Date: 2021-03-03T22:26:19+00:00

Actual source code: baijmkl.c
petsc-3.14.5 2021-03-03
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      1: /*
      2:   Defines basic operations for the MATSEQBAIJMKL matrix class.
      3:   Uses sparse BLAS operations from the Intel Math Kernel Library (MKL)
      4:   wherever possible. If used MKL verion is older than 11.3 PETSc default
      5:   code for sparse matrix operations is used.
      6: */

      8: #include <../src/mat/impls/baij/seq/baij.h>
      9: #include <../src/mat/impls/baij/seq/baijmkl/baijmkl.h>
     10: #include <mkl_spblas.h>

     12: static PetscBool PetscSeqBAIJSupportsZeroBased(void)
     13: {
     14:   static PetscBool set = PETSC_FALSE,value;
     15:   int              n=1,ia[1],ja[1];
     16:   float            a[1];
     17:   sparse_status_t  status;
     18:   sparse_matrix_t  A;

     20:   if (!set) {
     21:     status = mkl_sparse_s_create_bsr(&A,SPARSE_INDEX_BASE_ZERO,SPARSE_LAYOUT_COLUMN_MAJOR,n,n,n,ia,ia,ja,a);
     22:     value  = (status != SPARSE_STATUS_NOT_SUPPORTED) ? PETSC_TRUE : PETSC_FALSE;
     23:     (void)   mkl_sparse_destroy(A);
     24:     set    = PETSC_TRUE;
     25:   }
     26:   return value;
     27: }

     29: typedef struct {
     30:   PetscBool           sparse_optimized; /* If PETSC_TRUE, then mkl_sparse_optimize() has been called. */
     31:   sparse_matrix_t     bsrA; /* "Handle" used by SpMV2 inspector-executor routines. */
     32:   struct matrix_descr descr;
     33:   PetscInt            *ai1;
     34:   PetscInt            *aj1;
     35: } Mat_SeqBAIJMKL;

     37: static PetscErrorCode MatAssemblyEnd_SeqBAIJMKL(Mat A, MatAssemblyType mode);
     38: extern PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat,MatAssemblyType);

     40: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJMKL_SeqBAIJ(Mat A,MatType type,MatReuse reuse,Mat *newmat)
     41: {
     42:   /* This routine is only called to convert a MATBAIJMKL to its base PETSc type, */
     43:   /* so we will ignore 'MatType type'. */
     45:   Mat            B        = *newmat;
     46:   Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL*)A->spptr;

     49:   if (reuse == MAT_INITIAL_MATRIX) {
     50:     MatDuplicate(A,MAT_COPY_VALUES,&B);
     51:   }

     53:   /* Reset the original function pointers. */
     54:   B->ops->duplicate        = MatDuplicate_SeqBAIJ;
     55:   B->ops->assemblyend      = MatAssemblyEnd_SeqBAIJ;
     56:   B->ops->destroy          = MatDestroy_SeqBAIJ;
     57:   B->ops->multtranspose    = MatMultTranspose_SeqBAIJ;
     58:   B->ops->multtransposeadd = MatMultTransposeAdd_SeqBAIJ;
     59:   B->ops->scale            = MatScale_SeqBAIJ;
     60:   B->ops->diagonalscale    = MatDiagonalScale_SeqBAIJ;
     61:   B->ops->axpy             = MatAXPY_SeqBAIJ;

     63:   switch (A->rmap->bs) {
     64:     case 1:
     65:       B->ops->mult    = MatMult_SeqBAIJ_1;
     66:       B->ops->multadd = MatMultAdd_SeqBAIJ_1;
     67:       break;
     68:     case 2:
     69:       B->ops->mult    = MatMult_SeqBAIJ_2;
     70:       B->ops->multadd = MatMultAdd_SeqBAIJ_2;
     71:       break;
     72:     case 3:
     73:       B->ops->mult    = MatMult_SeqBAIJ_3;
     74:       B->ops->multadd = MatMultAdd_SeqBAIJ_3;
     75:       break;
     76:     case 4:
     77:       B->ops->mult    = MatMult_SeqBAIJ_4;
     78:       B->ops->multadd = MatMultAdd_SeqBAIJ_4;
     79:       break;
     80:     case 5:
     81:       B->ops->mult    = MatMult_SeqBAIJ_5;
     82:       B->ops->multadd = MatMultAdd_SeqBAIJ_5;
     83:       break;
     84:     case 6:
     85:       B->ops->mult    = MatMult_SeqBAIJ_6;
     86:       B->ops->multadd = MatMultAdd_SeqBAIJ_6;
     87:       break;
     88:     case 7:
     89:       B->ops->mult    = MatMult_SeqBAIJ_7;
     90:       B->ops->multadd = MatMultAdd_SeqBAIJ_7;
     91:       break;
     92:     case 11:
     93:       B->ops->mult    = MatMult_SeqBAIJ_11;
     94:       B->ops->multadd = MatMultAdd_SeqBAIJ_11;
     95:       break;
     96:     case 15:
     97:       B->ops->mult    = MatMult_SeqBAIJ_15_ver1;
     98:       B->ops->multadd = MatMultAdd_SeqBAIJ_N;
     99:       break;
    100:     default:
    101:       B->ops->mult    = MatMult_SeqBAIJ_N;
    102:       B->ops->multadd = MatMultAdd_SeqBAIJ_N;
    103:       break;
    104:   }
    105:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqbaijmkl_seqbaij_C",NULL);

    107:   /* Free everything in the Mat_SeqBAIJMKL data structure. Currently, this
    108:    * simply involves destroying the MKL sparse matrix handle and then freeing
    109:    * the spptr pointer. */
    110:   if (reuse == MAT_INITIAL_MATRIX) baijmkl = (Mat_SeqBAIJMKL*)B->spptr;

    112:   if (baijmkl->sparse_optimized) {
    113:     sparse_status_t stat;
    114:     stat = mkl_sparse_destroy(baijmkl->bsrA);
    115:     if (stat != SPARSE_STATUS_SUCCESS) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"Intel MKL error: error in mkl_sparse_destroy");
    116:   }
    117:   PetscFree2(baijmkl->ai1,baijmkl->aj1);
    118:   PetscFree(B->spptr);

    120:   /* Change the type of B to MATSEQBAIJ. */
    121:   PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJ);

    123:   *newmat = B;
    124:   return(0);
    125: }

    127: static PetscErrorCode MatDestroy_SeqBAIJMKL(Mat A)
    128: {
    130:   Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL*) A->spptr;

    133:   if (baijmkl) {
    134:     /* Clean up everything in the Mat_SeqBAIJMKL data structure, then free A->spptr. */
    135:     if (baijmkl->sparse_optimized) {
    136:       sparse_status_t stat = SPARSE_STATUS_SUCCESS;
    137:       stat = mkl_sparse_destroy(baijmkl->bsrA);
    138:       if (stat != SPARSE_STATUS_SUCCESS) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"Intel MKL error: error in mkl_sparse_destroy");
    139:     }
    140:     PetscFree2(baijmkl->ai1,baijmkl->aj1);
    141:     PetscFree(A->spptr);
    142:   }

    144:   /* Change the type of A back to SEQBAIJ and use MatDestroy_SeqBAIJ()
    145:    * to destroy everything that remains. */
    146:   PetscObjectChangeTypeName((PetscObject)A, MATSEQBAIJ);
    147:   MatDestroy_SeqBAIJ(A);
    148:   return(0);
    149: }

    151: static PetscErrorCode MatSeqBAIJMKL_create_mkl_handle(Mat A)
    152: {
    153:   Mat_SeqBAIJ     *a = (Mat_SeqBAIJ*)A->data;
    154:   Mat_SeqBAIJMKL  *baijmkl = (Mat_SeqBAIJMKL*)A->spptr;
    155:   PetscInt        mbs, nbs, nz, bs;
    156:   MatScalar       *aa;
    157:   PetscInt        *aj,*ai;
    158:   sparse_status_t stat;
    159:   PetscErrorCode  ierr;
    160:   PetscInt        i;

    163:   if (baijmkl->sparse_optimized) {
    164:     /* Matrix has been previously assembled and optimized. Must destroy old
    165:      * matrix handle before running the optimization step again. */
    166:     PetscFree2(baijmkl->ai1,baijmkl->aj1);
    167:     stat = mkl_sparse_destroy(baijmkl->bsrA);CHKERRMKL(stat);
    168:   }
    169:   baijmkl->sparse_optimized = PETSC_FALSE;

    171:   /* Now perform the SpMV2 setup and matrix optimization. */
    172:   baijmkl->descr.type        = SPARSE_MATRIX_TYPE_GENERAL;
    173:   baijmkl->descr.mode        = SPARSE_FILL_MODE_LOWER;
    174:   baijmkl->descr.diag        = SPARSE_DIAG_NON_UNIT;
    175:   mbs = a->mbs;
    176:   nbs = a->nbs;
    177:   nz  = a->nz;
    178:   bs  = A->rmap->bs;
    179:   aa  = a->a;

    181:   if ((nz!=0) & !(A->structure_only)) {
    182:     /* Create a new, optimized sparse matrix handle only if the matrix has nonzero entries.
    183:      * The MKL sparse-inspector executor routines don't like being passed an empty matrix. */
    184:     if (PetscSeqBAIJSupportsZeroBased()) {
    185:       aj   = a->j;
    186:       ai   = a->i;
    187:       stat = mkl_sparse_x_create_bsr(&(baijmkl->bsrA),SPARSE_INDEX_BASE_ZERO,SPARSE_LAYOUT_COLUMN_MAJOR,mbs,nbs,bs,ai,ai+1,aj,aa);CHKERRMKL(stat);
    188:     } else {
    189:       PetscMalloc2(mbs+1,&ai,nz,&aj);
    190:       for (i=0;i<mbs+1;i++) ai[i] = a->i[i]+1;
    191:       for (i=0;i<nz;i++) aj[i] = a->j[i]+1;
    192:       aa   = a->a;
    193:       stat = mkl_sparse_x_create_bsr(&baijmkl->bsrA,SPARSE_INDEX_BASE_ONE,SPARSE_LAYOUT_COLUMN_MAJOR,mbs,nbs,bs,ai,ai+1,aj,aa);CHKERRMKL(stat);
    194:       baijmkl->ai1 = ai;
    195:       baijmkl->aj1 = aj;
    196:     }
    197:     stat = mkl_sparse_set_mv_hint(baijmkl->bsrA,SPARSE_OPERATION_NON_TRANSPOSE,baijmkl->descr,1000);CHKERRMKL(stat);
    198:     stat = mkl_sparse_set_memory_hint(baijmkl->bsrA,SPARSE_MEMORY_AGGRESSIVE);CHKERRMKL(stat);
    199:     stat = mkl_sparse_optimize(baijmkl->bsrA);CHKERRMKL(stat);
    200:     baijmkl->sparse_optimized = PETSC_TRUE;
    201:   }
    202:   return(0);
    203: }

    205: static PetscErrorCode MatDuplicate_SeqBAIJMKL(Mat A, MatDuplicateOption op, Mat *M)
    206: {
    208:   Mat_SeqBAIJMKL *baijmkl;
    209:   Mat_SeqBAIJMKL *baijmkl_dest;

    212:   MatDuplicate_SeqBAIJ(A,op,M);
    213:   baijmkl = (Mat_SeqBAIJMKL*) A->spptr;
    214:   PetscNewLog((*M),&baijmkl_dest);
    215:   (*M)->spptr = (void*)baijmkl_dest;
    216:   PetscMemcpy(baijmkl_dest,baijmkl,sizeof(Mat_SeqBAIJMKL));
    217:   baijmkl_dest->sparse_optimized = PETSC_FALSE;
    218:   MatSeqBAIJMKL_create_mkl_handle(A);
    219:   return(0);
    220: }

    222: static PetscErrorCode MatMult_SeqBAIJMKL_SpMV2(Mat A,Vec xx,Vec yy)
    223: {
    224:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data;
    225:   Mat_SeqBAIJMKL     *baijmkl=(Mat_SeqBAIJMKL*)A->spptr;
    226:   const PetscScalar  *x;
    227:   PetscScalar        *y;
    228:   PetscErrorCode     ierr;
    229:   sparse_status_t    stat = SPARSE_STATUS_SUCCESS;

    232:   /* If there are no nonzero entries, zero yy and return immediately. */
    233:   if (!a->nz) {
    234:     VecSet(yy,0.0);
    235:     return(0);
    236:   }

    238:   VecGetArrayRead(xx,&x);
    239:   VecGetArray(yy,&y);

    241:   /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
    242:    * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
    243:    * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
    244:   if (!baijmkl->sparse_optimized) {
    245:     MatSeqBAIJMKL_create_mkl_handle(A);
    246:   }

    248:   /* Call MKL SpMV2 executor routine to do the MatMult. */
    249:   stat = mkl_sparse_x_mv(SPARSE_OPERATION_NON_TRANSPOSE,1.0,baijmkl->bsrA,baijmkl->descr,x,0.0,y);CHKERRMKL(stat);

    251:   PetscLogFlops(2.0*a->bs2*a->nz - a->nonzerorowcnt*A->rmap->bs);
    252:   VecRestoreArrayRead(xx,&x);
    253:   VecRestoreArray(yy,&y);
    254:   return(0);
    255: }

    257: static PetscErrorCode MatMultTranspose_SeqBAIJMKL_SpMV2(Mat A,Vec xx,Vec yy)
    258: {
    259:   Mat_SeqBAIJ       *a       = (Mat_SeqBAIJ*)A->data;
    260:   Mat_SeqBAIJMKL    *baijmkl = (Mat_SeqBAIJMKL*)A->spptr;
    261:   const PetscScalar *x;
    262:   PetscScalar       *y;
    263:   PetscErrorCode    ierr;
    264:   sparse_status_t   stat;

    267:   /* If there are no nonzero entries, zero yy and return immediately. */
    268:   if (!a->nz) {
    269:     VecSet(yy,0.0);
    270:     return(0);
    271:   }

    273:   VecGetArrayRead(xx,&x);
    274:   VecGetArray(yy,&y);

    276:   /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
    277:    * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
    278:    * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
    279:   if (!baijmkl->sparse_optimized) {
    280:     MatSeqBAIJMKL_create_mkl_handle(A);
    281:   }

    283:   /* Call MKL SpMV2 executor routine to do the MatMultTranspose. */
    284:   stat = mkl_sparse_x_mv(SPARSE_OPERATION_TRANSPOSE,1.0,baijmkl->bsrA,baijmkl->descr,x,0.0,y);CHKERRMKL(stat);

    286:   PetscLogFlops(2.0*a->bs2*a->nz - a->nonzerorowcnt*A->rmap->bs);
    287:   VecRestoreArrayRead(xx,&x);
    288:   VecRestoreArray(yy,&y);
    289:   return(0);
    290: }

    292: static PetscErrorCode MatMultAdd_SeqBAIJMKL_SpMV2(Mat A,Vec xx,Vec yy,Vec zz)
    293: {
    294:   Mat_SeqBAIJ        *a       = (Mat_SeqBAIJ*)A->data;
    295:   Mat_SeqBAIJMKL     *baijmkl = (Mat_SeqBAIJMKL*)A->spptr;
    296:   const PetscScalar  *x;
    297:   PetscScalar        *y,*z;
    298:   PetscErrorCode     ierr;
    299:   PetscInt           m=a->mbs*A->rmap->bs;
    300:   PetscInt           i;

    302:   sparse_status_t stat = SPARSE_STATUS_SUCCESS;

    305:   /* If there are no nonzero entries, set zz = yy and return immediately. */
    306:   if (!a->nz) {
    307:     VecCopy(yy,zz);
    308:     return(0);
    309:   }

    311:   VecGetArrayRead(xx,&x);
    312:   VecGetArrayPair(yy,zz,&y,&z);

    314:   /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
    315:    * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
    316:    * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
    317:   if (!baijmkl->sparse_optimized) {
    318:     MatSeqBAIJMKL_create_mkl_handle(A);
    319:   }

    321:   /* Call MKL sparse BLAS routine to do the MatMult. */
    322:   if (zz == yy) {
    323:     /* If zz and yy are the same vector, we can use mkl_sparse_x_mv, which calculates y = alpha*A*x + beta*y,
    324:      * with alpha and beta both set to 1.0. */
    325:     stat = mkl_sparse_x_mv(SPARSE_OPERATION_NON_TRANSPOSE,1.0,baijmkl->bsrA,baijmkl->descr,x,1.0,z);CHKERRMKL(stat);
    326:   } else {
    327:     /* zz and yy are different vectors, so we call mkl_sparse_x_mv with alpha=1.0 and beta=0.0, and then
    328:      * we add the contents of vector yy to the result; MKL sparse BLAS does not have a MatMultAdd equivalent. */
    329:     stat = mkl_sparse_x_mv(SPARSE_OPERATION_NON_TRANSPOSE,1.0,baijmkl->bsrA,baijmkl->descr,x,0.0,z);CHKERRMKL(stat);
    330:     for (i=0; i<m; i++) {
    331:       z[i] += y[i];
    332:     }
    333:   }

    335:   PetscLogFlops(2.0*a->bs2*a->nz);
    336:   VecRestoreArrayRead(xx,&x);
    337:   VecRestoreArrayPair(yy,zz,&y,&z);
    338:   return(0);
    339: }

    341: static PetscErrorCode MatMultTransposeAdd_SeqBAIJMKL_SpMV2(Mat A,Vec xx,Vec yy,Vec zz)
    342: {
    343:   Mat_SeqBAIJ       *a       = (Mat_SeqBAIJ*)A->data;
    344:   Mat_SeqBAIJMKL    *baijmkl = (Mat_SeqBAIJMKL*)A->spptr;
    345:   const PetscScalar *x;
    346:   PetscScalar       *y,*z;
    347:   PetscErrorCode    ierr;
    348:   PetscInt          n=a->nbs*A->rmap->bs;
    349:   PetscInt          i;
    350:   /* Variables not in MatMultTransposeAdd_SeqBAIJ. */
    351:   sparse_status_t   stat = SPARSE_STATUS_SUCCESS;

    354:   /* If there are no nonzero entries, set zz = yy and return immediately. */
    355:   if (!a->nz) {
    356:     VecCopy(yy,zz);
    357:     return(0);
    358:   }

    360:   VecGetArrayRead(xx,&x);
    361:   VecGetArrayPair(yy,zz,&y,&z);

    363:   /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
    364:    * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
    365:    * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
    366:   if (!baijmkl->sparse_optimized) {
    367:     MatSeqBAIJMKL_create_mkl_handle(A);
    368:   }

    370:   /* Call MKL sparse BLAS routine to do the MatMult. */
    371:   if (zz == yy) {
    372:     /* If zz and yy are the same vector, we can use mkl_sparse_x_mv, which calculates y = alpha*A*x + beta*y,
    373:      * with alpha and beta both set to 1.0. */
    374:     stat = mkl_sparse_x_mv(SPARSE_OPERATION_TRANSPOSE,1.0,baijmkl->bsrA,baijmkl->descr,x,1.0,z);CHKERRMKL(stat);
    375:   } else {
    376:     /* zz and yy are different vectors, so we call mkl_sparse_x_mv with alpha=1.0 and beta=0.0, and then
    377:      * we add the contents of vector yy to the result; MKL sparse BLAS does not have a MatMultAdd equivalent. */
    378:     stat = mkl_sparse_x_mv(SPARSE_OPERATION_TRANSPOSE,1.0,baijmkl->bsrA,baijmkl->descr,x,0.0,z);CHKERRMKL(stat);
    379:     for (i=0; i<n; i++) {
    380:       z[i] += y[i];
    381:     }
    382:   }

    384:   PetscLogFlops(2.0*a->bs2*a->nz);
    385:   VecRestoreArrayRead(xx,&x);
    386:   VecRestoreArrayPair(yy,zz,&y,&z);
    387:   return(0);
    388: }

    390: static PetscErrorCode MatScale_SeqBAIJMKL(Mat inA,PetscScalar alpha)
    391: {

    395:   MatScale_SeqBAIJ(inA,alpha);
    396:   MatSeqBAIJMKL_create_mkl_handle(inA);
    397:   return(0);
    398: }

    400: static PetscErrorCode MatDiagonalScale_SeqBAIJMKL(Mat A,Vec ll,Vec rr)
    401: {

    405:   MatDiagonalScale_SeqBAIJ(A,ll,rr);
    406:   MatSeqBAIJMKL_create_mkl_handle(A);
    407:   return(0);
    408: }

    410: static PetscErrorCode MatAXPY_SeqBAIJMKL(Mat Y,PetscScalar a,Mat X,MatStructure str)
    411: {

    415:   MatAXPY_SeqBAIJ(Y,a,X,str);
    416:   if (str == SAME_NONZERO_PATTERN) {
    417:     /* MatAssemblyEnd() is not called if SAME_NONZERO_PATTERN, so we need to force update of the MKL matrix handle. */
    418:     MatSeqBAIJMKL_create_mkl_handle(Y);
    419:   }
    420:   return(0);
    421: }
    422: /* MatConvert_SeqBAIJ_SeqBAIJMKL converts a SeqBAIJ matrix into a
    423:  * SeqBAIJMKL matrix.  This routine is called by the MatCreate_SeqMKLBAIJ()
    424:  * routine, but can also be used to convert an assembled SeqBAIJ matrix
    425:  * into a SeqBAIJMKL one. */
    426: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat A,MatType type,MatReuse reuse,Mat *newmat)
    427: {
    429:   Mat            B = *newmat;
    430:   Mat_SeqBAIJMKL *baijmkl;
    431:   PetscBool      sametype;

    434:   if (reuse == MAT_INITIAL_MATRIX) {
    435:     MatDuplicate(A,MAT_COPY_VALUES,&B);
    436:   }

    438:   PetscObjectTypeCompare((PetscObject)A,type,&sametype);
    439:   if (sametype) return(0);

    441:   PetscNewLog(B,&baijmkl);
    442:   B->spptr = (void*)baijmkl;

    444:   /* Set function pointers for methods that we inherit from BAIJ but override.
    445:    * We also parse some command line options below, since those determine some of the methods we point to. */
    446:   B->ops->assemblyend      = MatAssemblyEnd_SeqBAIJMKL;

    448:   baijmkl->sparse_optimized = PETSC_FALSE;

    450:   PetscObjectComposeFunction((PetscObject)B,"MatScale_SeqBAIJMKL_C",MatScale_SeqBAIJMKL);
    451:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqbaijmkl_seqbaij_C",MatConvert_SeqBAIJMKL_SeqBAIJ);

    453:   PetscObjectChangeTypeName((PetscObject)B,MATSEQBAIJMKL);
    454:   *newmat = B;
    455:   return(0);
    456: }

    458: static PetscErrorCode MatAssemblyEnd_SeqBAIJMKL(Mat A, MatAssemblyType mode)
    459: {
    460:   PetscErrorCode  ierr;

    463:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);
    464:   MatAssemblyEnd_SeqBAIJ(A, mode);
    465:   MatSeqBAIJMKL_create_mkl_handle(A);
    466:   A->ops->destroy          = MatDestroy_SeqBAIJMKL;
    467:   A->ops->mult             = MatMult_SeqBAIJMKL_SpMV2;
    468:   A->ops->multtranspose    = MatMultTranspose_SeqBAIJMKL_SpMV2;
    469:   A->ops->multadd          = MatMultAdd_SeqBAIJMKL_SpMV2;
    470:   A->ops->multtransposeadd = MatMultTransposeAdd_SeqBAIJMKL_SpMV2;
    471:   A->ops->scale            = MatScale_SeqBAIJMKL;
    472:   A->ops->diagonalscale    = MatDiagonalScale_SeqBAIJMKL;
    473:   A->ops->axpy             = MatAXPY_SeqBAIJMKL;
    474:   A->ops->duplicate        = MatDuplicate_SeqBAIJMKL;
    475:   return(0);
    476: }

    478: /*@C
    479:    MatCreateSeqBAIJMKL - Creates a sparse matrix of type SEQBAIJMKL.
    480:    This type inherits from BAIJ and is largely identical, but uses sparse BLAS
    481:    routines from Intel MKL whenever possible.
    482:    MatMult, MatMultAdd, MatMultTranspose, and MatMultTransposeAdd
    483:    operations are currently supported.
    484:    If the installed version of MKL supports the "SpMV2" sparse
    485:    inspector-executor routines, then those are used by default.
    486:    Default PETSc kernels are used otherwise.

    488:    Input Parameters:
    489: +  comm - MPI communicator, set to PETSC_COMM_SELF
    490: .  bs - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row
    491:           blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs()
    492: .  m - number of rows
    493: .  n - number of columns
    494: .  nz - number of nonzero blocks  per block row (same for all rows)
    495: -  nnz - array containing the number of nonzero blocks in the various block rows
    496:          (possibly different for each block row) or NULL

    499:    Output Parameter:
    500: .  A - the matrix

    502:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
    503:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
    504:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

    506:    Options Database Keys:
    507: +   -mat_no_unroll - uses code that does not unroll the loops in the
    508:                      block calculations (much slower)
    509: -   -mat_block_size - size of the blocks to use

    511:    Level: intermediate

    513:    Notes:
    514:    The number of rows and columns must be divisible by blocksize.

    516:    If the nnz parameter is given then the nz parameter is ignored

    518:    A nonzero block is any block that as 1 or more nonzeros in it

    520:    The block AIJ format is fully compatible with standard Fortran 77
    521:    storage.  That is, the stored row and column indices can begin at
    522:    either one (as in Fortran) or zero.  See the users' manual for details.

    524:    Specify the preallocated storage with either nz or nnz (not both).
    525:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    526:    allocation.  See Users-Manual: ch_mat for details.
    527:    matrices.

    529: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateBAIJ()

    531: @*/
    532: PetscErrorCode  MatCreateSeqBAIJMKL(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
    533: {

    537:   MatCreate(comm,A);
    538:   MatSetSizes(*A,m,n,m,n);
    539:   MatSetType(*A,MATSEQBAIJMKL);
    540:   MatSeqBAIJSetPreallocation_SeqBAIJ(*A,bs,nz,(PetscInt*)nnz);
    541:   return(0);
    542: }

    544: PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJMKL(Mat A)
    545: {

    549:   MatSetType(A,MATSEQBAIJ);
    550:   MatConvert_SeqBAIJ_SeqBAIJMKL(A,MATSEQBAIJMKL,MAT_INPLACE_MATRIX,&A);
    551:   return(0);
    552: }