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Accelerating Minimum Spanning Forest Computations on Multicore Platforms

  • Guojing CongEmail author
  • Ilie Tanase
  • Yinglong Xia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9523)

Abstract

We propose new approaches for accelerating minimum spanning forest algorithms on shared-memory platforms. Our approaches improve cache performance and reduce synchronization overhead of the base algorithms. On our target platform these optimizations achieve up to an order of magnitude speedup over the best prior parallel \({Bor{\mathring{u}}vka}\) implementation.

Keywords

Minimum spanning forest Locality Synchronization 

References

  1. 1.
    Adler, M., Dittrich, W., Juurlink, B., Kutyłowski, M., Rieping, I.: Communication-optimal parallel minimum spanning tree algorithms (extended abstract). In: SPAA 1998: Proceedings of the Tenth Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 27–36. ACM, New York (1998)Google Scholar
  2. 2.
    Agarwal, V., Petrini, F., Pasetto, D., Bader, D.: Scalable graph exploration on multicore processors. In: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2010, pp. 1–11. IEEE Computer Society, Washington, DC (2010)Google Scholar
  3. 3.
    An, L., Xiang, Q., Chavez, S.: A fast implementation of the minimum spanning tree method for phase unwrapping. IEEE Trans. Med. Imaging 19(8), 805–808 (2000)CrossRefGoogle Scholar
  4. 4.
    Bader, D.A., Cong, G.: Fast shared-memory algorithms for computing the minimum spanning forest of sparse graphs. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium, IPDPS 2004, Santa Fe, New Mexico, April 2004Google Scholar
  5. 5.
    Banerjee, D., Sharma, S., Kothapalli, K.: Work efficient parallel algorithms for large graph exploration. In: 2013 20th International Conference on High Performance Computing (HiPC), pp. 433–442, December 2013Google Scholar
  6. 6.
    Barnat, J., Bauch, P., Brim, L., Ceska, M.: Computing strongly connected components in parallel on cuda. In: 2011 IEEE International Parallel Distributed Processing Symposium (IPDPS), pp. 544–555, May 2011Google Scholar
  7. 7.
    Beamer, S., Asanović, K., Patterson, D.: Direction-optimizing breadth-first search. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2012, pp. 12:1–12:10. IEEE Computer Society Press, Los Alamitos, CA, USA (2012)Google Scholar
  8. 8.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vision 70(2), 109–131 (2006). http://dx.doi.org/10.1007/s11263-006-7934-5 CrossRefGoogle Scholar
  9. 9.
    Chakrabarti, D., Zhan, Y., Faloutsos, C.: R-MAT: a recursive model for graph mining. In: Proceedings of the 4th SIAM International Conference on Data Mining, April 2004Google Scholar
  10. 10.
    Chen, C., Morris, S.: Visualizing evolving networks: minimum spanning trees versus pathfinder networks. In: IEEE Symposium on Information Visualization, Seattle, WA, October 2003Google Scholar
  11. 11.
    Chong, K.W., Han, Y., Lam, T.W.: Concurrent threads and optimal parallel minimum spanning tree algorithm. J. ACM 48, 297–323 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Cong, G., Bader, D.A.: Lock-free parallel algorithms: an experimental study. In: Bougé, L., Prasanna, V.K. (eds.) HiPC 2004. LNCS, vol. 3296, pp. 516–527. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  13. 13.
    Fich, F., Ragde, P., Wigderson, A.: Simulations among concurrent-write prams. Algorithmica 3(1–4), 43–51 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Goh, K.I., Oh, E., Jeong, H., Kahng, B., Kim, D.: Classification of scale-free networks. Proc. Natl. Acad. Sci. 99, 12583 (2002). http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:cond-mat/0205232 zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Hong, S., Oguntebi, T., Olukotun, K.: Efficient parallel graph exploration on multi-core CPU and GPU. In: 2011 International Conference on Parallel Architectures and Compilation Techniques (PACT), pp. 78–88, october 2011Google Scholar
  16. 16.
    Kunegis, J.: KONECT - The Koblenz network collection. In: Proceedings of the International Conference on World Wide Web Companion, pp. 1343–1350 (2013). http://userpages.uni-koblenz.de/kunegis/paper/kunegis-koblenz-network-collection.pdf
  17. 17.
    Meguerdichian, S., Koushanfar, F., Potkonjak, M., Srivastava, M.: Coverage problems in wireless ad-hoc sensor networks. In: Proceedings of the INFOCOM 2001, pp. 1380–1387. IEEE Press, Anchorage, April 2001Google Scholar
  18. 18.
    Olman, V., Xu, D., Xu, Y.: Identification of regulatory binding sites using minimum spanning trees. In: Proceedings of the 8th Pacific Symposium on Biocomputing (PSB 2003), pp. 327–338. World Scientific Pub., Hawaii (2003)Google Scholar
  19. 19.
    Palmer, E.: Graphical Evolution. Wiley-Interscience Series in Discrete Mathematic. Wiley, New York (1985) zbMATHGoogle Scholar
  20. 20.
    Patwary, M., Ref, P., Manne, F.: Multi-core spanning forest algorithms using the disjoint-set data structure. In: Proceedings of the 2012 IEEE International Parallel & Distributed Processing Symposium, IPDPS 2012, pp. 827–835. IEEE Computer Society, Washington, DC (2012)Google Scholar
  21. 21.
    Pettie, S., Ramachandran, V.: A randomized time-work optimal parallel algorithm for finding a minimum spanning forest. SIAM J. Comput. 31(6), 1879–1895 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Stanford SNAP Large Network Dataset Collection. http://memetracker.org/data/index.html
  23. 23.
    Vishkin, U.: Implementation of simultaneous memory address access in models that forbid it. J. Algorithms 4(1), 45–50 (1983). http://dblp.uni-trier.de/db/journals/jal/jal4.html#Vishkin83 zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

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