Actual source code: mkl_pardiso.c
petsc-3.12.2 2019-11-22
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) {
114: SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_PLIB,"This should not happen");
115: }
116: *v = aa->a;
117: if (bs == 1) { /* already in the correct format */
118: /* though PetscInt and INT_TYPE are of the same size since they are defined differently the Intel compiler requires a cast */
119: *r = (INT_TYPE*)aa->i;
120: *c = (INT_TYPE*)aa->j;
121: *nnz = (INT_TYPE)aa->nz;
122: *free = PETSC_FALSE;
123: } else if (reuse == MAT_INITIAL_MATRIX) {
124: PetscInt m = A->rmap->n,nz = aa->nz;
125: PetscInt *row,*col;
126: PetscMalloc2(m+1,&row,nz,&col);
127: for (i=0; i<m+1; i++) {
128: row[i] = aa->i[i]+1;
129: }
130: for (i=0; i<nz; i++) {
131: col[i] = aa->j[i]+1;
132: }
133: *r = (INT_TYPE*)row;
134: *c = (INT_TYPE*)col;
135: *nnz = (INT_TYPE)nz;
136: *free = PETSC_TRUE;
137: }
138: return(0);
139: }
141: PetscErrorCode MatMKLPardiso_Convert_seqbaij(Mat A,PetscBool sym,MatReuse reuse,PetscBool *free,INT_TYPE *nnz,INT_TYPE **r,INT_TYPE **c,PetscScalar **v)
142: {
143: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ*)A->data;
144: PetscInt bs = A->rmap->bs,i;
148: if (!sym) {
149: *v = aa->a;
150: if (bs == 1) { /* already in the correct format */
151: /* though PetscInt and INT_TYPE are of the same size since they are defined differently the Intel compiler requires a cast */
152: *r = (INT_TYPE*)aa->i;
153: *c = (INT_TYPE*)aa->j;
154: *nnz = (INT_TYPE)aa->nz;
155: *free = PETSC_FALSE;
156: return(0);
157: } else if (reuse == MAT_INITIAL_MATRIX) {
158: PetscInt m = A->rmap->n,nz = aa->nz;
159: PetscInt *row,*col;
160: PetscMalloc2(m+1,&row,nz,&col);
161: for (i=0; i<m+1; i++) {
162: row[i] = aa->i[i]+1;
163: }
164: for (i=0; i<nz; i++) {
165: col[i] = aa->j[i]+1;
166: }
167: *r = (INT_TYPE*)row;
168: *c = (INT_TYPE*)col;
169: *nnz = (INT_TYPE)nz;
170: }
171: *free = PETSC_TRUE;
172: } else {
173: SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_PLIB,"This should not happen");
174: }
175: return(0);
176: }
178: PetscErrorCode MatMKLPardiso_Convert_seqaij(Mat A,PetscBool sym,MatReuse reuse,PetscBool *free,INT_TYPE *nnz,INT_TYPE **r,INT_TYPE **c,PetscScalar **v)
179: {
180: Mat_SeqAIJ *aa = (Mat_SeqAIJ*)A->data;
181: PetscScalar *aav;
185: MatSeqAIJGetArrayRead(A,(const PetscScalar**)&aav);
186: if (!sym) { /* already in the correct format */
187: *v = aav;
188: *r = (INT_TYPE*)aa->i;
189: *c = (INT_TYPE*)aa->j;
190: *nnz = (INT_TYPE)aa->nz;
191: *free = PETSC_FALSE;
192: } else if (reuse == MAT_INITIAL_MATRIX) { /* need to get the triangular part */
193: PetscScalar *vals,*vv;
194: PetscInt *row,*col,*jj;
195: PetscInt m = A->rmap->n,nz,i;
197: nz = 0;
198: for (i=0; i<m; i++) nz += aa->i[i+1] - aa->diag[i];
199: PetscMalloc2(m+1,&row,nz,&col);
200: PetscMalloc1(nz,&vals);
201: jj = col;
202: vv = vals;
204: row[0] = 0;
205: for (i=0; i<m; i++) {
206: PetscInt *aj = aa->j + aa->diag[i];
207: PetscScalar *av = aav + aa->diag[i];
208: PetscInt rl = aa->i[i+1] - aa->diag[i],j;
210: for (j=0; j<rl; j++) {
211: *jj = *aj; jj++; aj++;
212: *vv = *av; vv++; av++;
213: }
214: row[i+1] = row[i] + rl;
215: }
216: *v = vals;
217: *r = (INT_TYPE*)row;
218: *c = (INT_TYPE*)col;
219: *nnz = (INT_TYPE)nz;
220: *free = PETSC_TRUE;
221: } else {
222: PetscScalar *vv;
223: PetscInt m = A->rmap->n,i;
225: vv = *v;
226: for (i=0; i<m; i++) {
227: PetscScalar *av = aav + aa->diag[i];
228: PetscInt rl = aa->i[i+1] - aa->diag[i],j;
229: for (j=0; j<rl; j++) {
230: *vv = *av; vv++; av++;
231: }
232: }
233: *free = PETSC_TRUE;
234: }
235: MatSeqAIJRestoreArrayRead(A,(const PetscScalar**)&aav);
236: return(0);
237: }
240: static PetscErrorCode MatMKLPardisoSolveSchur_Private(Mat F, PetscScalar *B, PetscScalar *X)
241: {
242: Mat_MKL_PARDISO *mpardiso = (Mat_MKL_PARDISO*)F->data;
243: Mat S,Xmat,Bmat;
244: MatFactorSchurStatus schurstatus;
245: PetscErrorCode ierr;
248: MatFactorFactorizeSchurComplement(F);
249: MatFactorGetSchurComplement(F,&S,&schurstatus);
250: if (X == B && schurstatus == MAT_FACTOR_SCHUR_INVERTED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"X and B cannot point to the same address");
251: MatCreateSeqDense(PETSC_COMM_SELF,mpardiso->schur_size,mpardiso->nrhs,B,&Bmat);
252: MatCreateSeqDense(PETSC_COMM_SELF,mpardiso->schur_size,mpardiso->nrhs,X,&Xmat);
253: MatSetType(Bmat,((PetscObject)S)->type_name);
254: MatSetType(Xmat,((PetscObject)S)->type_name);
255: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
256: MatPinToCPU(Xmat,S->pinnedtocpu);
257: MatPinToCPU(Bmat,S->pinnedtocpu);
258: #endif
260: #if defined(PETSC_USE_COMPLEX)
261: if (mpardiso->iparm[12-1] == 1) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Hermitian solve not implemented yet");
262: #endif
264: switch (schurstatus) {
265: case MAT_FACTOR_SCHUR_FACTORED:
266: if (!mpardiso->iparm[12-1]) {
267: MatMatSolve(S,Bmat,Xmat);
268: } else { /* transpose solve */
269: MatMatSolveTranspose(S,Bmat,Xmat);
270: }
271: break;
272: case MAT_FACTOR_SCHUR_INVERTED:
273: if (!mpardiso->iparm[12-1]) {
274: MatMatMult(S,Bmat,MAT_REUSE_MATRIX,PETSC_DEFAULT,&Xmat);
275: } else { /* transpose solve */
276: MatTransposeMatMult(S,Bmat,MAT_REUSE_MATRIX,PETSC_DEFAULT,&Xmat);
277: }
278: break;
279: default:
280: SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
281: break;
282: }
283: MatFactorRestoreSchurComplement(F,&S,schurstatus);
284: MatDestroy(&Bmat);
285: MatDestroy(&Xmat);
286: return(0);
287: }
289: PetscErrorCode MatFactorSetSchurIS_MKL_PARDISO(Mat F, IS is)
290: {
291: Mat_MKL_PARDISO *mpardiso = (Mat_MKL_PARDISO*)F->data;
292: const PetscScalar *arr;
293: const PetscInt *idxs;
294: PetscInt size,i;
295: PetscMPIInt csize;
296: PetscBool sorted;
297: PetscErrorCode ierr;
300: MPI_Comm_size(PetscObjectComm((PetscObject)F),&csize);
301: if (csize > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MKL_PARDISO parallel Schur complements not yet supported from PETSc");
302: ISSorted(is,&sorted);
303: if (!sorted) {
304: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"IS for MKL_PARDISO Schur complements needs to be sorted");
305: }
306: ISGetLocalSize(is,&size);
307: PetscFree(mpardiso->schur_work);
308: PetscBLASIntCast(PetscMax(mpardiso->n,2*size),&mpardiso->schur_work_size);
309: PetscMalloc1(mpardiso->schur_work_size,&mpardiso->schur_work);
310: MatDestroy(&F->schur);
311: MatCreateSeqDense(PETSC_COMM_SELF,size,size,NULL,&F->schur);
312: MatDenseGetArrayRead(F->schur,&arr);
313: mpardiso->schur = (PetscScalar*)arr;
314: mpardiso->schur_size = size;
315: MatDenseRestoreArrayRead(F->schur,&arr);
316: if (mpardiso->mtype == 2) {
317: MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);
318: }
320: PetscFree(mpardiso->schur_idxs);
321: PetscMalloc1(size,&mpardiso->schur_idxs);
322: PetscArrayzero(mpardiso->perm,mpardiso->n);
323: ISGetIndices(is,&idxs);
324: PetscArraycpy(mpardiso->schur_idxs,idxs,size);
325: for (i=0;i<size;i++) mpardiso->perm[idxs[i]] = 1;
326: ISRestoreIndices(is,&idxs);
327: if (size) { /* turn on Schur switch if the set of indices is not empty */
328: mpardiso->iparm[36-1] = 2;
329: }
330: return(0);
331: }
333: PetscErrorCode MatDestroy_MKL_PARDISO(Mat A)
334: {
335: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
336: PetscErrorCode ierr;
339: if (mat_mkl_pardiso->CleanUp) {
340: mat_mkl_pardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;
342: MKL_PARDISO (mat_mkl_pardiso->pt,
343: &mat_mkl_pardiso->maxfct,
344: &mat_mkl_pardiso->mnum,
345: &mat_mkl_pardiso->mtype,
346: &mat_mkl_pardiso->phase,
347: &mat_mkl_pardiso->n,
348: NULL,
349: NULL,
350: NULL,
351: NULL,
352: &mat_mkl_pardiso->nrhs,
353: mat_mkl_pardiso->iparm,
354: &mat_mkl_pardiso->msglvl,
355: NULL,
356: NULL,
357: &mat_mkl_pardiso->err);
358: }
359: PetscFree(mat_mkl_pardiso->perm);
360: PetscFree(mat_mkl_pardiso->schur_work);
361: PetscFree(mat_mkl_pardiso->schur_idxs);
362: if (mat_mkl_pardiso->freeaij) {
363: PetscFree2(mat_mkl_pardiso->ia,mat_mkl_pardiso->ja);
364: if (mat_mkl_pardiso->iparm[34] == 1) {
365: PetscFree(mat_mkl_pardiso->a);
366: }
367: }
368: PetscFree(A->data);
370: /* clear composed functions */
371: PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
372: PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);
373: PetscObjectComposeFunction((PetscObject)A,"MatMkl_PardisoSetCntl_C",NULL);
374: return(0);
375: }
377: static PetscErrorCode MatMKLPardisoScatterSchur_Private(Mat_MKL_PARDISO *mpardiso, PetscScalar *whole, PetscScalar *schur, PetscBool reduce)
378: {
380: if (reduce) { /* data given for the whole matrix */
381: PetscInt i,m=0,p=0;
382: for (i=0;i<mpardiso->nrhs;i++) {
383: PetscInt j;
384: for (j=0;j<mpardiso->schur_size;j++) {
385: schur[p+j] = whole[m+mpardiso->schur_idxs[j]];
386: }
387: m += mpardiso->n;
388: p += mpardiso->schur_size;
389: }
390: } else { /* from Schur to whole */
391: PetscInt i,m=0,p=0;
392: for (i=0;i<mpardiso->nrhs;i++) {
393: PetscInt j;
394: for (j=0;j<mpardiso->schur_size;j++) {
395: whole[m+mpardiso->schur_idxs[j]] = schur[p+j];
396: }
397: m += mpardiso->n;
398: p += mpardiso->schur_size;
399: }
400: }
401: return(0);
402: }
404: PetscErrorCode MatSolve_MKL_PARDISO(Mat A,Vec b,Vec x)
405: {
406: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
407: PetscErrorCode ierr;
408: PetscScalar *xarray;
409: const PetscScalar *barray;
412: mat_mkl_pardiso->nrhs = 1;
413: VecGetArray(x,&xarray);
414: VecGetArrayRead(b,&barray);
416: if (!mat_mkl_pardiso->schur) mat_mkl_pardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
417: else mat_mkl_pardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;
419: if (barray == xarray) { /* if the two vectors share the same memory */
420: PetscScalar *work;
421: if (!mat_mkl_pardiso->schur_work) {
422: PetscMalloc1(mat_mkl_pardiso->n,&work);
423: } else {
424: work = mat_mkl_pardiso->schur_work;
425: }
426: mat_mkl_pardiso->iparm[6-1] = 1;
427: MKL_PARDISO (mat_mkl_pardiso->pt,
428: &mat_mkl_pardiso->maxfct,
429: &mat_mkl_pardiso->mnum,
430: &mat_mkl_pardiso->mtype,
431: &mat_mkl_pardiso->phase,
432: &mat_mkl_pardiso->n,
433: mat_mkl_pardiso->a,
434: mat_mkl_pardiso->ia,
435: mat_mkl_pardiso->ja,
436: NULL,
437: &mat_mkl_pardiso->nrhs,
438: mat_mkl_pardiso->iparm,
439: &mat_mkl_pardiso->msglvl,
440: (void*)xarray,
441: (void*)work,
442: &mat_mkl_pardiso->err);
443: if (!mat_mkl_pardiso->schur_work) {
444: PetscFree(work);
445: }
446: } else {
447: mat_mkl_pardiso->iparm[6-1] = 0;
448: MKL_PARDISO (mat_mkl_pardiso->pt,
449: &mat_mkl_pardiso->maxfct,
450: &mat_mkl_pardiso->mnum,
451: &mat_mkl_pardiso->mtype,
452: &mat_mkl_pardiso->phase,
453: &mat_mkl_pardiso->n,
454: mat_mkl_pardiso->a,
455: mat_mkl_pardiso->ia,
456: mat_mkl_pardiso->ja,
457: mat_mkl_pardiso->perm,
458: &mat_mkl_pardiso->nrhs,
459: mat_mkl_pardiso->iparm,
460: &mat_mkl_pardiso->msglvl,
461: (void*)barray,
462: (void*)xarray,
463: &mat_mkl_pardiso->err);
464: }
465: VecRestoreArrayRead(b,&barray);
467: 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);
469: if (mat_mkl_pardiso->schur) { /* solve Schur complement and expand solution */
470: PetscInt shift = mat_mkl_pardiso->schur_size;
472: /* if inverted, uses BLAS *MM subroutines, otherwise LAPACK *TRS */
473: if (A->schur_status != MAT_FACTOR_SCHUR_INVERTED) shift = 0;
475: if (!mat_mkl_pardiso->solve_interior) {
476: /* solve Schur complement */
477: MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work,PETSC_TRUE);
478: MatMKLPardisoSolveSchur_Private(A,mat_mkl_pardiso->schur_work,mat_mkl_pardiso->schur_work+shift);
479: MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work+shift,PETSC_FALSE);
480: } else { /* if we are solving for the interior problem, any value in barray[schur] forward-substituted to xarray[schur] will be neglected */
481: PetscInt i;
482: for (i=0;i<mat_mkl_pardiso->schur_size;i++) {
483: xarray[mat_mkl_pardiso->schur_idxs[i]] = 0.;
484: }
485: }
487: /* expansion phase */
488: mat_mkl_pardiso->iparm[6-1] = 1;
489: mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
490: MKL_PARDISO (mat_mkl_pardiso->pt,
491: &mat_mkl_pardiso->maxfct,
492: &mat_mkl_pardiso->mnum,
493: &mat_mkl_pardiso->mtype,
494: &mat_mkl_pardiso->phase,
495: &mat_mkl_pardiso->n,
496: mat_mkl_pardiso->a,
497: mat_mkl_pardiso->ia,
498: mat_mkl_pardiso->ja,
499: mat_mkl_pardiso->perm,
500: &mat_mkl_pardiso->nrhs,
501: mat_mkl_pardiso->iparm,
502: &mat_mkl_pardiso->msglvl,
503: (void*)xarray,
504: (void*)mat_mkl_pardiso->schur_work, /* according to the specs, the solution vector is always used */
505: &mat_mkl_pardiso->err);
507: 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);
508: mat_mkl_pardiso->iparm[6-1] = 0;
509: }
510: VecRestoreArray(x,&xarray);
511: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
512: return(0);
513: }
515: PetscErrorCode MatSolveTranspose_MKL_PARDISO(Mat A,Vec b,Vec x)
516: {
517: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
518: PetscInt oiparm12;
519: PetscErrorCode ierr;
522: oiparm12 = mat_mkl_pardiso->iparm[12 - 1];
523: mat_mkl_pardiso->iparm[12 - 1] = 2;
524: MatSolve_MKL_PARDISO(A,b,x);
525: mat_mkl_pardiso->iparm[12 - 1] = oiparm12;
526: return(0);
527: }
529: PetscErrorCode MatMatSolve_MKL_PARDISO(Mat A,Mat B,Mat X)
530: {
531: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)(A)->data;
532: PetscErrorCode ierr;
533: const PetscScalar *barray;
534: PetscScalar *xarray;
535: PetscBool flg;
538: PetscObjectBaseTypeCompare((PetscObject)B,MATSEQDENSE,&flg);
539: if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATSEQDENSE matrix");
540: PetscObjectBaseTypeCompare((PetscObject)X,MATSEQDENSE,&flg);
541: if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATSEQDENSE matrix");
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;
552: mat_mkl_pardiso->iparm[6-1] = 0;
554: MKL_PARDISO (mat_mkl_pardiso->pt,
555: &mat_mkl_pardiso->maxfct,
556: &mat_mkl_pardiso->mnum,
557: &mat_mkl_pardiso->mtype,
558: &mat_mkl_pardiso->phase,
559: &mat_mkl_pardiso->n,
560: mat_mkl_pardiso->a,
561: mat_mkl_pardiso->ia,
562: mat_mkl_pardiso->ja,
563: mat_mkl_pardiso->perm,
564: &mat_mkl_pardiso->nrhs,
565: mat_mkl_pardiso->iparm,
566: &mat_mkl_pardiso->msglvl,
567: (void*)barray,
568: (void*)xarray,
569: &mat_mkl_pardiso->err);
570: 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);
572: MatDenseRestoreArrayRead(B,&barray);
573: if (mat_mkl_pardiso->schur) { /* solve Schur complement and expand solution */
574: PetscScalar *o_schur_work = NULL;
575: PetscInt shift = mat_mkl_pardiso->schur_size*mat_mkl_pardiso->nrhs,scale;
576: PetscInt mem = mat_mkl_pardiso->n*mat_mkl_pardiso->nrhs;
578: /* allocate extra memory if it is needed */
579: scale = 1;
580: if (A->schur_status == MAT_FACTOR_SCHUR_INVERTED) scale = 2;
582: mem *= scale;
583: if (mem > mat_mkl_pardiso->schur_work_size) {
584: o_schur_work = mat_mkl_pardiso->schur_work;
585: PetscMalloc1(mem,&mat_mkl_pardiso->schur_work);
586: }
588: /* if inverted, uses BLAS *MM subroutines, otherwise LAPACK *TRS */
589: if (A->schur_status != MAT_FACTOR_SCHUR_INVERTED) shift = 0;
591: /* solve Schur complement */
592: if (!mat_mkl_pardiso->solve_interior) {
593: MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work,PETSC_TRUE);
594: MatMKLPardisoSolveSchur_Private(A,mat_mkl_pardiso->schur_work,mat_mkl_pardiso->schur_work+shift);
595: MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work+shift,PETSC_FALSE);
596: } else { /* if we are solving for the interior problem, any value in barray[schur,n] forward-substituted to xarray[schur,n] will be neglected */
597: PetscInt i,n,m=0;
598: for (n=0;n<mat_mkl_pardiso->nrhs;n++) {
599: for (i=0;i<mat_mkl_pardiso->schur_size;i++) {
600: xarray[mat_mkl_pardiso->schur_idxs[i]+m] = 0.;
601: }
602: m += mat_mkl_pardiso->n;
603: }
604: }
606: /* expansion phase */
607: mat_mkl_pardiso->iparm[6-1] = 1;
608: mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
609: MKL_PARDISO (mat_mkl_pardiso->pt,
610: &mat_mkl_pardiso->maxfct,
611: &mat_mkl_pardiso->mnum,
612: &mat_mkl_pardiso->mtype,
613: &mat_mkl_pardiso->phase,
614: &mat_mkl_pardiso->n,
615: mat_mkl_pardiso->a,
616: mat_mkl_pardiso->ia,
617: mat_mkl_pardiso->ja,
618: mat_mkl_pardiso->perm,
619: &mat_mkl_pardiso->nrhs,
620: mat_mkl_pardiso->iparm,
621: &mat_mkl_pardiso->msglvl,
622: (void*)xarray,
623: (void*)mat_mkl_pardiso->schur_work, /* according to the specs, the solution vector is always used */
624: &mat_mkl_pardiso->err);
625: if (o_schur_work) { /* restore original schur_work (minimal size) */
626: PetscFree(mat_mkl_pardiso->schur_work);
627: mat_mkl_pardiso->schur_work = o_schur_work;
628: }
629: 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);
630: mat_mkl_pardiso->iparm[6-1] = 0;
631: }
632: }
633: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
634: return(0);
635: }
637: PetscErrorCode MatFactorNumeric_MKL_PARDISO(Mat F,Mat A,const MatFactorInfo *info)
638: {
639: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)(F)->data;
640: PetscErrorCode ierr;
643: mat_mkl_pardiso->matstruc = SAME_NONZERO_PATTERN;
644: (*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);
646: mat_mkl_pardiso->phase = JOB_NUMERICAL_FACTORIZATION;
647: MKL_PARDISO (mat_mkl_pardiso->pt,
648: &mat_mkl_pardiso->maxfct,
649: &mat_mkl_pardiso->mnum,
650: &mat_mkl_pardiso->mtype,
651: &mat_mkl_pardiso->phase,
652: &mat_mkl_pardiso->n,
653: mat_mkl_pardiso->a,
654: mat_mkl_pardiso->ia,
655: mat_mkl_pardiso->ja,
656: mat_mkl_pardiso->perm,
657: &mat_mkl_pardiso->nrhs,
658: mat_mkl_pardiso->iparm,
659: &mat_mkl_pardiso->msglvl,
660: NULL,
661: (void*)mat_mkl_pardiso->schur,
662: &mat_mkl_pardiso->err);
663: 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);
665: /* report flops */
666: if (mat_mkl_pardiso->iparm[18] > 0) {
667: PetscLogFlops(PetscPowRealInt(10.,6)*mat_mkl_pardiso->iparm[18]);
668: }
670: if (F->schur) { /* schur output from pardiso is in row major format */
671: #if defined(PETSC_HAVE_CUDA)
672: F->schur->offloadmask = PETSC_OFFLOAD_CPU;
673: #endif
674: MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED);
675: MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur);
676: }
677: mat_mkl_pardiso->matstruc = SAME_NONZERO_PATTERN;
678: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
679: return(0);
680: }
682: PetscErrorCode PetscSetMKL_PARDISOFromOptions(Mat F, Mat A)
683: {
684: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;
685: PetscErrorCode ierr;
686: PetscInt icntl,threads=1;
687: PetscBool flg;
690: PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MKL_PARDISO Options","Mat");
692: PetscOptionsInt("-mat_mkl_pardiso_65","Number of threads to use within PARDISO","None",threads,&threads,&flg);
693: if (flg) PetscSetMKL_PARDISOThreads((int)threads);
695: 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);
696: if (flg) mat_mkl_pardiso->maxfct = icntl;
698: PetscOptionsInt("-mat_mkl_pardiso_67","Indicates the actual matrix for the solution phase","None",mat_mkl_pardiso->mnum,&icntl,&flg);
699: if (flg) mat_mkl_pardiso->mnum = icntl;
701: PetscOptionsInt("-mat_mkl_pardiso_68","Message level information","None",mat_mkl_pardiso->msglvl,&icntl,&flg);
702: if (flg) mat_mkl_pardiso->msglvl = icntl;
704: PetscOptionsInt("-mat_mkl_pardiso_69","Defines the matrix type","None",mat_mkl_pardiso->mtype,&icntl,&flg);
705: if (flg) {
706: void *pt[IPARM_SIZE];
707: mat_mkl_pardiso->mtype = icntl;
708: MKL_PARDISO_INIT(pt, &mat_mkl_pardiso->mtype, mat_mkl_pardiso->iparm);
709: #if defined(PETSC_USE_REAL_SINGLE)
710: mat_mkl_pardiso->iparm[27] = 1;
711: #else
712: mat_mkl_pardiso->iparm[27] = 0;
713: #endif
714: mat_mkl_pardiso->iparm[34] = 1; /* use 0-based indexing */
715: }
716: PetscOptionsInt("-mat_mkl_pardiso_1","Use default values (if 0)","None",mat_mkl_pardiso->iparm[0],&icntl,&flg);
718: if (flg && icntl != 0) {
719: PetscOptionsInt("-mat_mkl_pardiso_2","Fill-in reducing ordering for the input matrix","None",mat_mkl_pardiso->iparm[1],&icntl,&flg);
720: if (flg) mat_mkl_pardiso->iparm[1] = icntl;
722: PetscOptionsInt("-mat_mkl_pardiso_4","Preconditioned CGS/CG","None",mat_mkl_pardiso->iparm[3],&icntl,&flg);
723: if (flg) mat_mkl_pardiso->iparm[3] = icntl;
725: PetscOptionsInt("-mat_mkl_pardiso_5","User permutation","None",mat_mkl_pardiso->iparm[4],&icntl,&flg);
726: if (flg) mat_mkl_pardiso->iparm[4] = icntl;
728: PetscOptionsInt("-mat_mkl_pardiso_6","Write solution on x","None",mat_mkl_pardiso->iparm[5],&icntl,&flg);
729: if (flg) mat_mkl_pardiso->iparm[5] = icntl;
731: PetscOptionsInt("-mat_mkl_pardiso_8","Iterative refinement step","None",mat_mkl_pardiso->iparm[7],&icntl,&flg);
732: if (flg) mat_mkl_pardiso->iparm[7] = icntl;
734: PetscOptionsInt("-mat_mkl_pardiso_10","Pivoting perturbation","None",mat_mkl_pardiso->iparm[9],&icntl,&flg);
735: if (flg) mat_mkl_pardiso->iparm[9] = icntl;
737: PetscOptionsInt("-mat_mkl_pardiso_11","Scaling vectors","None",mat_mkl_pardiso->iparm[10],&icntl,&flg);
738: if (flg) mat_mkl_pardiso->iparm[10] = icntl;
740: PetscOptionsInt("-mat_mkl_pardiso_12","Solve with transposed or conjugate transposed matrix A","None",mat_mkl_pardiso->iparm[11],&icntl,&flg);
741: if (flg) mat_mkl_pardiso->iparm[11] = icntl;
743: PetscOptionsInt("-mat_mkl_pardiso_13","Improved accuracy using (non-) symmetric weighted matching","None",mat_mkl_pardiso->iparm[12],&icntl,&flg);
744: if (flg) mat_mkl_pardiso->iparm[12] = icntl;
746: PetscOptionsInt("-mat_mkl_pardiso_18","Numbers of non-zero elements","None",mat_mkl_pardiso->iparm[17],&icntl,&flg);
747: if (flg) mat_mkl_pardiso->iparm[17] = icntl;
749: PetscOptionsInt("-mat_mkl_pardiso_19","Report number of floating point operations (0 to disable)","None",mat_mkl_pardiso->iparm[18],&icntl,&flg);
750: if (flg) mat_mkl_pardiso->iparm[18] = icntl;
752: PetscOptionsInt("-mat_mkl_pardiso_21","Pivoting for symmetric indefinite matrices","None",mat_mkl_pardiso->iparm[20],&icntl,&flg);
753: if (flg) mat_mkl_pardiso->iparm[20] = icntl;
755: PetscOptionsInt("-mat_mkl_pardiso_24","Parallel factorization control","None",mat_mkl_pardiso->iparm[23],&icntl,&flg);
756: if (flg) mat_mkl_pardiso->iparm[23] = icntl;
758: PetscOptionsInt("-mat_mkl_pardiso_25","Parallel forward/backward solve control","None",mat_mkl_pardiso->iparm[24],&icntl,&flg);
759: if (flg) mat_mkl_pardiso->iparm[24] = icntl;
761: PetscOptionsInt("-mat_mkl_pardiso_27","Matrix checker","None",mat_mkl_pardiso->iparm[26],&icntl,&flg);
762: if (flg) mat_mkl_pardiso->iparm[26] = icntl;
764: PetscOptionsInt("-mat_mkl_pardiso_31","Partial solve and computing selected components of the solution vectors","None",mat_mkl_pardiso->iparm[30],&icntl,&flg);
765: if (flg) mat_mkl_pardiso->iparm[30] = icntl;
767: PetscOptionsInt("-mat_mkl_pardiso_34","Optimal number of threads for conditional numerical reproducibility (CNR) mode","None",mat_mkl_pardiso->iparm[33],&icntl,&flg);
768: if (flg) mat_mkl_pardiso->iparm[33] = icntl;
770: PetscOptionsInt("-mat_mkl_pardiso_60","Intel MKL_PARDISO mode","None",mat_mkl_pardiso->iparm[59],&icntl,&flg);
771: if (flg) mat_mkl_pardiso->iparm[59] = icntl;
772: }
773: PetscOptionsEnd();
774: return(0);
775: }
777: PetscErrorCode MatFactorMKL_PARDISOInitialize_Private(Mat A, MatFactorType ftype, Mat_MKL_PARDISO *mat_mkl_pardiso)
778: {
780: PetscInt i,bs;
781: PetscBool match;
784: for (i=0; i<IPARM_SIZE; i++) mat_mkl_pardiso->iparm[i] = 0;
785: for (i=0; i<IPARM_SIZE; i++) mat_mkl_pardiso->pt[i] = 0;
786: /* Default options for both sym and unsym */
787: mat_mkl_pardiso->iparm[ 0] = 1; /* Solver default parameters overriden with provided by iparm */
788: mat_mkl_pardiso->iparm[ 1] = 2; /* Metis reordering */
789: mat_mkl_pardiso->iparm[ 5] = 0; /* Write solution into x */
790: mat_mkl_pardiso->iparm[ 7] = 0; /* Max number of iterative refinement steps */
791: mat_mkl_pardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
792: mat_mkl_pardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
793: #if 0
794: mat_mkl_pardiso->iparm[23] = 1; /* Parallel factorization control*/
795: #endif
796: PetscObjectTypeCompareAny((PetscObject)A,&match,MATSEQBAIJ,MATSEQSBAIJ,"");
797: MatGetBlockSize(A,&bs);
798: if (!match || bs == 1) {
799: mat_mkl_pardiso->iparm[34] = 1; /* Cluster Sparse Solver use C-style indexing for ia and ja arrays */
800: mat_mkl_pardiso->n = A->rmap->N;
801: } else {
802: mat_mkl_pardiso->iparm[34] = 0; /* Cluster Sparse Solver use Fortran-style indexing for ia and ja arrays */
803: mat_mkl_pardiso->iparm[36] = bs;
804: mat_mkl_pardiso->n = A->rmap->N/bs;
805: }
806: mat_mkl_pardiso->iparm[39] = 0; /* Input: matrix/rhs/solution stored on master */
808: mat_mkl_pardiso->CleanUp = PETSC_FALSE;
809: mat_mkl_pardiso->maxfct = 1; /* Maximum number of numerical factorizations. */
810: mat_mkl_pardiso->mnum = 1; /* Which factorization to use. */
811: mat_mkl_pardiso->msglvl = 0; /* 0: do not print 1: Print statistical information in file */
812: mat_mkl_pardiso->phase = -1;
813: mat_mkl_pardiso->err = 0;
815: mat_mkl_pardiso->nrhs = 1;
816: mat_mkl_pardiso->err = 0;
817: mat_mkl_pardiso->phase = -1;
819: if (ftype == MAT_FACTOR_LU) {
820: mat_mkl_pardiso->iparm[ 9] = 13; /* Perturb the pivot elements with 1E-13 */
821: mat_mkl_pardiso->iparm[10] = 1; /* Use nonsymmetric permutation and scaling MPS */
822: mat_mkl_pardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
823: } else {
824: mat_mkl_pardiso->iparm[ 9] = 8; /* Perturb the pivot elements with 1E-8 */
825: mat_mkl_pardiso->iparm[10] = 0; /* Use nonsymmetric permutation and scaling MPS */
826: mat_mkl_pardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
827: #if defined(PETSC_USE_DEBUG)
828: mat_mkl_pardiso->iparm[26] = 1; /* Matrix checker */
829: #endif
830: }
831: PetscMalloc1(A->rmap->N*sizeof(INT_TYPE), &mat_mkl_pardiso->perm);
832: for (i=0; i<A->rmap->N; i++) {
833: mat_mkl_pardiso->perm[i] = 0;
834: }
835: mat_mkl_pardiso->schur_size = 0;
836: return(0);
837: }
839: PetscErrorCode MatFactorSymbolic_AIJMKL_PARDISO_Private(Mat F,Mat A,const MatFactorInfo *info)
840: {
841: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;
842: PetscErrorCode ierr;
845: mat_mkl_pardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
846: PetscSetMKL_PARDISOFromOptions(F,A);
848: /* throw away any previously computed structure */
849: if (mat_mkl_pardiso->freeaij) {
850: PetscFree2(mat_mkl_pardiso->ia,mat_mkl_pardiso->ja);
851: if (mat_mkl_pardiso->iparm[34] == 1) {
852: PetscFree(mat_mkl_pardiso->a);
853: }
854: }
855: (*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);
856: if (mat_mkl_pardiso->iparm[34] == 1) mat_mkl_pardiso->n = A->rmap->N;
857: else mat_mkl_pardiso->n = A->rmap->N/A->rmap->bs;
859: mat_mkl_pardiso->phase = JOB_ANALYSIS;
861: /* reset flops counting if requested */
862: if (mat_mkl_pardiso->iparm[18]) mat_mkl_pardiso->iparm[18] = -1;
864: MKL_PARDISO (mat_mkl_pardiso->pt,
865: &mat_mkl_pardiso->maxfct,
866: &mat_mkl_pardiso->mnum,
867: &mat_mkl_pardiso->mtype,
868: &mat_mkl_pardiso->phase,
869: &mat_mkl_pardiso->n,
870: mat_mkl_pardiso->a,
871: mat_mkl_pardiso->ia,
872: mat_mkl_pardiso->ja,
873: mat_mkl_pardiso->perm,
874: &mat_mkl_pardiso->nrhs,
875: mat_mkl_pardiso->iparm,
876: &mat_mkl_pardiso->msglvl,
877: NULL,
878: NULL,
879: &mat_mkl_pardiso->err);
880: 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);
882: mat_mkl_pardiso->CleanUp = PETSC_TRUE;
884: if (F->factortype == MAT_FACTOR_LU) F->ops->lufactornumeric = MatFactorNumeric_MKL_PARDISO;
885: else F->ops->choleskyfactornumeric = MatFactorNumeric_MKL_PARDISO;
887: F->ops->solve = MatSolve_MKL_PARDISO;
888: F->ops->solvetranspose = MatSolveTranspose_MKL_PARDISO;
889: F->ops->matsolve = MatMatSolve_MKL_PARDISO;
890: return(0);
891: }
893: PetscErrorCode MatLUFactorSymbolic_AIJMKL_PARDISO(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
894: {
898: MatFactorSymbolic_AIJMKL_PARDISO_Private(F, A, info);
899: return(0);
900: }
902: #if !defined(PETSC_USE_COMPLEX)
903: PetscErrorCode MatGetInertia_MKL_PARDISO(Mat F,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
904: {
905: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)F->data;
908: if (nneg) *nneg = mat_mkl_pardiso->iparm[22];
909: if (npos) *npos = mat_mkl_pardiso->iparm[21];
910: if (nzero) *nzero = F->rmap->N - (mat_mkl_pardiso->iparm[22] + mat_mkl_pardiso->iparm[21]);
911: return(0);
912: }
913: #endif
915: PetscErrorCode MatCholeskyFactorSymbolic_AIJMKL_PARDISO(Mat F,Mat A,IS r,const MatFactorInfo *info)
916: {
920: MatFactorSymbolic_AIJMKL_PARDISO_Private(F, A, info);
921: #if defined(PETSC_USE_COMPLEX)
922: F->ops->getinertia = NULL;
923: #else
924: F->ops->getinertia = MatGetInertia_MKL_PARDISO;
925: #endif
926: return(0);
927: }
929: PetscErrorCode MatView_MKL_PARDISO(Mat A, PetscViewer viewer)
930: {
931: PetscErrorCode ierr;
932: PetscBool iascii;
933: PetscViewerFormat format;
934: Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
935: PetscInt i;
938: if (A->ops->solve != MatSolve_MKL_PARDISO) return(0);
940: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
941: if (iascii) {
942: PetscViewerGetFormat(viewer,&format);
943: if (format == PETSC_VIEWER_ASCII_INFO) {
944: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO run parameters:\n");
945: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO phase: %d \n",mat_mkl_pardiso->phase);
946: for (i=1; i<=64; i++) {
947: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO iparm[%d]: %d \n",i, mat_mkl_pardiso->iparm[i - 1]);
948: }
949: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO maxfct: %d \n", mat_mkl_pardiso->maxfct);
950: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO mnum: %d \n", mat_mkl_pardiso->mnum);
951: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO mtype: %d \n", mat_mkl_pardiso->mtype);
952: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO n: %d \n", mat_mkl_pardiso->n);
953: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO nrhs: %d \n", mat_mkl_pardiso->nrhs);
954: PetscViewerASCIIPrintf(viewer,"MKL_PARDISO msglvl: %d \n", mat_mkl_pardiso->msglvl);
955: }
956: }
957: return(0);
958: }
961: PetscErrorCode MatGetInfo_MKL_PARDISO(Mat A, MatInfoType flag, MatInfo *info)
962: {
963: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)A->data;
966: info->block_size = 1.0;
967: info->nz_used = mat_mkl_pardiso->iparm[17];
968: info->nz_allocated = mat_mkl_pardiso->iparm[17];
969: info->nz_unneeded = 0.0;
970: info->assemblies = 0.0;
971: info->mallocs = 0.0;
972: info->memory = 0.0;
973: info->fill_ratio_given = 0;
974: info->fill_ratio_needed = 0;
975: info->factor_mallocs = 0;
976: return(0);
977: }
979: PetscErrorCode MatMkl_PardisoSetCntl_MKL_PARDISO(Mat F,PetscInt icntl,PetscInt ival)
980: {
981: Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;
984: if (icntl <= 64) {
985: mat_mkl_pardiso->iparm[icntl - 1] = ival;
986: } else {
987: if (icntl == 65) PetscSetMKL_PARDISOThreads(ival);
988: else if (icntl == 66) mat_mkl_pardiso->maxfct = ival;
989: else if (icntl == 67) mat_mkl_pardiso->mnum = ival;
990: else if (icntl == 68) mat_mkl_pardiso->msglvl = ival;
991: else if (icntl == 69) {
992: void *pt[IPARM_SIZE];
993: mat_mkl_pardiso->mtype = ival;
994: MKL_PARDISO_INIT(pt, &mat_mkl_pardiso->mtype, mat_mkl_pardiso->iparm);
995: #if defined(PETSC_USE_REAL_SINGLE)
996: mat_mkl_pardiso->iparm[27] = 1;
997: #else
998: mat_mkl_pardiso->iparm[27] = 0;
999: #endif
1000: mat_mkl_pardiso->iparm[34] = 1;
1001: } else if (icntl==70) mat_mkl_pardiso->solve_interior = (PetscBool)!!ival;
1002: }
1003: return(0);
1004: }
1006: /*@
1007: MatMkl_PardisoSetCntl - Set Mkl_Pardiso parameters
1009: Logically Collective on Mat
1011: Input Parameters:
1012: + F - the factored matrix obtained by calling MatGetFactor()
1013: . icntl - index of Mkl_Pardiso parameter
1014: - ival - value of Mkl_Pardiso parameter
1016: Options Database:
1017: . -mat_mkl_pardiso_<icntl> <ival>
1019: Level: beginner
1021: References:
1022: . Mkl_Pardiso Users' Guide
1024: .seealso: MatGetFactor()
1025: @*/
1026: PetscErrorCode MatMkl_PardisoSetCntl(Mat F,PetscInt icntl,PetscInt ival)
1027: {
1031: PetscTryMethod(F,"MatMkl_PardisoSetCntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
1032: return(0);
1033: }
1035: /*MC
1036: MATSOLVERMKL_PARDISO - A matrix type providing direct solvers (LU) for
1037: sequential matrices via the external package MKL_PARDISO.
1039: Works with MATSEQAIJ matrices
1041: Use -pc_type lu -pc_factor_mat_solver_type mkl_pardiso to use this direct solver
1043: Options Database Keys:
1044: + -mat_mkl_pardiso_65 - Number of threads to use within MKL_PARDISO
1045: . -mat_mkl_pardiso_66 - Maximum number of factors with identical sparsity structure that must be kept in memory at the same time
1046: . -mat_mkl_pardiso_67 - Indicates the actual matrix for the solution phase
1047: . -mat_mkl_pardiso_68 - Message level information
1048: . -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
1049: . -mat_mkl_pardiso_1 - Use default values
1050: . -mat_mkl_pardiso_2 - Fill-in reducing ordering for the input matrix
1051: . -mat_mkl_pardiso_4 - Preconditioned CGS/CG
1052: . -mat_mkl_pardiso_5 - User permutation
1053: . -mat_mkl_pardiso_6 - Write solution on x
1054: . -mat_mkl_pardiso_8 - Iterative refinement step
1055: . -mat_mkl_pardiso_10 - Pivoting perturbation
1056: . -mat_mkl_pardiso_11 - Scaling vectors
1057: . -mat_mkl_pardiso_12 - Solve with transposed or conjugate transposed matrix A
1058: . -mat_mkl_pardiso_13 - Improved accuracy using (non-) symmetric weighted matching
1059: . -mat_mkl_pardiso_18 - Numbers of non-zero elements
1060: . -mat_mkl_pardiso_19 - Report number of floating point operations
1061: . -mat_mkl_pardiso_21 - Pivoting for symmetric indefinite matrices
1062: . -mat_mkl_pardiso_24 - Parallel factorization control
1063: . -mat_mkl_pardiso_25 - Parallel forward/backward solve control
1064: . -mat_mkl_pardiso_27 - Matrix checker
1065: . -mat_mkl_pardiso_31 - Partial solve and computing selected components of the solution vectors
1066: . -mat_mkl_pardiso_34 - Optimal number of threads for conditional numerical reproducibility (CNR) mode
1067: - -mat_mkl_pardiso_60 - Intel MKL_PARDISO mode
1069: Level: beginner
1071: For more information please check mkl_pardiso manual
1073: .seealso: PCFactorSetMatSolverType(), MatSolverType
1075: M*/
1076: static PetscErrorCode MatFactorGetSolverType_mkl_pardiso(Mat A, MatSolverType *type)
1077: {
1079: *type = MATSOLVERMKL_PARDISO;
1080: return(0);
1081: }
1083: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mkl_pardiso(Mat A,MatFactorType ftype,Mat *F)
1084: {
1085: Mat B;
1086: PetscErrorCode ierr;
1087: Mat_MKL_PARDISO *mat_mkl_pardiso;
1088: PetscBool isSeqAIJ,isSeqBAIJ,isSeqSBAIJ;
1091: PetscObjectBaseTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
1092: PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);
1093: PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);
1094: MatCreate(PetscObjectComm((PetscObject)A),&B);
1095: MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1096: PetscStrallocpy("mkl_pardiso",&((PetscObject)B)->type_name);
1097: MatSetUp(B);
1099: PetscNewLog(B,&mat_mkl_pardiso);
1100: B->data = mat_mkl_pardiso;
1102: MatFactorMKL_PARDISOInitialize_Private(A, ftype, mat_mkl_pardiso);
1103: if (ftype == MAT_FACTOR_LU) {
1104: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_PARDISO;
1105: B->factortype = MAT_FACTOR_LU;
1106: mat_mkl_pardiso->needsym = PETSC_FALSE;
1107: if (isSeqAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqaij;
1108: else if (isSeqBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqbaij;
1109: else if (isSeqSBAIJ) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO LU factor with SEQSBAIJ format! Use MAT_FACTOR_CHOLESKY instead");
1110: else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO LU with %s format",((PetscObject)A)->type_name);
1111: #if defined(PETSC_USE_COMPLEX)
1112: mat_mkl_pardiso->mtype = 13;
1113: #else
1114: mat_mkl_pardiso->mtype = 11;
1115: #endif
1116: } else {
1117: #if defined(PETSC_USE_COMPLEX)
1118: SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO CHOLESKY with complex scalars! Use MAT_FACTOR_LU instead",((PetscObject)A)->type_name);
1119: #endif
1120: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_AIJMKL_PARDISO;
1121: B->factortype = MAT_FACTOR_CHOLESKY;
1122: if (isSeqAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqaij;
1123: else if (isSeqBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqbaij;
1124: else if (isSeqSBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqsbaij;
1125: else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO CHOLESKY with %s format",((PetscObject)A)->type_name);
1127: mat_mkl_pardiso->needsym = PETSC_TRUE;
1128: if (A->spd_set && A->spd) mat_mkl_pardiso->mtype = 2;
1129: else mat_mkl_pardiso->mtype = -2;
1130: }
1131: B->ops->destroy = MatDestroy_MKL_PARDISO;
1132: B->ops->view = MatView_MKL_PARDISO;
1133: B->ops->getinfo = MatGetInfo_MKL_PARDISO;
1134: B->factortype = ftype;
1135: B->assembled = PETSC_TRUE;
1137: PetscFree(B->solvertype);
1138: PetscStrallocpy(MATSOLVERMKL_PARDISO,&B->solvertype);
1140: PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mkl_pardiso);
1141: PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MKL_PARDISO);
1142: PetscObjectComposeFunction((PetscObject)B,"MatMkl_PardisoSetCntl_C",MatMkl_PardisoSetCntl_MKL_PARDISO);
1144: *F = B;
1145: return(0);
1146: }
1148: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MKL_Pardiso(void)
1149: {
1153: MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mkl_pardiso);
1154: MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mkl_pardiso);
1155: MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mkl_pardiso);
1156: MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mkl_pardiso);
1157: return(0);
1158: }