Actual source code: mpiaijviennaclcuda.cu

petsc-3.8.3 2017-12-09
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  1: #include <petscconf.h>
  2:  #include <../src/mat/impls/aij/mpi/mpiaij.h>
  3:  #include <../src/mat/impls/aij/seq/seqviennacl/viennaclmatimpl.h>

  5: PetscErrorCode  MatMPIAIJSetPreallocation_MPIAIJViennaCL(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
  6: {
  7:   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  MatCreateVecs_MPIAIJViennaCL(Mat mat,Vec *right,Vec *left)
 31: {
 33:   PetscInt rbs,cbs;

 36:   MatGetBlockSizes(mat,&rbs,&cbs);
 37:   if (right) {
 38:     VecCreate(PetscObjectComm((PetscObject)mat),right);
 39:     VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
 40:     VecSetBlockSize(*right,cbs);
 41:     VecSetType(*right,VECVIENNACL);
 42:     VecSetLayout(*right,mat->cmap);
 43:   }
 44:   if (left) {
 45:     VecCreate(PetscObjectComm((PetscObject)mat),left);
 46:     VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
 47:     VecSetBlockSize(*left,rbs);
 48:     VecSetType(*left,VECVIENNACL);
 49:     VecSetLayout(*left,mat->rmap);
 50:   }
 51:   return(0);
 52: }


 55: PetscErrorCode MatDestroy_MPIAIJViennaCL(Mat A)
 56: {

 60:   MatDestroy_MPIAIJ(A);
 61:   return(0);
 62: }

 64: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJViennaCL(Mat A)
 65: {

 69:   MatCreate_MPIAIJ(A);
 70:   PetscObjectComposeFunction((PetscObject)A,"MatMPIAIJSetPreallocation_C",MatMPIAIJSetPreallocation_MPIAIJViennaCL);
 71:   A->ops->getvecs        = MatCreateVecs_MPIAIJViennaCL;

 73:   PetscObjectChangeTypeName((PetscObject)A,MATMPIAIJVIENNACL);
 74:   return(0);
 75: }


 78: /*@
 79:    MatCreateAIJViennaCL - Creates a sparse matrix in AIJ (compressed row) format
 80:    (the default parallel PETSc format).  This matrix will ultimately be pushed down
 81:    to GPUs and use the ViennaCL library for calculations. For good matrix
 82:    assembly performance the user should preallocate the matrix storage by setting
 83:    the parameter nz (or the array nnz).  By setting these parameters accurately,
 84:    performance during matrix assembly can be increased substantially.


 87:    Collective on MPI_Comm

 89:    Input Parameters:
 90: +  comm - MPI communicator, set to PETSC_COMM_SELF
 91: .  m - number of rows
 92: .  n - number of columns
 93: .  nz - number of nonzeros per row (same for all rows)
 94: -  nnz - array containing the number of nonzeros in the various rows
 95:          (possibly different for each row) or NULL

 97:    Output Parameter:
 98: .  A - the matrix

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

104:    Notes:
105:    If nnz is given then nz is ignored

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

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

117:    Level: intermediate

119: .seealso: MatCreate(), MatCreateAIJ(), MatCreateAIJCUSP(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATMPIAIJVIENNACL, MATAIJVIENNACL
120: @*/
121: 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)
122: {
124:   PetscMPIInt    size;

127:   MatCreate(comm,A);
128:   MatSetSizes(*A,m,n,M,N);
129:   MPI_Comm_size(comm,&size);
130:   if (size > 1) {
131:     MatSetType(*A,MATMPIAIJVIENNACL);
132:     MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
133:   } else {
134:     MatSetType(*A,MATSEQAIJVIENNACL);
135:     MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
136:   }
137:   return(0);
138: }

140: /*M
141:    MATAIJVIENNACL - MATMPIAIJVIENNACL= "aijviennacl" = "mpiaijviennacl" - A matrix type to be used for sparse matrices.

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

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

152:    Options Database Keys:
153: +  -mat_type mpiaijviennacl - sets the matrix type to "mpiaijviennacl" during a call to MatSetFromOptions()

155:   Level: beginner

157:  .seealso: MatCreateAIJViennaCL(), MATSEQAIJVIENNACL, MatCreateSeqAIJVIENNACL()
158: M*/