Journal article

ANTIKAINEN Jukka, HAVEL Jiří, JOŠTH Radovan, HEROUT Adam, ZEMČÍK Pavel and HAUTA-KASARI Markku. Non-Negative Tensor Factorization Accelerated Using GPGPU. IEEE Transactions on Parallel and Distributed Systems (TPDS). 2011, vol. 2011, no. 1111, p. 7. ISSN 1045-9219.
Publication language:english
Original title:Non-Negative Tensor Factorization Accelerated Using GPGPU
Title (cs):Nezáporná faktorizace tenzorů akcelerovaná pomocí GPGPU
Journal:IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 2011, No. 1111, US
Non-negative tensor factorization, spectral analysis, GPU
This article presents an optimized algorithm for Non-Negative 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 speed-ups 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 speed-up 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.
   author = {Jukka Antikainen and Ji{\v{r}}{\'{i}} Havel and Radovan
	Jo{\v{s}}th and Adam Herout and Pavel Zem{\v{c}}{\'{i}}k and
	Markku Hauta-Kasari},
   title = {Non-Negative Tensor Factorization Accelerated Using GPGPU},
   pages = {7},
   journal = {IEEE Transactions on Parallel and Distributed Systems (TPDS)},
   volume = {2011},
   number = {1111},
   year = {2011},
   ISSN = {1045-9219},
   language = {english},
   url = {}

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