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Parallel Metric Tree Embedding Based on an Algebraic View on Moore-Bellman-Ford

Published: 22 November 2018 Publication History

Abstract

A metric tree embedding of expected stretch α ≥ 1 maps a weighted n-node graph G = (V, E, ω) to a weighted tree T = (VT, ET, ωT) with VVT such that, for all v,wV, dist(v, w, G) ≤ dist(v, w, T), and E[dist(v, w, T)] ≤ α dist(v, w, G). Such embeddings are highly useful for designing fast approximation algorithms as many hard problems are easy to solve on tree instances. However, to date, the best parallel polylog n)-depth algorithm that achieves an asymptotically optimal expected stretch of α ∈ O(log n) requires Ω (n2) work and a metric as input.
In this article, we show how to achieve the same guarantees using polylog n depth and Õ(m1+ε) work, where m = |E| and ε > 0 is an arbitrarily small constant. Moreover, one may further reduce the work to Õ(m + n1+ε) at the expense of increasing the expected stretch to O(ε−1 log n).
Our main tool in deriving these parallel algorithms is an algebraic characterization of a generalization of the classic Moore-Bellman-Ford algorithm. We consider this framework, which subsumes a variety of previous “Moore-Bellman-Ford-like” algorithms, to be of independent interest and discuss it in depth. In our tree embedding algorithm, we leverage it to provide efficient query access to an approximate metric that allows sampling the tree using polylog n depth and Õ(m) work.
We illustrate the generality and versatility of our techniques by various examples and a number of additional results. Specifically, we (1) improve the state of the art for determining metric tree embeddings in the Congest model, (2) determine a (1 + εˆ)-approximate metric regarding the distances in a graph G in polylogarithmic depth and Õ(n(m+n1 + ε )) work, and (3) improve upon the state of the art regarding the k-median and the buy-at-bulk network design problems.

References

[1]
Amir Abboud, Greg Bodwin, and Seth Pettie. 2017. A hierarchy of lower bounds for sublinear additive spanners. In Proceedings of the Symposium on Discrete Algorithms (SODA). SIAM, Philadelphia, PA, USA, 568--576.
[2]
Miklós Ajtai, János Komlós, and Endre Szemerédi. 1983. An O(n log n) sorting network. In Proceedings of the 15th ACM Symposium on Theory of Computing (STOC). ACM, New York, NY, USA, 1--9.
[3]
Noga Alon, Zvi Galil, and Oded Margalit. 1997. On the exponent of the all pairs shortest path problem. Journal of Computer and System Science 54, 2 (1997), 255--262.
[4]
Noga Alon, Richard M. Karp, David Peleg, and Douglas B. West. 1995. A graph-theoretic game and its application to the k-server problem. SIAM Journal on Computing 24, 1 (1995), 78--100.
[5]
Matthew Andrews. 2004. Hardness of buy-at-bulk network design. In Proceedings of the 45th Symposium on Foundations of Computer Science (FOCS). IEEE, Los Alamitos, CA, USA, 115--124.
[6]
Baruch Awerbuch and Yossi Azar. 1997. Buy-at-bulk network design. In Proceedings of the 38th Annual Symposium on Foundations of Computer Science (FOCS). 542--547.
[7]
Yair Bartal. 1996. Probabilistic approximations of metric spaces and its algorithmic applications. In Proceedings of the 37th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, Los Alamitos, CA, USA, 184--193.
[8]
Yair Bartal. 1998. On approximating arbitrary metrices by tree metrics. In Proceedings of the 30th Annual ACM Symposium on the Theory of Computing (STOC). ACM, New York, NY, USA, 161--168.
[9]
Surender Baswana and Sandeep Sen. 2007. A simple and linear time randomized algorithm for computing sparse spanners in weighted graphs. Random Structures 8 Algorithms 30, 4 (2007), 532--563.
[10]
Ruben Becker, Andreas Karrenbauer, Sebastian Krinninger, and Christoph Lenzen. 2016. Near-optimal approximate shortest paths and transshipment in distributed and streaming models. In 31st International Symposium on Distributed Computing (DISC’17) (Leibniz International Proceedings in Informatics (LIPIcs)), Andréa W. Richa (Ed.), Vol. 91. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 7:1--7:16. http://arxiv.org/abs/1607.05127
[11]
Richard E. Bellman. 1958. On a routing problem. Quarterly Applied Mathematics 16 (1958), 87--90.
[12]
Guy E. Blelloch, Yan Gu, Julian Shun, and Yihan Sun. 2016. Parallelism in randomized incremental algorithms. In Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA). ACM, New York, NY, USA, 467--478.
[13]
Guy E. Blelloch, Yan Gu, and Yihan Sun. 2016. Efficient construction of probabilistic tree embeddings. CoRR abs/1605.04651 (2016). http://arxiv.org/abs/1605.04651.
[14]
Guy E. Blelloch, Anupam Gupta, and Kanat Tangwongsan. 2012. Parallel probabilistic tree embeddings, k-median, and buy-at-bulk network design. In Proceedings of the 24th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA). ACM, New York, NY, USA, 205--213.
[15]
Guy E. Blelloch and Kanat Tangwongsan. 2010. Parallel approximation algorithms for facility-location problems. In Proceedings of the 22nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA). ACM, New York, NY, USA, 315--324.
[16]
Edith Cohen. 1997. Size-estimation framework with applications to transitive closure and reachability. Journal of Computer and System Sciences 55, 3 (1997), 441--453.
[17]
Edith Cohen. 2000. Polylog-time and near-linear work approximation scheme for undirected shortest paths. Journal of the ACM 47, 1 (2000), 132--166.
[18]
Edith Cohen and Haim Kaplan. 2007. Spatially-decaying aggregation over a network. Journal of Computer and System Sciences 73, 3 (2007), 265--288.
[19]
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2009. Introduction to Algorithms (3rd edition). MIT Press. http://mitpress.mit.edu/books/introduction-algorithms.
[20]
Atish Das Sarma, Stephan Holzer, Liah Kor, Amos Korman, Danupon Nanongkai, Gopal Pandurangan, David Peleg, and Roger Wattenhofer. 2012. Distributed verification and hardness of distributed approximation. SIAM Journal on Computing 41, 5 (2012), 1235--1265.
[21]
Edsger W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numerische Mathematik 1, 1 (1959), 269--271.
[22]
Michael Elkin and Ofer Neiman. 2016. Hopsets with constant hopbound, and applications to approximate shortest paths. In Proceedings of the 57th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, Los Alamitos, CA, USA, 128--137.
[23]
Jittat Fakcharoenphol, Satish Rao, and Kunal Talwar. 2004. A tight bound on approximating arbitrary metrics by tree metrics. Journal of Computer and System Sciences 69, 3 (2004), 485--497.
[24]
Lester R. Ford. 1956. Network Flow Theory. Technical Report. The RAND Corporation.
[25]
Stephan Friedrichs and Christoph Lenzen. 2016. Parallel metric tree embedding based on an algebraic view on Moore-Bellman-Ford. In Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA). ACM, New York, NY, USA, 455--466.
[26]
Mohsen Ghaffari and Christoph Lenzen. 2014. Near-optimal distributed tree embedding. In Proceedings of the 28th International Symposium on Distributed Computing (DISC). Springer, Berlin, Heidelberg, 197--211.
[27]
Mathias Hauptmann and Marek Karpinski. A Compendium on Steiner Tree Problems. http://theory.cs.uni-bonn.de/info5/steinerkompendium/netcompendium.html. Visited 2016-05-12.
[28]
Udo Hebisch and Hanns J. Weinert. 1998. Semirings: Algebraic Theory and Applications in Computer Science. World Scientific.
[29]
Monika Henzinger, Sebastian Krinninger, and Danupon Nanongkai. 2016. A deterministic almost-tight distributed algorithm for approximating single-source shortest paths. In Proceedings of the 48th Symposium on Theory of Computing (STOC). ACM, New York, NY, USA, 489--498.
[30]
Maleq Khan, Fabian Kuhn, Dahlia Malkhi, Gopal Pandurangan, and Kunal Talwar. 2012. Efficient distributed approximation algorithms via probabilistic tree embeddings. Distributed Computing 25, 3 (2012), 189--205.
[31]
Philip N. Klein and Sairam Subramanian. 1997. A randomized parallel algorithm for single-source shortest paths. Journal of Algorithms 25, 2 (1997), 205--220.
[32]
François Le Gall. 2014. Powers of tensors and fast matrix multiplication. In Proceedings of the International Symposium on Symbolic and Algebraic Computation (ISSAC). ACM, New York, NY, USA, 296--303.
[33]
Christoph Lenzen and Boaz Patt-Shamir. 2013. Fast routing table construction using small messages: Extended abstract. In Proceedings of the Symposium on Theory of Computing Conference (STOC). ACM, New York, NY, USA, 381--390.
[34]
Christoph Lenzen and Boaz Patt-Shamir. 2014. Improved distributed Steiner forest construction. In Proceedings of the ACM Symposium on Principles of Distributed Computing (PODC). ACM, New York, NY, USA, 262--271.
[35]
Christoph Lenzen and Boaz Patt-Shamir. 2015. Fast partial distance estimation and applications. In Proceedings of the ACM Symposium on Principles of Distributed Computing (PODC). ACM, New York, NY, USA, 153--162.
[36]
Christoph Lenzen and David Peleg. 2013. Efficient distributed source detection with limited bandwidth. In Proceedings of the ACM Symposium on Principles of Distributed Computing (PODC). ACM, New York, NY, USA, 375--382.
[37]
Manor Mendel and Chaya Schwob. 2009. Fast C-K-R partitions of sparse graphs. Chicago Journal of Theoretical Computer Science 2009, 2 (2009), 1--18. http://cjtcs.cs.uchicago.edu/articles/2009/2/contents.html.
[38]
Ramgopal R. Mettu and C. Greg Plaxton. 2004. Optimal time bounds for approximate clustering. Machine Learning 56, 1--3 (2004), 35--60.
[39]
Mehryar Mohri. 2002. Semiring frameworks and algorithms for shortest-distance problems. Journal of Automata, Languages and Combinatorics 7, 3 (2002), 321--350.
[40]
Edward F. Moore. 1959. The shortest path through a maze. In Proceedings of the Symposium on the Theory of Switching. 87--90.
[41]
David Peleg. 2000. Distributed Computing: A Locality-Sensitive Approach. SIAM, Philadelphia PA, USA.
[42]
Hanmao Shi and Thomas H. Spencer. 1999. Time-work tradeoffs of the single-source shortest paths problem. Journal of Algorithms 30, 1 (1999), 19--32.
[43]
Uri Zwick. 2002. All pairs shortest paths using bridging sets and rectangular matrix multiplication. Journal of the ACM 49, 3 (2002), 289--317.

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Published In

cover image Journal of the ACM
Journal of the ACM  Volume 65, Issue 6
December 2018
331 pages
ISSN:0004-5411
EISSN:1557-735X
DOI:10.1145/3293435
Issue’s Table of Contents
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2018
Accepted: 01 August 2018
Revised: 01 July 2018
Received: 01 August 2016
Published in JACM Volume 65, Issue 6

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Author Tags

  1. MBF-like algorithms
  2. PRAM
  3. approximate metric
  4. buy-at-bulk network design
  5. hop sets
  6. k-median

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