文章基本信息

标题：Parallelizing Kernel Polynomial Method Applying Graphics Processing Units
其他标题：Parallelizing Kernel Polynomial Method Applying Graphics Processing Units
本地全文：下载
作者：Shixun Zhang ; Shinichi Yamagiwa ; Masahiko Okumura 等
期刊名称：International Journal of Networking and Computing
印刷版ISSN：2185-2847
出版年度：2012
卷号：2
期号：1
页码：41-55
语种：English
出版社：International Journal of Networking and Computing
摘要：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-grain 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 describes performance evaluations regarding the cases when the actual simulation parameters are applied, where one parameter is applied for the increased intensive calculations and another is applied for the increased amount of memory usage. Moreover, the impact for applying the Compress Row Storage (CRS) format to the KPM algorithm is also discussed. Finally, it concludes that the performance on the GPU promises very high performance compared to the one on CPU and reduces the overall simulation time.
关键词：GPGPU; Kernel Polynomial Method; Condensed Matter Physics; CUDA; CRS