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https://gitlab.com/libeigen/eigen.git
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add a "using MKL" documentation page, add a minimal documentation of PARDISO wrapper classes, refine a bit the EIGEN_USE_* logic
This commit is contained in:
Gael Guennebaud
parent
fff25a4b46
commit
37f304a2e6
@ -24,7 +24,7 @@ namespace Eigen {
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#include "src/misc/Solve.h"
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#include "src/Cholesky/LLT.h"
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#include "src/Cholesky/LDLT.h"
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#ifdef EIGEN_USE_MKL_CLAPACK
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#ifdef EIGEN_USE_LAPACKE
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#include "src/Cholesky/LLT_MKL.h"
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#endif
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@ -36,7 +36,7 @@ namespace Eigen {
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#include "src/Eigenvalues/ComplexSchur.h"
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#include "src/Eigenvalues/ComplexEigenSolver.h"
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#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
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#ifdef EIGEN_USE_MKL_CLAPACK
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#ifdef EIGEN_USE_LAPACKE
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#include "src/Eigenvalues/RealSchur_MKL.h"
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#include "src/Eigenvalues/ComplexSchur_MKL.h"
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#include "src/Eigenvalues/SelfAdjointEigenSolver_MKL.h"
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2
Eigen/LU
2
Eigen/LU
@ -23,7 +23,7 @@ namespace Eigen {
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#include "src/misc/Image.h"
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#include "src/LU/FullPivLU.h"
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#include "src/LU/PartialPivLU.h"
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#ifdef EIGEN_USE_MKL_CLAPACK
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#ifdef EIGEN_USE_LAPACKE
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#include "src/LU/PartialPivLU_MKL.h"
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#endif
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#include "src/LU/Determinant.h"
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@ -11,10 +11,10 @@
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namespace Eigen {
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/** \ingroup Sparse_modules
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* \defgroup PARDISOSupport_Module Intel(R) MKL PARDISO support
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*
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/** \ingroup Support_modules
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* \defgroup PARDISOSupport_Module PARDISOSupport module
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*
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* This module brings support for the Intel(R) MKL PARDISO direct sparse solvers
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*
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* \code
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* #include <Eigen/PARDISOSupport>
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2
Eigen/QR
2
Eigen/QR
@ -28,7 +28,7 @@ namespace Eigen {
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#include "src/QR/HouseholderQR.h"
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#include "src/QR/FullPivHouseholderQR.h"
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#include "src/QR/ColPivHouseholderQR.h"
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#ifdef EIGEN_USE_MKL_CLAPACK
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#ifdef EIGEN_USE_LAPACKE
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#include "src/QR/HouseholderQR_MKL.h"
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#include "src/QR/ColPivHouseholderQR_MKL.h"
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#endif
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@ -24,7 +24,7 @@ namespace Eigen {
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#include "src/misc/Solve.h"
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#include "src/SVD/JacobiSVD.h"
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#if defined(EIGEN_ALLOW_JACOBISVD_USE_LAPACKSVD) && defined(EIGEN_USE_MKL_CLAPACK)
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#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
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#include "src/SVD/JacobiSVD_MKL.h"
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#endif
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#include "src/SVD/UpperBidiagonalization.h"
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@ -37,15 +37,19 @@
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#ifndef EIGEN_USE_BLAS
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#define EIGEN_USE_BLAS
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#endif
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#ifndef EIGEN_USE_MKL_CLAPACK
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#define EIGEN_USE_MKL_CLAPACK
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#ifndef EIGEN_USE_LAPACKE
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#define EIGEN_USE_LAPACKE
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#endif
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#ifndef EIGEN_USE_MKL_VML
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#define EIGEN_USE_MKL_VML
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#endif
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#endif
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#if defined(EIGEN_USE_MKL_CLAPACK) || defined(EIGEN_USE_MKL_VML)
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#ifdef EIGEN_USE_LAPACKE_STRICT
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#define EIGEN_USE_LAPACKE
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#endif
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#if defined(EIGEN_USE_BLAS) || defined(EIGEN_USE_LAPACKE) || defined(EIGEN_USE_MKL_VML)
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#define EIGEN_USE_MKL
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#endif
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@ -55,17 +59,6 @@
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#include <mkl_lapacke.h>
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#define EIGEN_MKL_VML_THRESHOLD 128
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#elif defined EIGEN_USE_BLAS
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#error Currently EIGEN_USE_BLAS requires Intel MKL. If you want to use MKL's BLAS and only it, then define EIGEN_USE_MKL too.
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#include "../../misc/blas.h"
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#define MKL_INT int
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#endif
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#if defined(EIGEN_USE_MKL) || defined(EIGEN_USE_BLAS)
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namespace Eigen {
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typedef std::complex<double> dcomplex;
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@ -83,7 +76,6 @@ static inline void assign_conj_scalar_eig2mkl(MKLType& mklScalar, const EigenTyp
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mklScalar=eigenScalar;
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}
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#ifdef EIGEN_USE_MKL
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template <>
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inline void assign_scalar_eig2mkl<MKL_Complex16,dcomplex>(MKL_Complex16& mklScalar, const dcomplex& eigenScalar) {
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mklScalar.real=eigenScalar.real();
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@ -108,8 +100,6 @@ inline void assign_conj_scalar_eig2mkl<MKL_Complex8,scomplex>(MKL_Complex8& mklS
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mklScalar.imag=-eigenScalar.imag();
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}
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#endif
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} // end namespace internal
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} // end namespace Eigen
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@ -312,6 +312,18 @@ bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b,
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}
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/** \ingroup PARDISOSupport_Module
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* \class PardisoLU
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* \brief A sparse direct LU factorization and solver based on the PARDISO library
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*
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* This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
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* using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
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* The vectors or matrices X and B can be either dense or sparse.
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*
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* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
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*
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* \sa \ref TutorialSparseDirectSolvers
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*/
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template<typename MatrixType>
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class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
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{
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@ -340,6 +352,18 @@ class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
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}
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};
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/** \ingroup PARDISOSupport_Module
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* \class PardisoLLT
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* \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
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*
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* This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
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* using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
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* The vectors or matrices X and B can be either dense or sparse.
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*
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* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
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*
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* \sa \ref TutorialSparseDirectSolvers
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*/
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template<typename MatrixType>
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class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType> >
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{
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@ -368,6 +392,18 @@ class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType> >
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}
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};
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/** \ingroup PARDISOSupport_Module
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* \class PardisoLDLT
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* \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
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*
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* This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
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* using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
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* The vectors or matrices X and B can be either dense or sparse.
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*
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* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
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*
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* \sa \ref TutorialSparseDirectSolvers
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*/
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template<typename MatrixType>
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class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType> >
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{
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@ -40,6 +40,7 @@ For a first contact with Eigen, the best place is to have a look at the \ref Get
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- \ref TopicClassHierarchy
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- \ref TopicFunctionTakingEigenTypes
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- \ref TopicTemplateKeyword
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- \ref TopicUsingIntelMKL
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- <b>Topics related to alignment issues</b>
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- \ref TopicUnalignedArrayAssert
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- \ref TopicFixedSizeVectorizable
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167
doc/UsingIntelMKL.dox
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167
doc/UsingIntelMKL.dox
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@ -0,0 +1,167 @@
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/*
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Copyright (c) 2011, Intel Corporation. All rights reserved.
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Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of Intel Corporation nor the names of its contributors may
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be used to endorse or promote products derived from this software without
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specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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********************************************************************************
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* Content : Documentation on the use of Intel MKL through Eigen
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********************************************************************************
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*/
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namespace Eigen {
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/** \page TopicUsingIntelMKL Using Intel® Math Kernel Library from Eigen
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\section TopicUsingIntelMKL_Intro Eigen and Intel® Math Kernel Library (Intel® MKL)
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Since Eigen version 3.1 and later, users can benefit from built-in Intel MKL optimizations with an installed copy of Intel MKL 10.3 (or later).
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<a href="http://eigen.tuxfamily.org/Counter/redirect_to_mkl.php"> Intel MKL </a> provides highly optimized multi-threaded mathematical routines for x86-compatible architectures.
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Intel MKL is available on Linux, Mac and Windows for both Intel64 and IA32 architectures.
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\warning Be aware that Intel® MKL is a proprietary software. It is the responsibility of the users to buy MKL licenses for their products. Moreover, the license of the user product has to allow linking to proprietary software that excludes any unmodified versions of the GPL. As a consequence, this also means that Eigen has to be used through the LGPL3+ license.
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Using Intel MKL through Eigen is easy:
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-# define the \c EIGEN_USE_MKL_ALL macro before including any Eigen's header
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-# link your program to MKL libraries (see the <a href="http://software.intel.com/en-us/articles/intel-mkl-link-line-advisor/">MKL linking advisor</a>)
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When doing so, a number of Eigen's algorithms are silently substituted with calls to Intel MKL routines.
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These substitutions apply only for \b Dynamic \b or \b large enough objects with one of the following four standard scalar types: \c float, \c double, \c complex<float>, and \c complex<double>.
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Operations on other scalar types or mixing reals and complexes will continue to use the built-in algorithms.
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In addition you can coarsely select choose which parts will be substituted by defining one or multiple of the following macros:
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<table class="manual">
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<tr><td>\c EIGEN_USE_BLAS </td><td>Enables the use of external BLAS level 2 and 3 routines (currently works with Intel MKL only)</td></tr>
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<tr class="alt"><td>\c EIGEN_USE_LAPACKE </td><td>Enables the use of external Lapack routines via the <a href="http://www.netlib.org/lapack/lapacke.html">Intel Lapacke</a> C interface to Lapack (currently works with Intel MKL only)</td></tr>
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<tr><td>\c EIGEN_USE_LAPACKE_STRICT </td><td>Same as \c EIGEN_USE_LAPACKE but algorithm of lower robustness are disabled. This currently concerns only JacobiSVD which otherwise would be replaced by \c gesvd that is less robust than Jacobi rotations.</td></tr>
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<tr class="alt"><td>\c EIGEN_USE_MKL_VML </td><td>Enables the use of Intel VML (vector operations)</td></tr>
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<tr><td>\c EIGEN_USE_MKL_ALL </td><td>Defines \c EIGEN_USE_BLAS, \c EIGEN_USE_LAPACKE, and \c EIGEN_USE_MKL_VML </td></tr>
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</table>
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Finally, the PARDISO sparse solver shipped with Intel MKL can be used through the \ref PardisoLU, \ref PardisoLLT and \ref PardisoLDLT classes of the \ref PARDISOSupport_Module.
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\section TopicUsingIntelMKL_SupportedFeatures List of supported features
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The breadth of Eigen functionality covered by Intel MKL is listed in the table below.
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<table class="manual">
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<tr><th>Functional domain</th><th>Code example</th><th>MKL routines</th></tr>
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<tr><td>Matrix-matrix operations \n \c EIGEN_USE_BLAS </td><td>\code
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m1*m2.transpose();
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m1.selfadjointView<Lower>()*m2;
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m1*m2.triangularView<Upper>();
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m1.selfadjointView<Lower>().rankUpdate(m2,1.0);
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\endcode</td><td>\code
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?gemm
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?symm/?hemm
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?trmm
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dsyrk/ssyrk
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\endcode</td></tr>
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<tr class="alt"><td>Matrix-vector operations \n \c EIGEN_USE_BLAS </td><td>\code
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m1.adjoint()*b;
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m1.selfadjointView<Lower>()*b;
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m1.triangularView<Upper>()*b;
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\endcode</td><td>\code
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?gemv
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?symv/?hemv
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?trmv
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\endcode</td></tr>
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<tr><td>LU decomposition \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT </td><td>\code
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v1 = m1.lu().solve(v2);
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\endcode</td><td>\code
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?getrf
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\endcode</td></tr>
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<tr class="alt"><td>Cholesky decomposition \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT </td><td>\code
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v1 = m2.selfadjointView<Upper>().llt().solve(v2);
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\endcode</td><td>\code
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?potrf
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\endcode</td></tr>
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<tr><td>QR decomposition \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT </td><td>\code
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m1.householderQr();
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m1.colPivHouseholderQr();
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\endcode</td><td>\code
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?geqrf
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?geqp3
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\endcode</td></tr>
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<tr class="alt"><td>Singular value decomposition \n \c EIGEN_USE_LAPACKE </td><td>\code
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JacobiSVD<MatrixXd> svd;
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svd.compute(m1, ComputeThinV);
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\endcode</td><td>\code
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?gesvd
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\endcode</td></tr>
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<tr><td>Eigen-value decompositions \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT </td><td>\code
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EigenSolver<MatrixXd> es(m1);
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ComplexEigenSolver<MatrixXcd> ces(m1);
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SelfAdjointEigenSolver<MatrixXd> saes(m1+m1.transpose());
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GeneralizedSelfAdjointEigenSolver<MatrixXd>
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gsaes(m1+m1.transpose(),m2+m2.transpose());
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\endcode</td><td>\code
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?gees
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?gees
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?syev/?heev
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?syev/?heev,
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?potrf
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\endcode</td></tr>
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<tr class="alt"><td>Schur decomposition \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT </td><td>\code
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RealSchur<MatrixXd> schurR(m1);
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ComplexSchur<MatrixXcd> schurC(m1);
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\endcode</td><td>\code
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?gees
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\endcode</td></tr>
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<tr><td>Vector Math \n \c EIGEN_USE_MKL_VML </td><td>\code
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v2=v1.array().sin();
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v2=v1.array().asin();
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v2=v1.array().cos();
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v2=v1.array().acos();
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v2=v1.array().tan();
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v2=v1.array().exp();
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v2=v1.array().log();
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v2=v1.array().sqrt();
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v2=v1.array().square();
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v2=v1.array().pow(1.5);
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\endcode</td><td>\code
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v?Sin
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v?Asin
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v?Cos
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v?Acos
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v?Tan
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v?Exp
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v?Ln
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v?Sqrt
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v?Sqr
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v?Powx
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\endcode</td></tr>
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</table>
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In the examples, m1 and m2 are dense matrices and v1 and v2 are dense vectors.
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\section TopicUsingIntelMKL_Links Links
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- Intel MKL can be purchased and downloaded <a href="http://eigen.tuxfamily.org/Counter/redirect_to_mkl.php">here</a>.
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- Intel MKL is also bundled with <a href="http://software.intel.com/en-us/articles/intel-composer-xe/">Intel Composer XE</a>.
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*/
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}
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