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Overcoming Congestion in Distributed Coloring

Published: 21 July 2022 Publication History

Abstract

We present a new technique to efficiently sample and communicate a large number of elements from a distributed sampling space. When used in the context of a recent Local algorithm for (degree +1)-list-coloring (D1LC), this allows us to solve D1LC in O(log5 logn) Congest rounds, and in only O(log* n) rounds when the graph has minimum degree Ω(log7 n), w.h.p.
The technique also has immediate applications in testing some graph properties locally, and for estimating the sparsity/density of local subgraphs in O(1) Congest rounds, w.h.p.

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Cited By

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  • (2024)Parallel Derandomization for Coloring*2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS57955.2024.00098(1058-1069)Online publication date: 27-May-2024
  • (2023)Distributed Graph Coloring Made EasyACM Transactions on Parallel Computing10.1145/360589610:4(1-21)Online publication date: 14-Dec-2023

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cover image ACM Conferences
PODC'22: Proceedings of the 2022 ACM Symposium on Principles of Distributed Computing
July 2022
509 pages
ISBN:9781450392624
DOI:10.1145/3519270
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Published: 21 July 2022

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Author Tags

  1. congest model
  2. distributed graph coloring
  3. pseudorandomness

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View all
  • (2024)Parallel Derandomization for Coloring*2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS57955.2024.00098(1058-1069)Online publication date: 27-May-2024
  • (2023)Distributed Graph Coloring Made EasyACM Transactions on Parallel Computing10.1145/360589610:4(1-21)Online publication date: 14-Dec-2023

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