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Graph Coloring on Coarse Grained Multicomputers
, 2002
"... We present an efficient and scalable Coarse Grained Multicomputer (CGM) coloring algorithm that colors a graph G with at most D+ 1 colors where D is the maximum degree in G. This algorithm is given in two variants: randomized and deterministic. We show that on a pprocessor CGM model the proposed al ..."
Abstract

Cited by 7 (1 self)
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We present an efficient and scalable Coarse Grained Multicomputer (CGM) coloring algorithm that colors a graph G with at most D+ 1 colors where D is the maximum degree in G. This algorithm is given in two variants: randomized and deterministic. We show that on a pprocessor CGM model the proposed algorithms require a parallel time of O( G p ) and a total work and overall communication cost of O(G). These bounds correspond to the average case for the randomized version and to the worstcase for the deterministic variant. Key words: graph algorithms, parallel algorithms, graph coloring, Coarse Grained Multicomputers 1
Parallel and Online Graph Coloring
"... We discover a surprising connection between graph coloring in two orthogonal paradigms: parallel and online computing. We present a randomized online coloring algorithm with a performance ratio of O(n = log n), an improvement of plog n factor over the previous best known algorithm of Vishwanathan. ..."
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Cited by 5 (3 self)
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We discover a surprising connection between graph coloring in two orthogonal paradigms: parallel and online computing. We present a randomized online coloring algorithm with a performance ratio of O(n = log n), an improvement of plog n factor over the previous best known algorithm of Vishwanathan. Also, from the same principles, we construct a parallel coloring algorithm with the same performance ratio, for the o/rst such result. As a byproduct, we obtain a parallel approximation for the independent set problem.
Abstract Graph Coloring on Coarse Grained
"... We present an efficient and scalable Coarse Grained Multicomputer (CGM) coloring algorithm that colors a graph G with at most ∆+1 colors where ∆ is the maximum degree in G. This algorithm is given in two variants: a randomized and a deterministic. We show that on a pprocessor CGM model the proposed ..."
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We present an efficient and scalable Coarse Grained Multicomputer (CGM) coloring algorithm that colors a graph G with at most ∆+1 colors where ∆ is the maximum degree in G. This algorithm is given in two variants: a randomized and a deterministic. We show that on a pprocessor CGM model the proposed algorithms require a parallel time of O ( G p) and a total work and overall communication cost of O(G). These bounds correspond to the average case for the randomized version and to the worst case for the deterministic variant.