References & Citations
Computer Science > Data Structures and Algorithms
Title: Parallel Derandomization for Coloring
(Submitted on 8 Feb 2023 (v1), last revised 25 Apr 2024 (this version, v3))
Abstract: 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 Halldorsson et al. (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.
Submission history
From: Gopinath Mishra [view email][v1] Wed, 8 Feb 2023 23:56:25 GMT (55kb)
[v2] Wed, 24 Apr 2024 09:14:19 GMT (57kb)
[v3] Thu, 25 Apr 2024 15:46:53 GMT (57kb)
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