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dc.contributor.authorShanbhag, Anil
dc.contributor.authorMadden, Samuel
dc.contributor.authorYu, Xiangyao
dc.date.accessioned2022-10-19T16:43:32Z
dc.date.available2021-11-05T15:20:30Z
dc.date.available2022-10-19T16:43:32Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/137522.2
dc.description.abstract© 2020 Association for Computing Machinery. There has been significant amount of excitement and recent work on GPU-based database systems. Previous work has claimed that these systems can perform orders of magnitude better than CPU-based database systems on analytical workloads such as those found in decision support and business intelligence applications. A hardware expert would view these claims with suspicion. Given the general notion that database operators are memory-bandwidth bound, one would expect the maximum gain to be roughly equal to the ratio of the memory bandwidth of GPU to that of CPU. In this paper, we adopt a model-based approach to understand when and why the performance gains of running queries on GPUs vs on CPUs vary from the bandwidth ratio (which is roughly 16× on modern hardware). We propose Crystal, a library of parallel routines that can be combined together to run full SQL queries on a GPU with minimal materialization overhead. We implement individual query operators to show that while the speedups for selection, projection, and sorts are near the bandwidth ratio, joins achieve less speedup due to differences in hardware capabilities. Interestingly, we show on a popular analytical workload that full query performance gain from running on GPU exceeds the bandwidth ratio despite individual operators having speedup less than bandwidth ratio, as a result of limitations of vectorizing chained operators on CPUs, resulting in a 25× speedup for GPUs over CPUs on the benchmark.en_US
dc.language.isoen
dc.publisherACMen_US
dc.relation.isversionof10.1145/3318464.3380595en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceACMen_US
dc.titleA Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analyticsen_US
dc.typeArticleen_US
dc.identifier.citationShanbhag, Anil, Madden, Samuel and Yu, Xiangyao. 2020. "A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics." Proceedings of the ACM SIGMOD International Conference on Management of Data.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalProceedings of the ACM SIGMOD International Conference on Management of Dataen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-01-29T19:07:21Z
dspace.orderedauthorsShanbhag, A; Madden, S; Yu, Xen_US
dspace.date.submission2021-01-29T19:07:28Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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