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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010
DOIs
Publication statusPublished - 2010
Event2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010 - Atlanta, GA, United States
Duration: Apr 19 2010Apr 23 2010

Other

Other2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010
CountryUnited States
CityAtlanta, GA
Period4/19/104/23/10

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking'. Together they form a unique fingerprint.

  • Cite this

    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] https://doi.org/10.1109/IPDPSW.2010.5470779