几种并行编程框架在数据挖掘领域的比较  

The Comparison of Several Parallel Model in the Data Dining Fields

作　　者：何渊淘[1] 齐兵辉[1] 

机构地区：[1]郑州航空工业管理学院,郑州450000

出　　处：《中原工学院学报》2015年第3期90-94,共5页Journal of Zhongyuan University of Technology

基　　金：国家自然科学基金项目(41171341)

摘　　要：将机器学习并行化是进行海量数据挖掘的重要方式,但由于并行计算框架、机器学习算法的多样性,导致计算框架的选取及算法并行化存在着困难。本文对几种常见的并行计算框架的模型结构和工作机理进行了分析,根据算法中变量的依存关系将其分类,并将这几类算法进行了实验对比。实验结果表明,算法中变量的依存关系对其在并行化后的性能有巨大的影响。To slove the date mining on large dataset, the parallelizaion of algorithm is the most important solu-tion. Due to the diversity of the parallel frameworks and the machine learning algorithms, it is difficult to choose a framework and algorithm parallelizaion. In this paper, the models and mechanism of the parallel framework are ana-lyzed, and it is classified based on the parameter relations. In the end, the experiments are conducted and the results show that the relation of the algorithm parameters have great impact on the performance.

关 键 词：MAPREDUCE Pregel HAMA GraphLab MPI 数据挖掘 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]