Application Power Signature Analysis

Author

Chung-Hsing Hsu ; Combs, Jacob ; Nazor, Jolie ; Santiago, Fabian ; Thysell, Rachelle ; Rivoire, S. ; Poole, Stephen W.

Author_Institution

Comput. Sci. & Math. Div., Oak Ridge Nat. Lab., Oak Ridge, TN, USA

fYear

2014

fDate

19-23 May 2014

Firstpage

782

Lastpage

789

Abstract

The high-performance computing (HPC) community has been greatly concerned about energy efficiency. To address this concern, it is essential to understand and characterize the electrical loads of HPC applications. In this work, we study whether HPC applications can be distinguished by their power-consumption patterns using quantitative measures in an automatic manner. Using a collection of 88 power traces from 4 different systems, we find that basic statistical measures do a surprisingly good job of summarizing applications´ distinctive power behavior. Moreover, this study opens up a new area of research in power-aware HPC that has a multitude of potential applications.

Keywords

parallel processing; power aware computing; statistical analysis; application power signature analysis; distinctive power behavior; high-performance computing community; power traces; power-aware HPC; power-consumption patterns; statistical measures; Benchmark testing; Context; Data collection; Feature extraction; Histograms; Power demand; Power measurement; clustering; high performance computing; power signature;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International

Conference_Location

Phoenix, AZ

Print_ISBN

978-1-4799-4117-9

Type

conf

DOI

10.1109/IPDPSW.2014.90

Filename

6969461