计算机科学
机器学习
人工神经网络
人工智能
星团(航天器)
班级(哲学)
性能预测
多元统计
数据挖掘
支持向量机
预测建模
模拟
操作系统
作者
Zhengxiong Hou,Shuxin Zhao,Chao Yin,Yunlan Wang,Jianhua Gu,Xingshe Zhou
标识
DOI:10.1109/pdcat46702.2019.00053
摘要
There are a lot of middle-class or small-class high-performance computing clusters at universities and research institutes, etc. Large volumes of job logs have been accumulated after many years of operation. In this paper, on the basis of accumulated job logs on a high-performance computing cluster, we examine and analyze the job logs. Then, we study machine learning based performance analysis and prediction methods for parallel jobs. Various machine learning methods such as multivariate linear fitting, artificial neural network are used to build performance prediction models. We compare the errors of each model, and select the optimal prediction model for different users. The experimental results show that we can obtain reasonable prediction accuracy using the selected machine learning algorithms.
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