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.
Type de document :
Communication dans un congrès
24th International Symposium on Computer Architecture and High Performance Computing, Oct 2012, New York City, United States. 2012


https://hal.inria.fr/hal-00752883
Contributeur : Ghislain Landry Tsafack Chetsa <>
Soumis le : vendredi 16 novembre 2012 - 15:43:08
Dernière modification le : mercredi 11 mai 2016 - 01:06:34

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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. 2012. <hal-00752883>

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