research-article

Automated empirical tuning of scientific codes for performance and power consumption

Authors:

Shah Faizur Rahman,

Qing YiAuthors Info & Claims

HiPEAC '11: Proceedings of the 6th International Conference on High Performance and Embedded Architectures and Compilers

Pages 107 - 116

https://doi.org/10.1145/1944862.1944880

Published: 24 January 2011 Publication History

Abstract

Automatic empirical tuning of compiler optimizations has been widely used to achieve portable high performance for scientific applications. However, as power dissipation becomes increasingly important in modern architecture design, few have attempted to empirically tune optimization configurations to reduce the power consumption of applications. We provide an automated empirical tuning framework that can be configured to optimize for both performance and energy efficiency. In particular, we extensively parameterize the configuration of a large number of compiler optimizations, including loop parallelization, blocking, unroll-and-jam, array copying, scalar replacement, strength reduction, and loop unrolling. We then use hardware counters combined with elapsed time to estimate both the performance and the power consumption of differently optimized code to automatically discover desirable configurations for these optimizations. We use a power meter to verify our tuning results on two multi-core computers and show that our approach can effectively achieve a balanced performance and energy efficiency on modern CMP machines.

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Cited By

Dongarra JTourancheau BEndrei MJin CDinh MAbramson DPoxon HDeRose Lde Supinski B(2019)Statistical and machine learning models for optimizing energy in parallel applicationsInternational Journal of High Performance Computing Applications10.1177/109434201984291533:6(1079-1097)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1177/1094342019842915
Endrei MJin CDinh MAbramson DPoxon HDeRose Lde Supinski B(2018)Energy efficiency modeling of parallel applicationsProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.5555/3291656.3291679(1-13)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.5555/3291656.3291679
Endrei MJin CDinh MAbramson DPoxon HDeRose Lde Supinski B(2018)Energy efficiency modeling of parallel applicationsProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC.2018.00020(1-13)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.1109/SC.2018.00020
Show More Cited By

Index Terms

Automated empirical tuning of scientific codes for performance and power consumption
1. Software and its engineering
  1. Software notations and tools
    1. Compilers

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cover image ACM Other conferences

HiPEAC '11: Proceedings of the 6th International Conference on High Performance and Embedded Architectures and Compilers

January 2011

226 pages

ISBN:9781450302418

DOI:10.1145/1944862

General Chairs:
Manolis Katevenis
FORTH-ICS and U.Crete, Greece
,
Margaret Martonosi
Princeton University
,
Program Chairs:
Christos Kozyrakis
Stanford University
,
Olivier Temam
INRIA, France

Copyright © 2011 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: 24 January 2011

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HIPEAC '11: International Conference on High-Performance and Embedded Architectures and Compilers

January 24 - 26, 2011

Heraklion, Greece

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Cited By

Dongarra JTourancheau BEndrei MJin CDinh MAbramson DPoxon HDeRose Lde Supinski B(2019)Statistical and machine learning models for optimizing energy in parallel applicationsInternational Journal of High Performance Computing Applications10.1177/109434201984291533:6(1079-1097)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1177/1094342019842915
Endrei MJin CDinh MAbramson DPoxon HDeRose Lde Supinski B(2018)Energy efficiency modeling of parallel applicationsProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.5555/3291656.3291679(1-13)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.5555/3291656.3291679
Endrei MJin CDinh MAbramson DPoxon HDeRose Lde Supinski B(2018)Energy efficiency modeling of parallel applicationsProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC.2018.00020(1-13)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.1109/SC.2018.00020
Jin Cde Supinski BAbramson DPoxon HDeRose LDinh MEndrei MJessup E(2017)A survey on software methods to improve the energy efficiency of parallel computingInternational Journal of High Performance Computing Applications10.1177/109434201666547131:6(517-549)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1177/1094342016665471
Kephart JMaggio MDiaconescu AGiese HHoffmann HKounev SKoziolek ALewis PRobertsson ASpinner S(2017)Reference Scenarios for Self-aware ComputingSelf-Aware Computing Systems10.1007/978-3-319-47474-8_4(87-106)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-47474-8_4
Arcelli DCortellessa VLeva A(2016)A Library of Modeling Components for Adaptive Queuing NetworksComputer Performance Engineering10.1007/978-3-319-46433-6_14(204-219)Online publication date: 16-Sep-2016
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Lang J(2016)Data‐aware tuning of scientific applications with model‐based autotuningConcurrency and Computation: Practice and Experience10.1002/cpe.388529:4Online publication date: 17-Jun-2016
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Faizur Rahman SYi QHomayoun HNapoli CSalapura VFranke HHou R(2015)Just-in-time component-wise power and thermal modelingProceedings of the 12th ACM International Conference on Computing Frontiers10.1145/2742854.2742880(1-8)Online publication date: 6-May-2015
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Arcelli DCortellessa VFilieri ALeva AKruchten POzkaya IKoziolek H(2015)Control Theory for Model-based Performance-driven Software AdaptationProceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures10.1145/2737182.2737187(11-20)Online publication date: 4-May-2015
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Lang JRunger GStocker P(2015)Towards energy-efficient linear algebra with an ATLAS library tuned for energy consumption2015 International Conference on High Performance Computing & Simulation (HPCS)10.1109/HPCSim.2015.7237022(63-70)Online publication date: Jul-2015
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