关键词: 数据挖掘, GPU加速, 并行计算, 协同过滤

Abstract: Transferring the procedure involving dense computation to GPU is known as an effective way to accelerate the whole procedure of many classical data mining algorithms. In this paper, features of GPU as well as existing programming models of GPU are introduced firstly. The representative works of fundamental data mining tasks are covered respectively, including classification, clustering, association analysis, time series analysis and deep learning. Two classical algorithms of collaborative filtering are implemented on CPU and GPU, and experiments with MovieLens data sets are conducted, which help to collect first-hand experience of applying GPU to accelerate the applications of data mining algorithms.

Key words: data mining, GPU accelerated, parallel computing, collaborative filtering