并行Alpha Beta剪枝算法在不同体系结构下的性能比较
Comparative study of performance of parallel Alpha Beta Pruning for different architectures
摘要：
在计算机系统中，根据环境状态、系统目标等各种状态搜索最佳可能动作的优化已经成为主要的研究领域。在任何一种搜索算法中，从已知的所有可能性中寻找最佳可能解可以导致整个状态搜索空间的构造，通常称为极大极小算法。这可能导致不切实际的时间复杂度，其可能不适合于实时的搜索操作。减少计算时间的实际解决方案之一是Alpha Beta剪枝。而不是搜索整个状态空间，我们修剪不必要的分支，这有助于减少大量的时间。本文重点介绍了Alpha Beta剪枝算法的各种可能的实现，并给出了一个什么样的算法可以用于并行洞察。关于如何使Alpha Beta剪枝更快，已经进行了各种研究。针对GPU特定架构（如网格（CUDA）等）或共享内存模型（OpenMP）的优化Alpha Beta剪枝有助于减少计算时间。本文以象棋博弈为例，研究了顺序和不同并行形式的Alpha Beta剪枝方法的比较及其各自的效率。
英文摘要：
Optimization of searching the best possible action depending on various states like state of environment, system goal etc. has been a major area of study in computer systems. In any search algorithm, searching best possible solution from the pool of every possibility known can lead to the construction of the whole state search space popularly called as minimax algorithm. This may lead to a impractical time complexities which may not be suitable for real time searching operations. One of the practical solution for the reduction in computational time is Alpha Beta pruning. Instead of searching for the whole state space, we prune the unnecessary branches, which helps reduce the time by significant amount. This paper focuses on the various possible implementations of the Alpha Beta pruning algorithms and gives an insight of what algorithm can be used for parallelism. Various studies have been conducted on how to make Alpha Beta pruning faster. Parallelizing Alpha Beta pruning for the GPUs specific architectures like mesh(CUDA) etc. or shared memory model(OpenMP) helps in the reduction of the computational time. This paper studies the comparison between sequential and different parallel forms of Alpha Beta pruning and their respective efficiency for the chess game as an application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
