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SIAM Journal on Scientific Computing
Article . 1993 . Peer-reviewed
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A Parallel Graph Coloring Heuristic

A parallel graph coloring heuristic
Authors: Jones, Mark T.; Plassmann, Paul E.;

A Parallel Graph Coloring Heuristic

Abstract

Summary: The problem of computing good graph colorings arises in many diverse applications, such as in the estimation of sparse Jacobians and in the development of efficient, parallel iterative methods for solving sparse linear systems. This paper presents an asynchronous graph coloring heuristic well suited to distributed memory parallel computers. Experimental results obtained on an Intel iPSC/860 are presented, which demonstrate that, for graphs arising from finite element applications, the heuristic exhibits scalable performance and generates colorings usually within three or four colors of the best-known linear time sequential heuristics. For bounded degree graphs, it is shown that the expected running time of the heuristic under the P-RAM computation model is bounded by \(EO(\log(n)/\log\log(n))\). This bound is an improvement over the previously known best upper bound for the expected running time of a random heuristic for the graph coloring problem.

Keywords

Iterative numerical methods for linear systems, Numerical solutions to overdetermined systems, pseudoinverses, Graph theory (including graph drawing) in computer science, sparse matrices, Distributed algorithms, Parallel numerical computation, distributed memory computers, random algorithms, graph coloring heuristics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
149
Top 1%
Top 1%
Top 10%
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