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The READEX Project for Dynamic Energy Efficiency Tuning

Name: The READEX Project for Dynamic Energy Efficiency Tuning
Creator: Gerndt, Michael
Keywords: 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy, 0503 education, ddc:

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Conference object 31 May 2016Publisher:ACMJournal:Proceedings of the ACM Workshop on Software Engineering Methods for Parallel and High Performance ApplicationsFunded by:EC | READEX

Authors: Gerndt, Michael;

doi: 10.1145/2916026.2916033

The READEX Project for Dynamic Energy Efficiency Tuning

- Summary
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Abstract

High Performance Computing (HPC) systems consume a lot of energy. The overall energy consumption is one of the biggest challenges on the way towards exascale computers. Therefore, energy reduction techniques have to be applied on all levels from the basic chip technology up to the data center infrastructure. The READEX project explores the potential of dynamically switching application and system parameters, such as the clock frequency of the cores, to reduce the overall energy consumption of applications. An analysis is performed during application design time to precompute a tuning model that is then input to the runtime tuning library. This library switches the application and system configuration at runtime to adapt to varying application characteristics.

Related Organizations

Technical University of Munich
Germany

Keywords

ddc: ddc:

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	1
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average