Parallel Computing of PVS Node based GTS Algorithm on GPU

Abstract

The developments in the field of computer architecture, it allows humans to play games like Chess, Tic-tac-toe, Go, etc. with computer machines using AI technology. In AI, Game tree search (GTS) is an important approach and is directed toward finding the finest choice of move for computer games. Using the traditional GTS algorithm the computer could not win a human. So there is a need for enhancing algorithms using dynamic parallelism of GPU. The block of thread set is organized on GPU is either one or two or three dimensional for parallel computations. On this, each thread designated by an inimitable mixture of indices. In this paper, parallel computing of node-based Principal Variation Search (PVS) GTS algorithm presented, which runs on GPU using libraries of CUDA. this experiment tested on chess games with different depths and results are compared with the threads on CPU and GPU. The results proved that GPU improves the performance of speedup up to 80 percent on the checkers game. Parallel computing greatly increasing the efficient use of CPU and improves the performances of the PVS-GTS algorithm on GPU to search deeper layers and find the optimal moves for the current players for two-player computer games.