research-article

An Incremental Methodology for Energy Measurement and Modeling

Authors:

Abdelhafid Mazouz,

William JalbyAuthors Info & Claims

ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering

Pages 15 - 26

https://doi.org/10.1145/3030207.3030224

Published: 17 April 2017 Publication History

Abstract

This paper presents an empirical approach to measuring and modeling the energy consumption of multicore processors.The modeling approach allows us to find a breakdown of the energy consumption among a set of key hardware components, also called HW nodes. We explicitly model the front-end and the back-end in terms of the number of instructions executed. We also model the L1, L2 and L3 caches. Furthermore, we explicitly model the static and dynamic energy consumed by the the uncore and core components. From a software perspective, our methodology allows us to correlate energy to the executed code, which helps find opportunities for code optimization and tuning.

We use binary analysis and hardware counters for performance characterization. Although, we use the on-chip counters (RAPL) for energy measurement, our methodology does not rely on a specific method for energy measurement. Thus, it is portable and easy to deploy in various computing environments. We validate our energy model using two Intel processors with a set of HPC codelets, where data sizes are varied to come from the L1, L2 and L3 caches and show 3% average modeling error. We present a comprehensive analysis and show energy consumption differences between kernels and relate those differences to the algorithms that are implemented. Finally, we discuss how vectorization leads to energy savings compared to non-vectorized codes.

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Cited By

Stuckmann FWeißbrich MPayá-Vayá G(2024)Energy-Aware Register Allocation for VLIW ProcessorsJournal of Signal Processing Systems10.1007/s11265-024-01930-x96:11(627-650)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11265-024-01930-x
Guimarães MCarneiro DPalumbo GOliveira FOliveira ÓAlves VNovais P(2023)Predicting Model Training Time to Optimize Distributed Machine Learning ApplicationsElectronics10.3390/electronics1204087112:4(871)Online publication date: 8-Feb-2023
https://doi.org/10.3390/electronics12040871
Gundu KDhyaram LRamana Rao GSurya Deepak G(2023)Comparative Analysis of Energy Consumption in Text Processing ModelsAdvancements in Smart Computing and Information Security10.1007/978-3-031-23092-9_9(107-116)Online publication date: 11-Jan-2023
https://doi.org/10.1007/978-3-031-23092-9_9
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Index Terms

An Incremental Methodology for Energy Measurement and Modeling

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Published In

cover image ACM Conferences

ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering

April 2017

450 pages

ISBN:9781450344043

DOI:10.1145/3030207

General Chairs:
Walter Binder
Università della Svizzera Italiana, Switzerland
,
Vittorio Cortellessa
Università dell'Aquila, Italy
,
Program Chairs:
Anne Koziolek
Karlsruhe Institute of Technology, Germany
,
Evgenia Smirni
College of William & Mary, USA
,
Meikel Poess
Oracle Corporation, USA

Copyright © 2017 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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SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGSOFT: ACM Special Interest Group on Software Engineering
SPEC: SPEC Research Group

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Association for Computing Machinery

New York, NY, United States

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Published: 17 April 2017

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ICPE '17: ACM/SPEC International Conference on Performance Engineering

April 22 - 26, 2017

L'Aquila, Italy

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ICPE '17 Paper Acceptance Rate 27 of 83 submissions, 33%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

Stuckmann FWeißbrich MPayá-Vayá G(2024)Energy-Aware Register Allocation for VLIW ProcessorsJournal of Signal Processing Systems10.1007/s11265-024-01930-x96:11(627-650)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11265-024-01930-x
Guimarães MCarneiro DPalumbo GOliveira FOliveira ÓAlves VNovais P(2023)Predicting Model Training Time to Optimize Distributed Machine Learning ApplicationsElectronics10.3390/electronics1204087112:4(871)Online publication date: 8-Feb-2023
https://doi.org/10.3390/electronics12040871
Gundu KDhyaram LRamana Rao GSurya Deepak G(2023)Comparative Analysis of Energy Consumption in Text Processing ModelsAdvancements in Smart Computing and Information Security10.1007/978-3-031-23092-9_9(107-116)Online publication date: 11-Jan-2023
https://doi.org/10.1007/978-3-031-23092-9_9
Carastan-Santos DPham T(2022)Understanding the Energy Consumption of HPC Scale Artificial IntelligenceHigh Performance Computing10.1007/978-3-031-23821-5_10(131-144)Online publication date: 21-Dec-2022
https://doi.org/10.1007/978-3-031-23821-5_10
Kruglov ASucci GDlamini GKruglov ASucci G(2022)System Energy Consumption MeasurementDeveloping Sustainable and Energy-Efficient Software Systems10.1007/978-3-031-11658-2_3(27-38)Online publication date: 27-Jul-2022
https://doi.org/10.1007/978-3-031-11658-2_3
Lipp MKogler AOswald DSchwarz MEasdon CCanella CGruss D(2021)PLATYPUS: Software-based Power Side-Channel Attacks on x862021 IEEE Symposium on Security and Privacy (SP)10.1109/SP40001.2021.00063(355-371)Online publication date: May-2021
https://doi.org/10.1109/SP40001.2021.00063
García-Martín ELavesson NGrahn HCasalicchio EBoeva V(2021)Energy-aware very fast decision treeInternational Journal of Data Science and Analytics10.1007/s41060-021-00246-411:2(105-126)Online publication date: 20-Mar-2021
https://doi.org/10.1007/s41060-021-00246-4
Guermouche AOrgerie A(2021)Thermal design power and vectorized instructions behaviorConcurrency and Computation: Practice and Experience10.1002/cpe.626134:2Online publication date: 30-Mar-2021
https://doi.org/10.1002/cpe.6261
Yao CLiu WTang WGuo JHu SLu YJiang W(2020)Evaluating and analyzing the energy efficiency of CNN inference on high‐performance GPUConcurrency and Computation: Practice and Experience10.1002/cpe.606433:6Online publication date: 21-Oct-2020
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García-Martín ELavesson NGrahn HCasalicchio EBoeva V(2019)How to Measure Energy Consumption in Machine Learning AlgorithmsECML PKDD 2018 Workshops10.1007/978-3-030-13453-2_20(243-255)Online publication date: 16-Feb-2019
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