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Diffstat (limited to 'Eigen/src/Core/products/SelfadjointMatrixVector.h')
| -rw-r--r-- | Eigen/src/Core/products/SelfadjointMatrixVector.h | 274 |
1 files changed, 274 insertions, 0 deletions
diff --git a/Eigen/src/Core/products/SelfadjointMatrixVector.h b/Eigen/src/Core/products/SelfadjointMatrixVector.h new file mode 100644 index 000000000..c3145c69a --- /dev/null +++ b/Eigen/src/Core/products/SelfadjointMatrixVector.h @@ -0,0 +1,274 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_H +#define EIGEN_SELFADJOINT_MATRIX_VECTOR_H + +namespace Eigen { + +namespace internal { + +/* Optimized selfadjoint matrix * vector product: + * This algorithm processes 2 columns at onces that allows to both reduce + * the number of load/stores of the result by a factor 2 and to reduce + * the instruction dependency. + */ + +template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version=Specialized> +struct selfadjoint_matrix_vector_product; + +template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version> +struct selfadjoint_matrix_vector_product + +{ +static EIGEN_DONT_INLINE void run( + Index size, + const Scalar* lhs, Index lhsStride, + const Scalar* _rhs, Index rhsIncr, + Scalar* res, + Scalar alpha) +{ + typedef typename packet_traits<Scalar>::type Packet; + typedef typename NumTraits<Scalar>::Real RealScalar; + const Index PacketSize = sizeof(Packet)/sizeof(Scalar); + + enum { + IsRowMajor = StorageOrder==RowMajor ? 1 : 0, + IsLower = UpLo == Lower ? 1 : 0, + FirstTriangular = IsRowMajor == IsLower + }; + + conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> cj0; + conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1; + conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex, ConjugateRhs> cjd; + + conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0; + conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1; + + Scalar cjAlpha = ConjugateRhs ? conj(alpha) : alpha; + + // FIXME this copy is now handled outside product_selfadjoint_vector, so it could probably be removed. + // if the rhs is not sequentially stored in memory we copy it to a temporary buffer, + // this is because we need to extract packets + ei_declare_aligned_stack_constructed_variable(Scalar,rhs,size,rhsIncr==1 ? const_cast<Scalar*>(_rhs) : 0); + if (rhsIncr!=1) + { + const Scalar* it = _rhs; + for (Index i=0; i<size; ++i, it+=rhsIncr) + rhs[i] = *it; + } + + Index bound = (std::max)(Index(0),size-8) & 0xfffffffe; + if (FirstTriangular) + bound = size - bound; + + for (Index j=FirstTriangular ? bound : 0; + j<(FirstTriangular ? size : bound);j+=2) + { + register const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride; + register const Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride; + + Scalar t0 = cjAlpha * rhs[j]; + Packet ptmp0 = pset1<Packet>(t0); + Scalar t1 = cjAlpha * rhs[j+1]; + Packet ptmp1 = pset1<Packet>(t1); + + Scalar t2(0); + Packet ptmp2 = pset1<Packet>(t2); + Scalar t3(0); + Packet ptmp3 = pset1<Packet>(t3); + + size_t starti = FirstTriangular ? 0 : j+2; + size_t endi = FirstTriangular ? j : size; + size_t alignedStart = (starti) + internal::first_aligned(&res[starti], endi-starti); + size_t alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize); + + // TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed + res[j] += cjd.pmul(internal::real(A0[j]), t0); + res[j+1] += cjd.pmul(internal::real(A1[j+1]), t1); + if(FirstTriangular) + { + res[j] += cj0.pmul(A1[j], t1); + t3 += cj1.pmul(A1[j], rhs[j]); + } + else + { + res[j+1] += cj0.pmul(A0[j+1],t0); + t2 += cj1.pmul(A0[j+1], rhs[j+1]); + } + + for (size_t i=starti; i<alignedStart; ++i) + { + res[i] += t0 * A0[i] + t1 * A1[i]; + t2 += conj(A0[i]) * rhs[i]; + t3 += conj(A1[i]) * rhs[i]; + } + // Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up) + // gcc 4.2 does this optimization automatically. + const Scalar* EIGEN_RESTRICT a0It = A0 + alignedStart; + const Scalar* EIGEN_RESTRICT a1It = A1 + alignedStart; + const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart; + Scalar* EIGEN_RESTRICT resIt = res + alignedStart; + for (size_t i=alignedStart; i<alignedEnd; i+=PacketSize) + { + Packet A0i = ploadu<Packet>(a0It); a0It += PacketSize; + Packet A1i = ploadu<Packet>(a1It); a1It += PacketSize; + Packet Bi = ploadu<Packet>(rhsIt); rhsIt += PacketSize; // FIXME should be aligned in most cases + Packet Xi = pload <Packet>(resIt); + + Xi = pcj0.pmadd(A0i,ptmp0, pcj0.pmadd(A1i,ptmp1,Xi)); + ptmp2 = pcj1.pmadd(A0i, Bi, ptmp2); + ptmp3 = pcj1.pmadd(A1i, Bi, ptmp3); + pstore(resIt,Xi); resIt += PacketSize; + } + for (size_t i=alignedEnd; i<endi; i++) + { + res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1); + t2 += cj1.pmul(A0[i], rhs[i]); + t3 += cj1.pmul(A1[i], rhs[i]); + } + + res[j] += alpha * (t2 + predux(ptmp2)); + res[j+1] += alpha * (t3 + predux(ptmp3)); + } + for (Index j=FirstTriangular ? 0 : bound;j<(FirstTriangular ? bound : size);j++) + { + register const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride; + + Scalar t1 = cjAlpha * rhs[j]; + Scalar t2(0); + // TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed + res[j] += cjd.pmul(internal::real(A0[j]), t1); + for (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++) + { + res[i] += cj0.pmul(A0[i], t1); + t2 += cj1.pmul(A0[i], rhs[i]); + } + res[j] += alpha * t2; + } +} +}; + +} // end namespace internal + +/*************************************************************************** +* Wrapper to product_selfadjoint_vector +***************************************************************************/ + +namespace internal { +template<typename Lhs, int LhsMode, typename Rhs> +struct traits<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true> > + : traits<ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>, Lhs, Rhs> > +{}; +} + +template<typename Lhs, int LhsMode, typename Rhs> +struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true> + : public ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>, Lhs, Rhs > +{ + EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix) + + enum { + LhsUpLo = LhsMode&(Upper|Lower) + }; + + SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} + + template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const + { + typedef typename Dest::Scalar ResScalar; + typedef typename Base::RhsScalar RhsScalar; + typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest; + + eigen_assert(dest.rows()==m_lhs.rows() && dest.cols()==m_rhs.cols()); + + typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs); + typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs); + + Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) + * RhsBlasTraits::extractScalarFactor(m_rhs); + + enum { + EvalToDest = (Dest::InnerStrideAtCompileTime==1), + UseRhs = (_ActualRhsType::InnerStrideAtCompileTime==1) + }; + + internal::gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,!EvalToDest> static_dest; + internal::gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!UseRhs> static_rhs; + + ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), + EvalToDest ? dest.data() : static_dest.data()); + + ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,rhs.size(), + UseRhs ? const_cast<RhsScalar*>(rhs.data()) : static_rhs.data()); + + if(!EvalToDest) + { + #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN + int size = dest.size(); + EIGEN_DENSE_STORAGE_CTOR_PLUGIN + #endif + MappedDest(actualDestPtr, dest.size()) = dest; + } + + if(!UseRhs) + { + #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN + int size = rhs.size(); + EIGEN_DENSE_STORAGE_CTOR_PLUGIN + #endif + Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, rhs.size()) = rhs; + } + + + internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<_ActualLhsType>::Flags&RowMajorBit) ? RowMajor : ColMajor, int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run + ( + lhs.rows(), // size + &lhs.coeffRef(0,0), lhs.outerStride(), // lhs info + actualRhsPtr, 1, // rhs info + actualDestPtr, // result info + actualAlpha // scale factor + ); + + if(!EvalToDest) + dest = MappedDest(actualDestPtr, dest.size()); + } +}; + +namespace internal { +template<typename Lhs, typename Rhs, int RhsMode> +struct traits<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false> > + : traits<ProductBase<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>, Lhs, Rhs> > +{}; +} + +template<typename Lhs, typename Rhs, int RhsMode> +struct SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false> + : public ProductBase<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>, Lhs, Rhs > +{ + EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix) + + enum { + RhsUpLo = RhsMode&(Upper|Lower) + }; + + SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} + + template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const + { + // let's simply transpose the product + Transpose<Dest> destT(dest); + SelfadjointProductMatrix<Transpose<const Rhs>, int(RhsUpLo)==Upper ? Lower : Upper, false, + Transpose<const Lhs>, 0, true>(m_rhs.transpose(), m_lhs.transpose()).scaleAndAddTo(destT, alpha); + } +}; + +} // end namespace Eigen + +#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_H |