Parallel and Improved PageRank Algorithm for GPU-CPU Collaborative Environment

Prasann Choudhari, Eikshith Baikampadi, Paresh Patil, Sanket Gadekar
Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India
International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 6 (3), 2003-2005, 2015

   title={Parallel and Improved PageRank Algorithm for GPU-CPU Collaborative Environment},

   author={Choudhari, Prasann and Baikampadi, Eikshith and Patil, Paresh and Gadekar, Sanket},



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The internet is a huge collection of websites in the order of 10^8 bytes. Around 90% of the world’s population uses search engines for getting relevant information. According to Wikipedia, more than 200 million Indians use the Internet every day. Thus the correct data retrieval least time domain is the most important task. Hence need of efficient and parallel PageRanking algorithm. All the existing implementations are cluster based and to process huge lists of data take awful lot of time. The difficulty in cluster based approach is latency among different nodes participating in the computation. Since internet has large distributions of weblinks, collaboration of partial results after processing is a major issue. Thus latency factor overcomes the performance achievement of parallel cluster computation. As complete list can be hosted on one data server, PCI based communication mechanism can be used as a solution in addition of high parallel computation power with GPUs. So our approach aims at providing a parallel solution to it.
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