Determining work partitioning on closely coupled heterogeneous computing systems using statistical design of experiments

Yectli A. Huerta, Brent Swartz, David J Lilja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In a closely coupled heterogeneous computing system the work is shared amongst all available computing resources. One challenge is to find an optimal division of work between the two or more very different kinds of processing units, each with their own optimal settings. We show that through the use of statistical techniques, a systematic search of the parameter space can be conducted. These techniques can be applied to variables that are categorical or continuous in nature and do not rely on the standard assumptions of linear models, mainly that the response variable can be described as a linear combination of the regression coefficients. Our search technique, when applied to the HPL benchmark, resulted in a performance gain of 14.5% over previously reported results.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 IEEE International Symposium on Workload Characterization, IISWC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages118-119
Number of pages2
ISBN (Electronic)9781538612323
DOIs
StatePublished - Dec 5 2017
Event2017 IEEE International Symposium on Workload Characterization, IISWC 2017 - Seattle, United States
Duration: Oct 1 2017Oct 3 2017

Publication series

NameProceedings of the 2017 IEEE International Symposium on Workload Characterization, IISWC 2017
Volume2017-January

Other

Other2017 IEEE International Symposium on Workload Characterization, IISWC 2017
CountryUnited States
CitySeattle
Period10/1/1710/3/17

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