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 |
|---|---|
| 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 |
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Keywords
- Automatic data decomposition
- Data-localization
- Multigrain parallel processing
- Parallelizing compilers
- Task scheduling
ASJC Scopus subject areas
- Information Systems
- Computer Graphics and Computer-Aided Design
- Software
Cite this
Data-localization scheduling inside processor-cluster for multigrain parallel processing. / Yoshida, Akimasa; Koshizuka, KeN'Ichi; Ogata, Wataru; Kasahara, Hironori.
In: IEICE Transactions on Information and Systems, Vol. E80-D, No. 4, 1997, p. 473-478.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Data-localization scheduling inside processor-cluster for multigrain parallel processing
AU - Yoshida, Akimasa
AU - Koshizuka, KeN'Ichi
AU - Ogata, Wataru
AU - Kasahara, Hironori
PY - 1997
Y1 - 1997
N2 - 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.
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.
KW - Automatic data decomposition
KW - Data-localization
KW - Multigrain parallel processing
KW - Parallelizing compilers
KW - Task scheduling
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UR - http://www.scopus.com/inward/citedby.url?scp=0031121930&partnerID=8YFLogxK
M3 - Article
VL - E80-D
SP - 473
EP - 478
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 4
ER -