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Title: Parallel Graph Coloring.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the SIAM Conference on Computational Science & Engineering (CSE) held March 14-18, 2015 in SLC, UT.
Country of Publication:
United States

Citation Formats

Boman, Erik G., and Rajamanickam, Sivasankaran. Parallel Graph Coloring.. United States: N. p., 2015. Web.
Boman, Erik G., & Rajamanickam, Sivasankaran. Parallel Graph Coloring.. United States.
Boman, Erik G., and Rajamanickam, Sivasankaran. 2015. "Parallel Graph Coloring.". United States. doi:.
title = {Parallel Graph Coloring.},
author = {Boman, Erik G. and Rajamanickam, Sivasankaran},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 3

Other availability
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  • The graph coloring problem is an NP-Complete problem with a wide array of applications, such as course scheduling, exam scheduling, register allocation, and parallelizing solutions for sparse systems of linear equations. Much theoretical effort has been put into designing heuristics that perform well on randomly generated graphs. The best sequential heuristics require large amounts of time and tuning of various parameters in the heuristics. We have used parallelism to combine exhaustive search with successful heuristic strategies to create a new heuristic, Hybrid, which does well on a wide variety of graphs, without any tuning of parameters. We have also gatheredmore » real application data and tested several heuristics on this data. Our study of real data points out some flaws in studying only random graphs and also suggests interesting new problems for study.« less
  • In large-scale parallel applications a graph coloring is often carried out to schedule computational tasks. In this paper, we describe a new distributed memory algorithm for doing the coloring itself in parallel. The algorithm operates in an iterative fashion; in each round vertices are speculatively colored based on limited information, and then a set of incorrectly colored vertices, to be recolored in the next round, is identified. Parallel speedup is achieved in part by reducing the frequency of communication among processors. Experimental results on a PC cluster using up to 16 processors show that the algorithm is scalable.
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