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The Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS

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dc.contributor.authorBreß, Sebastian
dc.date.accessioned2018-01-10T13:19:49Z
dc.date.available2018-01-10T13:19:49Z
dc.date.issued2014
dc.description.abstractNowadays, the performance of processors is primarily bound by a fixed energy budget, the power wall. This forces hardware vendors to optimize processors for specific tasks, which leads to an increasingly heterogeneous hardware landscape. Although efficient algorithms for modern processors such as GPUs are heavily investigated, we also need to prepare the database optimizer to handle computations on heterogeneous processors. GPUs are an interesting base for case studies, because they already offer many difficulties we will face tomorrow.In this paper, we present CoGaDB, a main-memory DBMS with built-in GPU acceleration, which is optimized for OLAP workloads. CoGaDB uses the self-tuning optimizer framework HyPE to build a hardware-oblivious optimizer, which learns cost models for database operators and efficiently distributes a workload on available processors. Furthermore, CoGaDB implements efficient algorithms on CPU and GPU and efficiently supports star joins. We show in this paper, how these novel techniques interact with each other in a single system. Our evaluation shows that CoGaDB quickly adapts to the underlying hardware by increasing the accuracy of its cost models at runtime.
dc.identifier.pissn1610-1995
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/11727
dc.publisherSpringer
dc.relation.ispartofDatenbank-Spektrum: Vol. 14, No. 3
dc.relation.ispartofseriesDatenbank-Spektrum
dc.subjectCo-processing
dc.subjectDBMS
dc.subjectDBMS architecture
dc.subjectGPU acceleration
dc.subjectMain-memory
dc.titleThe Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS
dc.typeText/Journal Article
gi.citation.endPage209
gi.citation.startPage199

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Datenbank Spektrum 14(3) - November 2014