Reference : Deskilling HPL - Using an Evolutionary Algorithm to Automate Cluster Benchmarking
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/12445
Deskilling HPL - Using an Evolutionary Algorithm to Automate Cluster Benchmarking
English
Dunlop, Dominic mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Varrette, Sébastien mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Sep-2009
Proc. of 8th Intl. conf. on Parallel Processing and Applied Mathematics (PPAM 2009)
Springer Verlag
6068
LNCS
102--114
Yes
International
8th Intl. Conf. on Parallel Processing and Applied Mathematics (PPAM 2009)
from 13-09-2009 to 16-09-2009
Wroclaw
Poland
[en] HPL ; Performance Evaluation ; Evolutionary Algorithm
[en] The High-Performance Linpack (HPL) benchmark is the ac- cepted standard for measuring the capacity of the world’s most powerful computers, which are ranked twice yearly in the Top 500 List. Since just a small deficit in performance can cost a computer several places, it is impor- tant to tune the benchmark to obtain the best possible result. However, the adjustment of HPL’s seventeen configuration parameters to obtain maximum performance is a time-consuming task that must be performed by hand. In a previous paper, we provided a preliminary study that proposed the tuning of HPL parameters by means of an Evolutionary Algorithm. The approach was validated on a small cluster hosted at the University of Luxembourg. In this article, we extend this initial work by describing Acbea, a fully-automatic benchmark tuning tool that performs both the configuration and installation of HPL followed by an automatic search for optimized parameters that will lead to the best benchmark results. Experiments have been conducted to validate this tool on several clusters, exploiting in particular the Grid’5000 infrastructure.
University of Luxembourg: High Performance Computing - ULHPC
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/12445
10.1007/978-3-642-14403-5
http://www.ppam.pl/2009/

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