Skip to main content
SpringerLink
Log in

A GPU Algorithm for Greedy Graph Matching

  • Chapter
Facing the Multicore - Challenge II

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7174))

Abstract

Greedy graph matching provides us with a fast way to coarsen a graph during graph partitioning. Direct algorithms on the CPU which perform such greedy matchings are simple and fast, but offer few handholds for parallelisation. To remedy this, we introduce a fine-grained shared-memory parallel algorithm for maximal greedy matching, together with an implementation on the GPU, which is faster (speedups up to 6.8 for random matching and 5.6 for weighted matching) than the serial CPU algorithms and produces matchings of similar (random matching) or better (weighted matching) quality.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aykanat, C., Pinar, A., Çatalyürek, U.V.: Permuting sparse rectangular matrices into block-diagonal form. SIAM J. Sci. Comput. 25(6), 1860–1879 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bader, D.A., Sanders, P., Wagner, D., Meyerhenke, H., Hendrickson, B., Johnson, D.S., Walshaw, C., Mattson, T.G.: 10th DIMACS implementation challenge - graph partitioning and graph clustering (2012), http://www.cc.gatech.edu/dimacs10/index.shtml

  3. Bell, N., Garland, M.: Cusp: Generic parallel algorithms for sparse matrix and graph computations, version 0.1.0 (2010), http://cusp-library.googlecode.com

  4. Bertsekas, D.P.: A distributed asynchronous relaxation algorithm for the assignment problem. In: 24th IEEE CDC, vol. 24, pp. 1703–1704 (1985)

    Google Scholar 

  5. Çatalyürek, U.V., Aykanat, C.: Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication. IEEE Trans. Par. Dist. Syst. 10(7), 673–693 (1999)

    Article  Google Scholar 

  6. Davis, T.A., Hu, Y.: The University of Florida Sparse Matrix Collection. ACM TOMS 38(1), 1:1–1:25 (2011)

    Google Scholar 

  7. Grigori, L., Boman, E.G., Donfack, S., Davis, T.A.: Hypergraph-based unsymmetric nested dissection ordering for sparse LU factorization. SIAM J. Sci. Comput. 32(6), 3426–3446 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Her, J.H., Pellegrini, F.: Efficient and scalable parallel graph partitioning. Parallel Computing (2010)

    Google Scholar 

  9. Kahng, A.B., Reda, S.: Match twice and stitch: a new TSP tour construction heuristic. Operations Research Letters 32(6), 499–509 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  10. Karp, R.M., Sipser, M.: Maximum matchings in sparse random graphs. In: Proc. 22nd FOCS, pp. 364–375 (1981)

    Google Scholar 

  11. Langguth, J., Manne, F., Sanders, P.: Heuristic initialization for bipartite matching problems. J. Exp. Algorithmics 15(1.3), 1.1–1.22 (2010)

    Google Scholar 

  12. Manne, F., Bisseling, R.H.: A Parallel Approximation Algorithm for the Weighted Maximum Matching Problem. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 708–717. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Patwary, M.A., Bisseling, R.H., Manne, F.: Parallel greedy graph matching using an edge partitioning approach. In: Proc. HLPP 2010, pp. 45–54. ACM (2010)

    Google Scholar 

  14. Preis, R.: Analyses and design of efficient graph partitioning methods. HNI-Verlagsschriftenreihe, Heinz Nixdorf Inst. Univ. Paderborn (2001)

    Google Scholar 

  15. Rivest, R.L.: The MD5 message-digest algorithm, Internet RFC 1321 (1992)

    Google Scholar 

  16. Segev, D.L., Gentry, S.E., Warren, D.S., Reeb, B., Montgomery, R.A.: Kidney paired donation and optimizing the use of live donor organs. JAMA 293(15), 1883–1890 (2005)

    Article  Google Scholar 

  17. Vasconcelos, C.N., Rosenhahn, B.: Bipartite Graph Matching Computation on GPU. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 42–55. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Vastenhouw, B., Bisseling, R.H.: A two-dimensional data distribution method for parallel sparse matrix-vector multiplication. SIAM Rev. 47(1), 67–95 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  19. Xing, G., Lu, C., Zhang, Y., Huang, Q., Pless, R.: Minimum power configuration for wireless communication in sensor networks. ACM Trans. Sen. Netw. 3(2) (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mathematics Institute, Utrecht University, Budapestlaan 6, 3584 CD, Utrecht, The Netherlands

    Bas O. Fagginger Auer & Rob H. Bisseling

Authors
  1. Bas O. Fagginger Auer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rob H. Bisseling
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. High Performance Computing Center Stuttgart (HLRS), Universität Stuttgart, Nobelstraße 19, 70569, Stuttgart, Germany

    Rainer Keller

  2. Institute of Computer Science and Engineering, Karlsruhe Institute of Technology (KIT), Haid-und-Neu-Straße 7, 76131, Karlsruhe, Germany

    David Kramer

  3. Institute for Applied and Numerical Mathematics, SRG New Frontiers in High Performance Computing and Karlsruhe Institute of Technology (KIT), 4, Fritz-Erler-Straße 23, 76133, Karlsruhe, Germany

    Jan-Philipp Weiss

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Fagginger Auer, B.O., Bisseling, R.H. (2012). A GPU Algorithm for Greedy Graph Matching. In: Keller, R., Kramer, D., Weiss, JP. (eds) Facing the Multicore - Challenge II. Lecture Notes in Computer Science, vol 7174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30397-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30397-5_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30396-8

  • Online ISBN: 978-3-642-30397-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation