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Nonnegative Tensor Factorization Accelerated Using GPGPU
July 2011 (vol. 22 no. 7)
pp. 1135-1141
ASCII Text
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Jukka Antikainen, Jiří Havel, Radovan Jošth, Adam Herout, Pavel Zemčík, Markku Hauta-Kasari, "Nonnegative Tensor Factorization Accelerated Using GPGPU," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 7, pp. 1135-1141, July, 2011.
 
 
BibTex
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@article{ 10.1109/TPDS.2010.194,
author = {Jukka Antikainen and Jiří Havel and Radovan Jošth and Adam Herout and Pavel Zemčík and Markku Hauta-Kasari},
title = {Nonnegative Tensor Factorization Accelerated Using GPGPU},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {22},
number = {7},
issn = {1045-9219},
year = {2011},
pages = {1135-1141},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2010.194},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - Nonnegative Tensor Factorization Accelerated Using GPGPU
IS - 7
SN - 1045-9219
SP1135
EP1141
EPD - 1135-1141
A1 - Jukka Antikainen,
A1 - Jiří Havel,
A1 - Radovan Jošth,
A1 - Adam Herout,
A1 - Pavel Zemčík,
A1 - Markku Hauta-Kasari,
PY - 2011
KW - Nonnegative tensor factorization
KW - spectral analysis
KW - GPU.
VL - 22
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Jukka Antikainen, University of Eastern Finland, Joensuu
Jiří Havel, Brno University of Technology, Brno
Radovan Jošth, Brno University of Technology, Brno
Adam Herout, Brno University of Technology, Brno
Pavel Zemčík, Brno University of Technology, Brno
Markku Hauta-Kasari, University of Eastern Finland, Joensuu
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 60-100{\times} 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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Index Terms:
Nonnegative tensor factorization, spectral analysis, GPU.
Citation:
Jukka Antikainen, Jiří Havel, Radovan Jošth, Adam Herout, Pavel Zemčík, Markku Hauta-Kasari, "Nonnegative Tensor Factorization Accelerated Using GPGPU," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 7, pp. 1135-1141, July 2011, doi:10.1109/TPDS.2010.194
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