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: #define PetscCallCluster(f) PetscStackCallExternalVoid("cluster_sparse_solver", f);
73: /*
74: * Internal data structure.
75: * For more information check mkl_cpardiso manual.
76: */
78: typedef struct {
79: /* Configuration vector */
80: INT_TYPE iparm[IPARM_SIZE];
82: /*
83: * Internal mkl_cpardiso memory location.
84: * After the first call to mkl_cpardiso do not modify pt, as that could cause a serious memory leak.
85: */
86: void *pt[IPARM_SIZE];
88: MPI_Fint comm_mkl_cpardiso;
90: /* Basic mkl_cpardiso info*/
91: INT_TYPE phase, maxfct, mnum, mtype, n, nrhs, msglvl, err;
93: /* Matrix structure */
94: PetscScalar *a;
96: INT_TYPE *ia, *ja;
98: /* Number of non-zero elements */
99: INT_TYPE nz;
101: /* Row permutaton vector*/
102: INT_TYPE *perm;
104: /* Define is matrix preserve sparse structure. */
105: MatStructure matstruc;
107: PetscErrorCode (*ConvertToTriples)(Mat, MatReuse, PetscInt *, PetscInt **, PetscInt **, PetscScalar **);
109: /* True if mkl_cpardiso function have been used. */
110: PetscBool CleanUp;
111: } Mat_MKL_CPARDISO;
113: /*
114: * Copy the elements of matrix A.
115: * Input:
116: * - Mat A: MATSEQAIJ matrix
117: * - int shift: matrix index.
118: * - 0 for c representation
119: * - 1 for fortran representation
120: * - MatReuse reuse:
121: * - MAT_INITIAL_MATRIX: Create a new aij representation
122: * - MAT_REUSE_MATRIX: Reuse all aij representation and just change values
123: * Output:
124: * - int *nnz: Number of nonzero-elements.
125: * - int **r pointer to i index
126: * - int **c pointer to j elements
127: * - MATRIXTYPE **v: Non-zero elements
128: */
129: static PetscErrorCode MatCopy_seqaij_seqaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
130: {
131: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
133: PetscFunctionBegin;
134: *v = aa->a;
135: if (reuse == MAT_INITIAL_MATRIX) {
136: *r = (INT_TYPE *)aa->i;
137: *c = (INT_TYPE *)aa->j;
138: *nnz = aa->nz;
139: }
140: PetscFunctionReturn(PETSC_SUCCESS);
141: }
143: static PetscErrorCode MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
144: {
145: const PetscInt *ai, *aj, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
146: PetscInt rstart, nz, i, j, countA, countB;
147: PetscInt *row, *col;
148: const PetscScalar *av, *bv;
149: PetscScalar *val;
150: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
151: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)mat->A->data;
152: Mat_SeqAIJ *bb = (Mat_SeqAIJ *)mat->B->data;
153: PetscInt colA_start, jB, jcol;
155: PetscFunctionBegin;
156: ai = aa->i;
157: aj = aa->j;
158: bi = bb->i;
159: bj = bb->j;
160: rstart = A->rmap->rstart;
161: av = aa->a;
162: bv = bb->a;
164: garray = mat->garray;
166: if (reuse == MAT_INITIAL_MATRIX) {
167: nz = aa->nz + bb->nz;
168: *nnz = nz;
169: PetscCall(PetscMalloc3(m + 1, &row, nz, &col, nz, &val));
170: *r = row;
171: *c = col;
172: *v = val;
173: } else {
174: row = *r;
175: col = *c;
176: val = *v;
177: }
179: nz = 0;
180: for (i = 0; i < m; i++) {
181: row[i] = nz;
182: countA = ai[i + 1] - ai[i];
183: countB = bi[i + 1] - bi[i];
184: ajj = aj + ai[i]; /* ptr to the beginning of this row */
185: bjj = bj + bi[i];
187: /* B part, smaller col index */
188: colA_start = rstart + ajj[0]; /* the smallest global col index of A */
189: jB = 0;
190: for (j = 0; j < countB; j++) {
191: jcol = garray[bjj[j]];
192: if (jcol > colA_start) break;
193: col[nz] = jcol;
194: val[nz++] = *bv++;
195: }
196: jB = j;
198: /* A part */
199: for (j = 0; j < countA; j++) {
200: col[nz] = rstart + ajj[j];
201: val[nz++] = *av++;
202: }
204: /* B part, larger col index */
205: for (j = jB; j < countB; j++) {
206: col[nz] = garray[bjj[j]];
207: val[nz++] = *bv++;
208: }
209: }
210: row[m] = nz;
211: PetscFunctionReturn(PETSC_SUCCESS);
212: }
214: static PetscErrorCode MatConvertToTriples_mpibaij_mpibaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
215: {
216: const PetscInt *ai, *aj, *bi, *bj, *garray, bs = A->rmap->bs, bs2 = bs * bs, m = A->rmap->n / bs, *ajj, *bjj;
217: PetscInt rstart, nz, i, j, countA, countB;
218: PetscInt *row, *col;
219: const PetscScalar *av, *bv;
220: PetscScalar *val;
221: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)A->data;
222: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)mat->A->data;
223: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)mat->B->data;
224: PetscInt colA_start, jB, jcol;
226: PetscFunctionBegin;
227: ai = aa->i;
228: aj = aa->j;
229: bi = bb->i;
230: bj = bb->j;
231: rstart = A->rmap->rstart / bs;
232: av = aa->a;
233: bv = bb->a;
235: garray = mat->garray;
237: if (reuse == MAT_INITIAL_MATRIX) {
238: nz = aa->nz + bb->nz;
239: *nnz = nz;
240: PetscCall(PetscMalloc3(m + 1, &row, nz, &col, nz * bs2, &val));
241: *r = row;
242: *c = col;
243: *v = val;
244: } else {
245: row = *r;
246: col = *c;
247: val = *v;
248: }
250: nz = 0;
251: for (i = 0; i < m; i++) {
252: row[i] = nz + 1;
253: countA = ai[i + 1] - ai[i];
254: countB = bi[i + 1] - bi[i];
255: ajj = aj + ai[i]; /* ptr to the beginning of this row */
256: bjj = bj + bi[i];
258: /* B part, smaller col index */
259: colA_start = rstart + (countA > 0 ? ajj[0] : 0); /* the smallest global col index of A */
260: jB = 0;
261: for (j = 0; j < countB; j++) {
262: jcol = garray[bjj[j]];
263: if (jcol > colA_start) break;
264: col[nz++] = jcol + 1;
265: }
266: jB = j;
267: PetscCall(PetscArraycpy(val, bv, jB * bs2));
268: val += jB * bs2;
269: bv += jB * bs2;
271: /* A part */
272: for (j = 0; j < countA; j++) col[nz++] = rstart + ajj[j] + 1;
273: PetscCall(PetscArraycpy(val, av, countA * bs2));
274: val += countA * bs2;
275: av += countA * bs2;
277: /* B part, larger col index */
278: for (j = jB; j < countB; j++) col[nz++] = garray[bjj[j]] + 1;
279: PetscCall(PetscArraycpy(val, bv, (countB - jB) * bs2));
280: val += (countB - jB) * bs2;
281: bv += (countB - jB) * bs2;
282: }
283: 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;
343: PetscFunctionReturn(PETSC_SUCCESS);
344: }
346: /*
347: * Free memory for Mat_MKL_CPARDISO structure and pointers to objects.
348: */
349: static PetscErrorCode MatDestroy_MKL_CPARDISO(Mat A)
350: {
351: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
352: MPI_Comm comm;
354: PetscFunctionBegin;
355: /* Terminate instance, deallocate memories */
356: if (mat_mkl_cpardiso->CleanUp) {
357: mat_mkl_cpardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;
359: PetscCallCluster(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,
360: mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err));
361: }
362: if (mat_mkl_cpardiso->ConvertToTriples != MatCopy_seqaij_seqaij_MKL_CPARDISO) PetscCall(PetscFree3(mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja, mat_mkl_cpardiso->a));
363: comm = MPI_Comm_f2c(mat_mkl_cpardiso->comm_mkl_cpardiso);
364: PetscCallMPI(MPI_Comm_free(&comm));
365: PetscCall(PetscFree(A->data));
367: /* clear composed functions */
368: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
369: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMkl_CPardisoSetCntl_C", NULL));
370: PetscFunctionReturn(PETSC_SUCCESS);
371: }
373: /*
374: * Computes Ax = b
375: */
376: static PetscErrorCode MatSolve_MKL_CPARDISO(Mat A, Vec b, Vec x)
377: {
378: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
379: PetscScalar *xarray;
380: const PetscScalar *barray;
382: PetscFunctionBegin;
383: mat_mkl_cpardiso->nrhs = 1;
384: PetscCall(VecGetArray(x, &xarray));
385: PetscCall(VecGetArrayRead(b, &barray));
387: /* solve phase */
388: mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
389: PetscCallCluster(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,
390: 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));
391: 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));
393: PetscCall(VecRestoreArray(x, &xarray));
394: PetscCall(VecRestoreArrayRead(b, &barray));
395: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
396: PetscFunctionReturn(PETSC_SUCCESS);
397: }
399: static PetscErrorCode MatForwardSolve_MKL_CPARDISO(Mat A, Vec b, Vec x)
400: {
401: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
402: PetscScalar *xarray;
403: const PetscScalar *barray;
405: PetscFunctionBegin;
406: mat_mkl_cpardiso->nrhs = 1;
407: PetscCall(VecGetArray(x, &xarray));
408: PetscCall(VecGetArrayRead(b, &barray));
410: /* solve phase */
411: mat_mkl_cpardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;
412: PetscCallCluster(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,
413: 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));
414: 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));
416: PetscCall(VecRestoreArray(x, &xarray));
417: PetscCall(VecRestoreArrayRead(b, &barray));
418: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
419: PetscFunctionReturn(PETSC_SUCCESS);
420: }
422: static PetscErrorCode MatBackwardSolve_MKL_CPARDISO(Mat A, Vec b, Vec x)
423: {
424: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
425: PetscScalar *xarray;
426: const PetscScalar *barray;
428: PetscFunctionBegin;
429: mat_mkl_cpardiso->nrhs = 1;
430: PetscCall(VecGetArray(x, &xarray));
431: PetscCall(VecGetArrayRead(b, &barray));
433: /* solve phase */
434: mat_mkl_cpardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
435: PetscCallCluster(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,
436: 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));
437: 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));
439: PetscCall(VecRestoreArray(x, &xarray));
440: PetscCall(VecRestoreArrayRead(b, &barray));
441: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
442: PetscFunctionReturn(PETSC_SUCCESS);
443: }
445: static PetscErrorCode MatSolveTranspose_MKL_CPARDISO(Mat A, Vec b, Vec x)
446: {
447: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
449: PetscFunctionBegin;
450: #if defined(PETSC_USE_COMPLEX)
451: mat_mkl_cpardiso->iparm[12 - 1] = 1;
452: #else
453: mat_mkl_cpardiso->iparm[12 - 1] = 2;
454: #endif
455: PetscCall(MatSolve_MKL_CPARDISO(A, b, x));
456: mat_mkl_cpardiso->iparm[12 - 1] = 0;
457: PetscFunctionReturn(PETSC_SUCCESS);
458: }
460: static PetscErrorCode MatMatSolve_MKL_CPARDISO(Mat A, Mat B, Mat X)
461: {
462: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
463: PetscScalar *xarray;
464: const PetscScalar *barray;
466: PetscFunctionBegin;
467: PetscCall(MatGetSize(B, NULL, (PetscInt *)&mat_mkl_cpardiso->nrhs));
469: if (mat_mkl_cpardiso->nrhs > 0) {
470: PetscCall(MatDenseGetArrayRead(B, &barray));
471: PetscCall(MatDenseGetArray(X, &xarray));
473: PetscCheck(barray != xarray, PETSC_COMM_SELF, PETSC_ERR_SUP, "B and X cannot share the same memory location");
475: /* solve phase */
476: mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
477: PetscCallCluster(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,
478: 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));
479: 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));
480: PetscCall(MatDenseRestoreArrayRead(B, &barray));
481: PetscCall(MatDenseRestoreArray(X, &xarray));
482: }
483: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
484: PetscFunctionReturn(PETSC_SUCCESS);
485: }
487: /*
488: * LU Decomposition
489: */
490: static PetscErrorCode MatFactorNumeric_MKL_CPARDISO(Mat F, Mat A, const MatFactorInfo *info)
491: {
492: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
494: PetscFunctionBegin;
495: mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
496: 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));
498: mat_mkl_cpardiso->phase = JOB_NUMERICAL_FACTORIZATION;
499: PetscCallCluster(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,
500: 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));
501: 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));
503: mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
504: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
505: PetscFunctionReturn(PETSC_SUCCESS);
506: }
508: /* Sets mkl_cpardiso options from the options database */
509: static PetscErrorCode MatSetFromOptions_MKL_CPARDISO(Mat F, Mat A)
510: {
511: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
512: PetscInt icntl, threads;
513: PetscBool flg;
515: PetscFunctionBegin;
516: PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MKL Cluster PARDISO Options", "Mat");
517: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_65", "Suggested number of threads to use within MKL Cluster PARDISO", "None", threads, &threads, &flg));
518: if (flg) mkl_set_num_threads((int)threads);
520: 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));
521: if (flg) mat_mkl_cpardiso->maxfct = icntl;
523: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_67", "Indicates the actual matrix for the solution phase", "None", mat_mkl_cpardiso->mnum, &icntl, &flg));
524: if (flg) mat_mkl_cpardiso->mnum = icntl;
526: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_68", "Message level information", "None", mat_mkl_cpardiso->msglvl, &icntl, &flg));
527: if (flg) mat_mkl_cpardiso->msglvl = icntl;
529: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_69", "Defines the matrix type", "None", mat_mkl_cpardiso->mtype, &icntl, &flg));
530: if (flg) mat_mkl_cpardiso->mtype = icntl;
531: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_1", "Use default values", "None", mat_mkl_cpardiso->iparm[0], &icntl, &flg));
533: if (flg && icntl != 0) {
534: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_2", "Fill-in reducing ordering for the input matrix", "None", mat_mkl_cpardiso->iparm[1], &icntl, &flg));
535: if (flg) mat_mkl_cpardiso->iparm[1] = icntl;
537: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_4", "Preconditioned CGS/CG", "None", mat_mkl_cpardiso->iparm[3], &icntl, &flg));
538: if (flg) mat_mkl_cpardiso->iparm[3] = icntl;
540: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_5", "User permutation", "None", mat_mkl_cpardiso->iparm[4], &icntl, &flg));
541: if (flg) mat_mkl_cpardiso->iparm[4] = icntl;
543: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_6", "Write solution on x", "None", mat_mkl_cpardiso->iparm[5], &icntl, &flg));
544: if (flg) mat_mkl_cpardiso->iparm[5] = icntl;
546: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_8", "Iterative refinement step", "None", mat_mkl_cpardiso->iparm[7], &icntl, &flg));
547: if (flg) mat_mkl_cpardiso->iparm[7] = icntl;
549: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_10", "Pivoting perturbation", "None", mat_mkl_cpardiso->iparm[9], &icntl, &flg));
550: if (flg) mat_mkl_cpardiso->iparm[9] = icntl;
552: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_11", "Scaling vectors", "None", mat_mkl_cpardiso->iparm[10], &icntl, &flg));
553: if (flg) mat_mkl_cpardiso->iparm[10] = icntl;
555: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_12", "Solve with transposed or conjugate transposed matrix A", "None", mat_mkl_cpardiso->iparm[11], &icntl, &flg));
556: if (flg) mat_mkl_cpardiso->iparm[11] = icntl;
558: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_13", "Improved accuracy using (non-) symmetric weighted matching", "None", mat_mkl_cpardiso->iparm[12], &icntl, &flg));
559: if (flg) mat_mkl_cpardiso->iparm[12] = icntl;
561: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_18", "Numbers of non-zero elements", "None", mat_mkl_cpardiso->iparm[17], &icntl, &flg));
562: if (flg) mat_mkl_cpardiso->iparm[17] = icntl;
564: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_19", "Report number of floating point operations", "None", mat_mkl_cpardiso->iparm[18], &icntl, &flg));
565: if (flg) mat_mkl_cpardiso->iparm[18] = icntl;
567: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_21", "Pivoting for symmetric indefinite matrices", "None", mat_mkl_cpardiso->iparm[20], &icntl, &flg));
568: if (flg) mat_mkl_cpardiso->iparm[20] = icntl;
570: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_24", "Parallel factorization control", "None", mat_mkl_cpardiso->iparm[23], &icntl, &flg));
571: if (flg) mat_mkl_cpardiso->iparm[23] = icntl;
573: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_25", "Parallel forward/backward solve control", "None", mat_mkl_cpardiso->iparm[24], &icntl, &flg));
574: if (flg) mat_mkl_cpardiso->iparm[24] = icntl;
576: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_27", "Matrix checker", "None", mat_mkl_cpardiso->iparm[26], &icntl, &flg));
577: if (flg) mat_mkl_cpardiso->iparm[26] = icntl;
579: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_31", "Partial solve and computing selected components of the solution vectors", "None", mat_mkl_cpardiso->iparm[30], &icntl, &flg));
580: if (flg) mat_mkl_cpardiso->iparm[30] = icntl;
582: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_34", "Optimal number of threads for conditional numerical reproducibility (CNR) mode", "None", mat_mkl_cpardiso->iparm[33], &icntl, &flg));
583: if (flg) mat_mkl_cpardiso->iparm[33] = icntl;
585: PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_60", "Intel MKL Cluster PARDISO mode", "None", mat_mkl_cpardiso->iparm[59], &icntl, &flg));
586: if (flg) mat_mkl_cpardiso->iparm[59] = icntl;
587: }
589: PetscOptionsEnd();
590: PetscFunctionReturn(PETSC_SUCCESS);
591: }
593: static PetscErrorCode PetscInitialize_MKL_CPARDISO(Mat A, Mat_MKL_CPARDISO *mat_mkl_cpardiso)
594: {
595: PetscInt bs;
596: PetscBool match;
597: PetscMPIInt size;
598: MPI_Comm comm;
600: PetscFunctionBegin;
601: PetscCallMPI(MPI_Comm_dup(PetscObjectComm((PetscObject)A), &comm));
602: PetscCallMPI(MPI_Comm_size(comm, &size));
603: mat_mkl_cpardiso->comm_mkl_cpardiso = MPI_Comm_c2f(comm);
605: mat_mkl_cpardiso->CleanUp = PETSC_FALSE;
606: mat_mkl_cpardiso->maxfct = 1;
607: mat_mkl_cpardiso->mnum = 1;
608: mat_mkl_cpardiso->n = A->rmap->N;
609: if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];
610: mat_mkl_cpardiso->msglvl = 0;
611: mat_mkl_cpardiso->nrhs = 1;
612: mat_mkl_cpardiso->err = 0;
613: mat_mkl_cpardiso->phase = -1;
614: #if defined(PETSC_USE_COMPLEX)
615: mat_mkl_cpardiso->mtype = 13;
616: #else
617: mat_mkl_cpardiso->mtype = 11;
618: #endif
620: #if defined(PETSC_USE_REAL_SINGLE)
621: mat_mkl_cpardiso->iparm[27] = 1;
622: #else
623: mat_mkl_cpardiso->iparm[27] = 0;
624: #endif
626: mat_mkl_cpardiso->iparm[0] = 1; /* Solver default parameters overridden with provided by iparm */
627: mat_mkl_cpardiso->iparm[1] = 2; /* Use METIS for fill-in reordering */
628: mat_mkl_cpardiso->iparm[5] = 0; /* Write solution into x */
629: mat_mkl_cpardiso->iparm[7] = 2; /* Max number of iterative refinement steps */
630: mat_mkl_cpardiso->iparm[9] = 13; /* Perturb the pivot elements with 1E-13 */
631: mat_mkl_cpardiso->iparm[10] = 1; /* Use nonsymmetric permutation and scaling MPS */
632: mat_mkl_cpardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
633: mat_mkl_cpardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
634: mat_mkl_cpardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
635: mat_mkl_cpardiso->iparm[26] = 1; /* Check input data for correctness */
637: mat_mkl_cpardiso->iparm[39] = 0;
638: if (size > 1) {
639: mat_mkl_cpardiso->iparm[39] = 2;
640: mat_mkl_cpardiso->iparm[40] = A->rmap->rstart;
641: mat_mkl_cpardiso->iparm[41] = A->rmap->rend - 1;
642: }
643: PetscCall(PetscObjectTypeCompareAny((PetscObject)A, &match, MATMPIBAIJ, MATMPISBAIJ, ""));
644: if (match) {
645: PetscCall(MatGetBlockSize(A, &bs));
646: mat_mkl_cpardiso->iparm[36] = bs;
647: mat_mkl_cpardiso->iparm[40] /= bs;
648: mat_mkl_cpardiso->iparm[41] /= bs;
649: mat_mkl_cpardiso->iparm[40]++;
650: mat_mkl_cpardiso->iparm[41]++;
651: mat_mkl_cpardiso->iparm[34] = 0; /* Fortran style */
652: } else {
653: mat_mkl_cpardiso->iparm[34] = 1; /* C style */
654: }
656: mat_mkl_cpardiso->perm = 0;
657: PetscFunctionReturn(PETSC_SUCCESS);
658: }
660: /*
661: * Symbolic decomposition. Mkl_Pardiso analysis phase.
662: */
663: static PetscErrorCode MatLUFactorSymbolic_AIJMKL_CPARDISO(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
664: {
665: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
667: PetscFunctionBegin;
668: mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
670: /* Set MKL_CPARDISO options from the options database */
671: PetscCall(MatSetFromOptions_MKL_CPARDISO(F, A));
672: 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));
674: mat_mkl_cpardiso->n = A->rmap->N;
675: if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];
677: /* analysis phase */
678: mat_mkl_cpardiso->phase = JOB_ANALYSIS;
680: PetscCallCluster(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,
681: 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));
682: 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));
684: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
685: F->ops->lufactornumeric = MatFactorNumeric_MKL_CPARDISO;
686: F->ops->solve = MatSolve_MKL_CPARDISO;
687: F->ops->forwardsolve = MatForwardSolve_MKL_CPARDISO;
688: F->ops->backwardsolve = MatBackwardSolve_MKL_CPARDISO;
689: F->ops->solvetranspose = MatSolveTranspose_MKL_CPARDISO;
690: F->ops->matsolve = MatMatSolve_MKL_CPARDISO;
691: PetscFunctionReturn(PETSC_SUCCESS);
692: }
694: static PetscErrorCode MatCholeskyFactorSymbolic_AIJMKL_CPARDISO(Mat F, Mat A, IS perm, const MatFactorInfo *info)
695: {
696: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
698: PetscFunctionBegin;
699: mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
701: /* Set MKL_CPARDISO options from the options database */
702: PetscCall(MatSetFromOptions_MKL_CPARDISO(F, A));
703: 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));
705: mat_mkl_cpardiso->n = A->rmap->N;
706: if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];
707: PetscCheck(!PetscDefined(USE_COMPLEX), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "No support for PARDISO CHOLESKY with complex scalars! Use MAT_FACTOR_LU instead");
708: if (A->spd == PETSC_BOOL3_TRUE) mat_mkl_cpardiso->mtype = 2;
709: else mat_mkl_cpardiso->mtype = -2;
711: /* analysis phase */
712: mat_mkl_cpardiso->phase = JOB_ANALYSIS;
714: PetscCallCluster(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,
715: 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));
716: 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));
718: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
719: F->ops->choleskyfactornumeric = MatFactorNumeric_MKL_CPARDISO;
720: F->ops->solve = MatSolve_MKL_CPARDISO;
721: F->ops->solvetranspose = MatSolveTranspose_MKL_CPARDISO;
722: F->ops->matsolve = MatMatSolve_MKL_CPARDISO;
723: if (A->spd == PETSC_BOOL3_TRUE) {
724: F->ops->forwardsolve = MatForwardSolve_MKL_CPARDISO;
725: F->ops->backwardsolve = MatBackwardSolve_MKL_CPARDISO;
726: }
727: PetscFunctionReturn(PETSC_SUCCESS);
728: }
730: static PetscErrorCode MatView_MKL_CPARDISO(Mat A, PetscViewer viewer)
731: {
732: PetscBool iascii;
733: PetscViewerFormat format;
734: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
735: PetscInt i;
737: PetscFunctionBegin;
738: /* check if matrix is mkl_cpardiso type */
739: if (A->ops->solve != MatSolve_MKL_CPARDISO) PetscFunctionReturn(PETSC_SUCCESS);
741: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
742: if (iascii) {
743: PetscCall(PetscViewerGetFormat(viewer, &format));
744: if (format == PETSC_VIEWER_ASCII_INFO) {
745: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO run parameters:\n"));
746: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO phase: %d \n", mat_mkl_cpardiso->phase));
747: for (i = 1; i <= 64; i++) PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO iparm[%d]: %d \n", i, mat_mkl_cpardiso->iparm[i - 1]));
748: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO maxfct: %d \n", mat_mkl_cpardiso->maxfct));
749: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO mnum: %d \n", mat_mkl_cpardiso->mnum));
750: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO mtype: %d \n", mat_mkl_cpardiso->mtype));
751: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO n: %d \n", mat_mkl_cpardiso->n));
752: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO nrhs: %d \n", mat_mkl_cpardiso->nrhs));
753: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO msglvl: %d \n", mat_mkl_cpardiso->msglvl));
754: }
755: }
756: PetscFunctionReturn(PETSC_SUCCESS);
757: }
759: static PetscErrorCode MatGetInfo_MKL_CPARDISO(Mat A, MatInfoType flag, MatInfo *info)
760: {
761: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
763: PetscFunctionBegin;
764: info->block_size = 1.0;
765: info->nz_allocated = mat_mkl_cpardiso->nz + 0.0;
766: info->nz_unneeded = 0.0;
767: info->assemblies = 0.0;
768: info->mallocs = 0.0;
769: info->memory = 0.0;
770: info->fill_ratio_given = 0;
771: info->fill_ratio_needed = 0;
772: info->factor_mallocs = 0;
773: PetscFunctionReturn(PETSC_SUCCESS);
774: }
776: static PetscErrorCode MatMkl_CPardisoSetCntl_MKL_CPARDISO(Mat F, PetscInt icntl, PetscInt ival)
777: {
778: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
780: PetscFunctionBegin;
781: if (icntl <= 64) {
782: mat_mkl_cpardiso->iparm[icntl - 1] = ival;
783: } else {
784: if (icntl == 65) mkl_set_num_threads((int)ival);
785: else if (icntl == 66) mat_mkl_cpardiso->maxfct = ival;
786: else if (icntl == 67) mat_mkl_cpardiso->mnum = ival;
787: else if (icntl == 68) mat_mkl_cpardiso->msglvl = ival;
788: else if (icntl == 69) mat_mkl_cpardiso->mtype = ival;
789: }
790: PetscFunctionReturn(PETSC_SUCCESS);
791: }
793: /*@
794: MatMkl_CPardisoSetCntl - Set MKL Cluster PARDISO parameters
795: <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>
797: Logically Collective
799: Input Parameters:
800: + F - the factored matrix obtained by calling `MatGetFactor()`
801: . icntl - index of MKL Cluster PARDISO parameter
802: - ival - value of MKL Cluster PARDISO parameter
804: Options Database Key:
805: . -mat_mkl_cpardiso_<icntl> <ival> - set the option numbered icntl to ival
807: Level: intermediate
809: Note:
810: 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
811: database approach when working with `TS`, `SNES`, or `KSP`. See `MATSOLVERMKL_CPARDISO` for the options
813: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MATMPIAIJ`, `MATSOLVERMKL_CPARDISO`
814: @*/
815: PetscErrorCode MatMkl_CPardisoSetCntl(Mat F, PetscInt icntl, PetscInt ival)
816: {
817: PetscFunctionBegin;
818: PetscTryMethod(F, "MatMkl_CPardisoSetCntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
819: PetscFunctionReturn(PETSC_SUCCESS);
820: }
822: /*MC
823: MATSOLVERMKL_CPARDISO - A matrix type providing direct solvers (LU) for parallel matrices via the external package MKL Cluster PARDISO
824: <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>
826: Works with `MATMPIAIJ` matrices
828: Use `-pc_type lu` `-pc_factor_mat_solver_type mkl_cpardiso` to use this direct solver
830: Options Database Keys:
831: + -mat_mkl_cpardiso_65 - Suggested number of threads to use within MKL Cluster PARDISO
832: . -mat_mkl_cpardiso_66 - Maximum number of factors with identical sparsity structure that must be kept in memory at the same time
833: . -mat_mkl_cpardiso_67 - Indicates the actual matrix for the solution phase
834: . -mat_mkl_cpardiso_68 - Message level information, use 1 to get detailed information on the solver options
835: . -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
836: . -mat_mkl_cpardiso_1 - Use default values
837: . -mat_mkl_cpardiso_2 - Fill-in reducing ordering for the input matrix
838: . -mat_mkl_cpardiso_4 - Preconditioned CGS/CG
839: . -mat_mkl_cpardiso_5 - User permutation
840: . -mat_mkl_cpardiso_6 - Write solution on x
841: . -mat_mkl_cpardiso_8 - Iterative refinement step
842: . -mat_mkl_cpardiso_10 - Pivoting perturbation
843: . -mat_mkl_cpardiso_11 - Scaling vectors
844: . -mat_mkl_cpardiso_12 - Solve with transposed or conjugate transposed matrix A
845: . -mat_mkl_cpardiso_13 - Improved accuracy using (non-) symmetric weighted matching
846: . -mat_mkl_cpardiso_18 - Numbers of non-zero elements
847: . -mat_mkl_cpardiso_19 - Report number of floating point operations
848: . -mat_mkl_cpardiso_21 - Pivoting for symmetric indefinite matrices
849: . -mat_mkl_cpardiso_24 - Parallel factorization control
850: . -mat_mkl_cpardiso_25 - Parallel forward/backward solve control
851: . -mat_mkl_cpardiso_27 - Matrix checker
852: . -mat_mkl_cpardiso_31 - Partial solve and computing selected components of the solution vectors
853: . -mat_mkl_cpardiso_34 - Optimal number of threads for conditional numerical reproducibility (CNR) mode
854: - -mat_mkl_cpardiso_60 - Intel MKL Cluster PARDISO mode
856: Level: beginner
858: Notes:
859: 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
860: information.
862: For more information on the options check
863: <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>
865: .seealso: [](ch_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMkl_CPardisoSetCntl()`, `MatGetFactor()`, `MATSOLVERMKL_PARDISO`
866: M*/
868: static PetscErrorCode MatFactorGetSolverType_mkl_cpardiso(Mat A, MatSolverType *type)
869: {
870: PetscFunctionBegin;
871: *type = MATSOLVERMKL_CPARDISO;
872: PetscFunctionReturn(PETSC_SUCCESS);
873: }
875: /* MatGetFactor for MPI AIJ matrices */
876: static PetscErrorCode MatGetFactor_mpiaij_mkl_cpardiso(Mat A, MatFactorType ftype, Mat *F)
877: {
878: Mat B;
879: Mat_MKL_CPARDISO *mat_mkl_cpardiso;
880: PetscBool isSeqAIJ, isMPIBAIJ, isMPISBAIJ;
882: PetscFunctionBegin;
883: /* Create the factorization matrix */
885: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
886: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &isMPIBAIJ));
887: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPISBAIJ, &isMPISBAIJ));
888: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
889: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
890: PetscCall(PetscStrallocpy("mkl_cpardiso", &((PetscObject)B)->type_name));
891: PetscCall(MatSetUp(B));
893: PetscCall(PetscNew(&mat_mkl_cpardiso));
895: if (isSeqAIJ) mat_mkl_cpardiso->ConvertToTriples = MatCopy_seqaij_seqaij_MKL_CPARDISO;
896: else if (isMPIBAIJ) mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpibaij_mpibaij_MKL_CPARDISO;
897: else if (isMPISBAIJ) mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij_MKL_CPARDISO;
898: else mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO;
900: if (ftype == MAT_FACTOR_LU) B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_CPARDISO;
901: else B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_AIJMKL_CPARDISO;
902: B->ops->destroy = MatDestroy_MKL_CPARDISO;
904: B->ops->view = MatView_MKL_CPARDISO;
905: B->ops->getinfo = MatGetInfo_MKL_CPARDISO;
907: B->factortype = ftype;
908: B->assembled = PETSC_TRUE; /* required by -ksp_view */
910: B->data = mat_mkl_cpardiso;
912: /* set solvertype */
913: PetscCall(PetscFree(B->solvertype));
914: PetscCall(PetscStrallocpy(MATSOLVERMKL_CPARDISO, &B->solvertype));
916: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mkl_cpardiso));
917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMkl_CPardisoSetCntl_C", MatMkl_CPardisoSetCntl_MKL_CPARDISO));
918: PetscCall(PetscInitialize_MKL_CPARDISO(A, mat_mkl_cpardiso));
920: *F = B;
921: PetscFunctionReturn(PETSC_SUCCESS);
922: }
924: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MKL_CPardiso(void)
925: {
926: PetscFunctionBegin;
927: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
928: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
929: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
930: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_mpiaij_mkl_cpardiso));
931: PetscFunctionReturn(PETSC_SUCCESS);
932: }