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Energy-Awareness and Performance Management with Parallel Dataflow Applications

Authors:

Simon Holmbacka,

Sébastien Lafond,

Johan LiliusAuthors Info & Claims

Journal of Signal Processing Systems, Volume 87, Issue 1

Pages 33 - 48

https://doi.org/10.1007/s11265-015-1059-4

Published: 01 April 2017 Publication History

Abstract

Applications have traditionally been executed as fast as possible (Race-to-Idle) and mapped to as many cores as possible (Fair scheduling) to minimize the energy consumption. With modern hardware, this method has become inefficient because of the power characteristics of the platforms. Instead, applications should utilize an optimal combination of clock frequency and number of cores to balance the dynamic and static power. Such approaches have been difficult to achieve since resource allocation is based only on CPU utilization. Resources are then allocated to prohibit over utilization rather than following software performance requirements. By adjusting the clock frequency directly according to software requirements and activating CPU cores according to the application parallelism, significant energy can be saved by lowering the average power dissipation. To enforce these recommendations, this paper provides means of expressing performance and parallelism in applications for more tight integration with the power management to balance the execution speed and mapping on multi-core systems. An interface between the applications and the hardware resources is provided in combination with a novel power management runtime system called Bricktop. A signal processing case study demonstrates real-world energy savings up to 50 % without performance degradation.

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Cited By

Ammar MBaklouti MPelcat MDesnos KAbid M(2018)Comparing Three Clustering-based Scheduling Methods for Energy-Aware Rapid Design of MP2SoCsJournal of Signal Processing Systems10.5555/3200212.320022290:4(537-570)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.5555/3200212.3200222

Energy-Awareness and Performance Management with Parallel Dataflow Applications
1. General and reference
  1. Cross-computing tools and techniques

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Published In

cover image Journal of Signal Processing Systems

Journal of Signal Processing Systems Volume 87, Issue 1

April 2017

172 pages

ISSN:1939-8018

EISSN:1939-8115

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Copyright © Copyright © 2017 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2017

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Cited By

Ammar MBaklouti MPelcat MDesnos KAbid M(2018)Comparing Three Clustering-based Scheduling Methods for Energy-Aware Rapid Design of MP2SoCsJournal of Signal Processing Systems10.5555/3200212.320022290:4(537-570)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.5555/3200212.3200222

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