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Diffstat (limited to 'Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h')
| -rw-r--r-- | Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h | 214 |
1 files changed, 214 insertions, 0 deletions
diff --git a/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h new file mode 100644 index 000000000..432d3a9dc --- /dev/null +++ b/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h @@ -0,0 +1,214 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009-2010 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_GENERAL_MATRIX_MATRIX_TRIANGULAR_H +#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H + +namespace Eigen { + +namespace internal { + +/********************************************************************** +* This file implements a general A * B product while +* evaluating only one triangular part of the product. +* This is more general version of self adjoint product (C += A A^T) +* as the level 3 SYRK Blas routine. +**********************************************************************/ + +// forward declarations (defined at the end of this file) +template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int UpLo> +struct tribb_kernel; + +/* Optimized matrix-matrix product evaluating only one triangular half */ +template <typename Index, + typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, + typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, + int ResStorageOrder, int UpLo, int Version = Specialized> +struct general_matrix_matrix_triangular_product; + +// as usual if the result is row major => we transpose the product +template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, + typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int UpLo, int Version> +struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,UpLo,Version> +{ + typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; + static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride, + const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha) + { + general_matrix_matrix_triangular_product<Index, + RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs, + LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs, + ColMajor, UpLo==Lower?Upper:Lower> + ::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resStride,alpha); + } +}; + +template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, + typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int UpLo, int Version> +struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,UpLo,Version> +{ + typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; + static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride, + const RhsScalar* _rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha) + { + const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> lhs(_lhs,lhsStride); + const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> rhs(_rhs,rhsStride); + + typedef gebp_traits<LhsScalar,RhsScalar> Traits; + + Index kc = depth; // cache block size along the K direction + Index mc = size; // cache block size along the M direction + Index nc = size; // cache block size along the N direction + computeProductBlockingSizes<LhsScalar,RhsScalar>(kc, mc, nc); + // !!! mc must be a multiple of nr: + if(mc > Traits::nr) + mc = (mc/Traits::nr)*Traits::nr; + + std::size_t sizeW = kc*Traits::WorkSpaceFactor; + std::size_t sizeB = sizeW + kc*size; + ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, kc*mc, 0); + ei_declare_aligned_stack_constructed_variable(RhsScalar, allocatedBlockB, sizeB, 0); + RhsScalar* blockB = allocatedBlockB + sizeW; + + gemm_pack_lhs<LhsScalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs; + gemm_pack_rhs<RhsScalar, Index, Traits::nr, RhsStorageOrder> pack_rhs; + gebp_kernel <LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp; + tribb_kernel<LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs, UpLo> sybb; + + for(Index k2=0; k2<depth; k2+=kc) + { + const Index actual_kc = (std::min)(k2+kc,depth)-k2; + + // note that the actual rhs is the transpose/adjoint of mat + pack_rhs(blockB, &rhs(k2,0), rhsStride, actual_kc, size); + + for(Index i2=0; i2<size; i2+=mc) + { + const Index actual_mc = (std::min)(i2+mc,size)-i2; + + pack_lhs(blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc); + + // the selected actual_mc * size panel of res is split into three different part: + // 1 - before the diagonal => processed with gebp or skipped + // 2 - the actual_mc x actual_mc symmetric block => processed with a special kernel + // 3 - after the diagonal => processed with gebp or skipped + if (UpLo==Lower) + gebp(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, (std::min)(size,i2), alpha, + -1, -1, 0, 0, allocatedBlockB); + + sybb(res+resStride*i2 + i2, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha, allocatedBlockB); + + if (UpLo==Upper) + { + Index j2 = i2+actual_mc; + gebp(res+resStride*j2+i2, resStride, blockA, blockB+actual_kc*j2, actual_mc, actual_kc, (std::max)(Index(0), size-j2), alpha, + -1, -1, 0, 0, allocatedBlockB); + } + } + } + } +}; + +// Optimized packed Block * packed Block product kernel evaluating only one given triangular part +// This kernel is built on top of the gebp kernel: +// - the current destination block is processed per panel of actual_mc x BlockSize +// where BlockSize is set to the minimal value allowing gebp to be as fast as possible +// - then, as usual, each panel is split into three parts along the diagonal, +// the sub blocks above and below the diagonal are processed as usual, +// while the triangular block overlapping the diagonal is evaluated into a +// small temporary buffer which is then accumulated into the result using a +// triangular traversal. +template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int UpLo> +struct tribb_kernel +{ + typedef gebp_traits<LhsScalar,RhsScalar,ConjLhs,ConjRhs> Traits; + typedef typename Traits::ResScalar ResScalar; + + enum { + BlockSize = EIGEN_PLAIN_ENUM_MAX(mr,nr) + }; + void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, ResScalar alpha, RhsScalar* workspace) + { + gebp_kernel<LhsScalar, RhsScalar, Index, mr, nr, ConjLhs, ConjRhs> gebp_kernel; + Matrix<ResScalar,BlockSize,BlockSize,ColMajor> buffer; + + // let's process the block per panel of actual_mc x BlockSize, + // again, each is split into three parts, etc. + for (Index j=0; j<size; j+=BlockSize) + { + Index actualBlockSize = std::min<Index>(BlockSize,size - j); + const RhsScalar* actual_b = blockB+j*depth; + + if(UpLo==Upper) + gebp_kernel(res+j*resStride, resStride, blockA, actual_b, j, depth, actualBlockSize, alpha, + -1, -1, 0, 0, workspace); + + // selfadjoint micro block + { + Index i = j; + buffer.setZero(); + // 1 - apply the kernel on the temporary buffer + gebp_kernel(buffer.data(), BlockSize, blockA+depth*i, actual_b, actualBlockSize, depth, actualBlockSize, alpha, + -1, -1, 0, 0, workspace); + // 2 - triangular accumulation + for(Index j1=0; j1<actualBlockSize; ++j1) + { + ResScalar* r = res + (j+j1)*resStride + i; + for(Index i1=UpLo==Lower ? j1 : 0; + UpLo==Lower ? i1<actualBlockSize : i1<=j1; ++i1) + r[i1] += buffer(i1,j1); + } + } + + if(UpLo==Lower) + { + Index i = j+actualBlockSize; + gebp_kernel(res+j*resStride+i, resStride, blockA+depth*i, actual_b, size-i, depth, actualBlockSize, alpha, + -1, -1, 0, 0, workspace); + } + } + } +}; + +} // end namespace internal + +// high level API + +template<typename MatrixType, unsigned int UpLo> +template<typename ProductDerived, typename _Lhs, typename _Rhs> +TriangularView<MatrixType,UpLo>& TriangularView<MatrixType,UpLo>::assignProduct(const ProductBase<ProductDerived, _Lhs,_Rhs>& prod, const Scalar& alpha) +{ + typedef typename internal::remove_all<typename ProductDerived::LhsNested>::type Lhs; + typedef internal::blas_traits<Lhs> LhsBlasTraits; + typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs; + typedef typename internal::remove_all<ActualLhs>::type _ActualLhs; + typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs()); + + typedef typename internal::remove_all<typename ProductDerived::RhsNested>::type Rhs; + typedef internal::blas_traits<Rhs> RhsBlasTraits; + typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs; + typedef typename internal::remove_all<ActualRhs>::type _ActualRhs; + typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs()); + + typename ProductDerived::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived()); + + internal::general_matrix_matrix_triangular_product<Index, + typename Lhs::Scalar, _ActualLhs::Flags&RowMajorBit ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate, + typename Rhs::Scalar, _ActualRhs::Flags&RowMajorBit ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate, + MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo> + ::run(m_matrix.cols(), actualLhs.cols(), + &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &actualRhs.coeffRef(0,0), actualRhs.outerStride(), + const_cast<Scalar*>(m_matrix.data()), m_matrix.outerStride(), actualAlpha); + + return *this; +} + +} // end namespace Eigen + +#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H |