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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F11%3APU89632%21RIV13-MSM-26230___
rdf:type
n8:Vysledek skos:Concept
dcterms:description
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 eng 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 eng
dcterms:title
Non-Negative Tensor Factorization Accelerated Using GPGPU Non-Negative Tensor Factorization Accelerated Using GPGPU
skos:prefLabel
Non-Negative Tensor Factorization Accelerated Using GPGPU Non-Negative Tensor Factorization Accelerated Using GPGPU
skos:notation
RIV/00216305:26230/11:PU89632!RIV13-MSM-26230___
n8:predkladatel
n16:orjk%3A26230
n3:aktivita
n5:Z n5:S n5:P
n3:aktivity
P(LC06008), S, Z(MSM0021630528)
n3:cisloPeriodika
1111
n3:dodaniDat
n10:2013
n3:domaciTvurceVysledku
n12:9340386 n12:1958313 Jošth, Radovan n12:1211811
n3:druhVysledku
n7:J
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
216255
n3:idVysledku
RIV/00216305:26230/11:PU89632
n3:jazykVysledku
n13:eng
n3:klicovaSlova
Non-negative tensor factorization, spectral analysis, GPU
n3:klicoveSlovo
n4:Non-negative%20tensor%20factorization n4:spectral%20analysis n4:GPU
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[36265A78A2A5]
n3:nazevZdroje
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
n3:obor
n9:JC
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
6
n3:projekt
n20:LC06008
n3:rokUplatneniVysledku
n10:2011
n3:svazekPeriodika
2011
n3:tvurceVysledku
Herout, Adam Hauta-Kasari, Markku Antikainen, Jukka Jošth, Radovan Zemčík, Pavel Havel, Jiří
n3:zamer
n18:MSM0021630528
s:issn
1045-9219
s:numberOfPages
7
n19:organizacniJednotka
26230