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Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems

Abstract : Modern high performance computing subsystems (HPC) - including processor, network, memory, and IO - are provided with power management mechanisms. These include dynamic speed scaling and dynamic resource sleeping. Understanding the behavioral patterns of high performance computing systems at runtime can lead to a multitude of optimization opportunities including controlling and limiting their energy usage. In this paper, we present a general purpose methodology for optimizing energy performance of HPC systems consid- ering processor, disk and network. We rely on the concept of execution vector along with a partial phase recognition technique for on-the-fly dynamic management without any a priori knowledge of the workload. We demonstrate the effectiveness of our management policy under two real-life workloads. Experimental results show that our management policy in comparison with baseline unmanaged execution saves up to 24% of energy with less than 4% performance overhead for our real-life workloads.
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Contributor : Ghislain Landry Tsafack Chetsa Connect in order to contact the contributor
Submitted on : Friday, November 16, 2012 - 3:43:08 PM
Last modification on : Thursday, January 20, 2022 - 4:14:36 PM
Long-term archiving on: : Saturday, December 17, 2016 - 11:14:41 AM


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  • HAL Id : hal-00752883, version 1


Ghislain Landry Tsafack Chetsa, Laurent Lefevre, Jean-Marc Pierson, Patricia Stolf, Georges da Costa. Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems. 24th International Symposium on Computer Architecture and High Performance Computing, Oct 2012, New York City, United States. ⟨hal-00752883⟩



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