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Pattern-guided big data processing on hybrid parallel architectures

dc.contributor.authorKhalid, Fahad
dc.contributor.authorFeinbube, Frank
dc.contributor.authorPolze, Andreas
dc.contributor.editorPlödereder, E.
dc.contributor.editorGrunske, L.
dc.contributor.editorSchneider, E.
dc.contributor.editorUll, D.
dc.date.accessioned2017-07-26T10:58:51Z
dc.date.available2017-07-26T10:58:51Z
dc.date.issued2014
dc.description.abstractThe advent of hybrid CPU-GPU architectures has significantly increased the number of raw FLOP/s. However, it is not obvious how these can be put to use when processing Big Data. In this paper, we present an approach for designing Big Data simulations for hybrid architectures, which is based on a hierarchal application of design patterns in parallel programming. We provide a detailed account of the step by step approach that results in efficient utilization of processing and memory resources, while simultaneously improving developer productivity. Finally, we present our vision of automated tools that will further simplify the development of efficient parallel implementations for Big Data processing on hybrid architectures.en
dc.identifier.isbn978-3-88579-626-8
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofInformatik 2014
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-232
dc.titlePattern-guided big data processing on hybrid parallel architecturesen
dc.typeText/Conference Paper
gi.citation.endPage1780
gi.citation.publisherPlaceBonn
gi.citation.startPage1767
gi.conference.date22.-26. September 2014
gi.conference.locationStuttgart

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