Optimization of parallel Genetic Algorithms for nVidia GPUs

2011 IEEE Congress of Evolutionary Computation, CEC 2011

Volumn , Issue , 2011, Pages 803-811

  Wahib, Mohamed ^a   Munawar, Asim ^a   Munetomo, Masaharu ^a   Akama, Kiyoshi ^a  

^a HOKKAIDO UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTING EFFICIENCY; DESIGN CHALLENGES; DESIGN ISSUES; EFFICIENT IMPLEMENTATION; GENERAL-PURPOSE COMPUTING; GRAPHICAL PROCESSING UNITS; MULTIPLE THREADS; PARALLEL GENETIC ALGORITHMS; PARALLEL MODELS; PARALLEL SYSTEM; RAPID CHANGES;

DESIGN; GASES; GENETIC ALGORITHMS; LEGACY SYSTEMS; OPTIMIZATION; PARALLEL ARCHITECTURES; PROGRAM PROCESSORS;

PARALLEL PROCESSING SYSTEMS;

EID: 80052017581     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2011.5949701     Document Type: Conference Paper

References (21)

1. E. Alba, Parallel Metaheuristics: A New Class of Algorithms. Wiley-Interscience, 2005.
2. E. D. Demaine, I. Foster, C. Kesselman, and M. Snir, Generalized communicators in the message passing interface, IEEE Trans. Parallel Distrib. Syst., vol. 12, no. 6, pp. 610-616, 2001. (Pubitemid 32611066)
3. Scaling Genetic Algorithms using MapReduce. IEEE Press, October 2009.
4. K. Asanovic, R. Bodik, J. Demmel, T. Keaveny, K. Keutzer, J. Kubiatowicz, N. Morgan, D. Patterson, K. Sen, J. Wawrzynek, D. Wessel, and K. Yelick, A view of the parallel computing landscape, Commun. ACM, vol. 52, no. 10, pp. 56-67, 2009.
5. nVidia CUDA Programming Guide 2.1 - CUDA 2.1 SDK Documentation, August 2008. [Online]. Available: http://www.nvidia.com/cuda/
6. J. Stratton, S. Stone, and W. Hwu, Mcuda: An efficient implementation of cuda kernels on multi-cores, University of Illinois at Urbana-Champaign, Tech. Rep. IMPACT-08-01, March 2008.
7. E. Cantu-Paz, Efficient and Accurate Parallel Genetic Algorithms. Norwell, MA, USA: Kluwer Academic Publishers, 2000.
8. Q. Yu, C. Chen, and Z. Pan, Parallel Genetic Algorithms on Programmable Graphics Hardware, July 2005, vol. 3612, pp. 1051-1059.
9. E. Alba and B. Dorronsoro, Cellular Genetic Algorithms, ser. Operations Research/Computer Science Interfaces Series. Springer, 2008, vol. 42.
10. H. Hoos and T. Stutzle, Satlib: An online resource for research on sat. IOS Press, 2000, pp. 283-292, sATLIB is available online at www.satlib.org.
11. D. Patterson, The top 10 innovations in the new nvidia fermi architecture, and the top 3 next challenges, Parallel Computing Research Laboratory (Par Lab), U. C. Berkeley, Tech. Rep., September 2009.
12. A. Munawar, M. Wahib, M. Munetomo, and K. Akama, Hybrid of genetic algorithm and local search to solve max-sat problem using nvidia cuda framework, Genetic Programming and Evolvable Machines, vol. 10, no. 4, pp. 391-415, 2009.
13. K. Fok, T. Wong, and M. Wong, Evolutionary computing on consumer graphics hardware, IEEE Intelligent Systems, vol. 22, no. 2, pp. 69-78, 2007. (Pubitemid 46495748)
14. J. Li, X. Wang, R. He, and Z. Chi, An efficient fine-grained parallel genetic algorithm based on gpu-accelerated, in NPC'07: Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops. IEEE Computer Society, 2007, pp. 855-862.
15. M. Tomassini, Spatially structured evolutionary algorithms. Berlin: Springer, 2005.
16. M. L. Wong, Parallel multi-objective evolutionary algorithms on graphics processing units, in GECCO'09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference. ACM, 2009, pp. 2515-2522.
17. S. Tsutsui and N. Fujimoto, Solving quadratic assignment problems by genetic algorithms with gpu computation: a case study, in GECCO'09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference. ACM, 2009, pp. 2523-2530.
18. M. Wong and T. Wong, Implementation of parallel genetic algorithms on graphics processing units, in Intelligent and Evolutionary Systems, 2009, pp. 197-216.
19. W. Zhu, A study of parallel evolution strategy: pattern search on a gpu computing platform, in GEC'09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation. ACM, 2009, pp. 765-772.
20. O. Maitre, L. A. Baumes, N. Lachiche, A. Corma, and P. Collet, Coarse grain parallelization of evolutionary algorithms on gpgpu cards with easea, in GECCO'09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation. ACM, 2009, pp. 1403-1410.
21. J. Dean and S. Ghemawat, Mapreduce: simplified data processing on large clusters, Commun. ACM, vol. 51, no. 1, pp. 107-113, 2008.