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Parallel derandomization for coloring
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DOI：10.1016/j.tcs.2026.115912.png)
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Abstract
En 中文
Graph coloring problems are among the most fundamental problems in parallel and distributed computing, and have been studied extensively in both settings. In this context, designing efficient deterministic algorithms for these problems has been found particularly challenging. In this work we consider this challenge, and design a novel framework for derandomizing algorithms for coloring-type problems in the Massively Parallel Computation (MPC) model with sublinear space. We give an application of this framework by showing that a recent (degree + 1)-list coloring algorithm by Halld & oacute;rsson, Kuhn, Nolin, and Tonoyan (STOC'22) in the LOCAL model of distributed computation can be translated to the MPC model and efficiently derandomized. Our algorithm runs in O(log log log n) rounds, which matches the complexity of the state of the art algorithm for the (Delta + 1)-coloring problem.
Keywords:
Parallel algorithms
Graph coloring
Derandomization
Journal
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Papers:
183
Citations:
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