research-article

Robust Query Processing in Co-Processor-accelerated Databases

Authors:

Sebastian Breß,

Jens TeubnerAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 1891 - 1906

https://doi.org/10.1145/2882903.2882936

Published: 26 June 2016 Publication History

Abstract

Technology limitations are making the use of heterogeneous computing devices much more than an academic curiosity. In fact, the use of such devices is widely acknowledged to be the only promising way to achieve application-speedups that users urgently need and expect. However, building a robust and efficient query engine for heterogeneous co-processor environments is still a significant challenge.

In this paper, we identify two effects that limit performance in case co-processor resources become scarce. Cache thrashing occurs when the working set of queries does not fit into the co-processor's data cache, resulting in performance degradations up to a factor of 24. Heap contention occurs when multiple operators run in parallel on a co-processor and when their accumulated memory footprint exceeds the main memory capacity of the co-processor, slowing down query execution by up to a factor of six.

We propose solutions for both effects. Data-driven operator placement avoids data movements when they might be harmful; query chopping limits co-processor memory usage and thus avoids contention. The combined approach-data-driven query chopping-achieves robust and scalable performance on co-processors. We validate our proposal with our open-source GPU-accelerated database engine CoGaDB and the popular star schema and TPC-H benchmarks.

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Cited By

Carvalho MSimitsis AQueralt ARomero O(2024)Workload Placement on Heterogeneous CPU-GPU SystemsProceedings of the VLDB Endowment10.14778/3685800.368584517:12(4241-4244)Online publication date: 8-Nov-2024
https://doi.org/10.14778/3685800.3685845
Kroviakov AKurapov PAnneser CGiceva J(2024)Heterogeneous Intra-Pipeline Device-Parallel AggregationsProceedings of the 20th International Workshop on Data Management on New Hardware10.1145/3662010.3663441(1-10)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3662010.3663441
Habich DPietrzyk JBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)SIMDified Data Processing - Foundations, Abstraction, and Advanced TechniquesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654694(613-621)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654694
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Index Terms

Robust Query Processing in Co-Processor-accelerated Databases
1. Information systems
  1. Data management systems
    1. Database management system engines

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cover image ACM Conferences

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, USA

Copyright © 2016 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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Published: 26 June 2016

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European Union's Horizon2020 Research & Innovation Program
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SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

California, San Francisco, USA

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Cited By

Carvalho MSimitsis AQueralt ARomero O(2024)Workload Placement on Heterogeneous CPU-GPU SystemsProceedings of the VLDB Endowment10.14778/3685800.368584517:12(4241-4244)Online publication date: 8-Nov-2024
Kroviakov AKurapov PAnneser CGiceva J(2024)Heterogeneous Intra-Pipeline Device-Parallel AggregationsProceedings of the 20th International Workshop on Data Management on New Hardware10.1145/3662010.3663441(1-10)Online publication date: 10-Jun-2024
Habich DPietrzyk JBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)SIMDified Data Processing - Foundations, Abstraction, and Advanced TechniquesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654694(613-621)Online publication date: 9-Jun-2024
Berthold ASchmidt LObersteiner AHabich DLehner WSchirmeier H(2024)On-The-Fly Data Distribution to Accelerate Query Processing in Heterogeneous Memory SystemsAdvances in Databases and Information Systems10.1007/978-3-031-70626-4_12(170-183)Online publication date: 1-Sep-2024
Thostrup LDoci GBoeschen NLuthra MBinnig C(2023)Distributed GPU Joins on Fast RDMA-capable NetworksProceedings of the ACM on Management of Data10.1145/35887091:1(1-26)Online publication date: 30-May-2023
Gurumurthy BBroneske DSchäler MPionteck TSaake G(2023)Novel insights on atomic synchronization for sort-based group-by on GPUsDistributed and Parallel Databases10.1007/s10619-023-07424-241:3(387-409)Online publication date: 24-Apr-2023
Yogatama BGong WYu X(2022)Orchestrating data placement and query execution in heterogeneous CPU-GPU DBMSProceedings of the VLDB Endowment10.14778/3551793.355180915:11(2491-2503)Online publication date: 29-Sep-2022
Lutz CBreß SZeuch SRabl TMarkl VIves ZBonifati AEl Abbadi A(2022)Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast InterconnectsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517911(1017-1032)Online publication date: 10-Jun-2022
Ailamaki ALiarou ETozun PPorobic DPsaroudakis I(2022)Databases on Modern HardwareundefinedOnline publication date: 25-Feb-2022
Beena Gopalakrishnan LBecher AWildermann SMeyer-Wegener KTeich J(2021)Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing SystemDatenbank-Spektrum10.1007/s13222-020-00363-721:1(55-64)Online publication date: 4-Jan-2021
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