libtrilinos-stokhos-dev 12.10.1-3 / usr / include / trilinos / Stokhos_FlatSparse3Tensor_kji.hpp

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#ifndef STOKHOS_FLAT_SPARSE_3_TENSOR_KJI_HPP
#define STOKHOS_FLAT_SPARSE_3_TENSOR_KJI_HPP

#include "Kokkos_Core.hpp"

#include "Stokhos_Multiply.hpp"
#include "Stokhos_ProductBasis.hpp"
#include "Stokhos_Sparse3Tensor.hpp"
#include "Teuchos_ParameterList.hpp"

//----------------------------------------------------------------------------
//----------------------------------------------------------------------------

namespace Stokhos {

/** \brief  Sparse product tensor with replicated entries
 *          to provide subsets with a given coordinate.
 */
template< typename ValueType , class ExecutionSpace >
class FlatSparse3Tensor_kji {
public:

  typedef ExecutionSpace                       execution_space ;
  typedef typename execution_space::size_type  size_type ;
  typedef ValueType                        value_type ;

private:

  typedef Kokkos::View< size_type[] , execution_space > coord_array_type ;
  typedef Kokkos::View< value_type[], execution_space > value_array_type ;
  typedef Kokkos::View< size_type[], execution_space > entry_array_type ;
  typedef Kokkos::View< size_type[], execution_space > row_map_array_type ;

  coord_array_type   m_j_coord ;
  coord_array_type   m_i_coord ;
  value_array_type   m_value ;
  entry_array_type   m_num_j ;
  entry_array_type   m_num_i ;
  row_map_array_type m_j_row_map ;
  row_map_array_type m_i_row_map ;
  size_type          m_nnz ;
  size_type          m_dim ;
  size_type          m_flops ;


public:

  inline
  ~FlatSparse3Tensor_kji() {}

  inline
  FlatSparse3Tensor_kji() :
    m_j_coord() ,
    m_i_coord() ,
    m_value() ,
    m_num_j() ,
    m_num_i() ,
    m_j_row_map() ,
    m_i_row_map() ,
    m_nnz(0) ,
    m_dim(0) ,
    m_flops(0) {}

  inline
  FlatSparse3Tensor_kji( const FlatSparse3Tensor_kji & rhs ) :
    m_j_coord( rhs.m_j_coord ) ,
    m_i_coord( rhs.m_i_coord ) ,
    m_value( rhs.m_value ) ,
    m_num_j( rhs.m_num_j ) ,
    m_num_i( rhs.m_num_i ) ,
    m_j_row_map( rhs.m_j_row_map ) ,
    m_i_row_map( rhs.m_i_row_map ) ,
    m_nnz( rhs.m_nnz ) ,
    m_dim( rhs.m_dim ) ,
    m_flops( rhs.m_flops ) {}

  inline
  FlatSparse3Tensor_kji & operator = ( const FlatSparse3Tensor_kji & rhs )
  {
    m_j_coord = rhs.m_j_coord ;
    m_i_coord = rhs.m_i_coord ;
    m_value = rhs.m_value ;
    m_num_j = rhs.m_num_j ;
    m_num_i = rhs.m_num_i ;
    m_j_row_map = rhs.m_j_row_map ;
    m_i_row_map = rhs.m_i_row_map ;
    m_nnz = rhs.m_nnz;
    m_dim = rhs.m_dim ;
    m_flops = rhs.m_flops ;
    return *this ;
  }

  /** \brief  Dimension of the tensor. */
  KOKKOS_INLINE_FUNCTION
  size_type dimension() const { return m_dim ; }

  /** \brief  Number of k entries. */
  KOKKOS_INLINE_FUNCTION
  size_type num_k() const { return m_j_row_map.dimension_0() - 1 ; }

  /** \brief  Number of sparse entries. */
  KOKKOS_INLINE_FUNCTION
  size_type entry_count() const
  { return m_i_coord.dimension_0(); }

  /** \brief  Begin j entries with a coordinate 'k' */
  KOKKOS_INLINE_FUNCTION
  size_type j_begin( size_type k ) const
  { return m_j_row_map[k]; }

  /** \brief  End j entries with a coordinate 'k' */
  KOKKOS_INLINE_FUNCTION
  size_type j_end( size_type k ) const
  { return m_j_row_map[k] + m_num_j(k); }

  /** \brief  Number of j entries with a coordinate 'k' */
  KOKKOS_INLINE_FUNCTION
  size_type num_j( size_type k ) const
  { return m_num_j(k); }

  /** \brief  j coordinate for j entry 'jEntry' */
  KOKKOS_INLINE_FUNCTION
  const size_type& j_coord( const size_type jEntry ) const
  { return m_j_coord( jEntry ); }

  /** \brief  Begin i entries with a j entry 'jEntry' */
  KOKKOS_INLINE_FUNCTION
  size_type i_begin( size_type jEntry ) const
  { return m_i_row_map[jEntry]; }

  /** \brief  End i entries with a j entry 'jEntry' */
  KOKKOS_INLINE_FUNCTION
  size_type i_end( size_type jEntry ) const
  { return m_i_row_map[jEntry] + m_num_i(jEntry); }

  /** \brief  Number of i entries with a j entry 'jEntry' */
  KOKKOS_INLINE_FUNCTION
  size_type num_i( size_type jEntry ) const
  { return m_num_i(jEntry); }

  /** \brief  i coordinate for i entry 'iEntry' */
  KOKKOS_INLINE_FUNCTION
  const size_type& i_coord( const size_type iEntry ) const
  { return m_i_coord( iEntry ); }

  /** \brief  Value for i entry 'iEntry' */
  KOKKOS_INLINE_FUNCTION
  const value_type & value( const size_type iEntry ) const
  { return m_value( iEntry ); }

  /** \brief Number of non-zero's */
  KOKKOS_INLINE_FUNCTION
  size_type num_non_zeros() const
  { return m_nnz; }

  /** \brief Number flop's per multiply-add */
  KOKKOS_INLINE_FUNCTION
  size_type num_flops() const
  { return m_flops; }

  template <typename OrdinalType>
  static FlatSparse3Tensor_kji
  create( const Stokhos::ProductBasis<OrdinalType,ValueType>& basis,
          const Stokhos::Sparse3Tensor<OrdinalType,ValueType>& Cijk,
          const Teuchos::ParameterList& params = Teuchos::ParameterList())
  {
    typedef Stokhos::Sparse3Tensor<OrdinalType,ValueType> Cijk_type;

    // Compute number of j's for each k
    const size_type dimension = basis.size();
    const size_type nk = Cijk.num_k();
    std::vector< size_t > j_coord_work( nk , (size_t) 0 );
    size_type j_entry_count = 0 ;
    for (typename Cijk_type::k_iterator k_it=Cijk.k_begin();
         k_it!=Cijk.k_end(); ++k_it) {
      OrdinalType k = index(k_it);
      for (typename Cijk_type::kj_iterator j_it = Cijk.j_begin(k_it);
           j_it != Cijk.j_end(k_it); ++j_it) {
        OrdinalType j = index(j_it);
        if (j >= k) {
          ++j_coord_work[k];
          ++j_entry_count;
        }
      }
    }

    // Compute number of i's for each k and j
    std::vector< size_t > i_coord_work( j_entry_count , (size_t) 0 );
    size_type i_entry_count = 0 ;
    size_type j_entry = 0 ;
    for (typename Cijk_type::k_iterator k_it=Cijk.k_begin();
         k_it!=Cijk.k_end(); ++k_it) {
      OrdinalType k = index(k_it);
      for (typename Cijk_type::kj_iterator j_it = Cijk.j_begin(k_it);
           j_it != Cijk.j_end(k_it); ++j_it) {
        OrdinalType j = index(j_it);
        if (j >= k) {
          for (typename Cijk_type::kji_iterator i_it = Cijk.i_begin(j_it);
               i_it != Cijk.i_end(j_it); ++i_it) {
            ++i_coord_work[j_entry];
            ++i_entry_count;
          }
          ++j_entry;
        }
      }
    }

    /*
    // Pad each row to have size divisible by alignment size
    enum { Align = Kokkos::Impl::is_same<ExecutionSpace,Kokkos::Cuda>::value ? 32 : 2 };
    for ( size_type i = 0 ; i < dimension ; ++i ) {
      const size_t rem = coord_work[i] % Align;
      if (rem > 0) {
        const size_t pad = Align - rem;
        coord_work[i] += pad;
        entry_count += pad;
      }
    }
    */

    // Allocate tensor data
    FlatSparse3Tensor_kji tensor ;
    tensor.m_dim = dimension;
    tensor.m_j_coord = coord_array_type( "j_coord" , j_entry_count );
    tensor.m_i_coord = coord_array_type( "i_coord" , i_entry_count );
    tensor.m_value = value_array_type( "value" , i_entry_count );
    tensor.m_num_j = entry_array_type( "num_j" , nk );
    tensor.m_num_i = entry_array_type( "num_i" , j_entry_count );
    tensor.m_j_row_map = row_map_array_type( "j_row_map" , nk+1 );
    tensor.m_i_row_map = row_map_array_type( "i_row_map" , j_entry_count+1 );
    tensor.m_flops = 3*j_entry_count + 2*i_entry_count;

    // Create mirror, is a view if is host memory
    typename coord_array_type::HostMirror
      host_j_coord = Kokkos::create_mirror_view( tensor.m_j_coord );
    typename coord_array_type::HostMirror
      host_i_coord = Kokkos::create_mirror_view( tensor.m_i_coord );
    typename value_array_type::HostMirror
      host_value = Kokkos::create_mirror_view( tensor.m_value );
    typename entry_array_type::HostMirror
      host_num_j = Kokkos::create_mirror_view( tensor.m_num_j );
    typename entry_array_type::HostMirror
      host_num_i = Kokkos::create_mirror_view( tensor.m_num_i );
    typename entry_array_type::HostMirror
      host_j_row_map = Kokkos::create_mirror_view( tensor.m_j_row_map );
    typename entry_array_type::HostMirror
      host_i_row_map = Kokkos::create_mirror_view( tensor.m_i_row_map );

    // Compute j row map
    size_type sum = 0;
    host_j_row_map(0) = 0;
    for ( size_type k = 0 ; k < nk ; ++k ) {
      sum += j_coord_work[k];
      host_j_row_map(k+1) = sum;
      host_num_j(k) = 0;
    }

    // Compute i row map
    sum = 0;
    host_i_row_map(0) = 0;
    for ( size_type j = 0 ; j < j_entry_count ; ++j ) {
      sum += i_coord_work[j];
      host_i_row_map(j+1) = sum;
      host_num_i(j) = 0;
    }

    for ( size_type k = 0 ; k < nk ; ++k ) {
      j_coord_work[k] = host_j_row_map[k];
    }
    for ( size_type j = 0 ; j < j_entry_count ; ++j ) {
      i_coord_work[j] = host_i_row_map[j];
    }

    for (typename Cijk_type::k_iterator k_it=Cijk.k_begin();
         k_it!=Cijk.k_end(); ++k_it) {
      OrdinalType k = index(k_it);
      for (typename Cijk_type::kj_iterator j_it = Cijk.j_begin(k_it);
           j_it != Cijk.j_end(k_it); ++j_it) {
        OrdinalType j = index(j_it);
        if (j >= k) {
          const size_type jEntry = j_coord_work[k];
          ++j_coord_work[k];
          host_j_coord(jEntry) = j ;
          ++host_num_j(k);
          for (typename Cijk_type::kji_iterator i_it = Cijk.i_begin(j_it);
               i_it != Cijk.i_end(j_it); ++i_it) {
            OrdinalType i = index(i_it);
            ValueType c = Stokhos::value(i_it);
            const size_type iEntry = i_coord_work[jEntry];
            ++i_coord_work[jEntry];
            host_value(iEntry) = (j != k) ? c : 0.5*c;
            host_i_coord(iEntry) = i ;
            ++host_num_i(jEntry);
            ++tensor.m_nnz;
          }
        }
      }
    }

    // Copy data to device if necessary
    Kokkos::deep_copy( tensor.m_j_coord , host_j_coord );
    Kokkos::deep_copy( tensor.m_i_coord , host_i_coord );
    Kokkos::deep_copy( tensor.m_value , host_value );
    Kokkos::deep_copy( tensor.m_num_j , host_num_j );
    Kokkos::deep_copy( tensor.m_num_i , host_num_i );
    Kokkos::deep_copy( tensor.m_j_row_map , host_j_row_map );
    Kokkos::deep_copy( tensor.m_i_row_map , host_i_row_map );

    return tensor ;
  }
};

template< class Device , typename OrdinalType , typename ValueType >
FlatSparse3Tensor_kji<ValueType, Device>
create_flat_sparse_3_tensor_kji(
  const Stokhos::ProductBasis<OrdinalType,ValueType>& basis,
  const Stokhos::Sparse3Tensor<OrdinalType,ValueType>& Cijk,
  const Teuchos::ParameterList& params = Teuchos::ParameterList())
{
  return FlatSparse3Tensor_kji<ValueType, Device>::create(
    basis, Cijk, params );
}

template < typename ValueType, typename Device >
class BlockMultiply< FlatSparse3Tensor_kji< ValueType , Device > >
{
public:

  typedef typename Device::size_type size_type ;
  typedef FlatSparse3Tensor_kji< ValueType , Device > tensor_type ;

  template< typename MatrixValue , typename VectorValue >
  KOKKOS_INLINE_FUNCTION
  static void apply( const tensor_type & tensor ,
                     const MatrixValue * const a ,
                     const VectorValue * const x ,
                           VectorValue * const y )
  {
    const size_type nk = tensor.num_k();

    // Loop over k
    for ( size_type k = 0; k < nk; ++k) {
      const MatrixValue ak = a[k];
      const VectorValue xk = x[k];

      // Loop over j for this k
      const size_type nj = tensor.num_j(k);
      const size_type jBeg = tensor.j_begin(k);
      const size_type jEnd = jBeg + nj;
      for (size_type jEntry = jBeg; jEntry < jEnd; ++jEntry) {
        const size_type j = tensor.j_coord(jEntry);
        VectorValue tmp = a[j] * xk + ak * x[j];

        // Loop over i for this k,j
        const size_type ni = tensor.num_i(jEntry);
        const size_type iBeg = tensor.i_begin(jEntry);
        const size_type iEnd = iBeg + ni;
        for (size_type iEntry = iBeg; iEntry < iEnd; ++iEntry) {
          const size_type i = tensor.i_coord(iEntry);
          y[i] += tensor.value(iEntry) * tmp;
        }
      }
    }
  }

  KOKKOS_INLINE_FUNCTION
  static size_type matrix_size( const tensor_type & tensor )
  { return tensor.dimension(); }

  KOKKOS_INLINE_FUNCTION
  static size_type vector_size( const tensor_type & tensor )
  { return tensor.dimension(); }
};

} /* namespace Stokhos */

//----------------------------------------------------------------------------
//----------------------------------------------------------------------------

#endif /* #ifndef STOKHOS_FLAT_SPARSE_3_TENSOR_KJI_HPP */