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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