Improved Deterministic $(\Delta+1)$-Coloring in Low-Space MPC

TL;DR

This paper introduces a deterministic low-space MPC algorithm for $()$-coloring that operates in $O(\u0012\u0012 )$ rounds, derandomizing a known randomized algorithm and breaking previous complexity barriers.

Contribution

It presents the first deterministic low-space MPC algorithm for $()$-coloring with sublogarithmic round complexity, derandomizing a state-of-the-art randomized method.

Findings

Achieves $O(\u0012\u0012)$ round complexity for $()$-coloring.

Uses novel derandomization techniques with PRGs and bounded-independence hash functions.

Matches the theoretical lower bound barrier for randomized algorithms in this model.

Abstract

We present a deterministic $O(\log \log \log n)$-round low-space Massively Parallel Computation (MPC) algorithm for the classical problem of $(\Delta+1)$-coloring on $n$-vertex graphs. In this model, every machine has a sublinear local memory of size $n^{\phi}$ for any arbitrary constant $\phi \in (0,1)$. Our algorithm works under the relaxed setting where each machine is allowed to perform exponential (in $n^{\phi}$) local computation, while respecting the $n^{\phi}$ space and bandwidth limitations. Our key technical contribution is a novel derandomization of the ingenious $(\Delta+1)$-coloring LOCAL algorithm by Chang-Li-Pettie (STOC 2018, SIAM J. Comput. 2020). The Chang-Li-Pettie algorithm runs in $T_{local}=poly(\log\log n)$ rounds, which sets the state-of-the-art randomized round complexity for the problem in the local model. Our derandomization employs a combination of tools, most notably pseudorandom generators (PRG) and bounded-independence hash functions. The achieved round complexity of $O(\log\log\log n)$ rounds matches the bound of $\log(T_{local})$, which currently serves an upper bound barrier for all known randomized algorithms for locally-checkable problems in this model. Furthermore, no deterministic sublogarithmic low-space MPC algorithms for the $(\Delta+1)$-coloring problem were previously known.