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      Site-based partitioning and repartitioning techniques for parallel pagerank computation

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      Author
      Cevahir, A.
      Aykanat, Cevdet
      Turk, A.
      Cambazoglu, B. B.
      Date
      2011-05
      Source Title
      IEEE Transactions on Parallel and Distributed Systems
      Print ISSN
      1045-9219
      Electronic ISSN
      1558-2183
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      22
      Issue
      5
      Pages
      786 - 802
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      The PageRank algorithm is an important component in effective web search. At the core of this algorithm are repeated sparse matrix-vector multiplications where the involved web matrices grow in parallel with the growth of the web and are stored in a distributed manner due to space limitations. Hence, the PageRank computation, which is frequently repeated, must be performed in parallel with high-efficiency and low-preprocessing overhead while considering the initial distributed nature of the web matrices. Our contributions in this work are twofold. We first investigate the application of state-of-the-art sparse matrix partitioning models in order to attain high efficiency in parallel PageRank computations with a particular focus on reducing the preprocessing overhead they introduce. For this purpose, we evaluate two different compression schemes on the web matrix using the site information inherently available in links. Second, we consider the more realistic scenario of starting with an initially distributed data and extend our algorithms to cover the repartitioning of such data for efficient PageRank computation. We report performance results using our parallelization of a state-of-the-art PageRank algorithm on two different PC clusters with 40 and 64 processors. Experiments show that the proposed techniques achieve considerably high speedups while incurring a preprocessing overhead of several iterations (for some instances even less than a single iteration) of the underlying sequential PageRank algorithm. © 2011 IEEE.
      Keywords
      Graph partitioning
      Hypergraph partitioning
      PageRank
      Parallelization
      Repartitioning
      Sparse matrix partitioning
      Sparse matrix - vector multiplication
      Web search
      Permalink
      http://hdl.handle.net/11693/22022
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TPDS.20http://dx.doi.org/10.119
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