MPAssembly/fenl_functors.hpp

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#ifndef KOKKOS_EXAMPLE_FENLFUNCTORS_HPP
#define KOKKOS_EXAMPLE_FENLFUNCTORS_HPP
 
#include <stdio.h>
 
#include <iostream>
#include <fstream>
#include <iomanip>
#include <cstdlib>
#include <cmath>
#include <limits>
 
#include <Kokkos_Pair.hpp>
#include <Kokkos_UnorderedMap.hpp>
 
#include <impl/Kokkos_Timer.hpp>
 
#include <BoxElemFixture.hpp>
#include <HexElement.hpp>
 
#include "Stokhos_KL_ExponentialRandomField.hpp"
 
//----------------------------------------------------------------------------
//----------------------------------------------------------------------------
 
namespace Kokkos {
namespace Example {
namespace FENL {
 
struct DeviceConfig {
  struct Dim3 {
    size_t x, y, z;
    Dim3(const size_t x_, const size_t y_ = 1, const size_t z_ = 1) :
      x(x_), y(y_), z(z_) {}
  };
 
  Dim3 block_dim;
  size_t num_blocks;
  size_t num_threads_per_block;
 
  DeviceConfig(const size_t num_blocks_ = 0,
               const size_t threads_per_block_x_ = 0,
               const size_t threads_per_block_y_ = 0,
               const size_t threads_per_block_z_ = 1) :
    block_dim(threads_per_block_x_,threads_per_block_y_,threads_per_block_z_),
    num_blocks(num_blocks_),
    num_threads_per_block(block_dim.x * block_dim.y * block_dim.z)
    {}
};
 
template< typename ValueType , class Space >
struct CrsMatrix {
#ifdef KOKKOS_ENABLE_DEPRECATED_CODE // Don't remove this until Kokkos has removed the deprecated code path probably around September 2018
  typedef Kokkos::StaticCrsGraph< unsigned , Space , void , unsigned >  StaticCrsGraphType ;
#else
  typedef Kokkos::StaticCrsGraph< unsigned , Space , void , void , unsigned >  StaticCrsGraphType ;
#endif
  typedef View< ValueType * , Space > values_type ;
 
  StaticCrsGraphType  graph ;
  values_type  values ;
 
  CrsMatrix() : graph(), values() {}
 
  CrsMatrix( const StaticCrsGraphType & arg_graph )
    : graph( arg_graph )
    , values( "crs_matrix_values" , arg_graph.entries.extent(0) )
    {}
};
 
// Traits class for creating strided local views for embedded ensemble-based,
// specialized for ensemble UQ scalar type
template <typename ViewType, typename Enabled = void>
struct LocalViewTraits {
  typedef ViewType view_type;
  // typedef Kokkos::View<typename view_type::data_type,
  //                      typename view_type::array_layout,
  //                      typename view_type::execution_space,
  //                      Kokkos::MemoryUnmanaged> local_view_type;
  typedef const view_type& local_view_type;
  typedef typename view_type::value_type local_value_type;
  static const bool use_team = false;
  KOKKOS_INLINE_FUNCTION
  static local_view_type create_local_view(const view_type& v,
                                           const unsigned local_rank)
  { return v; }
};
 
#if defined( KOKKOS_ENABLE_CUDA )
 
template <typename ViewType>
struct LocalViewTraits<
  ViewType,
  typename std::enable_if< std::is_same<typename ViewType::execution_space,
                                        Kokkos::Cuda>::value &&
                           Kokkos::is_view_mp_vector<ViewType>::value
                         >::type > {
  typedef ViewType view_type;
  typedef typename Kokkos::LocalMPVectorView<view_type,1>::type local_view_type;
  typedef typename local_view_type::value_type local_value_type;
  static const bool use_team = true;
 
  KOKKOS_INLINE_FUNCTION
  static local_view_type create_local_view(const view_type& v,
                                           const unsigned local_rank)
  {
    return Kokkos::partition<1>(v, local_rank);
  }
};
 
#endif /* #if defined( KOKKOS_ENABLE_CUDA ) */
 
// Compute DeviceConfig struct's based on scalar type
template <typename ScalarType>
struct CreateDeviceConfigs {
  static void eval( Kokkos::Example::FENL::DeviceConfig& dev_config_elem,
                    Kokkos::Example::FENL::DeviceConfig& dev_config_bc ) {
    dev_config_elem = Kokkos::Example::FENL::DeviceConfig( 0 , 1 , 1 );
    dev_config_bc   = Kokkos::Example::FENL::DeviceConfig( 0 , 1 , 1 );
  }
};
 
// Compute DeviceConfig struct's based on scalar type
template <typename StorageType>
struct CreateDeviceConfigs< Sacado::MP::Vector<StorageType> > {
  typedef typename StorageType::execution_space execution_space;
  static void eval( Kokkos::Example::FENL::DeviceConfig& dev_config_elem,
                    Kokkos::Example::FENL::DeviceConfig& dev_config_bc ) {
    static const unsigned VectorSize = StorageType::static_size;
#if defined( KOKKOS_ENABLE_CUDA )
    enum { is_cuda = Kokkos::Impl::is_same< execution_space, Kokkos::Cuda >::value };
#else
    enum { is_cuda = false };
#endif /* #if defined( KOKKOS_ENABLE_CUDA ) */
    if ( is_cuda ) {
      dev_config_elem = Kokkos::Example::FENL::DeviceConfig( 0 , VectorSize , 64/VectorSize  );
      dev_config_bc   = Kokkos::Example::FENL::DeviceConfig( 0 , VectorSize , 256/VectorSize );
    }
    else {
      dev_config_elem = Kokkos::Example::FENL::DeviceConfig( 0 , 1 , 1 );
      dev_config_bc   = Kokkos::Example::FENL::DeviceConfig( 0 , 1 , 1 );
    }
  }
};
 
template< class ElemNodeIdView , class CrsGraphType , unsigned ElemNode >
class NodeNodeGraph {
public:
 
  typedef typename ElemNodeIdView::execution_space execution_space ;
  typedef pair<unsigned,unsigned> key_type ;
 
  typedef Kokkos::UnorderedMap< key_type, void , execution_space > SetType ;
  typedef typename CrsGraphType::row_map_type::non_const_type  RowMapType ;
  typedef Kokkos::View< unsigned ,  execution_space >              UnsignedValue ;
 
  // Static dimensions of 0 generate compiler warnings or errors.
  typedef Kokkos::View< unsigned*[ElemNode][ElemNode] , execution_space >
    ElemGraphType ;
 
private:
 
  enum PhaseType { FILL_NODE_SET ,
                   SCAN_NODE_COUNT ,
                   FILL_GRAPH_ENTRIES ,
                   SORT_GRAPH_ENTRIES ,
                   FILL_ELEMENT_GRAPH };
 
  const unsigned        node_count ;
  const ElemNodeIdView  elem_node_id ;
  UnsignedValue         row_total ;
  RowMapType            row_count ;
  RowMapType            row_map ;
  SetType               node_node_set ;
  PhaseType             phase ;
 
public:
 
  CrsGraphType          graph ;
  ElemGraphType         elem_graph ;
 
  struct Times
  {
    double ratio;
    double fill_node_set;
    double scan_node_count;
    double fill_graph_entries;
    double sort_graph_entries;
    double fill_element_graph;
  };
 
  NodeNodeGraph( const ElemNodeIdView & arg_elem_node_id ,
                 const unsigned         arg_node_count,
                 Times & results
               )
    : node_count(arg_node_count)
    , elem_node_id( arg_elem_node_id )
    , row_total( "row_total" )
    , row_count(Kokkos::ViewAllocateWithoutInitializing("row_count") , node_count ) // will deep_copy to 0 inside loop
    , row_map( "graph_row_map" , node_count + 1 )
    , node_node_set()
    , phase( FILL_NODE_SET )
    , graph()
    , elem_graph()
   {
      //--------------------------------
      // Guess at capacity required for the map:
 
      Kokkos::Impl::Timer wall_clock ;
 
      wall_clock.reset();
      phase = FILL_NODE_SET ;
 
      // upper bound on the capacity
      size_t set_capacity = (((28ull * node_count) / 2ull)*4ull)/3ull;
 
 
      // Increase capacity until the (node,node) map is successfully filled.
      {
        // Zero the row count to restart the fill
        Kokkos::deep_copy( row_count , 0u );
 
        node_node_set = SetType( set_capacity );
 
        // May be larger that requested:
        set_capacity = node_node_set.capacity();
 
        Kokkos::parallel_for( elem_node_id.extent(0) , *this );
      }
 
      execution_space::fence();
      results.ratio = (double)node_node_set.size() / (double)node_node_set.capacity();
      results.fill_node_set = wall_clock.seconds();
      //--------------------------------
 
      wall_clock.reset();
      phase = SCAN_NODE_COUNT ;
 
      // Exclusive scan of row_count into row_map
      // including the final total in the 'node_count + 1' position.
      // Zero the 'row_count' values.
      Kokkos::parallel_scan( node_count , *this );
 
      // Zero the row count for the fill:
      Kokkos::deep_copy( row_count , 0u );
 
      unsigned graph_entry_count = 0 ;
 
      Kokkos::deep_copy( graph_entry_count , row_total );
 
      // Assign graph's row_map and allocate graph's entries
      graph.row_map = row_map ;
      graph.entries = typename CrsGraphType::entries_type( "graph_entries" , graph_entry_count );
 
      //--------------------------------
      // Fill graph's entries from the (node,node) set.
 
      execution_space::fence();
      results.scan_node_count = wall_clock.seconds();
 
      wall_clock.reset();
      phase = FILL_GRAPH_ENTRIES ;
      Kokkos::parallel_for( node_node_set.capacity() , *this );
 
      execution_space::fence();
      results.fill_graph_entries = wall_clock.seconds();
 
      //--------------------------------
      // Done with the temporary sets and arrays
      wall_clock.reset();
      phase = SORT_GRAPH_ENTRIES ;
 
      row_total = UnsignedValue();
      row_count = RowMapType();
      row_map   = RowMapType();
      node_node_set.clear();
 
      //--------------------------------
 
      Kokkos::parallel_for( node_count , *this );
 
      execution_space::fence();
      results.sort_graph_entries = wall_clock.seconds();
 
      //--------------------------------
      // Element-to-graph mapping:
      wall_clock.reset();
      phase = FILL_ELEMENT_GRAPH ;
      elem_graph = ElemGraphType("elem_graph", elem_node_id.extent(0) );
      Kokkos::parallel_for( elem_node_id.extent(0) , *this );
 
      execution_space::fence();
      results.fill_element_graph = wall_clock.seconds();
    }
 
  //------------------------------------
  // parallel_for: create map and count row length
 
  KOKKOS_INLINE_FUNCTION
  void fill_set( const unsigned ielem ) const
  {
    // Loop over element's (row_local_node,col_local_node) pairs:
    for ( unsigned row_local_node = 0 ; row_local_node < elem_node_id.extent(1) ; ++row_local_node ) {
 
      const unsigned row_node = elem_node_id( ielem , row_local_node );
 
      for ( unsigned col_local_node = row_local_node ; col_local_node < elem_node_id.extent(1) ; ++col_local_node ) {
 
        const unsigned col_node = elem_node_id( ielem , col_local_node );
 
        // If either node is locally owned then insert the pair into the unordered map:
 
        if ( row_node < row_count.extent(0) || col_node < row_count.extent(0) ) {
 
          const key_type key = (row_node < col_node) ? make_pair( row_node, col_node ) : make_pair( col_node, row_node ) ;
 
          const typename SetType::insert_result result = node_node_set.insert( key );
 
          if ( result.success() ) {
            if ( row_node < row_count.extent(0) ) { atomic_fetch_add( & row_count( row_node ) , (typename RowMapType::value_type)1 ); }
            if ( col_node < row_count.extent(0) && col_node != row_node ) { atomic_fetch_add( & row_count( col_node ) , (typename RowMapType::value_type)1 ); }
          }
        }
      }
    }
  }
 
  KOKKOS_INLINE_FUNCTION
  void fill_graph_entries( const unsigned iset ) const
  {
    if ( node_node_set.valid_at(iset) ) {
      const key_type key = node_node_set.key_at(iset) ;
      const unsigned row_node = key.first ;
      const unsigned col_node = key.second ;
 
      if ( row_node < row_count.extent(0) ) {
        const unsigned offset = graph.row_map( row_node ) + atomic_fetch_add( & row_count( row_node ) , (typename RowMapType::value_type)1 );
        graph.entries( offset ) = col_node ;
      }
 
      if ( col_node < row_count.extent(0) && col_node != row_node ) {
        const unsigned offset = graph.row_map( col_node ) + atomic_fetch_add( & row_count( col_node ) , (typename RowMapType::value_type)1 );
        graph.entries( offset ) = row_node ;
      }
    }
  }
 
  KOKKOS_INLINE_FUNCTION
  void sort_graph_entries( const unsigned irow ) const
  {
    typedef typename CrsGraphType::size_type size_type;
    const size_type row_beg = graph.row_map( irow );
    const size_type row_end = graph.row_map( irow + 1 );
    for ( size_type i = row_beg + 1 ; i < row_end ; ++i ) {
      const typename CrsGraphType::data_type col = graph.entries(i);
      size_type j = i ;
      for ( ; row_beg < j && col < graph.entries(j-1) ; --j ) {
        graph.entries(j) = graph.entries(j-1);
      }
      graph.entries(j) = col ;
    }
  }
 
  KOKKOS_INLINE_FUNCTION
  void fill_elem_graph_map( const unsigned ielem ) const
  {
    typedef typename CrsGraphType::data_type entry_type;
    for ( unsigned row_local_node = 0 ; row_local_node < elem_node_id.extent(1) ; ++row_local_node ) {
 
      const unsigned row_node = elem_node_id( ielem , row_local_node );
 
      for ( unsigned col_local_node = 0 ; col_local_node < elem_node_id.extent(1) ; ++col_local_node ) {
 
        const unsigned col_node = elem_node_id( ielem , col_local_node );
 
        entry_type entry = 0 ;
 
        if ( row_node + 1 < graph.row_map.extent(0) ) {
 
          const entry_type entry_end = static_cast<entry_type> (graph.row_map( row_node + 1 ));
 
          entry = graph.row_map( row_node );
 
          for ( ; entry < entry_end && graph.entries(entry) != static_cast<entry_type> (col_node) ; ++entry );
 
          if ( entry == entry_end ) entry = ~0u ;
        }
 
        elem_graph( ielem , row_local_node , col_local_node ) = entry ;
      }
    }
  }
 
  KOKKOS_INLINE_FUNCTION
  void operator()( const unsigned iwork ) const
  {
    if ( phase == FILL_NODE_SET ) {
      fill_set( iwork );
    }
    else if ( phase == FILL_GRAPH_ENTRIES ) {
      fill_graph_entries( iwork );
    }
    else if ( phase == SORT_GRAPH_ENTRIES ) {
      sort_graph_entries( iwork );
    }
    else if ( phase == FILL_ELEMENT_GRAPH ) {
      fill_elem_graph_map( iwork );
    }
  }
 
  //------------------------------------
  // parallel_scan: row offsets
 
  typedef unsigned value_type ;
 
  KOKKOS_INLINE_FUNCTION
  void operator()( const unsigned irow , unsigned & update , const bool final ) const
  {
    // exclusive scan
    if ( final ) { row_map( irow ) = update ; }
 
    update += row_count( irow );
 
    if ( final ) {
      if ( irow + 1 == row_count.extent(0) ) {
        row_map( irow + 1 ) = update ;
        row_total()         = update ;
      }
    }
  }
 
  KOKKOS_INLINE_FUNCTION
  void init( unsigned & update ) const { update = 0 ; }
 
  KOKKOS_INLINE_FUNCTION
  void join( volatile unsigned & update , const volatile unsigned & input ) const { update += input ; }
 
  //------------------------------------
};
 
} /* namespace FENL */
} /* namespace Example */
} /* namespace Kokkos  */
 
//----------------------------------------------------------------------------
//----------------------------------------------------------------------------
 
namespace Kokkos {
namespace Example {
namespace FENL {
 
struct ElementComputationConstantCoefficient {
  enum { is_constant = false };
 
  const double coeff_k ;
 
  KOKKOS_INLINE_FUNCTION
  double operator()( double pt[], unsigned ensemble_rank) const
    { return coeff_k * std::sin(pt[0]) * std::sin(pt[1]) * std::sin(pt[2]); }
 
  ElementComputationConstantCoefficient( const double val )
    : coeff_k( val ) {}
 
  ElementComputationConstantCoefficient( const ElementComputationConstantCoefficient & rhs )
    : coeff_k( rhs.coeff_k ) {}
};
 
// Exponential KL from Stokhos
template < typename Scalar, typename MeshScalar, typename Device >
class ExponentialKLCoefficient {
public:
 
  // Turn into a meta-function class usable with Sacado::mpl::apply
  template <typename T1, typename T2 = MeshScalar, typename T3 = Device>
  struct apply {
    typedef ExponentialKLCoefficient<T1,T2,T3> type;
  };
 
  enum { is_constant = false };
  typedef Kokkos::View<Scalar*, Kokkos::LayoutLeft, Device> RandomVariableView;
  typedef typename RandomVariableView::size_type            size_type;
 
  typedef LocalViewTraits< RandomVariableView >           local_rv_view_traits;
  typedef typename local_rv_view_traits::local_view_type  local_rv_view_type;
  typedef typename local_rv_view_traits::local_value_type local_scalar_type;
  typedef Stokhos::KL::ExponentialRandomField<MeshScalar, Device> rf_type;
 
  rf_type m_rf;                   // Exponential random field
  const MeshScalar m_mean;        // Mean of random field
  const MeshScalar m_variance;    // Variance of random field
  const MeshScalar m_corr_len;    // Correlation length of random field
  const size_type m_num_rv;       // Number of random variables
  RandomVariableView m_rv;        // KL random variables
 
public:
 
  ExponentialKLCoefficient(
    const MeshScalar mean ,
    const MeshScalar variance ,
    const MeshScalar correlation_length ,
    const size_type num_rv ) :
    m_mean( mean ),
    m_variance( variance ),
    m_corr_len( correlation_length ),
    m_num_rv( num_rv ),
    m_rv( "KL Random Variables", m_num_rv )
  {
    Teuchos::ParameterList solverParams;
    solverParams.set("Number of KL Terms", int(num_rv));
    solverParams.set("Mean", mean);
    solverParams.set("Standard Deviation", std::sqrt(variance));
    int ndim = 3;
    Teuchos::Array<double> domain_upper(ndim, 1.0), domain_lower(ndim, 0.0),
      correlation_lengths(ndim, correlation_length);
    solverParams.set("Domain Upper Bounds", domain_upper);
    solverParams.set("Domain Lower Bounds", domain_lower);
    solverParams.set("Correlation Lengths", correlation_lengths);
 
    m_rf = rf_type(solverParams);
  }
 
  ExponentialKLCoefficient( const ExponentialKLCoefficient & rhs ) :
    m_rf( rhs.m_rf ) ,
    m_mean( rhs.m_mean ) ,
    m_variance( rhs.m_variance ) ,
    m_corr_len( rhs.m_corr_len ) ,
    m_num_rv( rhs.m_num_rv ) ,
    m_rv( rhs.m_rv ) {}
 
  KOKKOS_INLINE_FUNCTION
  void setRandomVariables( const RandomVariableView& rv) { m_rv = rv; }
 
  KOKKOS_INLINE_FUNCTION
  RandomVariableView getRandomVariables() const { return m_rv; }
 
  KOKKOS_INLINE_FUNCTION
  local_scalar_type operator() ( const MeshScalar point[],
                                 const size_type  ensemble_rank ) const
  {
    local_rv_view_type local_rv =
      local_rv_view_traits::create_local_view(m_rv, ensemble_rank);
 
    local_scalar_type val = m_rf.evaluate(point, local_rv);
 
    return val;
  }
};
 
template< class FiniteElementMeshType , class SparseMatrixType
        , class CoeffFunctionType = ElementComputationConstantCoefficient
        >
class ElementComputation ;
 
 
template< class ExecutionSpace , BoxElemPart::ElemOrder Order , class CoordinateMap ,
          typename ScalarType , class CoeffFunctionType >
class ElementComputation
  < Kokkos::Example::BoxElemFixture< ExecutionSpace , Order , CoordinateMap >
  , Kokkos::Example::FENL::CrsMatrix< ScalarType , ExecutionSpace >
  , CoeffFunctionType >
{
public:
 
  typedef Kokkos::Example::BoxElemFixture< ExecutionSpace, Order, CoordinateMap >  mesh_type ;
  typedef Kokkos::Example::HexElement_Data< mesh_type::ElemNode >              element_data_type ;
 
  //------------------------------------
 
  typedef ExecutionSpace   execution_space ;
  typedef ScalarType   scalar_type ;
 
  typedef Kokkos::Example::FENL::CrsMatrix< ScalarType , ExecutionSpace >  sparse_matrix_type ;
  typedef typename sparse_matrix_type::StaticCrsGraphType                                       sparse_graph_type ;
  typedef typename sparse_matrix_type::values_type matrix_values_type ;
  typedef Kokkos::View< scalar_type* , Kokkos::LayoutLeft, execution_space > vector_type ;
 
  //------------------------------------
 
  typedef LocalViewTraits< vector_type > local_vector_view_traits;
  typedef LocalViewTraits< matrix_values_type> local_matrix_view_traits;
  typedef typename local_vector_view_traits::local_view_type local_vector_type;
  typedef typename local_matrix_view_traits::local_view_type local_matrix_type;
  typedef typename local_vector_view_traits::local_value_type local_scalar_type;
  static const bool use_team = local_vector_view_traits::use_team;
 
  static const unsigned SpatialDim       = element_data_type::spatial_dimension ;
  static const unsigned TensorDim        = SpatialDim * SpatialDim ;
  static const unsigned ElemNodeCount    = element_data_type::element_node_count ;
  static const unsigned FunctionCount    = element_data_type::function_count ;
  static const unsigned IntegrationCount = element_data_type::integration_count ;
 
  //------------------------------------
 
  typedef typename mesh_type::node_coord_type                                      node_coord_type ;
  typedef typename mesh_type::elem_node_type                                       elem_node_type ;
  typedef Kokkos::View< scalar_type*[FunctionCount][FunctionCount] , execution_space > elem_matrices_type ;
  typedef Kokkos::View< scalar_type*[FunctionCount] ,                execution_space > elem_vectors_type ;
 
  typedef LocalViewTraits< elem_matrices_type > local_elem_matrices_traits;
  typedef LocalViewTraits< elem_vectors_type > local_elem_vectors_traits;
  typedef typename local_elem_matrices_traits::local_view_type local_elem_matrices_type;
  typedef typename local_elem_vectors_traits::local_view_type local_elem_vectors_type;
 
  typedef typename NodeNodeGraph< elem_node_type , sparse_graph_type , ElemNodeCount >::ElemGraphType elem_graph_type ;
 
  //------------------------------------
 
 
  //------------------------------------
  // Computational data:
 
  const element_data_type   elem_data ;
  const elem_node_type      elem_node_ids ;
  const node_coord_type     node_coords ;
  const elem_graph_type     elem_graph ;
  const elem_matrices_type  elem_jacobians ;
  const elem_vectors_type   elem_residuals ;
  const vector_type         solution ;
  const vector_type         residual ;
  const sparse_matrix_type  jacobian ;
  const CoeffFunctionType   coeff_function ;
  const Kokkos::Example::FENL::DeviceConfig dev_config ;
 
  ElementComputation( const ElementComputation & rhs )
    : elem_data()
    , elem_node_ids( rhs.elem_node_ids )
    , node_coords(   rhs.node_coords )
    , elem_graph(    rhs.elem_graph )
    , elem_jacobians( rhs.elem_jacobians )
    , elem_residuals( rhs.elem_residuals )
    , solution( rhs.solution )
    , residual( rhs.residual )
    , jacobian( rhs.jacobian )
    , coeff_function( rhs.coeff_function )
    , dev_config( rhs.dev_config )
    {}
 
  // If the element->sparse_matrix graph is provided then perform atomic updates
  // Otherwise fill per-element contributions for subequent gather-add into a residual and jacobian.
  ElementComputation( const mesh_type          & arg_mesh ,
                      const CoeffFunctionType  & arg_coeff_function ,
                      const vector_type        & arg_solution ,
                      const elem_graph_type    & arg_elem_graph ,
                      const sparse_matrix_type & arg_jacobian ,
                      const vector_type        & arg_residual ,
                      const Kokkos::Example::FENL::DeviceConfig arg_dev_config )
    : elem_data()
    , elem_node_ids( arg_mesh.elem_node() )
    , node_coords(   arg_mesh.node_coord() )
    , elem_graph(    arg_elem_graph )
    , elem_jacobians()
    , elem_residuals()
    , solution( arg_solution )
    , residual( arg_residual )
    , jacobian( arg_jacobian )
    , coeff_function( arg_coeff_function )
    , dev_config( arg_dev_config )
    {}
 
  //------------------------------------
 
  void apply() const
  {
    const size_t nelem = elem_node_ids.extent(0);
    if ( use_team ) {
      const size_t team_size = dev_config.block_dim.x * dev_config.block_dim.y;
      const size_t league_size =
        (nelem + dev_config.block_dim.y-1) / dev_config.block_dim.y;
      Kokkos::TeamPolicy< execution_space > config( league_size, team_size );
      parallel_for( config , *this );
    }
    else {
      parallel_for( nelem , *this );
    }
  }
 
  //------------------------------------
 
  static const unsigned FLOPS_transform_gradients =
     /* Jacobian */           FunctionCount * TensorDim * 2 +
     /* Inverse jacobian */   TensorDim * 6 + 6 +
     /* Gradient transform */ FunctionCount * 15 ;
 
  KOKKOS_INLINE_FUNCTION
  double transform_gradients(
    const double grad[][ FunctionCount ] , // Gradient of bases master element
    const double x[] ,
    const double y[] ,
    const double z[] ,
    double dpsidx[] ,
    double dpsidy[] ,
    double dpsidz[] ) const
  {
    enum { j11 = 0 , j12 = 1 , j13 = 2 ,
           j21 = 3 , j22 = 4 , j23 = 5 ,
           j31 = 6 , j32 = 7 , j33 = 8 };
 
    // Jacobian accumulation:
 
    double J[ TensorDim ] = { 0, 0, 0,  0, 0, 0,  0, 0, 0 };
 
    for( unsigned i = 0; i < FunctionCount ; ++i ) {
      const double x1 = x[i] ;
      const double x2 = y[i] ;
      const double x3 = z[i] ;
 
      const double g1 = grad[0][i] ;
      const double g2 = grad[1][i] ;
      const double g3 = grad[2][i] ;
 
      J[j11] += g1 * x1 ;
      J[j12] += g1 * x2 ;
      J[j13] += g1 * x3 ;
 
      J[j21] += g2 * x1 ;
      J[j22] += g2 * x2 ;
      J[j23] += g2 * x3 ;
 
      J[j31] += g3 * x1 ;
      J[j32] += g3 * x2 ;
      J[j33] += g3 * x3 ;
    }
 
    // Inverse jacobian:
 
    double invJ[ TensorDim ] = {
      static_cast<double>( J[j22] * J[j33] - J[j23] * J[j32] ) ,
      static_cast<double>( J[j13] * J[j32] - J[j12] * J[j33] ) ,
      static_cast<double>( J[j12] * J[j23] - J[j13] * J[j22] ) ,
 
      static_cast<double>( J[j23] * J[j31] - J[j21] * J[j33] ) ,
      static_cast<double>( J[j11] * J[j33] - J[j13] * J[j31] ) ,
      static_cast<double>( J[j13] * J[j21] - J[j11] * J[j23] ) ,
 
      static_cast<double>( J[j21] * J[j32] - J[j22] * J[j31] ) ,
      static_cast<double>( J[j12] * J[j31] - J[j11] * J[j32] ) ,
      static_cast<double>( J[j11] * J[j22] - J[j12] * J[j21] ) };
 
    const double detJ = J[j11] * invJ[j11] +
                       J[j21] * invJ[j12] +
                       J[j31] * invJ[j13] ;
 
    const double detJinv = 1.0 / detJ ;
 
    for ( unsigned i = 0 ; i < TensorDim ; ++i ) { invJ[i] *= detJinv ; }
 
    // Transform gradients:
 
    for( unsigned i = 0; i < FunctionCount ; ++i ) {
      const double g0 = grad[0][i];
      const double g1 = grad[1][i];
      const double g2 = grad[2][i];
 
      dpsidx[i] = g0 * invJ[j11] + g1 * invJ[j12] + g2 * invJ[j13];
      dpsidy[i] = g0 * invJ[j21] + g1 * invJ[j22] + g2 * invJ[j23];
      dpsidz[i] = g0 * invJ[j31] + g1 * invJ[j32] + g2 * invJ[j33];
    }
 
    return detJ ;
  }
 
  KOKKOS_INLINE_FUNCTION
  void contributeResidualJacobian(
    const local_scalar_type dof_values[] ,
    const double  dpsidx[] ,
    const double  dpsidy[] ,
    const double  dpsidz[] ,
    const double  detJ ,
    const local_scalar_type  coeff_k ,
    const double  integ_weight ,
    const double  bases_vals[] ,
    local_scalar_type  elem_res[] ,
    local_scalar_type  elem_mat[][ FunctionCount ] ) const
  {
    local_scalar_type value_at_pt = 0 ;
    local_scalar_type gradx_at_pt = 0 ;
    local_scalar_type grady_at_pt = 0 ;
    local_scalar_type gradz_at_pt = 0 ;
 
    for ( unsigned m = 0 ; m < FunctionCount ; m++ ) {
      value_at_pt += dof_values[m] * bases_vals[m] ;
      gradx_at_pt += dof_values[m] * dpsidx[m] ;
      grady_at_pt += dof_values[m] * dpsidy[m] ;
      gradz_at_pt += dof_values[m] * dpsidz[m] ;
    }
 
    const local_scalar_type k_detJ_weight = coeff_k             * detJ * integ_weight ;
    const local_scalar_type res_val = value_at_pt * value_at_pt * detJ * integ_weight ;
    const local_scalar_type mat_val = 2.0 * value_at_pt         * detJ * integ_weight ;
 
    // $$ R_i = \int_{\Omega} \nabla \phi_i \cdot (k \nabla T) + \phi_i T^2 d \Omega $$
    // $$ J_{i,j} = \frac{\partial R_i}{\partial T_j} = \int_{\Omega} k \nabla \phi_i \cdot \nabla \phi_j + 2 \phi_i \phi_j T d \Omega $$
 
    for ( unsigned m = 0; m < FunctionCount; ++m) {
      local_scalar_type * const mat = elem_mat[m] ;
      const double bases_val_m = bases_vals[m];
      const double dpsidx_m    = dpsidx[m] ;
      const double dpsidy_m    = dpsidy[m] ;
      const double dpsidz_m    = dpsidz[m] ;
 
      elem_res[m] += k_detJ_weight * ( dpsidx_m * gradx_at_pt +
                                       dpsidy_m * grady_at_pt +
                                       dpsidz_m * gradz_at_pt ) +
                     res_val * bases_val_m ;
 
      for( unsigned n = 0; n < FunctionCount; n++) {
 
        mat[n] += k_detJ_weight * ( dpsidx_m * dpsidx[n] +
                                    dpsidy_m * dpsidy[n] +
                                    dpsidz_m * dpsidz[n] ) +
                  mat_val * bases_val_m * bases_vals[n];
      }
    }
  }
 
  KOKKOS_INLINE_FUNCTION
  void operator()( const typename TeamPolicy< execution_space >::member_type & dev ) const
  {
 
    const unsigned num_ensemble_threads = dev_config.block_dim.x ;
    const unsigned num_element_threads  = dev_config.block_dim.y ;
    const unsigned element_rank  = dev.team_rank() / num_ensemble_threads ;
    const unsigned ensemble_rank = dev.team_rank() % num_ensemble_threads ;
 
    const unsigned ielem =
      dev.league_rank() * num_element_threads + element_rank;
 
    if (ielem >= elem_node_ids.extent(0))
      return;
 
    (*this)( ielem, ensemble_rank );
 
  }
 
  KOKKOS_INLINE_FUNCTION
  void operator()( const unsigned ielem ,
                   const unsigned ensemble_rank = 0 ) const
  {
    local_vector_type local_solution =
      local_vector_view_traits::create_local_view(solution,
                                                  ensemble_rank);
    local_vector_type local_residual =
      local_vector_view_traits::create_local_view(residual,
                                                  ensemble_rank);
    local_matrix_type local_jacobian_values =
      local_matrix_view_traits::create_local_view(jacobian.values,
                                                  ensemble_rank);
 
    // Gather nodal coordinates and solution vector:
 
    double x[ FunctionCount ] ;
    double y[ FunctionCount ] ;
    double z[ FunctionCount ] ;
    local_scalar_type val[ FunctionCount ] ;
    unsigned node_index[ ElemNodeCount ];
 
    for ( unsigned i = 0 ; i < ElemNodeCount ; ++i ) {
      const unsigned ni = elem_node_ids( ielem , i );
 
      node_index[i] = ni ;
 
      x[i] = node_coords( ni , 0 );
      y[i] = node_coords( ni , 1 );
      z[i] = node_coords( ni , 2 );
 
      val[i] = local_solution( ni );
    }
 
 
    local_scalar_type elem_vec[ FunctionCount ] ;
    local_scalar_type elem_mat[ FunctionCount ][ FunctionCount ] ;
 
    for( unsigned i = 0; i < FunctionCount ; i++ ) {
      elem_vec[i] = 0 ;
      for( unsigned j = 0; j < FunctionCount ; j++){
        elem_mat[i][j] = 0 ;
      }
    }
 
 
    for ( unsigned i = 0 ; i < IntegrationCount ; ++i ) {
      double dpsidx[ FunctionCount ] ;
      double dpsidy[ FunctionCount ] ;
      double dpsidz[ FunctionCount ] ;
 
      local_scalar_type coeff_k = 0 ;
 
      {
        double pt[] = {0.0, 0.0, 0.0};
 
        // If function is not constant
        // then compute physical coordinates of integration point
        if ( ! coeff_function.is_constant ) {
          for ( unsigned j = 0 ; j < FunctionCount ; ++j ) {
            pt[0] += x[j] * elem_data.values[i][j] ;
            pt[1] += y[j] * elem_data.values[i][j] ;
            pt[2] += z[j] * elem_data.values[i][j] ;
          }
        }
 
        // Need to fix this for local_scalar_type!!!!!!
        coeff_k = coeff_function(pt, ensemble_rank);
      }
 
      const double detJ =
        transform_gradients( elem_data.gradients[i] , x , y , z ,
                             dpsidx , dpsidy , dpsidz );
 
      contributeResidualJacobian( val , dpsidx , dpsidy , dpsidz ,
                                  detJ , coeff_k ,
                                  elem_data.weights[i] ,
                                  elem_data.values[i] ,
                                  elem_vec , elem_mat );
    }
 
    for( unsigned i = 0 ; i < FunctionCount ; i++ ) {
      const unsigned row = node_index[i] ;
      if ( row < residual.extent(0) ) {
        atomic_add( & local_residual( row ) , elem_vec[i] );
 
        for( unsigned j = 0 ; j < FunctionCount ; j++ ) {
          const unsigned entry = elem_graph( ielem , i , j );
          if ( entry != ~0u ) {
            atomic_add( & local_jacobian_values( entry ) , elem_mat[i][j] );
          }
        }
      }
    }
  }
}; /* ElementComputation */
 
//----------------------------------------------------------------------------
 
template< class FixtureType , class SparseMatrixType >
class DirichletComputation ;
 
template< class ExecutionSpace , BoxElemPart::ElemOrder Order , class CoordinateMap ,
          typename ScalarType >
class DirichletComputation<
  Kokkos::Example::BoxElemFixture< ExecutionSpace , Order , CoordinateMap > ,
  Kokkos::Example::FENL::CrsMatrix< ScalarType , ExecutionSpace > >
{
public:
 
  typedef Kokkos::Example::BoxElemFixture< ExecutionSpace, Order, CoordinateMap >  mesh_type ;
  typedef typename mesh_type::node_coord_type                                  node_coord_type ;
  typedef typename node_coord_type::value_type                                 scalar_coord_type ;
 
  typedef ExecutionSpace   execution_space ;
  typedef ScalarType   scalar_type ;
 
  typedef Kokkos::Example::FENL::CrsMatrix< ScalarType , ExecutionSpace >  sparse_matrix_type ;
  typedef typename sparse_matrix_type::StaticCrsGraphType                                       sparse_graph_type ;
  typedef typename sparse_matrix_type::values_type matrix_values_type ;
  typedef Kokkos::View< scalar_type* , execution_space > vector_type ;
 
  //------------------------------------
 
  typedef LocalViewTraits< vector_type > local_vector_view_traits;
  typedef LocalViewTraits< matrix_values_type> local_matrix_view_traits;
  typedef typename local_vector_view_traits::local_view_type local_vector_type;
  typedef typename local_matrix_view_traits::local_view_type local_matrix_type;
  static const bool use_team = local_vector_view_traits::use_team;
 
  typedef double       bc_scalar_type ;
 
  //------------------------------------
  // Computational data:
 
  const node_coord_type     node_coords ;
  const vector_type         solution ;
  const sparse_matrix_type  jacobian ;
  const vector_type         residual ;
  const bc_scalar_type      bc_lower_value ;
  const bc_scalar_type      bc_upper_value ;
  const scalar_coord_type   bc_lower_limit ;
  const scalar_coord_type   bc_upper_limit ;
  const unsigned            bc_plane ;
  const unsigned            node_count ;
        bool                init ;
  const Kokkos::Example::FENL::DeviceConfig dev_config ;
 
 
  DirichletComputation( const mesh_type          & arg_mesh ,
                        const vector_type        & arg_solution ,
                        const sparse_matrix_type & arg_jacobian ,
                        const vector_type        & arg_residual ,
                        const unsigned             arg_bc_plane ,
                        const bc_scalar_type       arg_bc_lower_value ,
                        const bc_scalar_type       arg_bc_upper_value ,
                        const Kokkos::Example::FENL::DeviceConfig arg_dev_config )
    : node_coords( arg_mesh.node_coord() )
    , solution(    arg_solution )
    , jacobian(    arg_jacobian )
    , residual(    arg_residual )
    , bc_lower_value( arg_bc_lower_value )
    , bc_upper_value( arg_bc_upper_value )
    , bc_lower_limit( std::numeric_limits<scalar_coord_type>::epsilon() )
    , bc_upper_limit( scalar_coord_type(1) - std::numeric_limits<scalar_coord_type>::epsilon() )
    , bc_plane(       arg_bc_plane )
    , node_count( arg_mesh.node_count_owned() )
    , init( false )
    , dev_config( arg_dev_config )
    {
      parallel_for( node_count , *this );
      init = true ;
    }
 
  void apply() const
  {
    if ( use_team ) {
      const size_t team_size = dev_config.block_dim.x * dev_config.block_dim.y;
      const size_t league_size =
        (node_count + dev_config.block_dim.y-1) / dev_config.block_dim.y;
      Kokkos::TeamPolicy< execution_space > config( league_size, team_size );
      parallel_for( config , *this );
    }
    else
      parallel_for( node_count , *this );
  }
 
  //------------------------------------
 
  KOKKOS_INLINE_FUNCTION
  void operator()( const typename TeamPolicy< execution_space >::member_type & dev ) const
  {
 
    const unsigned num_ensemble_threads = dev_config.block_dim.x ;
    const unsigned num_node_threads     = dev_config.block_dim.y ;
    const unsigned node_rank     = dev.team_rank() / num_ensemble_threads ;
    const unsigned ensemble_rank = dev.team_rank() % num_ensemble_threads ;
 
    const unsigned inode = dev.league_rank() * num_node_threads + node_rank;
 
    if (inode >= node_count)
      return;
 
    (*this)( inode, ensemble_rank );
 
  }
 
  KOKKOS_INLINE_FUNCTION
  void operator()( const unsigned inode ,
                   const unsigned ensemble_rank = 0) const
  {
    local_vector_type local_residual =
      local_vector_view_traits::create_local_view(residual,
                                                  ensemble_rank);
    local_matrix_type local_jacobian_values =
      local_matrix_view_traits::create_local_view(jacobian.values,
                                                  ensemble_rank);
 
    //  Apply dirichlet boundary condition on the Solution and Residual vectors.
    //  To maintain the symmetry of the original global stiffness matrix,
    //  zero out the columns that correspond to boundary conditions, and
    //  update the residual vector accordingly
 
    const unsigned iBeg = jacobian.graph.row_map[inode];
    const unsigned iEnd = jacobian.graph.row_map[inode+1];
 
    const scalar_coord_type c = node_coords(inode,bc_plane);
    const bool bc_lower = c <= bc_lower_limit ;
    const bool bc_upper = bc_upper_limit <= c ;
 
    if ( ! init ) {
      solution(inode) = bc_lower ? bc_lower_value : (
                        bc_upper ? bc_upper_value : 0 );
    }
    else {
      if ( bc_lower || bc_upper ) {
 
        local_residual(inode) = 0 ;
 
        //  zero each value on the row, and leave a one
        //  on the diagonal
 
        for( unsigned i = iBeg ; i < iEnd ; ++i ) {
          local_jacobian_values(i) = int(inode) == int(jacobian.graph.entries(i)) ? 1 : 0 ;
        }
      }
      else {
 
        //  Find any columns that are boundary conditions.
        //  Clear them and adjust the residual vector
 
        for( unsigned i = iBeg ; i < iEnd ; ++i ) {
          const unsigned       cnode = jacobian.graph.entries(i) ;
          const scalar_coord_type cc = node_coords(cnode,bc_plane);
 
          if ( ( cc <= bc_lower_limit ) || ( bc_upper_limit <= cc ) ) {
            local_jacobian_values(i) = 0 ;
          }
        }
      }
    }
  }
};
 
template< typename FixtureType , typename VectorType >
class ResponseComputation
{
public:
 
  typedef FixtureType fixture_type ;
  typedef VectorType vector_type ;
  typedef typename vector_type::execution_space execution_space ;
  typedef typename vector_type::value_type value_type ;
 
  typedef Kokkos::Example::HexElement_Data< fixture_type::ElemNode > element_data_type ;
  static const unsigned SpatialDim       = element_data_type::spatial_dimension ;
  static const unsigned TensorDim        = SpatialDim * SpatialDim ;
  static const unsigned ElemNodeCount    = element_data_type::element_node_count ;
  static const unsigned IntegrationCount = element_data_type::integration_count ;
 
  //------------------------------------
  // Computational data:
 
  const element_data_type    elem_data ;
  const fixture_type         fixture ;
  const vector_type          solution ;
 
  ResponseComputation( const ResponseComputation & rhs )
    : elem_data()
    , fixture( rhs.fixture )
    , solution( rhs.solution )
    {}
 
  ResponseComputation( const fixture_type& arg_fixture ,
                       const vector_type & arg_solution )
    : elem_data()
    , fixture( arg_fixture )
    , solution( arg_solution )
    {}
 
  //------------------------------------
 
  value_type apply() const
  {
    value_type response = 0;
    //Kokkos::parallel_reduce( fixture.elem_count() , *this , response );
    Kokkos::parallel_reduce( solution.extent(0) , *this , response );
    return response;
  }
 
  //------------------------------------
 
   KOKKOS_INLINE_FUNCTION
  double compute_detJ(
    const double grad[][ ElemNodeCount ] , // Gradient of bases master element
    const double x[] ,
    const double y[] ,
    const double z[] ) const
  {
    enum { j11 = 0 , j12 = 1 , j13 = 2 ,
           j21 = 3 , j22 = 4 , j23 = 5 ,
           j31 = 6 , j32 = 7 , j33 = 8 };
 
    // Jacobian accumulation:
 
    double J[ TensorDim ] = { 0, 0, 0,  0, 0, 0,  0, 0, 0 };
 
    for( unsigned i = 0; i < ElemNodeCount ; ++i ) {
      const double x1 = x[i] ;
      const double x2 = y[i] ;
      const double x3 = z[i] ;
 
      const double g1 = grad[0][i] ;
      const double g2 = grad[1][i] ;
      const double g3 = grad[2][i] ;
 
      J[j11] += g1 * x1 ;
      J[j12] += g1 * x2 ;
      J[j13] += g1 * x3 ;
 
      J[j21] += g2 * x1 ;
      J[j22] += g2 * x2 ;
      J[j23] += g2 * x3 ;
 
      J[j31] += g3 * x1 ;
      J[j32] += g3 * x2 ;
      J[j33] += g3 * x3 ;
    }
 
    // Inverse jacobian:
 
    double invJ[ TensorDim ] = {
      static_cast<double>( J[j22] * J[j33] - J[j23] * J[j32] ) ,
      static_cast<double>( J[j13] * J[j32] - J[j12] * J[j33] ) ,
      static_cast<double>( J[j12] * J[j23] - J[j13] * J[j22] ) ,
 
      static_cast<double>( J[j23] * J[j31] - J[j21] * J[j33] ) ,
      static_cast<double>( J[j11] * J[j33] - J[j13] * J[j31] ) ,
      static_cast<double>( J[j13] * J[j21] - J[j11] * J[j23] ) ,
 
      static_cast<double>( J[j21] * J[j32] - J[j22] * J[j31] ) ,
      static_cast<double>( J[j12] * J[j31] - J[j11] * J[j32] ) ,
      static_cast<double>( J[j11] * J[j22] - J[j12] * J[j21] ) };
 
    const double detJ = J[j11] * invJ[j11] +
                       J[j21] * invJ[j12] +
                       J[j31] * invJ[j13] ;
 
    return detJ ;
  }
 
  KOKKOS_INLINE_FUNCTION
  value_type contributeResponse(
    const value_type dof_values[] ,
    const double  detJ ,
    const double  integ_weight ,
    const double  bases_vals[] ) const
  {
    // $$ g_i = \int_{\Omega} T^2 d \Omega $$
 
    value_type value_at_pt = 0 ;
    for ( unsigned m = 0 ; m < ElemNodeCount ; m++ ) {
      value_at_pt += dof_values[m] * bases_vals[m] ;
    }
 
    value_type elem_response =
      value_at_pt * value_at_pt * detJ * integ_weight ;
 
    return elem_response;
  }
 
  /*
  KOKKOS_INLINE_FUNCTION
  void operator()( const unsigned ielem , value_type& response ) const
  {
    // Gather nodal coordinates and solution vector:
 
    double x[ ElemNodeCount ] ;
    double y[ ElemNodeCount ] ;
    double z[ ElemNodeCount ] ;
    value_type val[ ElemNodeCount ] ;
 
    for ( unsigned i = 0 ; i < ElemNodeCount ; ++i ) {
      const unsigned ni = fixture.elem_node( ielem , i );
 
      x[i] = fixture.node_coord( ni , 0 );
      y[i] = fixture.node_coord( ni , 1 );
      z[i] = fixture.node_coord( ni , 2 );
 
      val[i] = solution( ni );
    }
 
    for ( unsigned i = 0 ; i < IntegrationCount ; ++i ) {
 
      const double detJ = compute_detJ( elem_data.gradients[i] , x , y , z );
 
      response += contributeResponse( val , detJ , elem_data.weights[i] ,
                                      elem_data.values[i] );
    }
  }
  */
 
  KOKKOS_INLINE_FUNCTION
  void operator()( const unsigned i , value_type& response ) const
  {
    value_type u = solution(i);
    response += (u * u) / fixture.node_count_global();
  }
 
  KOKKOS_INLINE_FUNCTION
  void init( value_type & response ) const
  { response = 0 ; }
 
  KOKKOS_INLINE_FUNCTION
  void join( volatile value_type & response ,
             volatile const value_type & input ) const
  { response += input ; }
 
}; /* ResponseComputation */
 
} /* namespace FENL */
} /* namespace Example */
} /* namespace Kokkos  */
 
//----------------------------------------------------------------------------
 
/* A Cuda-specific specialization for the element computation functor. */
#if defined( __CUDACC__ )
// #include <NonlinearElement_Cuda.hpp>
#endif
 
//----------------------------------------------------------------------------
 
#endif /* #ifndef KOKKOS_EXAMPLE_FENLFUNCTORS_HPP */