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GPU Parallel Computation in Bioinspired Algorithms: A Review

  • Chapter
Advances in Intelligent Modelling and Simulation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 422))

Abstract

As bioinspired methods usually need a high amount of computational resources, parallelization is an interesting alternative in order to decrease the execution time and to provide accurate results. In this sense, recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also referred as GPU computation. Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units that possess more memory bandwidth and computational capability than central processing units (CPUs). As GPUs are available in personal computers, and they are easy to use and manage through several GPU programming languages, graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. This chapter reviews the use of GPUs to solve scientific problems, giving an overview of current software systems.

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References

  1. Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley (2005) ISBN: 978-0-471-67806-9, http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471678066.html

  2. Alba, E., Nebro, A.J., Luna, F.: Advances in parallel heterogeneous genetic algorithms for continuous optimization. International Journal of Applied Mathematics and Computer Science 14(3), 101–117 (2004)

    MathSciNet  Google Scholar 

  3. Bernhard, F., Keriven, R.: Spiking neurons on gpus. In: International Conference on Computational Science, Workshop General Purpose Computation on Graphics Hardware (GPGPU): Methods, Algorithms and Applications, Reading, UK (2006)

    Google Scholar 

  4. billconan, kavinguy: Ann libraries to develop on gpus (2011), http://www.codeproject.com/KB/graphics/GPUNN.aspx

  5. Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., Hanrahan, P.: Brook for gpus: stream computing on graphics hardware. ACM Trans. Graph. 23, 777–786 (2004), Doi http://doi.acm.org/10.1145/1015706.1015800

    Article  Google Scholar 

  6. Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10 (1998)

    Google Scholar 

  7. Cotta, C., Talbi, E.-G., Alba, E.: Parallel hybrid metaheuristics. In: Parallel Metaheuristics, a New Class of Algorithms, pp. 347–370. John Wiley (2005)

    Google Scholar 

  8. Crainic, T.G., Gendreau, M.: Towards a taxonomy of parallel tabu search heuristics (1997)

    Google Scholar 

  9. Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic, pp. 11–32. McGraw-Hill Ltd., UK (1999), http://dl.acm.org/citation.cfm?id=329055.329062

    Google Scholar 

  10. Fan, Z., Qiu, F., Kaufman, A., Yoakum-Stover, S.: Gpu cluster for high performance computing. In: SC Conference, p. 47 (2004), Doi http://doi.ieeecomputersociety.org/10.1109/SC.2004.26

  11. Fernández, F., Tomassini, M., Vanneschi, L.: An empirical study of multipopulation genetic programming. Genetic Programming and Evolvable Machines 4, 21–51 (2003), http://dx.doi.org/10.1023/A:1021873026259 , doi:10.1023/A:1021873026259

    Article  MATH  Google Scholar 

  12. García-López, F., Melián-Batista, B., Moreno-Pérez, J.A., Moreno-Vega, J.M.: Parallelization of the scatter search for the p-median problem. Parallel Computing 29(5), 575–589 (2003), http://www.sciencedirect.com/science/article/pii/S01678191030%00437 , doi:10.1016/S0167-8191(03)00043-7

    Article  Google Scholar 

  13. García-López, F., Melián-batista, B., Moreno-pérez, J.A., Moreno-vega, J.M.: The parallel variable neighborhood search for the p-median problem. Journal of Heuristics 8, 200–222 (2004)

    Google Scholar 

  14. Genetic, D.B., Miki, M., Hiroyasu, T., Yoshida, T., Fushimi, T.: Parallel simulated annealing with adaptive temperature. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics 2002, pp. 1–6 (2002)

    Google Scholar 

  15. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Norwell (1997)

    Book  MATH  Google Scholar 

  16. Harding, S., Banzhaf, W.: Fast genetic programming and artificial developmental systems on gpus. In: 21st International Symposium on High Performance Computing Systems and Applications, HPCS 2007, p. 2 (2007)

    Google Scholar 

  17. Illinois, U.: The LLVM Compiler Infrastructure. University of Illinois at Urbana-Champaign (2011), http://llvm.org

  18. Janson, S., Merkle, D., Middendorf, M.: Parallel ant colony algorithms, Tech. rep., Parallel Metaheuristics. Wiley Book Series on Parallel and Distributed Computing (2005)

    Google Scholar 

  19. Koomey, J.G., Berard, S., Sanchez, M., Wong, H.: Implications of historical trends in the electrical efficiency of computing. IEEE Annals of the History of Computing 33, 46–54 (2011), Doi http://doi.ieeecomputersociety.org/10.1109/MAHC.2010.28

    Article  MathSciNet  Google Scholar 

  20. Li, J., Wang, X., He, R., Chi, Z.: An efficient fine-grained parallel genetic algorithm based on GPU-Accelerated. In: International Conference on Network and Parallel Computing, pp. 855–862 (2007)

    Google Scholar 

  21. Li, J., Zhang, L., Liu, L.: A parallel immune algorithm based on fine-grained model with gpu-acceleration. In: Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control, ICICIC 2009, pp. 683–686 (2009), Doi http://dx.doi.org/10.1109/ICICIC.2009.44 , http://dx.doi.org/10.1109/ICICIC.2009.44

  22. Luo, Z., Liu, H.: Cellular genetic algorithms and local search for 3-SAT problem on graphic hardware. In: IEEE CEC 2006, pp. 2988–2992 (2006)

    Google Scholar 

  23. Luo, Z., Liu, H., Wu, X.: Artificial neural network computation on graphic process unit. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, vol. 1, pp. 622–626 (2005)

    Google Scholar 

  24. Luong, T.V., Melab, N., Talbi, E.G.: GPU-based Island Model for Evolutionary Algorithms. In: Genetic and Evolutionary Computation Conference (GECCO), Portland, USA (2010)

    Google Scholar 

  25. Madera, J., Alba, E., Ochoa, A.: A parallel island model for estimation of distribution algorithms. In: Lozano, J., Larrańaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation. STUDFUZZ, vol. 192, pp. 159–186 (2006)

    Google Scholar 

  26. Maitre, O., Baumes, L.A., Lachiche, N., Corma, A., Collet, P.: Coarse grain parallelization of evolutionary algorithms on gpgpu cards with easea. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 1403–1410 (2009), http://doi.acm.org/10.1145/1569901.1570089 , http://doi.acm.org/10.1145/1569901.1570089

  27. Martínez-Zarzuela, M., Díaz Pernas, F.J., Díez Higuera, J.F., Rodríguez, M.A.: Fuzzy ART Neural Network Parallel Computing on the GPU. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 463–470. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  28. Martinez-Zarzuela, M., Diaz-Pernas, F., Diez, J., Anton, M., Gonzalez, D., Boto, D., Lopez, F., DelaTorre, I.: Multi-scale neural texture classification using the gpu as a stream processing engine. Machine Vision and Applicactions (in press 2010)

    Google Scholar 

  29. Meuth, R.J., Wunsch, D.C.: A survey of neural computation on graphics processing hardware. In: IEEE 22nd International Symposium on Intelligent Control (ISIC 2007), pp. 524–527 (2007)

    Google Scholar 

  30. Munawar, A., Wahib, M., Munetomo, M., Akama, K.: Hybrid of genetic algorithm and local search to solve max-sat problem using nvidia cuda framework. Genetic Programming and Evolvable Machines 10, 391–415 (2009)

    Article  Google Scholar 

  31. Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E.: Mocell: A cellular genetic algorithm for multiobjective optimization. International Journal of Intelligent Systems, 25–36 (2007)

    Google Scholar 

  32. Oh, K.S., Jung, K.: Gpu implementation of neural networks. Pattern Recognition 37(6), 1311–1314 (2004)

    Article  MATH  Google Scholar 

  33. Parejo, J., Ruiz-Cortés, A., Lozano, S., Fernandez, P.: Metaheuristic optimization frameworks: a survey and benchmarking. In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, pp. 1–35., http://dx.doi.org/10.1007/s00500-011-0754-8 , doi:10.1007/s00500-011-0754-8

  34. Pospichal, P., Jaros, J.: Gpu-based acceleration of the genetic algorithm, Tech. rep., GECOO competition (2009)

    Google Scholar 

  35. Pospichal, 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)

    Chapter  Google Scholar 

  36. Pospichal, 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 2010, pp. 64–70 (2010)

    Google Scholar 

  37. Resende, M.G.C., Ribeiro, C.C.: Parallel greedy randomized adaptive search procedures (2004)

    Google Scholar 

  38. Rudolph, G.: Parallel approaches to stochastic global optimization. In: Joosen, W., Milgrom, E. (eds.) Parallel Computing: From Theory to Sound Practice, pp. 256–267. IOS Press (1992)

    Google Scholar 

  39. Selman, B., Kautz, H.: Domain-independent extensions to gsat: Solving large structured satisfiability problems. In: Proc. IJCAI 1993, pp. 290–295 (1993)

    Google Scholar 

  40. Thompson, C.J., Hahn, S., Oskin, M.: Using modern graphics architectures for general-purpose computing: a framework and analysis. In: Proceedings of the 35th Annual ACM/IEEE International Symposium on Microarchitecture, MICRO 35, pp. 306–317 (2002), http://portal.acm.org/citation.cfm?id=774861.774894

  41. Tsutsui, S., Fujimoto, N.: Solving quadratic assignment problems by genetic algorithms with gpu computation: a case study. In: GECCO 2009, pp. 2523–2530 (2009)

    Google Scholar 

  42. Tsutsui, S., Fujimoto, N.: Aco with tabu search on a gpu for solving qaps using move-cost adjusted thread assignment. In: Krasnogor, N., Lanzi, P.L., Engelbrecht, A., Pelta, D., Gershenson, C., Squillero, G., Freitas, A., Ritchie, M., Preuss, M., Gagne, C., Ong, Y.S., Raidl, G., Gallager, M., Lozano, J., Coello-Coello, C., Silva, D.L., Hansen, N., Meyer-Nieberg, S., Smith, J., Eiben, G., Bernado-Mansilla, E., Browne, W., Spector, L., Yu, T., Clune, J., Hornby, G., Wong, M.L., Collet, P., Gustafson, S., Watson, J.P., Sipper, M., Poulding, S., Ochoa, G., Schoenauer, M., Witt, C., Auger, A. (eds.) GECCO 2011: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1547–1554 (2011)

    Google Scholar 

  43. 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. SCI, vol. 284, pp. 223–232. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  44. Wong, M., Wong, T.: Parallel hybrid genetic algorithms on Consumer-Level graphics hardware. In: IEEE CEC 2006, pp. 2973–2980 (2006)

    Google Scholar 

  45. Wong, M.L., Wong, T.T.: Implementation of Parallel Genetic Algorithms on Graphics Processing Units. In: Gen, M., Green, D., Katai, O., McKay, B., Namatame, A., Sarker, R.A., Zhang, B.-T. (eds.) Intelligent and Evolutionary Systems. SCI, vol. 187, pp. 197–216. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  46. Wong, M., Wong, T., Fok, K.: Parallel evolutionary algorithms on graphics processing unit. In: IEEE CEC 2005, vol. 3, pp. 2286–2293 (2005)

    Google Scholar 

  47. Yu, Q., Chen, C., Pan, Z.: Parallel Genetic Algorithms on Programmable Graphics Hardware. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 1051–1059. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  48. Zhang, S., He, Z.: Implementation of Parallel Genetic Algorithm Based on CUDA. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. LNCS, vol. 5821, pp. 24–30. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Architecture and Computer Technology, CITIC (University of Granada), Granada, Spain

    M. G. Arenas, G. Romero, A. M. Mora, P. A. Castillo & J. J. Merelo

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  1. M. G. Arenas
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  2. G. Romero
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  3. A. M. Mora
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  4. P. A. Castillo
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  5. J. J. Merelo
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Correspondence to M. G. Arenas .

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Editors and Affiliations

  1. Institute of Computer Science, Cracow University of Technology, ul. Warszawska 24, Cracow, 31-155, Poland

    Joanna Kołodziej

  2. , Department of Electrical and Computer, North Dakota State University, Fargo, 58108-6050, USA

    Samee Ullah Khan

  3. Department of Computer Science, Cracow University of Technology, ul. Warszawska 24, Cracow, 31-155, Poland

    Tadeusz Burczy´nski

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Arenas, M.G., Romero, G., Mora, A.M., Castillo, P.A., Merelo, J.J. (2012). GPU Parallel Computation in Bioinspired Algorithms: A Review. In: Kołodziej, J., Khan, S., Burczy´nski, T. (eds) Advances in Intelligent Modelling and Simulation. Studies in Computational Intelligence, vol 422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30154-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-30154-4_6

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