IEICE TRANSACTIONS on Information
Data-Localization Scheduling inside Processor-Cluster for Multigrain Parallel Processing
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E80-D No.4 pp.473-479
- Publication Date
- 1997/04/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Special Section PAPER (Special Issue on Parallel and Distributed Supercomputing)
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Akimasa YOSHIDA, Ken'ichi KOSHIZUKA, Wataru OGATA, Hironori KASAHARA, "Data-Localization Scheduling inside Processor-Cluster for Multigrain Parallel Processing" in IEICE TRANSACTIONS on Information,
vol. E80-D, no. 4, pp. 473-479, April 1997, doi: .
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e80-d_4_473/_p
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@ARTICLE{e80-d_4_473,
author={Akimasa YOSHIDA, Ken'ichi KOSHIZUKA, Wataru OGATA, Hironori KASAHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Data-Localization Scheduling inside Processor-Cluster for Multigrain Parallel Processing},
year={1997},
volume={E80-D},
number={4},
pages={473-479},
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.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - Data-Localization Scheduling inside Processor-Cluster for Multigrain Parallel Processing
T2 - IEICE TRANSACTIONS on Information
SP - 473
EP - 479
AU - Akimasa YOSHIDA
AU - Ken'ichi KOSHIZUKA
AU - Wataru OGATA
AU - Hironori KASAHARA
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 1997
AB - 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.
ER -
English
