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A survey on graphic processing unit computing for large-scale data mining

Alberto Cano
Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2017

@article{cano2017survey,

   author={Cano, Alberto},

   title={A survey on graphic processing unit computing for large-scale data mining},

   journal={Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},

   year={2017}

}

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General purpose computation using Graphic Processing Units (GPUs) is a well-established research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfully improved the scalability of data mining algorithms to address significantly larger dataset sizes in many application areas. The popularization of distributed computing frameworks for big data mining opens up new opportunities for transformative solutions combining GPUs and distributed frameworks. This survey analyzes current trends in the use of GPU computing for large-scale data mining, discusses GPU architecture advantages for handling volume and velocity of data, identifies limitation factors hampering the scalability of the problems, and discusses open issues and future directions.
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