Qi, X., Chen, J., Dong, Y. et al. (6 more authors) (2023) HighRPM: Combining Integrated Measurement and Sofware Power Modeling for High-Resolution Power Monitoring. In: ICPP '23: Proceedings of the 52nd International Conference on Parallel Processing. 52nd International Conference on Parallel Processing (ICPP), 07-10 Aug 2023, Salt Lake City, UT, USA. ACM , New York, NY, United States , pp. 369-379. ISBN 9798400708435
Abstract
In an era where power and energy are the first-class constraints of computing systems, accurate power information is crucial for energy efficiency optimization in parallel computing systems. Existing power monitoring techniques rely on either software-centric power models that suffer from poor accuracy or integrated hardware measurement schemes that have a low reading update frequency and coarse granularity. These result in a low spatiotemporal resolution for power monitoring. This paper introduces HighRPM, a new method for accurately measuring power consumption on parallel computing systems. HighRPM combines coarse-grained power sensor readings and software power modeling techniques to improve temporal and spatial resolutions. To provide high-frequent power readings in the temporal domain, HighRPM employs statistical modeling and machine learning techniques to predict the long-term power trend and the short-term fluctuations in power consumption. To improve spatial coverage, HighRPM takes low-time resolution node-level power consumption and uses a neural network to distribute the power readings to lower-level computing components like CPUs and memory components. We evaluate HighRPM by applying it to both ARM-based and X86-based platforms. Experimental results show that HighRPM improves time resolution by 10 times, provides accurate readings for CPUs and memory, and reduces error by 7-24% compared to other power modeling methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Owner/Authors | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ICPP '23: Proceedings of the 52nd International Conference on Parallel Processing, https://doi.org/10.1145/3605573.3605649. |
Keywords: | power monitoring; integrated measurement; spatiotemporal resolution; power model |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Aug 2023 16:02 |
Last Modified: | 07 Nov 2023 17:00 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3605573 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201596 |