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Fast Deterministic Massively Parallel Ruling Sets Algorithms
Conference proceeding   Open access

Fast Deterministic Massively Parallel Ruling Sets Algorithms

Hongyan Ji, Kishore Kothapalli, Sriram V Pemmaraju and Ajitanshu Singh
Proceedings of the 26th International Conference on Distributed Computing and Networking, pp.152-160
ACM Other Conferences
ICDCN 2025: 26th International Conference on Distributed Computing and Networking
01/04/2025
DOI: 10.1145/3700838.3700872
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https://doi.org/10.1145/3700838.3700872View
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Abstract

In this paper, we present a deterministic \(\tilde{O}(\log ^{1/3}\Delta) \)-round algorithm for the 2-ruling set problem in the sublinear Massively Parallel Computation (MPC) model. This improves upon the fastest known deterministic 2-ruling set algorithm for this model, which is the \(\tilde{O}(\sqrt {\log n}) \)-round algorithm by Giliberti and Parsaeian (PODC 2024). Our result is obtained by derandomizing the “sample-and-gather” approach of Kothapalli, Pai, and Pemmaraju (FSTTCS 2020). The “sample-and-gather” approach involves making random sampling decisions, not just for the current iteration, but a batch of future iterations. Thus, derandomizing this approach requires the “fixing” of randomness for a batch of future iterations. We further extend our results to show that a β -ruling set for β ≥ 2 can be obtained in \(\tilde{O}(\log ^{1/2^{\beta }-1} \Delta) \) deterministic rounds in the sublinear MPC model. Additionally, we present a deterministic β -ruling set algorithms for sparse graphs (i.e., bounded arboricity graphs) where β ≥ 2, which runs in \(\tilde{O}(\log ^{1/2^\beta - 1} \lambda) \) rounds for arboricity-λ graphs in the sublinear MPC model.
Theory of computation -- Distributed algorithms Theory of computation -- Massively parallel algorithms Theory of computation -- Models of computation UIOWA OA Agreement

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