Particle Routing in Distributed Particle Filters for Large-Scale Spatial Temporal Systems

被引：16
作者：
Bai, Fan [1 ]
Gu, Feng [2 ]
Hu, Xiaolin [1 ]
Guo, Song [1 ]
机构：
[1] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA
[2] CUNY Coll Staten Isl, Dept Comp Sci, Staten Isl, NY 10314 USA
来源：
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS | 2016年 / 27卷 / 02期
基金：
美国国家科学基金会;
关键词：
Distributed applications; Monte Carlo; routing and layout; simulation; WILDFIRE SPREAD; DEVS-FIRE; PERFORMANCE;
D O I：
10.1109/TPDS.2015.2405912
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Particle filters are important techniques to support data assimilation for large-scale spatial temporal simulation systems. Distributed particle filters improve the performance of particle filtering by distributing particles to multiple processing units (PUs). While different resampling algorithms have been developed for distributed particle filters, less research has been conducted to investigate how to route particles among the PUs after resampling in effective and efficient manners. This paper develops particle routing policies in distributed particle filters with both the centralized resampling and the distributed resampling. The developed routing policies are evaluated from the aspects of the communication cost and the data assimilation accuracy based on an application of data assimilation for large-scale wildfire spread simulations.
引用
收藏
页码：481 / 493
页数：13
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