Comparative study of performance of parallel Alpha Beta Pruning for different architectures

TL;DR

This paper compares the performance of sequential and various parallel implementations of Alpha Beta pruning algorithms on different architectures, specifically for chess game applications, highlighting efficiency gains through parallelism.

Contribution

It provides a comparative analysis of parallel Alpha Beta pruning algorithms on GPU and shared memory architectures, offering insights into their efficiency improvements.

Findings

Parallel algorithms outperform sequential in computational time.

GPU-based implementations show significant speedup.

Efficiency varies with architecture and implementation details.

Abstract

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.