| Project/Area Number |
12480068
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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 | 一般 |
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| Research Field |
計算機科学
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
MATSUOKA Satoshi Global Scientific Information and Computing Center, Tokyo Institute of Technology, Professor, 学術国際情報センター, 教授 (20221583)
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| Co-Investigator(Kenkyū-buntansha) |
AIDA Kento Inter Disciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Lecture, 大学院・情報理工学研究科, 講師 (80247212)
DAI You Graduate School of Science, Dept. of Information Science, Dept. of Mathematical and Computer Sciences, Tokyo Institute of Technology, Lecture, 大学院・情報理工学研究科, 講師 (40244678)
KOJIMA Masakazu Graduate School of Science, Dept. of Information Science, Dept. of Mathematical and Computer Sciences, Tokyo Institute of Technology, Professor, 大学院・情報理工学研究科, 教授 (90092551)
OGAWA Hirotaka Graduate School of Science, Dept. of Information Science, Dept. of Mathematical and Computer Sciences, Tokyo Institute of Technology, Assistant, 大学院・情報理工学研究科, 助手 (90302968)
FUJISAWA Katsuki Kyoto University, Graduate School of Engineering, Department of Architecture and Architectural Systems, Assistant, 大学院・工学研究科, 助手 (40303854)
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| Project Period (FY) |
2000 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥12,500,000 (Direct Cost: ¥12,500,000)
Fiscal Year 2001: ¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2000: ¥8,800,000 (Direct Cost: ¥8,800,000)
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| Keywords | Grid / Wide -Area / High -Performance / Ninf / Cluster Numerical Optimization / Semi-Definite Programming / SDP / Non-Convex Quadratic Optimization / SCRM / クラスタ計算機 / 並列化 / Gridコンピューティング / 性能評価 |
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| Research Abstract |
We employ the so-called Grid technology to construct a fleet of compute nodes as an aggregation of computing cluster nodes over a wide-area network, and using such "federation of cluster resources" attempt to tackle non-convex quadratic optimization problems of unprecedented scale, and made it accessible from throughout the Internet. More specifically, we developed an algorithm called SCRM (Successive Convex Relaxation Method) which is heavily based on using large numbers of SDP (Semidefinite Programming, SDP) subsolvers, which itself is called SDPA and is a very fast SDP solver using the Interior Point Methods. By efficiently spreading out the SDP solvers over the Grid we showed that we can solve non-convex quadratic problems of very large scale very efficiently, achieving almost linear speedup. For this purpose, we have constructed a fleet of PC clusters spread out throughout several locations, including Titech Oo-okayama Campus, Titech Suzukake-dai Campus, and Kyoto University. We h
… More
ave been able to achieve nearly 100-fold speedup using 128 processors. The key issue was not only the algorithm but efficient programming using the Ninf GridRPC system, which had to be modified extensively as well as new programming methodologies had to be 4eyeloped in order to cope with massive parallel execution of hundreds of tasks over the Grid.
More specifically, we parallelized SDPA with OpenMP using worksharing methodology to achieve nearly perfect parallel speedup for each cluster on the Grid. Also, we automated the process of selecting the best solver based on the data structure of the problem as well as the "shape" of the non-zero elements in the problem matrix. Then using the 256 nodes worth of clusters spread out over the -country, and using the Ninf GridRPC middleware, we constructed a "optimization solver server", achieving good speedup as mentioned above. The result not only set several world records for benchmark problems but also lead to even larger Grid research in the coming years. Less
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