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Runtime energy management of multi-core processors

Runtime energy management of multi-core processors
Runtime energy management of multi-core processors
Performance requirements of emerging applications and tighter power consumption constraints of mobile and embedded platforms mean that runtime management software is required to control these systems efficiently. In order for embedded systems to maintain their optimality, especially in dynamic environments, runtime software must be capable of learning and adaptability. This thesis investigates and develops runtime modelling methods, including their experimental validation, to reduce energy consumption in homogenous and heterogeneous multi-core processors. A multiple linear regression model is established to predict power and performance and drive runtime adaptations of an application and platform to maximise energy efficiency whilst meeting performance targets.

The proposed method is further validated with a parallel stereo matching application, which is developed to investigate the use of core scaling and analyse trade-os between power and performance through runtime adaptation for energy saving. Experimental results obtained from a 61-core Intel Xeon-Phi platform show that the same performance can be achieved with an average reduction in power consumption of 27.8% and increased energy efficiency by 30.0%, in comparison to baseline dynamic power management techniques.

To make energy management independent of the application and platform, this thesis presents a holistic approach to runtime management in the form of a runtime framework that is both application- and platform-agnostic. The framework unites the hardware and software layers by embedding the runtime management layer at the centre and enables cross-layer interactions through an API and dynamic knobs and monitors. The framework is demonstrated experimentally across multiple applications and two heterogeneous platforms, the Odroid-XU3 and Cyclone V. Two state-of-the-art runtime management approaches are validated that reduce the energy consumption of application execution by 18.2% and 17.2%. Trade-os between power, performance and accuracy are presented in three application-platform scenarios.
University of Southampton
Leech, Charles
0a507b6f-3212-478c-bb12-a8cac269d9d6
Leech, Charles
0a507b6f-3212-478c-bb12-a8cac269d9d6
Kazmierski, Tomasz
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Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

Leech, Charles (2018) Runtime energy management of multi-core processors. University of Southampton, Doctoral Thesis, 293pp.

Record type: Thesis (Doctoral)

Abstract

Performance requirements of emerging applications and tighter power consumption constraints of mobile and embedded platforms mean that runtime management software is required to control these systems efficiently. In order for embedded systems to maintain their optimality, especially in dynamic environments, runtime software must be capable of learning and adaptability. This thesis investigates and develops runtime modelling methods, including their experimental validation, to reduce energy consumption in homogenous and heterogeneous multi-core processors. A multiple linear regression model is established to predict power and performance and drive runtime adaptations of an application and platform to maximise energy efficiency whilst meeting performance targets.

The proposed method is further validated with a parallel stereo matching application, which is developed to investigate the use of core scaling and analyse trade-os between power and performance through runtime adaptation for energy saving. Experimental results obtained from a 61-core Intel Xeon-Phi platform show that the same performance can be achieved with an average reduction in power consumption of 27.8% and increased energy efficiency by 30.0%, in comparison to baseline dynamic power management techniques.

To make energy management independent of the application and platform, this thesis presents a holistic approach to runtime management in the form of a runtime framework that is both application- and platform-agnostic. The framework unites the hardware and software layers by embedding the runtime management layer at the centre and enables cross-layer interactions through an API and dynamic knobs and monitors. The framework is demonstrated experimentally across multiple applications and two heterogeneous platforms, the Odroid-XU3 and Cyclone V. Two state-of-the-art runtime management approaches are validated that reduce the energy consumption of application execution by 18.2% and 17.2%. Trade-os between power, performance and accuracy are presented in three application-platform scenarios.

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Published date: May 2018

Identifiers

Local EPrints ID: 422287
URI: http://eprints.soton.ac.uk/id/eprint/422287
PURE UUID: 6e9f53cd-3d23-49bc-a384-948de7d7555a
ORCID for Charles Leech: ORCID iD orcid.org/0000-0002-2403-3873

Catalogue record

Date deposited: 20 Jul 2018 16:30
Last modified: 29 Jan 2020 16:56

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