A Parallel MCMC Algorithm for the Balanced Graph Coloring Problem

Jianyi Lin, D. Conte, G. Grossi, R. Lanzarotti, A. Petrini

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

Abstract

In parallel computation domain, graph coloring is widely studied in its own and represents a reference problem for scheduling of parallel tasks. Unfortunately, common graph coloring strategies usually focus on minimizing the number of colors without any concern for the sizes of each color class, thus producing highly skewed color class distributions. However, to guarantee efficiency in parallel computations, but also in other application contexts, it is important to keep the color classes highly balanced in their sizes. In this paper we address this challenging issue for large scale graphs, proposing a fast parallel MCMC heuristic for sparse graphs that randomly generates good balanced colorings provided that a sufficient number of colors are made available. We show its effectiveness through some numerical simulations on random graphs.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages161-171
Number of pages11
Volume11510
DOIs
Publication statusPublished - 2019
Event12th IAPR-TC15 Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2019 - fra
Duration: 19 Jun 201921 Jun 2019

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE

Workshop

Workshop12th IAPR-TC15 Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2019
Cityfra
Period19/6/1921/6/19

Keywords

  • Balanced graph coloring
  • Parallel algorithms
  • Markov Chain Monte Carlo method
  • Greedy colorer

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