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Multifold Acceleration of Neural Network Computations Using GPU

  • Alexander Guzhva
  • Sergey Dolenko
  • Igor Persiantsev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

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).

Keywords

GPGPU neural networks perceptron NVIDIA CUDA parallel computations 

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References

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    NVIDIA Corporation, CUDA Zone, http://www.nvidia.com/object/cuda_home.html
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexander Guzhva
    • 1
  • Sergey Dolenko
    • 1
  • Igor Persiantsev
    • 1
  1. 1.D.V. Skobeltsyn Institute of Nuclear PhysicsM.V. Lomonosov Moscow State UniversityMoscowRussia

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