An experimental study of global and local search algorithms in empirical performance tuning

Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

The increasing complexity, heterogeneity, and rapid evolution of modern computer architectures present obstacles for achieving high performance of scientific codes on different machines. Empirical performance tuning is a viable approach to obtain high-performing code variants based on their measured performance on the target machine. In previous work, we formulated the search for the best code variant as a numerical optimization problem. Two classes of algorithms are available to tackle this problem: global and local algorithms. We present an experimental study of some global and local search algorithms on a number of problems from the recently introduced SPAPT test suite. We show that local search algorithms are particularly attractive, where finding high-preforming code variants in a short computation time is crucial.

Original languageEnglish
Title of host publicationHigh Performance Computing for Computational Science, VECPAR 2012 - 10th International Conference, Revised Selected Papers
Pages261-269
Number of pages9
DOIs
StatePublished - 2013
Externally publishedYes
Event10th International Conference on High Performance Computing for Computational Science, VECPAR 2012 - Kobe, Japan
Duration: Jul 17 2012Jul 20 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7851 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on High Performance Computing for Computational Science, VECPAR 2012
Country/TerritoryJapan
CityKobe
Period07/17/1207/20/12

Funding

This paper has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357.

FundersFunder number
U.S. Department of EnergyDE-AC02-06CH11357

    Keywords

    • automatic performance tuning
    • black-box optimization
    • search

    Fingerprint

    Dive into the research topics of 'An experimental study of global and local search algorithms in empirical performance tuning'. Together they form a unique fingerprint.

    Cite this