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Improved Parallel Computation of PageRank for Web Searching
Background/Objectives: PageRank given by Brin and Larry in 1998, emerged as a dominant link analysis method used by web search engines for ranking of its search results. Efficient and fast computation of PageRank values for prodigious web graphs is indeed an important issue for web search engines today. Recognizing and fighting with spam web pages is also considered to be another necessary issue in web searching. Methods/Statistical Analysis: In this paper, we have proposed an efficient and accelerated parallel computation of PageRank scores on Graphics Processing Units (GPUs) which uses non even distribution of PageRank values. This work is experimented on datasets taken from Stanford Large Network Dataset Collection, on a system equipped with NVIDIA Quadro 2000 Graphics card using CUDA programming language. Findings: The proposed work has a speed up of 3.22 to 7.5 and is also capable of dealing with spam web pages. Application: The proposed algorithm helps in detecting spam web pages.
CUDA, GPU, Parallel PageRank, Spam Web Pages
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