期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：4
页码：276-279
语种：English
出版社：Ayushmaan Technologies
摘要：The Page Rank design is an important component in effective intricacy search. A power method formulation, which efficiently handles the problem of dangling pages, is investigated for parallelization of Page Rank computation. We claim that hyper graph partitioning with multiple constraints and fixed vertices should be implemented using direct K-way refinement, instead of the widely adopted recursive bisection paradigm. Hence, the Page Rank computation, which is frequently chime, must be performed in coextending with high-efficiency and low-preprocessing overhead while considering the initial distributed nature of the intricacy matrices We first investigate the application of state-ofthe- art sparse matrix partitioning models in order toattain high efficiency in parallel Page Rank computations with a particular focus on reducing the preprocessing overhead they introduce. We also propose an efficient parallelization scheme for matrixvector multiplies in order to avoid possible communication due to the pages without in-links. Second, we consider the more astute scenario of starting with an initially distributed data and extend our designs to cover the Redetachment of such data for efficient Page Rank computation. Experimental results on provedent datasets validate the performance of theproposedmodels.
