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added a vectorized version of Product::_cacheOptimalProduct,

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added the possibility to disable the vectorization using EIGEN_DONT_VECTORIZE
(some architectures has SSE support by default)
This commit is contained in:
Gael Guennebaud 2008-04-10 12:34:22 +00:00
parent 613c49b475
commit 187b1543ce
2 changed files with 61 additions and 18 deletions
Eigen
Core
src/Core
Product.h

2
Eigen/Core

@ -1,12 +1,14 @@
#ifndef EIGEN_CORE_H
#define EIGEN_CORE_H
#ifndef EIGEN_DONT_VECTORIZE
#ifdef __SSE2__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_SSE
#include <emmintrin.h>
#include <xmmintrin.h>
#endif
#endif
#include <cstdlib>
#include <cmath>

77
Eigen/src/Core/Product.h

@ -108,7 +108,7 @@ struct ei_packet_product_unroller<RowMajor, Index, Dynamic, Lhs, Rhs, PacketScal
*/
template<typename Lhs, typename Rhs> struct ei_product_eval_mode
{
enum{ value = Lhs::MaxRowsAtCompileTime >= 8 && Rhs::MaxColsAtCompileTime >= 8
enum{ value = Lhs::MaxRowsAtCompileTime >= 16 && Rhs::MaxColsAtCompileTime >= 16
? CacheOptimalProduct : NormalProduct };
};
@ -139,7 +139,7 @@ struct ei_traits<Product<Lhs, Rhs, EvalMode> >
| (
(
!(Lhs::Flags & RowMajorBit) && (Lhs::Flags & VectorizableBit)
)
)
? VectorizableBit
: (
(
@ -215,7 +215,6 @@ template<typename Lhs, typename Rhs, int EvalMode> class Product : ei_no_assignm
? Lhs::ColsAtCompileTime : Dynamic,
Lhs, Rhs, PacketScalar>
::run(row, col, m_lhs, m_rhs, res);
// std::cout << "vec unrolled product\n";
}
else
{
@ -280,25 +279,67 @@ template<typename DestDerived>
void Product<Lhs,Rhs,EvalMode>::_cacheOptimalEval(DestDerived& res) const
{
res.setZero();
const int cols4 = m_lhs.cols()&0xfffffffC;
for (int k=0; k<m_rhs.cols(); ++k)
const int cols4 = m_lhs.cols() & 0xfffffffC;
#ifdef EIGEN_VECTORIZE
if( (Flags & VectorizableBit) && (!(Lhs::Flags & RowMajorBit)) )
{
int j=0;
for (; j<cols4; j+=4)
for(int k=0; k<m_rhs.cols(); k++)
{
const Scalar tmp0 = m_rhs.coeff(j ,k);
const Scalar tmp1 = m_rhs.coeff(j+1,k);
const Scalar tmp2 = m_rhs.coeff(j+2,k);
const Scalar tmp3 = m_rhs.coeff(j+3,k);
for (int i=0; i<m_lhs.rows(); ++i)
res.coeffRef(i,k) += tmp0 * m_lhs.coeff(i,j) + tmp1 * m_lhs.coeff(i,j+1)
+ tmp2 * m_lhs.coeff(i,j+2) + tmp3 * m_lhs.coeff(i,j+3);
int j=0;
for(; j<cols4; j+=4)
{
const typename ei_packet_traits<Scalar>::type tmp0 = ei_pset1(m_rhs.coeff(j+0,k));
const typename ei_packet_traits<Scalar>::type tmp1 = ei_pset1(m_rhs.coeff(j+1,k));
const typename ei_packet_traits<Scalar>::type tmp2 = ei_pset1(m_rhs.coeff(j+2,k));
const typename ei_packet_traits<Scalar>::type tmp3 = ei_pset1(m_rhs.coeff(j+3,k));
for (int i=0; i<m_lhs.rows(); i+=ei_packet_traits<Scalar>::size)
{
res.writePacketCoeff(i,k,
ei_padd(
res.packetCoeff(i,k),
ei_padd(
ei_padd(
ei_pmul(tmp0, m_lhs.packetCoeff(i,j)),
ei_pmul(tmp1, m_lhs.packetCoeff(i,j+1))),
ei_padd(
ei_pmul(tmp2, m_lhs.packetCoeff(i,j+2)),
ei_pmul(tmp3, m_lhs.packetCoeff(i,j+3))
)
)
)
);
}
}
for(; j<m_lhs.cols(); ++j)
{
const typename ei_packet_traits<Scalar>::type tmp = ei_pset1(m_rhs.coeff(j,k));
for (int i=0; i<m_lhs.rows(); ++i)
res.writePacketCoeff(i,k,ei_pmul(tmp, m_lhs.packetCoeff(i,j)));
}
}
for (; j<m_lhs.cols(); ++j)
}
else
#endif
{
for(int k=0; k<m_rhs.cols(); ++k)
{
const Scalar tmp = m_rhs.coeff(j,k);
for (int i=0; i<m_lhs.rows(); ++i)
res.coeffRef(i,k) += tmp * m_lhs.coeff(i,j);
int j=0;
for(; j<cols4; j+=4)
{
const Scalar tmp0 = m_rhs.coeff(j ,k);
const Scalar tmp1 = m_rhs.coeff(j+1,k);
const Scalar tmp2 = m_rhs.coeff(j+2,k);
const Scalar tmp3 = m_rhs.coeff(j+3,k);
for (int i=0; i<m_lhs.rows(); ++i)
res.coeffRef(i,k) += tmp0 * m_lhs.coeff(i,j) + tmp1 * m_lhs.coeff(i,j+1)
+ tmp2 * m_lhs.coeff(i,j+2) + tmp3 * m_lhs.coeff(i,j+3);
}
for(; j<m_lhs.cols(); ++j)
{
const Scalar tmp = m_rhs.coeff(j,k);
for (int i=0; i<m_lhs.rows(); ++i)
res.coeffRef(i,k) += tmp * m_lhs.coeff(i,j);
}
}
}
}