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ALEA: A Fine-Grained Energy Profiling Tool

Authors:

Pavlos Petoumenos,

Nikos Parasyris,

Dimitrios S. Nikolopoulos,

Bronis R. De Supinski,

Hugh LeatherAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 14, Issue 1

Article No.: 1, Pages 1 - 25

https://doi.org/10.1145/3050436

Published: 13 March 2017 Publication History

Abstract

Energy efficiency is becoming increasingly important, yet few developers understand how source code changes affect the energy and power consumption of their programs. To enable them to achieve energy savings, we must associate energy consumption with software structures, especially at the fine-grained level of functions and loops. Most research in the field relies on direct power/energy measurements taken from on-board sensors or performance counters. However, this coarse granularity does not directly provide the needed fine-grained measurements. This article presents ALEA, a novel fine-grained energy profiling tool based on probabilistic analysis for fine-grained energy accounting. ALEA overcomes the limitations of coarse-grained power-sensing instruments to associate energy information effectively with source code at a fine-grained level. We demonstrate and validate that ALEA can perform accurate energy profiling at various granularity levels on two different architectures: Intel Sandy Bridge and ARM big.LITTLE. ALEA achieves a worst-case error of only 2% for coarse-grained code structures and 6% for fine-grained ones, with less than 1% runtime overhead. Our use cases demonstrate that ALEA supports energy optimizations, with energy savings of up to 2.87 times for a latency-critical option pricing workload under a given power budget.

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https://dl.acm.org/doi/10.1145/3698038.3698531
Bangash AGrundy J(2023)Cost-Effective Strategies for Building Energy Efficient Mobile ApplicationsProceedings of the 45th International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion58688.2023.00076(281-285)Online publication date: 14-May-2023
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Lamprakos CBouras DCatthoor FSoudris D(2023)Reliable Basic Block Energy AccountingEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-031-46077-7_13(193-208)Online publication date: 2-Jul-2023
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Index Terms

ALEA: A Fine-Grained Energy Profiling Tool
1. Hardware
  1. Power and energy
    1. Power estimation and optimization
      1. Platform power issues
2. Software and its engineering
  1. Software notations and tools

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Published In

cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 14, Issue 1

March 2017

258 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/3058793

Editor:
Koen De Bosschere
Ghent University

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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 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 March 2017

Accepted: 01 November 2016

Revised: 01 September 2016

Received: 01 May 2016

Published in TACO Volume 14, Issue 1

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Cited By

Sharma PFuerst A(2024)Accountable Carbon Footprints and Energy Profiling For Serverless FunctionsProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698531(522-541)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698531
Bangash AGrundy J(2023)Cost-Effective Strategies for Building Energy Efficient Mobile ApplicationsProceedings of the 45th International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion58688.2023.00076(281-285)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE-Companion58688.2023.00076
Lamprakos CBouras DCatthoor FSoudris D(2023)Reliable Basic Block Energy AccountingEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-031-46077-7_13(193-208)Online publication date: 2-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-46077-7_13
Wang YZhang WHao MWang Z(2022)Online Power Management for Multi-Cores: A Reinforcement Learning Based ApproachIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.309227033:4(751-764)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TPDS.2021.3092270
Rao Vaddina KLefevre LOrgerie A(2021)Experimental Workflow for Energy and Temperature Profiling on HPC Systems2021 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC53001.2021.9631413(1-7)Online publication date: 5-Sep-2021
https://doi.org/10.1109/ISCC53001.2021.9631413
Ou ZChen JZhang YDong YYuan YWang Z(2020)Power modeling for Phytium FT-2000+/64 multi-core architectureProceedings of the Workshop on Benchmarking in the Datacenter10.1145/3380868.3398199(1-7)Online publication date: 22-Feb-2020
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Li JLiu KLi MLi D(2020)Combined Prediction Energy Model at Software Architecture LevelIEEE Access10.1109/ACCESS.2020.30414428(214565-214576)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3041442
Djupdal AGottschall BGhasemi FJahre M(2020)Lynsyn and LynsynLite: The STHEM Power Measurement UnitsTowards Ubiquitous Low-power Image Processing Platforms10.1007/978-3-030-53532-2_6(93-114)Online publication date: 16-Dec-2020
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Mukhanov LTovletoglou KVandierendonck HNikolopoulos DKarakonstantis G(2019)Workload-Aware DRAM Error Prediction using Machine Learning2019 IEEE International Symposium on Workload Characterization (IISWC)10.1109/IISWC47752.2019.9041963(106-118)Online publication date: Nov-2019
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Wei GQian DYang HLuan ZWang L(2019)FPowerTool: A Function-Level Power Profiling ToolIEEE Access10.1109/ACCESS.2019.29615077(185710-185719)Online publication date: 2019
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