1: #define PETSC_SKIP_IMMINTRIN_H_CUDAWORKAROUND 1

  3: #include <petscconf.h>
  4: #include <../src/mat/impls/aij/mpi/mpiaij.h>
  5: #include <../src/mat/impls/aij/seq/seqviennacl/viennaclmatimpl.h>

  7: PetscErrorCode  MatMPIAIJSetPreallocation_MPIAIJViennaCL(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
  8: {
  9:   Mat_MPIAIJ     *b = (Mat_MPIAIJ*)B->data;

 11:   PetscLayoutSetUp(B->rmap);
 12:   PetscLayoutSetUp(B->cmap);
 13:   if (!B->preallocated) {
 14:     /* Explicitly create the two MATSEQAIJVIENNACL matrices. */
 15:     MatCreate(PETSC_COMM_SELF,&b->A);
 16:     MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
 17:     MatSetType(b->A,MATSEQAIJVIENNACL);
 18:     PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
 19:     MatCreate(PETSC_COMM_SELF,&b->B);
 20:     MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
 21:     MatSetType(b->B,MATSEQAIJVIENNACL);
 22:     PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);
 23:   }
 24:   MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
 25:   MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
 26:   B->preallocated = PETSC_TRUE;
 27:   return 0;
 28: }

 30: PetscErrorCode MatAssemblyEnd_MPIAIJViennaCL(Mat A,MatAssemblyType mode)
 31: {
 32:   Mat_MPIAIJ     *b = (Mat_MPIAIJ*)A->data;
 33:   PetscBool      v;

 35:   MatAssemblyEnd_MPIAIJ(A,mode);
 36:   PetscObjectTypeCompare((PetscObject)b->lvec,VECSEQVIENNACL,&v);
 37:   if (!v) {
 38:     PetscInt m;
 39:     VecGetSize(b->lvec,&m);
 40:     VecDestroy(&b->lvec);
 41:     VecCreateSeqViennaCL(PETSC_COMM_SELF,m,&b->lvec);
 42:   }
 43:   return 0;
 44: }

 46: PetscErrorCode MatDestroy_MPIAIJViennaCL(Mat A)
 47: {
 48:   MatDestroy_MPIAIJ(A);
 49:   return 0;
 50: }

 52: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJViennaCL(Mat A)
 53: {
 54:   MatCreate_MPIAIJ(A);
 55:   A->boundtocpu = PETSC_FALSE;
 56:   PetscFree(A->defaultvectype);
 57:   PetscStrallocpy(VECVIENNACL,&A->defaultvectype);
 58:   PetscObjectComposeFunction((PetscObject)A,"MatMPIAIJSetPreallocation_C",MatMPIAIJSetPreallocation_MPIAIJViennaCL);
 59:   A->ops->assemblyend = MatAssemblyEnd_MPIAIJViennaCL;
 60:   PetscObjectChangeTypeName((PetscObject)A,MATMPIAIJVIENNACL);
 61:   return 0;
 62: }

 64: /*@C
 65:    MatCreateAIJViennaCL - Creates a sparse matrix in AIJ (compressed row) format
 66:    (the default parallel PETSc format).  This matrix will ultimately be pushed down
 67:    to GPUs and use the ViennaCL library for calculations. For good matrix
 68:    assembly performance the user should preallocate the matrix storage by setting
 69:    the parameter nz (or the array nnz).  By setting these parameters accurately,
 70:    performance during matrix assembly can be increased substantially.

 72:    Collective

 74:    Input Parameters:
 75: +  comm - MPI communicator, set to PETSC_COMM_SELF
 76: .  m - number of rows
 77: .  n - number of columns
 78: .  nz - number of nonzeros per row (same for all rows)
 79: -  nnz - array containing the number of nonzeros in the various rows
 80:          (possibly different for each row) or NULL

 82:    Output Parameter:
 83: .  A - the matrix

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

 89:    Notes:
 90:    If nnz is given then nz is ignored

 92:    The AIJ format (also called the Yale sparse matrix format or
 93:    compressed row storage), is fully compatible with standard Fortran 77
 94:    storage.  That is, the stored row and column indices can begin at
 95:    either one (as in Fortran) or zero.  See the users' manual for details.

 97:    Specify the preallocated storage with either nz or nnz (not both).
 98:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
 99:    allocation.  For large problems you MUST preallocate memory or you
100:    will get TERRIBLE performance, see the users' manual chapter on matrices.

102:    Level: intermediate

104: .seealso: MatCreate(), MatCreateAIJ(), MatCreateAIJCUSPARSE(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATMPIAIJVIENNACL, MATAIJVIENNACL
105: @*/
106: PetscErrorCode  MatCreateAIJViennaCL(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
107: {
108:   PetscMPIInt    size;

110:   MatCreate(comm,A);
111:   MatSetSizes(*A,m,n,M,N);
112:   MPI_Comm_size(comm,&size);
113:   if (size > 1) {
114:     MatSetType(*A,MATMPIAIJVIENNACL);
115:     MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
116:   } else {
117:     MatSetType(*A,MATSEQAIJVIENNACL);
118:     MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
119:   }
120:   return 0;
121: }

123: /*MC
124:    MATAIJVIENNACL - MATMPIAIJVIENNACL= "aijviennacl" = "mpiaijviennacl" - A matrix type to be used for sparse matrices.

126:    A matrix type (CSR format) whose data resides on GPUs.
127:    All matrix calculations are performed using the ViennaCL library.

129:    This matrix type is identical to MATSEQAIJVIENNACL when constructed with a single process communicator,
130:    and MATMPIAIJVIENNACL otherwise.  As a result, for single process communicators,
131:    MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
132:    for communicators controlling multiple processes.  It is recommended that you call both of
133:    the above preallocation routines for simplicity.

135:    Options Database Keys:
136: .  -mat_type mpiaijviennacl - sets the matrix type to "mpiaijviennacl" during a call to MatSetFromOptions()

138:   Level: beginner

140:  .seealso: MatCreateAIJViennaCL(), MATSEQAIJVIENNACL, MatCreateSeqAIJVIENNACL()
141: M*/