Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55589
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorFeng, Z-
dc.creatorLo, E-
dc.creatorKao, B-
dc.creatorXu, W-
dc.date.accessioned2016-09-07T02:53:26Z-
dc.date.available2016-09-07T02:53:26Z-
dc.identifier.isbn9781450327589-
dc.identifier.issn0730-8078-
dc.identifier.urihttp://hdl.handle.net/10397/55589-
dc.language.isoenen_US
dc.publisherACMen_US
dc.subjectColumn storeen_US
dc.subjectMain memoryen_US
dc.subjectOLAPen_US
dc.subjectSIMDen_US
dc.subjectStorage layouten_US
dc.titleByteslice : pushing the envelop of main memory data processing with a new storage layouten_US
dc.typeConference Paperen_US
dc.identifier.spage31-
dc.identifier.epage46-
dc.identifier.doi10.1145/2723372.2747642-
dcterms.abstractScan and lookup are two core operations in main memory column stores. A scan operation scans a column and returns a result bit vector that indicates which records satisfy a filter. Once a column scan is completed, the result bit vector is converted into a list of record numbers, which is then used to look up values from other columns of interest for a query. Recently there are several inmemory data layout proposals that aim to improve the performance of in-memory data processing. However, these solutions all stand at either end of a trade-off - each is either good in lookup performance or good in scan performance, but not both. In this paper we present ByteSlice, a new main memory storage layout that supports both highly efficient scans and lookups. ByteSlice is a bytelevel columnar layout that fully leverages SIMD data-parallelism. Micro-benchmark experiments show that ByteSlice achieves a data scan speed at less than 0.5 processor cycle per column value - a new limit of main memory data scan, without sacrificing lookup performance. Our experiments on TPC-H data and real data show that ByteSlice offers significant performance improvement over all state-of-the-art approaches.-
dcterms.bibliographicCitationSIGMOD'15 : proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, May 31-June 4, 2015, Melbourne, Victoria, Australia, p. 31-46. New York, NY: ACM, 2015-
dcterms.issued2015-
dc.identifier.scopus2-s2.0-84957589427-
dc.relation.ispartofbookSIGMOD'15 : proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, May 31-June 4, 2015, Melbourne, Victoria, Australia-
dc.relation.conferenceACM SIGMOD International Conference on Management of Data-
dc.publisher.placeNew York, NYen_US
dc.identifier.rosgroupid2014000420-
dc.description.ros2014-2015 > Academic research: refereed > Refereed conference paper-
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