/usr/include/trilinos/Kokkos_Parallel_MP_Vector.hpp is in libtrilinos-stokhos-dev 12.10.1-3.
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#ifndef KOKKOS_PARALLEL_MP_VECTOR_HPP
#define KOKKOS_PARALLEL_MP_VECTOR_HPP
#include "Sacado_MP_Vector.hpp"
#include "Kokkos_Core.hpp"
//----------------------------------------------------------------------------
// Kokkos execution policies useful for Sacado::MP::Vector scalar type
//----------------------------------------------------------------------------
namespace Kokkos {
/*!
* \brief Team-based parallel work configuration for Sacado::MP::Vector
*/
template< class ExecSpace >
struct MPVectorWorkConfig {
typedef MPVectorWorkConfig execution_policy ;
typedef ExecSpace execution_space ;
size_t range;
size_t team;
size_t shared;
MPVectorWorkConfig( const size_t range_,
const size_t team_,
const size_t shared_ = 0 ) :
range(range_), team(team_), shared(shared_) {}
};
namespace Impl {
#if defined( KOKKOS_HAVE_PTHREAD )
// Specialization of ParallelFor<> for MPVectorWorkConfig and Threads
// The default implementation ignores the team size and uses the standard
// work-range implementation. In the future maybe we should try and use
// hyperthreads in a useful way. That would require:
// -- interpreting the team-size differently, rather as the sacado size
// -- determining the vector size of the architecture
// -- laying out the threads differently to use hyperthreads across the
// the sacado dimension
template< class FunctorType >
class ParallelFor< FunctorType , MPVectorWorkConfig< Threads > > :
public ParallelFor< FunctorType , Kokkos::RangePolicy< Threads > > {
typedef Kokkos::RangePolicy< Threads > Policy ;
public:
ParallelFor( const FunctorType & functor ,
const MPVectorWorkConfig< Threads > & work_config ) :
ParallelFor< FunctorType , Policy >( functor ,
Policy( 0, work_config.range ) ) {}
};
#endif
#if defined( KOKKOS_HAVE_OPENMP )
// Specialization of ParallelFor<> for MPVectorWorkConfig and OpenMP
// The default implementation ignores the team size and uses the standard
// work-range implementation. In the future maybe we should try and use
// hyperthreads in a useful way. That would require:
// -- interpreting the team-size differently, rather as the sacado size
// -- determining the vector size of the architecture
// -- laying out the threads differently to use hyperthreads across the
// the sacado dimension
template< class FunctorType >
class ParallelFor< FunctorType , MPVectorWorkConfig< OpenMP > > :
public ParallelFor< FunctorType , Kokkos::RangePolicy< OpenMP > > {
typedef Kokkos::RangePolicy< OpenMP > Policy ;
public:
ParallelFor( const FunctorType & functor ,
const MPVectorWorkConfig< OpenMP > & work_config ) :
ParallelFor< FunctorType , Policy >( functor ,
Policy( 0, work_config.range ) ) {}
};
#endif
#if defined(KOKKOS_HAVE_SERIAL)
// Specialization of ParallelFor<> for MPVectorWorkConfig and Serial
// The default implementation ignores the team size and uses the standard
// work-range implementation. In the future maybe we should try and use
// hyperthreads in a useful way. That would require:
// -- interpreting the team-size differently, rather as the sacado size
// -- determining the vector size of the architecture
// -- laying out the threads differently to use hyperthreads across the
// the sacado dimension
template< class FunctorType >
class ParallelFor< FunctorType , MPVectorWorkConfig< Serial > > :
public ParallelFor< FunctorType , Kokkos::RangePolicy< Serial > > {
typedef Kokkos::RangePolicy< Serial > Policy ;
public:
ParallelFor( const FunctorType & functor ,
const MPVectorWorkConfig< Serial > & work_config ) :
ParallelFor< FunctorType , Policy >( functor ,
Policy( 0, work_config.range ) ) {}
};
#endif // defined(KOKKOS_HAVE_SERIAL)
#if defined( KOKKOS_HAVE_CUDA ) && defined( __CUDACC__ )
// Specialization of ParallelFor<> for MPVectorWorkConfig on Cuda
// Here we use threadIdx.x for each entry in the specified team-size
template< class FunctorType >
class ParallelFor< FunctorType , MPVectorWorkConfig< Cuda > > {
public:
const FunctorType m_functor ;
const MPVectorWorkConfig< Cuda > m_config;
const Cuda::size_type m_work ;
inline
__device__
void operator()(void) const
{
const Cuda::size_type work_stride = blockDim.y * gridDim.x ;
for ( Cuda::size_type iwork = threadIdx.y + blockDim.y * blockIdx.x ;
iwork < m_work ;
iwork += work_stride ) {
m_functor( iwork , threadIdx.x );
}
}
ParallelFor( const FunctorType & functor ,
const MPVectorWorkConfig< Cuda > & work_config )
: m_functor( functor ) ,
m_config( work_config ) ,
m_work( work_config.range )
{
}
inline
void execute() const
{
// To do: query number of registers used by functor and adjust
// nwarp accordingly to get maximum occupancy
Cuda::size_type nwarp = 0;
if (m_config.team > CudaTraits::WarpSize) {
const Cuda::size_type warps_per_team =
( m_config.team + CudaTraits::WarpSize-1 ) / CudaTraits::WarpSize;
nwarp = cuda_internal_maximum_warp_count() / warps_per_team;
}
else {
const Cuda::size_type teams_per_warp =
CudaTraits::WarpSize / m_config.team ;
nwarp = cuda_internal_maximum_warp_count() * teams_per_warp;
}
const dim3 block( m_config.team , nwarp , 1 );
Cuda::size_type nblock =
std::min( (m_work + block.y - 1 ) / block.y ,
cuda_internal_maximum_grid_count() );
const dim3 grid( nblock , 1 , 1 );
const Cuda::size_type shared = m_config.shared;
CudaParallelLaunch< ParallelFor >( *this , grid , block , shared );
}
};
#endif
} // namespace Impl
} // namespace Kokkos
//----------------------------------------------------------------------------
//----------------------------------------------------------------------------
#endif /* #ifndef KOKKOS_ATOMIC_MP_VECTOR_HPP */
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