On the relative significance of kernelization versus branching for parallel FPT implementations

Ronald D. Hagan, Charles A. Philips, Kai Wang, Gary L. Rogers, Callum Lowcay, Michael A. Langston

Research output: Contribution to book or proceedingConference articlepeer-review

2 Scopus citations

Abstract

We investigate the relative significance of kernelization versus branching for parallel FPT implementations. Using the well-known vertex cover problem as a familiar example, we build and experiment with a testbed of five different classes of difficult graphs. For some, we find that kernelization alone obviates the need for parallelism. For others, we show that kernelization and branching work in synergy to produce efficient implementations. And yet for others, kernelization fails completely, leaving branching to solve the entire problem. Structural graph properties are studied in an effort to explicate this trichotomy. The NP-completeness of vertex cover makes scalability an extreme challenge. We mainly employ Hopper, named after the famous computing pioneer Admiral Grace Murray Hopper. The Hopper platform is currently one of the world's fastest supercomputers.

Original languageEnglish
Title of host publicationIASTED Multiconferences - Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2013
Pages621-627
Number of pages7
DOIs
StatePublished - 2013
Event11th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2013 - Innsbruck, Austria
Duration: Feb 11 2013Feb 13 2013

Publication series

NameIASTED Multiconferences - Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2013

Conference

Conference11th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2013
Country/TerritoryAustria
CityInnsbruck
Period02/11/1302/13/13

Scopus Subject Areas

  • Computer Networks and Communications
  • Software

Keywords

  • Fixed-parameter tractability
  • High performance computation
  • Kernelization and branching
  • Parallel algorithms and implementations

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