Research on the influence of node deployment in cluster for modeling efficiency

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI:10.1117/12.2540983

Yuxiang Liu, Yang Peng, Xin Long, Maojun Zhang

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Abstract

With the rapid development of oblique photography (OP) in recent years, the accuracy of reality modeling has increased, which has led to a surge in computational complexity. To solve the problem, a lot of reality modeling software adopts the strategy of cluster parallel computing for modeling. In this paper, the regression analysis method is used to study the influence of the configuration of the compute nodes in the cluster, which aims at improving the computational efficiency of the cluster for the 3D reconstruction task. Furthermore, the M/M/S queuing model in queuing theory is used to model the multi-task assignment of the cluster, and the mathematical model between compute nodes and performance of the cluster is established, which achieves the effective quantitative evaluation of the cluster computing efficiency. Experiments show that the CPU performance of the compute nodes is the most critical hardware factor affecting the efficiency of the cluster.

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研究集群中节点部署对建模效率的影响

近年来，随着斜向摄影技术(OP)的迅速发展，现实建模的精度不断提高，计算复杂度也随之激增。为了解决这一问题，许多现实建模软件采用集群并行计算的策略进行建模。本文采用回归分析的方法研究集群中计算节点配置的影响，旨在提高集群对三维重建任务的计算效率。利用排队理论中的M/M/S排队模型对集群的多任务分配进行建模，建立了计算节点与集群性能之间的数学模型，实现了对集群计算效率的有效定量评价。实验表明，计算节点的CPU性能是影响集群效率最关键的硬件因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging

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