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/Territory	United States
City	Atlanta, GA
Period	4/19/10 → 4/23/10

ASJC Scopus subject areas

Computational Theory and Mathematics
Software
Theoretical Computer Science

Access to Document

10.1109/IPDPSW.2010.5470779

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, 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

@inproceedings{8b9d8d4ce7be42969d61010cfd4e89ab,

title = "Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking",

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.",

author = "Abderezak Touzene",

year = "2010",

doi = "10.1109/IPDPSW.2010.5470779",

language = "English",

isbn = "9781424465347",

booktitle = "Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010",

note = "2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010 ; Conference date: 19-04-2010 Through 23-04-2010",

}

TY - GEN

T1 - Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking

AU - Touzene, Abderezak

PY - 2010

Y1 - 2010

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=77954079327&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77954079327&partnerID=8YFLogxK

U2 - 10.1109/IPDPSW.2010.5470779

DO - 10.1109/IPDPSW.2010.5470779

M3 - Conference contribution

AN - SCOPUS:77954079327

SN - 9781424465347

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

T2 - 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010

Y2 - 18 April 2010 through 22 April 2010

ER -

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

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this