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A scalable parallel algorithm for large-scale reactive force-field molecular dynamics simulations

机译:用于大规模反应力场分子动力学仿真的可扩展并行算法

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A scalable parallel algorithm has been designed to perform multimillion-atom molecular dynamics (MD) simulations, in which first principles-based reactive force fields (ReaxFF) describe chemical reactions. Environment-dependent bond orders associated with atomic pairs and their derivatives are reused extensively with the aid of linked-list cells to minimize the computation associated with atomic n-tuple interactions (n <= 4 explicitly and <= 6 due to chain-rule differentiation). These n-tuple computations are made modular, so that they can be reconfigured effectively with a multiple time-step integrator to further reduce the computation time. Atomic charges are updated dynamically with an electronegativity equalization method, by iteratively minimizing the electrostatic energy with the charge-neutrality constraint. The ReaxFF-MD simulation algorithm has been implemented on parallel computers based on a spatial decomposition scheme combined with distributed n-tuple data structures. The measured parallel efficiency of the parallel ReaxFF-MD algorithm is 0.998 on 131,072 IBM BlueGene/L processors for a 1.01 billion-atom RDX system. (C) 2007 Elsevier B.V. All rights reserved.
机译:设计了一种可扩展的并行算法来执行数百万个原子的分子动力学(MD)模拟,其中基于第一原理的反作用力场(ReaxFF)描述了化学反应。与原子对及其衍生物相关的与环境有关的键序在链表单元的帮助下被大量重用,以最大程度地减少与原子n元组交互作用相关的计算(n <= 4且由于链规则差异而<= 6 )。这些n元组计算被模块化,因此可以使用多个时间步积分器对其进行有效地重新配置,以进一步减少计算时间。通过用电荷中性约束迭代地最小化静电能,通过电负性均衡方法动态更新原子电荷。 ReaxFF-MD仿真算法已在并行计算机上基于空间分解方案与分布式n元组数据结构相结合而实现。在131072个IBM BlueGene / L处理器上,针对10.1十亿个原子的RDX系统,并行ReaxFF-MD算法的并行效率为0.998。 (C)2007 Elsevier B.V.保留所有权利。

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