Data-intensive document clustering on GPU clusters

Yongpeng Zhang, Frank Mueller, Xiaohui Cui, Thomas Potok
Department of Computer Science, North Carolina State University, Raleigh, NC 27695-7534, United States
Journal of Parallel and Distributed Computing (22 August 2010)


   title={Data-intensive document clustering on GPU clusters},

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

   journal={Journal of Parallel and Distributed Computing},





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Document clustering is a central method to mine massive amounts of data. Due to the explosion of raw documents generated on the Internet and the necessity to analyze them efficiently in various intelligent information systems, clustering techniques have reached their limitations on single processors. Instead of single processors, general-purpose multi-core chips are increasingly deployed in response to diminishing returns in single processor speedup due to the frequency wall, but multi-core benefits only provide linear speedups while the number of documents in the Internet grows exponentially. Accelerating hardware devices represent a novel promise for improving the performance for data-intensive problems such as document clustering. They offer more radical designs with a higher level of parallelism but adaptation to novel programming environments. In this paper, we assess the benefits of exploiting the computational power of Graphics Processing Units (GPUs) to study two fundamental problems in document mining, namely to calculate TF-IDF (Term Frequency-Inverse Document Frequency) and cluster a large set of document. We transform traditional algorithms into accelerated parallel counterparts that can be efficiently executed on many-core GPU architectures. We assess our implementations on various platforms ranging from stand-alone GPU desktops to Beowulf-like clusters equipped with contemporary GPU cards. We observe at least one order of magnitude speedups over CPU-only desktops and clusters. This demonstrates the potential of exploiting GPU clusters to efficiently solve massive document mining problems. Such speedups combined with the scalability potential and accelerator-based parallelization are unique in the domain of document-based data mining, to the best of our knowledge.
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