[1]冯林静. 多核的并行相似连接[J].计算机技术与发展,2017,27(07):43-46.
　FENG Lin-jing. Parallel Similarity Join of Multi-core[J].,2017,27(07):43-46.
点击复制

 多核的并行相似连接()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年07期
页码:
43-46
栏目:
智能、算法、系统工程
出版日期:
2017-07-10

文章信息/Info

Title:
 Parallel Similarity Join of Multi-core
文章编号:
1673-629X（2017)07-0043-04
作者:
 冯林静
 天津工业大学
Author(s):
 FENG Lin-jing
关键词:
 ; 多核; 多线程; 并行; 相似连接
Keywords:
 ; multi-core; multi-thread; parallel; similar join
分类号:
TP311
文献标志码:
A
摘要:
 相似连接(similarity join)是指在给定的数据集中,根据给定的相似度度量函数来衡量数据之间的相似度,并找出所有相似度不小于给定阈值的数据对的操作.随着网络和移动应用等信息技术的不断发展,数据呈现爆炸式增长,海量数据的分析需要强大的计算能力,相似连接成为大数据处理领域的热点方式之一.传统的单核计算机平台的处理能力已经很难满足海量数据处理的计算要求.为了提高计算效率和性能,利用基于多核平台的多线程并行编程发挥多核体系结构的优势,已经成为实现个人低成本并行计算和多核技术发展的趋势.因此,为了提高相似连接的效率,充分利用现代体系结构的多核特性和多线程技术,提出了相似连接并行化的改进方法.实验结果表明,使用该方法极大地提升了效率.
Abstract:
 Similar join is an operation which is using a given similarity function to measure the similarity between data and find out all similarity less than a given threshold in a given data set.With the continuous development of Internet and mobile applications,the amount of data is increasing explosively,and along with the analyzing of huge amount of data,it requires a strong ability of calculation,so similar joins become one of the leading way of hotspots in the field of data processing.The processing capacity of traditional single-core computer platform has been difficult to meet the calculation of mass data processing requirements.Programming based on multi-core platform and using the multi-thread parallel can make full use of the advantage of multi-core architecture and improve the computational efficiency and computational performance,which has become the trend to realize personal low cost calculation and the development of multi-core technology.Therefore,based on the characteristics of multi-core and multi-thread technology,the improved method of similar connected parallelization is proposed.The experimental results show that the efficiency has been obviously improved.

相似文献/References:

[1]张军 刘羽 卢奉良.蚁群算法解决TSP问题的并行化研究与实现[J].计算机技术与发展,2011,(05):72.
　ZHANG Jun,LIU Yu,LU Feng-liang.Parallel Research and Implementation of Ant Colony Algorithm to Solve Problem of TSP[J].,2011,(07):72.
[2]唐毓毅,朱怡安,黄姝娟,等.一种有约束关系的实时周期任务调度算法研究[J].计算机技术与发展,2013,(07):1.
　TANG Yu-yi[],ZHU Yi-an[],HUANG Shu-juan[],et al.Research on a Real-time Scheduling Algorithm for Periodic Task with Constraint Relation[J].,2013,(07):1.
[3]林英,孟正,康雁,等.多核下一种线程调度算法的研究与实现[J].计算机技术与发展,2013,(10):19.
　LIN Ying,MENG Zheng,KANG Yan,et al.Research and Implementation of a Thread Scheduling Algorithm in Multi-core Environment[J].,2013,(07):19.
[4]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(07):1.
[5]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(07):5.
[6]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(07):13.
[7]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(07):21.
[8]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(07):25.
[9]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(07):29.
[10]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(07):34.

更新日期/Last Update: 2017-08-22