卫星时序数据挖掘节点级并行与优化方法

doi:10.13700/j.bh.1001-5965.2018.0334

北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (12): 2470-2478.doi: 10.13700/j.bh.1001-5965.2018.0334

卫星时序数据挖掘节点级并行与优化方法

鲍军鹏¹, 杨科^1,2, 周静²

1. 西安交通大学电子与信息工程学院, 西安 710049;
2. 宁夏军区, 银川 750021

收稿日期:2018-06-07 修回日期:2018-07-27 出版日期:2018-12-20 发布日期:2018-12-28
通讯作者: 鲍军鹏 E-mail:baojp@mail.xjtu.edu.cn
作者简介:鲍军鹏,男,博士,副教授,博士生导师。主要研究方向:机器学习、数据挖掘、人工智能;杨科,男,硕士研究生。主要研究方向:机器学习、数据挖掘;周静,女,硕士研究生。主要研究方向:机器学习、数据挖掘。
基金资助:
航天器在轨故障诊断与维修重点实验室课题

Node level parallel and optimization method of satellite time serial data mining

BAO Junpeng¹, YANG Ke^1,2, ZHOU Jing²

1. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
2. Military Region of Ningxia, Yinchuan 750021, China

Received:2018-06-07 Revised:2018-07-27 Online:2018-12-20 Published:2018-12-28
Supported by:
Supported by the Key Laboratory for Fault Diagnosis and Maintenance of Spacecraft in Orbit of China

摘要/Abstract

摘要： 智能卫星技术对卫星时间序列数据挖掘提出了越来越多的需求。通常卫星数据计算量都非常大，若串行执行则需要较长时间。以卫星异变过程多类型特征分析过程为典型代表，针对窗口划分与向量相似度计算、特征提取、傅里叶变换、聚类等常见数据挖掘操作，探讨了在多核CPU和GPU的典型异构计算节点中对时序数据挖掘过程进行并行优化的多种策略，包括向量化方法、多进程方法、GPU计算等方法。对这几种优化策略的适用情况进行了实验分析对比。结果表明，针对不同任务情况综合使用多种优化策略具有显著提升效果。

关键词: 航天大数据, 数据挖掘, 智能卫星, 并行化, GPU

Abstract: Intelligent satellite technology requires more and more data mining operations for satellite time series data. Usually, satellite data amount is very big that needs a lot of computation, so it will take a very long time to complete the computation in serial program. The satellite anomaly process multi-features analysis procedure is such a typical representation, which performs many common data mining operations, including windows segmentation, computation of vector similarity, feature extraction, Fourier transformation, and cluster-ing. The paper discusses several speed-up and parallel optimization strategies for a time series data mining procedure on a typical heterogeneous computing node with multi-cores CPUs and GPUs, including vector optimization, multi-process parallelization, and GPU computation. We test and compare these optimization strategies in different usage conditions. The experiment results show that the combined use of them can achieve obvious efficiency improvement for different task.

Key words: aerospace big data, data mining, intelligent satellite, parallelization, GPU

中图分类号:

V19
TP311.11

鲍军鹏, 杨科, 周静. 卫星时序数据挖掘节点级并行与优化方法[J]. 北京航空航天大学学报, 2018, 44(12): 2470-2478.

BAO Junpeng, YANG Ke, ZHOU Jing. Node level parallel and optimization method of satellite time serial data mining[J]. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2018, 44(12): 2470-2478.

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卫星时序数据挖掘节点级并行与优化方法

Node level parallel and optimization method of satellite time serial data mining

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