#include <../src/mat/impls/baij/mpi/mpibaij.h> /*I   "petscmat.h"   I*/

PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat, MatType, MatReuse, Mat *);

static PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJMKL(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt *d_nnz, PetscInt o_nz, const PetscInt *o_nnz)
{
  Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;

  PetscFunctionBegin;
  PetscCall(MatMPIBAIJSetPreallocation_MPIBAIJ(B, bs, d_nz, d_nnz, o_nz, o_nnz));
  PetscCall(MatConvert_SeqBAIJ_SeqBAIJMKL(b->A, MATSEQBAIJMKL, MAT_INPLACE_MATRIX, &b->A));
  PetscCall(MatConvert_SeqBAIJ_SeqBAIJMKL(b->B, MATSEQBAIJMKL, MAT_INPLACE_MATRIX, &b->B));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatConvert_MPIBAIJ_MPIBAIJMKL(Mat A, MatType type, MatReuse reuse, Mat *newmat)
{
  Mat B = *newmat;

  PetscFunctionBegin;
  if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));

  PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJMKL));
  PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJMKL));
  *newmat = B;
  PetscFunctionReturn(PETSC_SUCCESS);
}

// PetscClangLinter pragma disable: -fdoc-section-header-unknown
/*@
  MatCreateBAIJMKL - Creates a sparse parallel matrix in `MATBAIJMKL` format (block compressed row).

  Collective

  Input Parameters:
+ comm  - MPI communicator
. bs    - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
          blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
. m     - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given)
          This value should be the same as the local size used in creating the
          y vector for the matrix-vector product y = Ax.
. n     - number of local columns (or `PETSC_DECIDE` to have calculated if `N` is given)
          This value should be the same as the local size used in creating the
          x vector for the matrix-vector product y = Ax.
. M     - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
. N     - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
. d_nz  - number of nonzero blocks per block row in diagonal portion of local
          submatrix  (same for all local rows)
. d_nnz - array containing the number of nonzero blocks in the various block rows
          of the in diagonal portion of the local (possibly different for each block
          row) or `NULL`.  If you plan to factor the matrix you must leave room for the diagonal entry
          and set it even if it is zero.
. o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
          submatrix (same for all local rows).
- o_nnz - array containing the number of nonzero blocks in the various block rows of the
          off-diagonal portion of the local submatrix (possibly different for
          each block row) or `NULL`.

  Output Parameter:
. A - the matrix

  Options Database Keys:
+ -mat_block_size          - size of the blocks to use
- -mat_use_hash_table fact - set hash table factor

  Level: intermediate

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

  This type inherits from `MATBAIJ` and is largely identical, but uses sparse BLAS
  routines from Intel MKL whenever possible.
  `MatMult()`, `MatMultAdd()`, `MatMultTranspose()`, and `MatMultTransposeAdd()`
  operations are currently supported.
  If the installed version of MKL supports the "SpMV2" sparse
  inspector-executor routines, then those are used by default.
  Default PETSc kernels are used otherwise.
  For good matrix assembly performance the user should preallocate the matrix
  storage by setting the parameters `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
  By setting these parameters accurately, performance can be increased by more
  than a factor of 50.

  If the *_nnz parameter is given then the *_nz parameter is ignored

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

  The user MUST specify either the local or global matrix dimensions
  (possibly both).

  If `PETSC_DECIDE` or  `PETSC_DETERMINE` is used for a particular argument on one processor
  than it must be used on all processors that share the object for that argument.

  Storage Information:
  For a square global matrix we define each processor's diagonal portion
  to be its local rows and the corresponding columns (a square submatrix);
  each processor's off-diagonal portion encompasses the remainder of the
  local matrix (a rectangular submatrix).

  The user can specify preallocated storage for the diagonal part of
  the local submatrix with either `d_nz` or `d_nnz` (not both).  Set
  `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
  memory allocation.  Likewise, specify preallocated storage for the
  off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).

  Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
  the figure below we depict these three local rows and all columns (0-11).

.vb
           0 1 2 3 4 5 6 7 8 9 10 11
          --------------------------
   row 3  |o o o d d d o o o o  o  o
   row 4  |o o o d d d o o o o  o  o
   row 5  |o o o d d d o o o o  o  o
          --------------------------
.ve

  Thus, any entries in the d locations are stored in the d (diagonal)
  submatrix, and any entries in the o locations are stored in the
  o (off-diagonal) submatrix.  Note that the d and the o submatrices are
  stored simply in the `MATSEQBAIJMKL` format for compressed row storage.

  Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
  and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
  In general, for PDE problems in which most nonzeros are near the diagonal,
  one expects `d_nz` >> `o_nz`.

.seealso: [](ch_matrices), `Mat`, `MATBAIJMKL`, `MATBAIJ`, `MatCreate()`, `MatCreateSeqBAIJMKL()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
@*/
PetscErrorCode MatCreateBAIJMKL(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[], Mat *A)
{
  PetscMPIInt size;

  PetscFunctionBegin;
  PetscCall(MatCreate(comm, A));
  PetscCall(MatSetSizes(*A, m, n, M, N));
  PetscCallMPI(MPI_Comm_size(comm, &size));
  if (size > 1) {
    PetscCall(MatSetType(*A, MATMPIBAIJMKL));
    PetscCall(MatMPIBAIJSetPreallocation(*A, bs, d_nz, d_nnz, o_nz, o_nnz));
  } else {
    PetscCall(MatSetType(*A, MATSEQBAIJMKL));
    PetscCall(MatSeqBAIJSetPreallocation(*A, bs, d_nz, d_nnz));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJMKL(Mat A)
{
  PetscFunctionBegin;
  PetscCall(MatSetType(A, MATMPIBAIJ));
  PetscCall(MatConvert_MPIBAIJ_MPIBAIJMKL(A, MATMPIBAIJMKL, MAT_INPLACE_MATRIX, &A));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*MC
   MATBAIJMKL - MATBAIJMKL = "BAIJMKL" - A matrix type to be used for sparse matrices.

   This matrix type is identical to `MATSEQBAIJMKL` when constructed with a single process communicator,
   and `MATMPIBAIJMKL` otherwise.  As a result, for single process communicators,
  `MatSeqBAIJSetPreallocation()` is supported, and similarly `MatMPIBAIJSetPreallocation()` is supported
  for communicators controlling multiple processes.  It is recommended that you call both of
  the above preallocation routines for simplicity.

   Options Database Key:
. -mat_type baijmkl - sets the matrix type to `MATBAIJMKL` during a call to `MatSetFromOptions()`

  Level: beginner

.seealso: [](ch_matrices), `Mat`, `MatCreateBAIJMKL()`, `MATSEQBAIJMKL`, `MATMPIBAIJMKL`
M*/
