReal *)av, lu->Symbolic, &lu->Common);
160: PetscCheck(lu->Numeric, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Numeric factorization failed");
162: lu->flg = SAME_NONZERO_PATTERN;
163: lu->CleanUpKLU = PETSC_TRUE;
164: F->ops->solve = MatSolve_KLU;
165: F->ops->solvetranspose = MatSolveTranspose_KLU;
166: PetscFunctionReturn(PETSC_SUCCESS);
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: PetscFunctionBegin;
177: if (lu->PetscMatOrdering) {
178: PetscCall(ISGetIndices(r, &ra));
179: PetscCall(ISGetIndices(c, &ca));
180: PetscCall(PetscMalloc2(m, &lu->perm_r, n, &lu->perm_c));
181: /* we cannot simply memcpy on 64-bit archs */
182: for (i = 0; i < m; i++) lu->perm_r[i] = ra[i];
183: for (i = 0; i < n; i++) lu->perm_c[i] = ca[i];
184: PetscCall(ISRestoreIndices(r, &ra));
185: PetscCall(ISRestoreIndices(c, &ca));
186: }
188: /* symbolic factorization of A' */
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: }
195: PetscCheck(lu->Symbolic, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Symbolic Factorization failed");
197: lu->flg = DIFFERENT_NONZERO_PATTERN;
198: lu->CleanUpKLU = PETSC_TRUE;
199: (F)->ops->lufactornumeric = MatLUFactorNumeric_KLU;
200: PetscFunctionReturn(PETSC_SUCCESS);
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: PetscFunctionBegin;
209: PetscCall(PetscViewerASCIIPrintf(viewer, "KLU stats:\n"));
210: PetscCall(PetscViewerASCIIPrintf(viewer, " Number of diagonal blocks: %" PetscInt_FMT "\n", (PetscInt)(Numeric->nblocks)));
211: PetscCall(PetscViewerASCIIPrintf(viewer, " Total nonzeros=%" PetscInt_FMT "\n", (PetscInt)(Numeric->lnz + Numeric->unz)));
212: PetscCall(PetscViewerASCIIPrintf(viewer, "KLU runtime parameters:\n"));
213: /* Control parameters used by numeric factorization */
214: PetscCall(PetscViewerASCIIPrintf(viewer, " Partial pivoting tolerance: %g\n", lu->Common.tol));
215: /* BTF preordering */
216: PetscCall(PetscViewerASCIIPrintf(viewer, " BTF preordering enabled: %" PetscInt_FMT "\n", (PetscInt)(lu->Common.btf)));
217: /* mat ordering */
218: if (!lu->PetscMatOrdering) PetscCall(PetscViewerASCIIPrintf(viewer, " Ordering: %s (not using the PETSc ordering)\n", KluOrderingTypes[(int)lu->Common.ordering]));
219: /* matrix row scaling */
220: PetscCall(PetscViewerASCIIPrintf(viewer, " Matrix row scaling: %s\n", scale[(int)lu->Common.scale]));
221: PetscFunctionReturn(PETSC_SUCCESS);
222: }
224: static PetscErrorCode MatView_KLU(Mat A, PetscViewer viewer)
225: {
226: PetscBool iascii;
227: PetscViewerFormat format;
229: PetscFunctionBegin;
230: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
231: if (iascii) {
232: PetscCall(PetscViewerGetFormat(viewer, &format));
233: if (format == PETSC_VIEWER_ASCII_INFO) PetscCall(MatView_Info_KLU(A, viewer));
234: }
235: PetscFunctionReturn(PETSC_SUCCESS);
236: }
238: PetscErrorCode MatFactorGetSolverType_seqaij_klu(Mat A, MatSolverType *type)
239: {
240: PetscFunctionBegin;
241: *type = MATSOLVERKLU;
242: PetscFunctionReturn(PETSC_SUCCESS);
243: }
245: /*MC
246: MATSOLVERKLU = "klu" - A matrix type providing direct solvers, LU, for sequential matrices
247: via the external package KLU.
249: `./configure --download-suitesparse` to install PETSc to use KLU
251: Use `-pc_type lu` `-pc_factor_mat_solver_type klu` to use this direct solver
253: Consult KLU documentation for more information on the options database keys below.
255: Options Database Keys:
256: + -mat_klu_pivot_tol <0.001> - Partial pivoting tolerance
257: . -mat_klu_use_btf <1> - Use BTF preordering
258: . -mat_klu_ordering <AMD> - KLU reordering scheme to reduce fill-in (choose one of) `AMD`, `COLAMD`, `PETSC`
259: - -mat_klu_row_scale <NONE> - Matrix row scaling (choose one of) `NONE`, `SUM`, `MAX`
261: Level: beginner
263: Note:
264: KLU is part of SuiteSparse http://faculty.cse.tamu.edu/davis/suitesparse.html
266: .seealso: [](ch_matrices), `Mat`, `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;
273: PetscInt m = A->rmap->n, n = A->cmap->n, idx = 0, status;
274: PetscBool flg;
276: PetscFunctionBegin;
277: /* Create the factorization matrix F */
278: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
279: PetscCall(MatSetSizes(B, PETSC_DECIDE, PETSC_DECIDE, m, n));
280: PetscCall(PetscStrallocpy("klu", &((PetscObject)B)->type_name));
281: PetscCall(MatSetUp(B));
283: PetscCall(PetscNew(&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: PetscCall(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: PetscCall(PetscFree(B->solvertype));
298: PetscCall(PetscStrallocpy(MATSOLVERKLU, &B->solvertype));
299: B->canuseordering = PETSC_TRUE;
300: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
302: /* initializations */
303: /* get the default control parameters */
304: status = klu_K_defaults(&lu->Common);
305: PetscCheck(status > 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Initialization failed");
307: lu->Common.scale = 0; /* No row scaling */
309: PetscOptionsBegin(PetscObjectComm((PetscObject)B), ((PetscObject)B)->prefix, "KLU Options", "Mat");
310: /* Partial pivoting tolerance */
311: PetscCall(PetscOptionsReal("-mat_klu_pivot_tol", "Partial pivoting tolerance", "None", lu->Common.tol, &lu->Common.tol, NULL));
312: /* BTF pre-ordering */
313: PetscCall(PetscOptionsInt("-mat_klu_use_btf", "Enable BTF preordering", "None", (PetscInt)lu->Common.btf, (PetscInt *)&lu->Common.btf, NULL));
314: /* Matrix reordering */
315: PetscCall(PetscOptionsEList("-mat_klu_ordering", "Internal ordering method", "None", KluOrderingTypes, PETSC_STATIC_ARRAY_LENGTH(KluOrderingTypes), KluOrderingTypes[0], &idx, &flg));
316: lu->Common.ordering = (int)idx;
317: /* Matrix row scaling */
318: PetscCall(PetscOptionsEList("-mat_klu_row_scale", "Matrix row scaling", "None", scale, 3, scale[0], &idx, &flg));
319: PetscOptionsEnd();
320: *F = B;
321: PetscFunctionReturn(PETSC_SUCCESS);
322: }