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

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.