Actual source code: mkl_pardiso.c

petsc-master 2020-05-26
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  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:   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:     MatProductCreateWithMat(S,Bmat,NULL,Xmat);
272:     if (!mpardiso->iparm[12-1]) {
273:       MatProductSetType(Xmat,MATPRODUCT_AB);
274:     } else { /* transpose solve */
275:       MatProductSetType(Xmat,MATPRODUCT_AtB);
276:     }
277:     MatProductSetFromOptions(Xmat);
278:     MatProductNumeric(Xmat);
279:     MatProductClear(Xmat);
280:     break;
281:   default:
282:     SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
283:     break;
284:   }
285:   MatFactorRestoreSchurComplement(F,&S,schurstatus);
286:   MatDestroy(&Bmat);
287:   MatDestroy(&Xmat);
288:   return(0);
289: }

291: PetscErrorCode MatFactorSetSchurIS_MKL_PARDISO(Mat F, IS is)
292: {
293:   Mat_MKL_PARDISO   *mpardiso = (Mat_MKL_PARDISO*)F->data;
294:   const PetscScalar *arr;
295:   const PetscInt    *idxs;
296:   PetscInt          size,i;
297:   PetscMPIInt       csize;
298:   PetscBool         sorted;
299:   PetscErrorCode    ierr;

302:   MPI_Comm_size(PetscObjectComm((PetscObject)F),&csize);
303:   if (csize > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MKL_PARDISO parallel Schur complements not yet supported from PETSc");
304:   ISSorted(is,&sorted);
305:   if (!sorted) {
306:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"IS for MKL_PARDISO Schur complements needs to be sorted");
307:   }
308:   ISGetLocalSize(is,&size);
309:   PetscFree(mpardiso->schur_work);
310:   PetscBLASIntCast(PetscMax(mpardiso->n,2*size),&mpardiso->schur_work_size);
311:   PetscMalloc1(mpardiso->schur_work_size,&mpardiso->schur_work);
312:   MatDestroy(&F->schur);
313:   MatCreateSeqDense(PETSC_COMM_SELF,size,size,NULL,&F->schur);
314:   MatDenseGetArrayRead(F->schur,&arr);
315:   mpardiso->schur      = (PetscScalar*)arr;
316:   mpardiso->schur_size = size;
317:   MatDenseRestoreArrayRead(F->schur,&arr);
318:   if (mpardiso->mtype == 2) {
319:     MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);
320:   }

322:   PetscFree(mpardiso->schur_idxs);
323:   PetscMalloc1(size,&mpardiso->schur_idxs);
324:   PetscArrayzero(mpardiso->perm,mpardiso->n);
325:   ISGetIndices(is,&idxs);
326:   PetscArraycpy(mpardiso->schur_idxs,idxs,size);
327:   for (i=0;i<size;i++) mpardiso->perm[idxs[i]] = 1;
328:   ISRestoreIndices(is,&idxs);
329:   if (size) { /* turn on Schur switch if the set of indices is not empty */
330:     mpardiso->iparm[36-1] = 2;
331:   }
332:   return(0);
333: }

335: PetscErrorCode MatDestroy_MKL_PARDISO(Mat A)
336: {
337:   Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
338:   PetscErrorCode  ierr;

341:   if (mat_mkl_pardiso->CleanUp) {
342:     mat_mkl_pardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;

344:     MKL_PARDISO (mat_mkl_pardiso->pt,
345:       &mat_mkl_pardiso->maxfct,
346:       &mat_mkl_pardiso->mnum,
347:       &mat_mkl_pardiso->mtype,
348:       &mat_mkl_pardiso->phase,
349:       &mat_mkl_pardiso->n,
350:       NULL,
351:       NULL,
352:       NULL,
353:       NULL,
354:       &mat_mkl_pardiso->nrhs,
355:       mat_mkl_pardiso->iparm,
356:       &mat_mkl_pardiso->msglvl,
357:       NULL,
358:       NULL,
359:       &mat_mkl_pardiso->err);
360:   }
361:   PetscFree(mat_mkl_pardiso->perm);
362:   PetscFree(mat_mkl_pardiso->schur_work);
363:   PetscFree(mat_mkl_pardiso->schur_idxs);
364:   if (mat_mkl_pardiso->freeaij) {
365:     PetscFree2(mat_mkl_pardiso->ia,mat_mkl_pardiso->ja);
366:     if (mat_mkl_pardiso->iparm[34] == 1) {
367:       PetscFree(mat_mkl_pardiso->a);
368:     }
369:   }
370:   PetscFree(A->data);

372:   /* clear composed functions */
373:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
374:   PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);
375:   PetscObjectComposeFunction((PetscObject)A,"MatMkl_PardisoSetCntl_C",NULL);
376:   return(0);
377: }

379: static PetscErrorCode MatMKLPardisoScatterSchur_Private(Mat_MKL_PARDISO *mpardiso, PetscScalar *whole, PetscScalar *schur, PetscBool reduce)
380: {
382:   if (reduce) { /* data given for the whole matrix */
383:     PetscInt i,m=0,p=0;
384:     for (i=0;i<mpardiso->nrhs;i++) {
385:       PetscInt j;
386:       for (j=0;j<mpardiso->schur_size;j++) {
387:         schur[p+j] = whole[m+mpardiso->schur_idxs[j]];
388:       }
389:       m += mpardiso->n;
390:       p += mpardiso->schur_size;
391:     }
392:   } else { /* from Schur to whole */
393:     PetscInt i,m=0,p=0;
394:     for (i=0;i<mpardiso->nrhs;i++) {
395:       PetscInt j;
396:       for (j=0;j<mpardiso->schur_size;j++) {
397:         whole[m+mpardiso->schur_idxs[j]] = schur[p+j];
398:       }
399:       m += mpardiso->n;
400:       p += mpardiso->schur_size;
401:     }
402:   }
403:   return(0);
404: }

406: PetscErrorCode MatSolve_MKL_PARDISO(Mat A,Vec b,Vec x)
407: {
408:   Mat_MKL_PARDISO   *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
409:   PetscErrorCode    ierr;
410:   PetscScalar       *xarray;
411:   const PetscScalar *barray;

414:   mat_mkl_pardiso->nrhs = 1;
415:   VecGetArray(x,&xarray);
416:   VecGetArrayRead(b,&barray);

418:   if (!mat_mkl_pardiso->schur) mat_mkl_pardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
419:   else mat_mkl_pardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;

421:   if (barray == xarray) { /* if the two vectors share the same memory */
422:     PetscScalar *work;
423:     if (!mat_mkl_pardiso->schur_work) {
424:       PetscMalloc1(mat_mkl_pardiso->n,&work);
425:     } else {
426:       work = mat_mkl_pardiso->schur_work;
427:     }
428:     mat_mkl_pardiso->iparm[6-1] = 1;
429:     MKL_PARDISO (mat_mkl_pardiso->pt,
430:       &mat_mkl_pardiso->maxfct,
431:       &mat_mkl_pardiso->mnum,
432:       &mat_mkl_pardiso->mtype,
433:       &mat_mkl_pardiso->phase,
434:       &mat_mkl_pardiso->n,
435:       mat_mkl_pardiso->a,
436:       mat_mkl_pardiso->ia,
437:       mat_mkl_pardiso->ja,
438:       NULL,
439:       &mat_mkl_pardiso->nrhs,
440:       mat_mkl_pardiso->iparm,
441:       &mat_mkl_pardiso->msglvl,
442:       (void*)xarray,
443:       (void*)work,
444:       &mat_mkl_pardiso->err);
445:     if (!mat_mkl_pardiso->schur_work) {
446:       PetscFree(work);
447:     }
448:   } else {
449:     mat_mkl_pardiso->iparm[6-1] = 0;
450:     MKL_PARDISO (mat_mkl_pardiso->pt,
451:       &mat_mkl_pardiso->maxfct,
452:       &mat_mkl_pardiso->mnum,
453:       &mat_mkl_pardiso->mtype,
454:       &mat_mkl_pardiso->phase,
455:       &mat_mkl_pardiso->n,
456:       mat_mkl_pardiso->a,
457:       mat_mkl_pardiso->ia,
458:       mat_mkl_pardiso->ja,
459:       mat_mkl_pardiso->perm,
460:       &mat_mkl_pardiso->nrhs,
461:       mat_mkl_pardiso->iparm,
462:       &mat_mkl_pardiso->msglvl,
463:       (void*)barray,
464:       (void*)xarray,
465:       &mat_mkl_pardiso->err);
466:   }
467:   VecRestoreArrayRead(b,&barray);

469:   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);

471:   if (mat_mkl_pardiso->schur) { /* solve Schur complement and expand solution */
472:     PetscInt shift = mat_mkl_pardiso->schur_size;

474:     /* if inverted, uses BLAS *MM subroutines, otherwise LAPACK *TRS */
475:     if (A->schur_status != MAT_FACTOR_SCHUR_INVERTED) shift = 0;

477:     if (!mat_mkl_pardiso->solve_interior) {
478:       /* solve Schur complement */
479:       MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work,PETSC_TRUE);
480:       MatMKLPardisoSolveSchur_Private(A,mat_mkl_pardiso->schur_work,mat_mkl_pardiso->schur_work+shift);
481:       MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work+shift,PETSC_FALSE);
482:     } else { /* if we are solving for the interior problem, any value in barray[schur] forward-substituted to xarray[schur] will be neglected */
483:       PetscInt i;
484:       for (i=0;i<mat_mkl_pardiso->schur_size;i++) {
485:         xarray[mat_mkl_pardiso->schur_idxs[i]] = 0.;
486:       }
487:     }

489:     /* expansion phase */
490:     mat_mkl_pardiso->iparm[6-1] = 1;
491:     mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
492:     MKL_PARDISO (mat_mkl_pardiso->pt,
493:       &mat_mkl_pardiso->maxfct,
494:       &mat_mkl_pardiso->mnum,
495:       &mat_mkl_pardiso->mtype,
496:       &mat_mkl_pardiso->phase,
497:       &mat_mkl_pardiso->n,
498:       mat_mkl_pardiso->a,
499:       mat_mkl_pardiso->ia,
500:       mat_mkl_pardiso->ja,
501:       mat_mkl_pardiso->perm,
502:       &mat_mkl_pardiso->nrhs,
503:       mat_mkl_pardiso->iparm,
504:       &mat_mkl_pardiso->msglvl,
505:       (void*)xarray,
506:       (void*)mat_mkl_pardiso->schur_work, /* according to the specs, the solution vector is always used */
507:       &mat_mkl_pardiso->err);

509:     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);
510:     mat_mkl_pardiso->iparm[6-1] = 0;
511:   }
512:   VecRestoreArray(x,&xarray);
513:   mat_mkl_pardiso->CleanUp = PETSC_TRUE;
514:   return(0);
515: }

517: PetscErrorCode MatSolveTranspose_MKL_PARDISO(Mat A,Vec b,Vec x)
518: {
519:   Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
520:   PetscInt        oiparm12;
521:   PetscErrorCode  ierr;

524:   oiparm12 = mat_mkl_pardiso->iparm[12 - 1];
525:   mat_mkl_pardiso->iparm[12 - 1] = 2;
526:   MatSolve_MKL_PARDISO(A,b,x);
527:   mat_mkl_pardiso->iparm[12 - 1] = oiparm12;
528:   return(0);
529: }

531: PetscErrorCode MatMatSolve_MKL_PARDISO(Mat A,Mat B,Mat X)
532: {
533:   Mat_MKL_PARDISO   *mat_mkl_pardiso=(Mat_MKL_PARDISO*)(A)->data;
534:   PetscErrorCode    ierr;
535:   const PetscScalar *barray;
536:   PetscScalar       *xarray;
537:   PetscBool         flg;

540:   PetscObjectBaseTypeCompare((PetscObject)B,MATSEQDENSE,&flg);
541:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATSEQDENSE matrix");
542:   if (X != B) {
543:     PetscObjectBaseTypeCompare((PetscObject)X,MATSEQDENSE,&flg);
544:     if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATSEQDENSE matrix");
545:   }

547:   MatGetSize(B,NULL,(PetscInt*)&mat_mkl_pardiso->nrhs);

549:   if (mat_mkl_pardiso->nrhs > 0) {
550:     MatDenseGetArrayRead(B,&barray);
551:     MatDenseGetArray(X,&xarray);

553:     if (barray == xarray) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"B and X cannot share the same memory location");
554:     if (!mat_mkl_pardiso->schur) mat_mkl_pardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
555:     else mat_mkl_pardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;

557:     MKL_PARDISO (mat_mkl_pardiso->pt,
558:       &mat_mkl_pardiso->maxfct,
559:       &mat_mkl_pardiso->mnum,
560:       &mat_mkl_pardiso->mtype,
561:       &mat_mkl_pardiso->phase,
562:       &mat_mkl_pardiso->n,
563:       mat_mkl_pardiso->a,
564:       mat_mkl_pardiso->ia,
565:       mat_mkl_pardiso->ja,
566:       mat_mkl_pardiso->perm,
567:       &mat_mkl_pardiso->nrhs,
568:       mat_mkl_pardiso->iparm,
569:       &mat_mkl_pardiso->msglvl,
570:       (void*)barray,
571:       (void*)xarray,
572:       &mat_mkl_pardiso->err);
573:     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);

575:     MatDenseRestoreArrayRead(B,&barray);
576:     if (mat_mkl_pardiso->schur) { /* solve Schur complement and expand solution */
577:       PetscScalar *o_schur_work = NULL;
578:       PetscInt    shift = mat_mkl_pardiso->schur_size*mat_mkl_pardiso->nrhs,scale;
579:       PetscInt    mem = mat_mkl_pardiso->n*mat_mkl_pardiso->nrhs;

581:       /* allocate extra memory if it is needed */
582:       scale = 1;
583:       if (A->schur_status == MAT_FACTOR_SCHUR_INVERTED) scale = 2;

585:       mem *= scale;
586:       if (mem > mat_mkl_pardiso->schur_work_size) {
587:         o_schur_work = mat_mkl_pardiso->schur_work;
588:         PetscMalloc1(mem,&mat_mkl_pardiso->schur_work);
589:       }

591:       /* if inverted, uses BLAS *MM subroutines, otherwise LAPACK *TRS */
592:       if (A->schur_status != MAT_FACTOR_SCHUR_INVERTED) shift = 0;

594:       /* solve Schur complement */
595:       if (!mat_mkl_pardiso->solve_interior) {
596:         MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work,PETSC_TRUE);
597:         MatMKLPardisoSolveSchur_Private(A,mat_mkl_pardiso->schur_work,mat_mkl_pardiso->schur_work+shift);
598:         MatMKLPardisoScatterSchur_Private(mat_mkl_pardiso,xarray,mat_mkl_pardiso->schur_work+shift,PETSC_FALSE);
599:       } else { /* if we are solving for the interior problem, any value in barray[schur,n] forward-substituted to xarray[schur,n] will be neglected */
600:         PetscInt i,n,m=0;
601:         for (n=0;n<mat_mkl_pardiso->nrhs;n++) {
602:           for (i=0;i<mat_mkl_pardiso->schur_size;i++) {
603:             xarray[mat_mkl_pardiso->schur_idxs[i]+m] = 0.;
604:           }
605:           m += mat_mkl_pardiso->n;
606:         }
607:       }

609:       /* expansion phase */
610:       mat_mkl_pardiso->iparm[6-1] = 1;
611:       mat_mkl_pardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
612:       MKL_PARDISO (mat_mkl_pardiso->pt,
613:         &mat_mkl_pardiso->maxfct,
614:         &mat_mkl_pardiso->mnum,
615:         &mat_mkl_pardiso->mtype,
616:         &mat_mkl_pardiso->phase,
617:         &mat_mkl_pardiso->n,
618:         mat_mkl_pardiso->a,
619:         mat_mkl_pardiso->ia,
620:         mat_mkl_pardiso->ja,
621:         mat_mkl_pardiso->perm,
622:         &mat_mkl_pardiso->nrhs,
623:         mat_mkl_pardiso->iparm,
624:         &mat_mkl_pardiso->msglvl,
625:         (void*)xarray,
626:         (void*)mat_mkl_pardiso->schur_work, /* according to the specs, the solution vector is always used */
627:         &mat_mkl_pardiso->err);
628:       if (o_schur_work) { /* restore original schur_work (minimal size) */
629:         PetscFree(mat_mkl_pardiso->schur_work);
630:         mat_mkl_pardiso->schur_work = o_schur_work;
631:       }
632:       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);
633:       mat_mkl_pardiso->iparm[6-1] = 0;
634:     }
635:   }
636:   mat_mkl_pardiso->CleanUp = PETSC_TRUE;
637:   return(0);
638: }

640: PetscErrorCode MatFactorNumeric_MKL_PARDISO(Mat F,Mat A,const MatFactorInfo *info)
641: {
642:   Mat_MKL_PARDISO *mat_mkl_pardiso=(Mat_MKL_PARDISO*)(F)->data;
643:   PetscErrorCode  ierr;

646:   mat_mkl_pardiso->matstruc = SAME_NONZERO_PATTERN;
647:   (*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);

649:   mat_mkl_pardiso->phase = JOB_NUMERICAL_FACTORIZATION;
650:   MKL_PARDISO (mat_mkl_pardiso->pt,
651:     &mat_mkl_pardiso->maxfct,
652:     &mat_mkl_pardiso->mnum,
653:     &mat_mkl_pardiso->mtype,
654:     &mat_mkl_pardiso->phase,
655:     &mat_mkl_pardiso->n,
656:     mat_mkl_pardiso->a,
657:     mat_mkl_pardiso->ia,
658:     mat_mkl_pardiso->ja,
659:     mat_mkl_pardiso->perm,
660:     &mat_mkl_pardiso->nrhs,
661:     mat_mkl_pardiso->iparm,
662:     &mat_mkl_pardiso->msglvl,
663:     NULL,
664:     (void*)mat_mkl_pardiso->schur,
665:     &mat_mkl_pardiso->err);
666:   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);

668:   /* report flops */
669:   if (mat_mkl_pardiso->iparm[18] > 0) {
670:     PetscLogFlops(PetscPowRealInt(10.,6)*mat_mkl_pardiso->iparm[18]);
671:   }

673:   if (F->schur) { /* schur output from pardiso is in row major format */
674: #if defined(PETSC_HAVE_CUDA)
675:     F->schur->offloadmask = PETSC_OFFLOAD_CPU;
676: #endif
677:     MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED);
678:     MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur);
679:   }
680:   mat_mkl_pardiso->matstruc = SAME_NONZERO_PATTERN;
681:   mat_mkl_pardiso->CleanUp  = PETSC_TRUE;
682:   return(0);
683: }

685: PetscErrorCode PetscSetMKL_PARDISOFromOptions(Mat F, Mat A)
686: {
687:   Mat_MKL_PARDISO     *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;
688:   PetscErrorCode      ierr;
689:   PetscInt            icntl,bs,threads=1;
690:   PetscBool           flg;

693:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MKL_PARDISO Options","Mat");

695:   PetscOptionsInt("-mat_mkl_pardiso_65","Number of threads to use within PARDISO","None",threads,&threads,&flg);
696:   if (flg) PetscSetMKL_PARDISOThreads((int)threads);

698:   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);
699:   if (flg) mat_mkl_pardiso->maxfct = icntl;

701:   PetscOptionsInt("-mat_mkl_pardiso_67","Indicates the actual matrix for the solution phase","None",mat_mkl_pardiso->mnum,&icntl,&flg);
702:   if (flg) mat_mkl_pardiso->mnum = icntl;

704:   PetscOptionsInt("-mat_mkl_pardiso_68","Message level information","None",mat_mkl_pardiso->msglvl,&icntl,&flg);
705:   if (flg) mat_mkl_pardiso->msglvl = icntl;

707:   PetscOptionsInt("-mat_mkl_pardiso_69","Defines the matrix type","None",mat_mkl_pardiso->mtype,&icntl,&flg);
708:   if (flg) {
709:     void *pt[IPARM_SIZE];
710:     mat_mkl_pardiso->mtype = icntl;
711:     icntl = mat_mkl_pardiso->iparm[34];
712:     bs = mat_mkl_pardiso->iparm[36];
713:     MKL_PARDISO_INIT(pt, &mat_mkl_pardiso->mtype, mat_mkl_pardiso->iparm);
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:     mat_mkl_pardiso->iparm[34] = icntl;
720:     mat_mkl_pardiso->iparm[36] = bs;
721:   }
722:   PetscOptionsInt("-mat_mkl_pardiso_1","Use default values (if 0)","None",mat_mkl_pardiso->iparm[0],&icntl,&flg);

724:   if (flg && icntl != 0) {
725:     PetscOptionsInt("-mat_mkl_pardiso_2","Fill-in reducing ordering for the input matrix","None",mat_mkl_pardiso->iparm[1],&icntl,&flg);
726:     if (flg) mat_mkl_pardiso->iparm[1] = icntl;

728:     PetscOptionsInt("-mat_mkl_pardiso_4","Preconditioned CGS/CG","None",mat_mkl_pardiso->iparm[3],&icntl,&flg);
729:     if (flg) mat_mkl_pardiso->iparm[3] = icntl;

731:     PetscOptionsInt("-mat_mkl_pardiso_5","User permutation","None",mat_mkl_pardiso->iparm[4],&icntl,&flg);
732:     if (flg) mat_mkl_pardiso->iparm[4] = icntl;

734:     PetscOptionsInt("-mat_mkl_pardiso_6","Write solution on x","None",mat_mkl_pardiso->iparm[5],&icntl,&flg);
735:     if (flg) mat_mkl_pardiso->iparm[5] = icntl;

737:     PetscOptionsInt("-mat_mkl_pardiso_8","Iterative refinement step","None",mat_mkl_pardiso->iparm[7],&icntl,&flg);
738:     if (flg) mat_mkl_pardiso->iparm[7] = icntl;

740:     PetscOptionsInt("-mat_mkl_pardiso_10","Pivoting perturbation","None",mat_mkl_pardiso->iparm[9],&icntl,&flg);
741:     if (flg) mat_mkl_pardiso->iparm[9] = icntl;

743:     PetscOptionsInt("-mat_mkl_pardiso_11","Scaling vectors","None",mat_mkl_pardiso->iparm[10],&icntl,&flg);
744:     if (flg) mat_mkl_pardiso->iparm[10] = icntl;

746:     PetscOptionsInt("-mat_mkl_pardiso_12","Solve with transposed or conjugate transposed matrix A","None",mat_mkl_pardiso->iparm[11],&icntl,&flg);
747:     if (flg) mat_mkl_pardiso->iparm[11] = icntl;

749:     PetscOptionsInt("-mat_mkl_pardiso_13","Improved accuracy using (non-) symmetric weighted matching","None",mat_mkl_pardiso->iparm[12],&icntl,&flg);
750:     if (flg) mat_mkl_pardiso->iparm[12] = icntl;

752:     PetscOptionsInt("-mat_mkl_pardiso_18","Numbers of non-zero elements","None",mat_mkl_pardiso->iparm[17],&icntl,&flg);
753:     if (flg) mat_mkl_pardiso->iparm[17] = icntl;

755:     PetscOptionsInt("-mat_mkl_pardiso_19","Report number of floating point operations (0 to disable)","None",mat_mkl_pardiso->iparm[18],&icntl,&flg);
756:     if (flg) mat_mkl_pardiso->iparm[18] = icntl;

758:     PetscOptionsInt("-mat_mkl_pardiso_21","Pivoting for symmetric indefinite matrices","None",mat_mkl_pardiso->iparm[20],&icntl,&flg);
759:     if (flg) mat_mkl_pardiso->iparm[20] = icntl;

761:     PetscOptionsInt("-mat_mkl_pardiso_24","Parallel factorization control","None",mat_mkl_pardiso->iparm[23],&icntl,&flg);
762:     if (flg) mat_mkl_pardiso->iparm[23] = icntl;

764:     PetscOptionsInt("-mat_mkl_pardiso_25","Parallel forward/backward solve control","None",mat_mkl_pardiso->iparm[24],&icntl,&flg);
765:     if (flg) mat_mkl_pardiso->iparm[24] = icntl;

767:     PetscOptionsInt("-mat_mkl_pardiso_27","Matrix checker","None",mat_mkl_pardiso->iparm[26],&icntl,&flg);
768:     if (flg) mat_mkl_pardiso->iparm[26] = icntl;

770:     PetscOptionsInt("-mat_mkl_pardiso_31","Partial solve and computing selected components of the solution vectors","None",mat_mkl_pardiso->iparm[30],&icntl,&flg);
771:     if (flg) mat_mkl_pardiso->iparm[30] = icntl;

773:     PetscOptionsInt("-mat_mkl_pardiso_34","Optimal number of threads for conditional numerical reproducibility (CNR) mode","None",mat_mkl_pardiso->iparm[33],&icntl,&flg);
774:     if (flg) mat_mkl_pardiso->iparm[33] = icntl;

776:     PetscOptionsInt("-mat_mkl_pardiso_60","Intel MKL_PARDISO mode","None",mat_mkl_pardiso->iparm[59],&icntl,&flg);
777:     if (flg) mat_mkl_pardiso->iparm[59] = icntl;
778:   }
779:   PetscOptionsEnd();
780:   return(0);
781: }

783: PetscErrorCode MatFactorMKL_PARDISOInitialize_Private(Mat A, MatFactorType ftype, Mat_MKL_PARDISO *mat_mkl_pardiso)
784: {
786:   PetscInt       i,bs;
787:   PetscBool      match;

790:   for (i=0; i<IPARM_SIZE; i++) mat_mkl_pardiso->iparm[i] = 0;
791:   for (i=0; i<IPARM_SIZE; i++) mat_mkl_pardiso->pt[i] = 0;
792: #if defined(PETSC_USE_REAL_SINGLE)
793:   mat_mkl_pardiso->iparm[27] = 1;
794: #else
795:   mat_mkl_pardiso->iparm[27] = 0;
796: #endif
797:   /* Default options for both sym and unsym */
798:   mat_mkl_pardiso->iparm[ 0] =  1; /* Solver default parameters overriden with provided by iparm */
799:   mat_mkl_pardiso->iparm[ 1] =  2; /* Metis reordering */
800:   mat_mkl_pardiso->iparm[ 5] =  0; /* Write solution into x */
801:   mat_mkl_pardiso->iparm[ 7] =  0; /* Max number of iterative refinement steps */
802:   mat_mkl_pardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
803:   mat_mkl_pardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
804: #if 0
805:   mat_mkl_pardiso->iparm[23] =  1; /* Parallel factorization control*/
806: #endif
807:   PetscObjectTypeCompareAny((PetscObject)A,&match,MATSEQBAIJ,MATSEQSBAIJ,"");
808:   MatGetBlockSize(A,&bs);
809:   if (!match || bs == 1) {
810:     mat_mkl_pardiso->iparm[34] = 1; /* Cluster Sparse Solver use C-style indexing for ia and ja arrays */
811:     mat_mkl_pardiso->n         = A->rmap->N;
812:   } else {
813:     mat_mkl_pardiso->iparm[34] = 0; /* Cluster Sparse Solver use Fortran-style indexing for ia and ja arrays */
814:     mat_mkl_pardiso->iparm[36] = bs;
815:     mat_mkl_pardiso->n         = A->rmap->N/bs;
816:   }
817:   mat_mkl_pardiso->iparm[39] =  0; /* Input: matrix/rhs/solution stored on master */

819:   mat_mkl_pardiso->CleanUp   = PETSC_FALSE;
820:   mat_mkl_pardiso->maxfct    = 1; /* Maximum number of numerical factorizations. */
821:   mat_mkl_pardiso->mnum      = 1; /* Which factorization to use. */
822:   mat_mkl_pardiso->msglvl    = 0; /* 0: do not print 1: Print statistical information in file */
823:   mat_mkl_pardiso->phase     = -1;
824:   mat_mkl_pardiso->err       = 0;

826:   mat_mkl_pardiso->nrhs      = 1;
827:   mat_mkl_pardiso->err       = 0;
828:   mat_mkl_pardiso->phase     = -1;

830:   if (ftype == MAT_FACTOR_LU) {
831:     mat_mkl_pardiso->iparm[ 9] = 13; /* Perturb the pivot elements with 1E-13 */
832:     mat_mkl_pardiso->iparm[10] =  1; /* Use nonsymmetric permutation and scaling MPS */
833:     mat_mkl_pardiso->iparm[12] =  1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
834:   } else {
835:     mat_mkl_pardiso->iparm[ 9] = 8; /* Perturb the pivot elements with 1E-8 */
836:     mat_mkl_pardiso->iparm[10] = 0; /* Use nonsymmetric permutation and scaling MPS */
837:     mat_mkl_pardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
838: #if defined(PETSC_USE_DEBUG)
839:     mat_mkl_pardiso->iparm[26] = 1; /* Matrix checker */
840: #endif
841:   }
842:   PetscMalloc1(A->rmap->N*sizeof(INT_TYPE), &mat_mkl_pardiso->perm);
843:   for (i=0; i<A->rmap->N; i++) {
844:     mat_mkl_pardiso->perm[i] = 0;
845:   }
846:   mat_mkl_pardiso->schur_size = 0;
847:   return(0);
848: }

850: PetscErrorCode MatFactorSymbolic_AIJMKL_PARDISO_Private(Mat F,Mat A,const MatFactorInfo *info)
851: {
852:   Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;
853:   PetscErrorCode  ierr;

856:   mat_mkl_pardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
857:   PetscSetMKL_PARDISOFromOptions(F,A);

859:   /* throw away any previously computed structure */
860:   if (mat_mkl_pardiso->freeaij) {
861:     PetscFree2(mat_mkl_pardiso->ia,mat_mkl_pardiso->ja);
862:     if (mat_mkl_pardiso->iparm[34] == 1) {
863:       PetscFree(mat_mkl_pardiso->a);
864:     }
865:   }
866:   (*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);
867:   if (mat_mkl_pardiso->iparm[34] == 1) mat_mkl_pardiso->n = A->rmap->N;
868:   else mat_mkl_pardiso->n = A->rmap->N/A->rmap->bs;

870:   mat_mkl_pardiso->phase = JOB_ANALYSIS;

872:   /* reset flops counting if requested */
873:   if (mat_mkl_pardiso->iparm[18]) mat_mkl_pardiso->iparm[18] = -1;

875:   MKL_PARDISO (mat_mkl_pardiso->pt,
876:     &mat_mkl_pardiso->maxfct,
877:     &mat_mkl_pardiso->mnum,
878:     &mat_mkl_pardiso->mtype,
879:     &mat_mkl_pardiso->phase,
880:     &mat_mkl_pardiso->n,
881:     mat_mkl_pardiso->a,
882:     mat_mkl_pardiso->ia,
883:     mat_mkl_pardiso->ja,
884:     mat_mkl_pardiso->perm,
885:     &mat_mkl_pardiso->nrhs,
886:     mat_mkl_pardiso->iparm,
887:     &mat_mkl_pardiso->msglvl,
888:     NULL,
889:     NULL,
890:     &mat_mkl_pardiso->err);
891:   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);

893:   mat_mkl_pardiso->CleanUp = PETSC_TRUE;

895:   if (F->factortype == MAT_FACTOR_LU) F->ops->lufactornumeric = MatFactorNumeric_MKL_PARDISO;
896:   else F->ops->choleskyfactornumeric = MatFactorNumeric_MKL_PARDISO;

898:   F->ops->solve           = MatSolve_MKL_PARDISO;
899:   F->ops->solvetranspose  = MatSolveTranspose_MKL_PARDISO;
900:   F->ops->matsolve        = MatMatSolve_MKL_PARDISO;
901:   return(0);
902: }

904: PetscErrorCode MatLUFactorSymbolic_AIJMKL_PARDISO(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
905: {

909:   MatFactorSymbolic_AIJMKL_PARDISO_Private(F, A, info);
910:   return(0);
911: }

913: #if !defined(PETSC_USE_COMPLEX)
914: PetscErrorCode MatGetInertia_MKL_PARDISO(Mat F,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
915: {
916:   Mat_MKL_PARDISO   *mat_mkl_pardiso=(Mat_MKL_PARDISO*)F->data;

919:   if (nneg) *nneg = mat_mkl_pardiso->iparm[22];
920:   if (npos) *npos = mat_mkl_pardiso->iparm[21];
921:   if (nzero) *nzero = F->rmap->N - (mat_mkl_pardiso->iparm[22] + mat_mkl_pardiso->iparm[21]);
922:   return(0);
923: }
924: #endif

926: PetscErrorCode MatCholeskyFactorSymbolic_AIJMKL_PARDISO(Mat F,Mat A,IS r,const MatFactorInfo *info)
927: {

931:   MatFactorSymbolic_AIJMKL_PARDISO_Private(F, A, info);
932: #if defined(PETSC_USE_COMPLEX)
933:   F->ops->getinertia = NULL;
934: #else
935:   F->ops->getinertia = MatGetInertia_MKL_PARDISO;
936: #endif
937:   return(0);
938: }

940: PetscErrorCode MatView_MKL_PARDISO(Mat A, PetscViewer viewer)
941: {
942:   PetscErrorCode    ierr;
943:   PetscBool         iascii;
944:   PetscViewerFormat format;
945:   Mat_MKL_PARDISO   *mat_mkl_pardiso=(Mat_MKL_PARDISO*)A->data;
946:   PetscInt          i;

949:   if (A->ops->solve != MatSolve_MKL_PARDISO) return(0);

951:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
952:   if (iascii) {
953:     PetscViewerGetFormat(viewer,&format);
954:     if (format == PETSC_VIEWER_ASCII_INFO) {
955:       PetscViewerASCIIPrintf(viewer,"MKL_PARDISO run parameters:\n");
956:       PetscViewerASCIIPrintf(viewer,"MKL_PARDISO phase:             %d \n",mat_mkl_pardiso->phase);
957:       for (i=1; i<=64; i++) {
958:         PetscViewerASCIIPrintf(viewer,"MKL_PARDISO iparm[%d]:     %d \n",i, mat_mkl_pardiso->iparm[i - 1]);
959:       }
960:       PetscViewerASCIIPrintf(viewer,"MKL_PARDISO maxfct:     %d \n", mat_mkl_pardiso->maxfct);
961:       PetscViewerASCIIPrintf(viewer,"MKL_PARDISO mnum:     %d \n", mat_mkl_pardiso->mnum);
962:       PetscViewerASCIIPrintf(viewer,"MKL_PARDISO mtype:     %d \n", mat_mkl_pardiso->mtype);
963:       PetscViewerASCIIPrintf(viewer,"MKL_PARDISO n:     %d \n", mat_mkl_pardiso->n);
964:       PetscViewerASCIIPrintf(viewer,"MKL_PARDISO nrhs:     %d \n", mat_mkl_pardiso->nrhs);
965:       PetscViewerASCIIPrintf(viewer,"MKL_PARDISO msglvl:     %d \n", mat_mkl_pardiso->msglvl);
966:     }
967:   }
968:   return(0);
969: }


972: PetscErrorCode MatGetInfo_MKL_PARDISO(Mat A, MatInfoType flag, MatInfo *info)
973: {
974:   Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)A->data;

977:   info->block_size        = 1.0;
978:   info->nz_used           = mat_mkl_pardiso->iparm[17];
979:   info->nz_allocated      = mat_mkl_pardiso->iparm[17];
980:   info->nz_unneeded       = 0.0;
981:   info->assemblies        = 0.0;
982:   info->mallocs           = 0.0;
983:   info->memory            = 0.0;
984:   info->fill_ratio_given  = 0;
985:   info->fill_ratio_needed = 0;
986:   info->factor_mallocs    = 0;
987:   return(0);
988: }

990: PetscErrorCode MatMkl_PardisoSetCntl_MKL_PARDISO(Mat F,PetscInt icntl,PetscInt ival)
991: {
992:   PetscInt        backup,bs;
993:   Mat_MKL_PARDISO *mat_mkl_pardiso = (Mat_MKL_PARDISO*)F->data;

996:   if (icntl <= 64) {
997:     mat_mkl_pardiso->iparm[icntl - 1] = ival;
998:   } else {
999:     if (icntl == 65) PetscSetMKL_PARDISOThreads(ival);
1000:     else if (icntl == 66) mat_mkl_pardiso->maxfct = ival;
1001:     else if (icntl == 67) mat_mkl_pardiso->mnum = ival;
1002:     else if (icntl == 68) mat_mkl_pardiso->msglvl = ival;
1003:     else if (icntl == 69) {
1004:       void *pt[IPARM_SIZE];
1005:       backup = mat_mkl_pardiso->iparm[34];
1006:       bs = mat_mkl_pardiso->iparm[36];
1007:       mat_mkl_pardiso->mtype = ival;
1008:       MKL_PARDISO_INIT(pt, &mat_mkl_pardiso->mtype, mat_mkl_pardiso->iparm);
1009: #if defined(PETSC_USE_REAL_SINGLE)
1010:       mat_mkl_pardiso->iparm[27] = 1;
1011: #else
1012:       mat_mkl_pardiso->iparm[27] = 0;
1013: #endif
1014:       mat_mkl_pardiso->iparm[34] = backup;
1015:       mat_mkl_pardiso->iparm[36] = bs;
1016:     } else if (icntl==70) mat_mkl_pardiso->solve_interior = (PetscBool)!!ival;
1017:   }
1018:   return(0);
1019: }

1021: /*@
1022:   MatMkl_PardisoSetCntl - Set Mkl_Pardiso parameters

1024:    Logically Collective on Mat

1026:    Input Parameters:
1027: +  F - the factored matrix obtained by calling MatGetFactor()
1028: .  icntl - index of Mkl_Pardiso parameter
1029: -  ival - value of Mkl_Pardiso parameter

1031:   Options Database:
1032: .   -mat_mkl_pardiso_<icntl> <ival>

1034:    Level: beginner

1036:    References:
1037: .      Mkl_Pardiso Users' Guide

1039: .seealso: MatGetFactor()
1040: @*/
1041: PetscErrorCode MatMkl_PardisoSetCntl(Mat F,PetscInt icntl,PetscInt ival)
1042: {

1046:   PetscTryMethod(F,"MatMkl_PardisoSetCntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
1047:   return(0);
1048: }

1050: /*MC
1051:   MATSOLVERMKL_PARDISO -  A matrix type providing direct solvers (LU) for
1052:   sequential matrices via the external package MKL_PARDISO.

1054:   Works with MATSEQAIJ matrices

1056:   Use -pc_type lu -pc_factor_mat_solver_type mkl_pardiso to use this direct solver

1058:   Options Database Keys:
1059: + -mat_mkl_pardiso_65 - Number of threads to use within MKL_PARDISO
1060: . -mat_mkl_pardiso_66 - Maximum number of factors with identical sparsity structure that must be kept in memory at the same time
1061: . -mat_mkl_pardiso_67 - Indicates the actual matrix for the solution phase
1062: . -mat_mkl_pardiso_68 - Message level information
1063: . -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
1064: . -mat_mkl_pardiso_1  - Use default values
1065: . -mat_mkl_pardiso_2  - Fill-in reducing ordering for the input matrix
1066: . -mat_mkl_pardiso_4  - Preconditioned CGS/CG
1067: . -mat_mkl_pardiso_5  - User permutation
1068: . -mat_mkl_pardiso_6  - Write solution on x
1069: . -mat_mkl_pardiso_8  - Iterative refinement step
1070: . -mat_mkl_pardiso_10 - Pivoting perturbation
1071: . -mat_mkl_pardiso_11 - Scaling vectors
1072: . -mat_mkl_pardiso_12 - Solve with transposed or conjugate transposed matrix A
1073: . -mat_mkl_pardiso_13 - Improved accuracy using (non-) symmetric weighted matching
1074: . -mat_mkl_pardiso_18 - Numbers of non-zero elements
1075: . -mat_mkl_pardiso_19 - Report number of floating point operations
1076: . -mat_mkl_pardiso_21 - Pivoting for symmetric indefinite matrices
1077: . -mat_mkl_pardiso_24 - Parallel factorization control
1078: . -mat_mkl_pardiso_25 - Parallel forward/backward solve control
1079: . -mat_mkl_pardiso_27 - Matrix checker
1080: . -mat_mkl_pardiso_31 - Partial solve and computing selected components of the solution vectors
1081: . -mat_mkl_pardiso_34 - Optimal number of threads for conditional numerical reproducibility (CNR) mode
1082: - -mat_mkl_pardiso_60 - Intel MKL_PARDISO mode

1084:   Level: beginner

1086:   For more information please check  mkl_pardiso manual

1088: .seealso: PCFactorSetMatSolverType(), MatSolverType

1090: M*/
1091: static PetscErrorCode MatFactorGetSolverType_mkl_pardiso(Mat A, MatSolverType *type)
1092: {
1094:   *type = MATSOLVERMKL_PARDISO;
1095:   return(0);
1096: }

1098: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mkl_pardiso(Mat A,MatFactorType ftype,Mat *F)
1099: {
1100:   Mat             B;
1101:   PetscErrorCode  ierr;
1102:   Mat_MKL_PARDISO *mat_mkl_pardiso;
1103:   PetscBool       isSeqAIJ,isSeqBAIJ,isSeqSBAIJ;

1106:   PetscObjectBaseTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
1107:   PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);
1108:   PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);
1109:   MatCreate(PetscObjectComm((PetscObject)A),&B);
1110:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1111:   PetscStrallocpy("mkl_pardiso",&((PetscObject)B)->type_name);
1112:   MatSetUp(B);

1114:   PetscNewLog(B,&mat_mkl_pardiso);
1115:   B->data = mat_mkl_pardiso;

1117:   MatFactorMKL_PARDISOInitialize_Private(A, ftype, mat_mkl_pardiso);
1118:   if (ftype == MAT_FACTOR_LU) {
1119:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_PARDISO;
1120:     B->factortype            = MAT_FACTOR_LU;
1121:     mat_mkl_pardiso->needsym = PETSC_FALSE;
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) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO LU factor with SEQSBAIJ format! Use MAT_FACTOR_CHOLESKY instead");
1125:     else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO LU with %s format",((PetscObject)A)->type_name);
1126: #if defined(PETSC_USE_COMPLEX)
1127:     mat_mkl_pardiso->mtype = 13;
1128: #else
1129:     mat_mkl_pardiso->mtype = 11;
1130: #endif
1131:   } else {
1132:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_AIJMKL_PARDISO;
1133:     B->factortype                  = MAT_FACTOR_CHOLESKY;
1134:     if (isSeqAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqaij;
1135:     else if (isSeqBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqbaij;
1136:     else if (isSeqSBAIJ) mat_mkl_pardiso->Convert = MatMKLPardiso_Convert_seqsbaij;
1137:     else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO CHOLESKY with %s format",((PetscObject)A)->type_name);

1139:     mat_mkl_pardiso->needsym = PETSC_TRUE;
1140: #if !defined(PETSC_USE_COMPLEX)
1141:     if (A->spd_set && A->spd) mat_mkl_pardiso->mtype = 2;
1142:     else                      mat_mkl_pardiso->mtype = -2;
1143: #else
1144:     mat_mkl_pardiso->mtype = 6;
1145:     if (A->hermitian) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for PARDISO CHOLESKY with Hermitian matrices! Use MAT_FACTOR_LU instead");
1146: #endif
1147:   }
1148:   B->ops->destroy = MatDestroy_MKL_PARDISO;
1149:   B->ops->view    = MatView_MKL_PARDISO;
1150:   B->ops->getinfo = MatGetInfo_MKL_PARDISO;
1151:   B->factortype   = ftype;
1152:   B->assembled    = PETSC_TRUE;

1154:   PetscFree(B->solvertype);
1155:   PetscStrallocpy(MATSOLVERMKL_PARDISO,&B->solvertype);

1157:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mkl_pardiso);
1158:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MKL_PARDISO);
1159:   PetscObjectComposeFunction((PetscObject)B,"MatMkl_PardisoSetCntl_C",MatMkl_PardisoSetCntl_MKL_PARDISO);

1161:   *F = B;
1162:   return(0);
1163: }

1165: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MKL_Pardiso(void)
1166: {

1170:   MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mkl_pardiso);
1171:   MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mkl_pardiso);
1172:   MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mkl_pardiso);
1173:   MatSolverTypeRegister(MATSOLVERMKL_PARDISO,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mkl_pardiso);
1174:   return(0);
1175: }