Abstract
We investigate the performance of a highly parallel Particle Swarm Optimization (PSO) algorithm implemented on the graphics processing unit (GPU). In order to achieve this high degree of parallelism we implement a collaborative multi-swarm PSO algorithm on the GPU which relies on the use of many swarms rather than just one. We choose to apply our PSO algorithm against a real-world application: the task matching problem in a heterogeneous distributed computing environment. Due to the potential for large problem sizes with high dimensionality, the task matching problem proves to be very thorough in testing the GPU’s capabilities for handling PSO. Our results show that the GPU offers a high degree of performance and achieves a maximum of 37 times speedup over a sequential implementation when the problem size in terms of tasks is large and many swarms are used.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Change, C.W., Lee, Y.C., Lee, C.N., Chou, T.Y.: Ant colony optimisation for task matching and scheduling. IEEE Proc. Comput. Digit. Tech. 153(6), 373–380 (1997)
de Veronese, P.L., Krohling, R.A.: Swarm’s flight: accelerating the particles using C-CUDA. In: IEEE Congress on Evolutionary Computation, Trondheim, pp. 3264–3270 (2009)
Flynn, M.: Some computer organizations and their effectiveness. IEEE Trans. Comput. C-21(9), 948–960 (1972)
Freund, R.F., Gherrity, M., Ambrosius, S., Campbell, M., Halderman, M., Hensgen, D., Keith, E., Kidd, T., Kussow, M., Lima, J.D., Mirabile, F., Moore, L., Rust, B., Siegel, H.J.: Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet. In: The Seventh IEEE Heterogeneous Computing Workshop, Orlando, pp. 184–199 (1998)
Kang, Q., He, H., Wang, H., Jiang, C.: A novel discrete particle swarm optimization algorithm for job scheduling in grids. In: Fourth International Conference on Natural Computation, pp. 401–405. IEEE, Jinan (2008)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE, Perth (1995)
Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer with local search. In: IEEE Congress on Evolutionary Computation, Edinburgh, pp. 522–528 (2005)
Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.F.: Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems. In: The Eighth IEEE Heterogeneous Computing Workshop, San Juan, pp. 30–44 (1999)
Mussi, L., Cagnoni, S., Daolio, F.: GPU-based road sign detection using particle swarm optimization. In: Ninth International Conference on Intelligent Systems Design and Applications, pp. 152–157. IEEE, Pisa (2009)
Mussi, L., Daolio, F., Cagnoni, S.: Evaluation of parallel particle swarm optimization algorithms within the CUDA architecture. Inform. Sci. 181(20), 4642–4657 (2011)
NVIDIA: CUDA Programming Guide Version 3.1. NVIDIA, Santa Clara (2010)
NVIDIA: CUDA C Best Practices Guide. NVIDIA, Santa Clara (2011)
Nvidia: Nvidia CUDA developer zone. http://developer.nvidia.com/category/zone/cuda-zone (2011)
Sadasivam, G.S., Rajendran, V.: An efficient approach to task scheduling in computational grids. Int. J. Comput. Sci. Appl. 6(1), 53–69 (2009)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE, Anchorage (1998)
Solomon, S., Thulasiraman, P., Thulasiram, R.K.: Collaborative multi-swarm PSO for task matching using graphics processing units. In: 13th Annual Conference on Genetic and Evolutionary Computation (GECCO), Dublin, pp. 1563–1570 (2011)
Tasgetiren, M.F., Liang, Y.C., Sevkli, M., Gencyilmaz, G.: Particle swarm optimization and differential evolution for the single machine total weighted tardiness problem. Int. J. Prod. Res. 44(22), 4737–4754 (2006)
van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)
Vanneschi, L., Codecasa, D., Mauri, G.: An empirical comparison of parallel and distributed particle swarm optimization methods. In: The Genetic and Evolutionary Computation Conference, Portland, pp. 15–22 (2010)
Wang, L., Siegel, H.J., Roychowdhury, V.P., Maciejewski, A.A.: Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. J. Parallel Distr. Comput. 47(1), 8–22 (1997)
Yan-Ping, B., Wei, Z., Jin-Shou, Y.: An improved PSO algorithm and its application to grid scheduling problem. In: International Symposium on Computer Science and Computational Technology, pp. 352–355. IEEE, Shanghai (2008)
Zhang, L., Chen, Y., Sun, R., Jing, S., Yang, B.: A task scheduling algorithm based on PSO for grid computing. Int. J. Comput. Intell. Res. 4(1), 37–43 (2008)
Zhou, Y., Tan, Y.: GPU-based parallel particle swarm optimization. In: IEEE Congress on Evolutionary Computation, Trondheim, pp. 1493–1500 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Solomon, S., Thulasiraman, P., Thulasiram, R.K. (2013). Scheduling Using Multiple Swarm Particle Optimization with Memetic Features on Graphics Processing Units. In: Tsutsui, S., Collet, P. (eds) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-37959-8_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37958-1
Online ISBN: 978-3-642-37959-8
eBook Packages: Computer ScienceComputer Science (R0)