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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.
Cited literature [13 references]
https://hal.inria.fr/hal-00752883
Contributor : Ghislain Landry Tsafack Chetsa <>
Submitted on : Friday, November 16, 2012 - 3:43:08 PM
Last modification on : Tuesday, June 16, 2020 - 3:50:01 AM
Document(s) archivé(s) le : Saturday, December 17, 2016 - 11:14:41 AM
Contributor : Ghislain Landry Tsafack Chetsa <>
Submitted on : Friday, November 16, 2012 - 3:43:08 PM
Last modification on : Tuesday, June 16, 2020 - 3:50:01 AM
Document(s) archivé(s) le : Saturday, December 17, 2016 - 11:14:41 AM
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sbac-pad-fv.pdf
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