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首页> 外文期刊>International Journal of Networking and Computing >Parallelizing Kernel Polynomial Method Applying Graphics Processing Units
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Parallelizing Kernel Polynomial Method Applying Graphics Processing Units

机译:应用图形处理单元的并行核多项式方法

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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-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.
机译:核多项式方法(KPM)是在凝聚态物理和化学研究领域中用于量子系统模拟的快速对角化方法之一。由于细粒度的递归计算,该算法难以在集群计算机或超级计算机上并行化。本文提出了在最近的图形处理单元(GPU)上实施KPM的方法,该方法可以在大规模并行环境中并行执行递归计算。本文还介绍了在应用实际模拟参数的情况下的性能评估,其中一个参数用于增加的密集计算,而另一个参数用于增加的内存使用量。此外,还讨论了将压缩行存储(CRS)格式应用于KPM算法的影响。最后,得出的结论是,与CPU上的GPU相比,GPU上的性能具有很高的性能,并减少了总体仿真时间。

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