The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Distributed Pagerank for P2P Systems
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
Parallel PageRank Computation on a Gigabit PC Cluster
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
An Efficient Partition-Based Parallel PageRank Algorithm
ICPADS '05 Proceedings of the 11th International Conference on Parallel and Distributed Systems - Volume 01
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Efficient PageRank and SpMV Computation on AMD GPUs
ICPP '10 Proceedings of the 2010 39th International Conference on Parallel Processing
Fast sparse matrix-vector multiplication on GPUs: implications for graph mining
Proceedings of the VLDB Endowment
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
ACM SIGOPS 24th Symposium on Operating Systems Principles
Dandelion: a compiler and runtime for heterogeneous systems
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
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Fast & efficient computing of web rank scores is a necessary issue of search engines today. Because of the enormous size of data and the dynamic nature of World Wide Web, this computation is generally executed on large web graphs (to billions webpages) and requires refreshing quite often, so it becomes a challenging task. In this paper, we propose an efficient method for computing PageRank score -- a Google ranking method based on analyzing the link structure of the Web on graphics processing units (GPUs). We have employed a slightly modification of a storage data format called binary 'link structure file' which inspirited from [2] for storing the web graph data. We then divided the PageRank calculating phases into parallel operations for exploiting the computing power of the graphics cards. Our program was written in CUDA language to experiment on a system equipped two double NVIDIA GeForce GTX 295 graphics cards, using two real datasets which were crawled from Vietnamese sites containing 7 million pages, 132 million links and 15 million pages, 200 million links, respectively. The experimental results showed that the computation speed increase from 10 to 20 times when compared to a CPU Intel Q8400 at 2.67 GHz based version, on both datasets. Our method can also scale up well for larger web graphs.