Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore

  • Victoria María Sanz School of Computer Science, National University of La Plata, Argentina

Abstract

The contribution of the thesis is the development of two parallel Best-First Search algorithms, one that is suitable for execution on shared-memory machines (multicore), and another one that is suitable for execution on distributed memory machines (cluster). The former is based on the adaptation of the HDA* (Hash Distributed A*) algorithm for multicore machines proposed by (Burns et al., 2010), while the latter is based on the HDA* (Hash Distributed A*) algorithm proposed by (Kishimoto, et al., 2013). The implemented algorithms incorporate parameters and/or techniques that improve their performance, with respect to the original algorithms proposed by the authors mentioned above.

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References

[1] Burns, E., Lemons, S., Ruml, W. & Zhou, R., 2010. Best-First Heuristic Search for Multicore Machines.
Journal of Artificial Intelligence Research, Vol. 39, pp. 689-743.
[2] Hart, P., Nilsson, N. & Raphael, B., 1968. A Formal Basis for the Heuristic Determination of Minimum
Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), pp. 100-107.
[3] Kishimoto, A., Fukunaga, A. & Botea, A., 2013. Evaluation of a simple, scalable, parallel Best-First
Search strategy. Artificial Intelligence, Vol. 195, pp. 222–248.
[4] Russel, S. & Norvig, P., 2003. Artificial Intelligence: A Modern Approach. Second edition. NJ: Prentice
Hall.
[5] Sanz, V. M., 2015. Análisis de rendimiento y optimización de algoritmos paralelos Best-First Search
sobre multicore y cluster de multicore.
Available at: http://sedici.unlp.edu.ar/handle/10915/44478
Published
2016-04-01
How to Cite
Sanz, V. M. (2016). Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore. Journal of Computer Science and Technology, 16(01), p. 61-62. Retrieved from http://journal.info.unlp.edu.ar/JCST/article/view/510
Section
Thesis Overview