Abstract
In the context of distributed synchronous computing, processors perform in rounds, and the time complexity of a distributed algorithm is classically defined as the number of rounds before all computing nodes have output. Hence, this complexity measure captures the running time of the slowest node(s). In this paper, we are interested in the running time of the ordinary nodes, to be compared with the running time of the slowest nodes. The node-averaged time-complexity of a distributed algorithm on a given instance is defined as the average, taken over every node of the instance, of the number of rounds before that node output. We compare the node-averaged time-complexity with the classical one in the standard \(\mathsf {LOCAL}\) model for distributed network computing. We show that there can be an exponential gap between the node-averaged time-complexity and the classical time-complexity, as witnessed by, e.g., leader election. Our first main result is a positive one, stating that, in fact, the two time-complexities behave the same for a large class of problems on very sparse graphs. In particular, we show that, for \(\mathsf {LCL}\) problems on cycles, the node-averaged time complexity is of the same order of magnitude as the “slowest node” time-complexity. In addition, in the \(\mathsf {LOCAL}\) model, the time-complexity is computed as a worst case over all possible identity assignments to the nodes of the network. In this paper, we also investigate the ID-averaged time-complexity, when the number of rounds is averaged over all possible identity assignments of size \(O(\log n)\). Our second main result is that the ID-averaged time-complexity is essentially the same as the expected time-complexity of randomized algorithms (where the expectation is taken over all possible random bits used by the nodes, and the number of rounds is measured for the worst-case identity assignment). Finally, we study the node-averaged ID-averaged time-complexity. We show that 3-colouring the n-node ring requires \(\varTheta (\log ^*n)\) rounds if the number of rounds is averaged over the nodes, or if the number of rounds is averaged over the identity assignments. In contrast, we show that 3-colouring the ring requires only O(1) rounds if the number of rounds is averaged over the nodes, and over the identity assignments.
The author received additional support from ANR project DESCARTES, and Inria project GANG.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
There is a subtlety here, which is that after k rounds in the message-passing algorithm a node cannot know the edges that are between nodes at distance exactly k from it. For the sake of simplicity, we consider the proper k-neighbourhoods, as it does not affect the asymptotic of the algorithms.
- 2.
Remember that the nodes do not have the knowledge of the size of the network, thus they have exactly the same information in G and \(G'\).
- 3.
References
Alon, N., Babai, L., Itai, A.: A fast and simple randomized parallel algorithm for the maximal independent set problem. J. Algorithms 7(4), 567–583 (1986)
Attiya, H., Welch, J.: Distributed Computing: Fundamentals, Simulations, and Advanced Topics. Wiley, Hoboken (2004)
Barenboim, L., Elkin, M.: Sublogarithmic distributed MIS algorithm for sparse graphs using Nash-Williams decomposition. Distrib. Comput. 22(5–6), 363–379 (2010). https://doi.org/10.1007/s00446-009-0088-2
Brandt, S., Fischer, O., Hirvonen, J., Keller, B., Lempiäinen, T., Rybicki, J., Suomela, J., Uitto, J.: A lower bound for the distributed lovász local lemma. In: Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18–21, 2016, pp. 479–488 (2016). https://doi.org/10.1145/2897518.2897570
Brandt, S., Hirvonen, J., Korhonen, J.H., Lempiäinen, T., Östergård, P.R.J., Purcell, C., Rybicki, J., Suomela, J., Uznanski, P.: LCL problems on grids. CoRR, abs/1702.05456 (2017). arXiv:1702.05456
Chang, Y.J., Kopelowitz, T., Pettie, S.: An exponential separation between randomized and deterministic complexity in the local model. In: IEEE 57th Annual Symposium on Foundations of Computer Science, FOCS 2016, 9–11 October 2016, Hyatt Regency, New Brunswick, New Jersey, USA, pp. 615–624 (2016). https://doi.org/10.1109/FOCS.2016.72
Cole, R., Vishkin, U.: Deterministic coin tossing with applications to optimal parallel list ranking. Inf. Control 70(1), 32–53 (1986). https://doi.org/10.1016/S0019-9958(86)80023-7
Feuilloley, L.: Brief announcement: average complexity for the LOCAL model. In: Proceedings of the 2015 ACM Symposium on Principles of Distributed Computing, PODC 2015, Donostia-San Sebastián, Spain, July 21–23, 2015, pp. 335–337 (2015). https://doi.org/10.1145/2767386.2767446
Feuilloley, L., Fraigniaud, P.: Survey of distributed decision. Bull. EATCS 119 (2016). EATCS: The Distributed Computing Column by Stefan Schmid
Fraigniaud, P., Korman, A., Peleg, D.: Towards a complexity theory for local distributed computing. J. ACM 60(5), 35 (2013). https://doi.org/10.1145/2499228
Gamarnik, D., Sudan, M.: Limits of local algorithms over sparse random graphs. In: Proceedings of Innovations in Theoretical Computer Science, ITCS’14, Princeton, NJ, USA, January 12–14, 2014, pp. 369–376 (2014). https://doi.org/10.1145/2554797.2554831
Ghaffari, M.: An improved distributed algorithm for maximal independent set. In: Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016, Arlington, VA, USA, January 10–12, 2016, pp. 270–277 (2016). https://doi.org/10.1137/1.9781611974331.ch20
Goldreich, O.: Introduction to testing graph properties. In: Goldreich, O. (ed.) Property Testing - Current Research and Surveys. LNCS, vol. 6390, pp. 105–141. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16367-8_7
Harris, D.G., Schneider, J., Su, H.H.: Distributed \((\Delta +1)\)-coloring in sublogarithmic rounds. In: Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18–21, 2016, pp. 465–478 (2016). https://doi.org/10.1145/2897518.2897533
Korman, A., Sereni, J.-S., Viennot, L.: Toward more localized local algorithms: removing assumptions concerning global knowledge. Distrib. Comput. 26(5–6), 289–308 (2013). https://doi.org/10.1007/s00446-012-0174-8
Linial, N.: Locality in distributed graph algorithms. SIAM J. Comput. 21(1), 193–201 (1992)
Luby, M.: A simple parallel algorithm for the maximal independent set problem. SIAM J. Comput. 15(4), 1036–1053 (1986). https://doi.org/10.1137/0215074
Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann, Burlington (1996)
Musto, T.: Knowledge of degree bounds in local algorithms. Master’s thesis, University of Helsinki (2011)
Naor, M.: A lower bound on probabilistic algorithms for distributive ring coloring. SIAM J. Discrete Math. 4(3), 409–412 (1991). https://doi.org/10.1137/0404036
Naor, M., Stockmeyer, L.J.: What can be computed locally? SIAM J. Comput. 24(6), 1259–1277 (1995). https://doi.org/10.1137/S0097539793254571
Peleg, D.: Distributed Computing: A Locality-Sensitive Approach. SIAM, Philadelphia (2000)
Pettie, S., Ramachandran, V.: Minimizing randomness in minimum spanning tree, parallel connectivity, and set maxima algorithms. In: Proceedings of the Thirteenth Annual ACM-SIAM Symposium on Discrete Algorithms, January 6–8, 2002, San Francisco, CA, USA., pp. 713–722 (2002). acm:545381.545477
Santoro, N.: Design and Analysis of Distributed Algorithms, vol. 56. Wiley, Hoboken (2006)
Sloane, N.J.A.: The On-Line Encyclopedia of Integer Sequences. A000788
Yao, A.C.C.: Probabilistic computations: toward a unified measure of complexity (extended abstract). In: 18th Annual Symposium on Foundations of Computer Science, Providence, Rhode Island, USA, 31 October - 1 November 1977, pp. 222–227 (1977). https://doi.org/10.1109/SFCS.1977.24
Acknowledgements
I would like to thank Juho Hirvonen, Tuomo Lempiäinen and Jukka Suomela for fruitful discussions, and Pierre Fraigniaud for both discussions, and help for the writing. I thank the reviewers for helpful comments, and Mohsen Ghaffari for pointing out that randomized node-averaged complexity could be considered.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Feuilloley, L. (2017). How Long It Takes for an Ordinary Node with an Ordinary ID to Output?. In: Das, S., Tixeuil, S. (eds) Structural Information and Communication Complexity. SIROCCO 2017. Lecture Notes in Computer Science(), vol 10641. Springer, Cham. https://doi.org/10.1007/978-3-319-72050-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-72050-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72049-4
Online ISBN: 978-3-319-72050-0
eBook Packages: Computer ScienceComputer Science (R0)