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Molecular dynamics simulations with many-body potentials on multiple GPUs – the implementation, package and performance

Qing Hou, Min Li, Yulu Zhou, Jiechao Cui, Zhenguo Cui, Jun Wang
Key Lab of Radiation Physics and Technology, Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610064, China
arXiv:1212.6332 [physics.comp-ph], (27 Dec 2012)
@article{2012arXiv1212.6332H,

   author={Hou}, Q. and {Li}, M. and {Zhou}, Y. and {Cui}, J. and {Cui}, Z. and {Wang}, J.},

   title={"{Molecular dynamics simulations with many-body potentials on multiple GPUs – the implementation, package and performance}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1212.6332},

   primaryClass={"physics.comp-ph"},

   keywords={Physics – Computational Physics},

   year={2012},

   month={dec},

   adsurl={http://adsabs.harvard.edu/abs/2012arXiv1212.6332H},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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Molecular dynamics (MD) is an important research tool extensively applied in materials science. Running MD on a graphics processing unit (GPU) is an attractive new approach for accelerating MD simulations. Currently, GPU implementations of MD usually run in a one-host-process-one-GPU (OHPOG) scheme. This scheme may pose a limitation on the system size that an implementation can handle due to the small device memory relative to the host memory. In this paper, we present a one-host-process-multiple-GPU (OHPMG) implementation of MD with embedded-atom-model or semi-empirical tight-binding many-body potentials. Because more device memory is available in an OHPMG process, the system size that can be handled is increased to a few million or more atoms. In comparison with the CPU implementation, in which Newton’s third law is applied to improve the computational efficiency, our OHPMG implementation has achieved a 28.9x~86.0x speedup in double precision, depending on the system size, the cut-off ranges and the number of GPUs. The implementation can also handle a group of small boxes in one run by combining the small boxes into a large box. This approach greatly improves the GPU computing efficiency when a large number of MD simulations for small boxes are needed for statistical purposes.
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