Abstract
General purpose computing on graphics processing units (GPGPU) is a promising technique to cope with nowadays arising computational challenges due to the suitability of GPUs for parallel processing. Several libraries and functions are being released to boost the use of GPUs in real world problems. However, many of these packages require a deep knowledge in GPUs’ architecture and in low-level programming. As a result, end users find trouble in exploiting GPGPU advantages. In this paper, we focus on the GPU-acceleration of a prediction technique specially designed to deal with big datasets: the extreme learning machine (ELM). The intent of this study is to develop a user-friendly library in the open source R language and subsequently release the code in https://github.com/maaliam/EDMANS-elmNN-GPU.git. Therefore R users can freely implement it with the only requirement of having a NVIDIA graphic card. The most computationally demanding operations were identified by performing a sensitivity analysis. As a result, only matrix multiplications were executed in the GPU as they take around 99 % of total execution time. A speedup rate up to 15 times was obtained with this GPU-accelerated ELM in the most computationally expensive scenarios. Moreover, the applicability of the GPU-accelerated ELM was also tested with a typical case of model selection, in which genetic algorithms were used to fine-tune an ELM and training thousands of models is required. In this case, still a speedup of 6 times was obtained.
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References
Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Ullah Khan, S.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)
Chyzhyk, D., Savio, A., Graña, M.: Evolutionary ELM wrapper feature selection for alzheimer’s disease CAD on anatomical brain MRI. Neurocomputing 128, 73–80 (2014)
Peddie, J.: The new visualization engine - the heterogeneous processor unit. In: Dill, J., Earnshaw, R., Kasik, D., Vince, J., Wong, P.C. (eds.) Expanding The Frontiers Of Visual Analytics And Visualization, pp. 377–396. Springer International Publishing, London (2012)
Urraca, R., Antonanzas, J., Martinez-de Pison, F.J., Antonanzas-Torres, F.: Estimation of solar global irradiation in remote areas. J. Renew. Sustain. Energy (In Press)
van Heeswijk, M., Miche, Y., Oja, E., Lendasse, A.: GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing 74(16), 2430–2437 (2011)
Team, R.C.: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2014)
Buckner, J., Wilson, J., Seligman, M., Athey, B., Watson, S., Meng, F.: The gputools package enables GPU computing in R. Bioinformatics 26(1), 134–135 (2010)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)
Salcedo-Sanz, S., Casanova-Mateo, C., Pastor-Sanchez, A., Giron, M.S.: Daily global solar radiation prediction based on a hybrid coral reefs optimization - extreme learning machine approach. Sol. Energy 105, 91–98 (2014)
Huang, G.B.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 42(2), 513–529 (2012)
Gosso, A.: elmNN: Implementation of ELM (Extreme Learning Machine) algorithm for SLFN (Single Hidden Layer Feedforward Neural Networks). R package version 1.3 (2012)
Urraca-Valle, R., Sodupe-Ortega, E., Antoñanzas Torres, J., Antoñanzas-Torres, F., Martínez-de-Pisón, F.J.: An overall performance comparative of GA-PARSIMONY methodology with regression algorithms. In: de la Puerta, J.G., Ferreira, I.G., Bringas, P.G., Klett, F., Abraham, A., de Carvalho, A.C.P.L.F., Herrero, A., Baruque, B., Quintián, H., Corchado, E. (eds.) International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. AISC, vol. 299, pp. 53–62. Springer, Heidelberg (2014)
Ye, J.: On measuring and correcting the effects of data mining and model selection. J. Am. Stat. Assoc. 93(441), 120–131 (1998)
Seni, G., Elder, J.: Ensembe Methods In Data Mining. Improving Accuracy Through Combining Predictions. Morgan & Claypool, Chicago (2010)
Acknowledgments
R. Urraca and J. Antonanzas would like to acknowledge the fellowship FPI-UR-2014 granted by the University of La Rioja. F. Antonanzas-Torres would like to express his gratitude for the FPI-UR-2012 and ATUR grant No. 03061402 at the University of La Rioja. We are also greatly indebted to Banco Santander for the PROFAI-13/06 fellowship, to the Agencia de Desarrollo Económico de La Rioja for the ADER-2012-I-IDD-00126 (CONOBUILD) fellowship and to the Instituto de Estudios Riojanos (IER) for funding parts of this research.
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Alia-Martinez, M., Antonanzas, J., Antonanzas-Torres, F., Pernía-Espinoza, A., Urraca, R. (2015). A Straightforward Implementation of a GPU-accelerated ELM in R with NVIDIA Graphic Cards. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_54
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DOI: https://doi.org/10.1007/978-3-319-19644-2_54
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