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Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units

Shixun Zhang, Shinichi Yamagiwa, Masahiko Okumura, Seiji Yunoki
School of Information, Kochi University of Technology, JST PRESTO, Kami, Kochi 782-8502 Japan
arXiv:1105.5481v1 [physics.comp-ph] (27 May 2011)

@article{2011arXiv1105.5481Z,

   author={Zhang}, S. and {Yamagiwa}, S. and {Okumura}, M. and {Yunoki}, S.},

   title={"{Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1105.5481},

   primaryClass={"physics.comp-ph"},

   keywords={Physics – Computational Physics, Condensed Matter – Other Condensed Matter, Computer Science – Performance},

   year={2011},

   month={may},

   adsurl={http://adsabs.harvard.edu/abs/2011arXiv1105.5481Z},

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

}

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The Kernel Polynomial Method (KPM) is one of the fast diagonalization methods used for simulations of quantum systems in research fields of condensed matter physics and chemistry. The algorithm has a difficulty to be parallelized on a cluster computer or a supercomputer due to the fine-gain recursive calculations. This paper proposes an implementation of the KPM on the recent graphics processing units (GPU) where the recursive calculations are able to be parallelized in the massively parallel environment. This paper also illustrates performance evaluations regarding the cases when the actual simulation parameters are applied, the one for increased intensive calculations and the one for increased amount of memory usage. Finally, it concludes that the performance on GPU promises very high performance compared to the one on CPU and reduces the overall simulation time.
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