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基于区域匹配的并行区域匹配知识识别算法的大规模 HLA 模拟的实证研究。

Empirical Study of Large-Scale HLA Simulation of Parallel Region-Matching Knowledge Recognition Algorithm Based on Region Matching.

机构信息

School of Artificial Intelligence, Jianghan University, Wuhan 430056, Hubei, China.

出版信息

Comput Intell Neurosci. 2022 Apr 11;2022:1514396. doi: 10.1155/2022/1514396. eCollection 2022.

DOI:10.1155/2022/1514396
PMID:35449745
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9017540/
Abstract

Most of the existing region-matching algorithms need to match all regions, resulting in a waste of computing resources, increasing the cost of simulation technology and data redundancy, and resulting in the reduction of network data stream transmission efficiency. This paper presents a parallel region-matching knowledge recognition algorithm. Combined with the shortcomings of existing matching algorithms, a simulation technology is constructed to realize the parallel matching of multiple regions in HLA distributed simulation. The algorithm can realize the parallel matching calculation of multiple changed regions in one simulation. At the same time, the basic idea based on mobile intersection is adopted in the matching calculation, and the historical information before and after the region range is moved is used. The matching is limited to the moving interval, and the moving crossover theory is applied to the matching calculation to realize the relevant historical information before and after the region. Simulation results show that the parallel region-matching knowledge recognition algorithm can support HLA distributed simulation evaluation. In the matching calculation, the basic idea based on moving intersection is adopted, and the matching is limited to the moving interval by using the historical information before and after the region is moved, which reduces a large number of irrelevant calculations. Theoretical analysis and experimental results show that the algorithm is particularly suitable for the application needs of building large-scale distributed simulation based on multi-core computing platform.

摘要

大多数现有的区域匹配算法需要匹配所有区域，这导致计算资源的浪费，增加了模拟技术和数据冗余的成本，并降低了网络数据流传输效率。本文提出了一种并行区域匹配知识识别算法。该算法结合了现有匹配算法的缺点，构建了一种 HLA 分布式仿真中的多个区域并行匹配的模拟技术。该算法可以在一次仿真中实现多个变化区域的并行匹配计算。同时，在匹配计算中采用基于移动交集的基本思想，利用区域范围移动前后的历史信息，将匹配限制在移动区间内，并应用移动交叉理论实现区域前后的相关历史信息。仿真结果表明，该并行区域匹配知识识别算法可以支持 HLA 分布式仿真评估。在匹配计算中，采用基于移动交集的基本思想，利用区域移动前后的历史信息将匹配限制在移动区间内，减少了大量不相关的计算。理论分析和实验结果表明，该算法特别适用于基于多核计算平台构建大规模分布式仿真的应用需求。

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