Multifold Acceleration of Neural Network Computations Using GPU

Alexander Guzhva, Sergey Dolenko, Igor Persiantsev
D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia 119991
In Artificial Neural Networks ICANN 2009, Vol. 5768 (2009), pp. 373-380.


   title={Multifold Acceleration of Neural Network Computations Using GPU},

   author={Guzhva, A. and Dolenko, S. and Persiantsev, I.},

   journal={Artificial Neural Networks–ICANN 2009},





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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).
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