Actual source code: klu.c
1: /*
2: Provides an interface to the KLUv1.2 sparse solver
4: When build with PETSC_USE_64BIT_INDICES this will use SuiteSparse_long as the
5: integer type in KLU, otherwise it will use int. This means
6: all integers in this file are simply declared as PetscInt. Also it means
7: that KLU SuiteSparse_long version MUST be built with 64-bit integers when used.
9: */
10: #include <../src/mat/impls/aij/seq/aij.h>
12: #if defined(PETSC_USE_64BIT_INDICES)
13: #define klu_K_defaults klu_l_defaults
14: #define klu_K_analyze(a, b, c, d) klu_l_analyze((SuiteSparse_long)a, (SuiteSparse_long *)b, (SuiteSparse_long *)c, d)
15: #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)
16: #define klu_K_free_symbolic klu_l_free_symbolic
17: #define klu_K_free_numeric klu_l_free_numeric
18: #define klu_K_common klu_l_common
19: #define klu_K_symbolic klu_l_symbolic
20: #define klu_K_numeric klu_l_numeric
21: #if defined(PETSC_USE_COMPLEX)
22: #define klu_K_factor(a, b, c, d, e) klu_zl_factor((SuiteSparse_long *)a, (SuiteSparse_long *)b, c, d, e);
23: #define klu_K_solve klu_zl_solve
24: #define klu_K_tsolve klu_zl_tsolve
25: #define klu_K_refactor klu_zl_refactor
26: #define klu_K_sort klu_zl_sort
27: #define klu_K_flops klu_zl_flops
28: #define klu_K_rgrowth klu_zl_rgrowth
29: #define klu_K_condest klu_zl_condest
30: #define klu_K_rcond klu_zl_rcond
31: #define klu_K_scale klu_zl_scale
32: #else
33: #define klu_K_factor(a, b, c, d, e) klu_l_factor((SuiteSparse_long *)a, (SuiteSparse_long *)b, c, d, e);
34: #define klu_K_solve klu_l_solve
35: #define klu_K_tsolve klu_l_tsolve
36: #define klu_K_refactor klu_l_refactor
37: #define klu_K_sort klu_l_sort
38: #define klu_K_flops klu_l_flops
39: #define klu_K_rgrowth klu_l_rgrowth
40: #define klu_K_condest klu_l_condest
41: #define klu_K_rcond klu_l_rcond
42: #define klu_K_scale klu_l_scale
43: #endif
44: #else
45: #define klu_K_defaults klu_defaults
46: #define klu_K_analyze klu_analyze
47: #define klu_K_analyze_given klu_analyze_given
48: #define klu_K_free_symbolic klu_free_symbolic
49: #define klu_K_free_numeric klu_free_numeric
50: #define klu_K_common klu_common
51: #define klu_K_symbolic klu_symbolic
52: #define klu_K_numeric klu_numeric
53: #if defined(PETSC_USE_COMPLEX)
54: #define klu_K_factor klu_z_factor
55: #define klu_K_solve klu_z_solve
56: #define klu_K_tsolve klu_z_tsolve
57: #define klu_K_refactor klu_z_refactor
58: #define klu_K_sort klu_z_sort
59: #define klu_K_flops klu_z_flops
60: #define klu_K_rgrowth klu_z_rgrowth
61: #define klu_K_condest klu_z_condest
62: #define klu_K_rcond klu_z_rcond
63: #define klu_K_scale klu_z_scale
64: #else
65: #define klu_K_factor klu_factor
66: #define klu_K_solve klu_solve
67: #define klu_K_tsolve klu_tsolve
68: #define klu_K_refactor klu_refactor
69: #define klu_K_sort klu_sort
70: #define klu_K_flops klu_flops
71: #define klu_K_rgrowth klu_rgrowth
72: #define klu_K_condest klu_condest
73: #define klu_K_rcond klu_rcond
74: #define klu_K_scale klu_scale
75: #endif
76: #endif
78: EXTERN_C_BEGIN
79: #include <klu.h>
80: EXTERN_C_END
82: static const char *KluOrderingTypes[] = {"AMD", "COLAMD"};
83: static const char *scale[] = {"NONE", "SUM", "MAX"};
85: typedef struct {
86: klu_K_common Common;
87: klu_K_symbolic *Symbolic;
88: klu_K_numeric *Numeric;
89: PetscInt *perm_c, *perm_r;
90: MatStructure flg;
91: PetscBool PetscMatOrdering;
92: PetscBool CleanUpKLU;
93: } Mat_KLU;
95: static PetscErrorCode MatDestroy_KLU(Mat A)
96: {
97: Mat_KLU *lu = (Mat_KLU *)A->data;
99: PetscFunctionBegin;
100: if (lu->CleanUpKLU) {
101: klu_K_free_symbolic(&lu->Symbolic, &lu->Common);
102: klu_K_free_numeric(&lu->Numeric, &lu->Common);
103: PetscCall(PetscFree2(lu->perm_r, lu->perm_c));
104: }
105: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
106: PetscCall(PetscFree(A->data));
107: PetscFunctionReturn(PETSC_SUCCESS);
108: }
110: static PetscErrorCode MatSolveTranspose_KLU(Mat A, Vec b, Vec x)
111: {
112: Mat_KLU *lu = (Mat_KLU *)A->data;
113: PetscScalar *xa;
114: PetscInt status;
116: PetscFunctionBegin;
117: /* KLU uses a column major format, solve Ax = b by klu_*_solve */
118: PetscCall(VecCopy(b, x)); /* klu_solve stores the solution in rhs */
119: PetscCall(VecGetArray(x, &xa));
120: status = klu_K_solve(lu->Symbolic, lu->Numeric, A->rmap->n, 1, (PetscReal *)xa, &lu->Common);
121: PetscCheck(status == 1, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Solve failed");
122: PetscCall(VecRestoreArray(x, &xa));
123: PetscFunctionReturn(PETSC_SUCCESS);
124: }
126: static PetscErrorCode MatSolve_KLU(Mat A, Vec b, Vec x)
127: {
128: Mat_KLU *lu = (Mat_KLU *)A->data;
129: PetscScalar *xa;
130: PetscInt status;
132: PetscFunctionBegin;
133: /* KLU uses a column major format, solve A^Tx = b by klu_*_tsolve */
134: PetscCall(VecCopy(b, x)); /* klu_solve stores the solution in rhs */
135: PetscCall(VecGetArray(x, &xa));
136: #if defined(PETSC_USE_COMPLEX)
137: PetscInt conj_solve = 1;
138: status = klu_K_tsolve(lu->Symbolic, lu->Numeric, A->rmap->n, 1, (PetscReal *)xa, conj_solve, &lu->Common); /* conjugate solve */
139: #else
140: status = klu_K_tsolve(lu->Symbolic, lu->Numeric, A->rmap->n, 1, xa, &lu->Common);
141: #endif
142: PetscCheck(status == 1, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Solve failed");
143: PetscCall(VecRestoreArray(x, &xa));
144: PetscFunctionReturn(PETSC_SUCCESS);
145: }
147: static PetscErrorCode MatLUFactorNumeric_KLU(Mat F, Mat A, const MatFactorInfo *info)
148: {
149: Mat_KLU *lu = (Mat_KLU *)(F)->data;
150: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
151: PetscInt *ai = a->i, *aj = a->j;
152: PetscScalar *av = a->a;
154: PetscFunctionBegin;
155: /* numeric factorization of A' */
157: if (lu->flg == SAME_NONZERO_PATTERN && lu->Numeric) klu_K_free_numeric(&lu->Numeric, &lu->Common);
158: lu->Numeric = klu_K_factor(ai, aj, (PetscReal *)av, lu->Symbolic, &lu->Common);
159: PetscCheck(lu->Numeric, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Numeric factorization failed");
161: lu->flg = SAME_NONZERO_PATTERN;
162: lu->CleanUpKLU = PETSC_TRUE;
163: F->ops->solve = MatSolve_KLU;
164: F->ops->solvetranspose = MatSolveTranspose_KLU;
165: PetscFunctionReturn(PETSC_SUCCESS);
166: }
168: static PetscErrorCode MatLUFactorSymbolic_KLU(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
169: {
170: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
171: Mat_KLU *lu = (Mat_KLU *)(F->data);
172: PetscInt i, *ai = a->i, *aj = a->j, m = A->rmap->n, n = A->cmap->n;
173: const PetscInt *ra, *ca;
175: PetscFunctionBegin;
176: if (lu->PetscMatOrdering) {
177: PetscCall(ISGetIndices(r, &ra));
178: PetscCall(ISGetIndices(c, &ca));
179: PetscCall(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: PetscCall(ISRestoreIndices(r, &ra));
184: PetscCall(ISRestoreIndices(c, &ca));
185: }
187: /* symbolic factorization of A' */
188: if (r) {
189: lu->PetscMatOrdering = PETSC_TRUE;
190: lu->Symbolic = klu_K_analyze_given(n, ai, aj, lu->perm_c, lu->perm_r, &lu->Common);
191: } else { /* use klu internal ordering */
192: lu->Symbolic = klu_K_analyze(n, ai, aj, &lu->Common);
193: }
194: PetscCheck(lu->Symbolic, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Symbolic Factorization failed");
196: lu->flg = DIFFERENT_NONZERO_PATTERN;
197: lu->CleanUpKLU = PETSC_TRUE;
198: (F)->ops->lufactornumeric = MatLUFactorNumeric_KLU;
199: PetscFunctionReturn(PETSC_SUCCESS);
200: }
202: static PetscErrorCode MatView_Info_KLU(Mat A, PetscViewer viewer)
203: {
204: Mat_KLU *lu = (Mat_KLU *)A->data;
205: klu_K_numeric *Numeric = (klu_K_numeric *)lu->Numeric;
207: PetscFunctionBegin;
208: PetscCall(PetscViewerASCIIPrintf(viewer, "KLU stats:\n"));
209: PetscCall(PetscViewerASCIIPrintf(viewer, " Number of diagonal blocks: %" PetscInt_FMT "\n", (PetscInt)(Numeric->nblocks)));
210: PetscCall(PetscViewerASCIIPrintf(viewer, " Total nonzeros=%" PetscInt_FMT "\n", (PetscInt)(Numeric->lnz + Numeric->unz)));
211: PetscCall(PetscViewerASCIIPrintf(viewer, "KLU runtime parameters:\n"));
212: /* Control parameters used by numeric factorization */
213: PetscCall(PetscViewerASCIIPrintf(viewer, " Partial pivoting tolerance: %g\n", lu->Common.tol));
214: /* BTF preordering */
215: PetscCall(PetscViewerASCIIPrintf(viewer, " BTF preordering enabled: %" PetscInt_FMT "\n", (PetscInt)(lu->Common.btf)));
216: /* mat ordering */
217: if (!lu->PetscMatOrdering) PetscCall(PetscViewerASCIIPrintf(viewer, " Ordering: %s (not using the PETSc ordering)\n", KluOrderingTypes[(int)lu->Common.ordering]));
218: /* matrix row scaling */
219: PetscCall(PetscViewerASCIIPrintf(viewer, " Matrix row scaling: %s\n", scale[(int)lu->Common.scale]));
220: PetscFunctionReturn(PETSC_SUCCESS);
221: }
223: static PetscErrorCode MatView_KLU(Mat A, PetscViewer viewer)
224: {
225: PetscBool iascii;
226: PetscViewerFormat format;
228: PetscFunctionBegin;
229: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
230: if (iascii) {
231: PetscCall(PetscViewerGetFormat(viewer, &format));
232: if (format == PETSC_VIEWER_ASCII_INFO) PetscCall(MatView_Info_KLU(A, viewer));
233: }
234: PetscFunctionReturn(PETSC_SUCCESS);
235: }
237: static PetscErrorCode MatFactorGetSolverType_seqaij_klu(Mat A, MatSolverType *type)
238: {
239: PetscFunctionBegin;
240: *type = MATSOLVERKLU;
241: PetscFunctionReturn(PETSC_SUCCESS);
242: }
244: /*MC
245: MATSOLVERKLU = "klu" - A matrix type providing direct solvers, LU, for sequential matrices
246: via the external package KLU.
248: `./configure --download-suitesparse` to install PETSc to use KLU
250: Use `-pc_type lu` `-pc_factor_mat_solver_type klu` to use this direct solver
252: Consult KLU documentation for more information on the options database keys below.
254: Options Database Keys:
255: + -mat_klu_pivot_tol <0.001> - Partial pivoting tolerance
256: . -mat_klu_use_btf <1> - Use BTF preordering
257: . -mat_klu_ordering <AMD> - KLU reordering scheme to reduce fill-in (choose one of) `AMD`, `COLAMD`, `PETSC`
258: - -mat_klu_row_scale <NONE> - Matrix row scaling (choose one of) `NONE`, `SUM`, `MAX`
260: Level: beginner
262: Note:
263: KLU is part of SuiteSparse <http://faculty.cse.tamu.edu/davis/suitesparse.html>
265: .seealso: [](ch_matrices), `Mat`, `PCLU`, `MATSOLVERUMFPACK`, `MATSOLVERCHOLMOD`, `PCFactorSetMatSolverType()`, `MatSolverType`
266: M*/
268: PETSC_INTERN PetscErrorCode MatGetFactor_seqaij_klu(Mat A, MatFactorType ftype, Mat *F)
269: {
270: Mat B;
271: Mat_KLU *lu;
272: PetscInt m = A->rmap->n, n = A->cmap->n, idx = 0, status;
273: PetscBool flg;
275: PetscFunctionBegin;
276: /* Create the factorization matrix F */
277: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
278: PetscCall(MatSetSizes(B, PETSC_DECIDE, PETSC_DECIDE, m, n));
279: PetscCall(PetscStrallocpy("klu", &((PetscObject)B)->type_name));
280: PetscCall(MatSetUp(B));
282: PetscCall(PetscNew(&lu));
284: B->data = lu;
285: B->ops->getinfo = MatGetInfo_External;
286: B->ops->lufactorsymbolic = MatLUFactorSymbolic_KLU;
287: B->ops->destroy = MatDestroy_KLU;
288: B->ops->view = MatView_KLU;
290: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_seqaij_klu));
292: B->factortype = MAT_FACTOR_LU;
293: B->assembled = PETSC_TRUE; /* required by -ksp_view */
294: B->preallocated = PETSC_TRUE;
296: PetscCall(PetscFree(B->solvertype));
297: PetscCall(PetscStrallocpy(MATSOLVERKLU, &B->solvertype));
298: B->canuseordering = PETSC_TRUE;
299: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
301: /* initializations */
302: /* get the default control parameters */
303: status = klu_K_defaults(&lu->Common);
304: PetscCheck(status > 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "KLU Initialization failed");
306: lu->Common.scale = 0; /* No row scaling */
308: PetscOptionsBegin(PetscObjectComm((PetscObject)B), ((PetscObject)B)->prefix, "KLU Options", "Mat");
309: /* Partial pivoting tolerance */
310: PetscCall(PetscOptionsReal("-mat_klu_pivot_tol", "Partial pivoting tolerance", "None", lu->Common.tol, &lu->Common.tol, NULL));
311: /* BTF pre-ordering */
312: PetscCall(PetscOptionsInt("-mat_klu_use_btf", "Enable BTF preordering", "None", (PetscInt)lu->Common.btf, (PetscInt *)&lu->Common.btf, NULL));
313: /* Matrix reordering */
314: PetscCall(PetscOptionsEList("-mat_klu_ordering", "Internal ordering method", "None", KluOrderingTypes, PETSC_STATIC_ARRAY_LENGTH(KluOrderingTypes), KluOrderingTypes[0], &idx, &flg));
315: lu->Common.ordering = (int)idx;
316: /* Matrix row scaling */
317: PetscCall(PetscOptionsEList("-mat_klu_row_scale", "Matrix row scaling", "None", scale, 3, scale[0], &idx, &flg));
318: PetscOptionsEnd();
319: *F = B;
320: PetscFunctionReturn(PETSC_SUCCESS);
321: }