Application of Graph Sparsification in Developing Parallel Algorithms for Updating Connected Components

Sriram Srinivasan, Sanjukta Bhowmick, Sajal Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Analyzing large dynamic networks is an importantproblem with applications in a wide range of disciplines. A keyoperation is updating the network properties as its topologychanges. In this paper we present graph sparsification as anefficient abstraction for updating the properties of dynamic networks. We demonstrate the applicability of graph sparsificationin updating the connected components in random and scalefreenetworks on shared memory systems. Our results showthat the updating is scalable (10X on 16 processors for largernetworks). To the best of our knowledge this is the first parallelimplementation of graph sparsification. Based on these initialresults, we discuss how the current implementation can befurther improved and how graph sparsification can be appliedto updating other network properties.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages885-891
Number of pages7
ISBN (Electronic)9781509021406
DOIs
StatePublished - Jul 18 2016
Event30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 - Chicago, United States
Duration: May 23 2016May 27 2016

Publication series

NameProceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016

Other

Other30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016
Country/TerritoryUnited States
CityChicago
Period5/23/165/27/16

Keywords

  • Dynamic networks
  • Graph sparsification

ASJC Scopus subject areas

  • Computer Networks and Communications

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