research-article

Toward GPUs being mainstream in analytic processing: An initial argument using simple scan-aggregate queries

Authors:

Jignesh M. Patel,

David A. WoodAuthors Info & Claims

DaMoN'15: Proceedings of the 11th International Workshop on Data Management on New Hardware

Article No.: 11, Pages 1 - 8

https://doi.org/10.1145/2771937.2771941

Published: 31 May 2015 Publication History

Abstract

There have been a number of research proposals to use discrete graphics processing units (GPUs) to accelerate database operations. Although many of these works show up to an order of magnitude performance improvement, discrete GPUs are not commonly used in modern database systems. However, there is now a proliferation of integrated GPUs which are on the same silicon die as the conventional CPU. With the advent of new programming models like heterogeneous system architecture, these integrated GPUs are considered first-class compute units, with transparent access to CPU virtual addresses and very low overhead for computation offloading. We show that integrated GPUs significantly reduce the overheads of using GPUs in a database environment. Specifically, an integrated GPU is 3x faster than a discrete GPU even though the discrete GPU has 4x the computational capability. Therefore, we develop high performance scan and aggregate algorithms for the integrated GPU. We show that the integrated GPU can outperform a four-core CPU with SIMD extensions by an average of 30% (up to 3:2x) and provides an average of 45% reduction in energy on 16 TPC-H queries.

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Cited By

Cui WZhang QBlanas SCamacho-Rodríguez JHaynes BLi YRamamurthy RCheng PSen RInterlandi M(2023)Query Processing on Gaming ConsolesProceedings of the 19th International Workshop on Data Management on New Hardware10.1145/3592980.3595313(86-88)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3592980.3595313
Luan HFu Y(2022)Accelerating Group-By and Aggregation on Heterogeneous CPU-GPU PlatformsAdvances in Natural Computation, Fuzzy Systems and Knowledge Discovery10.1007/978-3-030-89698-0_100(980-990)Online publication date: 4-Jan-2022
https://doi.org/10.1007/978-3-030-89698-0_100
Tsog NGallardo MChakraborty SMartinson THengl AMoberg MSen AAhmed MBegum SBehnam MSjödin MMubeen S(2021)Supporting Autonomous Vehicle Applications on the Heterogeneous System Architecture7th Conference on the Engineering of Computer Based Systems10.1145/3459960.3459970(1-8)Online publication date: 26-May-2021
https://dl.acm.org/doi/10.1145/3459960.3459970
Show More Cited By

Index Terms

Toward GPUs being mainstream in analytic processing: An initial argument using simple scan-aggregate queries
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
2. Information systems
  1. Data management systems
    1. Database management system engines

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cover image ACM Conferences

DaMoN'15: Proceedings of the 11th International Workshop on Data Management on New Hardware

May 2015

100 pages

ISBN:9781450336383

DOI:10.1145/2771937

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Publication History

Published: 31 May 2015

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Funding Sources

National Science Foundation

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SIGMOD/PODS'15

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SIGMOD

SIGMOD/PODS'15: International Conference on Management of Data

May 31 - June 4, 2015

VIC, Melbourne, Australia

Acceptance Rates

DaMoN'15 Paper Acceptance Rate 12 of 16 submissions, 75%;

Overall Acceptance Rate 94 of 127 submissions, 74%

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Cited By

Cui WZhang QBlanas SCamacho-Rodríguez JHaynes BLi YRamamurthy RCheng PSen RInterlandi M(2023)Query Processing on Gaming ConsolesProceedings of the 19th International Workshop on Data Management on New Hardware10.1145/3592980.3595313(86-88)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3592980.3595313
Luan HFu Y(2022)Accelerating Group-By and Aggregation on Heterogeneous CPU-GPU PlatformsAdvances in Natural Computation, Fuzzy Systems and Knowledge Discovery10.1007/978-3-030-89698-0_100(980-990)Online publication date: 4-Jan-2022
https://doi.org/10.1007/978-3-030-89698-0_100
Tsog NGallardo MChakraborty SMartinson THengl AMoberg MSen AAhmed MBegum SBehnam MSjödin MMubeen S(2021)Supporting Autonomous Vehicle Applications on the Heterogeneous System Architecture7th Conference on the Engineering of Computer Based Systems10.1145/3459960.3459970(1-8)Online publication date: 26-May-2021
https://dl.acm.org/doi/10.1145/3459960.3459970
Li ZPeng BWeng C(2020)XeFlow: Streamlining Inter-Processor Pipeline Execution for the Discrete CPU-GPU PlatformIEEE Transactions on Computers10.1109/TC.2020.296830269:6(819-831)Online publication date: 1-Jun-2020
https://doi.org/10.1109/TC.2020.2968302
Dursun KBinnig CCetintemel USwart GGong W(2019)A morsel-driven query execution engine for heterogeneous multi-coresProceedings of the VLDB Endowment10.14778/3352063.335213712:12(2218-2229)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.14778/3352063.3352137
Guo CChen H(2019)In-Memory Join Algorithms on GPUs for Large-Data2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2019.00151(1060-1067)Online publication date: Aug-2019
https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00151
Yoon HLowe-Power JSohi G(2018)Filtering Translation Bandwidth with Virtual CachingACM SIGPLAN Notices10.1145/3296957.317319553:2(113-127)Online publication date: 19-Mar-2018
https://dl.acm.org/doi/10.1145/3296957.3173195
Funke HBreß SNoll SMarkl VTeubner JDas GJermaine CBernstein P(2018)Pipelined Query Processing in Coprocessor EnvironmentsProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3183734(1603-1618)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3183734
Yoon HLowe-Power JSohi GShen XTuck JBianchini RSarkar V(2018)Filtering Translation Bandwidth with Virtual CachingProceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3173162.3173195(113-127)Online publication date: 19-Mar-2018
https://dl.acm.org/doi/10.1145/3173162.3173195
Dursun KBinnig CCetintemel UPetrocelli R(2017)SiliconDBProceedings of the 13th International Workshop on Data Management on New Hardware10.1145/3076113.3076124(1-4)Online publication date: 14-May-2017
https://dl.acm.org/doi/10.1145/3076113.3076124
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