Actual source code: basfactor.c
1: #include <../src/mat/impls/aij/seq/aij.h>
2: #include <../src/mat/impls/sbaij/seq/sbaij.h>
3: #include <../src/mat/impls/aij/seq/bas/spbas.h>
5: static PetscErrorCode MatICCFactorSymbolic_SeqAIJ_Bas(Mat fact, Mat A, IS perm, const MatFactorInfo *info)
6: {
7: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8: Mat_SeqSBAIJ *b;
9: PetscBool perm_identity, diagDense;
10: PetscInt reallocs = 0, i, *ai = a->i, *aj = a->j, am = A->rmap->n, *ui;
11: const PetscInt *rip, *riip, *adiag;
12: PetscInt j;
13: PetscInt ncols, *cols, *uj;
14: PetscReal fill = info->fill, levels = info->levels;
15: IS iperm;
16: spbas_matrix Pattern_0, Pattern_P;
18: PetscFunctionBegin;
19: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Must be square matrix, rows %" PetscInt_FMT " columns %" PetscInt_FMT, A->rmap->n, A->cmap->n);
20: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, &diagDense));
21: PetscCheck(diagDense, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entries");
22: PetscCall(ISIdentity(perm, &perm_identity));
23: PetscCall(ISInvertPermutation(perm, PETSC_DECIDE, &iperm));
25: /* ICC(0) without matrix ordering: simply copies fill pattern */
26: if (!levels && perm_identity) {
27: PetscCall(PetscMalloc1(am + 1, &ui));
28: ui[0] = 0;
30: for (i = 0; i < am; i++) ui[i + 1] = ui[i] + ai[i + 1] - adiag[i];
31: PetscCall(PetscMalloc1(ui[am] + 1, &uj));
32: cols = uj;
33: for (i = 0; i < am; i++) {
34: aj = a->j + adiag[i];
35: ncols = ui[i + 1] - ui[i];
36: for (j = 0; j < ncols; j++) *cols++ = *aj++;
37: }
38: } else { /* case: levels>0 || (levels=0 && !perm_identity) */
39: PetscCall(ISGetIndices(iperm, &riip));
40: PetscCall(ISGetIndices(perm, &rip));
42: /* Create spbas_matrix for pattern */
43: PetscCall(spbas_pattern_only(am, am, ai, aj, &Pattern_0));
45: /* Apply the permutation */
46: PetscCall(spbas_apply_reordering(&Pattern_0, rip, riip));
48: /* Raise the power */
49: PetscCall(spbas_power(Pattern_0, (int)levels + 1, &Pattern_P));
50: PetscCall(spbas_delete(Pattern_0));
52: /* Keep only upper triangle of pattern */
53: PetscCall(spbas_keep_upper(&Pattern_P));
55: /* Convert to Sparse Row Storage */
56: PetscCall(spbas_matrix_to_crs(Pattern_P, NULL, &ui, &uj));
57: PetscCall(spbas_delete(Pattern_P));
58: } /* end of case: levels>0 || (levels=0 && !perm_identity) */
60: /* put together the new matrix in MATSEQSBAIJ format */
62: b = (Mat_SeqSBAIJ *)fact->data;
63: PetscCall(PetscMalloc1(ui[am], &b->a));
65: b->j = uj;
66: b->i = ui;
67: b->diag = NULL;
68: b->ilen = NULL;
69: b->imax = NULL;
70: b->row = perm;
71: b->col = perm;
73: PetscCall(PetscObjectReference((PetscObject)perm));
74: PetscCall(PetscObjectReference((PetscObject)perm));
76: b->icol = iperm;
77: b->pivotinblocks = PETSC_FALSE; /* need to get from MatFactorInfo */
78: PetscCall(PetscMalloc1(am, &b->solve_work));
79: b->maxnz = b->nz = ui[am];
80: b->free_a = PETSC_TRUE;
81: b->free_ij = PETSC_TRUE;
83: fact->info.factor_mallocs = reallocs;
84: fact->info.fill_ratio_given = fill;
85: if (ai[am] != 0) {
86: fact->info.fill_ratio_needed = (PetscReal)ui[am] / (PetscReal)ai[am];
87: } else {
88: fact->info.fill_ratio_needed = 0.0;
89: }
90: /* fact->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJ_inplace; */
91: PetscFunctionReturn(PETSC_SUCCESS);
92: }
94: static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ_Bas(Mat B, Mat A, const MatFactorInfo *info)
95: {
96: Mat C = B;
97: Mat_SeqSBAIJ *b = (Mat_SeqSBAIJ *)C->data;
98: IS ip = b->row, iip = b->icol;
99: const PetscInt *rip, *riip;
100: PetscInt mbs = A->rmap->n, *bi = b->i, *bj = b->j;
101: MatScalar *ba = b->a;
102: PetscReal shiftnz = info->shiftamount;
103: PetscReal droptol = -1;
104: PetscBool perm_identity;
105: spbas_matrix Pattern, matrix_L, matrix_LT;
106: PetscReal mem_reduction;
108: PetscFunctionBegin;
109: /* Reduce memory requirements: erase values of B-matrix */
110: PetscCall(PetscFree(ba));
111: /* Compress (maximum) sparseness pattern of B-matrix */
112: PetscCall(spbas_compress_pattern(bi, bj, mbs, mbs, SPBAS_DIAGONAL_OFFSETS, &Pattern, &mem_reduction));
113: PetscCall(PetscFree(bi));
114: PetscCall(PetscFree(bj));
116: PetscCall(PetscInfo(NULL, " compression rate for spbas_compress_pattern %g \n", (double)mem_reduction));
118: /* Make Cholesky decompositions with larger Manteuffel shifts until no more negative diagonals are found. */
119: PetscCall(ISGetIndices(ip, &rip));
120: PetscCall(ISGetIndices(iip, &riip));
122: if (info->usedt) droptol = info->dt;
124: for (int ierr = NEGATIVE_DIAGONAL; ierr == NEGATIVE_DIAGONAL;) {
125: PetscBool success;
127: ierr = (int)spbas_incomplete_cholesky(A, rip, riip, Pattern, droptol, shiftnz, &matrix_LT, &success);
128: if (!success) {
129: shiftnz *= 1.5;
130: if (shiftnz < 1e-5) shiftnz = 1e-5;
131: PetscCall(PetscInfo(NULL, "spbas_incomplete_cholesky found a negative diagonal. Trying again with Manteuffel shift=%g\n", (double)shiftnz));
132: }
133: }
134: PetscCall(spbas_delete(Pattern));
136: PetscCall(PetscInfo(NULL, " memory_usage for spbas_incomplete_cholesky %g bytes per row\n", (double)(spbas_memory_requirement(matrix_LT) / (PetscReal)mbs)));
138: PetscCall(ISRestoreIndices(ip, &rip));
139: PetscCall(ISRestoreIndices(iip, &riip));
141: /* Convert spbas_matrix to compressed row storage */
142: PetscCall(spbas_transpose(matrix_LT, &matrix_L));
143: PetscCall(spbas_delete(matrix_LT));
144: PetscCall(spbas_matrix_to_crs(matrix_L, &ba, &bi, &bj));
145: b->i = bi;
146: b->j = bj;
147: b->a = ba;
148: PetscCall(spbas_delete(matrix_L));
150: /* Set the appropriate solution functions */
151: PetscCall(ISIdentity(ip, &perm_identity));
152: if (perm_identity) {
153: B->ops->solve = MatSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
154: B->ops->solvetranspose = MatSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
155: B->ops->forwardsolve = MatForwardSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
156: B->ops->backwardsolve = MatBackwardSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
157: } else {
158: B->ops->solve = MatSolve_SeqSBAIJ_1_inplace;
159: B->ops->solvetranspose = MatSolve_SeqSBAIJ_1_inplace;
160: B->ops->forwardsolve = MatForwardSolve_SeqSBAIJ_1_inplace;
161: B->ops->backwardsolve = MatBackwardSolve_SeqSBAIJ_1_inplace;
162: }
164: C->assembled = PETSC_TRUE;
165: C->preallocated = PETSC_TRUE;
167: PetscCall(PetscLogFlops(C->rmap->n));
168: PetscFunctionReturn(PETSC_SUCCESS);
169: }
171: static PetscErrorCode MatFactorGetSolverType_seqaij_bas(Mat A, MatSolverType *type)
172: {
173: PetscFunctionBegin;
174: *type = MATSOLVERBAS;
175: PetscFunctionReturn(PETSC_SUCCESS);
176: }
178: PETSC_INTERN PetscErrorCode MatGetFactor_seqaij_bas(Mat A, MatFactorType ftype, Mat *B)
179: {
180: PetscInt n = A->rmap->n;
182: PetscFunctionBegin;
183: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
184: PetscCall(MatSetSizes(*B, n, n, n, n));
185: PetscCheck(ftype == MAT_FACTOR_ICC, PETSC_COMM_SELF, PETSC_ERR_SUP, "Factor type not supported");
186: PetscCall(MatSetType(*B, MATSEQSBAIJ));
187: PetscCall(MatSeqSBAIJSetPreallocation(*B, 1, MAT_SKIP_ALLOCATION, NULL));
189: (*B)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqAIJ_Bas;
190: (*B)->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJ_Bas;
191: PetscCall(PetscObjectComposeFunction((PetscObject)*B, "MatFactorGetSolverType_C", MatFactorGetSolverType_seqaij_bas));
192: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
193: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_CHOLESKY]));
194: (*B)->factortype = ftype;
196: PetscCall(PetscFree((*B)->solvertype));
197: PetscCall(PetscStrallocpy(MATSOLVERBAS, &(*B)->solvertype));
198: (*B)->canuseordering = PETSC_TRUE;
199: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ICC]));
200: PetscFunctionReturn(PETSC_SUCCESS);
201: }