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Parallel Computing Model of Multiple Dimensions Data Streams Canonical Correlation Analysis with GPU

Yong Zhou, Xiaowei Lu, Chuntian Cheng
Sch. of Software, Univ. of Technol., Dalian, China
2nd International Conference on Information Engineering and Computer Science (ICIECS), 2010

@inproceedings{zhou2010parallel,

   title={Parallel Computing Model of Multiple Dimensions Data Streams Canonical Correlation Analysis with GPU},

   author={Zhou, Y. and Lu, X. and Cheng, C.},

   booktitle={Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on},

   pages={1–4},

   organization={IEEE},

   year={2010}

}

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With view to satisfying the requirement of real-time under the circumstance of resource-constraints, specific and practical architecture for high-dimensional data streams are proposed, meanwhile, based on CUDA (Compute Unified Device Architecture), canonical correlation analysis between two multiple dimensions data streams using data cube pattern and dimensionality-reduction technique is carried out in this framework. The theoretical analysis and experimental results show that the parallel processing method can online detect correlations between multiple dimension data streams accurately in the synchronous sliding window mode. According to the pure CPU method, this method has significant speed advantage, well meeting the real-time requirements of high-dimensional data streaming and can be applied to the field of data stream mining widely.
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