大规模并行计算机的蒙特卡罗方法

M. Weigel
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引用次数: 12

摘要

如今,需要大量计算资源的应用程序无法避免使用高度并行的机器。拥抱并行计算的机会,特别是新一代大规模并行加速器设备(如gpu)提供的可能性,英特尔的Xeon Phi甚至fpga使串行程序无法访问的应用和研究成为可能。在这里,我们概述了统计物理中蒙特卡罗模拟的大规模并行计算的机遇和挑战,重点是模拟显示相变和临界现象的系统。这涵盖了一系列规范系综马尔可夫链技术以及广义系综,如多规范模拟和种群退火。虽然所讨论的例子是针对自旋系统的模拟,但许多方法是更一般的,适度的修改使它们能够应用于其他晶格和非晶格问题,包括聚合物和粒子系统。我们讨论了这种高度并行模拟的重要算法要求,例如在这种情况下随机数生成的挑战,并概述了并行蒙特卡罗代码良好运行的一些一般设计原则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monte Carlo methods for massively parallel computers
Applications that require substantial computational resources today cannot avoid the use of heavily parallel machines. Embracing the opportunities of parallel computing and especially the possibilities provided by a new generation of massively parallel accelerator devices such as GPUs, Intel's Xeon Phi or even FPGAs enables applications and studies that are inaccessible to serial programs. Here we outline the opportunities and challenges of massively parallel computing for Monte Carlo simulations in statistical physics, with a focus on the simulation of systems exhibiting phase transitions and critical phenomena. This covers a range of canonical ensemble Markov chain techniques as well as generalized ensembles such as multicanonical simulations and population annealing. While the examples discussed are for simulations of spin systems, many of the methods are more general and moderate modifications allow them to be applied to other lattice and off-lattice problems including polymers and particle systems. We discuss important algorithmic requirements for such highly parallel simulations, such as the challenges of random-number generation for such cases, and outline a number of general design principles for parallel Monte Carlo codes to perform well.
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