Abstract
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 language | English |
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Pages (from-to) | 473-478 |
Number of pages | 6 |
Journal | IEICE Transactions on Information and Systems |
Volume | E80-D |
Issue number | 4 |
Publication status | Published - 1997 Jan 1 |
Keywords
- 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