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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.identifier.isbn978-3-88579-684-8
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/21829
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.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
mci.reference.pages91-94
mci.conference.sessiontitle1st Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC)
mci.conference.locationRostock
mci.conference.date4.-8. März 2019
dc.identifier.doi10.18420/btw2019-ws-08


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