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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F10%3A86077801%21RIV11-MPO-27240___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
Today, the need of large data collection processing increase. Such type of data can has very large dimension and hidden relationships. Analyzing this type of data leads to many errors and noise, therefore, dimension reduction techniques are applied. Many techniques of reduction were developed, e.g. SVD, SDD, PCA, ICA. and NMF. Non-negative matrix factorization (NMF) has main advantage in processing of non-negative values which are easily interpretable as images, but other applications can be found in different areas as well. Both, data analysis and dimension reduction methods, need a lot of computation power. In these clays, many algorithms are rewritten with the GPU utilization, because GPU brings massive parallel architecture and very good ratio between performance and price. This paper introduce computation of NMF on GPU using CUDA technology. Today, the need of large data collection processing increase. Such type of data can has very large dimension and hidden relationships. Analyzing this type of data leads to many errors and noise, therefore, dimension reduction techniques are applied. Many techniques of reduction were developed, e.g. SVD, SDD, PCA, ICA. and NMF. Non-negative matrix factorization (NMF) has main advantage in processing of non-negative values which are easily interpretable as images, but other applications can be found in different areas as well. Both, data analysis and dimension reduction methods, need a lot of computation power. In these clays, many algorithms are rewritten with the GPU utilization, because GPU brings massive parallel architecture and very good ratio between performance and price. This paper introduce computation of NMF on GPU using CUDA technology.
dcterms:title
Non-negative Matrix Factorization on GPU Non-negative Matrix Factorization on GPU
skos:prefLabel
Non-negative Matrix Factorization on GPU Non-negative Matrix Factorization on GPU
skos:notation
RIV/61989100:27240/10:86077801!RIV11-MPO-27240___
n3:aktivita
n13:P
n3:aktivity
P(FR-TI1/420)
n3:dodaniDat
n19:2011
n3:domaciTvurceVysledku
n11:6026877 n11:6042570 n11:9175970 n11:4347269
n3:druhVysledku
n16:D
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
275070
n3:idVysledku
RIV/61989100:27240/10:86077801
n3:jazykVysledku
n15:eng
n3:klicovaSlova
GPU computing; CUDA; parallelism; NMF
n3:klicoveSlovo
n5:NMF n5:GPU%20computing n5:CUDA n5:parallelism
n3:kontrolniKodProRIV
[C08E892E8BCF]
n3:mistoKonaniAkce
Charles Univ, Prague, CZECH REPUBLIC
n3:mistoVydani
Berlin Heidelberg
n3:nazevZdroje
Networked Digital Technologie
n3:obor
n8:IN
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:projekt
n17:FR-TI1%2F420
n3:rokUplatneniVysledku
n19:2010
n3:tvurceVysledku
Snášel, Václav Gajdoš, Petr Krömer, Pavel Platoš, Jan
n3:typAkce
n21:WRD
n3:wos
000289452700004
n3:zahajeniAkce
2010-07-07+02:00
s:issn
1865-0929
s:numberOfPages
10
n12:hasPublisher
Springer-Verlag. (Berlin; Heidelberg)
n18:isbn
978-3-642-14291-8
n4:organizacniJednotka
27240