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A Three Step Blind Approach for Improving HPC Systems' Energy Performance

Abstract : Nowadays, there is no doubt that energy consumption has become a limiting factor in the design and operation of high performance computing (HPC) systems. This is evidenced by the rise of efforts both from the academia and the industry to reduce the energy consumption of those systems. Unlike hardware solutions, software initiatives targeting HPC systems' energy consumption reduction despite their effectiveness are often limited for reasons including: (i) the program specific nature of the solution proposed; (ii) the need of deep understanding of applications at hand; (iii) proposed solutions are often difficult to use by novices and/or are designed for single task environments. This paper propose a three step blind system-wide, application independent, fine-grain, and easy to use (user friendly) methodology for improving energy performance of HPC systems. The methodology typically breaks into phase detection, phase characterization, and phase identification and system reconfiguration. And it is blind in the sense that it does not require any knowledge from users. It relies upon reconfigurable capabilities offered by the majority of HPC subsystems -- including the processor, storage, memory, and communication subsystems -- to reduce the overall energy consumption of the system (excluding network equipments) at runtime. We also present an implementation of our methodology through which we demonstrate its effectiveness via static analyses and experiments using benchmarks representative of HPC workloads.
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Contributor : Ghislain Landry Tsafack Chetsa Connect in order to contact the contributor
Submitted on : Tuesday, January 7, 2014 - 7:00:40 PM
Last modification on : Tuesday, October 25, 2022 - 4:22:59 PM
Long-term archiving on: : Tuesday, April 8, 2014 - 12:21:14 AM


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Ghislain Landry Tsafack Chetsa, Laurent Lefèvre, Patricia Stolf. A Three Step Blind Approach for Improving HPC Systems' Energy Performance. EE-LSDS 2013, Energy Efficiency in Large Scale Distributed Systems conference, Apr 2013, Vienne, Austria. pp.168-181, ⟨10.1007/978-3-642-40517-4_15⟩. ⟨hal-00925302⟩



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