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Data-Localization Scheduling inside Processor-Cluster for Multigrain Parallel Processing
Akimasa YOSHIDA Ken'ichi KOSHIZUKA Wataru OGATA Hironori KASAHARA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1997/04/25
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Parallel and Distributed Supercomputing)
task scheduling, data-localization, automatic data decomposition, multigrain parallel processing, parallelizing compilers,
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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.