Computing with Near Data

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dc.contributor.authorTang, Xulongko
dc.contributor.authorKandemir, Mahmut Taylanko
dc.contributor.authorZhao, Huiko
dc.contributor.authorJung, Myoungsooko
dc.contributor.authorKarakoy, Mustafako
dc.date.accessioned2019-11-29T08:20:15Z-
dc.date.available2019-11-29T08:20:15Z-
dc.date.created2019-11-28-
dc.date.issued2018-12-
dc.identifier.citationJournal Proceedings of the ACM on Measurement and Analysis of Computing Systems , v.2, no.3-
dc.identifier.issn2476-1249-
dc.identifier.urihttp://hdl.handle.net/10203/268727-
dc.description.abstractOne cost that plays a significant role in shaping the overall performance of both single-threaded and multi-thread applications in modern computing systems is the cost of moving data between compute elements and storage elements. Traditional approaches to address this cost are code and data layout reorganizations and various hardware enhancements. More recently, an alternative paradigm, called Near Data Computing (NDC) or Near Data Processing (NDP), has been shown to be effective in reducing the data movements costs, by moving computation to data, instead of the traditional approach of moving data to computation. Unfortunately, the existing Near Data Computing proposals require significant modifications to hardware and are yet to be widely adopted.-
dc.languageEnglish-
dc.publisherACM-
dc.titleComputing with Near Data-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume2-
dc.citation.issue3-
dc.citation.publicationnameJournal Proceedings of the ACM on Measurement and Analysis of Computing Systems-
dc.identifier.doi10.1145/3287321-
dc.contributor.localauthorJung, Myoungsoo-
dc.contributor.nonIdAuthorTang, Xulong-
dc.contributor.nonIdAuthorKandemir, Mahmut Taylan-
dc.contributor.nonIdAuthorZhao, Hui-
dc.contributor.nonIdAuthorKarakoy, Mustafa-
dc.description.isOpenAccessN-
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