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

Akimasa Yoshida*, Ke N.Ichi Koshizuka, Wataru Ogata, Hironori Kasahara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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