Actual source code: mkl_pardiso.c
petsc-3.13.0 2020-03-29
1: #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/
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: int iparm_copy[IPARM_SIZE], mtype_copy, i;
45: mtype_copy = *mtype;
46: pardisoinit(pt, &mtype_copy, iparm_copy);
47: for (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
57: /*
58: * Internal data structure.
59: * For more information check mkl_pardiso manual.
60: */
61: typedef struct {
63: /* Configuration vector*/
64: INT_TYPE iparm[IPARM_SIZE];
66: /*
67: * Internal mkl_pardiso memory location.
68: * After the first call to mkl_pardiso do not modify pt, as that could cause a serious memory leak.
69: */
70: void *pt[IPARM_SIZE];
72: /* Basic mkl_pardiso info*/
73: INT_TYPE phase, maxfct, mnum, mtype, n, nrhs, msglvl, err;
75: /* Matrix structure*/
76: void *a;
77: INT_TYPE *ia, *ja;
79: /* Number of non-zero elements*/
80: INT_TYPE nz;
82: /* Row permutaton vector*/
83: INT_TYPE *perm;
85: /* Define if matrix preserves sparse structure.*/
86: MatStructure matstruc;
88: PetscBool needsym;
89: PetscBool freeaij;
91: /* Schur complement */
92: PetscScalar *schur;
93: PetscInt schur_size;
94: PetscInt *schur_idxs;
95: PetscScalar *schur_work;
96: PetscBLASInt schur_work_size;
97: PetscBool solve_interior;
99: /* True if mkl_pardiso function have been used.*/
100: PetscBool CleanUp;
102: /* Conversion to a format suitable for MKL */
103: PetscErrorCode (*Convert)(Mat, PetscBool, MatReuse, PetscBool*, INT_TYPE*, INT_TYPE**, INT_TYPE**, PetscScalar**);
104: } Mat_MKL_PARDISO;
106: PetscErrorCode MatMKLPardiso_Convert_seqsbaij(Mat A,PetscBool sym,MatReuse reuse,PetscBool *free,INT_TYPE *nnz,INT_TYPE **r,INT_TYPE **c,PetscScalar **v)
107: {
108: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ*)A->data;
109: PetscInt bs = A->rmap->bs,i;
113: if (!sym) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_PLIB,"This should not happen");
114: *v = aa->a;
115: if (bs == 1) { /* already in the correct format */
116: /* though PetscInt and INT_TYPE are of the same size since they are defined differently the Intel compiler requires a cast */
117: *r = (INT_TYPE*)aa->i;
118: *c = (INT_TYPE*)aa->j;
119: *nnz = (INT_TYPE)aa->nz;
120: *free = PETSC_FALSE;
121: } else if (reuse == MAT_INITIAL_MATRIX) {
122: PetscInt m = A->rmap->n,nz = aa->nz;
123: PetscInt *row,*col;
124: PetscMalloc2(m+1,&row,nz,&col);
125: for (i=0; i<m+1; i++) {
126: row[i] = aa->i[i]+1;
127: }
128: for (i=0; i<nz; i++) {
129: col[i] = aa->j[i]+1;
130: }
131: *r = (INT_TYPE*)row;
132: *c = (INT_TYPE*)col;
133: *nnz = (INT_TYPE)nz;
134: *free = PETSC_TRUE;
135: }
136: return(0);
137: }
139: PetscErrorCode MatMKLPardiso_Convert_seqbaij(Mat A,PetscBool sym,MatReuse reuse,PetscBool *free,INT_TYPE *nnz,INT_TYPE **r,INT_TYPE **c,PetscScalar **v)
140: {
141: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ*)A->data;
142: PetscInt bs = A->rmap->bs,i;
146: if (!sym) {
147: *v = aa->a;
148: if (bs == 1) { /* already in the correct format */
149: /* though PetscInt and INT_TYPE are of the same size since they are defined differently the Intel compiler requires a cast */
150: *r = (INT_TYPE*)aa->i;
151: *c = (INT_TYPE*)aa->j;
152: *nnz = (INT_TYPE)aa->nz;
153: *free = PETSC_FALSE;
154: return(0);
155: } else if (reuse == MAT_INITIAL_MATRIX) {
156: PetscInt m = A->rmap->n,nz = aa->nz;
157: PetscInt *row,*col;
158: PetscMalloc2(m+1,&row,nz,&col);
159: for (i=0; i<m+1; i++) {
160: row[i] = aa->i[i]+1;
161: }
162: for (i=0; i<nz; i++) {
163: col[i] = aa->j[i]+1;
164: }
165: *r = (INT_TYPE*)row;
166: *c = (INT_TYPE*)col;
167: *nnz = (INT_TYPE)nz;
168: }
169: *free = PETSC_TRUE;
170: } else {
171: SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_PLIB,"This should not happen");
172: }
173: return(0);
174: }
176: PetscErrorCode MatMKLPardiso_Convert_seqaij(Mat A,PetscBool sym,MatReuse reuse,PetscBool *free,INT_TYPE *nnz,INT_TYPE **r,INT_TYPE **c,PetscScalar **v)
177: {
178: Mat_SeqAIJ *aa = (Mat_SeqAIJ*)A->data;
179: PetscScalar *aav;
183: MatSeqAIJGetArrayRead(A,(const PetscScalar**)&aav);
184: if (!sym) { /* already in the correct format */
185: *v = aav;
186: *r = (INT_TYPE*)aa->i;
187: *c = (INT_TYPE*)aa->j;
188: *nnz = (INT_TYPE)aa->nz;
189: *free = PETSC_FALSE;
190: } else if (reuse == MAT_INITIAL_MATRIX) { /* need to get the triangular part */
191: PetscScalar *vals,*vv;
192: PetscInt *row,*col,*jj;
193: PetscInt m = A->rmap->n,nz,i;
195: nz = 0;
196: for (i=0; i<m; i++) nz += aa->i[i+1] - aa->diag[i];
197: PetscMalloc2(m+1,&row,nz,&col);
198: PetscMalloc1(nz,&vals);
199: jj = col;
200: vv = vals;
202: row[0] = 0;
203: for (i=0; i<m; i++) {
204: PetscInt *aj = aa->j + aa->diag[i];
205: PetscScalar *av = aav + aa->diag[i];
206: PetscInt rl = aa->i[i+1] - aa->diag[i],j;
208: for (j=0; j<rl; j++) {
209: *jj = *aj; jj++; aj++;
210: *vv = *av; vv++; av++;
211: }
212: row[i+1] = row[i] + rl;
213: }
214: *v = vals;
215: *r = (INT_TYPE*)row;
216: *c = (INT_TYPE*)col;
217: *nnz = (INT_TYPE)nz;
218: *free = PETSC_TRUE;
219: } else {
220: PetscScalar *vv;
221: PetscInt m = A->rmap->n,i;
223: vv = *v;
224: for (i=0; i<m; i++) {
225: PetscScalar *av = aav + aa->diag[i];
226: PetscInt rl = aa->i[i+1] - aa->diag[i],j;
227: for (j=0; j<rl; j++) {
228: *vv = *av; vv++; av++;
229: }
230: }
231: *free = PETSC_TRUE;
232: }
233: MatSeqAIJRestoreArrayRead(A,(const PetscScalar**)&aav);
234: return(0);
235: }
238: static PetscErrorCode MatMKLPardisoSolveSchur_Private(Mat F, PetscScalar *B, PetscScalar *X)
239: {
240: Mat_MKL_PARDISO *mpardiso = (Mat_MKL_PARDISO*)F->data;
241: Mat S,Xmat,Bmat;
242: MatFactorSchurStatus schurstatus;
243: PetscErrorCode ierr;
246: MatFactorFactorizeSchurComplement(F);
247: MatFactorGetSchurComplement(F,&S,&schurstatus);
248: if (X == B && schurstatus == MAT_FACTOR_SCHUR_INVERTED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"X and B cannot point to the same address");
249: MatCreateSeqDense(PETSC_COMM_SELF,mpardiso->schur_size,mpardiso->nrhs,B,&Bmat);
250: MatCreateSeqDense(PETSC_COMM_SELF,mpardiso->schur_size,mpardiso->nrhs,X,&Xmat);
251: MatSetType(Bmat,((PetscObject)S)->type_name);
252: MatSetType(Xmat,((PetscObject)S)->type_name);
253: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
254: MatBindToCPU(Xmat,S->boundtocpu);
255: MatBindToCPU(Bmat,S->boundtocpu);
256: #endif
258: #if defined(PETSC_USE_COMPLEX)
259: if (mpardiso->iparm[12-1] == 1) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Hermitian solve not implemented yet");
260: #endif
262: switch (schurstatus) {
263: case MAT_FACTOR_SCHUR_FACTORED:
264: if (!mpardiso->iparm[12-1]) {
265: MatMatSolve(S,Bmat,Xmat);
266: } else { /* transpose solve */
267: MatMatSolveTranspose(S,Bmat,Xmat);
268: }
269: break;
270: case MAT_FACTOR_SCHUR_INVERTED:
271: if (!mpardiso->iparm[12-1]) {
272: MatMatMult(S,Bmat,MAT_REUSE_MATRIX,PETSC_DEFAULT,&Xmat);
273: } else { /* transpose solve */
274: MatTransposeMatMult(S,Bmat,MAT_REUSE_MATRIX,PETSC_DEFAULT,&Xmat);
275: }
276: break;
277: default:
278: SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
279: break;
280: }
281: MatFactorRestoreSchurComplement(F,&S,schurstatus);
282: MatDestroy(&Bmat);
283: MatDestroy(&Xmat);
284: return(0);
285: }
287: 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;
295: PetscErrorCode ierr;
298: MPI_Comm_size(PetscObjectComm((PetscObject)F),&csize);
299: if (csize > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MKL_PARDISO parallel Schur complements not yet supported from PETSc");
300: ISSorted(is,&sorted);
301: if (!sorted) {
302: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"IS for MKL_PARDISO Schur complements needs to be sorted");
303: }
304: ISGetLocalSize(is,&size);
305: PetscFree(mpardiso->schur_work);
306: PetscBLASIntCast(PetscMax(mpardiso->n,2*size),&mpardiso->schur_work_size);
307: PetscMalloc1(mpardiso->schur_work_size,&mpardiso->schur_work);
308: MatDestroy(&F->schur);
309: MatCreateSeqDense(PETSC_COMM_SELF,size,size,NULL,&F->schur);
310: MatDenseGetArrayRead(F->schur,&arr);
311: mpardiso->schur = (PetscScalar*)arr;
312: mpardiso->schur_size = size;
313: MatDenseRestoreArrayRead(F->schur,&arr);
314: if (mpardiso->mtype == 2) {
315: MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);
316: }
318: PetscFree(mpardiso->schur_idxs);
319: PetscMalloc1(size,&mpardiso->schur_idxs);
320: PetscArrayzero(mpardiso->perm,mpardiso->n);
321: ISGetIndices(is,&idxs);
322: PetscArraycpy(mpardiso->schur_idxs,idxs,size);
323: for (i=0;i<size;i++) mpardiso->perm[idxs[i]] = 1;
324: ISRestoreIndices(is,&idxs);
325: if (size) { /* turn on Schur switch if the set of indices is not empty */
326: mpardiso->iparm[36-1] = 2;
327: }
328: return(0);
329: }
331: PetscErrorCode MatDestroy_MKL_PARDISO(Mat A)
332: {
333: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
334: PetscErrorCode ierr;
337: if (mat_mkl_pardiso->CleanUp) {
338: mat_mkl_pardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;
340: MKL_PARDISO (mat_mkl_pardiso->pt,
341: &mat_mkl_pardiso->maxfct,
342: &mat_mkl_pardiso->mnum,
343: &mat_mkl_pardiso->mtype,
344: &mat_mkl_pardiso->phase,
345: &mat_mkl_pardiso->n,
346: NULL,
347: NULL,
348: NULL,
349: NULL,
350: &mat_mkl_pardiso->nrhs,
351: mat_mkl_pardiso->iparm,
352: &mat_mkl_pardiso->msglvl,
353: NULL,
354: NULL,
355: &mat_mkl_pardiso->err);
356: }
357: PetscFree(mat_mkl_pardiso->perm);
358: PetscFree(mat_mkl_pardiso->schur_work);
359: PetscFree(mat_mkl_pardiso->schur_idxs);
360: if (mat_mkl_pardiso->freeaij) {
361: PetscFree2(mat_mkl_pardiso->ia,mat_mkl_pardiso->ja);
362: if (mat_mkl_pardiso->iparm[34] == 1) {
363: PetscFree(mat_mkl_pardiso->a);
364: }
365: }
366: PetscFree(A->data);
368: /* clear composed functions */
369: PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
370: PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);
371: PetscObjectComposeFunction((PetscObject)A,"MatMkl_PardisoSetCntl_C",NULL);
372: return(0);
373: }
375: static PetscErrorCode MatMKLPardisoScatterSchur_Private(Mat_MKL_PARDISO *mpardiso, PetscScalar *whole, PetscScalar *schur, PetscBool reduce)
376: {
378: if (reduce) { /* data given for the whole matrix */
379: PetscInt i,m=0,p=0;
380: for (i=0;i<mpardiso->nrhs;i++) {
381: PetscInt j;
382: for (j=0;j<mpardiso->schur_size;j++) {
383: schur[p+j] = whole[m+mpardiso->schur_idxs[j]];
384: }
385: m += mpardiso->n;
386: p += mpardiso->schur_size;
387: }
388: } else { /* from Schur to whole */
389: PetscInt i,m=0,p=0;
390: for (i=0;i<mpardiso->nrhs;i++) {
391: PetscInt j;
392: for (j=0;j<mpardiso->schur_size;j++) {
393: whole[m+mpardiso->schur_idxs[j]] = schur[p+j];
394: }
395: m += mpardiso->n;
396: p += mpardiso->schur_size;
397: }
398: }
399: return(0);
400: }
402: PetscErrorCode MatSolve_MKL_PARDISO(Mat A,Vec b,Vec x)
403: {
404: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
405: PetscErrorCode ierr;
406: PetscScalar *xarray;
407: const PetscScalar *barray;
410: mat_mkl_pardiso->nrhs = 1;
411: VecGetArray(x,&xarray);
412: VecGetArrayRead(b,&barray);
414: if (!mat_mkl_pardiso->schur) mat_mkl_pardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
415: else mat_mkl_pardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;
417: if (barray == xarray) { /* if the two vectors share the same memory */
418: PetscScalar *work;
419: if (!mat_mkl_pardiso->schur_work) {
420: PetscMalloc1(mat_mkl_pardiso->n,&work);
421: } else {
422: work = mat_mkl_pardiso->schur_work;
423: }
424: mat_mkl_pardiso->iparm[6-1] = 1;
425: MKL_PARDISO (mat_mkl_pardiso->pt,
426: &mat_mkl_pardiso->maxfct,
427: &mat_mkl_pardiso->mnum,
428: &mat_mkl_pardiso->mtype,
429: &mat_mkl_pardiso->phase,
430: &mat_mkl_pardiso->n,
431: mat_mkl_pardiso->a,
432: mat_mkl_pardiso->ia,
433: mat_mkl_pardiso->ja,
434: NULL,
435: &mat_mkl_pardiso->nrhs,
436: mat_mkl_pardiso->iparm,
437: &mat_mkl_pardiso->msglvl,
438: (void*)xarray,
439: (void*)work,
440: &mat_mkl_pardiso->err);
441: if (!mat_mkl_pardiso->schur_work) {
442: PetscFree(work);
443: }
444: } else {
445: mat_mkl_pardiso->iparm[6-1] = 0;
446: MKL_PARDISO (mat_mkl_pardiso->pt,
447: &mat_mkl_pardiso->maxfct,
448: &mat_mkl_pardiso->mnum,
449: &mat_mkl_pardiso->mtype,
450: &mat_mkl_pardiso->phase,
451: &mat_mkl_pardiso->n,
452: mat_mkl_pardiso->a,
453: mat_mkl_pardiso->ia,
454: mat_mkl_pardiso->ja,
455: mat_mkl_pardiso->perm,
456: &mat_mkl_pardiso->nrhs,
457: mat_mkl_pardiso->iparm,
458: &mat_mkl_pardiso->msglvl,
459: (void*)barray,
460: (void*)xarray,
461: &mat_mkl_pardiso->err);
462: }
463: VecRestoreArrayRead(b,&barray);
465: if (mat_mkl_pardiso->err < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_PARDISO: err=%d. Please check manual",mat_mkl_pardiso->err);
467: if (mat_mkl_pardiso->schur) { /* solve Schur complement and expand solution */
468: PetscInt shift = mat_mkl_pardiso->schur_size;
470: /* if inverted, uses BLAS *MM subroutines, otherwise LAPACK *TRS */
471: if (A->schur_status != MAT_FACTOR_SCHUR_INVERTED) shift = 0;
473: if (!mat_mkl_pardiso->solve_interior) {
474: /* solve Schur complement */
475: MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work,PETSC_TRUE);
476: MatMKLPardisoSolveSchur_Private(A,mat_mkl_pardiso->schur_work,mat_mkl_pardiso->schur_work+shift);
477: MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work+shift,PETSC_FALSE);
478: } else { /* if we are solving for the interior problem, any value in barray[schur] forward-substituted to xarray[schur] will be neglected */
479: PetscInt i;
480: for (i=0;i<mat_mkl_pardiso->schur_size;i++) {
481: xarray[mat_mkl_pardiso->schur_idxs[i]] = 0.;
482: }
483: }
485: /* expansion phase */
486: mat_mkl_pardiso->iparm[6-1] = 1;
487: mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
488: MKL_PARDISO (mat_mkl_pardiso->pt,
489: &mat_mkl_pardiso->maxfct,
490: &mat_mkl_pardiso->mnum,
491: &mat_mkl_pardiso->mtype,
492: &mat_mkl_pardiso->phase,
493: &mat_mkl_pardiso->n,
494: mat_mkl_pardiso->a,
495: mat_mkl_pardiso->ia,
496: mat_mkl_pardiso->ja,
497: mat_mkl_pardiso->perm,
498: &mat_mkl_pardiso->nrhs,
499: mat_mkl_pardiso->iparm,
500: &mat_mkl_pardiso->msglvl,
501: (void*)xarray,
502: (void*)mat_mkl_pardiso->schur_work, /* according to the specs, the solution vector is always used */
503: &mat_mkl_pardiso->err);
505: if (mat_mkl_pardiso->err < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_PARDISO: err=%d. Please check manual",mat_mkl_pardiso->err);
506: mat_mkl_pardiso->iparm[6-1] = 0;
507: }
508: VecRestoreArray(x,&xarray);
509: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
510: return(0);
511: }
513: PetscErrorCode MatSolveTranspose_MKL_PARDISO(Mat A,Vec b,Vec x)
514: {
515: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
516: PetscInt oiparm12;
517: PetscErrorCode ierr;
520: oiparm12 = mat_mkl_pardiso->iparm[12 - 1];
521: mat_mkl_pardiso->iparm[12 - 1] = 2;
522: MatSolve_MKL_PARDISO(A,b,x);
523: mat_mkl_pardiso->iparm[12 - 1] = oiparm12;
524: return(0);
525: }
527: PetscErrorCode MatMatSolve_MKL_PARDISO(Mat A,Mat B,Mat X)
528: {
529: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)(A)->data;
530: PetscErrorCode ierr;
531: const PetscScalar *barray;
532: PetscScalar *xarray;
533: PetscBool flg;
536: PetscObjectBaseTypeCompare((PetscObject)B,MATSEQDENSE,&flg);
537: if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATSEQDENSE matrix");
538: if (X != B) {
539: PetscObjectBaseTypeCompare((PetscObject)X,MATSEQDENSE,&flg);
540: if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATSEQDENSE matrix");
541: }
543: MatGetSize(B,NULL,(PetscInt*)&mat_mkl_pardiso->nrhs);
545: if (mat_mkl_pardiso->nrhs > 0) {
546: MatDenseGetArrayRead(B,&barray);
547: MatDenseGetArray(X,&xarray);
549: if (barray == xarray) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"B and X cannot share the same memory location");
550: if (!mat_mkl_pardiso->schur) mat_mkl_pardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
551: else mat_mkl_pardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;
553: MKL_PARDISO (mat_mkl_pardiso->pt,
554: &mat_mkl_pardiso->maxfct,
555: &mat_mkl_pardiso->mnum,
556: &mat_mkl_pardiso->mtype,
557: &mat_mkl_pardiso->phase,
558: &mat_mkl_pardiso->n,
559: mat_mkl_pardiso->a,
560: mat_mkl_pardiso->ia,
561: mat_mkl_pardiso->ja,
562: mat_mkl_pardiso->perm,
563: &mat_mkl_pardiso->nrhs,
564: mat_mkl_pardiso->iparm,
565: &mat_mkl_pardiso->msglvl,
566: (void*)barray,
567: (void*)xarray,
568: &mat_mkl_pardiso->err);
569: if (mat_mkl_pardiso->err < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_PARDISO: err=%d. Please check manual",mat_mkl_pardiso->err);
571: MatDenseRestoreArrayRead(B,&barray);
572: if (mat_mkl_pardiso->schur) { /* solve Schur complement and expand solution */
573: PetscScalar *o_schur_work = NULL;
574: PetscInt shift = mat_mkl_pardiso->schur_size*mat_mkl_pardiso->nrhs,scale;
575: PetscInt mem = mat_mkl_pardiso->n*mat_mkl_pardiso->nrhs;
577: /* allocate extra memory if it is needed */
578: scale = 1;
579: if (A->schur_status == MAT_FACTOR_SCHUR_INVERTED) scale = 2;
581: mem *= scale;
582: if (mem > mat_mkl_pardiso->schur_work_size) {
583: o_schur_work = mat_mkl_pardiso->schur_work;
584: PetscMalloc1(mem,&mat_mkl_pardiso->schur_work);
585: }
587: /* if inverted, uses BLAS *MM subroutines, otherwise LAPACK *TRS */
588: if (A->schur_status != MAT_FACTOR_SCHUR_INVERTED) shift = 0;
590: /* solve Schur complement */
591: if (!mat_mkl_pardiso->solve_interior) {
592: MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work,PETSC_TRUE);
593: MatMKLPardisoSolveSchur_Private(A,mat_mkl_pardiso->schur_work,mat_mkl_pardiso->schur_work+shift);
594: MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work+shift,PETSC_FALSE);
595: } else { /* if we are solving for the interior problem, any value in barray[schur,n] forward-substituted to xarray[schur,n] will be neglected */
596: PetscInt i,n,m=0;
597: for (n=0;n<mat_mkl_pardiso->nrhs;n++) {
598: for (i=0;i<mat_mkl_pardiso->schur_size;i++) {
599: xarray[mat_mkl_pardiso->schur_idxs[i]+m] = 0.;
600: }
601: m += mat_mkl_pardiso->n;
602: }
603: }
605: /* expansion phase */
606: mat_mkl_pardiso->iparm[6-1] = 1;
607: mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
608: MKL_PARDISO (mat_mkl_pardiso->pt,
609: &mat_mkl_pardiso->maxfct,
610: &mat_mkl_pardiso->mnum,
611: &mat_mkl_pardiso->mtype,
612: &mat_mkl_pardiso->phase,
613: &mat_mkl_pardiso->n,
614: mat_mkl_pardiso->a,
615: mat_mkl_pardiso->ia,
616: mat_mkl_pardiso->ja,
617: mat_mkl_pardiso->perm,
618: &mat_mkl_pardiso->nrhs,
619: mat_mkl_pardiso->iparm,
620: &mat_mkl_pardiso->msglvl,
621: (void*)xarray,
622: (void*)mat_mkl_pardiso->schur_work, /* according to the specs, the solution vector is always used */
623: &mat_mkl_pardiso->err);
624: if (o_schur_work) { /* restore original schur_work (minimal size) */
625: PetscFree(mat_mkl_pardiso->schur_work);
626: mat_mkl_pardiso->schur_work = o_schur_work;
627: }
628: if (mat_mkl_pardiso->err < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_PARDISO: err=%d. Please check manual",mat_mkl_pardiso->err);
629: mat_mkl_pardiso->iparm[6-1] = 0;
630: }
631: }
632: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
633: return(0);
634: }
636: PetscErrorCode MatFactorNumeric_MKL_PARDISO(Mat F,Mat A,const MatFactorInfo *info)
637: {
638: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)(F)->data;
639: PetscErrorCode ierr;
642: mat_mkl_pardiso->matstruc = SAME_NONZERO_PATTERN;
643: (*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);
645: mat_mkl_pardiso->phase = JOB_NUMERICAL_FACTORIZATION;
646: MKL_PARDISO (mat_mkl_pardiso->pt,
647: &mat_mkl_pardiso->maxfct,
648: &mat_mkl_pardiso->mnum,
649: &mat_mkl_pardiso->mtype,
650: &mat_mkl_pardiso->phase,
651: &mat_mkl_pardiso->n,
652: mat_mkl_pardiso->a,
653: mat_mkl_pardiso->ia,
654: mat_mkl_pardiso->ja,
655: mat_mkl_pardiso->perm,
656: &mat_mkl_pardiso->nrhs,
657: mat_mkl_pardiso->iparm,
658: &mat_mkl_pardiso->msglvl,
659: NULL,
660: (void*)mat_mkl_pardiso->schur,
661: &mat_mkl_pardiso->err);
662: if (mat_mkl_pardiso->err < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_PARDISO: err=%d. Please check manual",mat_mkl_pardiso->err);
664: /* report flops */
665: if (mat_mkl_pardiso->iparm[18] > 0) {
666: PetscLogFlops(PetscPowRealInt(10.,6)*mat_mkl_pardiso->iparm[18]);
667: }
669: if (F->schur) { /* schur output from pardiso is in row major format */
670: #if defined(PETSC_HAVE_CUDA)
671: F->schur->offloadmask = PETSC_OFFLOAD_CPU;
672: #endif
673: MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED);
674: MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur);
675: }
676: mat_mkl_pardiso->matstruc = SAME_NONZERO_PATTERN;
677: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
678: return(0);
679: }
681: PetscErrorCode PetscSetMKL_PARDISOFromOptions(Mat F, Mat A)
682: {
683: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;
684: PetscErrorCode ierr;
685: PetscInt icntl,threads=1;
686: PetscBool flg;
689: PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MKL_PARDISO Options","Mat");
691: PetscOptionsInt("-mat_mkl_pardiso_65","Number of threads to use within PARDISO","None",threads,&threads,&flg);
692: if (flg) PetscSetMKL_PARDISOThreads((int)threads);
694: 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);
695: if (flg) mat_mkl_pardiso->maxfct = icntl;
697: PetscOptionsInt("-mat_mkl_pardiso_67","Indicates the actual matrix for the solution phase","None",mat_mkl_pardiso->mnum,&icntl,&flg);
698: if (flg) mat_mkl_pardiso->mnum = icntl;
700: PetscOptionsInt("-mat_mkl_pardiso_68","Message level information","None",mat_mkl_pardiso->msglvl,&icntl,&flg);
701: if (flg) mat_mkl_pardiso->msglvl = icntl;
703: PetscOptionsInt("-mat_mkl_pardiso_69","Defines the matrix type","None",mat_mkl_pardiso->mtype,&icntl,&flg);
704: if (flg) {
705: void *pt[IPARM_SIZE];
706: mat_mkl_pardiso->mtype = icntl;
707: MKL_PARDISO_INIT(pt, &mat_mkl_pardiso->mtype, mat_mkl_pardiso->iparm);
708: #if defined(PETSC_USE_REAL_SINGLE)
709: mat_mkl_pardiso->iparm[27] = 1;
710: #else
711: mat_mkl_pardiso->iparm[27] = 0;
712: #endif
713: mat_mkl_pardiso->iparm[34] = 1; /* use 0-based indexing */
714: }
715: PetscOptionsInt("-mat_mkl_pardiso_1","Use default values (if 0)","None",mat_mkl_pardiso->iparm[0],&icntl,&flg);
717: if (flg && icntl != 0) {
718: PetscOptionsInt("-mat_mkl_pardiso_2","Fill-in reducing ordering for the input matrix","None",mat_mkl_pardiso->iparm[1],&icntl,&flg);
719: if (flg) mat_mkl_pardiso->iparm[1] = icntl;
721: PetscOptionsInt("-mat_mkl_pardiso_4","Preconditioned CGS/CG","None",mat_mkl_pardiso->iparm[3],&icntl,&flg);
722: if (flg) mat_mkl_pardiso->iparm[3] = icntl;
724: PetscOptionsInt("-mat_mkl_pardiso_5","User permutation","None",mat_mkl_pardiso->iparm[4],&icntl,&flg);
725: if (flg) mat_mkl_pardiso->iparm[4] = icntl;
727: PetscOptionsInt("-mat_mkl_pardiso_6","Write solution on x","None",mat_mkl_pardiso->iparm[5],&icntl,&flg);
728: if (flg) mat_mkl_pardiso->iparm[5] = icntl;
730: PetscOptionsInt("-mat_mkl_pardiso_8","Iterative refinement step","None",mat_mkl_pardiso->iparm[7],&icntl,&flg);
731: if (flg) mat_mkl_pardiso->iparm[7] = icntl;
733: PetscOptionsInt("-mat_mkl_pardiso_10","Pivoting perturbation","None",mat_mkl_pardiso->iparm[9],&icntl,&flg);
734: if (flg) mat_mkl_pardiso->iparm[9] = icntl;
736: PetscOptionsInt("-mat_mkl_pardiso_11","Scaling vectors","None",mat_mkl_pardiso->iparm[10],&icntl,&flg);
737: if (flg) mat_mkl_pardiso->iparm[10] = icntl;
739: PetscOptionsInt("-mat_mkl_pardiso_12","Solve with transposed or conjugate transposed matrix A","None",mat_mkl_pardiso->iparm[11],&icntl,&flg);
740: if (flg) mat_mkl_pardiso->iparm[11] = icntl;
742: PetscOptionsInt("-mat_mkl_pardiso_13","Improved accuracy using (non-) symmetric weighted matching","None",mat_mkl_pardiso->iparm[12],&icntl,&flg);
743: if (flg) mat_mkl_pardiso->iparm[12] = icntl;
745: PetscOptionsInt("-mat_mkl_pardiso_18","Numbers of non-zero elements","None",mat_mkl_pardiso->iparm[17],&icntl,&flg);
746: if (flg) mat_mkl_pardiso->iparm[17] = icntl;
748: PetscOptionsInt("-mat_mkl_pardiso_19","Report number of floating point operations (0 to disable)","None",mat_mkl_pardiso->iparm[18],&icntl,&flg);
749: if (flg) mat_mkl_pardiso->iparm[18] = icntl;
751: PetscOptionsInt("-mat_mkl_pardiso_21","Pivoting for symmetric indefinite matrices","None",mat_mkl_pardiso->iparm[20],&icntl,&flg);
752: if (flg) mat_mkl_pardiso->iparm[20] = icntl;
754: PetscOptionsInt("-mat_mkl_pardiso_24","Parallel factorization control","None",mat_mkl_pardiso->iparm[23],&icntl,&flg);
755: if (flg) mat_mkl_pardiso->iparm[23] = icntl;
757: PetscOptionsInt("-mat_mkl_pardiso_25","Parallel forward/backward solve control","None",mat_mkl_pardiso->iparm[24],&icntl,&flg);
758: if (flg) mat_mkl_pardiso->iparm[24] = icntl;
760: PetscOptionsInt("-mat_mkl_pardiso_27","Matrix checker","None",mat_mkl_pardiso->iparm[26],&icntl,&flg);
761: if (flg) mat_mkl_pardiso->iparm[26] = icntl;
763: PetscOptionsInt("-mat_mkl_pardiso_31","Partial solve and computing selected components of the solution vectors","None",mat_mkl_pardiso->iparm[30],&icntl,&flg);
764: if (flg) mat_mkl_pardiso->iparm[30] = icntl;
766: PetscOptionsInt("-mat_mkl_pardiso_34","Optimal number of threads for conditional numerical reproducibility (CNR) mode","None",mat_mkl_pardiso->iparm[33],&icntl,&flg);
767: if (flg) mat_mkl_pardiso->iparm[33] = icntl;
769: PetscOptionsInt("-mat_mkl_pardiso_60","Intel MKL_PARDISO mode","None",mat_mkl_pardiso->iparm[59],&icntl,&flg);
770: if (flg) mat_mkl_pardiso->iparm[59] = icntl;
771: }
772: PetscOptionsEnd();
773: return(0);
774: }
776: PetscErrorCode MatFactorMKL_PARDISOInitialize_Private(Mat A, MatFactorType ftype, Mat_MKL_PARDISO *mat_mkl_pardiso)
777: {
779: PetscInt i,bs;
780: PetscBool match;
783: for (i=0; i<IPARM_SIZE; i++) mat_mkl_pardiso->iparm[i] = 0;
784: for (i=0; i<IPARM_SIZE; i++) mat_mkl_pardiso->pt[i] = 0;
785: /* Default options for both sym and unsym */
786: mat_mkl_pardiso->iparm[ 0] = 1; /* Solver default parameters overriden with provided by iparm */
787: mat_mkl_pardiso->iparm[ 1] = 2; /* Metis reordering */
788: mat_mkl_pardiso->iparm[ 5] = 0; /* Write solution into x */
789: mat_mkl_pardiso->iparm[ 7] = 0; /* Max number of iterative refinement steps */
790: mat_mkl_pardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
791: mat_mkl_pardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
792: #if 0
793: mat_mkl_pardiso->iparm[23] = 1; /* Parallel factorization control*/
794: #endif
795: PetscObjectTypeCompareAny((PetscObject)A,&match,MATSEQBAIJ,MATSEQSBAIJ,"");
796: MatGetBlockSize(A,&bs);
797: if (!match || bs == 1) {
798: mat_mkl_pardiso->iparm[34] = 1; /* Cluster Sparse Solver use C-style indexing for ia and ja arrays */
799: mat_mkl_pardiso->n = A->rmap->N;
800: } else {
801: mat_mkl_pardiso->iparm[34] = 0; /* Cluster Sparse Solver use Fortran-style indexing for ia and ja arrays */
802: mat_mkl_pardiso->iparm[36] = bs;
803: mat_mkl_pardiso->n = A->rmap->N/bs;
804: }
805: mat_mkl_pardiso->iparm[39] = 0; /* Input: matrix/rhs/solution stored on master */
807: mat_mkl_pardiso->CleanUp = PETSC_FALSE;
808: mat_mkl_pardiso->maxfct = 1; /* Maximum number of numerical factorizations. */
809: mat_mkl_pardiso->mnum = 1; /* Which factorization to use. */
810: mat_mkl_pardiso->msglvl = 0; /* 0: do not print 1: Print statistical information in file */
811: mat_mkl_pardiso->phase = -1;
812: mat_mkl_pardiso->err = 0;
814: mat_mkl_pardiso->nrhs = 1;
815: mat_mkl_pardiso->err = 0;
816: mat_mkl_pardiso->phase = -1;
818: if (ftype == MAT_FACTOR_LU) {
819: mat_mkl_pardiso->iparm[ 9] = 13; /* Perturb the pivot elements with 1E-13 */
820: mat_mkl_pardiso->iparm[10] = 1; /* Use nonsymmetric permutation and scaling MPS */
821: mat_mkl_pardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
822: } else {
823: mat_mkl_pardiso->iparm[ 9] = 8; /* Perturb the pivot elements with 1E-8 */
824: mat_mkl_pardiso->iparm[10] = 0; /* Use nonsymmetric permutation and scaling MPS */
825: mat_mkl_pardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
826: #if defined(PETSC_USE_DEBUG)
827: mat_mkl_pardiso->iparm[26] = 1; /* Matrix checker */
828: #endif
829: }
830: PetscMalloc1(A->rmap->N*sizeof(INT_TYPE), &mat_mkl_pardiso->perm);
831: for (i=0; i<A->rmap->N; i++) {
832: mat_mkl_pardiso->perm[i] = 0;
833: }
834: mat_mkl_pardiso->schur_size = 0;
835: return(0);
836: }
838: PetscErrorCode MatFactorSymbolic_AIJMKL_PARDISO_Private(Mat F,Mat A,const MatFactorInfo *info)
839: {
840: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;
841: PetscErrorCode ierr;
844: mat_mkl_pardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
845: PetscSetMKL_PARDISOFromOptions(F,A);
847: /* throw away any previously computed structure */
848: if (mat_mkl_pardiso->freeaij) {
849: PetscFree2(mat_mkl_pardiso->ia,mat_mkl_pardiso->ja);
850: if (mat_mkl_pardiso->iparm[34] == 1) {
851: PetscFree(mat_mkl_pardiso->a);
852: }
853: }
854: (*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);
855: if (mat_mkl_pardiso->iparm[34] == 1) mat_mkl_pardiso->n = A->rmap->N;
856: else mat_mkl_pardiso->n = A->rmap->N/A->rmap->bs;
858: mat_mkl_pardiso->phase = JOB_ANALYSIS;
860: /* reset flops counting if requested */
861: if (mat_mkl_pardiso->iparm[18]) mat_mkl_pardiso->iparm[18] = -1;
863: MKL_PARDISO (mat_mkl_pardiso->pt,
864: &mat_mkl_pardiso->maxfct,
865: &mat_mkl_pardiso->mnum,
866: &mat_mkl_pardiso->mtype,
867: &mat_mkl_pardiso->phase,
868: &mat_mkl_pardiso->n,
869: mat_mkl_pardiso->a,
870: mat_mkl_pardiso->ia,
871: mat_mkl_pardiso->ja,
872: mat_mkl_pardiso->perm,
873: &mat_mkl_pardiso->nrhs,
874: mat_mkl_pardiso->iparm,
875: &mat_mkl_pardiso->msglvl,
876: NULL,
877: NULL,
878: &mat_mkl_pardiso->err);
879: if (mat_mkl_pardiso->err < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_PARDISO: err=%d. Please check manual",mat_mkl_pardiso->err);
881: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
883: if (F->factortype == MAT_FACTOR_LU) F->ops->lufactornumeric = MatFactorNumeric_MKL_PARDISO;
884: else F->ops->choleskyfactornumeric = MatFactorNumeric_MKL_PARDISO;
886: F->ops->solve = MatSolve_MKL_PARDISO;
887: F->ops->solvetranspose = MatSolveTranspose_MKL_PARDISO;
888: F->ops->matsolve = MatMatSolve_MKL_PARDISO;
889: return(0);
890: }
892: PetscErrorCode MatLUFactorSymbolic_AIJMKL_PARDISO(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
893: {
897: MatFactorSymbolic_AIJMKL_PARDISO_Private(F, A, info);
898: return(0);
899: }
901: #if !defined(PETSC_USE_COMPLEX)
902: PetscErrorCode MatGetInertia_MKL_PARDISO(Mat F,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
903: {
904: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)F->data;
907: if (nneg) *nneg = mat_mkl_pardiso->iparm[22];
908: if (npos) *npos = mat_mkl_pardiso->iparm[21];
909: if (nzero) *nzero = F->rmap->N - (mat_mkl_pardiso->iparm[22] + mat_mkl_pardiso->iparm[21]);
910: return(0);
911: }
912: #endif
914: PetscErrorCode MatCholeskyFactorSymbolic_AIJMKL_PARDISO(Mat F,Mat A,IS r,const MatFactorInfo *info)
915: {
919: MatFactorSymbolic_AIJMKL_PARDISO_Private(F, A, info);
920: #if defined(PETSC_USE_COMPLEX)
921: F->ops->getinertia = NULL;
922: #else
923: F->ops->getinertia = MatGetInertia_MKL_PARDISO;
924: #endif
925: return(0);
926: }
928: PetscErrorCode MatView_MKL_PARDISO(Mat A, PetscViewer viewer)
929: {
930: PetscErrorCode ierr;
931: PetscBool iascii;
932: PetscViewerFormat format;
933: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
934: PetscInt i;
937: if (A->ops->solve != MatSolve_MKL_PARDISO) return(0);
939: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
940: if (iascii) {
941: PetscViewerGetFormat(viewer,&format);
942: if (format == PETSC_VIEWER_ASCII_INFO) {
943: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO run parameters:\n");
944: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO phase: %d \n",mat_mkl_pardiso->phase);
945: for (i=1; i<=64; i++) {
946: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO iparm[%d]: %d \n",i, mat_mkl_pardiso->iparm[i - 1]);
947: }
948: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO maxfct: %d \n", mat_mkl_pardiso->maxfct);
949: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO mnum: %d \n", mat_mkl_pardiso->mnum);
950: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO mtype: %d \n", mat_mkl_pardiso->mtype);
951: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO n: %d \n", mat_mkl_pardiso->n);
952: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO nrhs: %d \n", mat_mkl_pardiso->nrhs);
953: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO msglvl: %d \n", mat_mkl_pardiso->msglvl);
954: }
955: }
956: return(0);
957: }
960: PetscErrorCode MatGetInfo_MKL_PARDISO(Mat A, MatInfoType flag, MatInfo *info)
961: {
962: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)A->data;
965: info->block_size = 1.0;
966: info->nz_used = mat_mkl_pardiso->iparm[17];
967: info->nz_allocated = mat_mkl_pardiso->iparm[17];
968: info->nz_unneeded = 0.0;
969: info->assemblies = 0.0;
970: info->mallocs = 0.0;
971: info->memory = 0.0;
972: info->fill_ratio_given = 0;
973: info->fill_ratio_needed = 0;
974: info->factor_mallocs = 0;
975: return(0);
976: }
978: PetscErrorCode MatMkl_PardisoSetCntl_MKL_PARDISO(Mat F,PetscInt icntl,PetscInt ival)
979: {
980: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;
983: if (icntl <= 64) {
984: mat_mkl_pardiso->iparm[icntl - 1] = ival;
985: } else {
986: if (icntl == 65) PetscSetMKL_PARDISOThreads(ival);
987: else if (icntl == 66) mat_mkl_pardiso->maxfct = ival;
988: else if (icntl == 67) mat_mkl_pardiso->mnum = ival;
989: else if (icntl == 68) mat_mkl_pardiso->msglvl = ival;
990: else if (icntl == 69) {
991: void *pt[IPARM_SIZE];
992: mat_mkl_pardiso->mtype = ival;
993: MKL_PARDISO_INIT(pt, &mat_mkl_pardiso->mtype, mat_mkl_pardiso->iparm);
994: #if defined(PETSC_USE_REAL_SINGLE)
995: mat_mkl_pardiso->iparm[27] = 1;
996: #else
997: mat_mkl_pardiso->iparm[27] = 0;
998: #endif
999: mat_mkl_pardiso->iparm[34] = 1;
1000: } else if (icntl==70) mat_mkl_pardiso->solve_interior = (PetscBool)!!ival;
1001: }
1002: return(0);
1003: }
1005: /*@
1006: MatMkl_PardisoSetCntl - Set Mkl_Pardiso parameters
1008: Logically Collective on Mat
1010: Input Parameters:
1011: + F - the factored matrix obtained by calling MatGetFactor()
1012: . icntl - index of Mkl_Pardiso parameter
1013: - ival - value of Mkl_Pardiso parameter
1015: Options Database:
1016: . -mat_mkl_pardiso_<icntl> <ival>
1018: Level: beginner
1020: References:
1021: . Mkl_Pardiso Users' Guide
1023: .seealso: MatGetFactor()
1024: @*/
1025: PetscErrorCode MatMkl_PardisoSetCntl(Mat F,PetscInt icntl,PetscInt ival)
1026: {
1030: PetscTryMethod(F,"MatMkl_PardisoSetCntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
1031: return(0);
1032: }
1034: /*MC
1035: MATSOLVERMKL_PARDISO - A matrix type providing direct solvers (LU) for
1036: sequential matrices via the external package MKL_PARDISO.
1038: Works with MATSEQAIJ matrices
1040: Use -pc_type lu -pc_factor_mat_solver_type mkl_pardiso to use this direct solver
1042: Options Database Keys:
1043: + -mat_mkl_pardiso_65 - Number of threads to use within MKL_PARDISO
1044: . -mat_mkl_pardiso_66 - Maximum number of factors with identical sparsity structure that must be kept in memory at the same time
1045: . -mat_mkl_pardiso_67 - Indicates the actual matrix for the solution phase
1046: . -mat_mkl_pardiso_68 - Message level information
1047: . -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
1048: . -mat_mkl_pardiso_1 - Use default values
1049: . -mat_mkl_pardiso_2 - Fill-in reducing ordering for the input matrix
1050: . -mat_mkl_pardiso_4 - Preconditioned CGS/CG
1051: . -mat_mkl_pardiso_5 - User permutation
1052: . -mat_mkl_pardiso_6 - Write solution on x
1053: . -mat_mkl_pardiso_8 - Iterative refinement step
1054: . -mat_mkl_pardiso_10 - Pivoting perturbation
1055: . -mat_mkl_pardiso_11 - Scaling vectors
1056: . -mat_mkl_pardiso_12 - Solve with transposed or conjugate transposed matrix A
1057: . -mat_mkl_pardiso_13 - Improved accuracy using (non-) symmetric weighted matching
1058: . -mat_mkl_pardiso_18 - Numbers of non-zero elements
1059: . -mat_mkl_pardiso_19 - Report number of floating point operations
1060: . -mat_mkl_pardiso_21 - Pivoting for symmetric indefinite matrices
1061: . -mat_mkl_pardiso_24 - Parallel factorization control
1062: . -mat_mkl_pardiso_25 - Parallel forward/backward solve control
1063: . -mat_mkl_pardiso_27 - Matrix checker
1064: . -mat_mkl_pardiso_31 - Partial solve and computing selected components of the solution vectors
1065: . -mat_mkl_pardiso_34 - Optimal number of threads for conditional numerical reproducibility (CNR) mode
1066: - -mat_mkl_pardiso_60 - Intel MKL_PARDISO mode
1068: Level: beginner
1070: For more information please check mkl_pardiso manual
1072: .seealso: PCFactorSetMatSolverType(), MatSolverType
1074: M*/
1075: static PetscErrorCode MatFactorGetSolverType_mkl_pardiso(Mat A, MatSolverType *type)
1076: {
1078: *type = MATSOLVERMKL_PARDISO;
1079: return(0);
1080: }
1082: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mkl_pardiso(Mat A,MatFactorType ftype,Mat *F)
1083: {
1084: Mat B;
1085: PetscErrorCode ierr;
1086: Mat_MKL_PARDISO *mat_mkl_pardiso;
1087: PetscBool isSeqAIJ,isSeqBAIJ,isSeqSBAIJ;
1090: PetscObjectBaseTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
1091: PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);
1092: PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);
1093: MatCreate(PetscObjectComm((PetscObject)A),&B);
1094: MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1095: PetscStrallocpy("mkl_pardiso",&((PetscObject)B)->type_name);
1096: MatSetUp(B);
1098: PetscNewLog(B,&mat_mkl_pardiso);
1099: B->data = mat_mkl_pardiso;
1101: MatFactorMKL_PARDISOInitialize_Private(A, ftype, mat_mkl_pardiso);
1102: if (ftype == MAT_FACTOR_LU) {
1103: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_PARDISO;
1104: B->factortype = MAT_FACTOR_LU;
1105: mat_mkl_pardiso->needsym = PETSC_FALSE;
1106: if (isSeqAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqaij;
1107: else if (isSeqBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqbaij;
1108: else if (isSeqSBAIJ) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO LU factor with SEQSBAIJ format! Use MAT_FACTOR_CHOLESKY instead");
1109: else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO LU with %s format",((PetscObject)A)->type_name);
1110: #if defined(PETSC_USE_COMPLEX)
1111: mat_mkl_pardiso->mtype = 13;
1112: #else
1113: mat_mkl_pardiso->mtype = 11;
1114: #endif
1115: } else {
1116: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_AIJMKL_PARDISO;
1117: B->factortype = MAT_FACTOR_CHOLESKY;
1118: if (isSeqAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqaij;
1119: else if (isSeqBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqbaij;
1120: else if (isSeqSBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqsbaij;
1121: else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO CHOLESKY with %s format",((PetscObject)A)->type_name);
1123: mat_mkl_pardiso->needsym = PETSC_TRUE;
1124: #if !defined(PETSC_USE_COMPLEX)
1125: if (A->spd_set && A->spd) mat_mkl_pardiso->mtype = 2;
1126: else mat_mkl_pardiso->mtype = -2;
1127: #else
1128: mat_mkl_pardiso->mtype = 6;
1129: if (A->hermitian) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO CHOLESKY with Hermitian matrices! Use MAT_FACTOR_LU instead");
1130: #endif
1131: }
1132: B->ops->destroy = MatDestroy_MKL_PARDISO;
1133: B->ops->view = MatView_MKL_PARDISO;
1134: B->ops->getinfo = MatGetInfo_MKL_PARDISO;
1135: B->factortype = ftype;
1136: B->assembled = PETSC_TRUE;
1138: PetscFree(B->solvertype);
1139: PetscStrallocpy(MATSOLVERMKL_PARDISO,&B->solvertype);
1141: PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mkl_pardiso);
1142: PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MKL_PARDISO);
1143: PetscObjectComposeFunction((PetscObject)B,"MatMkl_PardisoSetCntl_C",MatMkl_PardisoSetCntl_MKL_PARDISO);
1145: *F = B;
1146: return(0);
1147: }
1149: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MKL_Pardiso(void)
1150: {
1154: MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mkl_pardiso);
1155: MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mkl_pardiso);
1156: MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mkl_pardiso);
1157: MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mkl_pardiso);
1158: return(0);
1159: }