Actual source code: mkl_pardiso.c
1: #include <../src/mat/impls/aij/seq/aij.h>
2: #include <../src/mat/impls/sbaij/seq/sbaij.h>
3: #include <../src/mat/impls/dense/seq/dense.h>
5: #if defined(PETSC_HAVE_MKL_INTEL_ILP64)
6: #define MKL_ILP64
7: #endif
8: #include <mkl_pardiso.h>
10: PETSC_EXTERN void PetscSetMKL_PARDISOThreads(int);
12: /*
13: * Possible mkl_pardiso phases that controls the execution of the solver.
14: * For more information check mkl_pardiso manual.
15: */
16: #define JOB_ANALYSIS 11
17: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION 12
18: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 13
19: #define JOB_NUMERICAL_FACTORIZATION 22
20: #define JOB_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 23
21: #define JOB_SOLVE_ITERATIVE_REFINEMENT 33
22: #define JOB_SOLVE_FORWARD_SUBSTITUTION 331
23: #define JOB_SOLVE_DIAGONAL_SUBSTITUTION 332
24: #define JOB_SOLVE_BACKWARD_SUBSTITUTION 333
25: #define JOB_RELEASE_OF_LU_MEMORY 0
26: #define JOB_RELEASE_OF_ALL_MEMORY -1
28: #define IPARM_SIZE 64
30: #if defined(PETSC_USE_64BIT_INDICES)
31: #if defined(PETSC_HAVE_MKL_INTEL_ILP64)
32: #define INT_TYPE long long int
33: #define MKL_PARDISO pardiso
34: #define MKL_PARDISO_INIT pardisoinit
35: #else
36: /* this is the case where the MKL BLAS/LAPACK are 32-bit integers but the 64-bit integer version of
37: of PARDISO code is used; hence the need for the 64 below*/
38: #define INT_TYPE long long int
39: #define MKL_PARDISO pardiso_64
40: #define MKL_PARDISO_INIT pardiso_64init
41: void pardiso_64init(void *pt, INT_TYPE *mtype, INT_TYPE iparm[])
42: {
43: PetscBLASInt iparm_copy[IPARM_SIZE], mtype_copy;
45: PetscCallVoid(PetscBLASIntCast(*mtype, &mtype_copy));
46: pardisoinit(pt, &mtype_copy, iparm_copy);
47: for (PetscInt i = 0; i < IPARM_SIZE; i++) iparm[i] = iparm_copy[i];
48: }
49: #endif
50: #else
51: #define INT_TYPE int
52: #define MKL_PARDISO pardiso
53: #define MKL_PARDISO_INIT pardisoinit
54: #endif
56: #define PetscCallPardiso(f) PetscCallExternalVoid("MKL_PARDISO", f);
58: /*
59: Internal data structure.
60: */
61: typedef struct {
62: /* Configuration vector*/
63: INT_TYPE iparm[IPARM_SIZE];
65: /*
66: Internal MKL PARDISO memory location.
67: After the first call to MKL PARDISO do not modify pt, as that could cause a serious memory leak.
68: */
69: void *pt[IPARM_SIZE];
71: /* Basic MKL PARDISO info */
72: INT_TYPE phase, maxfct, mnum, mtype, n, nrhs, msglvl, err;
74: /* Matrix nonzero structure and values */
75: void *a;
76: INT_TYPE *ia, *ja;
78: /* Number of non-zero elements */
79: INT_TYPE nz;
81: /* Row permutaton vector */
82: INT_TYPE *perm;
84: /* Define if matrix preserves sparse structure. */
85: MatStructure matstruc;
87: PetscBool needsym;
88: PetscBool freeaij;
90: /* Schur complement */
91: PetscScalar *schur;
92: PetscInt schur_size;
93: PetscInt *schur_idxs;
94: PetscScalar *schur_work;
95: PetscBLASInt schur_work_size;
96: PetscBool solve_interior;
98: /* True if MKL PARDISO function have been used. */
99: PetscBool CleanUp;
101: /* Conversion to a format suitable for MKL */
102: PetscErrorCode (*Convert)(Mat, PetscBool, MatReuse, PetscBool *, INT_TYPE *, INT_TYPE **, INT_TYPE **, PetscScalar **);
103: } Mat_MKL_PARDISO;
105: static PetscErrorCode MatMKLPardiso_Convert_seqsbaij(Mat A, PetscBool sym, MatReuse reuse, PetscBool *free, INT_TYPE *nnz, INT_TYPE **r, INT_TYPE **c, PetscScalar **v)
106: {
107: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ *)A->data;
108: PetscInt bs = A->rmap->bs, i;
110: PetscFunctionBegin;
111: PetscCheck(sym, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "This should not happen");
112: *v = aa->a;
113: if (bs == 1) { /* already in the correct format */
114: /* though PetscInt and INT_TYPE are of the same size since they are defined differently the Intel compiler requires a cast */
115: *r = (INT_TYPE *)aa->i;
116: *c = (INT_TYPE *)aa->j;
117: *nnz = (INT_TYPE)aa->nz;
118: *free = PETSC_FALSE;
119: } else if (reuse == MAT_INITIAL_MATRIX) {
120: PetscInt m = A->rmap->n, nz = aa->nz;
121: PetscInt *row, *col;
122: PetscCall(PetscMalloc2(m + 1, &row, nz, &col));
123: for (i = 0; i < m + 1; i++) row[i] = aa->i[i] + 1;
124: for (i = 0; i < nz; i++) col[i] = aa->j[i] + 1;
125: *r = (INT_TYPE *)row;
126: *c = (INT_TYPE *)col;
127: *nnz = (INT_TYPE)nz;
128: *free = PETSC_TRUE;
129: }
130: PetscFunctionReturn(PETSC_SUCCESS);
131: }
133: static PetscErrorCode MatMKLPardiso_Convert_seqbaij(Mat A, PetscBool sym, MatReuse reuse, PetscBool *free, INT_TYPE *nnz, INT_TYPE **r, INT_TYPE **c, PetscScalar **v)
134: {
135: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)A->data;
136: PetscInt bs = A->rmap->bs, i;
138: PetscFunctionBegin;
139: if (!sym) {
140: *v = aa->a;
141: if (bs == 1) { /* already in the correct format */
142: /* though PetscInt and INT_TYPE are of the same size since they are defined differently the Intel compiler requires a cast */
143: *r = (INT_TYPE *)aa->i;
144: *c = (INT_TYPE *)aa->j;
145: *nnz = (INT_TYPE)aa->nz;
146: *free = PETSC_FALSE;
147: PetscFunctionReturn(PETSC_SUCCESS);
148: } else if (reuse == MAT_INITIAL_MATRIX) {
149: PetscInt m = A->rmap->n, nz = aa->nz;
150: PetscInt *row, *col;
151: PetscCall(PetscMalloc2(m + 1, &row, nz, &col));
152: for (i = 0; i < m + 1; i++) row[i] = aa->i[i] + 1;
153: for (i = 0; i < nz; i++) col[i] = aa->j[i] + 1;
154: *r = (INT_TYPE *)row;
155: *c = (INT_TYPE *)col;
156: *nnz = (INT_TYPE)nz;
157: }
158: *free = PETSC_TRUE;
159: } else {
160: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "This should not happen");
161: }
162: PetscFunctionReturn(PETSC_SUCCESS);
163: }
165: static PetscErrorCode MatMKLPardiso_Convert_seqaij(Mat A, PetscBool sym, MatReuse reuse, PetscBool *free, INT_TYPE *nnz, INT_TYPE **r, INT_TYPE **c, PetscScalar **v)
166: {
167: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
168: PetscScalar *aav;
170: PetscFunctionBegin;
171: PetscCall(MatSeqAIJGetArrayRead(A, (const PetscScalar **)&aav));
172: if (!sym) { /* already in the correct format */
173: *v = aav;
174: *r = (INT_TYPE *)aa->i;
175: *c = (INT_TYPE *)aa->j;
176: *nnz = (INT_TYPE)aa->nz;
177: *free = PETSC_FALSE;
178: } else if (reuse == MAT_INITIAL_MATRIX) { /* need to get the triangular part */
179: PetscScalar *vals, *vv;
180: PetscInt *row, *col, *jj;
181: PetscInt m = A->rmap->n, nz, i;
182: const PetscInt *adiag;
184: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
185: nz = 0;
186: for (i = 0; i < m; i++) nz += aa->i[i + 1] - adiag[i];
187: PetscCall(PetscMalloc2(m + 1, &row, nz, &col));
188: PetscCall(PetscMalloc1(nz, &vals));
189: jj = col;
190: vv = vals;
192: row[0] = 0;
193: for (i = 0; i < m; i++) {
194: PetscInt *aj = aa->j + adiag[i];
195: PetscScalar *av = aav + adiag[i];
196: PetscInt rl = aa->i[i + 1] - adiag[i], j;
198: for (j = 0; j < rl; j++) {
199: *jj = *aj;
200: jj++;
201: aj++;
202: *vv = *av;
203: vv++;
204: av++;
205: }
206: row[i + 1] = row[i] + rl;
207: }
208: *v = vals;
209: *r = (INT_TYPE *)row;
210: *c = (INT_TYPE *)col;
211: *nnz = (INT_TYPE)nz;
212: *free = PETSC_TRUE;
213: } else {
214: PetscScalar *vv;
215: PetscInt m = A->rmap->n, i;
216: const PetscInt *adiag;
218: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
219: vv = *v;
220: for (i = 0; i < m; i++) {
221: PetscScalar *av = aav + adiag[i];
222: PetscInt rl = aa->i[i + 1] - adiag[i], j;
223: for (j = 0; j < rl; j++) {
224: *vv = *av;
225: vv++;
226: av++;
227: }
228: }
229: *free = PETSC_TRUE;
230: }
231: PetscCall(MatSeqAIJRestoreArrayRead(A, (const PetscScalar **)&aav));
232: PetscFunctionReturn(PETSC_SUCCESS);
233: }
235: static PetscErrorCode MatMKLPardisoSolveSchur_Private(Mat F, PetscScalar *B, PetscScalar *X)
236: {
237: Mat_MKL_PARDISO *mpardiso = (Mat_MKL_PARDISO *)F->data;
238: Mat S, Xmat, Bmat;
239: MatFactorSchurStatus schurstatus;
241: PetscFunctionBegin;
242: PetscCall(MatFactorGetSchurComplement(F, &S, &schurstatus));
243: PetscCheck(X != B || schurstatus != MAT_FACTOR_SCHUR_INVERTED, PETSC_COMM_SELF, PETSC_ERR_SUP, "X and B cannot point to the same address");
244: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mpardiso->schur_size, mpardiso->nrhs, B, &Bmat));
245: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mpardiso->schur_size, mpardiso->nrhs, X, &Xmat));
246: PetscCall(MatSetType(Bmat, ((PetscObject)S)->type_name));
247: PetscCall(MatSetType(Xmat, ((PetscObject)S)->type_name));
248: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
249: PetscCall(MatBindToCPU(Xmat, S->boundtocpu));
250: PetscCall(MatBindToCPU(Bmat, S->boundtocpu));
251: #endif
253: #if defined(PETSC_USE_COMPLEX)
254: PetscCheck(mpardiso->iparm[12 - 1] != 1, PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Hermitian solve not implemented yet");
255: #endif
257: switch (schurstatus) {
258: case MAT_FACTOR_SCHUR_FACTORED:
259: if (!mpardiso->iparm[12 - 1]) {
260: PetscCall(MatMatSolve(S, Bmat, Xmat));
261: } else { /* transpose solve */
262: PetscCall(MatMatSolveTranspose(S, Bmat, Xmat));
263: }
264: break;
265: case MAT_FACTOR_SCHUR_INVERTED:
266: PetscCall(MatProductCreateWithMat(S, Bmat, NULL, Xmat));
267: if (!mpardiso->iparm[12 - 1]) {
268: PetscCall(MatProductSetType(Xmat, MATPRODUCT_AB));
269: } else { /* transpose solve */
270: PetscCall(MatProductSetType(Xmat, MATPRODUCT_AtB));
271: }
272: PetscCall(MatProductSetFromOptions(Xmat));
273: PetscCall(MatProductSymbolic(Xmat));
274: PetscCall(MatProductNumeric(Xmat));
275: PetscCall(MatProductClear(Xmat));
276: break;
277: default:
278: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", (int)F->schur_status);
279: break;
280: }
281: PetscCall(MatFactorRestoreSchurComplement(F, &S, schurstatus));
282: PetscCall(MatDestroy(&Bmat));
283: PetscCall(MatDestroy(&Xmat));
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: static PetscErrorCode MatFactorSetSchurIS_MKL_PARDISO(Mat F, IS is)
288: {
289: Mat_MKL_PARDISO *mpardiso = (Mat_MKL_PARDISO *)F->data;
290: const PetscScalar *arr;
291: const PetscInt *idxs;
292: PetscInt size, i;
293: PetscMPIInt csize;
294: PetscBool sorted;
296: PetscFunctionBegin;
297: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F), &csize));
298: PetscCheck(csize <= 1, PETSC_COMM_SELF, PETSC_ERR_SUP, "MKL PARDISO parallel Schur complements not yet supported from PETSc");
299: PetscCall(ISSorted(is, &sorted));
300: PetscCheck(sorted, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS for MKL PARDISO Schur complements needs to be sorted");
301: PetscCall(ISGetLocalSize(is, &size));
302: PetscCall(PetscFree(mpardiso->schur_work));
303: PetscCall(PetscBLASIntCast(PetscMax(mpardiso->n, 2 * size), &mpardiso->schur_work_size));
304: PetscCall(PetscMalloc1(mpardiso->schur_work_size, &mpardiso->schur_work));
305: PetscCall(MatDestroy(&F->schur));
306: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, size, size, NULL, &F->schur));
307: PetscCall(MatDenseGetArrayRead(F->schur, &arr));
308: mpardiso->schur = (PetscScalar *)arr;
309: mpardiso->schur_size = size;
310: PetscCall(MatDenseRestoreArrayRead(F->schur, &arr));
311: if (mpardiso->mtype == 2) PetscCall(MatSetOption(F->schur, MAT_SPD, PETSC_TRUE));
313: PetscCall(PetscFree(mpardiso->schur_idxs));
314: PetscCall(PetscMalloc1(size, &mpardiso->schur_idxs));
315: PetscCall(PetscArrayzero(mpardiso->perm, mpardiso->n));
316: PetscCall(ISGetIndices(is, &idxs));
317: PetscCall(PetscArraycpy(mpardiso->schur_idxs, idxs, size));
318: for (i = 0; i < size; i++) mpardiso->perm[idxs[i]] = 1;
319: PetscCall(ISRestoreIndices(is, &idxs));
320: if (size) { /* turn on Schur switch if the set of indices is not empty */
321: mpardiso->iparm[36 - 1] = 2;
322: }
323: PetscFunctionReturn(PETSC_SUCCESS);
324: }
326: static PetscErrorCode MatDestroy_MKL_PARDISO(Mat A)
327: {
328: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)A->data;
330: PetscFunctionBegin;
331: if (mat_mkl_pardiso->CleanUp) {
332: mat_mkl_pardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;
334: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, NULL, NULL, NULL, NULL, &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm,
335: &mat_mkl_pardiso->msglvl, NULL, NULL, &mat_mkl_pardiso->err));
336: }
337: PetscCall(PetscFree(mat_mkl_pardiso->perm));
338: PetscCall(PetscFree(mat_mkl_pardiso->schur_work));
339: PetscCall(PetscFree(mat_mkl_pardiso->schur_idxs));
340: if (mat_mkl_pardiso->freeaij) {
341: PetscCall(PetscFree2(mat_mkl_pardiso->ia, mat_mkl_pardiso->ja));
342: if (mat_mkl_pardiso->iparm[34] == 1) PetscCall(PetscFree(mat_mkl_pardiso->a));
343: }
344: PetscCall(PetscFree(A->data));
346: /* clear composed functions */
347: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
348: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorSetSchurIS_C", NULL));
349: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMkl_PardisoSetCntl_C", NULL));
350: PetscFunctionReturn(PETSC_SUCCESS);
351: }
353: static PetscErrorCode MatMKLPardisoScatterSchur_Private(Mat_MKL_PARDISO *mpardiso, PetscScalar *whole, PetscScalar *schur, PetscBool reduce)
354: {
355: PetscFunctionBegin;
356: if (reduce) { /* data given for the whole matrix */
357: PetscInt i, m = 0, p = 0;
358: for (i = 0; i < mpardiso->nrhs; i++) {
359: for (PetscInt j = 0; j < mpardiso->schur_size; j++) schur[p + j] = whole[m + mpardiso->schur_idxs[j]];
360: m += mpardiso->n;
361: p += mpardiso->schur_size;
362: }
363: } else { /* from Schur to whole */
364: PetscInt i, m = 0, p = 0;
365: for (i = 0; i < mpardiso->nrhs; i++) {
366: for (PetscInt j = 0; j < mpardiso->schur_size; j++) whole[m + mpardiso->schur_idxs[j]] = schur[p + j];
367: m += mpardiso->n;
368: p += mpardiso->schur_size;
369: }
370: }
371: PetscFunctionReturn(PETSC_SUCCESS);
372: }
374: static PetscErrorCode MatSolve_MKL_PARDISO(Mat A, Vec b, Vec x)
375: {
376: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)A->data;
377: PetscScalar *xarray;
378: const PetscScalar *barray;
380: PetscFunctionBegin;
381: mat_mkl_pardiso->nrhs = 1;
382: PetscCall(VecGetArrayWrite(x, &xarray));
383: PetscCall(VecGetArrayRead(b, &barray));
385: if (!mat_mkl_pardiso->schur) mat_mkl_pardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
386: else mat_mkl_pardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;
388: if (barray == xarray) { /* if the two vectors share the same memory */
389: PetscScalar *work;
390: if (!mat_mkl_pardiso->schur_work) {
391: PetscCall(PetscMalloc1(mat_mkl_pardiso->n, &work));
392: } else {
393: work = mat_mkl_pardiso->schur_work;
394: }
395: mat_mkl_pardiso->iparm[6 - 1] = 1;
396: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja, NULL,
397: &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, (void *)xarray, (void *)work, &mat_mkl_pardiso->err));
398: if (!mat_mkl_pardiso->schur_work) PetscCall(PetscFree(work));
399: } else {
400: mat_mkl_pardiso->iparm[6 - 1] = 0;
401: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja,
402: mat_mkl_pardiso->perm, &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_pardiso->err));
403: }
404: PetscCall(VecRestoreArrayRead(b, &barray));
406: PetscCheck(mat_mkl_pardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL PARDISO: err=%" PetscInt_FMT ". Please check manual", (PetscInt)mat_mkl_pardiso->err);
408: if (mat_mkl_pardiso->schur) { /* solve Schur complement and expand solution */
409: if (!mat_mkl_pardiso->solve_interior) {
410: PetscInt shift = mat_mkl_pardiso->schur_size;
412: PetscCall(MatFactorFactorizeSchurComplement(A));
413: /* if inverted, uses BLAS *MM subroutines, otherwise LAPACK *TRS */
414: if (A->schur_status != MAT_FACTOR_SCHUR_INVERTED) shift = 0;
416: /* solve Schur complement */
417: PetscCall(MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso, xarray, mat_mkl_pardiso->schur_work, PETSC_TRUE));
418: PetscCall(MatMKLPardisoSolveSchur_Private(A, mat_mkl_pardiso->schur_work, mat_mkl_pardiso->schur_work + shift));
419: PetscCall(MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso, xarray, mat_mkl_pardiso->schur_work + shift, PETSC_FALSE));
420: } else { /* if we are solving for the interior problem, any value in barray[schur] forward-substituted to xarray[schur] will be neglected */
421: PetscInt i;
422: for (i = 0; i < mat_mkl_pardiso->schur_size; i++) xarray[mat_mkl_pardiso->schur_idxs[i]] = 0.;
423: }
425: /* expansion phase */
426: mat_mkl_pardiso->iparm[6 - 1] = 1;
427: mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
428: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja,
429: mat_mkl_pardiso->perm, &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, (void *)xarray, (void *)mat_mkl_pardiso->schur_work, /* according to the specs, the solution vector is always used */
430: &mat_mkl_pardiso->err));
431: PetscCheck(mat_mkl_pardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL PARDISO: err=%" PetscInt_FMT ". Please check manual", (PetscInt)mat_mkl_pardiso->err);
432: mat_mkl_pardiso->iparm[6 - 1] = 0;
433: }
434: PetscCall(VecRestoreArrayWrite(x, &xarray));
435: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
436: PetscFunctionReturn(PETSC_SUCCESS);
437: }
439: static PetscErrorCode MatForwardSolve_MKL_PARDISO(Mat A, Vec b, Vec x)
440: {
441: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)A->data;
442: PetscScalar *xarray;
443: const PetscScalar *barray;
445: PetscFunctionBegin;
446: PetscCheck(!mat_mkl_pardiso->schur, PETSC_COMM_SELF, PETSC_ERR_SUP, "Forward substitution not supported with Schur complement");
448: mat_mkl_pardiso->nrhs = 1;
449: PetscCall(VecGetArrayWrite(x, &xarray));
450: PetscCall(VecGetArrayRead(b, &barray));
452: mat_mkl_pardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;
454: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja, mat_mkl_pardiso->perm,
455: &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_pardiso->err));
456: PetscCheck(mat_mkl_pardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL PARDISO: err=%" PetscInt_FMT ". Please check manual", (PetscInt)mat_mkl_pardiso->err);
458: PetscCall(VecRestoreArrayRead(b, &barray));
459: PetscCall(VecRestoreArrayWrite(x, &xarray));
460: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
461: PetscFunctionReturn(PETSC_SUCCESS);
462: }
464: static PetscErrorCode MatBackwardSolve_MKL_PARDISO(Mat A, Vec b, Vec x)
465: {
466: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)A->data;
467: PetscScalar *xarray;
468: const PetscScalar *barray;
470: PetscFunctionBegin;
471: PetscCheck(!mat_mkl_pardiso->schur, PETSC_COMM_SELF, PETSC_ERR_SUP, "Backward substitution not supported with Schur complement");
473: mat_mkl_pardiso->nrhs = 1;
474: PetscCall(VecGetArrayWrite(x, &xarray));
475: PetscCall(VecGetArrayRead(b, &barray));
477: mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
479: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja, mat_mkl_pardiso->perm,
480: &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_pardiso->err));
481: PetscCheck(mat_mkl_pardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL PARDISO: err=%" PetscInt_FMT ". Please check manual", (PetscInt)mat_mkl_pardiso->err);
483: PetscCall(VecRestoreArrayRead(b, &barray));
484: PetscCall(VecRestoreArrayWrite(x, &xarray));
485: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
486: PetscFunctionReturn(PETSC_SUCCESS);
487: }
489: static PetscErrorCode MatSolveTranspose_MKL_PARDISO(Mat A, Vec b, Vec x)
490: {
491: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)A->data;
492: PetscInt oiparm12;
494: PetscFunctionBegin;
495: oiparm12 = mat_mkl_pardiso->iparm[12 - 1];
496: mat_mkl_pardiso->iparm[12 - 1] = 2;
497: PetscCall(MatSolve_MKL_PARDISO(A, b, x));
498: mat_mkl_pardiso->iparm[12 - 1] = oiparm12;
499: PetscFunctionReturn(PETSC_SUCCESS);
500: }
502: static PetscErrorCode MatMatSolve_MKL_PARDISO(Mat A, Mat B, Mat X)
503: {
504: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)A->data;
505: const PetscScalar *barray;
506: PetscScalar *xarray;
507: PetscBool flg;
509: PetscFunctionBegin;
510: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQDENSE, &flg));
511: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Matrix B must be MATSEQDENSE matrix");
512: if (X != B) {
513: PetscCall(PetscObjectBaseTypeCompare((PetscObject)X, MATSEQDENSE, &flg));
514: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Matrix X must be MATSEQDENSE matrix");
515: }
517: PetscCall(MatGetSize(B, NULL, (PetscInt *)&mat_mkl_pardiso->nrhs));
519: if (mat_mkl_pardiso->nrhs > 0) {
520: PetscCall(MatDenseGetArrayRead(B, &barray));
521: PetscCall(MatDenseGetArrayWrite(X, &xarray));
523: PetscCheck(barray != xarray, PETSC_COMM_SELF, PETSC_ERR_SUP, "B and X cannot share the same memory location");
524: if (!mat_mkl_pardiso->schur) mat_mkl_pardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
525: else mat_mkl_pardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;
527: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja,
528: mat_mkl_pardiso->perm, &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_pardiso->err));
529: PetscCheck(mat_mkl_pardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL PARDISO: err=%" PetscInt_FMT ". Please check manual", (PetscInt)mat_mkl_pardiso->err);
531: PetscCall(MatDenseRestoreArrayRead(B, &barray));
532: if (mat_mkl_pardiso->schur) { /* solve Schur complement and expand solution */
533: PetscScalar *o_schur_work = NULL;
535: /* solve Schur complement */
536: if (!mat_mkl_pardiso->solve_interior) {
537: PetscInt shift = mat_mkl_pardiso->schur_size * mat_mkl_pardiso->nrhs, scale;
538: PetscInt mem = mat_mkl_pardiso->n * mat_mkl_pardiso->nrhs;
540: PetscCall(MatFactorFactorizeSchurComplement(A));
541: /* allocate extra memory if it is needed */
542: scale = 1;
543: if (A->schur_status == MAT_FACTOR_SCHUR_INVERTED) scale = 2;
544: mem *= scale;
545: if (mem > mat_mkl_pardiso->schur_work_size) {
546: o_schur_work = mat_mkl_pardiso->schur_work;
547: PetscCall(PetscMalloc1(mem, &mat_mkl_pardiso->schur_work));
548: }
549: /* if inverted, uses BLAS *MM subroutines, otherwise LAPACK *TRS */
550: if (A->schur_status != MAT_FACTOR_SCHUR_INVERTED) shift = 0;
551: PetscCall(MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso, xarray, mat_mkl_pardiso->schur_work, PETSC_TRUE));
552: PetscCall(MatMKLPardisoSolveSchur_Private(A, mat_mkl_pardiso->schur_work, mat_mkl_pardiso->schur_work + shift));
553: PetscCall(MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso, xarray, mat_mkl_pardiso->schur_work + shift, PETSC_FALSE));
554: } else { /* if we are solving for the interior problem, any value in barray[schur,n] forward-substituted to xarray[schur,n] will be neglected */
555: PetscInt i, n, m = 0;
556: for (n = 0; n < mat_mkl_pardiso->nrhs; n++) {
557: for (i = 0; i < mat_mkl_pardiso->schur_size; i++) xarray[mat_mkl_pardiso->schur_idxs[i] + m] = 0.;
558: m += mat_mkl_pardiso->n;
559: }
560: }
562: /* expansion phase */
563: mat_mkl_pardiso->iparm[6 - 1] = 1;
564: mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
565: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja,
566: mat_mkl_pardiso->perm, &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, (void *)xarray, (void *)mat_mkl_pardiso->schur_work, /* according to the specs, the solution vector is always used */
567: &mat_mkl_pardiso->err));
568: if (o_schur_work) { /* restore original Schur_work (minimal size) */
569: PetscCall(PetscFree(mat_mkl_pardiso->schur_work));
570: mat_mkl_pardiso->schur_work = o_schur_work;
571: }
572: PetscCheck(mat_mkl_pardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL PARDISO: err=%" PetscInt_FMT ". Please check manual", (PetscInt)mat_mkl_pardiso->err);
573: mat_mkl_pardiso->iparm[6 - 1] = 0;
574: }
575: PetscCall(MatDenseRestoreArrayWrite(X, &xarray));
576: }
577: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
578: PetscFunctionReturn(PETSC_SUCCESS);
579: }
581: static PetscErrorCode MatFactorNumeric_MKL_PARDISO(Mat F, Mat A, const MatFactorInfo *info)
582: {
583: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)F->data;
585: PetscFunctionBegin;
586: mat_mkl_pardiso->matstruc = SAME_NONZERO_PATTERN;
587: PetscCall((*mat_mkl_pardiso->Convert)(A, mat_mkl_pardiso->needsym, MAT_REUSE_MATRIX, &mat_mkl_pardiso->freeaij, &mat_mkl_pardiso->nz, &mat_mkl_pardiso->ia, &mat_mkl_pardiso->ja, (PetscScalar **)&mat_mkl_pardiso->a));
589: mat_mkl_pardiso->phase = JOB_NUMERICAL_FACTORIZATION;
590: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja, mat_mkl_pardiso->perm,
591: &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, NULL, (void *)mat_mkl_pardiso->schur, &mat_mkl_pardiso->err));
592: PetscCheck(mat_mkl_pardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL PARDISO: err=%" PetscInt_FMT ". Please check manual", (PetscInt)mat_mkl_pardiso->err);
594: /* report flops */
595: if (mat_mkl_pardiso->iparm[18] > 0) PetscCall(PetscLogFlops(PetscPowRealInt(10., 6) * mat_mkl_pardiso->iparm[18]));
597: if (F->schur) { /* schur output from pardiso is in row major format */
598: #if defined(PETSC_HAVE_CUDA)
599: F->schur->offloadmask = PETSC_OFFLOAD_CPU;
600: #endif
601: PetscCall(MatFactorRestoreSchurComplement(F, NULL, MAT_FACTOR_SCHUR_UNFACTORED));
602: PetscCall(MatTranspose(F->schur, MAT_INPLACE_MATRIX, &F->schur));
603: }
604: mat_mkl_pardiso->matstruc = SAME_NONZERO_PATTERN;
605: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
606: PetscFunctionReturn(PETSC_SUCCESS);
607: }
609: static PetscErrorCode MatSetFromOptions_MKL_PARDISO(Mat F, Mat A)
610: {
611: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)F->data;
612: PetscInt icntl, bs, threads = 1;
613: PetscBool flg;
615: PetscFunctionBegin;
616: PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MKL_PARDISO Options", "Mat");
618: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_65", "Suggested number of threads to use within MKL PARDISO", "None", threads, &threads, &flg));
619: if (flg) PetscSetMKL_PARDISOThreads((int)threads);
621: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_66", "Maximum number of factors with identical sparsity structure that must be kept in memory at the same time", "None", mat_mkl_pardiso->maxfct, &icntl, &flg));
622: if (flg) mat_mkl_pardiso->maxfct = icntl;
624: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_67", "Indicates the actual matrix for the solution phase", "None", mat_mkl_pardiso->mnum, &icntl, &flg));
625: if (flg) mat_mkl_pardiso->mnum = icntl;
627: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_68", "Message level information", "None", mat_mkl_pardiso->msglvl, &icntl, &flg));
628: if (flg) mat_mkl_pardiso->msglvl = icntl;
630: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_69", "Defines the matrix type", "None", mat_mkl_pardiso->mtype, &icntl, &flg));
631: if (flg) {
632: void *pt[IPARM_SIZE];
633: mat_mkl_pardiso->mtype = icntl;
634: icntl = mat_mkl_pardiso->iparm[34];
635: bs = mat_mkl_pardiso->iparm[36];
636: MKL_PARDISO_INIT(pt, &mat_mkl_pardiso->mtype, mat_mkl_pardiso->iparm);
637: #if defined(PETSC_USE_REAL_SINGLE)
638: mat_mkl_pardiso->iparm[27] = 1;
639: #else
640: mat_mkl_pardiso->iparm[27] = 0;
641: #endif
642: mat_mkl_pardiso->iparm[34] = icntl;
643: mat_mkl_pardiso->iparm[36] = bs;
644: }
646: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_1", "Use default values (if 0)", "None", mat_mkl_pardiso->iparm[0], &icntl, &flg));
647: if (flg) mat_mkl_pardiso->iparm[0] = icntl;
649: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_2", "Fill-in reducing ordering for the input matrix", "None", mat_mkl_pardiso->iparm[1], &icntl, &flg));
650: if (flg) mat_mkl_pardiso->iparm[1] = icntl;
652: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_4", "Preconditioned CGS/CG", "None", mat_mkl_pardiso->iparm[3], &icntl, &flg));
653: if (flg) mat_mkl_pardiso->iparm[3] = icntl;
655: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_5", "User permutation", "None", mat_mkl_pardiso->iparm[4], &icntl, &flg));
656: if (flg) mat_mkl_pardiso->iparm[4] = icntl;
658: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_6", "Write solution on x", "None", mat_mkl_pardiso->iparm[5], &icntl, &flg));
659: if (flg) mat_mkl_pardiso->iparm[5] = icntl;
661: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_8", "Iterative refinement step", "None", mat_mkl_pardiso->iparm[7], &icntl, &flg));
662: if (flg) mat_mkl_pardiso->iparm[7] = icntl;
664: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_10", "Pivoting perturbation", "None", mat_mkl_pardiso->iparm[9], &icntl, &flg));
665: if (flg) mat_mkl_pardiso->iparm[9] = icntl;
667: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_11", "Scaling vectors", "None", mat_mkl_pardiso->iparm[10], &icntl, &flg));
668: if (flg) mat_mkl_pardiso->iparm[10] = icntl;
670: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_12", "Solve with transposed or conjugate transposed matrix A", "None", mat_mkl_pardiso->iparm[11], &icntl, &flg));
671: if (flg) mat_mkl_pardiso->iparm[11] = icntl;
673: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_13", "Improved accuracy using (non-) symmetric weighted matching", "None", mat_mkl_pardiso->iparm[12], &icntl, &flg));
674: if (flg) mat_mkl_pardiso->iparm[12] = icntl;
676: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_18", "Numbers of non-zero elements", "None", mat_mkl_pardiso->iparm[17], &icntl, &flg));
677: if (flg) mat_mkl_pardiso->iparm[17] = icntl;
679: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_19", "Report number of floating point operations (0 to disable)", "None", mat_mkl_pardiso->iparm[18], &icntl, &flg));
680: if (flg) mat_mkl_pardiso->iparm[18] = icntl;
682: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_21", "Pivoting for symmetric indefinite matrices", "None", mat_mkl_pardiso->iparm[20], &icntl, &flg));
683: if (flg) mat_mkl_pardiso->iparm[20] = icntl;
685: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_24", "Parallel factorization control", "None", mat_mkl_pardiso->iparm[23], &icntl, &flg));
686: if (flg) mat_mkl_pardiso->iparm[23] = icntl;
688: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_25", "Parallel forward/backward solve control", "None", mat_mkl_pardiso->iparm[24], &icntl, &flg));
689: if (flg) mat_mkl_pardiso->iparm[24] = icntl;
691: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_27", "Matrix checker", "None", mat_mkl_pardiso->iparm[26], &icntl, &flg));
692: if (flg) mat_mkl_pardiso->iparm[26] = icntl;
694: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_31", "Partial solve and computing selected components of the solution vectors", "None", mat_mkl_pardiso->iparm[30], &icntl, &flg));
695: if (flg) mat_mkl_pardiso->iparm[30] = icntl;
697: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_34", "Optimal number of threads for conditional numerical reproducibility (CNR) mode", "None", mat_mkl_pardiso->iparm[33], &icntl, &flg));
698: if (flg) mat_mkl_pardiso->iparm[33] = icntl;
700: PetscCall(PetscOptionsInt("-mat_mkl_pardiso_60", "Intel MKL PARDISO mode", "None", mat_mkl_pardiso->iparm[59], &icntl, &flg));
701: if (flg) mat_mkl_pardiso->iparm[59] = icntl;
702: PetscOptionsEnd();
703: PetscFunctionReturn(PETSC_SUCCESS);
704: }
706: static PetscErrorCode MatFactorMKL_PARDISOInitialize_Private(Mat A, MatFactorType ftype, Mat_MKL_PARDISO *mat_mkl_pardiso)
707: {
708: PetscInt bs;
709: PetscBool match;
711: PetscFunctionBegin;
712: for (PetscInt i = 0; i < IPARM_SIZE; i++) mat_mkl_pardiso->iparm[i] = 0;
713: for (PetscInt i = 0; i < IPARM_SIZE; i++) mat_mkl_pardiso->pt[i] = 0;
714: #if defined(PETSC_USE_REAL_SINGLE)
715: mat_mkl_pardiso->iparm[27] = 1;
716: #else
717: mat_mkl_pardiso->iparm[27] = 0;
718: #endif
719: /* Default options for both sym and unsym */
720: mat_mkl_pardiso->iparm[0] = 1; /* Solver default parameters overridden with provided by iparm */
721: mat_mkl_pardiso->iparm[1] = 2; /* Metis reordering */
722: mat_mkl_pardiso->iparm[5] = 0; /* Write solution into x */
723: mat_mkl_pardiso->iparm[7] = 0; /* Max number of iterative refinement steps */
724: mat_mkl_pardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
725: mat_mkl_pardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
726: #if 0
727: mat_mkl_pardiso->iparm[23] = 1; /* Parallel factorization control*/
728: #endif
729: PetscCall(PetscObjectTypeCompareAny((PetscObject)A, &match, MATSEQBAIJ, MATSEQSBAIJ, ""));
730: PetscCall(MatGetBlockSize(A, &bs));
731: if (!match || bs == 1) {
732: mat_mkl_pardiso->iparm[34] = 1; /* Cluster Sparse Solver use C-style indexing for ia and ja arrays */
733: mat_mkl_pardiso->n = A->rmap->N;
734: } else {
735: mat_mkl_pardiso->iparm[34] = 0; /* Cluster Sparse Solver use Fortran-style indexing for ia and ja arrays */
736: mat_mkl_pardiso->iparm[36] = bs;
737: mat_mkl_pardiso->n = A->rmap->N / bs;
738: }
739: mat_mkl_pardiso->iparm[39] = 0; /* Input: matrix/rhs/solution stored on rank-0 */
741: mat_mkl_pardiso->CleanUp = PETSC_FALSE;
742: mat_mkl_pardiso->maxfct = 1; /* Maximum number of numerical factorizations. */
743: mat_mkl_pardiso->mnum = 1; /* Which factorization to use. */
744: mat_mkl_pardiso->msglvl = 0; /* 0: do not print 1: Print statistical information in file */
745: mat_mkl_pardiso->phase = -1;
746: mat_mkl_pardiso->err = 0;
748: mat_mkl_pardiso->nrhs = 1;
749: mat_mkl_pardiso->err = 0;
750: mat_mkl_pardiso->phase = -1;
752: if (ftype == MAT_FACTOR_LU) {
753: mat_mkl_pardiso->iparm[9] = 13; /* Perturb the pivot elements with 1E-13 */
754: mat_mkl_pardiso->iparm[10] = 1; /* Use nonsymmetric permutation and scaling MPS */
755: mat_mkl_pardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
756: } else {
757: mat_mkl_pardiso->iparm[9] = 8; /* Perturb the pivot elements with 1E-8 */
758: mat_mkl_pardiso->iparm[10] = 0; /* Use nonsymmetric permutation and scaling MPS */
759: mat_mkl_pardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
760: #if defined(PETSC_USE_DEBUG)
761: mat_mkl_pardiso->iparm[26] = 1; /* Matrix checker */
762: #endif
763: }
764: PetscCall(PetscCalloc1(A->rmap->N * sizeof(INT_TYPE), &mat_mkl_pardiso->perm));
765: mat_mkl_pardiso->schur_size = 0;
766: PetscFunctionReturn(PETSC_SUCCESS);
767: }
769: static PetscErrorCode MatFactorSymbolic_AIJMKL_PARDISO_Private(Mat F, Mat A, const MatFactorInfo *info)
770: {
771: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)F->data;
773: PetscFunctionBegin;
774: mat_mkl_pardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
775: PetscCall(MatSetFromOptions_MKL_PARDISO(F, A));
776: /* throw away any previously computed structure */
777: if (mat_mkl_pardiso->freeaij) {
778: PetscCall(PetscFree2(mat_mkl_pardiso->ia, mat_mkl_pardiso->ja));
779: if (mat_mkl_pardiso->iparm[34] == 1) PetscCall(PetscFree(mat_mkl_pardiso->a));
780: }
781: PetscCall((*mat_mkl_pardiso->Convert)(A, mat_mkl_pardiso->needsym, MAT_INITIAL_MATRIX, &mat_mkl_pardiso->freeaij, &mat_mkl_pardiso->nz, &mat_mkl_pardiso->ia, &mat_mkl_pardiso->ja, (PetscScalar **)&mat_mkl_pardiso->a));
782: if (mat_mkl_pardiso->iparm[34] == 1) mat_mkl_pardiso->n = A->rmap->N;
783: else mat_mkl_pardiso->n = A->rmap->N / A->rmap->bs;
785: mat_mkl_pardiso->phase = JOB_ANALYSIS;
787: /* reset flops counting if requested */
788: if (mat_mkl_pardiso->iparm[18]) mat_mkl_pardiso->iparm[18] = -1;
790: PetscCallPardiso(MKL_PARDISO(mat_mkl_pardiso->pt, &mat_mkl_pardiso->maxfct, &mat_mkl_pardiso->mnum, &mat_mkl_pardiso->mtype, &mat_mkl_pardiso->phase, &mat_mkl_pardiso->n, mat_mkl_pardiso->a, mat_mkl_pardiso->ia, mat_mkl_pardiso->ja, mat_mkl_pardiso->perm,
791: &mat_mkl_pardiso->nrhs, mat_mkl_pardiso->iparm, &mat_mkl_pardiso->msglvl, NULL, NULL, &mat_mkl_pardiso->err));
792: PetscCheck(mat_mkl_pardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL PARDISO: err=%" PetscInt_FMT ". Please check manual", (PetscInt)mat_mkl_pardiso->err);
794: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
796: if (F->factortype == MAT_FACTOR_LU) F->ops->lufactornumeric = MatFactorNumeric_MKL_PARDISO;
797: else F->ops->choleskyfactornumeric = MatFactorNumeric_MKL_PARDISO;
799: F->ops->solve = MatSolve_MKL_PARDISO;
800: F->ops->solvetranspose = MatSolveTranspose_MKL_PARDISO;
801: F->ops->matsolve = MatMatSolve_MKL_PARDISO;
802: if (F->factortype == MAT_FACTOR_LU || (!PetscDefined(USE_COMPLEX) && F->factortype == MAT_FACTOR_CHOLESKY && A->spd == PETSC_BOOL3_TRUE)) {
803: F->ops->backwardsolve = MatBackwardSolve_MKL_PARDISO;
804: F->ops->forwardsolve = MatForwardSolve_MKL_PARDISO;
805: }
806: PetscFunctionReturn(PETSC_SUCCESS);
807: }
809: static PetscErrorCode MatLUFactorSymbolic_AIJMKL_PARDISO(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
810: {
811: PetscFunctionBegin;
812: PetscCall(MatFactorSymbolic_AIJMKL_PARDISO_Private(F, A, info));
813: PetscFunctionReturn(PETSC_SUCCESS);
814: }
816: #if !defined(PETSC_USE_COMPLEX)
817: static PetscErrorCode MatGetInertia_MKL_PARDISO(Mat F, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
818: {
819: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)F->data;
821: PetscFunctionBegin;
822: if (nneg) *nneg = mat_mkl_pardiso->iparm[22];
823: if (npos) *npos = mat_mkl_pardiso->iparm[21];
824: if (nzero) *nzero = F->rmap->N - (mat_mkl_pardiso->iparm[22] + mat_mkl_pardiso->iparm[21]);
825: PetscFunctionReturn(PETSC_SUCCESS);
826: }
827: #endif
829: static PetscErrorCode MatCholeskyFactorSymbolic_AIJMKL_PARDISO(Mat F, Mat A, IS r, const MatFactorInfo *info)
830: {
831: PetscFunctionBegin;
832: PetscCall(MatFactorSymbolic_AIJMKL_PARDISO_Private(F, A, info));
833: F->ops->getinertia = NULL;
834: #if !defined(PETSC_USE_COMPLEX)
835: F->ops->getinertia = MatGetInertia_MKL_PARDISO;
836: #endif
837: PetscFunctionReturn(PETSC_SUCCESS);
838: }
840: static PetscErrorCode MatView_MKL_PARDISO(Mat A, PetscViewer viewer)
841: {
842: PetscBool isascii;
843: PetscViewerFormat format;
844: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)A->data;
846: PetscFunctionBegin;
847: if (A->ops->solve != MatSolve_MKL_PARDISO) PetscFunctionReturn(PETSC_SUCCESS);
849: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
850: if (isascii) {
851: PetscCall(PetscViewerGetFormat(viewer, &format));
852: if (format == PETSC_VIEWER_ASCII_INFO) {
853: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO run parameters:\n"));
854: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO phase: %" PetscInt_FMT "\n", (PetscInt)mat_mkl_pardiso->phase));
855: for (PetscInt i = 1; i <= 64; i++) PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO iparm[%" PetscInt_FMT "]: %" PetscInt_FMT "\n", i, (PetscInt)mat_mkl_pardiso->iparm[i - 1]));
856: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO maxfct: %" PetscInt_FMT "\n", (PetscInt)mat_mkl_pardiso->maxfct));
857: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO mnum: %" PetscInt_FMT "\n", (PetscInt)mat_mkl_pardiso->mnum));
858: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO mtype: %" PetscInt_FMT "\n", (PetscInt)mat_mkl_pardiso->mtype));
859: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO n: %" PetscInt_FMT "\n", (PetscInt)mat_mkl_pardiso->n));
860: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO nrhs: %" PetscInt_FMT "\n", (PetscInt)mat_mkl_pardiso->nrhs));
861: PetscCall(PetscViewerASCIIPrintf(viewer, "MKL PARDISO msglvl: %" PetscInt_FMT "\n", (PetscInt)mat_mkl_pardiso->msglvl));
862: }
863: }
864: PetscFunctionReturn(PETSC_SUCCESS);
865: }
867: static PetscErrorCode MatGetInfo_MKL_PARDISO(Mat A, MatInfoType flag, MatInfo *info)
868: {
869: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)A->data;
871: PetscFunctionBegin;
872: info->block_size = 1.0;
873: info->nz_used = mat_mkl_pardiso->iparm[17];
874: info->nz_allocated = mat_mkl_pardiso->iparm[17];
875: info->nz_unneeded = 0.0;
876: info->assemblies = 0.0;
877: info->mallocs = 0.0;
878: info->memory = 0.0;
879: info->fill_ratio_given = 0;
880: info->fill_ratio_needed = 0;
881: info->factor_mallocs = 0;
882: PetscFunctionReturn(PETSC_SUCCESS);
883: }
885: static PetscErrorCode MatMkl_PardisoSetCntl_MKL_PARDISO(Mat F, PetscInt icntl, PetscInt ival)
886: {
887: PetscInt backup, bs;
888: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO *)F->data;
890: PetscFunctionBegin;
891: if (icntl <= 64) {
892: mat_mkl_pardiso->iparm[icntl - 1] = ival;
893: } else {
894: if (icntl == 65) PetscSetMKL_PARDISOThreads((int)ival);
895: else if (icntl == 66) mat_mkl_pardiso->maxfct = ival;
896: else if (icntl == 67) mat_mkl_pardiso->mnum = ival;
897: else if (icntl == 68) mat_mkl_pardiso->msglvl = ival;
898: else if (icntl == 69) {
899: void *pt[IPARM_SIZE];
900: backup = mat_mkl_pardiso->iparm[34];
901: bs = mat_mkl_pardiso->iparm[36];
902: mat_mkl_pardiso->mtype = ival;
903: MKL_PARDISO_INIT(pt, &mat_mkl_pardiso->mtype, mat_mkl_pardiso->iparm);
904: #if defined(PETSC_USE_REAL_SINGLE)
905: mat_mkl_pardiso->iparm[27] = 1;
906: #else
907: mat_mkl_pardiso->iparm[27] = 0;
908: #endif
909: mat_mkl_pardiso->iparm[34] = backup;
910: mat_mkl_pardiso->iparm[36] = bs;
911: } else if (icntl == 70) mat_mkl_pardiso->solve_interior = (PetscBool)!!ival;
912: }
913: PetscFunctionReturn(PETSC_SUCCESS);
914: }
916: /*@
917: MatMkl_PardisoSetCntl - Set MKL PARDISO <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html> parameters
919: Logically Collective
921: Input Parameters:
922: + F - the factored matrix obtained by calling `MatGetFactor()`
923: . icntl - index of MKL PARDISO parameter
924: - ival - value of MKL PARDISO parameter
926: Options Database Key:
927: . -mat_mkl_pardiso_ICNTL ival - change the option numbered ICNTL to the value `ival`
929: Level: beginner
931: .seealso: [](ch_matrices), `Mat`, `MATSOLVERMKL_PARDISO`, `MatGetFactor()`
932: @*/
933: PetscErrorCode MatMkl_PardisoSetCntl(Mat F, PetscInt icntl, PetscInt ival)
934: {
935: PetscFunctionBegin;
936: PetscTryMethod(F, "MatMkl_PardisoSetCntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
937: PetscFunctionReturn(PETSC_SUCCESS);
938: }
940: /*MC
941: MATSOLVERMKL_PARDISO - A matrix type providing direct solvers, LU, for
942: `MATSEQAIJ` matrices via the external package MKL PARDISO
943: <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2024-0/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>.
945: Use `-pc_type lu` `-pc_factor_mat_solver_type mkl_pardiso` to use this direct solver
947: Options Database Keys:
948: + -mat_mkl_pardiso_65 - Suggested number of threads to use within MKL PARDISO
949: . -mat_mkl_pardiso_66 - Maximum number of factors with identical sparsity structure that must be kept in memory at the same time
950: . -mat_mkl_pardiso_67 - Indicates the actual matrix for the solution phase
951: . -mat_mkl_pardiso_68 - Message level information, use 1 to get detailed information on the solver options
952: . -mat_mkl_pardiso_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
953: . -mat_mkl_pardiso_1 - Use default values
954: . -mat_mkl_pardiso_2 - Fill-in reducing ordering for the input matrix
955: . -mat_mkl_pardiso_4 - Preconditioned CGS/CG
956: . -mat_mkl_pardiso_5 - User permutation
957: . -mat_mkl_pardiso_6 - Write solution on x
958: . -mat_mkl_pardiso_8 - Iterative refinement step
959: . -mat_mkl_pardiso_10 - Pivoting perturbation
960: . -mat_mkl_pardiso_11 - Scaling vectors
961: . -mat_mkl_pardiso_12 - Solve with transposed or conjugate transposed matrix A
962: . -mat_mkl_pardiso_13 - Improved accuracy using (non-) symmetric weighted matching
963: . -mat_mkl_pardiso_18 - Numbers of non-zero elements
964: . -mat_mkl_pardiso_19 - Report number of floating point operations
965: . -mat_mkl_pardiso_21 - Pivoting for symmetric indefinite matrices
966: . -mat_mkl_pardiso_24 - Parallel factorization control
967: . -mat_mkl_pardiso_25 - Parallel forward/backward solve control
968: . -mat_mkl_pardiso_27 - Matrix checker
969: . -mat_mkl_pardiso_31 - Partial solve and computing selected components of the solution vectors
970: . -mat_mkl_pardiso_34 - Optimal number of threads for conditional numerical reproducibility (CNR) mode
971: - -mat_mkl_pardiso_60 - Intel MKL PARDISO mode
973: Level: beginner
975: Notes:
976: Use `-mat_mkl_pardiso_68 1` to display the number of threads the solver is using. MKL does not provide a way to directly access this
977: information.
979: For more information on the options check the MKL PARDISO manual
981: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMkl_PardisoSetCntl()`, `MATSOLVERMKL_CPARDISO`
982: M*/
983: static PetscErrorCode MatFactorGetSolverType_mkl_pardiso(Mat A, MatSolverType *type)
984: {
985: PetscFunctionBegin;
986: *type = MATSOLVERMKL_PARDISO;
987: PetscFunctionReturn(PETSC_SUCCESS);
988: }
990: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mkl_pardiso(Mat A, MatFactorType ftype, Mat *F)
991: {
992: Mat B;
993: Mat_MKL_PARDISO *mat_mkl_pardiso;
994: PetscBool isSeqAIJ, isSeqBAIJ, isSeqSBAIJ;
996: PetscFunctionBegin;
997: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
998: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQBAIJ, &isSeqBAIJ));
999: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSBAIJ, &isSeqSBAIJ));
1000: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1001: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
1002: PetscCall(PetscStrallocpy("mkl_pardiso", &((PetscObject)B)->type_name));
1003: PetscCall(MatSetUp(B));
1005: PetscCall(PetscNew(&mat_mkl_pardiso));
1006: B->data = mat_mkl_pardiso;
1008: PetscCall(MatFactorMKL_PARDISOInitialize_Private(A, ftype, mat_mkl_pardiso));
1009: if (ftype == MAT_FACTOR_LU) {
1010: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_PARDISO;
1011: B->factortype = MAT_FACTOR_LU;
1012: mat_mkl_pardiso->needsym = PETSC_FALSE;
1013: if (isSeqAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqaij;
1014: else if (isSeqBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqbaij;
1015: else {
1016: PetscCheck(!isSeqSBAIJ, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "No support for MKL PARDISO LU factor with SEQSBAIJ format! Use MAT_FACTOR_CHOLESKY instead");
1017: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "No support for MKL PARDISO LU with %s format", ((PetscObject)A)->type_name);
1018: }
1019: #if defined(PETSC_USE_COMPLEX)
1020: mat_mkl_pardiso->mtype = 13;
1021: #else
1022: mat_mkl_pardiso->mtype = 11;
1023: #endif
1024: } else {
1025: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_AIJMKL_PARDISO;
1026: B->factortype = MAT_FACTOR_CHOLESKY;
1027: if (isSeqAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqaij;
1028: else if (isSeqBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqbaij;
1029: else if (isSeqSBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqsbaij;
1030: else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "No support for PARDISO CHOLESKY with %s format", ((PetscObject)A)->type_name);
1032: mat_mkl_pardiso->needsym = PETSC_TRUE;
1033: #if !defined(PETSC_USE_COMPLEX)
1034: if (A->spd == PETSC_BOOL3_TRUE) mat_mkl_pardiso->mtype = 2;
1035: else mat_mkl_pardiso->mtype = -2;
1036: #else
1037: mat_mkl_pardiso->mtype = 6;
1038: PetscCheck(A->hermitian != PETSC_BOOL3_TRUE, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "No support for MKL PARDISO CHOLESKY with Hermitian matrices! Use MAT_FACTOR_LU instead");
1039: #endif
1040: }
1041: B->ops->destroy = MatDestroy_MKL_PARDISO;
1042: B->ops->view = MatView_MKL_PARDISO;
1043: B->ops->getinfo = MatGetInfo_MKL_PARDISO;
1044: B->factortype = ftype;
1045: B->assembled = PETSC_TRUE;
1047: PetscCall(PetscFree(B->solvertype));
1048: PetscCall(PetscStrallocpy(MATSOLVERMKL_PARDISO, &B->solvertype));
1050: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mkl_pardiso));
1051: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MKL_PARDISO));
1052: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMkl_PardisoSetCntl_C", MatMkl_PardisoSetCntl_MKL_PARDISO));
1054: *F = B;
1055: PetscFunctionReturn(PETSC_SUCCESS);
1056: }
1058: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_MKL_Pardiso(void)
1059: {
1060: PetscFunctionBegin;
1061: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_PARDISO, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mkl_pardiso));
1062: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_PARDISO, MATSEQAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mkl_pardiso));
1063: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_PARDISO, MATSEQBAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mkl_pardiso));
1064: PetscCall(MatSolverTypeRegister(MATSOLVERMKL_PARDISO, MATSEQSBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mkl_pardiso));
1065: PetscFunctionReturn(PETSC_SUCCESS);
1066: }