Advertisement

Effect of the Block Occupancy in GPGPU over the Performance of Particle Swarm Algorithm

  • Miguel Cárdenas-Montes
  • Miguel A. Vega-Rodríguez
  • Juan José Rodríguez-Vázquez
  • Antonio Gómez-Iglesias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)

Abstract

Diverse technologies have been used to accelerate the execution of Evolutionary Algorithms. Nowadays, the GPGPU cards have demonstrated a high efficiency in the improvement of the execution times in a wide range of scientific problems, including some excellent examples with diverse categories of Evolutionary Algorithms. Nevertheless, the studies in depth of the efficiency of each one of these technologies, and how they affect to the final performance are still scarce. These studies are relevant in order to reduce the execution time budget, and therefore affront higher dimensional problems. In this work, the improvement of the speed-up face to the percentage of threads used per block in the GPGPU card is analysed. The results conclude that a correct election of the occupancy —number of the threads per block— contributes to win an additional speed-up.

Keywords

GPGPU Performance Analysis Particle Swarm Algorithm (PSO) Schwefel Problem 1.2 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st edn. Addison-Wesley Professional, Reading (July 2010)Google Scholar
  2. 2.
    Kirk, D.B., Hwu, W.: Programming Massively Parallel Processors: A Hands-on Approach, 1st edn. Morgan Kaufmann, San Francisco (February 2010)Google Scholar
  3. 3.
    Pospíchal, P., Schwarz, J., Jaros, J.: Parallel genetic algorithm solving 0/1 knapsack problem running on the gpu. In: 16th International Conference on Soft Computing MENDEL, Brno University of Technology, pp. 64–70 (2010)Google Scholar
  4. 4.
    Pospíchal, P., Jaros, J., Schwarz, J.: Parallel genetic algorithm on the CUDA architecture. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 442–451. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Vidal, P., Alba, E.: Cellular genetic algorithm on graphic processing units. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. Studies in Computational Intelligence, vol. 284, pp. 223–232. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Franco, M.A., Krasnogor, N., Bacardit, J.: Speeding up the evaluation of evolutionary learning systems using gpgpus. In: GECCO 2010: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1039–1046. ACM, New York (2010)Google Scholar
  7. 7.
    Zhou, Y., Tan, Y.: Particle swarm optimization with triggered mutation and its implementation based on gpu. In: Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2010, Portland, Oregon, USA, July 7-11, pp. 1–8. ACM, New York (2010)Google Scholar
  8. 8.
    Zhou, Y., Tan, Y.: Gpu-based parallel particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, May 18-21, pp. 1493–1500. IEEE, Los Alamitos (2009)CrossRefGoogle Scholar
  9. 9.
    Luong, T.V., Melab, N., Talbi, E.G.: Gpu-based island model for evolutionary algorithms. In: Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2010, Portland, Oregon, USA, July 7-11, pp. 1089–1096. ACM, New York (2010)Google Scholar
  10. 10.
    Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. Evolutionary Computation 6(5), 443–462 (2002)CrossRefGoogle Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)Google Scholar
  12. 12.
    Eberhart, R.C.: Computational Intelligence: Concepts to Implementations. Morgan Kaufmann Publishers Inc., San Francisco (2007)CrossRefzbMATHGoogle Scholar
  13. 13.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)Google Scholar
  14. 14.
    Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the cec’2010 special session and competition on large-scale global optimization. Technical report, Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China (USTC), Electric Building No. 2, Room 504, West Campus, Huangshan Road, Hefei 230027, Anhui, China (2009)Google Scholar
  15. 15.
    Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark functions for the CEC 2008 special session and competition on large scale global optimization. Technical report, Nature Inspired Computation and Applications Laboratory, USTC, China (2007)Google Scholar
  16. 16.
    Matsumoto, M., Nishimura, T.: Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8(1), 3–30 (1998)CrossRefzbMATHGoogle Scholar
  17. 17.
    Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers, 4th edn. John Wiley & Sons, Chichester (May 2006)zbMATHGoogle Scholar
  18. 18.
    Sheskin, D.: Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca Raton (2004)zbMATHGoogle Scholar
  19. 19.
    García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec’2005 special session on real parameter optimization. J. Heuristics 15(6), 617–644 (2009)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Miguel Cárdenas-Montes
    • 1
  • Miguel A. Vega-Rodríguez
    • 2
  • Juan José Rodríguez-Vázquez
    • 1
  • Antonio Gómez-Iglesias
    • 3
  1. 1.Department of Fundamental ResearchCentro de Investigaciones Energéticas Medioambientales y TecnológicasMadridSpain
  2. 2.ARCO Research Group, Dept. Technologies of Computers and CommunicationsUniversity of ExtremaduraCáceresSpain
  3. 3.National Laboratory of FusionCentro de Investigaciones Energéticas Medioambientales y TecnológicasMadridSpain

Personalised recommendations