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H. Migallón¹, V. Migallón², J.A. Palomino² and J. Penadés²

¹Department of Physics and Computer Architectures, University Miguel Hernández, Elche, Alicante, Spain
²Department of Computer Science and Artificial Intelligence, University of Alicante, Spain

doi:10.4203/ccp.105.29

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, "Parallel Computation of Pagerank using Extrapolation and Relaxation Techniques", in , (Editors), "Proceedings of the Ninth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 29, 2014. doi:10.4203/ccp.105.29

Keywords: pagerank, parallel algorithms, power method, relaxation and extrapolation, shared memory, distributed memory..

Summary

The pagerank algorithm for determining the importance of web pages has become a central technique in web search. This algorithm uses the power method to compute successive iterates that converge to the principal eigenvector of the Markov chain representing the web link graph. In this paper we present an effective heuristic relaxed and extrapolated algorithm based on the power method that accelerates its convergence. Two strategies of data distribution have been used: row-wise partitioning and nonzero elements partitioning. An hybrid parallel implementation has been designed by combining various OpenMP threads for each MPI process. The results show that the proposed algorithms can speed up the convergence time significantly with respect to the parallel Power algorithm.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 105 PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: Paper 29 Parallel Computation of Pagerank using Extrapolation and Relaxation Techniques H. Migallón¹, V. Migallón², J.A. Palomino² and J. Penadés² ¹Department of Physics and Computer Architectures, University Miguel Hernández, Elche, Alicante, Spain ²Department of Computer Science and Artificial Intelligence, University of Alicante, Spain doi:10.4203/ccp.105.29 purchase the full-text of this paper Full Bibliographic Reference for this paper , "Parallel Computation of Pagerank using Extrapolation and Relaxation Techniques", in , (Editors), "Proceedings of the Ninth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 29, 2014. doi:10.4203/ccp.105.29 Keywords: pagerank, parallel algorithms, power method, relaxation and extrapolation, shared memory, distributed memory.. Summary The pagerank algorithm for determining the importance of web pages has become a central technique in web search. This algorithm uses the power method to compute successive iterates that converge to the principal eigenvector of the Markov chain representing the web link graph. In this paper we present an effective heuristic relaxed and extrapolated algorithm based on the power method that accelerates its convergence. Two strategies of data distribution have been used: row-wise partitioning and nonzero elements partitioning. An hybrid parallel implementation has been designed by combining various OpenMP threads for each MPI process. The results show that the proposed algorithms can speed up the convergence time significantly with respect to the parallel Power algorithm. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £45 +P&P)
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