非规则流中高维数据流典型相关性分析并行计算方法

为了满足在计算资源受限的环境下高维数据流处理的实时性要求,提出一种方法——基于GPU(graphic processing unit)的非规则流中高维数据流的处理模型和具体的可行架构,并分析设计了相关的并行算法.该六层模型是将GPU处理数据的高宽带性能结合进滑动窗口中数据流的分析,进而在该框架下基于统一计算设备架构(compute unified device architecture,简称CUDA),使用数据立方模型以及降维约简技术并行分析了多条高维数据流的典型相关性.理论分析和实验结果均表明,该并行处理方法能够在线精确地识别同步滑动窗口模式下高维数据流之间的相关性.相对于纯CPU方法,该方法具有显著的速度优势,很好地满足了高维数据流的实时性需求,可以作为通用的分析方法广泛应用于数据流挖掘领域.

Parallel Computing Method of Canonical Correlation Analysis for High-Dimensional Data Streams in Irregular Streams

This paper addresses an approach that uses GPU(graphic processing unit)-based processing architecture model and its parallel algorithm for high-dimensional data streams over the irregular streams in order to satisfy the real-time requirement under the resource-constraints.This six layers model combines the GPU high wide-band property of data processing with analysis data stream in a sliding window.Next,canonical correlation analysis is carried out between two high-dimensional data streams,by a data cube pattern,and a dimensionality-reduction method in this framework based on compute unified device architecture(CUDA).The theoretical analysis and experimental results show that the parallel processing method can detect correlations on high dimension data streams,online,accurately in the synchronous sliding window mode.According to the pure CPU method,this technique has significant speed advantage and conducts the real-time requirement of high-dimensional data stream very well.It provides a common strategy for the applied field of data stream mining.