Skip to main content

Accelerating Clustering Coefficient Calculations on a GPU Using OPENCL

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 83))

Abstract

The growth in multicore CPUs and the emergence of powerful manycore GPUs has led to proliferation of parallel applications. Many applications are not straight forward to be parallelized. This paper examines the performance of a parallelized implementation for calculating measurements of Complex Networks. We present an algorithm for calculating complex networks topological feature clustering coefficient, and conducted an execution of the serial, parallel and parallel GPU implementations. A hash-table based structure was used for encoding the complex network’s data, which is different than the standard representation, and also speedups the parallel GPU implementations. Our results demonstrate that the parallelization of the sequential implementations on a multicore CPU, using OpenMP produces a significant speedup. Using OpenCL on a GPU produces even larger speedup depending of the volume of data being processed.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kirk, D.B., Hwu, W.W.: Programming Massively Parallel Processors: A Hands-on Approach, Published February 5 (2010)

    Google Scholar 

  2. Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A.E., Purcell, T.J.: A Survey of General-Purpose Computation on Graphics Hardware. In: Eurographics 2005, State of the Art Reports, August 2005, pp. 21–51 (2005)

    Google Scholar 

  3. Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)

    Article  Google Scholar 

  4. Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–97 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  5. Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  6. Batagelj, V., Mrvar, A.: Pajek – Analysis and Visualization of Large Networks. In: Junger, M., Mutzel, P. (eds.) Graph Drawing Software. Series Mathematics and Visualization, pp. 77–103. Springer, Berlin (2003)

    Google Scholar 

  7. Borgatti, S.P., Everett, M.G., Freeman, L.C.: Ucinet 6 for Windows: Software for Social Network Analysis, H.A. Technologies, Editor (2002)

    Google Scholar 

  8. Gleich, D.: Matlab BGL v1.0, April 27 (2006), http://www.stanford.edu/dgleich/programs/matlab_bgl/ (retrieved April 2010)

  9. Harish, P., Narayanan, P.J.: Accelerating Large Graph Algorithms on the GPU Using CUDA. In: Aluru, S., Parashar, M., Badrinath, R., Prasanna, V.K. (eds.) HiPC 2007. LNCS, vol. 4873, pp. 197–208. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Hyvoenen, J., Saramaeki, J., Kaski, K.: Efficient data structures for sparse network representation. International Journal of Computer Mathematics 85(8), 1219–1233 (2008)

    Article  MathSciNet  Google Scholar 

  11. Lessig, C.: Eigenvalue Computation with CUDA, NVIDIA CUDA SDK 1.1 (2007)

    Google Scholar 

  12. Volkov, V., Demmel, J.W.: LAPACK working note 197: Using GPUs to accelerate the bisection algorithm for finding eigenvalues of symmetric tridiagonal matrices. Technical Report UCB/EECS-2007-179, EECS Department, University of California, Berkeley (2007)

    Google Scholar 

  13. Katz, G.J., Kider, Jr. J.T.: All-Pairs Shortest-Paths for Large Graphs on the GPU. In: Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware (2008)

    Google Scholar 

  14. Cantin, J., Hill, M.: Cache performance for selected SPEC CPU2000 benchmarks. ACM SIGARCH Computer Architecture News 29, 13–18 (2001)

    Article  Google Scholar 

  15. Direct Compute Support on NVIDIA’s CUDA Architecture GPUs, http://developer.nvidia.com/object/directcompute_home.html/

  16. Nigel, D.: Senior VP and CMO at AMD about DirectCompute, http://developer.nvidia.com/object/directcompute_home.html/

  17. OpenCL Programming for the CUDA Architecture, Version 2.3 (8/31/2009)

    Google Scholar 

  18. The OpenCL Specification, Version 1.0, document Revision 43 (2009), http://www.khronos.org/opencl/ (retrieved February 2010)

  19. The Khronos Group, Open Standard for Media Authoring and Acceleration, http://www.khronos.org/

  20. Chapman, B., Jost, G., van der Pas, R.: Using OpenMP, Portable Shared Memory Parallel Programming. The MIT Press, Cambridge

    Google Scholar 

  21. OpenMP Application Program Interface, OpenMP Architecture Review Board, Version 3.0 (May 2008)

    Google Scholar 

  22. OpenMP Application Program Interface, OpenMP Architecture Review Board, Version 2.5 (May 2005)

    Google Scholar 

  23. NVIDIA OpenCL, Best Practices Guide, Version 1.0, August 10 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Djinevski, L., Mishkovski, I., Trajanov, D. (2011). Accelerating Clustering Coefficient Calculations on a GPU Using OPENCL. In: Gusev, M., Mitrevski, P. (eds) ICT Innovations 2010. ICT Innovations 2010. Communications in Computer and Information Science, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19325-5_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19325-5_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19324-8

  • Online ISBN: 978-3-642-19325-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics