and
Abstract:
We present a new parallel game-tree search algorithm. Our approach classifies a processor's available work as either mandatory (necessary for the solution) or speculative (may be necessary for the solution). Due to the nature of parallel game tree search, it is not possible to keep all processors busy with mandatory work. Our algorithm ER allows potential speculative work to be dynamically ordered, thereby reducing starvation without incurring an equivalent increase in speculative loss. Measurements of ER's performance on both random trees and trees from an actual game show that at least 16 processors can be applied profitably to a single search. These results contrast with previously published studies, which report a rapid drop-off of efficiency as the number of processors increases. 1.
Citations
| 193 | An analysis of Alpha-Beta Pruning – Knuth, Moore - 1975 |
| 86 | DIB—a distributed implementation of backtracking – Finkel, Manber - 1987 |
| 79 | Parallel Search of Strongly Ordered Game Trees – Marsland, Campbell - 1982 |
| 41 | The Design and Analysis of Algorithms for Asynchronous Multiprocessors – Baudet - 1978 |
| 38 | Analysis of Speedup in Distributed Algorithms – Fishburn - 1981 |
| 32 | A World-Championship-level Othello program – Rosenbloom |
| 31 | Parallelism in Alpha-Beta Search – Finkel, Fishburn - 1982 |
| 23 | Design, analysis and implementation of a parallel tree search algorithm – Akl, Barnard, et al. - 1982 |
| 17 | Pattern knowledge and search: the SUPREM architecture – Berliner, Ebeling - 1989 |
| 12 | On the branching factor of the alpha-beta pruning algorithm – Baudet - 1978 |
| 11 | Problem-Heap: A Paradigm for Multiprocessor Algorithms – Moller-Nielsen, Staunstrup - 1987 |
| 8 | Parallel game tree search – Marsland, Popowich - 1985 |
