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dc.contributor.authorHordemann, Glen
dc.contributor.authorKwan Lee, Jong
dc.contributor.authorSmith, Andries H.
dc.contributor.editorSkala, Václav
dc.date.accessioned2017-11-08T08:54:24Z
dc.date.available2017-11-08T08:54:24Z
dc.date.issued2014
dc.identifier.citationWSCG 2014: communication papers proceedings: 22nd International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 247-256.en
dc.identifier.isbn978-80-86943-71-8
dc.identifier.uriwscg.zcu.cz/WSCG2014/!!_2014-WSCG-Communication.pdf
dc.identifier.urihttp://hdl.handle.net/11025/26420
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2014: communication papers proceedingsen
dc.rights@ Václav Skala - UNION Agencycs
dc.subjectdatabázecs
dc.subjectSQLitecs
dc.subjectGPU zpracovánícs
dc.subjectCUDAcs
dc.titleAccelerated SQLite database using GPUsen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper introduces the development of a new GPU-based database to accelerate data retrieval. The main goal is to explore new ways of handling complex data types and managing data and workloads in massively parallel databases. This paper presents three novel innovations to create an efficient virtual database engine that executes the majority of database operations directly on the GPU. The GPU database executes a subset of SQLite’s SELECT queries, which are typically the most computationally expensive operations in a transactional database. This database engine extends existing research by exploring methods of table caching on the GPU, handling irregular and complex data types, and executing multiple table joins and managing the resulting workload on the GPU. The GPU database discussed in this paper is implemented on a consumer grade GPU to demonstrate the high-performance computing benefits of relatively inexpensive hardware. Advances are compared both to existing CPU standards and to alternate implementations of the GPU database.en
dc.subject.translateddatabaseen
dc.subject.translatedSQLiteen
dc.subject.translatedGPU processingen
dc.subject.translatedCUDAen
dc.type.statusPeer-revieweden
Appears in Collections:WSCG 2014: Communication Papers Proceedings

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