Actual source code: mkl_cpardiso.c
1: #include <petscsys.h>
2: #include <../src/mat/impls/aij/mpi/mpiaij.h>
3: #include <../src/mat/impls/sbaij/mpi/mpisbaij.h>
5: #if defined(PETSC_HAVE_MKL_INTEL_ILP64)
6: #define MKL_ILP64
7: #endif
8: #include <mkl.h>
9: #include <mkl_cluster_sparse_solver.h>
11: /*
12: * Possible mkl_cpardiso phases that controls the execution of the solver.
13: * For more information check mkl_cpardiso manual.
14: */
15: #define JOB_ANALYSIS 11
16: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION 12
17: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 13
18: #define JOB_NUMERICAL_FACTORIZATION 22
19: #define JOB_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 23
20: #define JOB_SOLVE_ITERATIVE_REFINEMENT 33
21: #define JOB_SOLVE_FORWARD_SUBSTITUTION 331
22: #define JOB_SOLVE_DIAGONAL_SUBSTITUTION 332
23: #define JOB_SOLVE_BACKWARD_SUBSTITUTION 333
24: #define JOB_RELEASE_OF_LU_MEMORY 0
25: #define JOB_RELEASE_OF_ALL_MEMORY -1
27: #define IPARM_SIZE 64
28: #define INT_TYPE MKL_INT
30: static const char *Err_MSG_CPardiso(int errNo)
31: {
32: switch (errNo) {
33: case -1:
34: return "input inconsistent";
35: break;
36: case -2:
37: return "not enough memory";
38: break;
39: case -3:
40: return "reordering problem";
41: break;
42: case -4:
43: return "zero pivot, numerical factorization or iterative refinement problem";
44: break;
45: case -5:
46: return "unclassified (internal) error";
47: break;
48: case -6:
49: return "preordering failed (matrix types 11, 13 only)";
50: break;
51: case -7:
52: return "diagonal matrix problem";
53: break;
54: case -8:
55: return "32-bit integer overflow problem";
56: break;
57: case -9:
58: return "not enough memory for OOC";
59: break;
60: case -10:
61: return "problems with opening OOC temporary files";
62: break;
63: case -11:
64: return "read/write problems with the OOC data file";
65: break;
66: default:
67: return "unknown error";
68: }
69: }
71: /*
72: * Internal data structure.
73: * For more information check mkl_cpardiso manual.
74: */
76: typedef struct {
77: /* Configuration vector */
78: INT_TYPE iparm[IPARM_SIZE];
80: /*
81: * Internal mkl_cpardiso memory location.
82: * After the first call to mkl_cpardiso do not modify pt, as that could cause a serious memory leak.
83: */
84: void *pt[IPARM_SIZE];
86: MPI_Fint comm_mkl_cpardiso;
88: /* Basic mkl_cpardiso info*/
89: INT_TYPE phase, maxfct, mnum, mtype, n, nrhs, msglvl, err;
91: /* Matrix structure */
92: PetscScalar *a;
94: INT_TYPE *ia, *ja;
96: /* Number of non-zero elements */
97: INT_TYPE nz;
99: /* Row permutaton vector*/
100: INT_TYPE *perm;
102: /* Define is matrix preserve sparse structure. */
103: MatStructure matstruc;
105: PetscErrorCode (*ConvertToTriples)(Mat, MatReuse, PetscInt *, PetscInt **, PetscInt **, PetscScalar **);
107: /* True if mkl_cpardiso function have been used. */
108: PetscBool CleanUp;
109: } Mat_MKL_CPARDISO;
111: /*
112: * Copy the elements of matrix A.
113: * Input:
114: * - Mat A: MATSEQAIJ matrix
115: * - int shift: matrix index.
116: * - 0 for c representation
117: * - 1 for fortran representation
118: * - MatReuse reuse:
119: * - MAT_INITIAL_MATRIX: Create a new aij representation
120: * - MAT_REUSE_MATRIX: Reuse all aij representation and just change values
121: * Output:
122: * - int *nnz: Number of nonzero-elements.
123: * - int **r pointer to i index
124: * - int **c pointer to j elements
125: * - MATRIXTYPE **v: Non-zero elements
126: */
127: static PetscErrorCode MatCopy_seqaij_seqaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
128: {
129: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
131: PetscFunctionBegin;
132: *v = aa->a;
133: if (reuse == MAT_INITIAL_MATRIX) {
134: *r = (INT_TYPE *)aa->i;
135: *c = (INT_TYPE *)aa->j;
136: *nnz = aa->nz;
137: }
138: PetscFunctionReturn(PETSC_SUCCESS);
139: }
141: static PetscErrorCode MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
142: {
143: const PetscInt *ai, *aj, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
144: PetscInt rstart, nz, i, j, countA, countB;
145: PetscInt *row, *col;
146: const PetscScalar *av, *bv;
147: PetscScalar *val;
148: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
149: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)(mat->A)->data;
150: Mat_SeqAIJ *bb = (Mat_SeqAIJ *)(mat->B)->data;
151: PetscInt colA_start, jB, jcol;
153: PetscFunctionBegin;
154: ai = aa->i;
155: aj = aa->j;
156: bi = bb->i;
157: bj = bb->j;
158: rstart = A->rmap->rstart;
159: av = aa->a;
160: bv = bb->a;
162: garray = mat->garray;
164: if (reuse == MAT_INITIAL_MATRIX) {
165: nz = aa->nz + bb->nz;
166: *nnz = nz;
167: PetscCall(PetscMalloc3(m + 1, &row, nz, &col, nz, &val));
168: *r = row;
169: *c = col;
170: *v = val;
171: } else {
172: row = *r;
173: col = *c;
174: val = *v;
175: }
177: nz = 0;
178: for (i = 0; i < m; i++) {
179: row[i] = nz;
180: countA = ai[i + 1] - ai[i];
181: countB = bi[i + 1] - bi[i];
182: ajj = aj + ai[i]; /* ptr to the beginning of this row */
183: bjj = bj + bi[i];
185: /* B part, smaller col index */
186: colA_start = rstart + ajj[0]; /* the smallest global col index of A */
187: jB = 0;
188: for (j = 0; j < countB; j++) {
189: jcol = garray[bjj[j]];
190: if (jcol > colA_start) break;
191: col[nz] = jcol;
192: val[nz++] = *bv++;
193: }
194: jB = j;
196: /* A part */
197: for (j = 0; j < countA; j++) {
198: col[nz] = rstart + ajj[j];
199: val[nz++] = *av++;
200: }
202: /* B part, larger col index */
203: for (j = jB; j < countB; j++) {
204: col[nz] = garray[bjj[j]];
205: val[nz++] = *bv++;
206: }
207: }
208: row[m] = nz;
210: PetscFunctionReturn(PETSC_SUCCESS);
211: }
213: static PetscErrorCode MatConvertToTriples_mpibaij_mpibaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
214: {
215: const PetscInt *ai, *aj, *bi, *bj, *garray, bs = A->rmap->bs, bs2 = bs * bs, m = A->rmap->n / bs, *ajj, *bjj;
216: PetscInt rstart, nz, i, j, countA, countB;
217: PetscInt *row, *col;
218: const PetscScalar *av, *bv;
219: PetscScalar *val;
220: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)A->data;
221: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)(mat->A)->data;
222: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)(mat->B)->data;
223: PetscInt colA_start, jB, jcol;
225: PetscFunctionBegin;
226: ai = aa->i;
227: aj = aa->j;
228: bi = bb->i;
229: bj = bb->j;
230: rstart = A->rmap->rstart / bs;
231: av = aa->a;
232: bv = bb->a;
234: garray = mat->garray;
236: if (reuse == MAT_INITIAL_MATRIX) {
237: nz = aa->nz + bb->nz;
238: *nnz = nz;
239: PetscCall(PetscMalloc3(m + 1, &row, nz, &col, nz * bs2, &val));
240: *r = row;
241: *c = col;
242: *v = val;
243: } else {
244: row = *r;
245: col = *c;
246: val = *v;
247: }
249: nz = 0;
250: for (i = 0; i < m; i++) {
251: row[i] = nz + 1;
252: countA = ai[i + 1] - ai[i];
253: countB = bi[i + 1] - bi[i];
254: ajj = aj + ai[i]; /* ptr to the beginning of this row */
255: bjj = bj + bi[i];
257: /* B part, smaller col index */
258: colA_start = rstart + (countA > 0 ? ajj[0] : 0); /* the smallest global col index of A */
259: jB = 0;
260: for (j = 0; j < countB; j++) {
261: jcol = garray[bjj[j]];
262: if (jcol > colA_start) break;
263: col[nz++] = jcol + 1;
264: }
265: jB = j;
266: PetscCall(PetscArraycpy(val, bv, jB * bs2));
267: val += jB * bs2;
268: bv += jB * bs2;
270: /* A part */
271: for (j = 0; j < countA; j++) col[nz++] = rstart + ajj[j] + 1;
272: PetscCall(PetscArraycpy(val, av, countA * bs2));
273: val += countA * bs2;
274: av += countA * bs2;
276: /* B part, larger col index */
277: for (j = jB; j < countB; j++) col[nz++] = garray[bjj[j]] + 1;
278: PetscCall(PetscArraycpy(val, bv, (countB - jB) * bs2));
279: val += (countB - jB) * bs2;
280: bv += (countB - jB) * bs2;
281: }
282: row[m] = nz + 1;
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: static PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
288: {
289: const PetscInt *ai, *aj, *bi, *bj, *garray, bs = A->rmap->bs, bs2 = bs * bs, m = A->rmap->n / bs, *ajj, *bjj;
290: PetscInt rstart, nz, i, j, countA, countB;
291: PetscInt *row, *col;
292: const PetscScalar *av, *bv;
293: PetscScalar *val;
294: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ *)A->data;
295: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ *)(mat->A)->data;
296: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)(mat->B)->data;
298: PetscFunctionBegin;
299: ai = aa->i;
300: aj = aa->j;
301: bi = bb->i;
302: bj = bb->j;
303: rstart = A->rmap->rstart / bs;
304: av = aa->a;
305: bv = bb->a;
307: garray = mat->garray;
309: if (reuse == MAT_INITIAL_MATRIX) {
310: nz = aa->nz + bb->nz;
311: *nnz = nz;
312: PetscCall(PetscMalloc3(m + 1, &row, nz, &col, nz * bs2, &val));
313: *r = row;
314: *c = col;
315: *v = val;
316: } else {
317: row = *r;
318: col = *c;
319: val = *v;
320: }
322: nz = 0;
323: for (i = 0; i < m; i++) {
324: row[i] = nz + 1;
325: countA = ai[i + 1] - ai[i];
326: countB = bi[i + 1] - bi[i];
327: ajj = aj + ai[i]; /* ptr to the beginning of this row */
328: bjj = bj + bi[i];
330: /* A part */
331: for (j = 0; j < countA; j++) col[nz++] = rstart + ajj[j] + 1;
332: PetscCall(PetscArraycpy(val, av, countA * bs2));
333: val += countA * bs2;
334: av += countA * bs2;
336: /* B part, larger col index */
337: for (j = 0; j < countB; j++) col[nz++] = garray[bjj[j]] + 1;
338: PetscCall(PetscArraycpy(val, bv, countB * bs2));
339: val += countB * bs2;
340: bv += countB * bs2;
341: }
342: row[m] = nz + 1;
344: PetscFunctionReturn(PETSC_SUCCESS);
345: }
347: /*
348: * Free memory for Mat_MKL_CPARDISO structure and pointers to objects.
349: */
350: static PetscErrorCode MatDestroy_MKL_CPARDISO(Mat A)
351: {
352: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
353: MPI_Comm comm;
355: PetscFunctionBegin;
356: /* Terminate instance, deallocate memories */
357: if (mat_mkl_cpardiso->CleanUp) {
358: mat_mkl_cpardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;
360: cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, NULL, NULL, NULL, mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs,
361: mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err);
362: }
364: if (mat_mkl_cpardiso->ConvertToTriples != MatCopy_seqaij_seqaij_MKL_CPARDISO) PetscCall(PetscFree3(mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja, mat_mkl_cpardiso->a));
365: comm = MPI_Comm_f2c(mat_mkl_cpardiso->comm_mkl_cpardiso);
366: PetscCallMPI(MPI_Comm_free(&comm));
367: PetscCall(PetscFree(A->data));
369: /* clear composed functions */
370: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
371: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMkl_CPardisoSetCntl_C", NULL));
372: PetscFunctionReturn(PETSC_SUCCESS);
373: }
375: /*
376: * Computes Ax = b
377: */
378: static PetscErrorCode MatSolve_MKL_CPARDISO(Mat A, Vec b, Vec x)
379: {
380: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)(A)->data;
381: PetscScalar *xarray;
382: const PetscScalar *barray;
384: PetscFunctionBegin;
385: mat_mkl_cpardiso->nrhs = 1;
386: PetscCall(VecGetArray(x, &xarray));
387: PetscCall(VecGetArrayRead(b, &barray));
389: /* solve phase */
390: mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
391: cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
392: mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err);
394: PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\". Please check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));
396: PetscCall(VecRestoreArray(x, &xarray));
397: PetscCall(VecRestoreArrayRead(b, &barray));
398: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
399: PetscFunctionReturn(PETSC_SUCCESS);
400: }
402: static PetscErrorCode MatSolveTranspose_MKL_CPARDISO(Mat A, Vec b, Vec x)
403: {
404: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
406: PetscFunctionBegin;
407: #if defined(PETSC_USE_COMPLEX)
408: mat_mkl_cpardiso->iparm[12 - 1] = 1;
409: #else
410: mat_mkl_cpardiso->iparm[12 - 1] = 2;
411: #endif
412: PetscCall(MatSolve_MKL_CPARDISO(A, b, x));
413: mat_mkl_cpardiso->iparm[12 - 1] = 0;
414: PetscFunctionReturn(PETSC_SUCCESS);
415: }
417: static PetscErrorCode MatMatSolve_MKL_CPARDISO(Mat A, Mat B, Mat X)
418: {
419: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)(A)->data;
420: PetscScalar *xarray;
421: const PetscScalar *barray;
423: PetscFunctionBegin;
424: PetscCall(MatGetSize(B, NULL, (PetscInt *)&mat_mkl_cpardiso->nrhs));
426: if (mat_mkl_cpardiso->nrhs > 0) {
427: PetscCall(MatDenseGetArrayRead(B, &barray));
428: PetscCall(MatDenseGetArray(X, &xarray));
430: PetscCheck(barray != xarray, PETSC_COMM_SELF, PETSC_ERR_SUP, "B and X cannot share the same memory location");
432: /* solve phase */
433: mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
434: cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
435: mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err);
436: PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\". Please check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));
437: PetscCall(MatDenseRestoreArrayRead(B, &barray));
438: PetscCall(MatDenseRestoreArray(X, &xarray));
439: }
440: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
441: PetscFunctionReturn(PETSC_SUCCESS);
442: }
444: /*
445: * LU Decomposition
446: */
447: static PetscErrorCode MatFactorNumeric_MKL_CPARDISO(Mat F, Mat A, const MatFactorInfo *info)
448: {
449: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)(F)->data;
451: PetscFunctionBegin;
452: mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
453: PetscCall((*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_REUSE_MATRIX, &mat_mkl_cpardiso->nz, &mat_mkl_cpardiso->ia, &mat_mkl_cpardiso->ja, &mat_mkl_cpardiso->a));
455: mat_mkl_cpardiso->phase = JOB_NUMERICAL_FACTORIZATION;
456: cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
457: mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, &mat_mkl_cpardiso->err);
458: PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\". Please check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));
460: mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
461: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
462: PetscFunctionReturn(PETSC_SUCCESS);
463: }
465: /* Sets mkl_cpardiso options from the options database */
466: static PetscErrorCode MatSetFromOptions_MKL_CPARDISO(Mat F, Mat A)
467: {
468: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
469: PetscInt icntl, threads;
470: PetscBool flg;
472: PetscFunctionBegin;
473: PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MKL Cluster PARDISO Options", "Mat");
474: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_65", "Suggested number of threads to use within MKL Cluster PARDISO", "None", threads, &threads, &flg));
475: if (flg) mkl_set_num_threads((int)threads);
477: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_66", "Maximum number of factors with identical sparsity structure that must be kept in memory at the same time", "None", mat_mkl_cpardiso->maxfct, &icntl, &flg));
478: if (flg) mat_mkl_cpardiso->maxfct = icntl;
480: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_67", "Indicates the actual matrix for the solution phase", "None", mat_mkl_cpardiso->mnum, &icntl, &flg));
481: if (flg) mat_mkl_cpardiso->mnum = icntl;
483: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_68", "Message level information", "None", mat_mkl_cpardiso->msglvl, &icntl, &flg));
484: if (flg) mat_mkl_cpardiso->msglvl = icntl;
486: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_69", "Defines the matrix type", "None", mat_mkl_cpardiso->mtype, &icntl, &flg));
487: if (flg) mat_mkl_cpardiso->mtype = icntl;
488: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_1", "Use default values", "None", mat_mkl_cpardiso->iparm[0], &icntl, &flg));
490: if (flg && icntl != 0) {
491: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_2", "Fill-in reducing ordering for the input matrix", "None", mat_mkl_cpardiso->iparm[1], &icntl, &flg));
492: if (flg) mat_mkl_cpardiso->iparm[1] = icntl;
494: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_4", "Preconditioned CGS/CG", "None", mat_mkl_cpardiso->iparm[3], &icntl, &flg));
495: if (flg) mat_mkl_cpardiso->iparm[3] = icntl;
497: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_5", "User permutation", "None", mat_mkl_cpardiso->iparm[4], &icntl, &flg));
498: if (flg) mat_mkl_cpardiso->iparm[4] = icntl;
500: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_6", "Write solution on x", "None", mat_mkl_cpardiso->iparm[5], &icntl, &flg));
501: if (flg) mat_mkl_cpardiso->iparm[5] = icntl;
503: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_8", "Iterative refinement step", "None", mat_mkl_cpardiso->iparm[7], &icntl, &flg));
504: if (flg) mat_mkl_cpardiso->iparm[7] = icntl;
506: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_10", "Pivoting perturbation", "None", mat_mkl_cpardiso->iparm[9], &icntl, &flg));
507: if (flg) mat_mkl_cpardiso->iparm[9] = icntl;
509: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_11", "Scaling vectors", "None", mat_mkl_cpardiso->iparm[10], &icntl, &flg));
510: if (flg) mat_mkl_cpardiso->iparm[10] = icntl;
512: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_12", "Solve with transposed or conjugate transposed matrix A", "None", mat_mkl_cpardiso->iparm[11], &icntl, &flg));
513: if (flg) mat_mkl_cpardiso->iparm[11] = icntl;
515: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_13", "Improved accuracy using (non-) symmetric weighted matching", "None", mat_mkl_cpardiso->iparm[12], &icntl, &flg));
516: if (flg) mat_mkl_cpardiso->iparm[12] = icntl;
518: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_18", "Numbers of non-zero elements", "None", mat_mkl_cpardiso->iparm[17], &icntl, &flg));
519: if (flg) mat_mkl_cpardiso->iparm[17] = icntl;
521: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_19", "Report number of floating point operations", "None", mat_mkl_cpardiso->iparm[18], &icntl, &flg));
522: if (flg) mat_mkl_cpardiso->iparm[18] = icntl;
524: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_21", "Pivoting for symmetric indefinite matrices", "None", mat_mkl_cpardiso->iparm[20], &icntl, &flg));
525: if (flg) mat_mkl_cpardiso->iparm[20] = icntl;
527: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_24", "Parallel factorization control", "None", mat_mkl_cpardiso->iparm[23], &icntl, &flg));
528: if (flg) mat_mkl_cpardiso->iparm[23] = icntl;
530: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_25", "Parallel forward/backward solve control", "None", mat_mkl_cpardiso->iparm[24], &icntl, &flg));
531: if (flg) mat_mkl_cpardiso->iparm[24] = icntl;
533: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_27", "Matrix checker", "None", mat_mkl_cpardiso->iparm[26], &icntl, &flg));
534: if (flg) mat_mkl_cpardiso->iparm[26] = icntl;
536: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_31", "Partial solve and computing selected components of the solution vectors", "None", mat_mkl_cpardiso->iparm[30], &icntl, &flg));
537: if (flg) mat_mkl_cpardiso->iparm[30] = icntl;
539: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_34", "Optimal number of threads for conditional numerical reproducibility (CNR) mode", "None", mat_mkl_cpardiso->iparm[33], &icntl, &flg));
540: if (flg) mat_mkl_cpardiso->iparm[33] = icntl;
542: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_60", "Intel MKL Cluster PARDISO mode", "None", mat_mkl_cpardiso->iparm[59], &icntl, &flg));
543: if (flg) mat_mkl_cpardiso->iparm[59] = icntl;
544: }
546: PetscOptionsEnd();
547: PetscFunctionReturn(PETSC_SUCCESS);
548: }
550: static PetscErrorCode PetscInitialize_MKL_CPARDISO(Mat A, Mat_MKL_CPARDISO *mat_mkl_cpardiso)
551: {
552: PetscInt bs;
553: PetscBool match;
554: PetscMPIInt size;
555: MPI_Comm comm;
557: PetscFunctionBegin;
559: PetscCallMPI(MPI_Comm_dup(PetscObjectComm((PetscObject)A), &comm));
560: PetscCallMPI(MPI_Comm_size(comm, &size));
561: mat_mkl_cpardiso->comm_mkl_cpardiso = MPI_Comm_c2f(comm);
563: mat_mkl_cpardiso->CleanUp = PETSC_FALSE;
564: mat_mkl_cpardiso->maxfct = 1;
565: mat_mkl_cpardiso->mnum = 1;
566: mat_mkl_cpardiso->n = A->rmap->N;
567: if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];
568: mat_mkl_cpardiso->msglvl = 0;
569: mat_mkl_cpardiso->nrhs = 1;
570: mat_mkl_cpardiso->err = 0;
571: mat_mkl_cpardiso->phase = -1;
572: #if defined(PETSC_USE_COMPLEX)
573: mat_mkl_cpardiso->mtype = 13;
574: #else
575: mat_mkl_cpardiso->mtype = 11;
576: #endif
578: #if defined(PETSC_USE_REAL_SINGLE)
579: mat_mkl_cpardiso->iparm[27] = 1;
580: #else
581: mat_mkl_cpardiso->iparm[27] = 0;
582: #endif
584: mat_mkl_cpardiso->iparm[0] = 1; /* Solver default parameters overridden with provided by iparm */
585: mat_mkl_cpardiso->iparm[1] = 2; /* Use METIS for fill-in reordering */
586: mat_mkl_cpardiso->iparm[5] = 0; /* Write solution into x */
587: mat_mkl_cpardiso->iparm[7] = 2; /* Max number of iterative refinement steps */
588: mat_mkl_cpardiso->iparm[9] = 13; /* Perturb the pivot elements with 1E-13 */
589: mat_mkl_cpardiso->iparm[10] = 1; /* Use nonsymmetric permutation and scaling MPS */
590: mat_mkl_cpardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
591: mat_mkl_cpardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
592: mat_mkl_cpardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
593: mat_mkl_cpardiso->iparm[26] = 1; /* Check input data for correctness */
595: mat_mkl_cpardiso->iparm[39] = 0;
596: if (size > 1) {
597: mat_mkl_cpardiso->iparm[39] = 2;
598: mat_mkl_cpardiso->iparm[40] = A->rmap->rstart;
599: mat_mkl_cpardiso->iparm[41] = A->rmap->rend - 1;
600: }
601: PetscCall(PetscObjectTypeCompareAny((PetscObject)A, &match, MATMPIBAIJ, MATMPISBAIJ, ""));
602: if (match) {
603: PetscCall(MatGetBlockSize(A, &bs));
604: mat_mkl_cpardiso->iparm[36] = bs;
605: mat_mkl_cpardiso->iparm[40] /= bs;
606: mat_mkl_cpardiso->iparm[41] /= bs;
607: mat_mkl_cpardiso->iparm[40]++;
608: mat_mkl_cpardiso->iparm[41]++;
609: mat_mkl_cpardiso->iparm[34] = 0; /* Fortran style */
610: } else {
611: mat_mkl_cpardiso->iparm[34] = 1; /* C style */
612: }
614: mat_mkl_cpardiso->perm = 0;
615: PetscFunctionReturn(PETSC_SUCCESS);
616: }
618: /*
619: * Symbolic decomposition. Mkl_Pardiso analysis phase.
620: */
621: static PetscErrorCode MatLUFactorSymbolic_AIJMKL_CPARDISO(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
622: {
623: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
625: PetscFunctionBegin;
626: mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
628: /* Set MKL_CPARDISO options from the options database */
629: PetscCall(MatSetFromOptions_MKL_CPARDISO(F, A));
630: PetscCall((*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_INITIAL_MATRIX, &mat_mkl_cpardiso->nz, &mat_mkl_cpardiso->ia, &mat_mkl_cpardiso->ja, &mat_mkl_cpardiso->a));
632: mat_mkl_cpardiso->n = A->rmap->N;
633: if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];
635: /* analysis phase */
636: mat_mkl_cpardiso->phase = JOB_ANALYSIS;
638: cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
639: mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err);
641: PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\".Check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));
643: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
644: F->ops->lufactornumeric = MatFactorNumeric_MKL_CPARDISO;
645: F->ops->solve = MatSolve_MKL_CPARDISO;
646: F->ops->solvetranspose = MatSolveTranspose_MKL_CPARDISO;
647: F->ops->matsolve = MatMatSolve_MKL_CPARDISO;
648: PetscFunctionReturn(PETSC_SUCCESS);
649: }
651: static PetscErrorCode MatCholeskyFactorSymbolic_AIJMKL_CPARDISO(Mat F, Mat A, IS perm, const MatFactorInfo *info)
652: {
653: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
655: PetscFunctionBegin;
656: mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
658: /* Set MKL_CPARDISO options from the options database */
659: PetscCall(MatSetFromOptions_MKL_CPARDISO(F, A));
660: PetscCall((*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_INITIAL_MATRIX, &mat_mkl_cpardiso->nz, &mat_mkl_cpardiso->ia, &mat_mkl_cpardiso->ja, &mat_mkl_cpardiso->a));
662: mat_mkl_cpardiso->n = A->rmap->N;
663: if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];
664: PetscCheck(!PetscDefined(USE_COMPLEX), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "No support for PARDISO CHOLESKY with complex scalars! Use MAT_FACTOR_LU instead");
665: if (A->spd == PETSC_BOOL3_TRUE) mat_mkl_cpardiso->mtype = 2;
666: else mat_mkl_cpardiso->mtype = -2;
668: /* analysis phase */
669: mat_mkl_cpardiso->phase = JOB_ANALYSIS;
671: cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
672: mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err);
674: PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\".Check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));
676: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
677: F->ops->choleskyfactornumeric = MatFactorNumeric_MKL_CPARDISO;
678: F->ops->solve = MatSolve_MKL_CPARDISO;
679: F->ops->solvetranspose = MatSolveTranspose_MKL_CPARDISO;
680: F->ops->matsolve = MatMatSolve_MKL_CPARDISO;
681: PetscFunctionReturn(PETSC_SUCCESS);
682: }
684: static PetscErrorCode MatView_MKL_CPARDISO(Mat A, PetscViewer viewer)
685: {
686: PetscBool iascii;
687: PetscViewerFormat format;
688: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
689: PetscInt i;
691: PetscFunctionBegin;
692: /* check if matrix is mkl_cpardiso type */
693: if (A->ops->solve != MatSolve_MKL_CPARDISO) PetscFunctionReturn(PETSC_SUCCESS);
695: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
696: if (iascii) {
697: PetscCall(PetscViewerGetFormat(viewer, &format));
698: if (format == PETSC_VIEWER_ASCII_INFO) {
699: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO run parameters:\n"));
700: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO phase: %d \n", mat_mkl_cpardiso->phase));
701: for (i = 1; i <= 64; i++) PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO iparm[%d]: %d \n", i, mat_mkl_cpardiso->iparm[i - 1]));
702: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO maxfct: %d \n", mat_mkl_cpardiso->maxfct));
703: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO mnum: %d \n", mat_mkl_cpardiso->mnum));
704: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO mtype: %d \n", mat_mkl_cpardiso->mtype));
705: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO n: %d \n", mat_mkl_cpardiso->n));
706: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO nrhs: %d \n", mat_mkl_cpardiso->nrhs));
707: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO msglvl: %d \n", mat_mkl_cpardiso->msglvl));
708: }
709: }
710: PetscFunctionReturn(PETSC_SUCCESS);
711: }
713: static PetscErrorCode MatGetInfo_MKL_CPARDISO(Mat A, MatInfoType flag, MatInfo *info)
714: {
715: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
717: PetscFunctionBegin;
718: info->block_size = 1.0;
719: info->nz_allocated = mat_mkl_cpardiso->nz + 0.0;
720: info->nz_unneeded = 0.0;
721: info->assemblies = 0.0;
722: info->mallocs = 0.0;
723: info->memory = 0.0;
724: info->fill_ratio_given = 0;
725: info->fill_ratio_needed = 0;
726: info->factor_mallocs = 0;
727: PetscFunctionReturn(PETSC_SUCCESS);
728: }
730: static PetscErrorCode MatMkl_CPardisoSetCntl_MKL_CPARDISO(Mat F, PetscInt icntl, PetscInt ival)
731: {
732: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
734: PetscFunctionBegin;
735: if (icntl <= 64) {
736: mat_mkl_cpardiso->iparm[icntl - 1] = ival;
737: } else {
738: if (icntl == 65) mkl_set_num_threads((int)ival);
739: else if (icntl == 66) mat_mkl_cpardiso->maxfct = ival;
740: else if (icntl == 67) mat_mkl_cpardiso->mnum = ival;
741: else if (icntl == 68) mat_mkl_cpardiso->msglvl = ival;
742: else if (icntl == 69) mat_mkl_cpardiso->mtype = ival;
743: }
744: PetscFunctionReturn(PETSC_SUCCESS);
745: }
747: /*@
748: MatMkl_CPardisoSetCntl - Set MKL Cluster PARDISO parameters
749: <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>
751: Logically Collective
753: Input Parameters:
754: + F - the factored matrix obtained by calling `MatGetFactor()`
755: . icntl - index of MKL Cluster PARDISO parameter
756: - ival - value of MKL Cluster PARDISO parameter
758: Options Database Key:
759: . -mat_mkl_cpardiso_<icntl> <ival> - set the option numbered icntl to ival
761: Level: intermediate
763: Note:
764: This routine cannot be used if you are solving the linear system with `TS`, `SNES`, or `KSP`, only if you directly call `MatGetFactor()` so use the options
765: database approach when working with `TS`, `SNES`, or `KSP`. See `MATSOLVERMKL_CPARDISO` for the options
767: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MATMPIAIJ`, `MATSOLVERMKL_CPARDISO`
768: @*/
769: PetscErrorCode MatMkl_CPardisoSetCntl(Mat F, PetscInt icntl, PetscInt ival)
770: {
771: PetscFunctionBegin;
772: PetscTryMethod(F, "MatMkl_CPardisoSetCntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
773: PetscFunctionReturn(PETSC_SUCCESS);
774: }
776: /*MC
777: MATSOLVERMKL_CPARDISO - A matrix type providing direct solvers (LU) for parallel matrices via the external package MKL Cluster PARDISO
778: <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>
780: Works with `MATMPIAIJ` matrices
782: Use `-pc_type lu` `-pc_factor_mat_solver_type mkl_cpardiso` to use this direct solver
784: Options Database Keys:
785: + -mat_mkl_cpardiso_65 - Suggested number of threads to use within MKL Cluster PARDISO
786: . -mat_mkl_cpardiso_66 - Maximum number of factors with identical sparsity structure that must be kept in memory at the same time
787: . -mat_mkl_cpardiso_67 - Indicates the actual matrix for the solution phase
788: . -mat_mkl_cpardiso_68 - Message level information, use 1 to get detailed information on the solver options
789: . -mat_mkl_cpardiso_69 - Defines the matrix type. IMPORTANT: When you set this flag, iparm parameters are going to be set to the default ones for the matrix type
790: . -mat_mkl_cpardiso_1 - Use default values
791: . -mat_mkl_cpardiso_2 - Fill-in reducing ordering for the input matrix
792: . -mat_mkl_cpardiso_4 - Preconditioned CGS/CG
793: . -mat_mkl_cpardiso_5 - User permutation
794: . -mat_mkl_cpardiso_6 - Write solution on x
795: . -mat_mkl_cpardiso_8 - Iterative refinement step
796: . -mat_mkl_cpardiso_10 - Pivoting perturbation
797: . -mat_mkl_cpardiso_11 - Scaling vectors
798: . -mat_mkl_cpardiso_12 - Solve with transposed or conjugate transposed matrix A
799: . -mat_mkl_cpardiso_13 - Improved accuracy using (non-) symmetric weighted matching
800: . -mat_mkl_cpardiso_18 - Numbers of non-zero elements
801: . -mat_mkl_cpardiso_19 - Report number of floating point operations
802: . -mat_mkl_cpardiso_21 - Pivoting for symmetric indefinite matrices
803: . -mat_mkl_cpardiso_24 - Parallel factorization control
804: . -mat_mkl_cpardiso_25 - Parallel forward/backward solve control
805: . -mat_mkl_cpardiso_27 - Matrix checker
806: . -mat_mkl_cpardiso_31 - Partial solve and computing selected components of the solution vectors
807: . -mat_mkl_cpardiso_34 - Optimal number of threads for conditional numerical reproducibility (CNR) mode
808: - -mat_mkl_cpardiso_60 - Intel MKL Cluster PARDISO mode
810: Level: beginner
812: Notes:
813: Use `-mat_mkl_cpardiso_68 1` to display the number of threads the solver is using. MKL does not provide a way to directly access this
814: information.
816: For more information on the options check
817: <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>
819: .seealso: [](ch_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMkl_CPardisoSetCntl()`, `MatGetFactor()`, `MATSOLVERMKL_PARDISO`
820: M*/
822: static PetscErrorCode MatFactorGetSolverType_mkl_cpardiso(Mat A, MatSolverType *type)
823: {
824: PetscFunctionBegin;
825: *type = MATSOLVERMKL_CPARDISO;
826: PetscFunctionReturn(PETSC_SUCCESS);
827: }
829: /* MatGetFactor for MPI AIJ matrices */
830: static PetscErrorCode MatGetFactor_mpiaij_mkl_cpardiso(Mat A, MatFactorType ftype, Mat *F)
831: {
832: Mat B;
833: Mat_MKL_CPARDISO *mat_mkl_cpardiso;
834: PetscBool isSeqAIJ, isMPIBAIJ, isMPISBAIJ;
836: PetscFunctionBegin;
837: /* Create the factorization matrix */
839: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
840: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &isMPIBAIJ));
841: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPISBAIJ, &isMPISBAIJ));
842: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
843: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
844: PetscCall(PetscStrallocpy("mkl_cpardiso", &((PetscObject)B)->type_name));
845: PetscCall(MatSetUp(B));
847: PetscCall(PetscNew(&mat_mkl_cpardiso));
849: if (isSeqAIJ) mat_mkl_cpardiso->ConvertToTriples = MatCopy_seqaij_seqaij_MKL_CPARDISO;
850: else if (isMPIBAIJ) mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpibaij_mpibaij_MKL_CPARDISO;
851: else if (isMPISBAIJ) mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij_MKL_CPARDISO;
852: else mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO;
854: if (ftype == MAT_FACTOR_LU) B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_CPARDISO;
855: else B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_AIJMKL_CPARDISO;
856: B->ops->destroy = MatDestroy_MKL_CPARDISO;
858: B->ops->view = MatView_MKL_CPARDISO;
859: B->ops->getinfo = MatGetInfo_MKL_CPARDISO;
861: B->factortype = ftype;
862: B->assembled = PETSC_TRUE; /* required by -ksp_view */
864: B->data = mat_mkl_cpardiso;
866: /* set solvertype */
867: PetscCall(PetscFree(B->solvertype));
868: PetscCall(PetscStrallocpy(MATSOLVERMKL_CPARDISO, &B->solvertype));
870: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mkl_cpardiso));
871: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMkl_CPardisoSetCntl_C", MatMkl_CPardisoSetCntl_MKL_CPARDISO));
872: PetscCall(PetscInitialize_MKL_CPARDISO(A, mat_mkl_cpardiso));
874: *F = B;
875: PetscFunctionReturn(PETSC_SUCCESS);
876: }
878: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MKL_CPardiso(void)
879: {
880: PetscFunctionBegin;
881: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
882: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
883: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
884: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_mpiaij_mkl_cpardiso));
885: PetscFunctionReturn(PETSC_SUCCESS);
886: }