Parallel Derandomization for Coloring

Coy, Sam; Czumaj, Artur; Davies-Peck, Peter; Mishra, Gopinath

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 (∆ + 1)-coloring problem.

Citation

Coy, S., Czumaj, A., Davies-Peck, P., & Mishra, G. (in press). Parallel Derandomization for Coloring.

• Conference Name: 38th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2024)
• Conference Location: San Francisco
• Start Date: May 27, 2024
• End Date: May 31, 2024
• Acceptance Date: Jan 31, 2024
• Deposit Date: Feb 23, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Keywords: Parallel algorithms; Graph coloring; Derandomization
• Public URL: https://durham-repository.worktribe.com/output/2272976
• Publisher URL: https://ieeexplore.ieee.org/xpl/conhome/1800044/all-proceedings