Many-threaded implementation of differential evolution for the CUDA platform

Genetic and Evolutionary Computation Conference, GECCO'11

Volumn , Issue , 2011, Pages 1595-1602

  Krömer, Pavel ^a   Snášel, Václav ^a   Platoš, Jan ^a   Abraham, Ajith ^a  

^a VSB TECHNICAL UNIVERSITY OF OSTRAVA   (Czech Republic)

Author keywords

CUDA; Differential evolution; Parallelization; Scheduling

Indexed keywords

BENCHMARK FUNCTIONS; COMBINATORIAL OPTIMIZATION PROBLEMS; CUDA; DIFFERENTIAL EVOLUTION; HEURISTIC OPTIMIZATION; MULTI-CORE SYSTEMS; NP-HARD; PARALLEL IMPLEMENTATIONS; PARALLELIZATIONS; REAL-WORLD PROBLEM;

COMBINATORIAL OPTIMIZATION; COMPUTATIONAL EFFICIENCY; DATA STRUCTURES; EVOLUTIONARY ALGORITHMS; OPTIMIZATION;

BIOLOGY;

EID: 84860421849     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2001576.2001791     Document Type: Conference Paper

References (24)

1. S. Ali, T. Braun, H. Siegel, and A. Maciejewski. Heterogeneous computing. In J. Urbana and P. Dasgupta, editors, Encyclopedia of Distributed Computing. Kluwer Academic Publishers, Norwell. MA, 2002.
2. M. Andrecut. Fast gpu implementation of sparse signal recovery from random projections. Engineering Letters, 17(3):151-158, 2009.
3. T. D. Braun, H. J. Siegel, N. Beck, L. L. Bölöni, M. Maheswaran, A. I. Reuther, J. P. Robertson, M. D. Theys, B. Yao, D. Hensgen, and R. F. Freund. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput., 61:810-837, June 2001. (Pubitemid 33658407)
4. J. Carretero, F. Xhafa, and A. Abraham. Genetic algorithm based schedulers for grid computing systems. International Journal of Innovative Computing, Information and Control, 3(7), 2007.
5. L. de Veronese and R. Krohling. Differential evolution algorithm on the gpu with c-cuda. In Evolutionary Computation (CEC), 2010 IEEE Congress on, pages 1-7, 2010.
6. T. J. Desell, D. P. Anderson, M. Magdon-Ismail, H. J. Newberg, B. K. Szymanski, and C. A. Varela. An analysis of massively distributed evolutionary algorithms. In IEEE Congress on Evolutionary Computation, pages 1-8. IEEE, 2010.
7. D. Fernandez-Baca. Allocating modules to processors in a distributed system. IEEE Trans. Softw. Eng., 15(11):1427-1436, 1989. (Pubitemid 20652945)
8. G. Hager, T. Zeiser, and G. Wellein. Data access optimizations for highly threaded multi-core cpus with multiple memory controllers. In Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on, pages 1-7, 2008.
9. H. Izakian, A. Abraham, and V. Snasel. Comparison of heuristics for scheduling independent tasks on heterogeneous distributed environments. In Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on, volume 1, pages 8-12, april 2009.
10. P. Kromer, V. Snasel, J. Platos, A. Abraham, and H. Ezakian. Evolving schedules of independent tasks by differential evolution. In S. Caballé, F. Xhafa, and A. Abraham, editors, Intelligent Networking, Collaborative Systems and Applications, volume 329 of Studies in Computational Intelligence, pages 79-94. Springer Berlin / Heidelberg, 2011.
11. W. Langdon and W. Banzhaf. A simd interpreter for genetic programming on gpu graphics cards. In M. O'Neill, L. Vanneschi, S. Gustafson, A. Esparcia Alcázar, I. De Falco, A. Della Cioppa, and E. Tarantino, editors, Genetic Programming, volume 4971 of Lecture Notes in Computer Science, pages 73-85. Springer Berlin / Heidelberg, 2008.
12. E. Munir, J.-Z. Li, S.-F. Shi, and Q. Rasool. Performance analysis of task scheduling heuristics in grid. In Machine Learning and Cybernetics, 2007 International Conference on, volume 6, pages 3093-3098, aug. 2007.
13. NVIDIA. NVIDIA CUDA Programming Guide 3.2. 2010.
14. NVIDIA. CUDA Toolkit 3.2 Math Library Performance. 2011.
15. A. J. Page and T. J. Naughton. Framework for task scheduling in heterogeneous distributed computing using genetic algorithms. Artificial Intelligence Review, 24:137-146, 2004.
16. D. Patnaik, S. P. Ponce, Y. Cao, and N. Ramakrishnan. Accelerator-oriented algorithm transformation for temporal data mining. Network and Parallel Computing Workshops, IFIP International Conference on, 0:93-100, 2009.
17. P. Pospíchal, J. Jarǒs, and J. Schwarz. Parallel genetic algorithm on the cuda architecture. In Applications of Evolutionary Computation, LNCS 6024, pages 442-451. Springer Verlag, 2010.
18. T. Preis, P. Virnau, W. Paul, and J. J. Schneider. Accelerated fluctuation analysis by graphic cards and complex pattern formation in Econophysics. New J. Phys., 11:093024, 2009.
19. K. V. Price, R. M. Storn, and J. A. Lampinen. Differential Evolution A Practical Approach to Global Optimization. Natural Computing Series. Springer-Verlag, Berlin, Germany, 2005.
20. G. Ritchie and J. Levine. A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments. In Proceedings of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group, December 2004.
21. D. Robilliard, V. Marion, and C. Fonlupt. High performance genetic programming on gpu. In Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems, BADS '09, pages 85-94, New York, NY, USA, 2009. ACM.
22. A. YarKhan and J. Dongarra. Experiments with scheduling using simulated annealing in a grid environment. In GRID '02: Proceedings of the Third International Workshop on Grid Computing, pages 232-242, London, UK, 2002. Springer-Verlag.
23. W. Zhu. Massively parallel differential evolution - pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems. Journal of Global Optimization, pages 1-21, 2010. 10.1007/s10898-010-9590-0.
24. W. Zhu and Y. Li. Gpu-accelerated differential evolutionary markov chain monte carlo method for multi-objective optimization over continuous space. In Proceeding of the 2nd workshop on Bio-inspired algorithms for distributed systems, BADS '10, pages 1-8, New York, NY, USA, 2010. ACM.