A historic knowledge based approach for dynamic optimization

Saber Feki*, Edgar Gabriel

*Corresponding author for this work

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

    2 Scopus citations

    Abstract

    Dynamic runtime optimization is a means to tune the performance of operations on a given platform while executing the application itself. However, most approaches discussed in literature so far fail for applications which have an adaptive and irregular behavior. In this paper, we present an algorithm which is able to incorporate knowledge gathered from previous optimizations to speed up the dynamic tuning procedure. We present the integration of the algorithm within a dynamic runtime optimization library along with a smoothing mechanism of the historic data entries to deal with outliers and inaccuracies in the knowledge base. The approach is evaluated for two separate parallel adaptive application kernels on three different platforms.

    Original languageEnglish (US)
    Title of host publicationParallel Computing
    Subtitle of host publicationFrom Multicores and GPU's to Petascale
    PublisherIOS Press BV
    Pages389-396
    Number of pages8
    ISBN (Print)9781607505297
    DOIs
    StatePublished - Jan 1 2010

    Publication series

    NameAdvances in Parallel Computing
    Volume19
    ISSN (Print)0927-5452

    Keywords

    • adaptive applications
    • adaptive communication library
    • historic learning

    ASJC Scopus subject areas

    • Computer Science(all)

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