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Graph Partitioning Using Heuristic Kernighan-Lin Algorithm for Parallel Computing

  • Siddheshwar V. PatilEmail author
  • Dinesh B. Kulkarni
Conference paper
  • 33 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1162)

Abstract

The goal of parallel computing is to distribute the load on available processors such that all processors should be utilized in a fair manner. This minimizes the overall execution time required to execute a complex task. So, the load balancing issue becomes an important aspect of parallel computing. It is abstracted as a graph partitioning problem in which the nodes represent computation cost, edges represent communication cost, and number of partitions should be equal to number of available processing units. So, the objective is to cut the graph into k-partitions such that—(i) total node weight should be equal for each partition and—(ii) minimize total edge weight across the partitions. A heuristic Kernighan-Lin graph partitioning algorithm for two-way partitioning is evaluated in this paper. It starts with an initial random graph partition and consecutively exchanges the nodes between partitions, determines cut size at each stage and saves the minimum cut found so far. After the desired number of swaps has been performed, the saved minimum cut will give optimal partitions. The graph data for experimental work is obtained from DIMACS tenth implementation challenge Web site. The experimental results show minimum cut-cost with respect to balance constraint. The results are compared with ground truth results for validation.

Keywords

Graph partitioning Parallel computing 

References

  1. 1.
    Doe, J.: Load balancing strategies in parallel computing: short survey (2015)
  2. 2.
    Kushwaha, M., Gupta, S.: Various schemes of load balancing in distributed systems—a review. Int. J. Sci. Res. Eng. Technol. 4(7), 741–748 (2015)
  3. 3.
    Prasad, V.: Load balancing and scheduling of tasks in parallel processing environment. Int. J. Inf. Comput. Technol. 4(16), 1727–1732 (2014)
  4. 4.
    Sakouhi, C., Khaldi, A., Ghezal, H.B. : An overview of recent graph partitioning algorithms. In: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, pp. 408–414 (2018)
  5. 5.
    Kumar, S., Das, S. K., Biswas, R. : Graph partitioning for parallel applications in heterogeneous grid environments. In: Proceedings of International Parallel and Distributed Processing Symposium, pp. 7-pp. IEEE (2002)
  6. 6.
    Patil, S.V., Kulkarni, D.B.: A review of dimensionality reduction in high-dimensional data using multi-core and many-core architecture. In: Workshop on Software Challenges to Exascale Computing, pp. 54–63. Springer (2018)
  7. 7.
    Patil, S.V., Kulkarni, D.B.: Parallel computing approaches for dimensionality reduction in the high-dimensional data. Int. J. Comput. Sci. Eng. 7(5), 1750–1755 (2019)
  8. 8.
    Sheblaev, M.V., Sheblaeva, A.S.: A method of improving initial partition of Fiduccia-Mattheyses algorithm. Lobachevskii J. Math. 39(9), 1270–1276 (2018). Springer
  9. 9.
    Bui, T.N., Moon, B.R.: Genetic algorithm and graph partitioning. IEEE Trans. Comput. 45(7), 841–855 (1996)
  10. 10.
    Schloegel, K., Karypis, G., Kumar, V.: Parallel static and dynamic multi-constraint graph partitioning. Wiley Concurrency Comput. Pract. Experience 14(3), 219–240 (2002)
  11. 11.
    Leng, M., Yu, S., Chen, Y.: An effective refinement algorithm based on multilevel paradigm for graph bipartitioning. In: International Conference on Programming Languages for Manufacturing, pp. 294–303. Springer (2006)
  12. 12.
    Andreev, K., Racke, H.: Balanced graph partitioning. Theor. Comput. Syst. 39(6), 929–939 (2006). ACM
  13. 13.
    Andersen, R., Chung, F., Lang, K.: Local graph partitioning using pagerank vectors. In: 47th Annual Symposium on Foundations of Computer Science, pp. 475–486. IEEE (2006)
  14. 14.
    Peng, R., Sun, H., Zanetti, L.: Partitioning well-clustered graphs: spectral clustering works! In: Conference on Learning Theory, pp. 1423–1455 (2015)
  15. 15.
    Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Techn. J. 49(2), 291–307 (1970)
  16. 16.
    Bader, D., Kappes, A., Meyerhenke, H., Sanders, P., Schulz, C., Wagner, D.: Benchmarking for graph clustering and partitioning. In: Encyclopedia of Social Network Analysis and Mining, pp. 73–82. Springer (2014)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringWalchand College of EngineeringSangliIndia
  2. 2.Department of Information TechnologyWalchand College of EngineeringSangliIndia

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