Parallel Graph Partitioning on Multicore Architectures

  • Xin Sui
  • Donald Nguyen
  • Martin Burtscher
  • Keshav Pingali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6548)


Graph partitioning is a common and frequent preprocessing step in many high-performance parallel applications on distributed- and shared-memory architectures. It is used to distribute graphs across memory and to improve spatial locality. There are several parallel implementations of graph partitioning for distributed-memory architectures.

In this paper, we present a parallel graph partitioner that implements a variation of the Metis partitioner for shared-memory, multicore architectures. We show that (1) the parallelism in this algorithm is an instance of the general amorphous data-parallelism pattern, and (2) a parallel implementation can be derived systematically from a sequential specification of the algorithm. The resulting program can be executed in parallel using the Galois system for optimistic parallelization. The scalability of this parallel implementation compares favorably with that of a publicly available, hand-parallelized C implementation of the algorithm, ParMetis, but absolute performance is lower because of missing sequential optimizations in our system. On a set of 15 large, publicly available graphs, we achieve an average scalability of 2.98X on 8 cores with our implementation, compared with 1.77X for ParMetis, and we achieve an average speedup of 2.80X over Metis, compared with 3.60X for ParMetis. These results show that our systematic approach for parallelizing irregular algorithms on multicore architectures is promising.


Active Node Input Graph Graph Partitioning Runtime System Parallel Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chevalier, C., Pellegrini, F.: Pt-scotch: A tool for efficient parallel graph ordering. Parallel Computing 34(6-8), 318–331 (2008)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Davis, T.A., Hu, Y.F.: The university of florida sparse matrix collection (in submission). ACM Transactions on Mathematical Software (2010)Google Scholar
  3. 3.
    DIMACS. 9th dimacs implementation challenge—shortest paths (2005),
  4. 4.
    George, A.: Nested dissection of a regular finite element mesh. SIAM Journal on Numerical Analysis 10(2), 345–363 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Gupta, A.: An evaluation of parallel graph partitioning and ordering software on a massively parallel computer. Technical Report RC25008 (W1006-029), IBM Research Division, Thomas J. Watson Research Center (2010)Google Scholar
  6. 6.
    Karypis, G., Kumar, V.: A coarse-grain parallel formulation of multilevel k-way graph-partitioning algorithm. In: Proc. 8th SIAM Conference on Parallel Processing for Scientific Computing (1997)Google Scholar
  7. 7.
    Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. Journal of Parallel and Distributed Computing 48(1), 96–129 (1998)CrossRefzbMATHGoogle Scholar
  8. 8.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing 20(1), 359–392 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Kernighan, B.W., Lin, S.: An effective heuristic procedure for partitioning graphs. The Bell System Technical Journal, 291–308 (February 1970)Google Scholar
  10. 10.
    Kulkarni, M., Pingali, K., Walter, B., Ramanarayanan, G., Bala, K., Chew, L.P.: Optimistic parallelism requires abstractions. SIGPLAN Not. (Proceedings of PLDI 2007) 42(6), 211–222 (2007)CrossRefGoogle Scholar
  11. 11.
    Mendez-Lojo, M., Nguyen, D., Prountzos, D., Sui, X., Hassaan, M.A., Kulkarni, M., Burtscher, M., Pingali, K.: Structure-driven optimizations for amorphous data-parallel programs. In: Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 3–14 (2010)Google Scholar
  12. 12.
    Pingali, K., Kulkarni, M., Nguyen, D., Burtscher, M., Mendez-Lojo, M., Prountzos, D., Sui, X., Zhong, Z.: Amorphous data-parallelism in irregular algorithms. regular tech report TR-09-05, The University of Texas at Austin (2009)Google Scholar
  13. 13.
    Walshaw, C., Cross, M.: Jostle: Parallel multilevel graph-partitioning software—an overview. In: Magoules, F. (ed.) Mesh Partitioning Techniques and Domain Decomposition Techniques, pp. 27–58. Civil-Comp. Ltd. (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xin Sui
    • 1
  • Donald Nguyen
    • 1
  • Martin Burtscher
    • 2
  • Keshav Pingali
    • 1
    • 3
  1. 1.Department of Computer ScienceUniversity of TexasAustinUSA
  2. 2.Department of Computer ScienceTexas State UniversitySan MarcosUSA
  3. 3.Institute for Computational Engineering and SciencesUniversity of TexasAustinUSA

Personalised recommendations