Runtime Performance and Power Optimization of Parallel Disparity Estimation on Many-Core Platforms

Leech, Charles and Kumar, Charan and Acharyya, Amit and Yang, Sheng and Merrett, Geoff V and Al-Hashimi, Bashir M (2017) Runtime Performance and Power Optimization of Parallel Disparity Estimation on Many-Core Platforms. ACM Transactions on Embedded Computing Systems, 17 (2). pp. 1-19. ISSN 1539-9087

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Abstract

This article investigates the use of many-core systems to execute the disparity estimation algorithm, used in stereo vision applications, as these systems can provide flexibility between performance scaling and power consumption. We present a learning-based runtime management approach that achieves a required performance threshold while minimizing power consumption through dynamic control of frequency and core allocation. Experimental results are obtained from a 61-core Intel Xeon Phi platform for the aforementioned investigation. The same performance can be achieved with an average reduction in power consumption of 27.8% and increased energy efficiency by 30.04% when compared to Dynamic Voltage and Frequency Scaling control alone without runtime management.

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IITH Creators:
IITH CreatorsORCiD
Acharyya, Amithttp://orcid.org/0000-0002-5636-0676
Item Type: Article
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 01 Mar 2019 11:51
Last Modified: 01 Mar 2019 11:51
URI: http://raiith.iith.ac.in/id/eprint/4862
Publisher URL: http://doi.org/10.1145/3133560
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