This HTML5 document contains 42 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n20http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n15http://localhost/temp/predkladatel/
n10http://purl.org/net/nknouf/ns/bibtex#
n8http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n17http://linked.opendata.cz/ontology/domain/vavai/
n9http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F10%3A00177603%21RIV11-MSM-21230___/
n16http://linked.opendata.cz/resource/domain/vavai/zamer/
n11https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n5http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n21http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n14http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n12http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F10%3A00177603%21RIV11-MSM-21230___
rdf:type
n17:Vysledek skos:Concept
dcterms:description
The evolutionary algorithms work with large sets of individuals. In our case these individuals represent neural networks. The speed of evaluation of the networks is the very crucial factor because it affects the overall speed of the whole evolution process. In this paper we describe our implementation of the fully recurrent neural networks on the general-purpose graphics processing units. We are using the nVidia CUDA technology to accelerate the simulation of the population of the networks. We have created package for Wolfram Mathematica that provides interface to our accelerated simulator from high-level programming environment. Our library supports the client-server architecture, so you can run the simulations on dedicated CUDA-enabled computational server and process the results of the simulations on your desktop using the TCP/IP communication protocol. In this paper we present the results of the speedup experiments. The evolutionary algorithms work with large sets of individuals. In our case these individuals represent neural networks. The speed of evaluation of the networks is the very crucial factor because it affects the overall speed of the whole evolution process. In this paper we describe our implementation of the fully recurrent neural networks on the general-purpose graphics processing units. We are using the nVidia CUDA technology to accelerate the simulation of the population of the networks. We have created package for Wolfram Mathematica that provides interface to our accelerated simulator from high-level programming environment. Our library supports the client-server architecture, so you can run the simulations on dedicated CUDA-enabled computational server and process the results of the simulations on your desktop using the TCP/IP communication protocol. In this paper we present the results of the speedup experiments.
dcterms:title
High-performance Implementation of Recurrent Neural Networks on Graphics Processing Units High-performance Implementation of Recurrent Neural Networks on Graphics Processing Units
skos:prefLabel
High-performance Implementation of Recurrent Neural Networks on Graphics Processing Units High-performance Implementation of Recurrent Neural Networks on Graphics Processing Units
skos:notation
RIV/68407700:21230/10:00177603!RIV11-MSM-21230___
n3:aktivita
n7:Z
n3:aktivity
Z(MSM6840770012)
n3:dodaniDat
n12:2011
n3:domaciTvurceVysledku
n8:2655802 n8:7035586
n3:druhVysledku
n18:D
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
261587
n3:idVysledku
RIV/68407700:21230/10:00177603
n3:jazykVysledku
n21:eng
n3:klicovaSlova
Neural Networks; CUDA; Parallel Computing; Graphics Processing Units
n3:klicoveSlovo
n5:CUDA n5:Parallel%20Computing n5:Neural%20Networks n5:Graphics%20Processing%20Units
n3:kontrolniKodProRIV
[3797BEC11682]
n3:mistoKonaniAkce
Praha
n3:mistoVydani
Prague
n3:nazevZdroje
Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, Vol. 2: Full Papers
n3:obor
n14:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n12:2010
n3:tvurceVysledku
Buk, Zdeněk Šnorek, Miroslav
n3:typAkce
n20:EUR
n3:zahajeniAkce
2010-09-06+02:00
n3:zamer
n16:MSM6840770012
s:numberOfPages
4
n10:hasPublisher
Department of Computer Science and Engineering, FEE, CTU in Prague
n11:isbn
978-80-01-04589-3
n15:organizacniJednotka
21230