Abstract
This paper presents methods for implementing SOMA (Self-Organizing Migrating Algorithm) in parallel with the CUDA (Compute Unified Device Architecture) system that can be used to perform the dominant of up-speed when using SOMA algorithm. SOMA has many individual points to find the global minimum which is the key for paralleling this system because each individual can work separately and share the position for all when it moves. Nowadays, due to the humongous size of data and the limitation of the process in single Central Processing Unit (CPU), it becomes impossible to deal with. As a result of these limitations, we need more CPUs working at the same time to do the same job or take advantage of the power of parallel processing in GPGPU (General-Purpose graphics processing unit). Additionally, many supercomputers are built with the need of Parallel Processing in order to meet the power of hardware. Based on the architecture of CUDA, it can handle the threads in SOMA independence. We use two methods with different architecture in CUDA to help SOMA run much faster than single threading method. This paper also uses some techniques to help SOMA work more effective.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ghorpade, J., et al.: GPGPU processing in CUDA architecture. arXiv preprint arXiv:1202.4347 (2012)
Kadlec, P., Raida, Z.: Multi-objective self-organizing migrating algorithm. In: Davendra, D., Zelinka, I. (eds.) Self-Organizing Migrating Algorithm: Methodology and Implementation. SCI, vol. 626, pp. 83–103. Springer, Heidelberg (2016). doi:10.1007/978-3-319-28161-2_4
Tran, T.D., Zelinka, I.: Using method of artificial intelligence to optimize and control chemical reactor. In: Proceedings of the 15th International Conference on Soft Computing (MENDEL 2009), Brno, Czech Republic, June 2009
Tran, T.D.: Investigation on evolutionary computation techniques of a nonlinear system. Modell. Simul. Eng., 2011, Article ID 496732, 21 pages (2011). doi:10.1155/2011/496732
Tran, T.D., Zelinka, I.: Investigation on optimization of process parameters and chemical reactor geometry by evolutionary algorithms. In: 23rd European Conference on Modelling and Simulation – ECMS 2009, Madrid, Spain, 9th June - 12th 2009 (2009)
Zelinka, I.: Umělá inteligence v problémech globální optimalizace, BEN, Praha (2002). ISBN 80-7300-069-5
Zelinka, I., Davendra, D.D., Chadli, M., Senkerik, R., Dao, T.T., Skanderova, L.: Evolutionary dynamics as the structure of complex networks. In: Zelinka, I., Snasel, V., Abraham, A. (eds.) Handbook of Optimization: From Classical to Modern Approach. ISRL, vol. 38, pp. 215–243. Springer, Heidelberg (2013)
Zelinka, I.: SOMA—self organizing migrating algorithm. In: Babu, B.V., Onwubolu, G. (eds.) New Optimization Techniques in Engineering, pp. 167–218. Springer, New York (2004)
Zelinka, I.: SOMA. In: Babu, B.V., Onwubolu, G. (eds.) New Optimization Techniques in Engineering. Springer - Verlag (2004). ISBN 3-540-20167-X, Chap. 7
Acknowledgement
This work is part of the Science activities of MERLIN at the Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Dao, T.T., Toan, N.M., Duy, V.H., Zelinka, I. (2017). Accelerate SOMA Using Parallel Processing in GPGPU. In: Duy, V., Dao, T., Kim, S., Tien, N., Zelinka, I. (eds) AETA 2016: Recent Advances in Electrical Engineering and Related Sciences. AETA 2016. Lecture Notes in Electrical Engineering, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-319-50904-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-50904-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50903-7
Online ISBN: 978-3-319-50904-4
eBook Packages: EngineeringEngineering (R0)