Data-localization scheduling inside processor-cluster for multigrain parallel processing

Akimasa Yoshida, KeN'Ichi Koshizuka, Wataru Ogata, Hironori Kasahara

    Research output: Contribution to journalArticle


    This paper proposes a data-localization scheduling scheme inside a processor-cluster for multigrain parallel processing, which hierarchically exploits parallelism among coarsegrain tasks like loops, medium-grain tasks like loop iterations and near-fine-grain tasks like statements. The proposed scheme assigns near-fine-grain or medium-grain tasks inside coarse-grain tasks onto processors inside a processor-cluster so that maximum parallelism can be exploited and inter-processor data transfer can be minimum after data-localization for coarse-grain tasks across processor-clusters. Performance evaluation on a multiprocessor system OSCAR shows that multigrain parallel processing with the proposed data-localization scheduling can reduce execution time for application programs by 10% compared with multigrain parallel processing without data-localization.

    Original languageEnglish
    Pages (from-to)473-478
    Number of pages6
    JournalIEICE Transactions on Information and Systems
    Issue number4
    Publication statusPublished - 1997



    • Automatic data decomposition
    • Data-localization
    • Multigrain parallel processing
    • Parallelizing compilers
    • Task scheduling

    ASJC Scopus subject areas

    • Information Systems
    • Computer Graphics and Computer-Aided Design
    • Software

    Cite this