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Stokhos_StochasticProductTensor.hpp
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41
42#ifndef STOKHOS_STOCHASTICPRODUCTTENSOR_HPP
43#define STOKHOS_STOCHASTICPRODUCTTENSOR_HPP
44
45#include <ostream>
46
47#include "Kokkos_Core.hpp"
48
50#include "Teuchos_ParameterList.hpp"
51
52namespace Stokhos {
53
54//----------------------------------------------------------------------------
55
77template< typename ValueType , typename TensorType, class Device >
79public:
80
81 typedef Device execution_space ;
82 typedef ValueType value_type ;
83 typedef TensorType tensor_type ;
84 typedef typename tensor_type::size_type size_type ;
85
86private:
87
89 Kokkos::View< size_type** , execution_space > m_degree_map ;
91
92public:
93
94 inline
96
97 inline
99 : m_tensor()
100 , m_degree_map()
101 , m_variable(0)
102 {}
103
104 inline
106 : m_tensor( rhs.m_tensor )
108 , m_variable( rhs.m_variable )
109 {}
110
111 inline
113 {
114 m_tensor = rhs.m_tensor ;
116 m_variable = rhs.m_variable ;
117 return *this ;
118 }
119
120 KOKKOS_INLINE_FUNCTION
121 const tensor_type & tensor() const { return m_tensor ; }
122
126 KOKKOS_INLINE_FUNCTION
127 size_type dimension() const { return m_tensor.dimension(); }
128
131 KOKKOS_INLINE_FUNCTION
133 const bool is_cuda =
134#if defined( KOKKOS_ENABLE_CUDA )
135 std::is_same<execution_space,Kokkos::Cuda>::value;
136#else
137 false ;
138#endif
139 const size_type AlignBytes = is_cuda ? 128 : 64;
140 const size_type NumAlign = AlignBytes/sizeof(value_type);
141 return (dimension() + NumAlign-1) & ~(NumAlign-1);
142 }
143
145 KOKKOS_INLINE_FUNCTION
147
149 template< typename iType >
150 KOKKOS_INLINE_FUNCTION
151 size_type variable_degree( const iType & iVariable ) const
152 { return m_degree_map( 0 , iVariable ); }
153
158 template< typename iType , typename jType >
159 KOKKOS_INLINE_FUNCTION
160 size_type bases_degree( const iType & iBasis , const jType & iVariable ) const
161 { return m_degree_map( iBasis + 1 , iVariable ); }
162
163 void print( std::ostream & s ) const
164 {
165 for ( unsigned i = 1 ; i < m_degree_map.extent(0) ; ++i ) {
166 s << " bases[" << i - 1 << "] (" ;
167 for ( unsigned j = 0 ; j < m_degree_map.extent(1) ; ++j ) {
168 s << " " << m_degree_map(i,j);
169 }
170 s << " )" << std::endl ;
171 }
172 }
173
174 template <typename OrdinalType, typename CijkType>
177 const CijkType& Cijk,
178 const Teuchos::ParameterList& params = Teuchos::ParameterList())
179 {
181
182 // Allocate and transfer data to the device-resident object.
183
184 typedef Kokkos::View< size_type** , execution_space > int_array_type ;
185 typedef typename int_array_type::HostMirror host_int_array_type ;
186
187 OrdinalType basis_sz = basis.size();
188 OrdinalType basis_dim = basis.dimension();
190
191 spt.m_degree_map =
192 int_array_type( "stochastic_tensor_degree_map" ,
193 basis_sz + 1 ,
194 basis_dim );
195
196 spt.m_variable = basis_dim ;
197
198 // Build degree_map
199 host_int_array_type degree_map =
200 Kokkos::create_mirror_view( spt.m_degree_map );
201 for ( OrdinalType j = 0 ; j < basis_dim ; ++j )
202 degree_map(0,j) = max_orders[j];
203 for ( OrdinalType i = 0 ; i < basis_sz ; ++i ) {
204 const Stokhos::MultiIndex<OrdinalType>& term = basis.term(i);
205 for ( OrdinalType j = 0 ; j < basis_dim ; ++j ) {
206 degree_map(i+1,j) = term[j];
207 }
208 }
209 Kokkos::deep_copy( spt.m_degree_map , degree_map );
210
211 // Build 3 tensor
212 spt.m_tensor = tensor_type::create( basis, Cijk, params );
213
214 return spt ;
215 }
216};
217
218template< typename TensorType, typename OrdinalType , typename ValueType, typename CijkType >
219StochasticProductTensor<ValueType, TensorType, typename TensorType::execution_space>
222 const CijkType& Cijk,
223 const Teuchos::ParameterList& params = Teuchos::ParameterList())
224{
225 typedef typename TensorType::execution_space Device;
227 basis, Cijk, params);
228}
229
230template < typename ValueType , typename Device, class TensorType >
231class BlockMultiply< StochasticProductTensor< ValueType, TensorType, Device > >
232{
233public:
234 typedef Device execution_space ;
235 typedef typename execution_space::size_type size_type ;
237
238 template< typename MatrixValue , typename VectorValue >
239 KOKKOS_INLINE_FUNCTION
240 static void apply( const block_type & block ,
241 const MatrixValue * a ,
242 const VectorValue * const x ,
243 VectorValue * const y )
244 {
246
247 tensor_multiply::apply( block.tensor() , a , x , y );
248 }
249};
250
251//----------------------------------------------------------------------------
252//----------------------------------------------------------------------------
253
254
255
256} // namespace Stokhos
257
258#endif /* #ifndef STOKHOS_STOCHASTICPRODUCTTENSOR_HPP */
static KOKKOS_INLINE_FUNCTION void apply(const block_type &block, const MatrixValue *a, const VectorValue *const x, VectorValue *const y)
A multidimensional index.
virtual ordinal_type size() const =0
Return total size of basis.
virtual ordinal_type dimension() const =0
Return dimension of basis.
Abstract base class for multivariate orthogonal polynomials generated from tensor products of univari...
virtual MultiIndex< ordinal_type > getMaxOrders() const =0
Return maximum order allowable for each coordinate basis.
virtual const MultiIndex< ordinal_type > & term(ordinal_type i) const =0
Get orders of each coordinate polynomial given an index i.
Bases defined by combinatorial product of polynomial bases.
KOKKOS_INLINE_FUNCTION size_type variable_count() const
How many variables are being expanded.
KOKKOS_INLINE_FUNCTION size_type dimension() const
Dimension: number of bases and length of the vector block (and tensor).
Kokkos::View< size_type **, execution_space > m_degree_map
KOKKOS_INLINE_FUNCTION size_type variable_degree(const iType &iVariable) const
Polynomial degree of a given variable.
static StochasticProductTensor create(const Stokhos::ProductBasis< OrdinalType, ValueType > &basis, const CijkType &Cijk, const Teuchos::ParameterList &params=Teuchos::ParameterList())
KOKKOS_INLINE_FUNCTION size_type bases_degree(const iType &iBasis, const jType &iVariable) const
Basis function 'iBasis' is the product of 'variable_count()' polynomials. Return the polynomial degre...
StochasticProductTensor & operator=(const StochasticProductTensor &rhs)
KOKKOS_INLINE_FUNCTION size_type aligned_dimension() const
Aligned dimension: length of the vector block properly aligned.
KOKKOS_INLINE_FUNCTION const tensor_type & tensor() const
StochasticProductTensor(const StochasticProductTensor &rhs)
Top-level namespace for Stokhos classes and functions.
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())