GPU-Accelerated Text Mining

Yongpeng Zhang, Frank Mueller, Xiaohui Cui, Thomas Potok
North Carolina State University, Department of Computer Science, Raleigh, NC 27695-7534
Framework (2009) Volume: 7534, Issue: 1, Publisher: Oak Ridge National Laboratory (ORNL), Pages: 1-6


   title={GPU-accelerated text mining},

   author={Zhang, Y. and Mueller, F. and Cui, X. and Potok, T.},

   booktitle={Workshop on Exploiting Parallelism using GPUs and other Hardware-Assisted Methods},



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Accelerating hardware devices represent a novel promise for im- proving the performance for many problem domains but it is not clear for which domains what accelerators are suitable. While there is no room in general-purpose processor design to significantly in- crease the processor frequency, developers are instead resorting to multi-core chips duplicating conventional computing capabilities on a single die. Yet, accelerators offer more radical designs with a much higher level of parallelism and novel programming environ- ments. This present work assesses the viability of textmining on CUDA. Text mining is one of the key concepts that has become prominent as an effectivemeans to index the Internet, but its applications range beyond this scope and extend to providing document similaritymet- rics, the subject of thiswork. We have developed and optimized text search algorithms for GPUs to exploit their potential for massive data processing. We discuss the algorithmic challenges of paral- lelization for text search problems on GPUs and demonstrate the potential of these devices in experiments by reporting significant speedups. Our study may be one of the first to assessmore complex text search problems for suitability forGPUdevices, and itmay also be one of the first to exploit and report on atomic instruction usage that have recently become available in NVIDIA devices.
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