NDLI: Large-scale multi-dimensional document clustering on GPU clusters

Content Provider	IEEE Xplore Digital Library
Author	Yongpeng Zhang Mueller, F. Xiaohui Cui Potok, T.
Copyright Year	2010
Description	Author affiliation: Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, TN 37831 (Xiaohui Cui; Potok, T.) \|\| Dept. of Computer Science, North Carolina State University, Raleigh, NC 27695-7534 (Yongpeng Zhang; Mueller, F.)
Abstract	Document clustering plays an important role in data mining systems. Recently, a flocking-based document clustering algorithm has been proposed to solve the problem through simulation resembling the flocking behavior of birds in nature. This method is superior to other clustering algorithms, including k-means, in the sense that the outcome is not sensitive to the initial state. One limitation of this approach is that the algorithmic complexity is inherently quadratic in the number of documents. As a result, execution time becomes a bottleneck with large number of documents. In this paper, we assess the benefits of exploiting the computational power of Beowulf-like clusters equipped with contemporary Graphics Processing Units (GPUs) as a means to significantly reduce the runtime of flocking-based document clustering. Our framework scales up to over one million documents processed simultaneously in a sixteen-node moderate GPU cluster. Results are also compared to a four-node cluster with higher-end GPUs. On these clusters, we observe 30X-50X speedups, which demonstrate the potential of GPU clusters to efficiently solve massive data 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.
Starting Page	1
Ending Page	10
File Size	574091
Page Count	10
File Format	PDF
ISBN	9781424464425
ISSN	15302075
e-ISBN	9781424464432
DOI	10.1109/IPDPS.2010.5470429
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Large-scale systems Clustering algorithms Computational modeling Data mining Acceleration Birds Information analysis Computer science Laboratories Data engineering
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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