Web-site-based partitioning techniques for efficient parallelization of the PageRank computation
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Web search engines use ranking techniques to order Web pages in query results. PageRank is an important technique, which orders Web pages according to the linkage structure of the Web. The efficiency of the PageRank computation is important since the constantly evolving nature of the Web requires this computation to be repeated many times. PageRank computation includes repeated iterative sparse matrix-vector multiplications. Due to the enormous size of the Web matrix to be multiplied, PageRank computations are usually carried out on parallel systems. However, efficiently parallelizing PageRank is not an easy task, because of the irregular sparsity pattern of the Web matrix. Graph and hypergraphpartitioning-based techniques are widely used for efficiently parallelizing matrixvector multiplications. Recently, a hypergraph-partitioning-based decomposition technique for fast parallel computation of PageRank is proposed. This technique aims to minimize the communication overhead of the parallel matrix-vector multiplication. However, the proposed technique has a high prepropocessing time, which makes the technique impractical. In this work, we propose 1D (rowwise and columnwise) and 2D (fine-grain and checkerboard) decomposition models using web-site-based graph and hypergraph-partitioning techniques. Proposed models minimize the communication overhead of the parallel PageRank computations with a reasonable preprocessing time. The models encapsulate not only the matrix-vector multiplication, but the overall iterative algorithm. Conducted experiments show that the proposed models achieve fast PageRank computation with low preprocessing time, compared with those in the literature.
Parallel Sparse-Matrix Vector Multiplication
Graph and Hypergraph Partitioning
QA188 .C49 2006
Sparse matrices Data processing.