Actual source code: mkl_cpardiso.c

      1: #include <petscsys.h>
      2: #include <../src/mat/impls/aij/mpi/mpiaij.h>
      3: #include <../src/mat/impls/sbaij/mpi/mpisbaij.h>

      5: #if defined(PETSC_HAVE_MKL_INTEL_ILP64)
      6:   #define MKL_ILP64
      7: #endif
      8: #include <mkl.h>
      9: #include <mkl_cluster_sparse_solver.h>

     11: /*
     12:  *  Possible mkl_cpardiso phases that controls the execution of the solver.
     13:  *  For more information check mkl_cpardiso manual.
     14:  */
     15: #define JOB_ANALYSIS                                                    11
     16: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION                            12
     17: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 13
     18: #define JOB_NUMERICAL_FACTORIZATION                                     22
     19: #define JOB_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT          23
     20: #define JOB_SOLVE_ITERATIVE_REFINEMENT                                  33
     21: #define JOB_SOLVE_FORWARD_SUBSTITUTION                                  331
     22: #define JOB_SOLVE_DIAGONAL_SUBSTITUTION                                 332
     23: #define JOB_SOLVE_BACKWARD_SUBSTITUTION                                 333
     24: #define JOB_RELEASE_OF_LU_MEMORY                                        0
     25: #define JOB_RELEASE_OF_ALL_MEMORY                                       -1

     27: #define IPARM_SIZE 64
     28: #define INT_TYPE   MKL_INT

     30: static const char *Err_MSG_CPardiso(int errNo)
     31: {
     32:   switch (errNo) {
     33:   case -1:
     34:     return "input inconsistent";
     35:     break;
     36:   case -2:
     37:     return "not enough memory";
     38:     break;
     39:   case -3:
     40:     return "reordering problem";
     41:     break;
     42:   case -4:
     43:     return "zero pivot, numerical factorization or iterative refinement problem";
     44:     break;
     45:   case -5:
     46:     return "unclassified (internal) error";
     47:     break;
     48:   case -6:
     49:     return "preordering failed (matrix types 11, 13 only)";
     50:     break;
     51:   case -7:
     52:     return "diagonal matrix problem";
     53:     break;
     54:   case -8:
     55:     return "32-bit integer overflow problem";
     56:     break;
     57:   case -9:
     58:     return "not enough memory for OOC";
     59:     break;
     60:   case -10:
     61:     return "problems with opening OOC temporary files";
     62:     break;
     63:   case -11:
     64:     return "read/write problems with the OOC data file";
     65:     break;
     66:   default:
     67:     return "unknown error";
     68:   }
     69: }

     71: #define PetscCallCluster(f) PetscCallExternalVoid("cluster_sparse_solver", f);

     73: /*
     74:  *  Internal data structure.
     75:  *  For more information check mkl_cpardiso manual.
     76:  */

     78: typedef struct {
     79:   /* Configuration vector */
     80:   INT_TYPE iparm[IPARM_SIZE];

     82:   /*
     83:    * Internal mkl_cpardiso memory location.
     84:    * After the first call to mkl_cpardiso do not modify pt, as that could cause a serious memory leak.
     85:    */
     86:   void *pt[IPARM_SIZE];

     88:   MPI_Fint comm_mkl_cpardiso;

     90:   /* Basic mkl_cpardiso info*/
     91:   INT_TYPE phase, maxfct, mnum, mtype, n, nrhs, msglvl, err;

     93:   /* Matrix values and matrix nonzero structure */
     94:   PetscScalar *a;

     96:   INT_TYPE *ia, *ja;

     98:   /* Number of non-zero elements */
     99:   INT_TYPE nz;

    101:   /* Row permutaton vector*/
    102:   INT_TYPE *perm;

    104:   /* Define is matrix preserve sparse structure. */
    105:   MatStructure matstruc;

    107:   PetscErrorCode (*ConvertToTriples)(Mat, MatReuse, PetscInt *, PetscInt **, PetscInt **, PetscScalar **);

    109:   /* True if mkl_cpardiso function have been used. */
    110:   PetscBool CleanUp;
    111: } Mat_MKL_CPARDISO;

    113: /*
    114:  * Copy the elements of matrix A.
    115:  * Input:
    116:  *   - Mat A: MATSEQAIJ matrix
    117:  *   - int shift: matrix index.
    118:  *     - 0 for c representation
    119:  *     - 1 for fortran representation
    120:  *   - MatReuse reuse:
    121:  *     - MAT_INITIAL_MATRIX: Create a new aij representation
    122:  *     - MAT_REUSE_MATRIX: Reuse all aij representation and just change values
    123:  * Output:
    124:  *   - int *nnz: Number of nonzero-elements.
    125:  *   - int **r pointer to i index
    126:  *   - int **c pointer to j elements
    127:  *   - MATRIXTYPE **v: Non-zero elements
    128:  */
    129: static PetscErrorCode MatCopy_seqaij_seqaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
    130: {
    131:   Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;

    133:   PetscFunctionBegin;
    134:   *v = aa->a;
    135:   if (reuse == MAT_INITIAL_MATRIX) {
    136:     *r   = (INT_TYPE *)aa->i;
    137:     *c   = (INT_TYPE *)aa->j;
    138:     *nnz = aa->nz;
    139:   }
    140:   PetscFunctionReturn(PETSC_SUCCESS);
    141: }

    143: static PetscErrorCode MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
    144: {
    145:   const PetscInt    *ai, *aj, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
    146:   PetscInt           rstart, nz, i, j, countA, countB;
    147:   PetscInt          *row, *col;
    148:   const PetscScalar *av, *bv;
    149:   PetscScalar       *val;
    150:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
    151:   Mat_SeqAIJ        *aa  = (Mat_SeqAIJ *)mat->A->data;
    152:   Mat_SeqAIJ        *bb  = (Mat_SeqAIJ *)mat->B->data;
    153:   PetscInt           colA_start, jB, jcol;

    155:   PetscFunctionBegin;
    156:   ai     = aa->i;
    157:   aj     = aa->j;
    158:   bi     = bb->i;
    159:   bj     = bb->j;
    160:   rstart = A->rmap->rstart;
    161:   av     = aa->a;
    162:   bv     = bb->a;

    164:   garray = mat->garray;

    166:   if (reuse == MAT_INITIAL_MATRIX) {
    167:     nz   = aa->nz + bb->nz;
    168:     *nnz = nz;
    169:     PetscCall(PetscMalloc3(m + 1, &row, nz, &col, nz, &val));
    170:     *r = row;
    171:     *c = col;
    172:     *v = val;
    173:   } else {
    174:     row = *r;
    175:     col = *c;
    176:     val = *v;
    177:   }

    179:   nz = 0;
    180:   for (i = 0; i < m; i++) {
    181:     row[i] = nz;
    182:     countA = ai[i + 1] - ai[i];
    183:     countB = bi[i + 1] - bi[i];
    184:     ajj    = aj + ai[i]; /* ptr to the beginning of this row */
    185:     bjj    = bj + bi[i];

    187:     /* B part, smaller col index */
    188:     colA_start = rstart + ajj[0]; /* the smallest global col index of A */
    189:     jB         = 0;
    190:     for (j = 0; j < countB; j++) {
    191:       jcol = garray[bjj[j]];
    192:       if (jcol > colA_start) break;
    193:       col[nz]   = jcol;
    194:       val[nz++] = *bv++;
    195:     }
    196:     jB = j;

    198:     /* A part */
    199:     for (j = 0; j < countA; j++) {
    200:       col[nz]   = rstart + ajj[j];
    201:       val[nz++] = *av++;
    202:     }

    204:     /* B part, larger col index */
    205:     for (j = jB; j < countB; j++) {
    206:       col[nz]   = garray[bjj[j]];
    207:       val[nz++] = *bv++;
    208:     }
    209:   }
    210:   row[m] = nz;
    211:   PetscFunctionReturn(PETSC_SUCCESS);
    212: }

    214: static PetscErrorCode MatConvertToTriples_mpibaij_mpibaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
    215: {
    216:   const PetscInt    *ai, *aj, *bi, *bj, *garray, bs = A->rmap->bs, bs2 = bs * bs, m = A->rmap->n / bs, *ajj, *bjj;
    217:   PetscInt           rstart, nz, i, j, countA, countB;
    218:   PetscInt          *row, *col;
    219:   const PetscScalar *av, *bv;
    220:   PetscScalar       *val;
    221:   Mat_MPIBAIJ       *mat = (Mat_MPIBAIJ *)A->data;
    222:   Mat_SeqBAIJ       *aa  = (Mat_SeqBAIJ *)mat->A->data;
    223:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ *)mat->B->data;
    224:   PetscInt           colA_start, jB, jcol;

    226:   PetscFunctionBegin;
    227:   ai     = aa->i;
    228:   aj     = aa->j;
    229:   bi     = bb->i;
    230:   bj     = bb->j;
    231:   rstart = A->rmap->rstart / bs;
    232:   av     = aa->a;
    233:   bv     = bb->a;

    235:   garray = mat->garray;

    237:   if (reuse == MAT_INITIAL_MATRIX) {
    238:     nz   = aa->nz + bb->nz;
    239:     *nnz = nz;
    240:     PetscCall(PetscMalloc3(m + 1, &row, nz, &col, nz * bs2, &val));
    241:     *r = row;
    242:     *c = col;
    243:     *v = val;
    244:   } else {
    245:     row = *r;
    246:     col = *c;
    247:     val = *v;
    248:   }

    250:   nz = 0;
    251:   for (i = 0; i < m; i++) {
    252:     row[i] = nz + 1;
    253:     countA = ai[i + 1] - ai[i];
    254:     countB = bi[i + 1] - bi[i];
    255:     ajj    = aj + ai[i]; /* ptr to the beginning of this row */
    256:     bjj    = bj + bi[i];

    258:     /* B part, smaller col index */
    259:     colA_start = rstart + (countA > 0 ? ajj[0] : 0); /* the smallest global col index of A */
    260:     jB         = 0;
    261:     for (j = 0; j < countB; j++) {
    262:       jcol = garray[bjj[j]];
    263:       if (jcol > colA_start) break;
    264:       col[nz++] = jcol + 1;
    265:     }
    266:     jB = j;
    267:     PetscCall(PetscArraycpy(val, bv, jB * bs2));
    268:     val += jB * bs2;
    269:     bv += jB * bs2;

    271:     /* A part */
    272:     for (j = 0; j < countA; j++) col[nz++] = rstart + ajj[j] + 1;
    273:     PetscCall(PetscArraycpy(val, av, countA * bs2));
    274:     val += countA * bs2;
    275:     av += countA * bs2;

    277:     /* B part, larger col index */
    278:     for (j = jB; j < countB; j++) col[nz++] = garray[bjj[j]] + 1;
    279:     PetscCall(PetscArraycpy(val, bv, (countB - jB) * bs2));
    280:     val += (countB - jB) * bs2;
    281:     bv += (countB - jB) * bs2;
    282:   }
    283:   row[m] = nz + 1;
    284:   PetscFunctionReturn(PETSC_SUCCESS);
    285: }

    287: static PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
    288: {
    289:   const PetscInt    *ai, *aj, *bi, *bj, *garray, bs = A->rmap->bs, bs2 = bs * bs, m = A->rmap->n / bs, *ajj, *bjj;
    290:   PetscInt           rstart, nz, i, j, countA, countB;
    291:   PetscInt          *row, *col;
    292:   const PetscScalar *av, *bv;
    293:   PetscScalar       *val;
    294:   Mat_MPISBAIJ      *mat = (Mat_MPISBAIJ *)A->data;
    295:   Mat_SeqSBAIJ      *aa  = (Mat_SeqSBAIJ *)mat->A->data;
    296:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ *)mat->B->data;

    298:   PetscFunctionBegin;
    299:   ai     = aa->i;
    300:   aj     = aa->j;
    301:   bi     = bb->i;
    302:   bj     = bb->j;
    303:   rstart = A->rmap->rstart / bs;
    304:   av     = aa->a;
    305:   bv     = bb->a;

    307:   garray = mat->garray;

    309:   if (reuse == MAT_INITIAL_MATRIX) {
    310:     nz   = aa->nz + bb->nz;
    311:     *nnz = nz;
    312:     PetscCall(PetscMalloc3(m + 1, &row, nz, &col, nz * bs2, &val));
    313:     *r = row;
    314:     *c = col;
    315:     *v = val;
    316:   } else {
    317:     row = *r;
    318:     col = *c;
    319:     val = *v;
    320:   }

    322:   nz = 0;
    323:   for (i = 0; i < m; i++) {
    324:     row[i] = nz + 1;
    325:     countA = ai[i + 1] - ai[i];
    326:     countB = bi[i + 1] - bi[i];
    327:     ajj    = aj + ai[i]; /* ptr to the beginning of this row */
    328:     bjj    = bj + bi[i];

    330:     /* A part */
    331:     for (j = 0; j < countA; j++) col[nz++] = rstart + ajj[j] + 1;
    332:     PetscCall(PetscArraycpy(val, av, countA * bs2));
    333:     val += countA * bs2;
    334:     av += countA * bs2;

    336:     /* B part, larger col index */
    337:     for (j = 0; j < countB; j++) col[nz++] = garray[bjj[j]] + 1;
    338:     PetscCall(PetscArraycpy(val, bv, countB * bs2));
    339:     val += countB * bs2;
    340:     bv += countB * bs2;
    341:   }
    342:   row[m] = nz + 1;
    343:   PetscFunctionReturn(PETSC_SUCCESS);
    344: }

    346: /*
    347:  * Free memory for Mat_MKL_CPARDISO structure and pointers to objects.
    348:  */
    349: static PetscErrorCode MatDestroy_MKL_CPARDISO(Mat A)
    350: {
    351:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
    352:   MPI_Comm          comm;

    354:   PetscFunctionBegin;
    355:   /* Terminate instance, deallocate memories */
    356:   if (mat_mkl_cpardiso->CleanUp) {
    357:     mat_mkl_cpardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;

    359:     PetscCallCluster(cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, NULL, NULL, NULL, mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs,
    360:                                            mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err));
    361:   }
    362:   if (mat_mkl_cpardiso->ConvertToTriples != MatCopy_seqaij_seqaij_MKL_CPARDISO) PetscCall(PetscFree3(mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja, mat_mkl_cpardiso->a));
    363:   comm = MPI_Comm_f2c(mat_mkl_cpardiso->comm_mkl_cpardiso);
    364:   PetscCallMPI(MPI_Comm_free(&comm));
    365:   PetscCall(PetscFree(A->data));

    367:   /* clear composed functions */
    368:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
    369:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMkl_CPardisoSetCntl_C", NULL));
    370:   PetscFunctionReturn(PETSC_SUCCESS);
    371: }

    373: /*
    374:  * Computes Ax = b
    375:  */
    376: static PetscErrorCode MatSolve_MKL_CPARDISO(Mat A, Vec b, Vec x)
    377: {
    378:   Mat_MKL_CPARDISO  *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
    379:   PetscScalar       *xarray;
    380:   const PetscScalar *barray;

    382:   PetscFunctionBegin;
    383:   mat_mkl_cpardiso->nrhs = 1;
    384:   PetscCall(VecGetArray(x, &xarray));
    385:   PetscCall(VecGetArrayRead(b, &barray));

    387:   /* solve phase */
    388:   mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
    389:   PetscCallCluster(cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
    390:                                          mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err));
    391:   PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\". Please check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));

    393:   PetscCall(VecRestoreArray(x, &xarray));
    394:   PetscCall(VecRestoreArrayRead(b, &barray));
    395:   mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
    396:   PetscFunctionReturn(PETSC_SUCCESS);
    397: }

    399: static PetscErrorCode MatForwardSolve_MKL_CPARDISO(Mat A, Vec b, Vec x)
    400: {
    401:   Mat_MKL_CPARDISO  *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
    402:   PetscScalar       *xarray;
    403:   const PetscScalar *barray;

    405:   PetscFunctionBegin;
    406:   mat_mkl_cpardiso->nrhs = 1;
    407:   PetscCall(VecGetArray(x, &xarray));
    408:   PetscCall(VecGetArrayRead(b, &barray));

    410:   /* solve phase */
    411:   mat_mkl_cpardiso->phase = JOB_SOLVE_FORWARD_SUBSTITUTION;
    412:   PetscCallCluster(cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
    413:                                          mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err));
    414:   PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\". Please check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));

    416:   PetscCall(VecRestoreArray(x, &xarray));
    417:   PetscCall(VecRestoreArrayRead(b, &barray));
    418:   mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
    419:   PetscFunctionReturn(PETSC_SUCCESS);
    420: }

    422: static PetscErrorCode MatBackwardSolve_MKL_CPARDISO(Mat A, Vec b, Vec x)
    423: {
    424:   Mat_MKL_CPARDISO  *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
    425:   PetscScalar       *xarray;
    426:   const PetscScalar *barray;

    428:   PetscFunctionBegin;
    429:   mat_mkl_cpardiso->nrhs = 1;
    430:   PetscCall(VecGetArray(x, &xarray));
    431:   PetscCall(VecGetArrayRead(b, &barray));

    433:   /* solve phase */
    434:   mat_mkl_cpardiso->phase = JOB_SOLVE_BACKWARD_SUBSTITUTION;
    435:   PetscCallCluster(cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
    436:                                          mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err));
    437:   PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\". Please check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));

    439:   PetscCall(VecRestoreArray(x, &xarray));
    440:   PetscCall(VecRestoreArrayRead(b, &barray));
    441:   mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
    442:   PetscFunctionReturn(PETSC_SUCCESS);
    443: }

    445: static PetscErrorCode MatSolveTranspose_MKL_CPARDISO(Mat A, Vec b, Vec x)
    446: {
    447:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;

    449:   PetscFunctionBegin;
    450:   mat_mkl_cpardiso->iparm[12 - 1] = PetscDefined(USE_COMPLEX) ? 1 : 2;
    451:   PetscCall(MatSolve_MKL_CPARDISO(A, b, x));
    452:   mat_mkl_cpardiso->iparm[12 - 1] = 0;
    453:   PetscFunctionReturn(PETSC_SUCCESS);
    454: }

    456: static PetscErrorCode MatMatSolve_MKL_CPARDISO(Mat A, Mat B, Mat X)
    457: {
    458:   Mat_MKL_CPARDISO  *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
    459:   PetscScalar       *xarray;
    460:   const PetscScalar *barray;

    462:   PetscFunctionBegin;
    463:   PetscCall(MatGetSize(B, NULL, (PetscInt *)&mat_mkl_cpardiso->nrhs));

    465:   if (mat_mkl_cpardiso->nrhs > 0) {
    466:     PetscCall(MatDenseGetArrayRead(B, &barray));
    467:     PetscCall(MatDenseGetArray(X, &xarray));

    469:     PetscCheck(barray != xarray, PETSC_COMM_SELF, PETSC_ERR_SUP, "B and X cannot share the same memory location");

    471:     /* solve phase */
    472:     mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
    473:     PetscCallCluster(cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
    474:                                            mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, (void *)barray, (void *)xarray, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err));
    475:     PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\". Please check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));
    476:     PetscCall(MatDenseRestoreArrayRead(B, &barray));
    477:     PetscCall(MatDenseRestoreArray(X, &xarray));
    478:   }
    479:   mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
    480:   PetscFunctionReturn(PETSC_SUCCESS);
    481: }

    483: /*
    484:  * LU Decomposition
    485:  */
    486: static PetscErrorCode MatFactorNumeric_MKL_CPARDISO(Mat F, Mat A, const MatFactorInfo *info)
    487: {
    488:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;

    490:   PetscFunctionBegin;
    491:   mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
    492:   PetscCall((*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_REUSE_MATRIX, &mat_mkl_cpardiso->nz, &mat_mkl_cpardiso->ia, &mat_mkl_cpardiso->ja, &mat_mkl_cpardiso->a));

    494:   mat_mkl_cpardiso->phase = JOB_NUMERICAL_FACTORIZATION;
    495:   PetscCallCluster(cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
    496:                                          mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, &mat_mkl_cpardiso->err));
    497:   PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\". Please check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));

    499:   mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
    500:   mat_mkl_cpardiso->CleanUp  = PETSC_TRUE;
    501:   PetscFunctionReturn(PETSC_SUCCESS);
    502: }

    504: /* Sets mkl_cpardiso options from the options database */
    505: static PetscErrorCode MatSetFromOptions_MKL_CPARDISO(Mat F, Mat A)
    506: {
    507:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;
    508:   PetscInt          icntl, threads;
    509:   PetscBool         flg;

    511:   PetscFunctionBegin;
    512:   PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MKL Cluster PARDISO Options", "Mat");
    513:   PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_65", "Suggested number of threads to use within MKL Cluster PARDISO", "None", threads, &threads, &flg));
    514:   if (flg) mkl_set_num_threads((int)threads);

    516:   PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_66", "Maximum number of factors with identical sparsity structure that must be kept in memory at the same time", "None", mat_mkl_cpardiso->maxfct, &icntl, &flg));
    517:   if (flg) mat_mkl_cpardiso->maxfct = icntl;

    519:   PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_67", "Indicates the actual matrix for the solution phase", "None", mat_mkl_cpardiso->mnum, &icntl, &flg));
    520:   if (flg) mat_mkl_cpardiso->mnum = icntl;

    522:   PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_68", "Message level information", "None", mat_mkl_cpardiso->msglvl, &icntl, &flg));
    523:   if (flg) mat_mkl_cpardiso->msglvl = icntl;

    525:   PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_69", "Defines the matrix type", "None", mat_mkl_cpardiso->mtype, &icntl, &flg));
    526:   if (flg) mat_mkl_cpardiso->mtype = icntl;
    527:   PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_1", "Use default values", "None", mat_mkl_cpardiso->iparm[0], &icntl, &flg));

    529:   if (flg && icntl != 0) {
    530:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_2", "Fill-in reducing ordering for the input matrix", "None", mat_mkl_cpardiso->iparm[1], &icntl, &flg));
    531:     if (flg) mat_mkl_cpardiso->iparm[1] = icntl;

    533:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_4", "Preconditioned CGS/CG", "None", mat_mkl_cpardiso->iparm[3], &icntl, &flg));
    534:     if (flg) mat_mkl_cpardiso->iparm[3] = icntl;

    536:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_5", "User permutation", "None", mat_mkl_cpardiso->iparm[4], &icntl, &flg));
    537:     if (flg) mat_mkl_cpardiso->iparm[4] = icntl;

    539:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_6", "Write solution on x", "None", mat_mkl_cpardiso->iparm[5], &icntl, &flg));
    540:     if (flg) mat_mkl_cpardiso->iparm[5] = icntl;

    542:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_8", "Iterative refinement step", "None", mat_mkl_cpardiso->iparm[7], &icntl, &flg));
    543:     if (flg) mat_mkl_cpardiso->iparm[7] = icntl;

    545:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_10", "Pivoting perturbation", "None", mat_mkl_cpardiso->iparm[9], &icntl, &flg));
    546:     if (flg) mat_mkl_cpardiso->iparm[9] = icntl;

    548:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_11", "Scaling vectors", "None", mat_mkl_cpardiso->iparm[10], &icntl, &flg));
    549:     if (flg) mat_mkl_cpardiso->iparm[10] = icntl;

    551:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_12", "Solve with transposed or conjugate transposed matrix A", "None", mat_mkl_cpardiso->iparm[11], &icntl, &flg));
    552:     if (flg) mat_mkl_cpardiso->iparm[11] = icntl;

    554:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_13", "Improved accuracy using (non-) symmetric weighted matching", "None", mat_mkl_cpardiso->iparm[12], &icntl, &flg));
    555:     if (flg) mat_mkl_cpardiso->iparm[12] = icntl;

    557:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_18", "Numbers of non-zero elements", "None", mat_mkl_cpardiso->iparm[17], &icntl, &flg));
    558:     if (flg) mat_mkl_cpardiso->iparm[17] = icntl;

    560:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_19", "Report number of floating point operations", "None", mat_mkl_cpardiso->iparm[18], &icntl, &flg));
    561:     if (flg) mat_mkl_cpardiso->iparm[18] = icntl;

    563:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_21", "Pivoting for symmetric indefinite matrices", "None", mat_mkl_cpardiso->iparm[20], &icntl, &flg));
    564:     if (flg) mat_mkl_cpardiso->iparm[20] = icntl;

    566:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_24", "Parallel factorization control", "None", mat_mkl_cpardiso->iparm[23], &icntl, &flg));
    567:     if (flg) mat_mkl_cpardiso->iparm[23] = icntl;

    569:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_25", "Parallel forward/backward solve control", "None", mat_mkl_cpardiso->iparm[24], &icntl, &flg));
    570:     if (flg) mat_mkl_cpardiso->iparm[24] = icntl;

    572:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_27", "Matrix checker", "None", mat_mkl_cpardiso->iparm[26], &icntl, &flg));
    573:     if (flg) mat_mkl_cpardiso->iparm[26] = icntl;

    575:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_31", "Partial solve and computing selected components of the solution vectors", "None", mat_mkl_cpardiso->iparm[30], &icntl, &flg));
    576:     if (flg) mat_mkl_cpardiso->iparm[30] = icntl;

    578:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_34", "Optimal number of threads for conditional numerical reproducibility (CNR) mode", "None", mat_mkl_cpardiso->iparm[33], &icntl, &flg));
    579:     if (flg) mat_mkl_cpardiso->iparm[33] = icntl;

    581:     PetscCall(PetscOptionsInt("-mat_mkl_cpardiso_60", "Intel MKL Cluster PARDISO mode", "None", mat_mkl_cpardiso->iparm[59], &icntl, &flg));
    582:     if (flg) mat_mkl_cpardiso->iparm[59] = icntl;
    583:   }

    585:   PetscOptionsEnd();
    586:   PetscFunctionReturn(PETSC_SUCCESS);
    587: }

    589: static PetscErrorCode PetscInitialize_MKL_CPARDISO(Mat A, Mat_MKL_CPARDISO *mat_mkl_cpardiso)
    590: {
    591:   PetscInt    bs;
    592:   PetscBool   match;
    593:   PetscMPIInt size;
    594:   MPI_Comm    comm;

    596:   PetscFunctionBegin;
    597:   PetscCallMPI(MPI_Comm_dup(PetscObjectComm((PetscObject)A), &comm));
    598:   PetscCallMPI(MPI_Comm_size(comm, &size));
    599:   mat_mkl_cpardiso->comm_mkl_cpardiso = MPI_Comm_c2f(comm);

    601:   mat_mkl_cpardiso->CleanUp = PETSC_FALSE;
    602:   mat_mkl_cpardiso->maxfct  = 1;
    603:   mat_mkl_cpardiso->mnum    = 1;
    604:   mat_mkl_cpardiso->n       = A->rmap->N;
    605:   if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];
    606:   mat_mkl_cpardiso->msglvl = 0;
    607:   mat_mkl_cpardiso->nrhs   = 1;
    608:   mat_mkl_cpardiso->err    = 0;
    609:   mat_mkl_cpardiso->phase  = -1;
    610:   mat_mkl_cpardiso->mtype  = PetscDefined(USE_COMPLEX) ? 13 : 11;

    612:   mat_mkl_cpardiso->iparm[27] = PetscDefined(USE_REAL_SINGLE) ? 1 : 0;

    614:   mat_mkl_cpardiso->iparm[0]  = 1;  /* Solver default parameters overridden with provided by iparm */
    615:   mat_mkl_cpardiso->iparm[1]  = 2;  /* Use METIS for fill-in reordering */
    616:   mat_mkl_cpardiso->iparm[5]  = 0;  /* Write solution into x */
    617:   mat_mkl_cpardiso->iparm[7]  = 2;  /* Max number of iterative refinement steps */
    618:   mat_mkl_cpardiso->iparm[9]  = 13; /* Perturb the pivot elements with 1E-13 */
    619:   mat_mkl_cpardiso->iparm[10] = 1;  /* Use nonsymmetric permutation and scaling MPS */
    620:   mat_mkl_cpardiso->iparm[12] = 1;  /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
    621:   mat_mkl_cpardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
    622:   mat_mkl_cpardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
    623:   mat_mkl_cpardiso->iparm[26] = 1;  /* Check input data for correctness */

    625:   mat_mkl_cpardiso->iparm[39] = 0;
    626:   if (size > 1) {
    627:     mat_mkl_cpardiso->iparm[39] = 2;
    628:     mat_mkl_cpardiso->iparm[40] = A->rmap->rstart;
    629:     mat_mkl_cpardiso->iparm[41] = A->rmap->rend - 1;
    630:   }
    631:   PetscCall(PetscObjectTypeCompareAny((PetscObject)A, &match, MATMPIBAIJ, MATMPISBAIJ, ""));
    632:   if (match) {
    633:     PetscCall(MatGetBlockSize(A, &bs));
    634:     mat_mkl_cpardiso->iparm[36] = bs;
    635:     mat_mkl_cpardiso->iparm[40] /= bs;
    636:     mat_mkl_cpardiso->iparm[41] /= bs;
    637:     mat_mkl_cpardiso->iparm[40]++;
    638:     mat_mkl_cpardiso->iparm[41]++;
    639:     mat_mkl_cpardiso->iparm[34] = 0; /* Fortran style */
    640:   } else {
    641:     mat_mkl_cpardiso->iparm[34] = 1; /* C style */
    642:   }

    644:   mat_mkl_cpardiso->perm = 0;
    645:   PetscFunctionReturn(PETSC_SUCCESS);
    646: }

    648: /*
    649:  * Symbolic decomposition. Mkl_Pardiso analysis phase.
    650:  */
    651: static PetscErrorCode MatLUFactorSymbolic_AIJMKL_CPARDISO(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
    652: {
    653:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;

    655:   PetscFunctionBegin;
    656:   mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN;

    658:   /* Set MKL_CPARDISO options from the options database */
    659:   PetscCall(MatSetFromOptions_MKL_CPARDISO(F, A));
    660:   PetscCall((*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_INITIAL_MATRIX, &mat_mkl_cpardiso->nz, &mat_mkl_cpardiso->ia, &mat_mkl_cpardiso->ja, &mat_mkl_cpardiso->a));

    662:   mat_mkl_cpardiso->n = A->rmap->N;
    663:   if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];

    665:   /* analysis phase */
    666:   mat_mkl_cpardiso->phase = JOB_ANALYSIS;

    668:   PetscCallCluster(cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
    669:                                          mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err));
    670:   PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\".Check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));

    672:   mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
    673:   F->ops->lufactornumeric   = MatFactorNumeric_MKL_CPARDISO;
    674:   F->ops->solve             = MatSolve_MKL_CPARDISO;
    675:   F->ops->forwardsolve      = MatForwardSolve_MKL_CPARDISO;
    676:   F->ops->backwardsolve     = MatBackwardSolve_MKL_CPARDISO;
    677:   F->ops->solvetranspose    = MatSolveTranspose_MKL_CPARDISO;
    678:   F->ops->matsolve          = MatMatSolve_MKL_CPARDISO;
    679:   PetscFunctionReturn(PETSC_SUCCESS);
    680: }

    682: static PetscErrorCode MatCholeskyFactorSymbolic_AIJMKL_CPARDISO(Mat F, Mat A, IS perm, const MatFactorInfo *info)
    683: {
    684:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;

    686:   PetscFunctionBegin;
    687:   mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN;

    689:   /* Set MKL_CPARDISO options from the options database */
    690:   PetscCall(MatSetFromOptions_MKL_CPARDISO(F, A));
    691:   PetscCall((*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_INITIAL_MATRIX, &mat_mkl_cpardiso->nz, &mat_mkl_cpardiso->ia, &mat_mkl_cpardiso->ja, &mat_mkl_cpardiso->a));

    693:   mat_mkl_cpardiso->n = A->rmap->N;
    694:   if (mat_mkl_cpardiso->iparm[36]) mat_mkl_cpardiso->n /= mat_mkl_cpardiso->iparm[36];
    695:   PetscCheck(!PetscDefined(USE_COMPLEX), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "No support for PARDISO CHOLESKY with complex scalars! Use MAT_FACTOR_LU instead");
    696:   if (A->spd == PETSC_BOOL3_TRUE) mat_mkl_cpardiso->mtype = 2;
    697:   else mat_mkl_cpardiso->mtype = -2;

    699:   /* analysis phase */
    700:   mat_mkl_cpardiso->phase = JOB_ANALYSIS;

    702:   PetscCallCluster(cluster_sparse_solver(mat_mkl_cpardiso->pt, &mat_mkl_cpardiso->maxfct, &mat_mkl_cpardiso->mnum, &mat_mkl_cpardiso->mtype, &mat_mkl_cpardiso->phase, &mat_mkl_cpardiso->n, mat_mkl_cpardiso->a, mat_mkl_cpardiso->ia, mat_mkl_cpardiso->ja,
    703:                                          mat_mkl_cpardiso->perm, &mat_mkl_cpardiso->nrhs, mat_mkl_cpardiso->iparm, &mat_mkl_cpardiso->msglvl, NULL, NULL, &mat_mkl_cpardiso->comm_mkl_cpardiso, (PetscInt *)&mat_mkl_cpardiso->err));
    704:   PetscCheck(mat_mkl_cpardiso->err >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MKL Cluster PARDISO: err=%d, msg = \"%s\".Check manual", mat_mkl_cpardiso->err, Err_MSG_CPardiso(mat_mkl_cpardiso->err));

    706:   mat_mkl_cpardiso->CleanUp     = PETSC_TRUE;
    707:   F->ops->choleskyfactornumeric = MatFactorNumeric_MKL_CPARDISO;
    708:   F->ops->solve                 = MatSolve_MKL_CPARDISO;
    709:   F->ops->solvetranspose        = MatSolveTranspose_MKL_CPARDISO;
    710:   F->ops->matsolve              = MatMatSolve_MKL_CPARDISO;
    711:   if (A->spd == PETSC_BOOL3_TRUE) {
    712:     F->ops->forwardsolve  = MatForwardSolve_MKL_CPARDISO;
    713:     F->ops->backwardsolve = MatBackwardSolve_MKL_CPARDISO;
    714:   }
    715:   PetscFunctionReturn(PETSC_SUCCESS);
    716: }

    718: static PetscErrorCode MatView_MKL_CPARDISO(Mat A, PetscViewer viewer)
    719: {
    720:   PetscBool         isascii;
    721:   PetscViewerFormat format;
    722:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;
    723:   PetscInt          i;

    725:   PetscFunctionBegin;
    726:   /* check if matrix is mkl_cpardiso type */
    727:   if (A->ops->solve != MatSolve_MKL_CPARDISO) PetscFunctionReturn(PETSC_SUCCESS);

    729:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
    730:   if (isascii) {
    731:     PetscCall(PetscViewerGetFormat(viewer, &format));
    732:     if (format == PETSC_VIEWER_ASCII_INFO) {
    733:       PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO run parameters:\n"));
    734:       PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO phase:             %d \n", mat_mkl_cpardiso->phase));
    735:       for (i = 1; i <= 64; i++) PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO iparm[%d]:     %d \n", i, mat_mkl_cpardiso->iparm[i - 1]));
    736:       PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO maxfct:     %d \n", mat_mkl_cpardiso->maxfct));
    737:       PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO mnum:     %d \n", mat_mkl_cpardiso->mnum));
    738:       PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO mtype:     %d \n", mat_mkl_cpardiso->mtype));
    739:       PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO n:     %d \n", mat_mkl_cpardiso->n));
    740:       PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO nrhs:     %d \n", mat_mkl_cpardiso->nrhs));
    741:       PetscCall(PetscViewerASCIIPrintf(viewer, "MKL Cluster PARDISO msglvl:     %d \n", mat_mkl_cpardiso->msglvl));
    742:     }
    743:   }
    744:   PetscFunctionReturn(PETSC_SUCCESS);
    745: }

    747: static PetscErrorCode MatGetInfo_MKL_CPARDISO(Mat A, MatInfoType flag, MatInfo *info)
    748: {
    749:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)A->data;

    751:   PetscFunctionBegin;
    752:   info->block_size        = 1.0;
    753:   info->nz_allocated      = mat_mkl_cpardiso->nz + 0.0;
    754:   info->nz_unneeded       = 0.0;
    755:   info->assemblies        = 0.0;
    756:   info->mallocs           = 0.0;
    757:   info->memory            = 0.0;
    758:   info->fill_ratio_given  = 0;
    759:   info->fill_ratio_needed = 0;
    760:   info->factor_mallocs    = 0;
    761:   PetscFunctionReturn(PETSC_SUCCESS);
    762: }

    764: static PetscErrorCode MatMkl_CPardisoSetCntl_MKL_CPARDISO(Mat F, PetscInt icntl, PetscInt ival)
    765: {
    766:   Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO *)F->data;

    768:   PetscFunctionBegin;
    769:   if (icntl <= 64) {
    770:     mat_mkl_cpardiso->iparm[icntl - 1] = ival;
    771:   } else {
    772:     if (icntl == 65) mkl_set_num_threads((int)ival);
    773:     else if (icntl == 66) mat_mkl_cpardiso->maxfct = ival;
    774:     else if (icntl == 67) mat_mkl_cpardiso->mnum = ival;
    775:     else if (icntl == 68) mat_mkl_cpardiso->msglvl = ival;
    776:     else if (icntl == 69) mat_mkl_cpardiso->mtype = ival;
    777:   }
    778:   PetscFunctionReturn(PETSC_SUCCESS);
    779: }

    781: /*@
    782:   MatMkl_CPardisoSetCntl - Set MKL Cluster PARDISO parameters
    783:   <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>

    785:   Logically Collective

    787:   Input Parameters:
    788: + F     - the factored matrix obtained by calling `MatGetFactor()`
    789: . icntl - index of MKL Cluster PARDISO parameter
    790: - ival  - value of MKL Cluster PARDISO parameter

    792:   Options Database Key:
    793: . -mat_mkl_cpardiso_icntl ival - set the option numbered icntl to ival

    795:   Level: intermediate

    797:   Note:
    798:   This routine cannot be used if you are solving the linear system with `TS`, `SNES`, or `KSP`, only if you directly call `MatGetFactor()` so use the options
    799:   database approach when working with `TS`, `SNES`, or `KSP`. See `MATSOLVERMKL_CPARDISO` for the options

    801: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MATMPIAIJ`, `MATSOLVERMKL_CPARDISO`
    802: @*/
    803: PetscErrorCode MatMkl_CPardisoSetCntl(Mat F, PetscInt icntl, PetscInt ival)
    804: {
    805:   PetscFunctionBegin;
    806:   PetscTryMethod(F, "MatMkl_CPardisoSetCntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
    807:   PetscFunctionReturn(PETSC_SUCCESS);
    808: }

    810: /*MC
    811:   MATSOLVERMKL_CPARDISO -  A matrix type providing direct solvers (LU) for parallel matrices via the external package MKL Cluster PARDISO
    812:   <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>

    814:   Works with `MATMPIAIJ` matrices

    816:   Use `-pc_type lu` `-pc_factor_mat_solver_type mkl_cpardiso` to use this direct solver

    818:   Options Database Keys:
    819: + -mat_mkl_cpardiso_65 - Suggested number of threads to use within MKL Cluster PARDISO
    820: . -mat_mkl_cpardiso_66 - Maximum number of factors with identical sparsity structure that must be kept in memory at the same time
    821: . -mat_mkl_cpardiso_67 - Indicates the actual matrix for the solution phase
    822: . -mat_mkl_cpardiso_68 - Message level information, use 1 to get detailed information on the solver options
    823: . -mat_mkl_cpardiso_69 - Defines the matrix type. IMPORTANT: When you set this flag, iparm parameters are going to be set to the default ones for the matrix type
    824: . -mat_mkl_cpardiso_1  - Use default values
    825: . -mat_mkl_cpardiso_2  - Fill-in reducing ordering for the input matrix
    826: . -mat_mkl_cpardiso_4  - Preconditioned CGS/CG
    827: . -mat_mkl_cpardiso_5  - User permutation
    828: . -mat_mkl_cpardiso_6  - Write solution on x
    829: . -mat_mkl_cpardiso_8  - Iterative refinement step
    830: . -mat_mkl_cpardiso_10 - Pivoting perturbation
    831: . -mat_mkl_cpardiso_11 - Scaling vectors
    832: . -mat_mkl_cpardiso_12 - Solve with transposed or conjugate transposed matrix A
    833: . -mat_mkl_cpardiso_13 - Improved accuracy using (non-) symmetric weighted matching
    834: . -mat_mkl_cpardiso_18 - Numbers of non-zero elements
    835: . -mat_mkl_cpardiso_19 - Report number of floating point operations
    836: . -mat_mkl_cpardiso_21 - Pivoting for symmetric indefinite matrices
    837: . -mat_mkl_cpardiso_24 - Parallel factorization control
    838: . -mat_mkl_cpardiso_25 - Parallel forward/backward solve control
    839: . -mat_mkl_cpardiso_27 - Matrix checker
    840: . -mat_mkl_cpardiso_31 - Partial solve and computing selected components of the solution vectors
    841: . -mat_mkl_cpardiso_34 - Optimal number of threads for conditional numerical reproducibility (CNR) mode
    842: - -mat_mkl_cpardiso_60 - Intel MKL Cluster PARDISO mode

    844:   Level: beginner

    846:   Notes:
    847:   Use `-mat_mkl_cpardiso_68 1` to display the number of threads the solver is using. MKL does not provide a way to directly access this
    848:   information.

    850:   For more information on the options check
    851:   <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>

    853: .seealso: [](ch_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMkl_CPardisoSetCntl()`, `MatGetFactor()`, `MATSOLVERMKL_PARDISO`
    854: M*/

    856: static PetscErrorCode MatFactorGetSolverType_mkl_cpardiso(Mat A, MatSolverType *type)
    857: {
    858:   PetscFunctionBegin;
    859:   *type = MATSOLVERMKL_CPARDISO;
    860:   PetscFunctionReturn(PETSC_SUCCESS);
    861: }

    863: /* MatGetFactor for MPI AIJ matrices */
    864: static PetscErrorCode MatGetFactor_mpiaij_mkl_cpardiso(Mat A, MatFactorType ftype, Mat *F)
    865: {
    866:   Mat               B;
    867:   Mat_MKL_CPARDISO *mat_mkl_cpardiso;
    868:   PetscBool         isSeqAIJ, isMPIBAIJ, isMPISBAIJ;

    870:   PetscFunctionBegin;
    871:   /* Create the factorization matrix */

    873:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
    874:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &isMPIBAIJ));
    875:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPISBAIJ, &isMPISBAIJ));
    876:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
    877:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
    878:   PetscCall(PetscStrallocpy("mkl_cpardiso", &((PetscObject)B)->type_name));
    879:   PetscCall(MatSetUp(B));

    881:   PetscCall(PetscNew(&mat_mkl_cpardiso));

    883:   if (isSeqAIJ) mat_mkl_cpardiso->ConvertToTriples = MatCopy_seqaij_seqaij_MKL_CPARDISO;
    884:   else if (isMPIBAIJ) mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpibaij_mpibaij_MKL_CPARDISO;
    885:   else if (isMPISBAIJ) mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij_MKL_CPARDISO;
    886:   else mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO;

    888:   if (ftype == MAT_FACTOR_LU) B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_CPARDISO;
    889:   else B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_AIJMKL_CPARDISO;
    890:   B->ops->destroy = MatDestroy_MKL_CPARDISO;

    892:   B->ops->view    = MatView_MKL_CPARDISO;
    893:   B->ops->getinfo = MatGetInfo_MKL_CPARDISO;

    895:   B->factortype = ftype;
    896:   B->assembled  = PETSC_TRUE; /* required by -ksp_view */

    898:   B->data = mat_mkl_cpardiso;

    900:   /* set solvertype */
    901:   PetscCall(PetscFree(B->solvertype));
    902:   PetscCall(PetscStrallocpy(MATSOLVERMKL_CPARDISO, &B->solvertype));

    904:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mkl_cpardiso));
    905:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMkl_CPardisoSetCntl_C", MatMkl_CPardisoSetCntl_MKL_CPARDISO));
    906:   PetscCall(PetscInitialize_MKL_CPARDISO(A, mat_mkl_cpardiso));

    908:   *F = B;
    909:   PetscFunctionReturn(PETSC_SUCCESS);
    910: }

    912: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_MKL_CPardiso(void)
    913: {
    914:   PetscFunctionBegin;
    915:   PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
    916:   PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
    917:   PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_mpiaij_mkl_cpardiso));
    918:   PetscCall(MatSolverTypeRegister(MATSOLVERMKL_CPARDISO, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_mpiaij_mkl_cpardiso));
    919:   PetscFunctionReturn(PETSC_SUCCESS);
    920: }