research-article

Heterogeneity-conscious parallel query execution: getting a better mileage while driving faster!

Authors:

Tobias Mühlbauer,

Robert Seilbeck,

Thomas NeumannAuthors Info & Claims

DaMoN '14: Proceedings of the Tenth International Workshop on Data Management on New Hardware

Article No.: 2, Pages 1 - 10

https://doi.org/10.1145/2619228.2619230

Published: 23 June 2014 Publication History

Abstract

Physical and thermal restrictions hinder commensurate performance gains from the ever increasing transistor density. While multi-core scaling helped alleviate dimmed or dark silicon for some time, future processors will need to become more heterogeneous. To this end, single instruction set architecture (ISA) heterogeneous processors are a particularly interesting solution that combines multiple cores with the same ISA but asymmetric performance and power characteristics. These processors, however, are no free lunch for database systems. Mapping jobs to the core that fits best is notoriously hard for the operating system or a compiler. To achieve optimal performance and energy efficiency, heterogeneity needs to be exposed to the database system.

In this paper, we provide a thorough study of parallelized core database operators and TPC-H query processing on a heterogeneous single-ISA multi-core architecture. Using these insights we design a heterogeneity-conscious job-to-core mapping approach for our high-performance main memory database system HyPer and show that it is indeed possible to get a better mileage while driving faster compared to static and operating-system-controlled mappings. Our approach improves the energy delay product of a TPC-H power run by 31% and up to over 60% for specific TPC-H queries.

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Cited By

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
Rosenfeld VBreß SMarkl V(2022)Query Processing on Heterogeneous CPU/GPU SystemsACM Computing Surveys10.1145/348512655:1(1-38)Online publication date: 17-Jan-2022
https://dl.acm.org/doi/10.1145/3485126
Song JMa ZThomas RYu G(2019)Energy efficiency optimization in big data processing platform by improving resources utilizationSustainable Computing: Informatics and Systems10.1016/j.suscom.2018.11.01121(80-89)Online publication date: Mar-2019
https://doi.org/10.1016/j.suscom.2018.11.011
Show More Cited By

Index Terms

Heterogeneity-conscious parallel query execution: getting a better mileage while driving faster!
1. Information systems
  1. Data management systems
    1. Database management system engines

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cover image ACM Conferences

DaMoN '14: Proceedings of the Tenth International Workshop on Data Management on New Hardware

June 2014

71 pages

ISBN:9781450329712

DOI:10.1145/2619228

Editors:
Alfons Kemper
Technische Universitat Munchen
,
Ippokratis Pandis
Cloudera

Copyright © 2014.

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 June 2014

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Bundesministerium für Bildung und Forschung

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SIGMOD/PODS'14

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SIGMOD

SIGMOD/PODS'14: International Conference on Management of Data

June 23, 2014

Utah, Snowbird, USA

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Overall Acceptance Rate 94 of 127 submissions, 74%

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Cited By

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
Rosenfeld VBreß SMarkl V(2022)Query Processing on Heterogeneous CPU/GPU SystemsACM Computing Surveys10.1145/348512655:1(1-38)Online publication date: 17-Jan-2022
https://dl.acm.org/doi/10.1145/3485126
Song JMa ZThomas RYu G(2019)Energy efficiency optimization in big data processing platform by improving resources utilizationSustainable Computing: Informatics and Systems10.1016/j.suscom.2018.11.01121(80-89)Online publication date: Mar-2019
https://doi.org/10.1016/j.suscom.2018.11.011
Krishna JNasre R(2018)Optimizing Graph Algorithms in Asymmetric Multicore ProcessorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2018.285836637:11(2673-2684)Online publication date: Nov-2018
https://doi.org/10.1109/TCAD.2018.2858366
Chang HHuang ZChang C(2018)Latency-aware task scheduling on big.LITTLE heterogeneous computing architecture2018 IEEE International Conference on Applied System Invention (ICASI)10.1109/ICASI.2018.8394254(13-14)Online publication date: Apr-2018
https://doi.org/10.1109/ICASI.2018.8394254
Ungethüm ADamme PPietrzyk JKrause AHabich DLehner W(2017)Balancing Performance and Energy for Lightweight Data Compression AlgorithmsNew Trends in Databases and Information Systems10.1007/978-3-319-67162-8_5(37-44)Online publication date: 9-Sep-2017
https://doi.org/10.1007/978-3-319-67162-8_5
Khan MShafique MHenkel JKhan MShafique MHenkel J(2017)Background and Related WorkEnergy Efficient Embedded Video Processing Systems10.1007/978-3-319-61455-7_2(25-65)Online publication date: 19-Sep-2017
https://doi.org/10.1007/978-3-319-61455-7_2
Ungethüm AKissinger THabich DLehner W(2017)Work-Energy Profiles: General Approach and In-Memory Database ApplicationPerformance Evaluation and Benchmarking. Traditional - Big Data - Interest of Things10.1007/978-3-319-54334-5_10(142-158)Online publication date: 18-Feb-2017
https://doi.org/10.1007/978-3-319-54334-5_10
Khan MShafique MGupta ASchumann THenkel JFanucci LTeich J(2016)Power-efficient load-balancing on heterogeneous computing platformsProceedings of the 2016 Conference on Design, Automation & Test in Europe10.5555/2971808.2972150(1469-1472)Online publication date: 14-Mar-2016
https://dl.acm.org/doi/10.5555/2971808.2972150
Ungethüm AKissinger TMentzel WHabich DLehner WÖzcan FKoutrika GMadden S(2016)Energy Elasticity on Heterogeneous Hardware using Adaptive Resource Reconfiguration LIVEProceedings of the 2016 International Conference on Management of Data10.1145/2882903.2899390(2173-2176)Online publication date: 26-Jun-2016
https://dl.acm.org/doi/10.1145/2882903.2899390
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