Parallel Alpha-Beta Algorithm on the GPU

In the paper we present the parallel implementation of the alpha-beta algorithm running on the graphics processing unit (GPU). We compare the speed of the parallel player with the standard serial one using the game of reversi with boards of different sizes. We show that for small boards the level of available parallelism is insufficient for efficient GPU utilization, but for larger boards substantial speed-ups can be achieved on the GPU. The results indicate that the GPU-based alpha-beta implementation would be advantageous for similar games of higher computational complexity (e.g. hex and go) in their standard form

The ABDADA Distributed Minimax Search Algorithm

This method is then compared to the "Young Brother Wait Concept " algorithm in an Othello program implementation and in a Chess program. Results of tests done on a 32-node CM5 and a 128-node CRAY T3D computers are given. 1 INTRODUCTION In the search for power to run our game-playing programs as fast as possible, the use of parallel computers is a stimulating choice. During the past few years, parallel algorithms have evolved from fixed master-slave relationships, where the master looked for slaves to complete its task, to more dynamic master-slave relationships, where unemployed processors look for a master in order to find some task to do. But those master-slave relationships, which are exemplified by the work-stealing schedulers of Jamboree[12] and YBWC[7] still suffer from synchronization overheads. In this paper, we describe a non-synchronized parallel algorithm named ABDADA