Abstract
We introduce a well-optimized implementation of PSO algorithm based on, Compute Unified Device Architecture (CUDA), using global neighborhood topology with extremely large swarms (greater than 1000 particles). The algorithm optimization is based on effective data organization in GPU memory such as transfer and thread optimization, pinned memory and the zero-copy mechanism usage. Experimental results show that the implementation on GPU is significantly faster than implementation on CPU.
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Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Swarm Intelligence Symposium, SIS 2007, pp. 120–127. IEEE, April 2007
Cagnoni, S., Bacchini, A., Mussi, L.: OpenCL implementation of particle swarm optimization: a comparison between multi-core CPU and GPU performances. In: Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 406–415. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29178-4_41
Calazan, R., Nedjah, N., de Macedo Mourelle, L.: Parallel gpu-based implementation of high dimension particle swarm optimizations. In: 2013 IEEE Fourth Latin American Symposium on Circuits and Systems (LASCAS), pp. 1–4, February 2013
Calazan, R.M., Nedjah, N., Macedo Mourelle, L.: Swarm grid: a proposal for high performance of parallel particle swarm optimization using GPGPU. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012. LNCS, vol. 7333, pp. 148–160. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31125-3_12
Cardenas-Montes, M., Vega-Rodriguez, M.A., Rodriguez-Vazquez, J.J., Gomez-Iglesias, A.: Accelerating particle swarm algorithm with gpgpu. In: 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing, pp. 560–564, February 2011
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 2, pp. 1671–1676 (2002)
Kennedy, J., Mendes, R.: Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 36(4), 515–519 (2006)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Laguna-Sánchez, G.A., Olguín-Carbajal, M., Cruz-Cortés, N., Barrón-Fernández, R., Álvarez-Cedillo, J.A.: Comparative study of parallel variants for a particle swarm optimization algorithm implemented on a multithreading gpu. J. Appl. Res. Technol. 7(3), 292–307 (2009)
Li, J., Wan, D., Chi, Z., Hu, X.: An efficient fine-grained parallel particle swarm optimization method based on gpu-acceleration. Int. J. Innov. Comput. Inf. Control 3(6(B)), 1707–1714 (2007)
Mussi, L., Daolio, F., Cagnoni, S.: Evaluation of parallel particle swarm optimization algorithms within the cuda architecture. Inf. Sci. 181(20), 4642–4657 (2011). specialIssueonInterpretableFuzzySystems, http://www.sciencedirect.com/science/article/pii/S0020025510004263
Mussi, L., Nashed, Y.S., Cagnoni, S.: GPU-based asynchronous particle swarm optimization. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, NY, USA, pp. 1555–1562 (2011). http://doi.acm.org/10.1145/2001576.2001786
nVidia.com: CUDA C Best Practices Guide, DG-05603-001 v6.0 edn. (February 2014)
de P. Veronese, L., Krohling, R.: Swarm’s flight: accelerating the particles using c-cuda. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 3264–3270 (May 2009)
Solomon, S., Thulasiraman, P., Thulasiram, R.: Collaborative multi-swarm pso for task matching using graphics processing units. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, NY, USA, pp. 1563–1570 (2011). http://doi.acm.org/10.1145/2001576.2001787
Wachowiak, M.P., Foster, A.E.L.: GPU-based asynchronous global optimization with particle swarm. In: Journal of Physics Conference HPCS 2012, vol. 385 (2012)
Wang, W.: Particle swarm optimization on GPU. In: Workshop on GPU Supercomputing. Center for Quantum Science and Engineering National Taiwan University (2009)
Zhou, Y., Tan, Y.: GPU-based parallel particle swarm optimization. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1493–1500, May 2009
Zhou, Y., Tan, Y.: Particle swarm optimization with triggered mutation and its implementation based on gpu. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, NY, USA, pp. 1–8 (2010). http://doi.acm.org/10.1145/1830483.1830485
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Kołodziejczyk, J., Sychel, D., Bera, A. (2017). Improved CUDA PSO Based on Global Topology. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_31
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DOI: https://doi.org/10.1007/978-3-319-59063-9_31
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