Abstract
Parallel implementation of Self-organizing Map (SOM) has been studied since last decade. Graphic Processing Unit (GPU) is one of most promising architecture for executing SOM in parallel. However, there are performances issues are highlighted when imposing larger mapping and dataset size onto parallel SOM that executed on the GPU. Alternatively, heterogeneous systems that soldered GPU together with Central Processing Unit (CPU) are introduced in order to improve communication between CPU and GPU. Shared Virtual Memory (SVM) is one of features in OpenCL 2.0 which allows the host and the device to share a common virtual address range. Thus this research proposes to introduce a parallel SOM architecture that suitable for both GPU and heterogeneous system with the aim to compare the performance in term of computation time. The architecture comprises of three kernels that executed on two different platforms (1) discrete GPU platform and (2) heterogeneous system platform that tested using SVM buffers. The experimental results show the parallel SOM running on heterogeneous platform has significant improvement in computation time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Perelygin, K., Lam, S., Wu, X.: Graphics Processing Units and Open Computing Language for parallel computing. Comput. Electr. Eng. 40(1), 241–251 (2014)
Kirk, D.B., Hwu, W.W.: Programming Massively Parallel Processors. Elsevier (2013)
Wittek, P., Darányi, S.: Accelerating text mining workloads in a MapReduce-based distributed GPU environment. J. Parallel Distrib. Comput. 73(2), 198–206 (2013)
Lachmair, J., Merényi, E., Porrmann, M., Rückert, U.: A reconfigurable neuroprocessor for self-organizing feature maps. Neurocomputing 112, 189–199 (2013)
Gajdos, P., Platos, J.: GPU based parallelism for self-organizing map. In: Advances in Intelligent Systems and Computing, IHCI 2011, vol. 179, pp. 3–12 (2013)
Hasan, S., Shamsuddin, S.M., Lopes, N.: Machine learning big data framework and analytics for big data problems. Int. J. Adv. Soft Comput. Appl. 6(2), 1–17 (2014)
McConnell, S., Sturgeon, R., Henry, G., Mayne, A., Hurley, R.: Scalability of self-organizing maps on a GPU cluster using OpenCL and CUDA. J. Phys. Conf. Ser. 341, 12018 (2012)
Moraes, F.C., Botelho, S.C., Filho, N.D., Gaya, J.F.O.: Parallel high dimensional self organizing maps using CUDA. In: 2012 Brazilian Robotics Symposium Latin American Robotics Symposium, pp. 302–306 (Oct. 2012)
Khan, S.Q., Ismail, M.A.: Design and implementation of parallel SOM model on GPGPU. In: 2013 5th International Conference Computer Science Information Technology, pp. 233–237 (Mar. 2013)
Wang, H., Zhang, N., Créput, J.-C.: A Massive Parallel Cellular GPU Implementation of Neural Network to Large Scale Euclidean TSP. In: Castro, F., Gelbukh, A., González, M. (eds.) Advances in Soft Computing and Its Applications: 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Mexico City, Mexico, 24–30 November 2013, Proceedings, Part II, pp. 118–129. Springer, Berlin, Heidelberg (2013)
Faro, A., Giordano, D., Palazzo, S.: Integrating unsupervised and supervised clustering methods on a GPU platform for fast image segmentation. In: 2012 3rd International Conference Image Processing Theory, Tools Applications IPTA 2012, pp. 85–90 (2012)
Khronos OpenCL: OpenCL Specification (2014)
Brodtkorb, A.R., Hagen, T.R., Sætra, M.L.: Graphics processing unit (GPU) programming strategies and trends in GPU computing. J. Parallel Distrib. Comput. 73(1), 4–13 (2013)
Mukherjee, S., Sun, Y., Blinzer, P., Ziabari, A.K., Kaeli, D.: A comprehensive performance analysis of HSA and OpenCL 2.0. In: 2016 IEEE International Symposium Performance Analysis System Software (April, 2016)
Acknowledgements
This work was funded by Ministry of Higher Education (MOHE) of Malaysia, under the Fundamental Research Grant Scheme (FRGS), grant no. FRGS/81/2015 and Academic Staff Bumiputera Training Scheme (SLAB). The authors also would like to thank the Universiti Teknologi MARA for supporting this study.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Khalid, N.E.B.A., Mustapha, M.F.B., Ismail, A.B., Manaf, M.B. (2017). Parallel Self-organizing Map Using Shared Virtual Memory Buffers. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-56660-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56659-7
Online ISBN: 978-3-319-56660-3
eBook Packages: EngineeringEngineering (R0)