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* added optimized paths for matrix-vector and vector-matrix products
(using either a cache friendly strategy or re-using dot-product vectorized implementation) * add LinearAccessBit to Transpose
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@ -41,12 +41,12 @@ namespace Eigen {
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#include "src/Core/CwiseUnaryOp.h"
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#include "src/Core/CwiseNullaryOp.h"
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#include "src/Core/InverseProduct.h"
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#include "src/Core/Dot.h"
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#include "src/Core/Product.h"
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#include "src/Core/DiagonalProduct.h"
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#include "src/Core/Block.h"
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#include "src/Core/Minor.h"
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#include "src/Core/Transpose.h"
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#include "src/Core/Dot.h"
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#include "src/Core/DiagonalMatrix.h"
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#include "src/Core/DiagonalCoeffs.h"
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#include "src/Core/Sum.h"
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@ -155,7 +155,7 @@ template<typename MatrixType, int BlockRows, int BlockCols> class Block
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return m_matrix.const_cast_derived()
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.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
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m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
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}
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inline const Scalar coeff(int index) const
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@ -168,20 +168,23 @@ template<typename MatrixType, int BlockRows, int BlockCols> class Block
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template<int LoadMode>
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inline PacketScalar packet(int row, int col) const
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{
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return m_matrix.template packet<Unaligned>(row + m_startRow.value(), col + m_startCol.value());
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return m_matrix.template packet<Unaligned>
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(row + m_startRow.value(), col + m_startCol.value());
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}
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template<int LoadMode>
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inline void writePacket(int row, int col, const PacketScalar& x)
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{
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m_matrix.const_cast_derived().template writePacket<Unaligned>(row + m_startRow.value(), col + m_startCol.value(), x);
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m_matrix.const_cast_derived().template writePacket<Unaligned>
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(row + m_startRow.value(), col + m_startCol.value(), x);
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}
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template<int LoadMode>
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inline PacketScalar packet(int index) const
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{
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return m_matrix.template packet<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
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m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
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return m_matrix.template packet<Unaligned>
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(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
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m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
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}
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template<int LoadMode>
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@ -219,7 +219,7 @@ template<typename Derived> class MatrixBase
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/** Overloaded for cache friendly product evaluation */
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template<typename OtherDerived>
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Derived& lazyAssign(const Flagged<OtherDerived, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit>& other)
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{ lazyAssign(other._expression()); }
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{ return lazyAssign(other._expression()); }
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/** Overloaded for sparse product evaluation */
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template<typename Derived1, typename Derived2>
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@ -89,9 +89,11 @@ template<typename Lhs, typename Rhs> struct ei_product_mode
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? DiagonalProduct
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: (Rhs::Flags & Lhs::Flags & SparseBit)
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? SparseProduct
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: Lhs::MaxRowsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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&& Rhs::MaxColsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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&& Lhs::MaxColsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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: Lhs::MaxColsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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&& ( Lhs::MaxRowsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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|| ((Rhs::Flags&RowMajorBit) && Lhs::IsVectorAtCompileTime))
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&& ( Rhs::MaxColsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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|| ((!(Lhs::Flags&RowMajorBit)) && Rhs::IsVectorAtCompileTime))
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? CacheFriendlyProduct : NormalProduct };
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};
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@ -161,7 +163,7 @@ struct ei_traits<Product<LhsNested, RhsNested, ProductMode> >
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* the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
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*/
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CanVectorizeInner = LhsRowMajor && (!RhsRowMajor) && (LhsFlags & RhsFlags & ActualPacketAccessBit)
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&& (InnerSize!=Dynamic) && (InnerSize % ei_packet_traits<Scalar>::size == 0)
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&& (InnerSize % ei_packet_traits<Scalar>::size == 0)
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};
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};
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@ -181,7 +183,7 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
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PacketSize = ei_packet_traits<Scalar>::size,
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InnerSize = ei_traits<Product>::InnerSize,
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Unroll = CoeffReadCost <= EIGEN_UNROLLING_LIMIT,
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CanVectorizeInner = ei_traits<Product>::CanVectorizeInner && Unroll
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CanVectorizeInner = ei_traits<Product>::CanVectorizeInner
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};
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typedef ei_product_coeff_impl<CanVectorizeInner ? InnerVectorization : NoVectorization,
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@ -206,10 +208,13 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
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/** \internal
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* \returns whether it is worth it to use the cache friendly product.
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*/
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inline bool _useCacheFriendlyProduct() const {
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return rows()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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&& cols()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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&& m_lhs.cols()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD;
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inline bool _useCacheFriendlyProduct() const
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{
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return m_lhs.cols()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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&& (rows()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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|| ((_RhsNested::Flags&RowMajorBit) && _LhsNested::IsVectorAtCompileTime))
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&& (cols()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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|| ((!(_LhsNested::Flags&RowMajorBit)) && _RhsNested::IsVectorAtCompileTime));
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}
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inline int rows() const { return m_lhs.rows(); }
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@ -245,6 +250,9 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
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return res;
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}
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const _LhsNested& lhs() const { return m_lhs; }
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const _RhsNested& rhs() const { return m_rhs; }
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protected:
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const LhsNested m_lhs;
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const RhsNested m_rhs;
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@ -324,18 +332,18 @@ struct ei_product_coeff_impl<NoVectorization, -1, Lhs, Rhs>
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*******************************************/
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template<int Index, typename Lhs, typename Rhs, typename PacketScalar>
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struct ei_product_coeff_vectorized_impl
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struct ei_product_coeff_vectorized_unroller
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{
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enum { PacketSize = ei_packet_traits<typename Lhs::Scalar>::size };
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inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres)
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{
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ei_product_coeff_vectorized_impl<Index-PacketSize, Lhs, Rhs, PacketScalar>::run(row, col, lhs, rhs, pres);
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ei_product_coeff_vectorized_unroller<Index-PacketSize, Lhs, Rhs, PacketScalar>::run(row, col, lhs, rhs, pres);
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pres = ei_padd(pres, ei_pmul( lhs.template packet<Aligned>(row, Index) , rhs.template packet<Aligned>(Index, col) ));
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}
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};
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template<typename Lhs, typename Rhs, typename PacketScalar>
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struct ei_product_coeff_vectorized_impl<0, Lhs, Rhs, PacketScalar>
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struct ei_product_coeff_vectorized_unroller<0, Lhs, Rhs, PacketScalar>
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{
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inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres)
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{
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@ -351,12 +359,59 @@ struct ei_product_coeff_impl<InnerVectorization, Index, Lhs, Rhs>
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inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
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{
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PacketScalar pres;
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ei_product_coeff_vectorized_impl<Index+1-PacketSize, Lhs, Rhs, PacketScalar>::run(row, col, lhs, rhs, pres);
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ei_product_coeff_vectorized_unroller<Index+1-PacketSize, Lhs, Rhs, PacketScalar>::run(row, col, lhs, rhs, pres);
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ei_product_coeff_impl<NoVectorization,Index,Lhs,Rhs>::run(row, col, lhs, rhs, res);
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res = ei_predux(pres);
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}
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};
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// FIXME the following is a hack to get very high perf with matrix-vector product,
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// however, it would be preferable to switch for more general dynamic alignment queries
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template<typename Lhs, typename Rhs, int LhsRows = Lhs::RowsAtCompileTime, int RhsCols = Rhs::ColsAtCompileTime>
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struct ei_product_coeff_vectorized_dyn_selector
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{
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inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
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{
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res = ei_dot_impl<
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Block<Lhs, 1, ei_traits<Lhs>::ColsAtCompileTime>,
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Block<Rhs, ei_traits<Rhs>::RowsAtCompileTime, 1>,
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LinearVectorization, NoUnrolling>::run(lhs.row(row), rhs.col(col));
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}
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};
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template<typename Lhs, typename Rhs, int RhsCols>
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struct ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,RhsCols>
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{
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inline static void run(int /*row*/, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
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{
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res = ei_dot_impl<
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Lhs,
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Block<Rhs, ei_traits<Rhs>::RowsAtCompileTime, 1>,
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LinearVectorization, NoUnrolling>::run(lhs, rhs.col(col));
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}
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};
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template<typename Lhs, typename Rhs, int LhsRows>
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struct ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs,LhsRows,1>
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{
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inline static void run(int row, int /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
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{
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res = ei_dot_impl<
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Block<Lhs, 1, ei_traits<Lhs>::ColsAtCompileTime>,
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Rhs,
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LinearVectorization, NoUnrolling>::run(lhs.row(row), rhs);
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}
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};
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template<typename Lhs, typename Rhs>
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struct ei_product_coeff_impl<InnerVectorization, Dynamic, Lhs, Rhs>
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{
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inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
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{
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ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs>::run(row, col, lhs, rhs, res);
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}
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};
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/*******************
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*** Packet path ***
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*******************/
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@ -425,6 +480,46 @@ struct ei_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, PacketScalar, LoadMod
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* Cache friendly product callers and specific nested evaluation strategies
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***************************************************************************/
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template<typename ProductType,
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int LhsRows = ei_traits<ProductType>::RowsAtCompileTime,
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int LhsOrder = int(ei_traits<ProductType>::LhsFlags)&RowMajorBit ? RowMajor : ColMajor,
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int RhsCols = ei_traits<ProductType>::ColsAtCompileTime,
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int RhsOrder = int(ei_traits<ProductType>::RhsFlags)&RowMajorBit ? RowMajor : ColMajor>
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struct ei_cache_friendly_product_selector
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{
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template<typename DestDerived>
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inline static void run(DestDerived& res, const ProductType& product)
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{
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product._cacheFriendlyEvalAndAdd(res);
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}
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};
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// optimized colmajor * vector path
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template<typename ProductType, int LhsRows, int RhsOrder>
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struct ei_cache_friendly_product_selector<ProductType,LhsRows,ColMajor,1,RhsOrder>
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{
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template<typename DestDerived>
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inline static void run(DestDerived& res, const ProductType& product)
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{
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const int rows = product.rhs().rows();
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for (int j=0; j<rows; ++j)
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res += product.rhs().coeff(j) * product.lhs().col(j);
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}
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};
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// optimized vector * rowmajor path
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template<typename ProductType, int LhsOrder, int RhsCols>
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struct ei_cache_friendly_product_selector<ProductType,1,LhsOrder,RhsCols,RowMajor>
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{
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template<typename DestDerived>
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inline static void run(DestDerived& res, const ProductType& product)
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{
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const int cols = product.lhs().cols();
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for (int j=0; j<cols; ++j)
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res += product.lhs().coeff(j) * product.rhs().row(j);
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}
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};
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/** \internal */
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template<typename Derived>
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template<typename Lhs,typename Rhs>
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@ -432,7 +527,7 @@ inline Derived&
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MatrixBase<Derived>::operator+=(const Flagged<Product<Lhs,Rhs,CacheFriendlyProduct>, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit>& other)
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{
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if (other._expression()._useCacheFriendlyProduct())
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other._expression()._cacheFriendlyEvalAndAdd(const_cast_derived());
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ei_cache_friendly_product_selector<Product<Lhs,Rhs,CacheFriendlyProduct> >::run(const_cast_derived(), other._expression());
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else
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lazyAssign(derived() + other._expression());
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return derived();
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@ -445,7 +540,7 @@ inline Derived& MatrixBase<Derived>::lazyAssign(const Product<Lhs,Rhs,CacheFrien
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if (product._useCacheFriendlyProduct())
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{
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setZero();
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product._cacheFriendlyEvalAndAdd(derived());
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ei_cache_friendly_product_selector<Product<Lhs,Rhs,CacheFriendlyProduct> >::run(const_cast_derived(), product);
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}
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else
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{
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MaxRowsAtCompileTime = MatrixType::MaxColsAtCompileTime,
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MaxColsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
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Flags = ((int(_MatrixTypeNested::Flags) ^ RowMajorBit)
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& ~( LinearAccessBit | LowerTriangularBit | UpperTriangularBit))
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& ~(LowerTriangularBit | UpperTriangularBit))
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| (int(_MatrixTypeNested::Flags)&UpperTriangularBit ? LowerTriangularBit : 0)
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| (int(_MatrixTypeNested::Flags)&LowerTriangularBit ? UpperTriangularBit : 0),
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CoeffReadCost = _MatrixTypeNested::CoeffReadCost
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@ -84,6 +84,16 @@ template<typename MatrixType> class Transpose
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return m_matrix.coeff(col, row);
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}
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inline const Scalar coeff(int index) const
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{
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return m_matrix.coeff(index);
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}
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inline Scalar& coeffRef(int index)
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{
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return m_matrix.const_cast_derived().coeffRef(index);
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}
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template<int LoadMode>
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inline const PacketScalar packet(int row, int col) const
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{
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@ -96,6 +106,18 @@ template<typename MatrixType> class Transpose
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m_matrix.const_cast_derived().template writePacket<LoadMode>(col, row, x);
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}
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template<int LoadMode>
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inline const PacketScalar packet(int index) const
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{
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return m_matrix.template packet<LoadMode>(index);
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}
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template<int LoadMode>
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inline void writePacket(int index, const PacketScalar& x)
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{
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m_matrix.const_cast_derived().template writePacket<LoadMode>(index, x);
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}
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protected:
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const typename MatrixType::Nested m_matrix;
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};
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