/usr/include/trilinos/Stokhos_StochasticProductTensor.hpp is in libtrilinos-stokhos-dev 12.10.1-3.
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// Stokhos Package
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#ifndef STOKHOS_STOCHASTICPRODUCTTENSOR_HPP
#define STOKHOS_STOCHASTICPRODUCTTENSOR_HPP
#include <ostream>
#include "Kokkos_Core.hpp"
#include "Stokhos_ProductBasis.hpp"
#include "Teuchos_ParameterList.hpp"
namespace Stokhos {
//----------------------------------------------------------------------------
/** \brief Bases defined by combinatorial product of polynomial bases.
*
* Bases: \prod_{j=0}^{N-1} P_k(x) \forall j and k \in M(j)
* Where: P_k is a polynomial of degree k
* Where: <P_a,P_b> is the the integral on [-1,1]
* Where: <P_a,P_b> is the Kronecker delta \delta_{a,b}
* thus the polynomials are normalized
* with respect to this inner product.
*
* Where: N = the number of variables expanded via polynomial bases
* Where: M(j) = the degree of a particular variable
*
* Where: \psi_I(x) = is one basis function and I is a multi-index
* of rank N, denoting one function from each
* variable's polynomial bases.
*
* Were: <\psi_I,\psi_J,\psi_K> is the integral on [-1,1]
*
* The bases space is sparse due to orthogonality within the
* expansion.
*/
template< typename ValueType , typename TensorType, class Device >
class StochasticProductTensor {
public:
typedef Device execution_space ;
typedef ValueType value_type ;
typedef TensorType tensor_type ;
typedef typename tensor_type::size_type size_type ;
private:
tensor_type m_tensor ;
Kokkos::View< size_type** , execution_space > m_degree_map ;
size_type m_variable ;
public:
inline
~StochasticProductTensor() {}
inline
StochasticProductTensor()
: m_tensor()
, m_degree_map()
, m_variable(0)
{}
inline
StochasticProductTensor( const StochasticProductTensor & rhs )
: m_tensor( rhs.m_tensor )
, m_degree_map( rhs.m_degree_map )
, m_variable( rhs.m_variable )
{}
inline
StochasticProductTensor & operator = ( const StochasticProductTensor & rhs )
{
m_tensor = rhs.m_tensor ;
m_degree_map = rhs.m_degree_map ;
m_variable = rhs.m_variable ;
return *this ;
}
KOKKOS_INLINE_FUNCTION
const tensor_type & tensor() const { return m_tensor ; }
/** \brief Dimension: number of bases and
* length of the vector block (and tensor).
*/
KOKKOS_INLINE_FUNCTION
size_type dimension() const { return m_tensor.dimension(); }
/** \brief Aligned dimension: length of the vector block properly aligned.
*/
KOKKOS_INLINE_FUNCTION
size_type aligned_dimension() const {
const bool is_cuda =
#if defined( KOKKOS_HAVE_CUDA )
Kokkos::Impl::is_same<execution_space,Kokkos::Cuda>::value;
#else
false ;
#endif
const size_type AlignBytes = is_cuda ? 128 : 64;
const size_type NumAlign = AlignBytes/sizeof(value_type);
return (dimension() + NumAlign-1) & ~(NumAlign-1);
}
/** \brief How many variables are being expanded. */
KOKKOS_INLINE_FUNCTION
size_type variable_count() const { return m_variable ; }
/** \brief Polynomial degree of a given variable */
template< typename iType >
KOKKOS_INLINE_FUNCTION
size_type variable_degree( const iType & iVariable ) const
{ return m_degree_map( 0 , iVariable ); }
/** \brief Basis function 'iBasis' is the product of
* 'variable_count()' polynomials. Return the
* polynomial degree of component 'iVariable'.
*/
template< typename iType , typename jType >
KOKKOS_INLINE_FUNCTION
size_type bases_degree( const iType & iBasis , const jType & iVariable ) const
{ return m_degree_map( iBasis + 1 , iVariable ); }
void print( std::ostream & s ) const
{
for ( unsigned i = 1 ; i < m_degree_map.dimension_0() ; ++i ) {
s << " bases[" << i - 1 << "] (" ;
for ( unsigned j = 0 ; j < m_degree_map.dimension_1() ; ++j ) {
s << " " << m_degree_map(i,j);
}
s << " )" << std::endl ;
}
}
template <typename OrdinalType, typename CijkType>
static StochasticProductTensor
create( const Stokhos::ProductBasis<OrdinalType,ValueType>& basis,
const CijkType& Cijk,
const Teuchos::ParameterList& params = Teuchos::ParameterList())
{
StochasticProductTensor spt ;
// Allocate and transfer data to the device-resident object.
typedef Kokkos::View< size_type** , execution_space > int_array_type ;
typedef typename int_array_type::HostMirror host_int_array_type ;
OrdinalType basis_sz = basis.size();
OrdinalType basis_dim = basis.dimension();
Stokhos::MultiIndex<OrdinalType> max_orders = basis.getMaxOrders();
spt.m_degree_map =
int_array_type( "stochastic_tensor_degree_map" ,
basis_sz + 1 ,
basis_dim );
spt.m_variable = basis_dim ;
// Build degree_map
host_int_array_type degree_map =
Kokkos::create_mirror_view( spt.m_degree_map );
for ( OrdinalType j = 0 ; j < basis_dim ; ++j )
degree_map(0,j) = max_orders[j];
for ( OrdinalType i = 0 ; i < basis_sz ; ++i ) {
const Stokhos::MultiIndex<OrdinalType>& term = basis.term(i);
for ( OrdinalType j = 0 ; j < basis_dim ; ++j ) {
degree_map(i+1,j) = term[j];
}
}
Kokkos::deep_copy( spt.m_degree_map , degree_map );
// Build 3 tensor
spt.m_tensor = tensor_type::create( basis, Cijk, params );
return spt ;
}
};
template< typename TensorType, typename OrdinalType , typename ValueType, typename CijkType >
StochasticProductTensor<ValueType, TensorType, typename TensorType::execution_space>
create_stochastic_product_tensor(
const Stokhos::ProductBasis<OrdinalType,ValueType>& basis,
const CijkType& Cijk,
const Teuchos::ParameterList& params = Teuchos::ParameterList())
{
typedef typename TensorType::execution_space Device;
return StochasticProductTensor<ValueType, TensorType, Device>::create(
basis, Cijk, params);
}
template < typename ValueType , typename Device, class TensorType >
class BlockMultiply< StochasticProductTensor< ValueType, TensorType, Device > >
{
public:
typedef Device execution_space ;
typedef typename execution_space::size_type size_type ;
typedef StochasticProductTensor< ValueType, TensorType, execution_space > block_type ;
template< typename MatrixValue , typename VectorValue >
KOKKOS_INLINE_FUNCTION
static void apply( const block_type & block ,
const MatrixValue * a ,
const VectorValue * const x ,
VectorValue * const y )
{
typedef BlockMultiply< typename block_type::tensor_type > tensor_multiply ;
tensor_multiply::apply( block.tensor() , a , x , y );
}
};
//----------------------------------------------------------------------------
//----------------------------------------------------------------------------
} // namespace Stokhos
#endif /* #ifndef STOKHOS_STOCHASTICPRODUCTTENSOR_HPP */
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