Abstract
With emergence of graphics processing units (GPU) of the latest generation, it became possible to undertake neural network based computations using GPU on serially produced video display adapters. In this study, NVIDIA CUDA technology has been used to implement standard back-propagation algorithm for training multiple perceptrons simultaneously on GPU. For the problem considered, GPU-based implementation (on NVIDIA GTX 260 GPU) has lead to a 50x speed increase compared to a highly optimized CPU-based computer program, and more than 150x compared to a commercially available CPU-based software (NeuroShell 2) (AMD Athlon 64 Dual core 6000+ processor).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
NVIDIA Corporation, CUDA Zone, http://www.nvidia.com/object/cuda_home.html
Kyoung-Su Oh, K.S., Jung, K.: GPU implementation of neural networks. Pattern Recognition 37, 1311–1314 (2004)
Zhongwen, L., Hongzhi, L., Zhengping, Y., Xincai, W.: Self-Organizing Maps Computing on Graphic Process Unit. In: ESANN 2005 proceedings, European Symposium on Artificial Neural Networks Bruges, Belgium, April 27-29. d-side publi. (2005) ISBN 2-930307-05-6, http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2005-135.pdf
Lahabar, S., Agrawal, P., Narayanan, P.J.: High Performance Pattern Recognition on GPU, http://web.iiit.ac.in/~sheetal/Paper/NN.pdf
Guzhva, A.G., Dolenko, S.A., Obornev, E.A., Persiantsev, I.G., Shimelevich, M.I., Shugai, J.S.: Use of Significant Feature Selection Adaptive Algorithms in Neural Networks Based Solution of the Inverse Problem of Electrical Prospecting. In: 9th International Conference Pattern Recognition and Image Analysis: New Informational Technologies (PRIA 2008), Conference Proceedings, vol. 1, pp. 215–218. Nizhni Novgorod, Russia (2008)
NVIDIA Corporation, http://www.nvidia.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guzhva, A., Dolenko, S., Persiantsev, I. (2009). Multifold Acceleration of Neural Network Computations Using GPU. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-04274-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
eBook Packages: Computer ScienceComputer Science (R0)