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Conference Papers Year : 2012

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

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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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Dates and versions

hal-00752883 , version 1 (16-11-2012)

Identifiers

Cite

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 (SBAC-PAD 2012), Oct 2012, New York City, United States. pp.132-138, ⟨10.1109/SBAC-PAD.2012.32⟩. ⟨hal-00752883⟩
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