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DC poleHodnotaJazyk
dc.contributor.authorMachlica, Lukáš
dc.contributor.authorVaněk, Jan
dc.contributor.authorZají­c, Zbyněk
dc.date.accessioned2015-12-17T09:32:11Z-
dc.date.available2015-12-17T09:32:11Z-
dc.date.issued2011
dc.identifier.citationMACHLICA, Lukáš; VANĚK, Jan; ZAJÍC, Zbyněk. Fast estimation of gaussian mixture model parameters on GPU using CUDA. In: The 12th International Conference on Parallel and Distributed Computing, Applications and Technologies: 20-22 October 2011, Gwangju, Jižní­ Korea. Gwangju: IEEE Press, 2011, p. 167-172. ISBN 978-0-7695-4564-6.en
dc.identifier.isbn978-0-7695-4564-6
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/LukasMachlica_2011_FastEstimationof
dc.identifier.urihttp://hdl.handle.net/11025/17041
dc.format6 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEE Pressen
dc.rights© Lukáš Machlica - Jan Vaněk - Zbyněk Zajíccs
dc.subjectCUDAcs
dc.subjectSSEcs
dc.subjectGMMcs
dc.subjectGMMcs
dc.subjectEMcs
dc.subjectparalelní­ implementacecs
dc.titleFast estimation of gaussian mixture model parameters on GPU using CUDAen
dc.title.alternativeRychlá estimace parametrů model Gaussovských směsí za využití GPU a architektury CUDAcs
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedGaussian Mixture Model (GMM) statistics are required for maximum likelihood training as well as for adaptation techniques. In order to train/adapt a reliable model a lot of data are needed, what makes the estimation process time consuming. The paper presents an efficient implementation of estimation of GMM statistics on GPU using NVIDIA's Compute Unified Device Architecture (CUDA). Also an augmentation of the standard CPU version is proposed utilizing SSE instructions. Time consumptions of presented methods are tested on a large dataset of real speech data from the NIST Speaker Recognition Evaluation 2008. Estimation on GPU proves to be 100 times faster than the standard CPU version and 30 times faster than the SSE version assuming more than 256 mixtures, thus a huge speed-up was achieved without any approximations made in the estimation formulas. Proposed implementation was also compared to other implementations developed by other departments over the world and proved to be the fastest.en
dc.subject.translatedCUDAen
dc.subject.translatedSSEen
dc.subject.translatedGMMen
dc.subject.translatedGMMen
dc.subject.translatedEMen
dc.subject.translatedparallel implementationen
dc.identifier.doi10.1109/PDCAT.2011.40
dc.type.statusPeer-revieweden
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