Skip to main content

Graph Partitioning Using Heuristic Kernighan-Lin Algorithm for Parallel Computing

  • Conference paper
  • First Online:
Next Generation Information Processing System

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1162 ))


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.

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

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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


  1. Doe, J.: Load balancing strategies in parallel computing: short survey (2015)

    Google Scholar 

  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)

    Google Scholar 

  3. Prasad, V.: Load balancing and scheduling of tasks in parallel processing environment. Int. J. Inf. Comput. Technol. 4(16), 1727–1732 (2014)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  9. Bui, T.N., Moon, B.R.: Genetic algorithm and graph partitioning. IEEE Trans. Comput. 45(7), 841–855 (1996)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  12. Andreev, K., Racke, H.: Balanced graph partitioning. Theor. Comput. Syst. 39(6), 929–939 (2006). ACM

    Article  MathSciNet  Google Scholar 

  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)

    Google Scholar 

  14. Peng, R., Sun, H., Zanetti, L.: Partitioning well-clustered graphs: spectral clustering works! In: Conference on Learning Theory, pp. 1423–1455 (2015)

    Google Scholar 

  15. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Techn. J. 49(2), 291–307 (1970)

    Article  Google Scholar 

  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)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Siddheshwar V. Patil .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Patil, S.V., Kulkarni, D.B. (2021). Graph Partitioning Using Heuristic Kernighan-Lin Algorithm for Parallel Computing. In: Deshpande, P., Abraham, A., Iyer, B., Ma, K. (eds) Next Generation Information Processing System. Advances in Intelligent Systems and Computing, vol 1162 . Springer, Singapore.

Download citation

Publish with us

Policies and ethics