CUDA accelerated computation of Non-negative Matrix Factorizations (NMF)
The Non-negative Matrix Factorization (NMF) was first described by Paatero and Tapper [1] and further developed by Lee & Seung [2]. Since then various algorithms for different needs were developed, such as Alternating Hoyer Constrained Least Squared (AHCLS) [3], Gradient Descent Constrained Least Squares (GDCLS) [4] and Non-smooth Non-negative Matrix Factorization (nsNMF) [5]. This library implements a set of algorithms and initialization strategies using the CUDA platforms. A binding to this library exists for the R language and can be found here.
@Article{koitka-friedrich:2016,
author = {Sven Koitka and Christoph M. Friedrich},
title = {{nmfgpu4R}: {GPU}-Accelerated Computation of the Non-Negative Matrix Factorization ({NMF}) Using {CUDA} Capable Hardware},
journal = {The R Journal},
year = 2016,
volume = 8,
number = 2,
pages = {382--392},
month = december,
url = {http://journal.r-project.org/archive/2016-2/koitka-friedrich.pdf}
}
This library is primary distributed under the terms of the General Public Licence Version 3 (GPLv3).
Furthermore if you are planning to use this work in proprietary software, please do not hesitate to get in contact for a commercial licence.
- CUDA Toolkit 7.0 or newer
- CMake 3.2.0 or newer
- Platform type x64 (CUDA libraries like cuBLAS are only available for x64 platforms)
- C++11 compatible compiler (supported compiler depends on CUDA Toolkit version)
- Windows: Visual Studio 2013
- Linux: g++ 4.8.x or newer
- Mac OS X: clang x.x.x
- Optional: Doxygen
- CUDA capable device with compute capabilities 3.0 (Kepler) or greater
- First use git to clone the repository into a new directory:
git clone https://github.com/razorx89/nmfgpu.git
If you don't have git installed on your system, then you can download the repository as a zip compressed archive and extract it.
2. #### Using the GUI interface
Switch into the newly created directory and run cmake-gui:
cd nmfgpu
cmake-gui
Create a new build folder for an out-of-source build and press Configure. Select the appropriate toolchain depending on your operating system (see Prerequisites). When the configuration has finished, then make sure to set CMAKE_INSTALL_PREFIX to the location where the compiled library should be installed to. If you want to build the documentation from source, then enable NMFGPU_BUILD_DOCUMENTATION (requires Doxygen). Press Generate to generate the required build files.
If you are using a console environment, then you can configure cmake using console commands:
cd nmfgpu
mkdir build
cd build
cmake .. -G "<generator>"
All supported CMake generators are listed in CMake's help. Specify the generator according to your operating system (see Prerequisites). For example under Linux the Unix Makefiles generator would be used to compile with g++:
cmake .. -G "Unix Makefiles"
If the generator has an optional configuration for the platform, then you must explicitly define the x64 platform. Otherwise the FindCUDA.cmake script won't be able to resolve library paths for CUDA libraries. For Visual Studio 2013 the appropriate generator would be the following:
cmake .. -G "Visual Studio 12 2013 Win64"
Furthermore specify the installation directory, where the compiled library should be installed to:
cmake . -DCMAKE_INSTALL_PREFIX="/usr/local/nmfgpu/"
- After the build files have been generated, the library must be build. This step highly depends on the chosen generator and operating system. In the following two common workflows are described.
Open the generated solution file nmfgpu.sln and compile the ALL_BUILD project. Wait until the compilation has successfully finished and compile the INSTALL project, which will install the library to the configured location.
If you are using MSBuild to compile your projects automaticly, then start for example the VS2013 x64 Native Tools Command Prompt and type:
msbuild ALL_BUILD.vcxproj
msbuild INSTALL.vcxproj
Once the Unix Makefiles generator has generated make files in the build directory, the build process is started by invoking make:
make
If the compilation was successful the library can be installed to the configured location:
make install
- For ease of usage you should define an environment variable called NMFGPU_ROOT which points to the location of the installed library (the path provided to CMAKE_INSTALL_PREFIX).
[1] Paatero, P. and Tapper, U. [1994], "Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values", Environmetrics 5(2), 111–126.
[2] Lee, D. D. and Seung, H. S. [1999], "Learning the parts of objects by non-negative matrix factorization", Nature 401(6755), 788–791.
[3] Langville, A. N., Meyer, C. D., Albright, R., Cox, J. and Duling, D. [2014], "Algorithms, initializations, and convergence for the nonnegative matrix factorization", CoRR abs/1407.7299.
[4] Shahnaz, F., Berry, M. W., Pauca, V. and Plemmons, R. J. [2006], ‘Document clustering using nonnegative matrix factorization’, Information Processing & Management 42(2), 373–386.
[5] Pascual-Montano, A., Carazo, J., Kochi, K., Lehmann, D. and Pascual-Marqui, R. [2006], "Nonsmooth nonnegative matrix factorization (nsNMF)", IEEE Transactions on Pattern Analysis and Machine Intelligence 28(3), 403–415.