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A new parallel block aggregated algorithm for solving Markov chains

作者:Abderezak Touzene;
4.5

In this paper, we propose a new scalable parallel block aggregated iterative method (PBA) for computing the stationary distribution of a Markov chain. The PBA technique is based on aggregation of groups (block) of Markov chain states. Scalability of the PBA algorithm depends on varying the number of blocks and their size, assigned to each processor. PBA solves the aggregated blocks very efficiently using a modified LU factorization technique. Some Markov chains have been tested to compare the performance of PBA algorithm with other block techniques such as parallel block Jacobi and block Gauss-Seidel. In all the tested models PBA outperforms the other parallel block methods.

作者单位Computer Science Department, Sultan Qaboos University, P.O. Box 36, Al-khod 123, Oman;

页码p.573-587

总页数15

正文语种eng

中图分类

关键词:performance evaluation; markov chains; parallel block methods