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Updating kokkos lib

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git-svn-id: svn://svn.icms.temple.edu/lammps-ro/trunk@14918 f3b2605a-c512-4ea7-a41b-209d697bcdaa
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stamoor
2016-05-02 22:06:50 +00:00
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lib/kokkos/example/multi_fem/SparseLinearSystem.hpp
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/*
//@HEADER
// ************************************************************************
//
// Kokkos v. 2.0
// Copyright (2014) Sandia Corporation
//
// Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
// the U.S. Government retains certain rights in this software.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact H. Carter Edwards (hcedwar@sandia.gov)
//
// ************************************************************************
//@HEADER
*/
#ifndef SPARSELINEARSYSTEM_HPP
#define SPARSELINEARSYSTEM_HPP
#include <cmath>
#include <impl/Kokkos_Timer.hpp>
#include <Kokkos_Core.hpp>
#include <Kokkos_StaticCrsGraph.hpp>
#include <LinAlgBLAS.hpp>
namespace Kokkos {
template< typename ScalarType , class Device >
struct CrsMatrix {
typedef Device execution_space ;
typedef ScalarType value_type ;
typedef StaticCrsGraph< int , execution_space , void , int > graph_type ;
typedef View< value_type* , execution_space > coefficients_type ;
graph_type graph ;
coefficients_type coefficients ;
};
//----------------------------------------------------------------------------
namespace Impl {
template< class Matrix , class OutputVector , class InputVector >
struct Multiply ;
}
}
//----------------------------------------------------------------------------
//----------------------------------------------------------------------------
namespace Kokkos {
namespace Impl {
template< typename AScalarType ,
typename VScalarType ,
class DeviceType >
struct Multiply< CrsMatrix<AScalarType,DeviceType> ,
View<VScalarType*,DeviceType > ,
View<VScalarType*,DeviceType > >
{
typedef DeviceType execution_space ;
typedef typename execution_space::size_type size_type ;
typedef View< VScalarType*, execution_space, MemoryUnmanaged > vector_type ;
typedef View< const VScalarType*, execution_space, MemoryUnmanaged > vector_const_type ;
typedef CrsMatrix< AScalarType , execution_space > matrix_type ;
private:
matrix_type m_A ;
vector_const_type m_x ;
vector_type m_y ;
public:
//--------------------------------------------------------------------------
KOKKOS_INLINE_FUNCTION
void operator()( const size_type iRow ) const
{
const size_type iEntryBegin = m_A.graph.row_map[iRow];
const size_type iEntryEnd = m_A.graph.row_map[iRow+1];
double sum = 0 ;
#if defined( __INTEL_COMPILER )
#pragma simd reduction(+:sum)
#pragma ivdep
for ( size_type iEntry = iEntryBegin ; iEntry < iEntryEnd ; ++iEntry ) {
sum += m_A.coefficients(iEntry) * m_x( m_A.graph.entries(iEntry) );
}
#else
for ( size_type iEntry = iEntryBegin ; iEntry < iEntryEnd ; ++iEntry ) {
sum += m_A.coefficients(iEntry) * m_x( m_A.graph.entries(iEntry) );
}
#endif
m_y(iRow) = sum ;
}
Multiply( const matrix_type & A ,
const size_type nrow ,
const size_type , // ncol ,
const vector_type & x ,
const vector_type & y )
: m_A( A ), m_x( x ), m_y( y )
{
parallel_for( nrow , *this );
}
};
//----------------------------------------------------------------------------
} // namespace Impl
} // namespace Kokkos
//----------------------------------------------------------------------------
//----------------------------------------------------------------------------
namespace Kokkos {
//----------------------------------------------------------------------------
template< typename AScalarType ,
typename VScalarType ,
class Device >
class Operator {
typedef CrsMatrix<AScalarType,Device> matrix_type ;
typedef View<VScalarType*,Device> vector_type ;
private:
const CrsMatrix<AScalarType,Device> A ;
ParallelDataMap data_map ;
AsyncExchange< VScalarType , Device , ParallelDataMap > exchange ;
public:
Operator( const ParallelDataMap & arg_data_map ,
const CrsMatrix<AScalarType,Device> & arg_A )
: A( arg_A )
, data_map( arg_data_map )
, exchange( arg_data_map , 1 )
{}
void apply( const View<VScalarType*,Device> & x ,
const View<VScalarType*,Device> & y )
{
// Gather off-processor data for 'x'
PackArray< vector_type >::pack( exchange.buffer() ,
data_map.count_interior ,
data_map.count_send , x );
exchange.setup();
// If interior & boundary matrices then could launch interior multiply
exchange.send_receive();
UnpackArray< vector_type >::unpack( x , exchange.buffer() ,
data_map.count_owned ,
data_map.count_receive );
const typename Device::size_type nrow = data_map.count_owned ;
const typename Device::size_type ncol = data_map.count_owned +
data_map.count_receive ;
Impl::Multiply<matrix_type,vector_type,vector_type>( A, nrow, ncol, x, y);
}
};
//----------------------------------------------------------------------------
template< typename AScalarType , typename VScalarType , class Device >
void cgsolve(
const ParallelDataMap data_map ,
const CrsMatrix<AScalarType,Device> A ,
const View<VScalarType*,Device> b ,
const View<VScalarType*,Device> x ,
size_t & iteration ,
double & normr ,
double & iter_time ,
const size_t maximum_iteration = 200 ,
const double tolerance = std::numeric_limits<VScalarType>::epsilon() )
{
typedef View<VScalarType*,Device> vector_type ;
//typedef View<VScalarType, Device> value_type ; // unused
const size_t count_owned = data_map.count_owned ;
const size_t count_total = data_map.count_owned + data_map.count_receive ;
Operator<AScalarType,VScalarType,Device> matrix_operator( data_map , A );
// Need input vector to matvec to be owned + received
vector_type pAll ( "cg::p" , count_total );
vector_type p = Kokkos::subview( pAll , std::pair<size_t,size_t>(0,count_owned) );
vector_type r ( "cg::r" , count_owned );
vector_type Ap( "cg::Ap", count_owned );
/* r = b - A * x ; */
/* p = x */ deep_copy( p , x );
/* Ap = A * p */ matrix_operator.apply( pAll , Ap );
/* r = b - Ap */ waxpby( count_owned , 1.0 , b , -1.0 , Ap , r );
/* p = r */ deep_copy( p , r );
double old_rdot = dot( count_owned , r , data_map.machine );
normr = sqrt( old_rdot );
iteration = 0 ;
Kokkos::Impl::Timer wall_clock ;
while ( tolerance < normr && iteration < maximum_iteration ) {
/* pAp_dot = dot( p , Ap = A * p ) */
/* Ap = A * p */ matrix_operator.apply( pAll , Ap );
const double pAp_dot = dot( count_owned , p , Ap , data_map.machine );
const double alpha = old_rdot / pAp_dot ;
/* x += alpha * p ; */ axpy( count_owned, alpha, p , x );
/* r -= alpha * Ap ; */ axpy( count_owned, -alpha, Ap, r );
const double r_dot = dot( count_owned , r , data_map.machine );
const double beta = r_dot / old_rdot ;
/* p = r + beta * p ; */ xpby( count_owned , r , beta , p );
normr = sqrt( old_rdot = r_dot );
++iteration ;
}
iter_time = wall_clock.seconds();
}
//----------------------------------------------------------------------------
} // namespace Kokkos
//----------------------------------------------------------------------------
//----------------------------------------------------------------------------
#if defined( KOKKOS_HAVE_CUDA )
#if ( CUDA_VERSION < 6000 )
#pragma message "cusparse_v2.h"
#include <cusparse_v2.h>
#else
#pragma message "cusparse.h"
#include <cusparse.h>
#endif
namespace Kokkos {
namespace Impl {
struct CudaSparseSingleton {
cusparseHandle_t handle;
cusparseMatDescr_t descra;
CudaSparseSingleton()
{
cusparseCreate( & handle );
cusparseCreateMatDescr( & descra );
cusparseSetMatType( descra , CUSPARSE_MATRIX_TYPE_GENERAL );
cusparseSetMatIndexBase( descra , CUSPARSE_INDEX_BASE_ZERO );
}
static CudaSparseSingleton & singleton();
};
template<>
struct Multiply< CrsMatrix<double,Cuda> ,
View<double*,Cuda > ,
View<double*,Cuda > >
{
typedef Cuda execution_space ;
typedef execution_space::size_type size_type ;
typedef double scalar_type ;
typedef View< scalar_type* , execution_space > vector_type ;
typedef CrsMatrix< scalar_type , execution_space > matrix_type ;
public:
Multiply( const matrix_type & A ,
const size_type nrow ,
const size_type ncol ,
const vector_type & x ,
const vector_type & y )
{
CudaSparseSingleton & s = CudaSparseSingleton::singleton();
const scalar_type alpha = 1 , beta = 0 ;
cusparseStatus_t status =
cusparseDcsrmv( s.handle ,
CUSPARSE_OPERATION_NON_TRANSPOSE ,
nrow , ncol , A.coefficients.dimension_0() ,
&alpha ,
s.descra ,
A.coefficients.ptr_on_device() ,
A.graph.row_map.ptr_on_device() ,
A.graph.entries.ptr_on_device() ,
x.ptr_on_device() ,
&beta ,
y.ptr_on_device() );
if ( CUSPARSE_STATUS_SUCCESS != status ) {
throw std::runtime_error( std::string("ERROR - cusparseDcsrmv " ) );
}
}
};
template<>
struct Multiply< CrsMatrix<float,Cuda> ,
View<float*,Cuda > ,
View<float*,Cuda > >
{
typedef Cuda execution_space ;
typedef execution_space::size_type size_type ;
typedef float scalar_type ;
typedef View< scalar_type* , execution_space > vector_type ;
typedef CrsMatrix< scalar_type , execution_space > matrix_type ;
public:
Multiply( const matrix_type & A ,
const size_type nrow ,
const size_type ncol ,
const vector_type & x ,
const vector_type & y )
{
CudaSparseSingleton & s = CudaSparseSingleton::singleton();
const scalar_type alpha = 1 , beta = 0 ;
cusparseStatus_t status =
cusparseScsrmv( s.handle ,
CUSPARSE_OPERATION_NON_TRANSPOSE ,
nrow , ncol , A.coefficients.dimension_0() ,
&alpha ,
s.descra ,
A.coefficients.ptr_on_device() ,
A.graph.row_map.ptr_on_device() ,
A.graph.entries.ptr_on_device() ,
x.ptr_on_device() ,
&beta ,
y.ptr_on_device() );
if ( CUSPARSE_STATUS_SUCCESS != status ) {
throw std::runtime_error( std::string("ERROR - cusparseDcsrmv " ) );
}
}
};
} /* namespace Impl */
} /* namespace Kokkos */
#endif /* #if defined( KOKKOS_HAVE_CUDA ) */
//----------------------------------------------------------------------------
//----------------------------------------------------------------------------
#endif /* #ifndef SPARSELINEARSYSTEM_HPP */