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http://dx.doi.org/10.3745/KIPSTB.2012.19B.4.243

Improving the Performance of Document Similarity by using GPU Parallelism  

Park, Il-Nam (국민대학교 컴퓨터공학과)
Bae, Byung-Gurl (국민대학교 컴퓨터공학과)
Im, Eun-Jin (국민대학교 컴퓨터공학부)
Kang, Seung-Shik (국민대학교 컴퓨터공학부)
Publication Information
The KIPS Transactions:PartB / v.19B, no.4, 2012 , pp. 243-248 More about this Journal
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.
Keywords
GPU Parallelism; Document Similarity; Document Clustering; CUDA Library;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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