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dc.contributor.authorBroneske, David
dc.contributor.authorMeister, Andreas
dc.contributor.authorSaake, Gunter
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-20T20:24:31Z
dc.date.available2017-06-20T20:24:31Z
dc.date.issued2017
dc.identifier.isbn978-3-88579-659-6
dc.identifier.issn1617-5468
dc.description.abstractThe ever-increasing demand for performance on huge data sets forces database systems to tweak the last bit of performance out of their operators. Especially query compiled plans allow for several tuning opportunities that can be applied depending on the query plan and the underlying data. Apart from classical query optimization opportunities, it includes to tune the code using code optimizations for processor specifics, e.g., using Single Instruction Multiple Data processing or predication. In this paper, we examine code optimizations that can be applied for compiled scan pipelines that include aggregations, evaluate impact factors that influence the performance of the scan pipelines, and derive guidelines that a query compiler should implement to choose the best variant for a given query plan and workload.en
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-265
dc.subjectMulti-Column Selection Predicates
dc.subjectScans
dc.subjectHardware Sensitivity
dc.subjectSIMD
dc.subjectPredication
dc.titleHardware-Sensitive Scan Operator Variants for Compiled Selection Pipelinesen
dc.typeText/Conference Paper
mci.reference.pages403-412
mci.conference.sessiontitleScientific Data and Hardware
mci.conference.locationStuttgart
mci.conference.date6.-10. März 2017


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