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Hardware support for accurate per-task energy metering in multicore systems

Authors:

Victor Jimenez,

Francisco J. Cazorla,

Mateo ValeroAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 10, Issue 4

Article No.: 34, Pages 1 - 27

https://doi.org/10.1145/2541228.2555291

Published: 01 December 2013 Publication History

Abstract

Accurately determining the energy consumed by each task in a system will become of prominent importance in future multicore-based systems because it offers several benefits, including (i) better application energy/performance optimizations, (ii) improved energy-aware task scheduling, and (iii) energy-aware billing in data centers. Unfortunately, existing methods for energy metering in multicores fail to provide accurate energy estimates for each task when several tasks run simultaneously.

This article makes a case for accurate Per-Task Energy Metering (PTEM) based on tracking the resource utilization and occupancy of each task. Different hardware implementations with different trade-offs between energy prediction accuracy and hardware-implementation complexity are proposed. Our evaluation shows that the energy consumed in a multicore by each task can be accurately measured. For a 32-core, 2-way, simultaneous multithreaded core setup, PTEM reduces the average accuracy error from more than 12% when our hardware support is not used to less than 4% when it is used. The maximum observed error for any task in the workload we used reduces from 58% down to 9% when our hardware support is used.

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O’brien KPietri IReddy RLastovetsky ASakellariou R(2017)A Survey of Power and Energy Predictive Models in HPC Systems and ApplicationsACM Computing Surveys10.1145/307881150:3(1-38)Online publication date: 29-Jun-2017
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Index Terms

Hardware support for accurate per-task energy metering in multicore systems

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Published In

cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 10, Issue 4

December 2013

1046 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/2541228

Issue’s Table of Contents

Copyright © 2013 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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Association for Computing Machinery

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Publication History

Published: 01 December 2013

Accepted: 01 October 2013

Revised: 01 September 2013

Received: 01 June 2013

Published in TACO Volume 10, Issue 4

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Cited By

Liu YLe DTan R(2022)A data-assisted first-principle approach to modeling server outlet temperature in air free-cooled data centersFuture Generation Computer Systems10.1016/j.future.2021.12.003129:C(225-235)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.future.2021.12.003
O’brien KPietri IReddy RLastovetsky ASakellariou R(2017)A Survey of Power and Energy Predictive Models in HPC Systems and ApplicationsACM Computing Surveys10.1145/307881150:3(1-38)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3078811
Catalán SIgual FMayo RRodríguez-Sánchez RQuintana-Ortí E(2017)Time and energy modeling of a high-performance multi-threaded Cholesky factorizationThe Journal of Supercomputing10.1007/s11227-016-1654-673:1(139-151)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s11227-016-1654-6
Liu QMoreto MAbella JCazorla FValero M(2016)DReAMACM Transactions on Design Automation of Electronic Systems10.1145/293937022:1(1-26)Online publication date: 23-Nov-2016
https://dl.acm.org/doi/10.1145/2939370
Dayarathna MWen YFan R(2016)Data Center Energy Consumption Modeling: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2015.248118318:1(732-794)Online publication date: Sep-2017
https://doi.org/10.1109/COMST.2015.2481183
Marcu MBoncalo OGhenea MAmaricai AWeinstock JLeupers RWang ZGeorgakoudis GNikolopoulos DCernazanu-Glavan CBara LIonascu M(2016)Low-Cost Hardware Infrastructure for Runtime Thread Level Energy AccountingProceedings of the 29th International Conference on Architecture of Computing Systems -- ARCS 2016 - Volume 963710.1007/978-3-319-30695-7_21(277-289)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1007/978-3-319-30695-7_21
Liu QMoreto MAbella JCazorla FJimenez DValero M(2015)Sensible Energy Accounting with Abstract Metering for Multicore SystemsACM Transactions on Architecture and Code Optimization10.1145/284261612:4(1-26)Online publication date: 22-Dec-2015
https://dl.acm.org/doi/10.1145/2842616
Alonso PCatalán SIgual FMayo RRodríguez-Sánchez RQuintana-Ortí E(2015)Time and energy modeling of high–performance Level-3 BLAS on x86 architecturesSimulation Modelling Practice and Theory10.1016/j.simpat.2015.04.00355(77-94)Online publication date: Jun-2015
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Liu QMoreto MAbella JCazorla FValero M(2014)DReAM: Per-Task DRAM Energy Metering in Multicore SystemsEuro-Par 2014 Parallel Processing10.1007/978-3-319-09873-9_10(111-123)Online publication date: 2014
https://doi.org/10.1007/978-3-319-09873-9_10

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