On Evaluating Graph Partitioning Algorithms for Distributed Agent Based Models on Networks

  • Alessia Antelmi
  • Gennaro Cordasco
  • Carmine SpagnuoloEmail author
  • Luca Vicidomini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9523)


Graph Partitioning is a key challenge problem with application in many scientific and technological fields. The problem is very well studied with a rich literature and is known to be NP-hard. Several heuristic solutions, which follow diverse approaches, have been proposed, they are based on different initial assumptions that make them difficult to compare. An analytical comparison was performed based on an Implementation Challenge [3], however being a multi-objective problem (two opposing goals are for instance load balancing and edge-cut size), the results are difficult to compare and it is hard to foresee what can be the impact of one solution, instead of another, in a real scenario. In this paper we analyze the problem in a real context: the development of a distributed agent-based simulation model on a network field (which for instance can model social interactions).

We present an extensive evaluation of the most efficient and effective solutions for the balanced k-way partitioning problem. We evaluate several strategies both analytically and on real distributed simulation settings (D-Mason). Results show that, a good partitioning strategy strongly influences the performances of the distributed simulation environment. Moreover, we show that there is a strong correlation between the edge-cut size and the real performances. Analyzing the results in details we were also able to discover the parameters that need to be optimized for best performances on networks in ABMs.


Agent-Based Simulation Models Graph partitioning D-Mason Parallel computing Distributed systems High performance computing 


  1. 1.
    Alam, S., Geller, A.: Networks in agent-based social simulation. In: Heppenstall, A.J., Crooks, A.T., See, L.M., Batty, M. (eds.) Agent-Based Models of Geographical Systems, pp. 199–216. Springer, Netherlands (2012).
  2. 2.
    Alpert, C.J., Kahng, A.B.: Recent directions in netlist partitioning: a survey. Integr. VLSI J. 19, 1–81 (1995)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bader, D.A., Meyerhenke, H., Sanders, P., Wagner, D. (eds.): Graph Partitioning and Graph Clustering - 10th DIMACS Implementation Challenge Workshop, Georgia Institute of Technology, Atlanta, GA, USA, February 13–14, 2012. Proceedings, Contemporary Mathematics, vol. 588, American Mathematical Society (2013).
  4. 4.
    Balan, G.C., Cioffi-Revilla, C., Luke, S., Panait, L., Paus, S.: MASON: a java multi-agent simulation library. In: Proceedings of the Agent 2003 Conference (2003)Google Scholar
  5. 5.
    Cordasco, G., De Chiara, R., Mancuso, A., Mazzeo, D., Scarano, V., Spagnuolo, C.: A Framework for distributing Agent-based simulations. In: 9th International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (2011)Google Scholar
  6. 6.
    Cordasco, G., De Chiara, R., Mancuso, A., Mazzeo, D., Scarano, V., Spagnuolo, C.: Bringing together efficiency and effectiveness in distributed simulations: the experience with D-MASON. SIMULATION Trans. Soc. Model. Simul. Int. 89(10), 1236–1253 (2013)CrossRefGoogle Scholar
  7. 7.
    Cordasco, G., Mancuso, A., Milone, F., Spagnuolo, C.: Communication strategies in distributed agent-based simulations: the experience with D-Mason. In: an Mey, D., Alexander, M., Bientinesi, P., Cannataro, M., Clauss, C., Costan, A., Kecskemeti, G., Morin, C., Ricci, L., Sahuquillo, J., Schulz, M., Scarano, V., Scott, S.L., Weidendorfer, J. (eds.) Euro-Par 2013. LNCS, vol. 8374, pp. 533–543. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  8. 8.
    Cordasco, G., Milone, F., Spagnuolo, C., Vicidomini, L.: Exploiting D-Mason on parallel platforms: a novel communication strategy. In: Proceedings of the 2nd Workshop on Parallel and Distributed Agent-Based Simulations (PADABS), Euro-Par 2014 (2014)Google Scholar
  9. 9.
    Cosenza, B., Cordasco, G., De Chiara, R., Scarano, V.: Distributed load balancing for parallel agent-based simulations. In: Proceedings of the 19th International Euromicro Conference on Parallel, Distributed, and Network-Based Processing, (PDP 2011), pp. 62–69 (2011)Google Scholar
  10. 10.
    Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York (2010) CrossRefGoogle Scholar
  11. 11.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman & Co, New York (1990) Google Scholar
  12. 12.
    Gupta, A.: Graph partitioning based sparse matrix orderings for interior-point algorithms. IBM Thomas J, Watson Research Division (1996)Google Scholar
  13. 13.
    Karypis, G., Aggarwal, R., Kumar, V., Shekhar, S.: Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 7(1), 69–79 (1999)CrossRefGoogle Scholar
  14. 14.
    Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. J. Parallel Distrib. Comput. 48(1), 96–129 (1998)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Kernighan, B., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)zbMATHCrossRefGoogle Scholar
  16. 16.
    Luke, S., Cioffi-revilla, C., Panait, L., Sullivan, K.: MASON: a new multi-agent simulation toolkit. In: Proceedings of the 2004 SwarmFest Workshop (2004)Google Scholar
  17. 17.
    Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: MASON: a multiagent simulation environment. Simulation 81(7), 517–527 (2005).
  18. 18.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Nat. Acad. Sci. (PNAS) 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  19. 19.
    Rahimian, F., Payberah, A.H., Girdzijauskas, S., Jelasity, M., Haridi, S.: JA-BE-JA: a distributed algorithm for balanced graph partitioning. In: 7th International Conference on Self-Adaptive and SelfOrganizing Systems. IEEE (2013)Google Scholar
  20. 20.
    Sanders, P., Schulz, C.: Think locally, act globally: highly balanced graph partitioning. In: Bonifaci, V., Demetrescu, C., Marchetti-Spaccamela, A. (eds.) SEA 2013. LNCS, vol. 7933, pp. 164–175. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  21. 21.
    Spielmat, D., Teng, S.H.: Spectral partitioning works: planar graphs and finite element meshes. In: Proceedings 37th Annual Symposium on Foundations of Computer Science, pp. 96–105, October 1996Google Scholar
  22. 22.
    Sutter, H.: The free lunch is over: a fundamental turn toward concurrency in software. Dr. Dobb’s J. 30(3), 202–210 (2005)Google Scholar
  23. 23.
    Tekin, E., Sabuncuoglu, I.: Simulation optimization: a comprehensive review on theory and applications. IIE Trans. 36(11), 1067–1081 (2004)CrossRefGoogle Scholar
  24. 24. Ja-be-Ja GitHub repository, Accessed on May 2015
  25. 25. GraphChi’s Java version, Accessed on May 2015
  26. 26. D-MASON Official GitHub Repository, Accessed on June 2015
  27. 27. The Graph Partitioning Archive, Accessed on May 2015
  28. 28. D-MASON Official Website, Accessed on May 2015

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alessia Antelmi
    • 1
  • Gennaro Cordasco
    • 2
  • Carmine Spagnuolo
    • 1
    Email author
  • Luca Vicidomini
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di SalernoFiscianoItaly
  2. 2.Dipartimento di PsicologiaSeconda Università degli Studi di NapoliCasertaItaly

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