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Computing

, Volume 100, Issue 9, pp 907–926 | Cite as

Evaluation of the computational efficacy in GPU-accelerated simulations of spiking neurons

  • Kazuhisa Fujita
  • Shun Okuno
  • Yoshiki Kashimori
Article

Abstract

To understand the mechanism of information processing by a biological neural network, computer simulation of a large-scale spiking neural network is an important method. However, because of a high computation cost of the simulation of a large-scale spiking neural network, the simulation requires high performance computing implemented by a supercomputer or a computer cluster. Recently, hardware for parallel computing such as a multi-core CPU and a graphics card with a graphics processing unit (GPU) is built in a gaming computer and a workstation. Thus, parallel computing using this hardware is becoming widespread, allowing us to obtain powerful computing power for simulation of a large-scale spiking neural network. However, it is not clear how much increased performance the parallel computing method using a new GPU yields in the simulation of a large-scale spiking neural network. In this study, we compared computation time between the computing methods with CPUs and GPUs in a simulation of neuronal models. We developed computer programs of neuronal simulations for the computing systems that consist of a gaming graphics card with new architecture (the NVIDIA GTX 1080) and an accelerator board using a GPU (the NVIDIA Tesla K20C). Our results show that the computing systems can perform a simulation of a large number of neurons faster than CPU-based systems. Furthermore, we investigated the accuracy of a simulation using single precision floating point. We show that the simulation results of single precision were accurate enough compared with those of double precision, but chaotic neuronal response calculated by a GPU using single precision is prominently different from that calculated by a CPU using double precision. Furthermore, the difference in chaotic dynamics appeared even if we used double precision of a GPU. In conclusion, the GPU-based computing system exhibits a higher computing performance than the CPU-based system, even if the GPU system includes data transfer from a graphics card to host memory.

Keywords

GPGPU CUDA Acceleration Spiking neural network Neuron model 

Mathematics Subject Classification

Simulation and numerical modeling Numerical chaos 

Notes

Acknowledgements

This work was supported by JSPS KAKENHI Grant No. 15K07146.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Engineering ScienceUniversity of Electro-CommunicationsChofuJapan
  2. 2.Department of Integrated Science and TechnologyNational Institute of Technology, Tsuyama CollageTsuyamaJapan

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