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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.

A Scalable Parallel Graph Coloring Algorithm for Distributed Memory Computers