| Project/Area Number |
11558029
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
計算機科学
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| Research Institution | THE UNIVERSITY OF TOKYO |
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Principal Investigator |
SHIMIZU Kentaro Graduate School of Agriculture and Life Sciences, THE UNIVERSITY OF TOKYO, PROFESSOR, 大学院・農学生命科学研究科, 教授 (80178970)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAURA Shugo Graduate School of Agriculture and Life Sciences, THE UNIVERSITY OF TOKYO, RESEARCH ASSOCIATE, 大学院・農学生命科学研究科, 助教授 (90272442)
池口 満徳 東京大学, 大学院・農学生命科学研究科, 助手 (60261955)
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| Project Period (FY) |
1999 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥6,500,000 (Direct Cost: ¥6,500,000)
Fiscal Year 2001: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2000: ¥4,800,000 (Direct Cost: ¥4,800,000)
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| Keywords | parallel computing / parallel programming environment / MP1 / processor allocation / scheduling / molecular dynamics / computational chemistry / 分子動力学計算 / 大規模並列計算機 / 科学技術計算 / 動的資源管理 / 分子動力学シュミレーション / エネルギー最小化計算 |
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| Research Abstract |
We designed and implemented a new parallel programming environment called Parsley, which provides fine-grained scheduling services based on the structures of application programs. In Parsley, application programs are divided into subtasks that can run serially or in parallel. It provides a programming interface that allows a user to define subtasks and to easily specify precedence constraints among them. Parsley uses these constraints to schedule subtasks at run time. The scheduling' policy. is automatically improved to reflect the hardware environment and resource usage. The basic 'scheduling policy is an incremental scheduling algorithm based on the critical path method. In this algorithm, subtask priorities are dynamically determined by using the execution time of each subtask, as monitored by the resource management facilities of Parsley. We developed a parallel molecular dynamics (MD) simulation program on the Parsley system and' executed it on three scalable multiprocessor systems. We found that Parsley is efficient for large-scale MD simulation and that load balancing facilities of Parsley can be adopted to the scalable multiprocessor systems of different architectures. The MD simulation on Parsley is 3.5 times faster than the conventional parallel algorithm. The Parsley's automatic improvement of scheduling policies further enhances processor utilization by 35 to 55 %. In addition, Parsley is useful in the heterogeneous environment (e.g. a network of different workstations and clusters) as well as the homogeneous environment. Users need not be aware of the individual performance of the computers and networks, because of the dynamic processor allocation facilities of Parsley.
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