/usr/include/trilinos/Kokkos_CrsMatrix.hpp is in libtrilinos-tpetra-dev 12.10.1-3.
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//@HEADER
// ************************************************************************
//
// Kokkos: Node API and Parallel Node Kernels
// Copyright (2008) 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 Michael A. Heroux (maherou@sandia.gov)
//
// ************************************************************************
//@HEADER
*/
#ifndef KOKKOS_CRSMATRIX_H_
#define KOKKOS_CRSMATRIX_H_
/// \file Kokkos_CrsMatrix.hpp
/// \brief Local sparse matrix interface
/// \warning Do NOT include this file. This file is DEPRECATED!!!
/// Include Kokkos_Sparse_CrsMatrix.hpp instead.
#include <algorithm>
#include <Kokkos_Core.hpp>
#include <Kokkos_ArithTraits.hpp>
#include <Kokkos_StaticCrsGraph.hpp>
#ifdef KOKKOS_USE_CUSPARSE
# include <cusparse.h>
# include <Kokkos_CrsMatrix_CuSparse.hpp>
#endif // KOKKOS_USE_CUSPARSE
#ifdef KOKKOS_USE_MKL
# include <mkl.h>
# include <mkl_spblas.h>
# include <Kokkos_CrsMatrix_MKL.hpp>
#endif // KOKKOS_USE_MKL
//#include <Kokkos_Vectorization.hpp>
#include <impl/Kokkos_Error.hpp>
#include <Kokkos_Sparse_CrsMatrix.hpp>
namespace Kokkos {
#if true
using KokkosSparse::CrsMatrix;
using KokkosSparse::RowsPerThread;
using KokkosSparse::SparseRowView;
using KokkosSparse::SparseRowViewConst;
using KokkosSparse::DeviceConfig;
#endif // true
template<class DeviceType, typename ScalarType, int NNZPerRow=27>
struct MV_MultiplyShflThreadsPerRow {
private:
typedef typename Kokkos::Impl::remove_const< ScalarType >::type value_type;
// The shuffle operation only works with CUDA, and only works for
// certain ScalarType types.
#ifdef KOKKOS_HAVE_CUDA
enum { shfl_possible =
Kokkos::Impl::is_same< DeviceType , Kokkos::Cuda >::value &&
(
Kokkos::Impl::is_same< value_type , unsigned int >::value ||
Kokkos::Impl::is_same< value_type , int >::value ||
Kokkos::Impl::is_same< value_type , float >::value ||
Kokkos::Impl::is_same< value_type , double >::value
)};
#else // NOT KOKKOS_HAVE_CUDA
enum { shfl_possible = 0 };
#endif // KOKKOS_HAVE_CUDA
public:
#if defined( __CUDA_ARCH__ )
enum { device_value = shfl_possible && ( 300 <= __CUDA_ARCH__ ) ?
(NNZPerRow<8?2:
(NNZPerRow<16?4:
(NNZPerRow<32?8:
(NNZPerRow<64?16:
32))))
:1 };
#else
enum { device_value = 1 };
#endif
#ifdef KOKKOS_HAVE_CUDA
inline static int host_value()
{ return shfl_possible && ( 300 <= Kokkos::Cuda::device_arch() ) ?
(NNZPerRow<8?2:
(NNZPerRow<16?4:
(NNZPerRow<32?8:
(NNZPerRow<64?16:
32))))
:1; }
#else // NOT KOKKOS_HAVE_CUDA
inline static int host_value() { return 1; }
#endif // KOKKOS_HAVE_CUDA
};
//----------------------------------------------------------------------------
template<class RangeVector,
class CrsMatrix,
class DomainVector,
class CoeffVector1,
class CoeffVector2,
int doalpha,
int dobeta>
struct MV_MultiplyFunctor {
typedef typename CrsMatrix::execution_space execution_space;
typedef typename CrsMatrix::size_type size_type;
typedef typename CrsMatrix::ordinal_type ordinal_type;
typedef typename CrsMatrix::non_const_value_type value_type;
typedef typename Kokkos::View<value_type*, execution_space> range_values;
typedef typename Kokkos::TeamPolicy<execution_space> team_policy;
typedef typename team_policy::member_type team_member;
CoeffVector1 beta;
CoeffVector2 alpha;
CrsMatrix m_A;
DomainVector m_x;
RangeVector m_y;
/// \brief The number of columns in the input and output MultiVectors.
///
/// Its approxpriate type is therefore size_type, but we don't
/// expect the input and output MultiVectors to have more columns
/// than the sparse matrix has rows or columns. Thus, we prefer the
/// (likely both smaller and signed, vs. the larger and likely
/// unsigned) size_type.
ordinal_type n;
int rows_per_thread;
MV_MultiplyFunctor (const CoeffVector1 beta_,
const CoeffVector2 alpha_,
const CrsMatrix m_A_,
const DomainVector m_x_,
const RangeVector m_y_,
const ordinal_type n_,
const int rows_per_thread_) :
beta (beta_), alpha (alpha_),
m_A (m_A_), m_x (m_x_), m_y (m_y_), n (n_),
rows_per_thread (rows_per_thread_)
{}
template<int UNROLL>
KOKKOS_INLINE_FUNCTION void
strip_mine (const team_member& dev, const size_type& iRow, const size_type& kk) const
{
value_type sum[UNROLL];
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (int k = 0; k < UNROLL; ++k) {
// NOTE (mfh 09 Aug 2013) This requires that assignment from int
// (in this case, 0) to value_type be defined. It's not for
// types like arprec and dd_real.
//
// mfh 29 Sep 2013: On the other hand, arprec and dd_real won't
// work on CUDA devices anyway, since their methods aren't
// device functions. arprec has other issues (e.g., dynamic
// memory allocation, and the array-of-structs memory layout
// which is unfavorable to GPUs), but could be dealt with in the
// same way as Sacado's AD types.
sum[k] = 0;
}
const SparseRowViewConst<CrsMatrix> row = m_A.rowConst (iRow);
// NOTE (mfh 20 Mar 2015) Unfortunately, Kokkos::Vectorization
// lacks a typedef for determining the type of the return value of
// begin(). I know that it returns int now, but this may change
// at some point.
//
// The correct type of iEntry is ordinal_type. This is because we
// assume that rows have no duplicate entries. As a result, a row
// cannot have more entries than the number of columns in the
// matrix.
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
#ifdef KOKKOS_HAVE_PRAGMA_LOOPCOUNT
#pragma loop count (15)
#endif
#ifdef __CUDA_ARCH__
for (ordinal_type iEntry = static_cast<ordinal_type> (threadIdx.x);
iEntry < static_cast<ordinal_type> (row.length);
iEntry += static_cast<ordinal_type> (blockDim.x)) {
#else
for (ordinal_type iEntry = 0;
iEntry < static_cast<ordinal_type> (row.length);
iEntry ++) {
#endif
const value_type val = row.value(iEntry);
const ordinal_type ind = row.colidx(iEntry);
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (int k = 0; k < UNROLL; ++k) {
sum[k] += val * m_x(ind, kk + k);
}
}
if (doalpha == -1) {
for (int ii=0; ii < UNROLL; ++ii) {
value_type sumt=sum[ii];
#if defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
if (blockDim.x > 1)
sumt += shfl_down(sumt, 1,blockDim.x);
if (blockDim.x > 2)
sumt += shfl_down(sumt, 2,blockDim.x);
if (blockDim.x > 4)
sumt += shfl_down(sumt, 4,blockDim.x);
if (blockDim.x > 8)
sumt += shfl_down(sumt, 8,blockDim.x);
if (blockDim.x > 16)
sumt += shfl_down(sumt, 16,blockDim.x);
#endif // defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
sum[ii] = - sumt;
}
}
else {
for (int ii=0; ii < UNROLL; ++ii) {
value_type sumt = sum[ii];
#if defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
if (blockDim.x > 1)
sumt += shfl_down(sumt, 1,blockDim.x);
if (blockDim.x > 2)
sumt += shfl_down(sumt, 2,blockDim.x);
if (blockDim.x > 4)
sumt += shfl_down(sumt, 4,blockDim.x);
if (blockDim.x > 8)
sumt += shfl_down(sumt, 8,blockDim.x);
if (blockDim.x > 16)
sumt += shfl_down(sumt, 16,blockDim.x);
#endif // defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
sum[ii] = sumt;
}
}
#if defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
if (threadIdx.x==0) {
#else
if (true) {
#endif // defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
if (doalpha * doalpha != 1) {
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (int k = 0; k < UNROLL; ++k) {
sum[k] *= alpha(kk + k);
}
}
if (dobeta == 0) {
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (int k = 0; k < UNROLL; ++k) {
m_y(iRow, kk + k) = sum[k];
}
} else if (dobeta == 1) {
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (int k = 0; k < UNROLL; ++k) {
m_y(iRow, kk + k) += sum[k];
}
} else if (dobeta == -1) {
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (int k = 0; k < UNROLL; ++k) {
m_y(iRow, kk + k) = -m_y(iRow, kk + k) + sum[k];
}
} else {
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (int k = 0; k < UNROLL; ++k) {
m_y(iRow, kk + k) = beta(kk + k) * m_y(iRow, kk + k) + sum[k] ;
}
}
}
}
KOKKOS_INLINE_FUNCTION void
strip_mine_1 (const team_member& dev, const size_type& iRow) const
{
value_type sum = 0;
const SparseRowViewConst<CrsMatrix> row = m_A.rowConst (iRow);
// NOTE (mfh 20 Mar 2015) Unfortunately, Kokkos::Vectorization
// lacks a typedef for determining the type of the return value of
// begin(). I know that it returns int now, but this may change
// at some point.
//
// The correct type of iEntry is ordinal_type. This is because we
// assume that rows have no duplicate entries. As a result, a row
// cannot have more entries than the number of columns in the
// matrix.
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
#ifdef KOKKOS_HAVE_PRAGMA_LOOPCOUNT
#pragma loop count (15)
#endif
#ifdef __CUDA_ARCH__
for (ordinal_type iEntry = static_cast<ordinal_type> (threadIdx.x);
iEntry < static_cast<ordinal_type> (row.length);
iEntry += static_cast<ordinal_type> (blockDim.x)) {
#else
for (ordinal_type iEntry = 0;
iEntry < static_cast<ordinal_type> (row.length);
iEntry ++) {
#endif
sum += row.value(iEntry) * m_x(row.colidx(iEntry),0);
}
#if defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
if (blockDim.x > 1)
sum += shfl_down(sum, 1,blockDim.x);
if (blockDim.x > 2)
sum += shfl_down(sum, 2,blockDim.x);
if (blockDim.x > 4)
sum += shfl_down(sum, 4,blockDim.x);
if (blockDim.x > 8)
sum += shfl_down(sum, 8,blockDim.x);
if (blockDim.x > 16)
sum += shfl_down(sum, 16,blockDim.x);
#endif // defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
#ifdef __CUDA_ARCH__
if (threadIdx.x==0) {
#else
if (true) {
#endif
if (doalpha == -1) {
sum *= value_type(-1);
} else if (doalpha * doalpha != 1) {
sum *= alpha(0);
}
if (dobeta == 0) {
m_y(iRow, 0) = sum ;
} else if (dobeta == 1) {
m_y(iRow, 0) += sum ;
} else if (dobeta == -1) {
m_y(iRow, 0) = -m_y(iRow, 0) + sum;
} else {
m_y(iRow, 0) = beta(0) * m_y(iRow, 0) + sum;
}
}
}
KOKKOS_INLINE_FUNCTION void
operator() (const team_member& dev) const
{
for (int loop = 0; loop < rows_per_thread; ++loop) {
// NOTE (mfh 20 Mar 2015) Unfortunately, Kokkos::Vectorization
// lacks a typedef for determining the type of the return value
// of global_thread_rank(). I know that it returns int now, but
// this may change at some point.
//
// iRow represents a row of the matrix, so its correct type is
// ordinal_type.
const ordinal_type iRow = (dev.league_rank() * dev.team_size() + dev.team_rank())
* rows_per_thread + loop;
if (iRow >= m_A.numRows ()) {
return;
}
// mfh 20 Mar 2015: This relates to n, so its correct type is
// ordinal_type. Once we can use C++11 without protection, the
// right thing to do would be to use decltype to pick up n's
// type here, rather than assuming that it's ordinal_type.
ordinal_type kk = 0;
#ifdef KOKKOS_FAST_COMPILE
for (; kk + 4 <= n; kk += 4) {
strip_mine<4>(dev, iRow, kk);
}
for( ; kk < n; ++kk) {
strip_mine<1>(dev, iRow, kk);
}
#else
# ifdef __CUDA_ARCH__
if ((n > 8) && (n % 8 == 1)) {
strip_mine<9>(dev, iRow, kk);
kk += 9;
}
for(; kk + 8 <= n; kk += 8)
strip_mine<8>(dev, iRow, kk);
if(kk < n)
switch(n - kk) {
# else // NOT a CUDA device
if ((n > 16) && (n % 16 == 1)) {
strip_mine<17>(dev, iRow, kk);
kk += 17;
}
for (; kk + 16 <= n; kk += 16) {
strip_mine<16>(dev, iRow, kk);
}
if(kk < n)
switch(n - kk) {
case 15:
strip_mine<15>(dev, iRow, kk);
break;
case 14:
strip_mine<14>(dev, iRow, kk);
break;
case 13:
strip_mine<13>(dev, iRow, kk);
break;
case 12:
strip_mine<12>(dev, iRow, kk);
break;
case 11:
strip_mine<11>(dev, iRow, kk);
break;
case 10:
strip_mine<10>(dev, iRow, kk);
break;
case 9:
strip_mine<9>(dev, iRow, kk);
break;
case 8:
strip_mine<8>(dev, iRow, kk);
break;
# endif // __CUDA_ARCH__
case 7:
strip_mine<7>(dev, iRow, kk);
break;
case 6:
strip_mine<6>(dev, iRow, kk);
break;
case 5:
strip_mine<5>(dev, iRow, kk);
break;
case 4:
strip_mine<4>(dev, iRow, kk);
break;
case 3:
strip_mine<3>(dev, iRow, kk);
break;
case 2:
strip_mine<2>(dev, iRow, kk);
break;
case 1:
strip_mine_1(dev, iRow);
break;
}
#endif // KOKKOS_FAST_COMPILE
}
}
};
template<class RangeVector,
class CrsMatrix,
class DomainVector,
class CoeffVector1,
class CoeffVector2,
int doalpha,
int dobeta>
struct MV_MultiplySingleFunctor {
typedef typename CrsMatrix::execution_space execution_space;
typedef typename CrsMatrix::ordinal_type ordinal_type;
typedef typename CrsMatrix::non_const_value_type value_type;
typedef typename Kokkos::View<value_type*, typename CrsMatrix::execution_space> range_values;
typedef typename Kokkos::TeamPolicy<execution_space> team_policy;
typedef typename team_policy::member_type team_member;
CoeffVector1 beta;
CoeffVector2 alpha;
CrsMatrix m_A;
DomainVector m_x;
RangeVector m_y;
ordinal_type rows_per_thread;
MV_MultiplySingleFunctor (const CoeffVector1 beta_,
const CoeffVector2 alpha_,
const CrsMatrix m_A_,
const DomainVector m_x_,
const RangeVector m_y_,
const int rows_per_thread_) :
beta (beta_), alpha (alpha_),
m_A (m_A_), m_x (m_x_), m_y (m_y_),
rows_per_thread (rows_per_thread_)
{}
KOKKOS_INLINE_FUNCTION void
operator() (const team_member& dev) const
{
// This should be a thread loop as soon as we can use C++11.
//
// FIXME (mfh 20 Mar 2015, 11 Apr 2015) The correct type of
// 'loop' should be ordinal_type, not int. Ditto for
// rows_per_thread. The cast avoids a build warning.
for (int loop = 0; loop < static_cast<int> (rows_per_thread); ++loop) {
// NOTE (mfh 20 Mar 2015) Unfortunately, Kokkos::Vectorization
// lacks a typedef for determining the type of the return
// value of global_thread_rank(). I know that it returns int
// now, but this may change at some point.
//
// iRow represents a row of the matrix, so its correct type is
// ordinal_type.
const ordinal_type iRow = (dev.league_rank() * dev.team_size() + dev.team_rank())
* rows_per_thread + loop;
if (iRow >= m_A.numRows ()) {
return;
}
const SparseRowViewConst<CrsMatrix> row = m_A.rowConst (iRow);
const ordinal_type row_length = static_cast<ordinal_type> (row.length);
value_type sum = 0;
// Use explicit Cuda below to avoid C++11 for now. This should be a vector reduce loop !
#ifdef KOKKOS_HAVE_PRAGMA_IVDEP
#pragma ivdep
#endif
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
#ifdef KOKKOS_HAVE_PRAGMA_LOOPCOUNT
#pragma loop count (15)
#endif
#ifdef __CUDA_ARCH__
for (ordinal_type iEntry = static_cast<ordinal_type> (threadIdx.x);
iEntry < static_cast<ordinal_type> (row_length);
iEntry += static_cast<ordinal_type> (blockDim.x)) {
#else
for (ordinal_type iEntry = 0;
iEntry < static_cast<ordinal_type> (row_length);
iEntry ++) {
#endif
sum += row.value(iEntry) * m_x(row.colidx(iEntry));
}
#if defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
if (blockDim.x > 1)
sum += shfl_down(sum, 1,blockDim.x);
if (blockDim.x > 2)
sum += shfl_down(sum, 2,blockDim.x);
if (blockDim.x > 4)
sum += shfl_down(sum, 4,blockDim.x);
if (blockDim.x > 8)
sum += shfl_down(sum, 8,blockDim.x);
if (blockDim.x > 16)
sum += shfl_down(sum, 16,blockDim.x);
if (threadIdx.x==0) {
#else
if (true) {
#endif // defined(__CUDA_ARCH__) && defined(KOKKOS_HAVE_CUDA)
if (doalpha == -1) {
sum *= value_type(-1);
} else if (doalpha * doalpha != 1) {
sum *= alpha(0);
}
if (dobeta == 0) {
m_y(iRow) = sum ;
} else if (dobeta == 1) {
m_y(iRow) += sum ;
} else if (dobeta == -1) {
m_y(iRow) = -m_y(iRow) + sum;
} else {
m_y(iRow) = beta(0) * m_y(iRow) + sum;
}
}
}
}
};
namespace Impl {
template <class RangeVector,
class CrsMatrix,
class DomainVector,
class CoeffVector1,
class CoeffVector2>
void
MV_Multiply_Check_Compatibility (const CoeffVector1 &betav,
const RangeVector &y,
const CoeffVector2 &alphav,
const CrsMatrix &A,
const DomainVector &x,
const int& doalpha,
const int& dobeta)
{
typename DomainVector::size_type numVecs = x.dimension_1();
typename DomainVector::size_type numRows = A.numRows();
typename DomainVector::size_type numCols = A.numCols();
if (y.dimension_1() != numVecs) {
std::ostringstream msg;
msg << "Error in CRSMatrix - Vector Multiply (y = by + aAx): 2nd dimensions of y and x do not match\n";
msg << "\t Labels are: y(" << y.tracker().label() << ") b("
<< betav.tracker().label() << ") a("
<< alphav.tracker().label() << ") x("
<< A.values.tracker().label() << ") x("
<< x.tracker().label() << ")\n";
msg << "\t Dimensions are: y(" << y.dimension_0() << "," << y.dimension_1() << ") x(" << x.dimension_0() << "," << x.dimension_1() << ")\n";
Impl::throw_runtime_exception( msg.str() );
}
if (numRows > y.dimension_0()) {
std::ostringstream msg;
msg << "Error in CRSMatrix - Vector Multiply (y = by + aAx): dimensions of y and A do not match\n";
msg << "\t Labels are: y(" << y.tracker().label() << ") b("
<< betav.tracker().label() << ") a("
<< alphav.tracker().label() << ") x("
<< A.values.tracker().label() << ") x("
<< x.tracker().label() << ")\n";
msg << "\t Dimensions are: y(" << y.dimension_0() << "," << y.dimension_1() << ") A(" << A.numRows() << "," << A.numCols() << ")\n";
Impl::throw_runtime_exception( msg.str() );
}
if (numCols > x.dimension_0()) {
std::ostringstream msg;
msg << "Error in CRSMatrix - Vector Multiply (y = by + aAx): dimensions of x and A do not match\n";
msg << "\t Labels are: y(" << y.tracker().label() << ") b("
<< betav.tracker().label() << ") a("
<< alphav.tracker().label() << ") x("
<< A.values.tracker().label() << ") x("
<< x.tracker().label() << ")\n";
msg << "\t Dimensions are: x(" << x.dimension_0() << "," << x.dimension_1() << ") A(" << A.numRows() << "," << A.numCols() << ")\n";
Impl::throw_runtime_exception( msg.str() );
}
if (dobeta==2) {
if (betav.dimension_0()!=numVecs) {
std::ostringstream msg;
msg << "Error in CRSMatrix - Vector Multiply (y = by + aAx): 2nd dimensions of y and b do not match\n";
msg << "\t Labels are: y(" << y.tracker().label() << ") b("
<< betav.tracker().label() << ") a("
<< alphav.tracker().label() << ") x("
<< A.values.tracker().label() << ") x("
<< x.tracker().label() << ")\n";
msg << "\t Dimensions are: y(" << y.dimension_0() << "," << y.dimension_1() << ") b(" << betav.dimension_0() << ")\n";
Impl::throw_runtime_exception( msg.str() );
}
}
if(doalpha==2) {
if(alphav.dimension_0()!=numVecs) {
std::ostringstream msg;
msg << "Error in CRSMatrix - Vector Multiply (y = by + aAx): 2nd dimensions of x and b do not match\n";
msg << "\t Labels are: y(" << y.tracker().label() << ") b("
<< betav.tracker().label() << ") a("
<< alphav.tracker().label() << ") x("
<< A.values.tracker().label() << ") x("
<< x.tracker().label() << ")\n";
msg << "\t Dimensions are: x(" << x.dimension_0() << "," << x.dimension_1() << ") b(" << betav.dimension_0() << ")\n";
Impl::throw_runtime_exception( msg.str() );
}
}
}
} // namespace Impl
// This TransposeFunctor is functional, but not necessarily performant.
template<class RangeVector,
class CrsMatrix,
class DomainVector,
class CoeffVector1,
class CoeffVector2,
int doalpha,
int dobeta,
bool conjugate = false,
int NNZPerRow = 27>
struct MV_MultiplyTransposeFunctor {
typedef typename CrsMatrix::execution_space execution_space;
typedef typename CrsMatrix::ordinal_type ordinal_type;
typedef typename CrsMatrix::size_type size_type;
typedef typename CrsMatrix::non_const_value_type value_type;
typedef typename Kokkos::View<value_type*, execution_space> range_values;
typedef MV_MultiplyShflThreadsPerRow<execution_space, value_type, NNZPerRow> ShflThreadsPerRow;
CoeffVector1 beta;
CoeffVector2 alpha;
CrsMatrix m_A ;
DomainVector m_x ;
RangeVector m_y ;
ordinal_type n;
// This is an iteration over rows of the matrix (modulo the
// shuffle width), so the correct type of i is ordinal_type.
KOKKOS_INLINE_FUNCTION
void operator() (const ordinal_type i) const {
typedef Kokkos::Details::ArithTraits<value_type> ATV;
const ordinal_type iRow = i / ShflThreadsPerRow::device_value;
const int lane = static_cast<int> (i) % ShflThreadsPerRow::device_value;
const SparseRowViewConst<CrsMatrix> row = m_A.rowConst (iRow);
for (ordinal_type iEntry = static_cast<ordinal_type> (lane);
iEntry < static_cast<ordinal_type> (row.length);
iEntry += static_cast<ordinal_type> (ShflThreadsPerRow::device_value)) {
const value_type val = conjugate ?
ATV::conj (row.value(iEntry)) :
row.value(iEntry);
const ordinal_type ind = row.colidx(iEntry);
if (doalpha != 1) {
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (ordinal_type k = 0; k < n; ++k) {
atomic_add (&m_y(ind,k), value_type(alpha(k) * val * m_x(iRow, k)));
}
} else {
#ifdef KOKKOS_HAVE_PRAGMA_UNROLL
#pragma unroll
#endif
for (ordinal_type k = 0; k < n; ++k) {
atomic_add (&m_y(ind,k), value_type(val * m_x(iRow, k)));
}
}
}
}
};
// This TansposeFunctor is functional, but not necessarily performant.
template<class RangeVector,
class CrsMatrix,
class DomainVector,
class CoeffVector1,
class CoeffVector2,
int doalpha,
int dobeta,
bool conjugate = false,
int NNZPerRow = 27 >
struct MV_MultiplyTransposeSingleFunctor {
typedef typename CrsMatrix::execution_space execution_space;
typedef typename CrsMatrix::ordinal_type ordinal_type;
typedef typename CrsMatrix::non_const_value_type value_type;
typedef typename Kokkos::View<value_type*, execution_space> range_values;
typedef MV_MultiplyShflThreadsPerRow< execution_space , value_type , NNZPerRow > ShflThreadsPerRow ;
CoeffVector1 beta;
CoeffVector2 alpha;
CrsMatrix m_A ;
DomainVector m_x ;
RangeVector m_y ;
ordinal_type n;
KOKKOS_INLINE_FUNCTION
void operator() (const ordinal_type i) const {
typedef Kokkos::Details::ArithTraits<value_type> ATV;
const ordinal_type iRow = i / ShflThreadsPerRow::device_value;
const int lane = static_cast<int> (i) % ShflThreadsPerRow::device_value;
const SparseRowViewConst<CrsMatrix> row = m_A.rowConst (iRow);
for (ordinal_type iEntry = static_cast<ordinal_type> (lane);
iEntry < static_cast<ordinal_type> (row.length);
iEntry += static_cast<ordinal_type> (ShflThreadsPerRow::device_value)) {
const value_type val = conjugate ?
ATV::conj (row.value(iEntry)) :
row.value(iEntry);
const ordinal_type ind = row.colidx(iEntry);
if (doalpha != 1) {
atomic_add (&m_y(ind), value_type(alpha(0) * val * m_x(iRow)));
} else {
atomic_add (&m_y(ind), value_type(val * m_x(iRow)));
}
}
}
};
template <class RangeVector,
class TCrsMatrix,
class DomainVector,
class CoeffVector1,
class CoeffVector2,
int doalpha,
int dobeta>
void
MV_MultiplyTranspose (typename Kokkos::Impl::enable_if<DomainVector::Rank == 2, const CoeffVector1>::type& betav,
const RangeVector &y,
const CoeffVector2 &alphav,
const TCrsMatrix &A,
const DomainVector &x,
const bool conjugate = false)
{
typedef typename TCrsMatrix::ordinal_type ordinal_type;
// FIXME (mfh 02 Jan 2015) Is numRows() always signed? More
// importantly, if the calling process owns zero rows in the row
// Map, numRows() should return 0, not -1.
//
//Special case for zero Rows RowMap
if (A.numRows () == static_cast<ordinal_type> (-1)) {
return;
}
if (doalpha == 0) {
if (dobeta == 2) {
MV_MulScalar (y, betav, y);
} else {
MV_MulScalar (y, static_cast<typename RangeVector::const_value_type> (dobeta), y);
}
return;
} else {
typedef View< typename RangeVector::non_const_data_type ,
typename RangeVector::array_layout ,
typename RangeVector::execution_space ,
typename RangeVector::memory_traits >
RangeVectorType;
typedef View< typename DomainVector::const_data_type ,
typename DomainVector::array_layout ,
typename DomainVector::execution_space ,
Kokkos::MemoryRandomAccess >
DomainVectorType;
typedef View< typename CoeffVector1::const_data_type ,
typename CoeffVector1::array_layout ,
typename CoeffVector1::execution_space ,
Kokkos::MemoryRandomAccess >
CoeffVector1Type;
typedef View< typename CoeffVector2::const_data_type ,
typename CoeffVector2::array_layout ,
typename CoeffVector2::execution_space ,
Kokkos::MemoryRandomAccess >
CoeffVector2Type;
typedef CrsMatrix<typename TCrsMatrix::const_value_type,
typename TCrsMatrix::ordinal_type,
typename TCrsMatrix::execution_space,
typename TCrsMatrix::memory_traits,
typename TCrsMatrix::size_type> CrsMatrixType;
// FIXME (mfh 20 Mar 2015) The dimension doesn't have type int.
// On the other hand, this is the number of columns in the input
// (and output) MultiVectors, so it's not likely to be large (the
// typical case is < 100).
int numVecs = x.dimension_1();
CoeffVector1 beta = betav;
CoeffVector2 alpha = alphav;
if (doalpha != 2) {
alpha = CoeffVector2("CrsMatrix::auto_a", numVecs);
typename CoeffVector2::HostMirror h_a = Kokkos::create_mirror_view(alpha);
typename CoeffVector2::value_type s_a = (typename CoeffVector2::value_type) doalpha;
for (int i = 0; i < numVecs; ++i) {
h_a(i) = s_a;
}
Kokkos::deep_copy (alpha, h_a);
}
if (dobeta != 2) {
beta = CoeffVector1("CrsMatrix::auto_b", numVecs);
typename CoeffVector1::HostMirror h_b = Kokkos::create_mirror_view(beta);
typename CoeffVector1::value_type s_b = (typename CoeffVector1::value_type) dobeta;
for (int i = 0; i < numVecs; ++i) {
h_b(i) = s_b;
}
Kokkos::deep_copy (beta, h_b);
}
if (dobeta == 2) {
MV_MulScalar (y, betav, y);
} else {
if (dobeta != 1) {
MV_MulScalar (y, static_cast<typename RangeVector::const_value_type> (dobeta), y);
}
}
const ordinal_type nrow = A.numRows ();
if (conjugate) {
typedef MV_MultiplyTransposeFunctor<RangeVectorType, CrsMatrixType,
DomainVectorType, CoeffVector1Type,
CoeffVector2Type, 2, 2, true> OpType;
OpType op ;
op.m_A = A;
op.m_x = x;
op.m_y = y;
op.beta = beta;
op.alpha = alpha;
op.n = x.dimension_1();
Kokkos::parallel_for (nrow * OpType::ShflThreadsPerRow::host_value (), op);
}
else {
typedef MV_MultiplyTransposeFunctor<RangeVectorType, CrsMatrixType,
DomainVectorType, CoeffVector1Type,
CoeffVector2Type, 2, 2, false> OpType;
OpType op ;
op.m_A = A;
op.m_x = x;
op.m_y = y;
op.beta = beta;
op.alpha = alpha;
op.n = x.dimension_1();
Kokkos::parallel_for (nrow * OpType::ShflThreadsPerRow::host_value (), op);
}
//#endif // KOKKOS_FAST_COMPILE
}
}
template<class RangeVector,
class CrsMatrix,
class DomainVector,
class CoeffVector1,
class CoeffVector2>
void
MV_MultiplyTranspose (const CoeffVector1& betav,
const RangeVector& y,
const CoeffVector2& alphav,
const CrsMatrix& A,
const DomainVector& x,
int beta,
int alpha,
const bool conjugate = false)
{
if (beta == 0) {
if (alpha == 0) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 0, 0 > (betav, y, alphav, A, x, conjugate);
}
else if (alpha == 1) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 1, 0 > (betav, y, alphav, A, x, conjugate);
}
else if (alpha == -1) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, -1, 0 > (betav, y, alphav, A, x, conjugate);
}
else {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 2, 0 > (betav, y, alphav, A, x, conjugate);
}
} else if (beta == 1) {
if (alpha == 0) {
return;
}
else if (alpha == 1) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 1, 1 > (betav, y, alphav, A, x, conjugate);
}
else if (alpha == -1) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, -1, 1 > (betav, y, alphav, A, x, conjugate);
}
else {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 2, 1 > (betav, y, alphav, A, x, conjugate);
}
} else if (beta == -1) {
if (alpha == 0) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 0, -1 > (betav, y, alphav, A, x, conjugate);
}
else if (alpha == 1) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 1, -1 > (betav, y, alphav, A, x, conjugate);
}
else if (alpha == -1) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, -1, -1 > (betav, y, alphav, A, x, conjugate);
}
else {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 2, -1 > (betav, y, alphav, A, x, conjugate);
}
} else {
if (alpha == 0) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 0, 2 > (betav, y, alphav, A, x, conjugate);
}
else if (alpha == 1) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 1, 2 > (betav, y, alphav, A, x, conjugate);
}
else if (alpha == -1) {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, -1, 2 > (betav, y, alphav, A, x, conjugate);
}
else {
MV_MultiplyTranspose<RangeVector, CrsMatrix, DomainVector, CoeffVector1,
CoeffVector2, 2, 2> (betav, y, alphav, A, x, conjugate);
}
}
}
template<class RangeVector, class CrsMatrix, class DomainVector>
void
MV_MultiplyTranspose (typename RangeVector::const_value_type s_b,
const RangeVector& y,
typename DomainVector::const_value_type s_a,
const CrsMatrix& A,
const DomainVector& x,
const bool conjugate = false)
{
/*#ifdef KOKKOS_USE_CUSPARSE
if (MV_Multiply_Try_CuSparse (s_b, y, s_a, A, x, conjugate)) {
return;
}
#endif // KOKKOSE_USE_CUSPARSE
#ifdef KOKKOS_USE_MKL
if (MV_Multiply_Try_MKL (s_b, y, s_a, A, x, conjugate)) {
return;
}
#endif // KOKKOS_USE_MKL*/
typedef Kokkos::View<typename RangeVector::value_type*,
typename RangeVector::execution_space> aVector;
aVector a;
aVector b;
int numVecs = x.dimension_1();
if (s_b == 0) {
if (s_a == 0)
return MV_MultiplyTranspose (a, y, a, A, x, 0, 0, conjugate);
else if (s_a == 1)
return MV_MultiplyTranspose (a, y, a, A, x, 0, 1, conjugate);
else if (s_a == static_cast<typename DomainVector::const_value_type> (-1))
return MV_MultiplyTranspose (a, y, a, A, x, 0, -1, conjugate);
else {
a = aVector("a", numVecs);
typename aVector::HostMirror h_a = Kokkos::create_mirror_view (a);
for (int i = 0; i < numVecs; ++i) {
h_a(i) = s_a;
}
Kokkos::deep_copy (a, h_a);
return MV_MultiplyTranspose (a, y, a, A, x, 0, 2, conjugate);
}
} else if (s_b == 1) {
if (s_a == 0)
return MV_MultiplyTranspose (a, y, a, A, x, 1, 0, conjugate);
else if (s_a == 1)
return MV_MultiplyTranspose (a, y, a, A, x, 1, 1, conjugate);
else if (s_a == static_cast<typename DomainVector::const_value_type> (-1))
return MV_MultiplyTranspose (a, y, a, A, x, 1, -1, conjugate);
else {
a = aVector("a", numVecs);
typename aVector::HostMirror h_a = Kokkos::create_mirror_view (a);
for (int i = 0; i < numVecs; ++i) {
h_a(i) = s_a;
}
Kokkos::deep_copy (a, h_a);
return MV_MultiplyTranspose (a, y, a, A, x, 1, 2, conjugate);
}
} else if (s_b == static_cast<typename RangeVector::const_value_type> (-1)) {
if (s_a == 0)
return MV_MultiplyTranspose (a, y, a, A, x, -1, 0, conjugate);
else if (s_a == 1)
return MV_MultiplyTranspose (a, y, a, A, x, -1, 1, conjugate);
else if (s_a == static_cast<typename DomainVector::const_value_type> (-1))
return MV_MultiplyTranspose (a, y, a, A, x, -1, -1, conjugate);
else {
a = aVector("a", numVecs);
typename aVector::HostMirror h_a = Kokkos::create_mirror_view (a);
for (int i = 0; i < numVecs; ++i) {
h_a(i) = s_a;
}
Kokkos::deep_copy (a, h_a);
return MV_MultiplyTranspose (a, y, a, A, x, -1, 2, conjugate);
}
} else {
b = aVector("b", numVecs);
typename aVector::HostMirror h_b = Kokkos::create_mirror_view (b);
for (int i = 0; i < numVecs; ++i) {
h_b(i) = s_b;
}
Kokkos::deep_copy (b, h_b);
if (s_a == 0)
return MV_MultiplyTranspose (b, y, a, A, x, 2, 0, conjugate);
else if (s_a == 1)
return MV_MultiplyTranspose (b, y, a, A, x, 2, 1, conjugate);
else if (s_a == static_cast<typename DomainVector::const_value_type> (-1))
return MV_MultiplyTranspose (b, y, a, A, x, 2, -1, conjugate);
else {
a = aVector("a", numVecs);
typename aVector::HostMirror h_a = Kokkos::create_mirror_view (a);
for (int i = 0; i < numVecs; ++i) {
h_a(i) = s_a;
}
Kokkos::deep_copy (a, h_a);
return MV_MultiplyTranspose (b, y, a, A, x, 2, 2, conjugate);
}
}
}
template<class RangeVector,
class TCrsMatrix,
class DomainVector,
class CoeffVector1,
class CoeffVector2,
int doalpha,
int dobeta>
void
MV_MultiplySingle (typename Kokkos::Impl::enable_if<DomainVector::Rank == 1, const CoeffVector1>::type& betav,
const RangeVector &y,
const CoeffVector2 &alphav,
const TCrsMatrix& A,
const DomainVector& x)
{
typedef typename TCrsMatrix::ordinal_type ordinal_type;
if (A.numRows () <= static_cast<ordinal_type> (0)) {
return;
}
if (doalpha == 0) {
if (dobeta==2) {
V_MulScalar (y, betav, y);
}
else {
V_MulScalar (y, typename RangeVector::value_type (dobeta), y);
}
return;
} else {
typedef View< typename RangeVector::non_const_data_type ,
typename RangeVector::array_layout ,
typename RangeVector::execution_space ,
typename RangeVector::memory_traits >
RangeVectorType;
typedef View< typename DomainVector::const_data_type ,
typename DomainVector::array_layout ,
typename DomainVector::execution_space ,
//typename DomainVector::memory_traits >
Kokkos::MemoryRandomAccess >
DomainVectorType;
typedef View< typename CoeffVector1::const_data_type ,
typename CoeffVector1::array_layout ,
typename CoeffVector1::execution_space ,
Kokkos::MemoryRandomAccess >
CoeffVector1Type;
typedef View< typename CoeffVector2::const_data_type ,
typename CoeffVector2::array_layout ,
typename CoeffVector2::execution_space ,
Kokkos::MemoryRandomAccess >
CoeffVector2Type;
typedef CrsMatrix<typename TCrsMatrix::const_value_type,
typename TCrsMatrix::ordinal_type,
typename TCrsMatrix::execution_space,
typename TCrsMatrix::memory_traits,
typename TCrsMatrix::size_type>
CrsMatrixType;
typedef typename CrsMatrixType::size_type size_type;
Impl::MV_Multiply_Check_Compatibility(betav,y,alphav,A,x,doalpha,dobeta);
// NNZPerRow could be anywhere from 0, to A.numRows()*A.numCols().
// Thus, the appropriate type is size_type.
const size_type NNZPerRow = A.nnz () / A.numRows ();
int vector_length = 1;
while( (static_cast<size_type> (vector_length*2*3) <= NNZPerRow) && (vector_length<32) ) vector_length*=2;
#ifndef KOKKOS_FAST_COMPILE // This uses templated fucntions on doalpha and dobeta and will produce 16 kernels
typedef MV_MultiplySingleFunctor<RangeVectorType, CrsMatrixType, DomainVectorType,
CoeffVector1Type, CoeffVector2Type, doalpha, dobeta > OpType ;
const typename CrsMatrixType::ordinal_type nrow = A.numRows();
OpType op(betav,alphav,A,x,y,RowsPerThread<typename RangeVector::execution_space >(NNZPerRow)) ;
const int rows_per_thread = RowsPerThread<typename RangeVector::execution_space >(NNZPerRow);
const int team_size = Kokkos::TeamPolicy< typename RangeVector::execution_space >::team_size_recommended(op,vector_length);
const int rows_per_team = rows_per_thread * team_size;
const int nteams = (nrow+rows_per_team-1)/rows_per_team;
Kokkos::parallel_for( Kokkos::TeamPolicy< typename RangeVector::execution_space >
( nteams , team_size , vector_length ) , op );
#else // NOT KOKKOS_FAST_COMPILE
typedef MV_MultiplySingleFunctor<RangeVectorType, CrsMatrixType, DomainVectorType,
CoeffVector1Type, CoeffVector2Type, 2, 2> OpType ;
int numVecs = x.dimension_1(); // == 1
CoeffVector1 beta = betav;
CoeffVector2 alpha = alphav;
if(doalpha!=2) {
alpha = CoeffVector2("CrsMatrix::auto_a", numVecs);
typename CoeffVector2::HostMirror h_a = Kokkos::create_mirror_view(alpha);
typename CoeffVector2::value_type s_a = (typename CoeffVector2::value_type) doalpha;
for(int i = 0; i < numVecs; i++)
h_a(i) = s_a;
Kokkos::deep_copy(alpha, h_a);
}
if(dobeta!=2) {
beta = CoeffVector1("CrsMatrix::auto_b", numVecs);
typename CoeffVector1::HostMirror h_b = Kokkos::create_mirror_view(beta);
typename CoeffVector1::value_type s_b = (typename CoeffVector1::value_type) dobeta;
for(int i = 0; i < numVecs; i++)
h_b(i) = s_b;
Kokkos::deep_copy(beta, h_b);
}
const typename CrsMatrixType::ordinal_type nrow = A.numRows();
OpType op(beta,alpha,A,x,y,RowsPerThread<typename RangeVector::execution_space >(NNZPerRow)) ;
const int team_size = Kokkos::TeamPolicy< typename RangeVector::execution_space >::team_size_recommended(op,vector_length);
const int rows_per_team = rows_per_thread * team_size;
const int nteams = (nrow+rows_per_team-1)/rows_per_team;
Kokkos::parallel_for( Kokkos::TeamPolicy< typename RangeVector::execution_space >
( nteams , team_size , vector_length ) , op );
#endif // KOKKOS_FAST_COMPILE
}
}
} // namespace Kokkos
#endif /* KOKKOS_CRSMATRIX_H_ */
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