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Simple, Deterministic, Constant-Round Coloring in the Congested Clique

Published: 31 July 2020 Publication History

Abstract

We settle the complexity of the (Δ + 1)-coloring and (Δ + 1)-list coloring problems in the CONGESTED CLIQUE model by presenting a simple deterministic algorithm for both problems running in a constant number of rounds. This matches the complexity of the recent breakthrough randomized constant-round (Δ + 1)-list coloring algorithm due to Chang et al. (PODC'19), and significantly improves upon the state-of-the-art O(log Δ)-round deterministic (Δ + 1)-coloring bound of Parter (ICALP'18).
A remarkable property of our algorithm is its simplicity. Whereas the state-of-the-art randomized algorithms for this problem are based on the quite involved local coloring algorithm of Chang et al. (STOC'18), our algorithm can be described in just a few lines. At a high level, it applies a careful derandomization of a recursive procedure which partitions the nodes and their respective palettes into separate bins. We show that after O(1) recursion steps, the remaining uncolored subgraph within each bin has linear size, and thus can be solved locally by collecting it to a single node. This algorithm can also be implemented in the Massively Parallel Computation (MPC) model provided that each machine has linear (in n, the number of nodes in the input graph) space.
We also show an extension of our algorithm to the MPC regime in which machines have sublinear space: we present the first deterministic (Δ + 1)-list coloring algorithm designed for sublinear-space MPC, which runs in O(log Δ + log log n) rounds.

References

[1]
Philipp Bamberger, Fabian Kuhn, and Yannic Maus. Efficient deterministic distributed coloring with small bandwidth. In Proceedings of the 38th ACM Symposium on Principles of Distributed Computing (PODC), 2020.
[2]
Leonid Barenboim and Victor Khazanov. Distributed symmetry-breaking algorithms for Congested Cliques. In Proceedings of the 13th International Computer Science Symposium in Russia (CSR), pages 41--52, 2018.
[3]
Soheil Behnezhad, Mahsa Derakhshan, and MohammadTaghi Hajiaghayi. Brief announcement: Semi-MapReduce meets Congested Clique. CoRR abs/1802.10297, 2018.
[4]
Mihir Bellare and John Rompel. Randomness-efficient oblivious sampling. In Proceedings of the 35th IEEE Symposium on Foundations of Computer Science (FOCS), pages 276--287, 1994.
[5]
Keren Censor-Hillel, Merav Parter, and Gregory Schwartzman. Derandomizing local distributed algorithms under bandwidth restrictions. In Proceedings of the 31st International Symposium on Distributed Computing (DISC), pages 11:1--11:16, 2017.
[6]
Yi-Jun Chang, Manuela Fischer, Mohsen Ghaffari, Jara Uitto, and Yufan Zheng. The complexity of (Δ + 1) coloring in Congested Clique, massively parallel computation, and centralized local computation. In Proceedings of the 38th ACM Symposium on Principles of Distributed Computing (PODC), pages 471--480, 2019.
[7]
Artur Czumaj, Peter Davies, and Merav Parter. Graph sparsification for derandomizing massively parallel computation with low space. In Proceedings of the 32nd Annual ACM Symposium on Parallel Algorithms and Architectures (SPAA), 2020. Also in CoRR abs/1912.05390, December 2019.
[8]
Jeffrey Dean and Sanjay Ghemawat. MapReduce: Simplified data processing on large clusters. In Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation (OSDI), pages 10--10, 2004.
[9]
Jeffrey Dean and Sanjay Ghemawat. MapReduce: Simplified data processing on large clusters. Commununication of the ACM, 51(1):107--113, January 2008.
[10]
Mohsen Ghaffari, Fabian Kuhn, and Jara Uitto. Conditional hardness results for massively parallel computation from distributed lower bounds. In Proceedings of the 60th IEEE Symposium on Foundations of Computer Science (FOCS), pages 1650--1663, 2019.
[11]
Michael T. Goodrich, Nodari Sitchinava, and Qin Zhang. Sorting, searching, and simulation in the MapReduce framework. In Proceedings of the 22nd International Symposium on Algorithms and Computation (ISAAC), pages 374--383, 2011.
[12]
Yijie Han. A fast derandomization scheme and its applications. SIAM Journal on Computing, 25(1):52--82, 1996.
[13]
Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly. Dryad: Distributed data-parallel programs from sequential building blocks. SIGOPS Operating Systems Review, 41(3):59--72, March 2007.
[14]
Howard J. Karloff, Siddharth Suri, and Sergei Vassilvitskii. A model of computation for MapReduce. In Proceedings of the 21st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 938--948, 2010.
[15]
Christoph Lenzen. Optimal deterministic routing and sorting on the Congested Clique. In Proceedings of the 32nd ACM Symposium on Principles of Distributed Computing (PODC), pages 42--50, 2013.
[16]
Zvi Lotker, Boaz Patt-Shamir, and Seth Pettie. Improved distributed approximate matching. Journal of the ACM, 62(5):38:1--38:17, November 2015.
[17]
Michael Luby. A simple parallel algorithm for the maximal independent set problem. SIAM Journal on Computing, 15(4):1036--1053, 1986.
[18]
Merav Parter. (Δ + 1) coloring in the Congested Clique model. In Proceedings of the 45th Annual International Colloquium on Automata, Languages and Programming (ICALP), pages 160:1--160:14, 2018.
[19]
Merav Parter and Hsin-Hao Su. Randomized (Δ + 1)-coloring in O(log* Δ) Congested Clique rounds. In Proceedings of the 32nd International Symposium on Distributed Computing (DISC), pages 39:1--39:18, 2018.
[20]
Václav Rozhoň and Mohsen Ghaffari. Polylogarithmic-time deterministic network decomposition and distributed derandomization. In Proceedings of the 52nd Annual ACM Symposium on Theory of Computing (STOC), pages 350--363, 2020.
[21]
Salil P. Vadhan. Pseudorandomness. Foundations and Trends in Theoretical Computer Science, 7(1-3):1--336, 2012.
[22]
Tom White. Hadoop: The Definitive Guide. O'Reilly Media, Inc., 2012.
[23]
Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, and Ion Stoica. Spark: Cluster computing with working sets. In Proceedings of the 2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud), 2010.

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cover image ACM Conferences
PODC '20: Proceedings of the 39th Symposium on Principles of Distributed Computing
July 2020
539 pages
ISBN:9781450375825
DOI:10.1145/3382734
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Published: 31 July 2020

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Author Tags

  1. coloring
  2. congested clique
  3. derandomization
  4. massively parallel computation

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