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Adaptive Energy Minimization of OpenMP Parallel Applications on Many-Core Systems
Conference proceeding   Open access

Adaptive Energy Minimization of OpenMP Parallel Applications on Many-Core Systems

Rishad Shafik, Anup Das, Sheng Yang, Geoff Merrett and Bashir Al-Hashimi
Proceedings of the 6th Workshop on parallel programming and run-time management techniques for many-core architectures, v 19-21-, pp 19-24
19 Jan 2015
DOI: https://doi.org/10.1145/2701310.2701311
url
https://eprints.soton.ac.uk/372773/1/aspdac2015.pdfView
Accepted (AM)Open Access (License Unspecified),  Open

Abstract

Energy minimization Many-core OpenMP
Energy minimization of parallel applications is an emerging challenge for current and future generations of many-core computing systems. In this paper, we propose a novel and scalable energy minimization approach that suitably applies DVFS in the sequential part and jointly considers DVFS and dynamic core allocations in the parallel part. Fundamental to this approach is an iterative learning based control algorithm that adapt the voltage/frequency scaling and core allocations dynamically based on workload predictions and is guided by the CPU performance counters at regular intervals. The adaptation is facilitated through performance annotations in the application codes, defined in a modified OpenMP runtime library. The proposed approach is validated on an Intel Xeon E5-2630 platform with up to 24 CPUs running NAS parallel benchmark applications. We show that our proposed approach can effectively adapt to different architecture and core allocations and minimize energy consumption by up to 17% compared to the existing approaches for a given performance requirement.

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25 citations in Scopus

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