Abstract
The paper describes results of minimax tree searching algorithm implemented within CUDA platform. The problem regards move choice strategy in the game of Reversi. The parallelization scheme and performance aspects are discussed, focusing mainly on warp divergence problem and data transfer size. Moreover, a method of minimizing warp divergence and performance degradation is described. The paper contains both the results of test performed on multiple CPUs and GPUs. Additionally, it discusses αβ parallel pruning implementation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Knuth, D.E., Moore, R.W.: An analysis of alpha-beta pruning. Artificial Intelligence 6, 293–326 (1975)
Manohararajah, V.: Parallel Alpha-Beta Search on Shared Memory Multiprocessors, Master Thesis, Graduate Department of Electrical and Computer Engineering, University of Toronto (2001)
Warren, H.S.: Hacker’s Delight. Addison-Wesley, Reading (2002)
Schaeffer, J.: Improved Parallel Alpha Beta Search. In: Proceedings of 1986 ACM Fall Joint Computer Conference (1986)
Borovska, P., Lazarova, M.: Efficiency of Parallel Minimax Algorithm for Game Tree Search. In: Proceedings of the International Conference on Computer Systems and Technologies (2007)
Schaeffer, J., Brockington, M.G.: The APHID Parallel αβ algorithm. In: Proceedings of the 8th IEEE Symposium on Parallel and Distributed Processing, p. 428 (1996)
Hewett, R., Ganesan, K.: Consistent Linear speedup in Parallel Alpha-beta Search. In: Proceedings of the ICCI 1992, Fourth International Conference on Computing and Information, pp. 237–240 (1992)
Hopp, H., Sanders, P.: Parallel Game Tree Search on SIMD Machines. In: Ferreira, A., Rolim, J.D.P. (eds.) IRREGULAR 1995. LNCS, vol. 980, pp. 349–361. Springer, Heidelberg (1995)
Sanders, P.: Efficient Emulation of MIMD behavior on SIMD Machines. In: Proceedings of the International Conference on Massively Parallel Processing Applications and Development, pp. 313–321 (1995)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, pp. 163–171. Prentice Hall, Englewood Cliffs (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rocki, K., Suda, R. (2010). Parallel Minimax Tree Searching on GPU. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2009. Lecture Notes in Computer Science, vol 6067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14390-8_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-14390-8_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14389-2
Online ISBN: 978-3-642-14390-8
eBook Packages: Computer ScienceComputer Science (R0)