Improving the Performance of Document Similarity by using GPU Parallelism


The KIPS Transactions:PartB , Vol. 19, No. 4, pp. 243-248, Aug. 2012
10.3745/KIPSTB.2012.19.4.243,   PDF Download:

Abstract

In the information retrieval systems like vector model implementation and document clustering, document similarity calculation takes a great part on the overall performance of the system. In this paper, GPU parallelism has been explored to enhance the processing speed of document similarity calculation in a CUDA framework. The proposed method increased the similarity calculation speed almost 15 times better compared to the typical CPU-based framework. It is 5.2 and 3.4 times better than the methods by using CUBLAS and Thrust, respectively.


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Cite this article
[IEEE Style]
S. S. Kang, I. N. Park, B. G. Bae, E. J. Im, "Improving the Performance of Document Similarity by using GPU Parallelism," The KIPS Transactions:PartB , vol. 19, no. 4, pp. 243-248, 2012. DOI: 10.3745/KIPSTB.2012.19.4.243.

[ACM Style]
Seung Shik Kang, Il Nam Park, Byung Gurl Bae, and Eun Jin Im. 2012. Improving the Performance of Document Similarity by using GPU Parallelism. The KIPS Transactions:PartB , 19, 4, (2012), 243-248. DOI: 10.3745/KIPSTB.2012.19.4.243.