research-article

Energy-Performance Co-Management of Mixed-Sensitivity Workloads on Heterogeneous Multi-core Systems

Authors:

Amir M. Rahmani,

Pasi LiljebergAuthors Info & Claims

ASPDAC '21: Proceedings of the 26th Asia and South Pacific Design Automation Conference

Pages 421 - 427

https://doi.org/10.1145/3394885.3431516

Published: 29 January 2021 Publication History

Abstract

Satisfying performance of complex workload scenarios with respect to energy consumption on Heterogeneous Multi-core Platforms (HMPs) is challenging when considering i) the increasing variety of applications, and ii) the large space of resource management configurations. Existing run-time resource management approaches use online and offline learning to handle such complexity. However, they focus on one type of application, neglecting concurrent execution of mixed sensitivity workloads. In this work, we propose an energy-performance co-management method which prioritizes mixed type of applications at run-time, and searches in the configuration space to find the optimal configuration for each application which satisfies the performance requirements while saving energy. We evaluate our approach on a real Odroid XU3 platform over mixed-sensitivity embedded workloads. Experimental results show our approach provides 54% lower performance violation with 50% higher energy saving compared to the existing approaches.

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Cited By

Hu BYang XZhao M(2023)Online energy-efficient scheduling of DAG tasks on heterogeneous embedded platformsJournal of Systems Architecture10.1016/j.sysarc.2023.102894140(102894)Online publication date: Jul-2023
https://doi.org/10.1016/j.sysarc.2023.102894
Liang JLin WXu YLiu YMo RLuo X(2023)Energy-aware parameter tuning for mixed workloads in cloud serverCluster Computing10.1007/s10586-023-04212-627:4(4805-4821)Online publication date: 27-Dec-2023
https://doi.org/10.1007/s10586-023-04212-6
Qiu WChen YChen DSu TYang S(2022)Run-Time Hierarchical Management of Mapping, Per-Cluster DVFS and Per-Core DPM for Energy OptimizationElectronics10.3390/electronics1107109411:7(1094)Online publication date: 30-Mar-2022
https://doi.org/10.3390/electronics11071094
Show More Cited By

Index Terms

Energy-Performance Co-Management of Mixed-Sensitivity Workloads on Heterogeneous Multi-core Systems
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
    2. System on a chip

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cover image ACM Conferences

ASPDAC '21: Proceedings of the 26th Asia and South Pacific Design Automation Conference

January 2021

930 pages

ISBN:9781450379991

DOI:10.1145/3394885

Copyright © 2021 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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SIGDA: ACM Special Interest Group on Design Automation
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New York, NY, United States

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Published: 29 January 2021

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ASPDAC '21

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ASPDAC '21: 26th Asia and South Pacific Design Automation Conference

January 18 - 21, 2021

Tokyo, Japan

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ASPDAC '21 Paper Acceptance Rate 111 of 368 submissions, 30%;

Overall Acceptance Rate 466 of 1,454 submissions, 32%

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Cited By

Hu BYang XZhao M(2023)Online energy-efficient scheduling of DAG tasks on heterogeneous embedded platformsJournal of Systems Architecture10.1016/j.sysarc.2023.102894140(102894)Online publication date: Jul-2023
https://doi.org/10.1016/j.sysarc.2023.102894
Liang JLin WXu YLiu YMo RLuo X(2023)Energy-aware parameter tuning for mixed workloads in cloud serverCluster Computing10.1007/s10586-023-04212-627:4(4805-4821)Online publication date: 27-Dec-2023
https://doi.org/10.1007/s10586-023-04212-6
Qiu WChen YChen DSu TYang S(2022)Run-Time Hierarchical Management of Mapping, Per-Cluster DVFS and Per-Core DPM for Energy OptimizationElectronics10.3390/electronics1107109411:7(1094)Online publication date: 30-Mar-2022
https://doi.org/10.3390/electronics11071094
Bhat GMandal SManchukonda SVadlamudi SAgarwal AWang JOgras U(2021)Per-Core Power Modeling for Heterogenous SoCsElectronics10.3390/electronics1019242810:19(2428)Online publication date: 7-Oct-2021
https://doi.org/10.3390/electronics10192428

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