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FEPAC: A Framework for Evaluating Parallel Algorithms on Cluster Architectures

Authors:

Jean-Guy Schneider,

Kevin LeeAuthors Info & Claims

ACSW '21: Proceedings of the 2021 Australasian Computer Science Week Multiconference

Article No.: 6, Pages 1 - 10

https://doi.org/10.1145/3437378.3444363

Published: 01 February 2021 Publication History

Abstract

For many years, computer scientists have explored the computing power of so-called computing clusters to address performance requirements of computationally intensive tasks. Historically, computing clusters have been optimized with run-time performance in mind, but increasingly energy consumption has emerged as a second dimension that needs to be considered when optimizing cluster configurations. However, there is a lack of generally available tool support to experiment with cluster and algorithm configurations in order to identify “sweet-spots” with regards to both, run-time performance and energy consumption, respectively. In this work, we are introducing FEPAC, a framework for the automated evaluation of parallel algorithms on different cluster architectures and different deployments of software processes to hardware nodes, allowing users to explore the impact of different configurations on run-time properties of their computations. As proof of concept, the utility of the framework is demonstrated on a custom-built Raspberry Pi 3B+ cluster using different types of parallel algorithms as benchmarks. The experiments evaluate matrix multiplication, kmeans, and OpenCV on varying sizes of cluster, and showed that although a larger cluster improves performance, there is often a trade-off between energy and computation time.

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Cited By

Warade MLee KRanaweera CSchneider J(2023)Optimising workflow execution for energy consumption and performance2023 IEEE/ACM 7th International Workshop on Green And Sustainable Software (GREENS)10.1109/GREENS59328.2023.00011(24-29)Online publication date: May-2023
https://doi.org/10.1109/GREENS59328.2023.00011
Warade MLee KRanaweera CSchneider J(2023)Monitoring the Energy Consumption of Docker Containers2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC57700.2023.00263(1703-1710)Online publication date: Jun-2023
https://doi.org/10.1109/COMPSAC57700.2023.00263
Hosny KSalah AMagdi A(2023)Parallel Image Processing Applications Using Raspberry PiRecent Advances in Computer Vision Applications Using Parallel Processing10.1007/978-3-031-18735-3_6(107-119)Online publication date: 24-Jan-2023
https://doi.org/10.1007/978-3-031-18735-3_6
Show More Cited By

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Information & Contributors

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Published In

cover image ACM Other conferences

ACSW '21: Proceedings of the 2021 Australasian Computer Science Week Multiconference

February 2021

211 pages

ISBN:9781450389563

DOI:10.1145/3437378

Editors:
Nigel Stanger
University of Otago
,
Brendon Woodford
University of Otago
,
Michael Winikoff
Victoria University of Wellington
,
David Eyers
University of Otago
,
Veronica Liesaputra Joachim
University of Otago
,
Daniel Alencar da Costa
University of Otago
,
Andrew Trotman
University of Otago
,
Nigel Stanger
University of Otago
,
Veronica Liesaputra Joachim
University of Otago

Copyright © 2021 ACM.

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Association for Computing Machinery

New York, NY, United States

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Published: 01 February 2021

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ACSW '21

ACSW '21: 2021 Australasian Computer Science Week Multiconference

February 1 - 5, 2021

Dunedin, New Zealand

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Cited By

Warade MLee KRanaweera CSchneider J(2023)Optimising workflow execution for energy consumption and performance2023 IEEE/ACM 7th International Workshop on Green And Sustainable Software (GREENS)10.1109/GREENS59328.2023.00011(24-29)Online publication date: May-2023
https://doi.org/10.1109/GREENS59328.2023.00011
Warade MLee KRanaweera CSchneider J(2023)Monitoring the Energy Consumption of Docker Containers2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC57700.2023.00263(1703-1710)Online publication date: Jun-2023
https://doi.org/10.1109/COMPSAC57700.2023.00263
Hosny KSalah AMagdi A(2023)Parallel Image Processing Applications Using Raspberry PiRecent Advances in Computer Vision Applications Using Parallel Processing10.1007/978-3-031-18735-3_6(107-119)Online publication date: 24-Jan-2023
https://doi.org/10.1007/978-3-031-18735-3_6
Warade MSchneider JLee K(2022)Measuring the Energy and Performance of Scientific Workflows on Low-Power ClustersElectronics10.3390/electronics1111180111:11(1801)Online publication date: 6-Jun-2022
https://doi.org/10.3390/electronics11111801
Warade MLee KRanaweera CSchneider J(2022)Energy Aware Adaptive Scheduling of Workflows2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078(562-570)Online publication date: Dec-2022
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078
Warade MSchneider JLee K(2022)Towards Energy-aware Scheduling of Scientific Workflows2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST)10.1109/GECOST55694.2022.10010634(93-98)Online publication date: 26-Oct-2022
https://doi.org/10.1109/GECOST55694.2022.10010634

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