Detail publikačního výsledku

Non-Negative Tensor Factorization Accelerated Using GPGPU

ANTIKAINEN, J.; HAVEL, J.; JOŠTH, R.; HEROUT, A.; ZEMČÍK, P.; HAUTA-KASARI, M.

Originální název

Non-Negative Tensor Factorization Accelerated Using GPGPU

Anglický název

Non-Negative Tensor Factorization Accelerated Using GPGPU

Druh

Článek recenzovaný mimo WoS a Scopus

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.

Anglický 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

Klíčová slova v angličtině

Non-negative tensor factorization, spectral analysis, GPU

Autoři

ANTIKAINEN, J.; HAVEL, J.; JOŠTH, R.; HEROUT, A.; ZEMČÍK, P.; HAUTA-KASARI, M.

Rok RIV

2012

Vydáno

14.03.2011

ISSN

1045-9219

Periodikum

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS

Svazek

2011

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"
}