The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
I/O-efficient techniques for computing pagerank
Proceedings of the eleventh international conference on Information and knowledge management
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Parallel PageRank Computation on a Gigabit PC Cluster
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
UbiCrawler: a scalable fully distributed web crawler
Software—Practice & Experience
Streaming Algorithms for Biological Sequence Alignment on GPUs
IEEE Transactions on Parallel and Distributed Systems
Data mining on the cell broadband engine
Proceedings of the 22nd annual international conference on Supercomputing
Parallel Computing Experiences with CUDA
IEEE Micro
Solving Systems of Linear Equations on the CELL Processor Using Cholesky Factorization
IEEE Transactions on Parallel and Distributed Systems
Parallel Genomic Alignments on the Cell Broadband Engine
IEEE Transactions on Parallel and Distributed Systems
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PageRank is a prominent metric used by search engines for ranking of search results. Page rank of a particular web page is a function of page ranks of all the web pages pointing to this page. The algorithm works on a large number of web pages and is thus computational intensive. The need of hardware is currently served by connecting thousands of computers in cluster. But faster and less complex alternatives to this system can be found in multi-core processors. In this paper, we identify major issues involved in porting PageRank algorithm on Cell BE Processor and CUDA, and their possible solutions. The work is evaluated on three input graphs of different sizes ranging from 0.35 million nodes to 1.3 million. Our results show that PageRank algorithm runs 2.8 times fast on CUDA compared to Xeon dual core 3.0 GHz.