Implementing Random Indexing on GPU

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F11%3APU96124" target="_blank" >RIV/00216305:26230/11:PU96124 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Implementing Random Indexing on GPU

Popis výsledku v původním jazyce

Vector space models (also word space models or term space models) are algebraic models, used for representing text documents as vectors of terms. They have received much attention recently as they have wide spectrum of applications, including informationfiltering, information retrieval, indexing and relevancy ranking. They can be advantageous over the other representations because vector spaces are mathematically well defined and there&#8217;s large set of tools for manipulating them. Random Indexing is one of methods used for calculating vector space models from set of documents, based on distributional statistics of term cooccurrences. To  produce useful results it may therefore require large amounts of data and significant computational power. We present an efficient implementation of Random Indexing on GPU, allowing fast training even on large datasets. It is only limited by amount of memory available on GPU, some techniques to overcome this limitation are sugg

Název v anglickém jazyce

Implementing Random Indexing on GPU

Popis výsledku anglicky

Vector space models (also word space models or term space models) are algebraic models, used for representing text documents as vectors of terms. They have received much attention recently as they have wide spectrum of applications, including informationfiltering, information retrieval, indexing and relevancy ranking. They can be advantageous over the other representations because vector spaces are mathematically well defined and there&#8217;s large set of tools for manipulating them. Random Indexing is one of methods used for calculating vector space models from set of documents, based on distributional statistics of term cooccurrences. To  produce useful results it may therefore require large amounts of data and significant computational power. We present an efficient implementation of Random Indexing on GPU, allowing fast training even on large datasets. It is only limited by amount of memory available on GPU, some techniques to overcome this limitation are sugg

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/7H10012" target="_blank" >7H10012: Embedded Service Oriented Monitoring, Diagnostics and Control: Towards the Asset-aware and Self-Recovery Factory</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

High Performance Computing Symposium 2011

ISBN

978-1-61782-840-9

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

127-135

Název nakladatele

SCS Publication House

Místo vydání

Boston

Místo konání akce

Boston, MA, USA

Datum konání akce

4. 4. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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