Parallel Derandomization for Coloring

TL;DR

This paper introduces a new derandomization framework for graph coloring algorithms in the MPC model, enabling efficient deterministic solutions that match the best randomized algorithms in terms of complexity.

Contribution

The paper presents a novel framework for derandomizing coloring algorithms in the MPC model, bridging distributed and parallel computing approaches.

Findings

Derandomized a $( ext{degree}+1)$-list coloring algorithm in MPC.

Achieved $O( ext{log} ext{log} ext{log} n)$ round complexity.

Matched the complexity of the best randomized algorithms for coloring.

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.