Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking

Abderezak Touzene

doi:10.1109/IPDPSW.2010.5470779

Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking

Abderezak Touzene^*

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we propose two parallel Aggregation-Isolation iterative methods for solving Markov chains. These parallel methods conserves as much as possible the benefits of aggregation, and Gauss-Seidel effects. Some experiments have been conducted testing models from queuing systems and models from Google Page Ranking. The results of the experiments show super linear speed-up for the parallel Aggregation-Isolation method.

Original language	English
Title of host publication	Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010
DOIs	https://doi.org/10.1109/IPDPSW.2010.5470779
Publication status	Published - 2010
Event	2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010 - Atlanta, GA, United States Duration: Apr 19 2010 → Apr 23 2010

Other

Other	2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010
Country	United States
City	Atlanta, GA
Period	4/19/10 → 4/23/10

ASJC Scopus subject areas

Computational Theory and Mathematics
Software
Theoretical Computer Science

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Touzene, A 2010, Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking. in Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010., 5470779, 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010, Atlanta, GA, United States, 4/19/10. https://doi.org/10.1109/IPDPSW.2010.5470779

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AB - In this paper, we propose two parallel Aggregation-Isolation iterative methods for solving Markov chains. These parallel methods conserves as much as possible the benefits of aggregation, and Gauss-Seidel effects. Some experiments have been conducted testing models from queuing systems and models from Google Page Ranking. The results of the experiments show super linear speed-up for the parallel Aggregation-Isolation method.

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BT - Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010

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Y2 - 19 April 2010 through 23 April 2010

ER -

Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking

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