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ANTIKAINEN, J. HAVEL, J. JOŠTH, R. HEROUT, A. ZEMČÍK, P. HAUTA-KASARI, M.
Originální název
Non-Negative Tensor Factorization Accelerated Using GPGPU
Typ
článek v časopise - ostatní, Jost
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
Non-negative tensor factorization, spectral analysis, GPU
Autoři
ANTIKAINEN, J.; HAVEL, J.; JOŠTH, R.; HEROUT, A.; ZEMČÍK, P.; HAUTA-KASARI, M.
Rok RIV
2011
Vydáno
14. 3. 2011
ISSN
1045-9219
Periodikum
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Ročník
Číslo
1111
Stát
Spojené státy americké
Strany počet
7
BibTex
@article{BUT50517, author="Jukka {Antikainen} and Jiří {Havel} and Radovan {Jošth} and Adam {Herout} and Pavel {Zemčík} and Markku {Hauta-Kasari}", title="Non-Negative Tensor Factorization Accelerated Using GPGPU", journal="IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS", year="2011", volume="2011", number="1111", pages="7", issn="1045-9219" }