并行马尔可夫链蒙特卡罗模拟

Parallel Markov chain Monte Carlo simulations.

作者信息

Ren Ruichao, Orkoulas G

机构信息

Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, USA.

出版信息

J Chem Phys. 2007 Jun 7;126(21):211102. doi: 10.1063/1.2743003.

DOI:10.1063/1.2743003

PMID:17567181

Abstract

With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. In this work, the parallel version of Markov chain theory and its role in accelerating Monte Carlo simulations via cluster computing is explored. It is shown that sequential updating is the key to improving efficiency in parallel simulations through domain decomposition. A parallel scheme is proposed to reduce interprocessor communication or synchronization, which slows down parallel simulation with increasing number of processors. Parallel simulation results for the two-dimensional lattice gas model show substantial reduction of simulation time for systems of moderate and large size.

摘要

对于严格的细致平衡，通过区域分解进行的并行蒙特卡罗模拟无法用传统的马尔可夫链理论来验证，因为传统理论描述的是一个本质上串行的随机过程。在这项工作中，探索了马尔可夫链理论的并行版本及其在通过集群计算加速蒙特卡罗模拟中的作用。结果表明，顺序更新是通过区域分解提高并行模拟效率的关键。提出了一种并行方案以减少处理器间的通信或同步，因为随着处理器数量的增加，这种通信或同步会减慢并行模拟的速度。二维晶格气体模型的并行模拟结果表明，对于中等和大尺寸的系统，模拟时间大幅减少。