Abstract
The interest on applications that analyse large volumes of high dimensional data has grown recently. Many of these applications related to the so-called Big Data show different implicit parallelism that can benefit from the efficient use, in terms of performance and power consumption, of Graphics Processing Unit (GPU) accelerators. Although the GPU microarchitectures make possible the acceleration of applications by exploiting parallelism at different levels, the characteristics of their memory hierarchy and the location of GPUs as coprocessors require a careful organization of the memory access patterns and data transferences to get efficient speedups. This paper aims to take advantage of heterogeneous parallel codes on GPUs to accelerate evolutionary approaches in Electroencephalogram (EEG) classification and feature selection in the context of Brain Computer Interface (BCI) tasks. The results show the benefits of taking into account not only the data parallelism achievable by GPUs, but also the memory access patterns, in order to increase the speedups achieved by superscalar cores.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Rupp, R., Kleih, S., Leeb, R., Millan, J., Kübler, A., Müller-Putz, G.: Brain-computer interfaces and assistive technology. In: Grübler, G., Hildt, E. (eds.) Brain-Computer-Interfaces in their Ethical, Social and Cultural Contexts. The International Library of Ethics, Law and Technology, pp. 7–38. Springer, Heidelberg (2014)
Collet, P.: Why GPGPUs for evolutionary computation? In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series, pp. 3–14. Springer, Heidelberg (2013)
Alba, E., Luque, G., Nesmachnow, S.: Parallel metaheuristics: recent advances and new trends. Int. Trans. Oper. Res. 20(1), 1–48 (2013)
Sharma, D., Collet, P.: Implementation techniques for massively parallel multi-objective optimization. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series, pp. 267–286. Springer, Heidelberg (2013)
Wong, M., Cui, G.: Data mining using parallel multi-objective evolutionary algorithms on graphics processing units. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series, pp. 287–307. Springer, Heidelberg (2013)
Baramkar, P., Kulkarni, D.: Review for k-means on graphics processing units (GPU). Int. J. Eng. Res. Technol. 3(6), 1911–1914 (2014)
Wu, R., Zhang, B., Hsu, M.: Clustering billions of data points using gpus. In: Hast, A., Buchty, R., Tao, J., Weidendorfer, J. (eds.) Proceedings of the Combined Workshops on UnConventional High Performance Computing workshop plus Memory Access Workshop, pp. 1–6. UCHPC-MAW 2009. ACM, Ischia, May 2009
Zechner, M., Granitzer, M.: Accelerating k-means on the graphics processor via CUDA. In: Proceedings of the First International Conference on Intensive Applications and Services, INTENSIVE 2009, pp. 7–15. IEEE, Valencia, April 2009
Escobar, J.J., Ortega, J., González, J., Damas, M.: Assessing parallel heterogeneous computer architectures for multiobjective feature selection on EEG classification. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2016. LNCS, vol. 9656, pp. 277–289. Springer, Heidelberg (2016). doi:10.1007/978-3-319-31744-1_25
Escobar, J.J., Ortega, J., González, J., Damas, M.: Improving memory accesses for heterogeneous parallel multi-objective feature selection on eeg classification. In: Proceedings of the 4th International Workshop on Parallelism in Bioinformatics, PBIO 2016. Springer, Grenoble, France (2016)
Khronos Group: Khronos opencl registry. https://www.khronos.org/registry/cl/. Accessed: 30 November 2015
Bellman, R.: Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961)
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., Coello Coello, C.: A survey of multiobjective evolutionary algorithms for data mining: Part I. IEEE Trans. Evol. Comput. 18(1), 4–19 (2014)
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., Coello Coello, C.: A survey of multiobjective evolutionary algorithms for data mining: Part II. IEEE Trans. Evol. Comput. 18(1), 20–35 (2014)
Handl, J., Knowles, J.: Feature subset selection in unsupervised learning via multiobjective optimization. Int. J. Comput. Intell. Res. 2(3), 217–238 (2006)
Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)
Lopez-Novoa, U., Mendiburu, A., Miguel-Alonso, J.: A survey of performance modeling and simulation techniques for accelerator-based computing. IEEE Trans. Parallel Distrib. Syst. 26(1), 272–281 (2015)
Hong, S., Kim, H.: An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness. In: Proceedings of the 36th Annual International Symposium on Computer Architecture, pp. 152–163. ISCA 2009. ACM, New York, June 2009
Dao, T., Kim, J., Seo, S., Egger, B., Lee, J.: A performance model for gpus with caches. IEEE Trans. Parallel Distrib. Syst. 26(7), 1800–1813 (2015)
Kimovski, D., Ortega, J., Ortiz, A., Baños, R.: Leveraging cooperation for parallel multi-objective feature selection in high-dimensional eeg data. Concurrency Comput. Pract. Experience 27(18), 5476–5499 (2015)
Fazendeiro, P., Padole, C., Sequeira, P., Prata, P.: OpenCL implementations of a genetic algorithm for feature selection in periocular biometric recognition. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds.) SEMCCO 2012. LNCS, vol. 7677, pp. 729–737. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35380-2_85
Dhanasekaran, B., Rubin, N.: A new method for GPU based irregular reductions and its application to k-means clustering. In: Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units, pp. 729–737. GPGPU-4, ACM, Newport Beach, March 2011
Gunarathne, T., Salpitikorala, B., Chauhan, A., Fox, G.: Optimizing OpenCL kernels for iterative statistical algorithms on GPUs. In: Proceedings of the Second International Workshop on GPUs and Scientific Applications, GPUScA 2011, pp. 33–44. Galveston Island, October 2011
Asensio-Cubero, J., Gan, J., Palaniappan, R.: Multiresolution analysis over simple graphs for brain computer interfaces. J. Neural Eng. 10(4) (2013)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). doi:10.1007/3-540-45356-3_83
Fonseca, C., López-Ibáñez, M., Paquete, L., Guerreiro, A.: Computation of the hypervolume indicator. http://lopez-ibanez.eu/hypervolume. Accessed: 30 November 2015
Acknowledgements
This work has been funded by project TIN2015-67020-P (Spanish “Ministerio de Economá y Competitividad” and FEDER funds). We also thank the BCI laboratory of the University of Essex, and especially prof. John Q. Gan, for allowing us to use their databases.
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
Escobar, J.J., Ortega, J., González, J., Damas, M., Prieto, B. (2017). Issues on GPU Parallel Implementation of Evolutionary High-Dimensional Multi-objective Feature Selection. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-55849-3_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55848-6
Online ISBN: 978-3-319-55849-3
eBook Packages: Computer ScienceComputer Science (R0)