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Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units

机译:应用图形处理单元核多项式方法的性能加速度

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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.
机译:内核多项式方法(KPM)是用于凝聚物理学和化学研究领域的量子系统模拟的快速对角化方法之一。由于细增益递归计算,该算法难以在群集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)上的KPM的实现,其中递归计算能够在大规模平行环境中平行化。本文还说明了关于应用实际仿真参数的情况的性能评估,用于增加密集型计算的情况和用于增加内存使用量的一个。最后,它得出结论,与CPU上的一个,GPU上的性能承诺非常高的性能,并降低了整体模拟时间。

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