| Project/Area Number |
10680340
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | The University of Electro-Communications |
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Principal Investigator |
NOSHITA Kohei Department of Electro-Communications, University of Electro-Communications, Professor, 電気通信学部, 教授 (60011706)
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| Co-Investigator(Kenkyū-buntansha) |
YANAI Keiji Department of Electro-Communications, University of Electro-Communications, Research Associate, 電気通信学部, 助手 (20301179)
NAKAYAMA Yasuichi Department of Electro-Communications, University of Electro-Communications, Assistant Professor, 電気通信学部, 講師 (70251709)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 1999: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1998: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | game-tree / parallel searching / hash method / transposition table |
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| Research Abstract |
In game-tree searching, transposition tables are used for eliminating repetitions of the identical computation for reappeared (identical or similar) positions. Transposition tables are also expected to be effective for efficient parallel searching. On a distributed parallel computer-cluster, a new method for sharing the global transposition table among component processors is designed, implemented, applied and evaluated.
The basic parallel software system for communicating among network-connected processors is designed, implemented and improved. The global transposition table consists of shared-hashing tables which are distributed on processors. Two types of the shared-hashing tables are experimentally compared.
Two games are used for evaluating our distributed shared-hashing method. They are mini-othello and parallel selection. Some theoretical results concerning properties of those games are obtained.
By executing some parallel algorithms, several kinds of overheads as well as the computation time are counted. Based on these experimental results, various aspects of our method are evaluated. The speedup factor is shown by comparing our method (together with local hashing tables) with the local-hashing method (without the global shared-hashing table). The excellent speedups in terms of the number of processors are achieved. By our method, several instances of the parallel selection problem are solved, which have not been solved so far on a single computer.
The experiments prove that our distributed shared-hashing method is efficient enough to show a good performance near the maximum on a distributed parallel environment with slow interprocessor communication
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