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Workload-Driven Data Placement for GPU-Accelerated Database Management Systems

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dc.contributor.authorSchmidt, Christopher
dc.contributor.authorUflacker, Matthias
dc.contributor.editorMeyer, Holger
dc.contributor.editorRitter, Norbert
dc.contributor.editorThor, Andreas
dc.contributor.editorNicklas, Daniela
dc.contributor.editorHeuer, Andreas
dc.contributor.editorKlettke, Meike
dc.date.accessioned2019-04-15T11:40:42Z
dc.date.available2019-04-15T11:40:42Z
dc.date.issued2019
dc.description.abstractAn increase in the memory capacity of current Graphics Processing Unit (GPU) generations and advances in multi-GPU systems enables a large unified GPU memory space to be utilized by modern coprocessor-accelerated Database Management System (DBMS). We take this as an opportunity to revisit the idea of using GPU memory as a hot cache for the DBMS. In particular, we focus on the data placement for the hot cache. Based on previous approaches and their shortcomings, we present a new workload-driven data placement for a GPU-accelerated DBMS. Lastly, we outline how we aim to implement and evaluate our proposed approach by comparing it to existing data placement approaches in future work.en
dc.identifier.doi10.18420/btw2019-ws-08
dc.identifier.isbn978-3-88579-684-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/21829
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2019 – Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) – Proceedings, Volume P-290
dc.subjectGPU-Acceleration
dc.subjectCoprocessor-Accelerated Databases
dc.subjectData Placement
dc.titleWorkload-Driven Data Placement for GPU-Accelerated Database Management Systemsen
gi.citation.endPage94
gi.citation.startPage91
gi.conference.date4.-8. März 2019
gi.conference.locationRostock
gi.conference.sessiontitle1st Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC)

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P290 - BTW2019 - Datenbanksysteme für Business, Technologie und Web - Workshopband