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Nonnegative Tensor Factorization Accelerated Using GPGPU

Jukka Antikainen, Jiri Havel, Radovan Josth, Adam Herout, Pavel Zemcik, Markku Hauta-Kasari
Sch. of Comput., University of Eastern Finland, Joensuu, Finland
IEEE Transactions on Parallel and Distributed Systems, 2010

@article{antikainen2010non,

   title={Non-Negative Tensor Factorization Accelerated Using GPGPU},

   author={Antikainen, J. and Havel, J. and Jo{v{s}}th, R. and Herout, A. and Zem{v{c}}{‘i}k, P. and Hauta-Kasari, M.},

   journal={IEEE Transactions on Parallel and Distributed Systems},

   year={2010},

   publisher={Published by the IEEE Computer Society}

}

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This article presents an optimized algorithm for Nonnegative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including dimensionality reduction, feature extraction, and other tasks related to spectral imaging; however, the algorithm and its implementation are not limited to spectral imaging. The speedups measured on real spectral images are around 60 – 100x compared to a traditional C implementation compiled with an optimizing compiler. Since common problems in the field of spectral imaging may take hours on a state-of-the-art CPU, the speedup achieved using a graphics card is attractive. The implementation is publicly available in the form of a dynamically linked library, including an interface to MATLAB, and thus may be of help to researchers and engineers using NTF on large problems.
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