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    Readme Readme Document Transcript

    • <prompt> === Contents === 1: Introduction 1.1: Citation Details 2: Installation 2.0: Preliminaries 2.1: Manual Installation 2.2: Installation on Linux / Mac OS X 2.3: Installation on MS Windows 3: Compiling Programs and Linking 3.0: Examples 3.1: Compiling & Linking on Linux / Mac OS X 3.2: Compiling & Linking on MS Windows 4: Caveats 4.0: Support for ATLAS 4.1: Support for ACML and Intel MKL 5: Documentation / Reference Manual 6: FAQs and Bug Reports 7: Credits 8: License === 1.0: Introduction === Armadillo is a C++ linear algebra library (matrix maths) aiming towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK or high-performance LAPACK-compatible libraries. A delayed evaluation approach is employed (during compile time) to combine several operations into one and reduce (or eliminate) the need for temporaries. This is accomplished through recursive templates and template meta-programming. This library is useful if C++ has been decided as the language of choice (due to speed and/or integration capabilities), rather than another language like Matlab or Octave. It is distributed under a license that is useful in both open-source and proprietary contexts. Armadillo is primarily developed at NICTA (Australia), with contributions from around the world. More information about NICTA can be obtained from: http://nicta.com.au
    • === 1.1: Citation Details === If you use Armadillo in your research and/or software, we would appreciate a citation to the following tech report: Conrad Sanderson. Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments. Technical Report, NICTA, 2010. === 2.0: Installation: Preliminaries === Armadillo makes extensive use of template meta-programming, recursive templates and template based function overloading. As such, C++ compilers which do not fully implement the C++ standard may not work correctly. The functionality of Armadillo is partly dependent on other libraries -- mainly LAPACK and BLAS. Armadillo can work without LAPACK or BLAS, but its functionality will be reduced. In particular, basic functionality will be available (eg. matrix addition and multiplication), but things like eigen decomposition or will not be. Matrix multiplication (mainly for big matrices) may not be as fast. For manual installation on all systems, see section 2.1. For installation on Linux / Mac OS X systems, see section 2.2. The Linux installation is also likely to work on other Unix-like systems, such as FreeBSD, NetBSD, OpenBSD, Solaris, CygWin, etc. For installation on MS Windows, see section 2.3. === 2.1: Manual Installation === The manual installation is comprised of 3 steps: * Step 1: Copy the entire "include" folder to a convenient location and tell your compiler to use that location for header files (in addition to the locations it uses already). Alternatively, you can use the "include" folder directly. * Step 2: Modify "include/armadillo_bits/config.hpp" to indicate which libraries are currently available on your system. For example, if you have LAPACK and BLAS present, uncomment the following lines: #define ARMA_USE_LAPACK #define ARMA_USE_BLAS * Step 3: If you have LAPACK and/or BLAS present, configure your compiler to link with these libraries. You can also link with the the equivalent of LAPACK and BLAS, eg. Intel's MKL or AMD's ACML. Under Mac OS X, link using
    • -framework Accelerate === 2.2: Installation on Linux / Mac OS X === If you have installed Armadillo using an RPM or DEB package, you don't need to do anything else. Otherwise read on. You can use the manual installation process as described in section 2.1, or the following CMake based automatic installation. * Step 1: If CMake is not already be present on your system, download it from http://www.cmake.org On major Linux systems (such as Fedora, Ubuntu, Debian, etc), cmake is available as a pre-built package, though it may need to be explicitly installed (using a tool such as PackageKit, yum, rpm, apt, aptitude, etc). * Step 2: If you have BLAS and/or LAPACK, install them before installing Armadillo. Under Mac OS X this is not necessary. On Linux systems it is recommended that the following libraries are present: LAPACK, BLAS, ATLAS and Boost. LAPACK and BLAS are the most important. If you have ATLAS and Boost, it's also necessary to have the corresponding header files installed. * Step 3: Open a shell (command line), change into the directory that was created by unpacking the armadillo archive, and type the following commands: cmake . make The full stop separated from "cmake" by a space is important. CMake will figure out what other libraries are currently installed and will modify Armadillo's configuration correspondingly. CMake will also generate a run-time armadillo library, which is a combined alias for all the relevant libraries present on your system (eg. BLAS, LAPACK and ATLAS). If you need to re-run cmake, it's a good idea to first delete the "CMakeCache.txt" file (not "CMakeLists.txt"). * Step 4: If you have access to root/administrator/superuser privileges, first enable the privileges (eg. through "su" or "sudo") and then type the following command: make install If you don't have root/administrator/superuser privileges, type the following command: make install DESTDIR=my_usr_dir where "my_usr_dir" is for storing C++ headers and library files. Make sure your C++ compiler is configured to use the sub-directories present within this directory.
    • === 2.3: Installation on MS Windows === There is currently no automatic installation for Windows. Please use the manual installation process described in section 2.1. Pre-compiled 32 bit versions of BLAS and LAPACK libraries for Windows are provided in the "examples/libs_win32" folder. If the provided libraries don't work for you, see section 3.2. === 3.0: Compiling Programs and Linking: Examples === The "examples" directory contains several quick example programs that use the Armadillo library. If Armadillo was installed manually (ie. according to section 2.1), you will also need to explicitly link your programs with the libraries that were specified in "include/armadillo_bits/config.hpp". "example1.cpp" may require the BLAS library or its equivalent. "example2.cpp" requires the LAPACK library or its equivalent (eg. the Accelerate framework on Mac OS X). You may get errors at compile or run time if BLAS and/or LAPACK functions are not available. NOTE: As Armadillo is a template library, we recommended that optimisation is enabled during compilation. For example, for the GCC compiler use -O1 or -O2 === 3.1: Compiling & Linking on Linux / Mac OS X === Please see "examples/Makefile", which may may need to be configured for your system. If Armadillo header files were installed in a non-standard location, you will need to modify "examples/Makefile" to tell the compiler where they are. In general, programs which use Armadillo are compiled along these lines: g++ example1.cpp -o example1 -O1 -larmadillo (you may also need to specify the include directory via the -I switch) If you get linking errors, or if Armadillo was installed manually and you specified that LAPACK and BLAS are available, you will need to explicitly link with LAPACK and BLAS (or their equivalents), for example: g++ example1.cpp -o example1 -O1 -llapack -lblas (you may also need to specify the library directory via the -L switch) Notes: * under most Linux systems, using "-llapack -lblas" should be enough; however, on Ubuntu and Debian you may need to add "-lgfortran" * under Mac OS X, try "-framework Accelerate" or "-llapack -lblas" (the Accelerate option is usually the fastest) * under the Sun Studio compiler, try "-library=sunperf"
    • === 3.2: Compiling & Linking on MS Windows === As a courtesy, we've provided pre-compiled 32 bit versions of LAPACK and BLAS for Windows, as well as MSVC project files to compile example1.cpp and example2.cpp. The project files are stored in the following folders: examples/example1_win32 examples/example2_win32 The LAPACK and BLAS libraries are stored in: examples/lib_win32 Note that on 64 bit systems (such as Windows 7), dedicated 64 bit versions of BLAS and LAPACK are considerably faster. If you're not using MSVC, you will need to manually modify "include/armadillo_bits/config.hpp" to enable the use of LAPACK and BLAS. Please see section 2.1 for more information. The MSCV project files were tested on 32 bit Windows XP with Visual C++ 2008 (Express Edition). You may need to make adaptations for 64 bit systems, later versions of Windows and/or the compiler. For example, you may have to enable or disable the ARMA_BLAS_LONG and ARMA_BLAS_UNDERSCORE macros in "armadillo_bits/config.hpp". To preserve our sanity, we (Armadillo developers) don't use Windows on a regular basis, and as such can't help you with the adaptations. If you are serious about doing experimentation and/or prototyping involving linear algebra, you shouldn't be using lower-grade (ie. unreliable) operating systems such as Windows anyway. The pre-compiled versions of LAPACK and BLAS were downloaded from: http://www.fi.muni.cz/~xsvobod2/misc/lapack/ If the provided libraries don't work for you, try these versions: http://www.stanford.edu/~vkl/code/libs.html http://icl.cs.utk.edu/lapack-for-windows/lapack/ http://software.intel.com/en-us/intel-mkl/ http://www.amd.com/acml (the MKL and ACML libraries are generally the fastest) If you want to compile BLAS and LAPACK yourself, you can find the original sources at: http://www.netlib.org/blas/ http://www.netlib.org/lapack/ If you encounter issues with the MS Visual C++ compiler, the following high-quality compilers are useful alternatives: * Intel's C++ compiler http://software.intel.com/en-us/intel-compilers/ * GCC (part MinGW) http://www.mingw.org/ * GCC (part of CygWin) http://www.cygwin.com/ If using Intel's C++ compiler, you'll need version 10.0 or better. If using GCC, you'll need version 4.0 or better.
    • === 4.0: Caveats: Support for ATLAS === Armadillo can use the ATLAS library for faster versions of certain LAPACK and BLAS functions. Not all ATLAS functions are currently used, and as such LAPACK should still be installed. The minimum recommended version of ATLAS is 3.8. Old versions (eg. 3.6) can produce incorrect results as well as corrupting memory, leading to random crashes. Users of Ubuntu and Debian based systems should explicitly check that version 3.6 is not installed. It's better to remove the old version and use the standard LAPACK library. === 4.1: Caveats: Support for ACML and Intel MKL === Armadillo can work with AMD Core Math Library and Intel's Math Kernel Library (MKL), however there are several caveats. On Linux systems, ACML and MKL are typically installed in a non-standard location, which can cause problems during linking. Before installing Armadillo, the system should know where the ACML or MKL libraries are located (eg., "/opt/intel/mkl/10.2.2.025/lib/em64t/"). This can be achieved by setting the LD_LIBRARY_PATH environment variable, or, for a more permanent solution, adding the location of the libraries to "/etc/ld.so.conf". It may also be possible to store a text file with the location in the "/etc/ld.so.conf.d" directory. In the latter two cases you will need to run "ldconfig" afterwards. The default installations of ACML 4.4.0 and MKL 10.2.2.025 are known to have issues with SELinux, which is turned on by default in Fedora (and possibly RHEL). The problem may manifest itself during run-time, where the run-time linker reports permission problems. It is possible to work around the problem by applying an appropriate SELinux type to all ACML and MKL libraries. If you have ACML or MKL installed and they are persistently giving you problems during linking, you can disable the support for them by editing the "CMakeLists.txt" file, deleting "CMakeCache.txt" and re-running the CMake based installation. Specifically, comment out the lines containing: INCLUDE(ARMA_FindMKL) INCLUDE(ARMA_FindACMLMP) INCLUDE(ARMA_FindACML) === 5: Documentation / Reference Manual === A reference manual (user documentation) is available at http://arma.sourceforge.net or in the "docs" directory. Use a web browser to open the "docs/index.html" file. The user documentation explains how to use Armadillo's classes and functions, with snippets of example code.
    • === 6: FAQs and Bug Reports === Answers to Frequently Asked Questions (FAQs) can be found at: http://arma.sourceforge.net/faq.html This library has gone through extensive testing and has been successfully used in production environments. However, as with almost all software, it's impossible to guarantee 100% correct functionality. If you find a bug in the library (or the documentation), we are interested in hearing about it. Please make a small self-contained program which exposes the bug and send the program source (as well as the bug description) to the developers. The developers' contact details are available at: http://arma.sourceforge.net/contact.html === 7: Credits === Main sponsoring organisation: - NICTA http://nicta.com.au Main developers: - Conrad Sanderson - http://itee.uq.edu.au/~conrad/ - Ian Cullinan - Dimitrios Bouzas - Stanislav Funiak Contributors: - Eric R. Anderson - Benoît Bayol - Salim Bcoin - Justin Bedo - Darius Braziunas - Ted Campbell - Clement Creusot - Ryan Curtin - Chris Davey - Dirk Eddelbuettel - Romain Francois - Piotr Gawron - Charles Gretton - Benjamin Herzog - Edmund Highcock - Kshitij Kulshreshtha - Oka Kurniawan - Simen Kvaal - David Lawrence - Carlos Mendes - Artem Novikov - Martin Orlob - Ken Panici - Adam Piątyszek - Jayden Platell - Vikas Reddy - Ola Rinta-Koski - James Sanders - Alexander Scherbatey - Gerhard Schreiber - Shane Stainsby - Petter Strandmark
    • - Paul Torfs Simon Urbanek Arnold Wiliem Yong Kang Wong === 8: License === Please see the "LICENSE.txt" file.