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let redux use the new ByOuterInner accessors
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@ -40,7 +40,7 @@ struct ei_redux_traits
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private:
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enum {
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PacketSize = ei_packet_traits<typename Derived::Scalar>::size,
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InnerMaxSize = int(Derived::Flags)&RowMajorBit
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InnerMaxSize = int(Derived::IsRowMajor)
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? Derived::MaxColsAtCompileTime
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: Derived::MaxRowsAtCompileTime
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};
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@ -100,15 +100,15 @@ template<typename Func, typename Derived, int Start>
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struct ei_redux_novec_unroller<Func, Derived, Start, 1>
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{
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enum {
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col = Start / Derived::RowsAtCompileTime,
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row = Start % Derived::RowsAtCompileTime
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outer = Start / Derived::InnerSizeAtCompileTime,
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inner = Start % Derived::InnerSizeAtCompileTime
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};
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typedef typename Derived::Scalar Scalar;
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EIGEN_STRONG_INLINE static Scalar run(const Derived &mat, const Func&)
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{
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return mat.coeff(row, col);
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return mat.coeffByOuterInner(outer, inner);
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}
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};
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@ -148,12 +148,8 @@ struct ei_redux_vec_unroller<Func, Derived, Start, 1>
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{
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enum {
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index = Start * ei_packet_traits<typename Derived::Scalar>::size,
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row = int(Derived::Flags)&RowMajorBit
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? index / int(Derived::ColsAtCompileTime)
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: index % Derived::RowsAtCompileTime,
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col = int(Derived::Flags)&RowMajorBit
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? index % int(Derived::ColsAtCompileTime)
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: index / Derived::RowsAtCompileTime,
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outer = index / int(Derived::InnerSizeAtCompileTime),
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inner = index % int(Derived::InnerSizeAtCompileTime),
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alignment = (Derived::Flags & AlignedBit) ? Aligned : Unaligned
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};
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@ -162,7 +158,7 @@ struct ei_redux_vec_unroller<Func, Derived, Start, 1>
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EIGEN_STRONG_INLINE static PacketScalar run(const Derived &mat, const Func&)
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{
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return mat.template packet<alignment>(row, col);
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return mat.template packetByOuterInner<alignment>(outer, inner);
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}
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};
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@ -184,12 +180,12 @@ struct ei_redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>
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{
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ei_assert(mat.rows()>0 && mat.cols()>0 && "you are using a non initialized matrix");
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Scalar res;
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res = mat.coeff(0, 0);
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for(int i = 1; i < mat.rows(); ++i)
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res = func(res, mat.coeff(i, 0));
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for(int j = 1; j < mat.cols(); ++j)
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for(int i = 0; i < mat.rows(); ++i)
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res = func(res, mat.coeff(i, j));
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res = mat.coeffByOuterInner(0, 0);
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for(int i = 1; i < mat.innerSize(); ++i)
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res = func(res, mat.coeffByOuterInner(0, i));
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for(int i = 1; i < mat.outerSize(); ++i)
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for(int j = 0; j < mat.innerSize(); ++j)
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res = func(res, mat.coeffByOuterInner(i, j));
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return res;
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}
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};
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@ -253,8 +249,7 @@ struct ei_redux_impl<Func, Derived, SliceVectorizedTraversal, NoUnrolling>
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const int innerSize = mat.innerSize();
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const int outerSize = mat.outerSize();
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enum {
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packetSize = ei_packet_traits<Scalar>::size,
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isRowMajor = Derived::Flags&RowMajorBit?1:0
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packetSize = ei_packet_traits<Scalar>::size
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};
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const int packetedInnerSize = ((innerSize)/packetSize)*packetSize;
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Scalar res;
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@ -263,13 +258,12 @@ struct ei_redux_impl<Func, Derived, SliceVectorizedTraversal, NoUnrolling>
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PacketScalar packet_res = mat.template packet<Unaligned>(0,0);
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for(int j=0; j<outerSize; ++j)
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for(int i=0; i<packetedInnerSize; i+=int(packetSize))
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packet_res = func.packetOp(packet_res, mat.template packet<Unaligned>
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(isRowMajor?j:i, isRowMajor?i:j));
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packet_res = func.packetOp(packet_res, mat.template packetByOuterInner<Unaligned>(j,i));
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res = func.predux(packet_res);
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for(int j=0; j<outerSize; ++j)
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for(int i=packetedInnerSize; i<innerSize; ++i)
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res = func(res, mat.coeff(isRowMajor?j:i, isRowMajor?i:j));
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res = func(res, mat.coeffByOuterInner(j,i));
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}
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else // too small to vectorize anything.
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// since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
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