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Parallelization Strategies for Spatial Agent-Based Models

  • Nuno Fachada
  • Vitor V. Lopes
  • Rui C. Martins
  • Agostinho C. Rosa
Article

Abstract

Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. Large scale emergent behavior in ABMs is population sensitive. As such, the number of agents in a simulation should be able to reflect the reality of the system being modeled, which can be in the order of millions or billions of individuals in certain domains. A natural solution to reach acceptable scalability in commodity multi-core processors consists of decomposing models such that each component can be independently processed by a different thread in a concurrent manner. In this paper we present a multithreaded Java implementation of the PPHPC ABM, with two goals in mind: (1) compare the performance of this implementation with an existing NetLogo implementation; and, (2) study how different parallelization strategies impact simulation performance on a shared memory architecture. Results show that: (1) model parallelization can yield considerable performance gains; (2) distinct parallelization strategies offer specific trade-offs in terms of performance and simulation reproducibility; and, (3) PPHPC is a valid reference model for comparing distinct implementations or parallelization strategies, from both performance and statistical accuracy perspectives.

Keywords

Agent-based modeling Parallelization strategies Shared memory Multithreading 

References

  1. 1.
    Aaby, B., Perumalla, K., Seal, S.: Efficient simulation of agent-based models on multi-GPU and multi-core clusters. In: Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques, pp. 1–10. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2010)Google Scholar
  2. 2.
    Axtell, R., Axelrod, R., Epstein, J.M., Cohen, M.D.: Aligning simulation models: a case study and results. Comput. Math. Organ. Theory 1(2), 123–141 (1996). doi: 10.1007/BF01299065 CrossRefGoogle Scholar
  3. 3.
    Berryman, M.: Review of software platforms for agent based models. Technical report DSTO-GD-0532, Land Operations Division, Defence Science and Technology Organisation, PO Box 1500, Edinburgh, South Australia 5111, Australia (2008)Google Scholar
  4. 4.
    Brugger, C., Weithoffer, S., Schryver, C.d., Wasenmüller, U., Wehn, N.: On parallel random number generation for accelerating simulations of communication systems. Adv. Radio Sci. 12, 75–81 (2014). doi: 10.5194/ars-12-75-2014. http://www.adv-radio-sci.net/12/75/2014/
  5. 5.
    Chen, D., Wang, L., Zomaya, A., Dou, M., Chen, J., Deng, Z., Hariri, S.: Parallel simulation of complex evacuation scenarios with adaptive agent models. IEEE Trans. Parall. Distrib. 26(3), 847–857 (2015). doi: 10.1109/TPDS.2014.2311805 CrossRefGoogle Scholar
  6. 6.
    Coakley, S., Gheorghe, M., Holcombe, M., Chin, S., Worth, D., Greenough, C.: Exploitation of high performance computing in the FLAME agent-based simulation framework. In: Proceedings of the 14th International Conference on High Performance Computing and Communications, pp. 538–545. Liverpool, UK (2012). doi: 10.1109/HPCC.2012.79
  7. 7.
    Coddington, P.D.: Random number generators for parallel computers. Northeast Parallel Architecture Center (1997), Paper 13. http://surface.syr.edu/npac/13/
  8. 8.
    Collier, N., North, M.: Repast HPC: A platform for large-scale agent-based modeling. In: W. Dubitzky, K. Kurowski, B. Schott (eds.) Large-Scale Computing Techniques for Complex System Simulations, Chap. 5, pp. 81–109. Wiley, New York (2011). doi: 10.1002/9781118130506.ch5
  9. 9.
    Cosenza, B., Cordasco, G., De Chiara, R., Scarano, V.: Distributed load balancing for parallel agent-based simulations. In: 19th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 62–69 (2011). doi: 10.1109/PDP.2011.22
  10. 10.
    D’Souza, R., Lysenko, M., Marino, S., Kirschner, D.: Data-parallel algorithms for agent-based model simulation of tuberculosis on graphics processing units. In: Proceedings of the 2009 Spring Simulation Multiconference, SpringSim’09, pp. 21:1–21:12. Society for Computer Simulation International, San Diego, CA, USA (2009)Google Scholar
  11. 11.
    D’Souza, R., Lysenko, M., Rahmani, K.: Sugarscape on steroids: simulating over a million agents at interactive rates. In: Proceedings of Agent 2007 Conference, Chicago, USA (2007)Google Scholar
  12. 12.
    Dyer, D.W.: Uncommons Maths (2012). http://maths.uncommons.org/
  13. 13.
    Edmonds, B., Hales, D.: Replication, replication and replication: some hard lessons from model alignment. J. Artif. Soc. Soc. Simul. 6(4), 11 (2003). http://jasss.soc.surrey.ac.uk/6/4/11.html
  14. 14.
    Fachada, N.: Agent-based simulation of the immune system. Master’s thesis, Instituto Superior Técnico, Universidade Técnica de Lisboa, Lisboa (2008)Google Scholar
  15. 15.
    Fachada, N., Lopes, V.V., Martins, R.C., Rosa, A.C.: Towards a standard model for research in agent-based modeling and simulation. PeerJ Comput. Sci. 1, e36 (2015). doi: 10.7717/peerj-cs.36 CrossRefGoogle Scholar
  16. 16.
    Fachada, N., Lopes, V.V., Rosa, A.: Simulating antigenic drift and shift in influenza A. In: Proceedings of the 2009 ACM symposium on Applied Computing, SAC’09, pp. 2093–2100. ACM, New York, NY, USA (2009). doi: 10.1145/1529282.1529744
  17. 17.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design patterns: elements of reusable object-oriented software. Pearson Education, New York (1994)zbMATHGoogle Scholar
  18. 18.
    Gibbons, J.D., Chakraborti, S.: Nonparametric statistical inference. Statistics: Textbooks & Monographs, 5th edn. Chapman and Hall/CRC, Boca Raton (2010)Google Scholar
  19. 19.
    Glass, K., Livingston, M., Conery, J.: Distributed simulation of spatially explicit ecological models. SIGSIM Simul. Dig. 27(1), 60–63 (1997). doi: 10.1145/268823.268902 CrossRefGoogle Scholar
  20. 20.
    Göetz, B., Peierls, T., Bloch, J., Bowbeer, J., Holmes, D., Lea, D.: Java Concurrency in Practice. Addison-Wesley, Cambridge (2006)Google Scholar
  21. 21.
    Goldsby, M.E., Pancerella, C.M.: Multithreaded agent-based simulation. In: Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World. WSC’13, pp. 1581–1591. IEEE Press, Washington, DC, USA (2013)Google Scholar
  22. 22.
    Gong, Z., Tang, W., Bennett, D.A., Thill, J.C.: Parallel agent-based simulation of individual-level spatial interactions within a multicore computing environment. Int. J. Geogr. Inf. Sci. 27(6), 1152–1170 (2013). doi: 10.1080/13658816.2012.741240 CrossRefGoogle Scholar
  23. 23.
    Gosling, J., Joy, B., Steele, G., Bracha, G., Buckley, A.: The Java Language Specification (Java SE 8). Oracle Corporation, Redwood City (2014)Google Scholar
  24. 24.
    Grimm, V., Berger, U., DeAngelis, D., Polhill, J., Giske, J., Railsback, S.: The ODD protocol: a review and first update. Ecol. Model. 221(23), 2760–2768 (2010)CrossRefGoogle Scholar
  25. 25.
    Gulyás, L., Szabó, A., Legéndi, R., Máhr, T., Bocsi, R., Kampis, G.: Tools for large scale (distributed) agent-based computational experiments. In: Proceedings of The Computational Social Science Society of the Americas (2011)Google Scholar
  26. 26.
    Gulyás, L., Szemes, G., Kampis, G., de Back, W.: A modeler-friendly API for ABM partitioning. In: Proceedings of the ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 219–226. San Diego, CA, USA (2009)Google Scholar
  27. 27.
    Hill, D.R.C., Mazel, C., Passerat-Palmbach, J., Traore, M.K.: Distribution of random streams for simulation practitioners. Concurr. Comput. Pract. Exp. 25(10), 1427–1442 (2013). doi: 10.1002/cpe.2942 CrossRefGoogle Scholar
  28. 28.
    Husselmann, A., Hawick, K.: Spatial agent-based modelling and simulations—a review. Res. Lett. Inf. Math. Sci. 7, 101–111 (2008)Google Scholar
  29. 29.
    Isaac, A.G.: The ABM template models: A reformulation with reference implementations. J. Artif. Soc. Soc. Simulat. 14(2), 5 (2011). http://jasss.soc.surrey.ac.uk/14/2/5.html
  30. 30.
    Keenan, M., Komarov, I., D’Souza, R.M., Riolo, R.: Novel graphics processing unit-based parallel algorithms for understanding species diversity in forests. In: Proceedings of the 2012 Symposium on High Performance Computing. Society for Computer Simulation International (2012)Google Scholar
  31. 31.
    Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583–621 (1952). doi: 10.1080/01621459.1952.10483441 CrossRefzbMATHGoogle Scholar
  32. 32.
    Law, A.M.: Simulation Modeling and Analysis, 5th edn. McGraw-Hill, Columbus (2015)Google Scholar
  33. 33.
    Lenoski, D.E., Weber, W.D.: Scalable Shared-Memory Multiprocessing. Elsevier, Amsterdam (1991)Google Scholar
  34. 34.
    Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: MASON: a multiagent simulation environment. Simulation 81(7), 517–527 (2005). doi: 10.1177/0037549705058073 CrossRefGoogle Scholar
  35. 35.
    Lysenko, M., D’Souza, R.: A framework for megascale agent based model simulations on graphics processing units. J. Artif. Soc. Soc. Simul. 11(4), 10 (2008). http://jasss.soc.surrey.ac.uk/11/4/10.html
  36. 36.
    Lytinen, S.L., Railsback, S.F.: The evolution of agent-based simulation platforms: a review of NetLogo 5.0 and ReLogo. In: Proceedings of the Fourth International Symposium on Agent-Based Modeling and Simulation (2012)Google Scholar
  37. 37.
    Macal, C., North, M.: Tutorial on agent-based modelling and simulation. J. Simul. 4(3), 151–162 (2010)CrossRefGoogle Scholar
  38. 38.
    Merlone, U., Sonnessa, M., Terna, P.: Horizontal and vertical multiple implementations in a model of industrial districts. J. Artif. Soc. Soc. Simul. 11(2), 5 (2008). http://jasss.soc.surrey.ac.uk/11/2/5.html
  39. 39.
    Müller, B., Balbi, S., Buchmann, C.M., de Sousa, L., Dressler, G., Groeneveld, J., Klassert, C.J., Le, Q.B., Millington, J.D., Nolzen, H., Parker, D.C., Polhill, J.G., Schlüter, M., Schulze, J., Schwarz, N., Sun, Z., Taillandier, P., Weise, H.: Standardised and transparent model descriptions for agent-based models: current status and prospects. Environ. Model. Softw. 55, 156–163 (2014). doi: 10.1016/j.envsoft.2014.01.029 CrossRefGoogle Scholar
  40. 40.
    Nikolai, C., Madey, G.: Tools of the trade: a survey of various agent based modeling platforms. J. Artif. Soc. Soc. Simul. 12(2), 2 (2009). http://jasss.soc.surrey.ac.uk/12/2/2.html
  41. 41.
    North, M., Collier, N., Vos, J.: Experiences creating three implementations of the repast agent modeling toolkit. ACM Trans. Model. Comput. Simul. 16(1), 1–25 (2006)CrossRefGoogle Scholar
  42. 42.
    North, M.J., Collier, N.T., Ozik, J., Tatara, E.R., Macal, C.M., Bragen, M., Sydelko, P.: Complex adaptive systems modeling with Repast Simphony. Complex Adapt. Syst. Model. 1(1), 1–26 (2013). doi: 10.1186/2194-3206-1-3 CrossRefGoogle Scholar
  43. 43.
    Parry, H.R., Bithell, M.: Large scale agent-based modelling: a review and guidelines for model scaling. In: Heppenstall, A.J., Crooks, A.T., See, L.M., Batty, M. (eds.) Agent-Based Models Geogr. Syst., pp. 271–308. Springer, Netherlands (2012)CrossRefGoogle Scholar
  44. 44.
    Perumalla, K.S.: Computational spectrum of agent model simulation. In: Cakaj, S. (ed.) Modeling Simulation and Optimization—Focus on Applications, Numerical Analysis and Scientific Computing, Chap. 12, pp. 185–204. InTech (2010)Google Scholar
  45. 45.
    Railsback, S., Lytinen, S., Jackson, S.: Agent-based simulation platforms: review and development recommendations. Simulation 82(9), 609–623 (2006)CrossRefGoogle Scholar
  46. 46.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. SIGGRAPH Comput. Graph. 21(4), 25–34 (1987). doi: 10.1145/37402.37406 CrossRefGoogle Scholar
  47. 47.
    Richmond, P., Coakley, S., Romano, D.: Cellular level agent based modelling on the graphics processing unit. In: International Workshop on High Performance Computational Systems Biology, 2009. HIBI’09. pp. 43–50 (2009)Google Scholar
  48. 48.
    Richmond, P., Walker, D., Coakley, S., Romano, D.: High performance cellular level agent-based simulation with FLAME for the GPU. Brief Bioinform. 11(3), 334–347 (2010). doi: 10.1093/bib/bbp073 CrossRefGoogle Scholar
  49. 49.
    Scheutz, M., Schermerhorn, P.: Adaptive algorithms for the dynamic distribution and parallel execution of agent-based models. J. Parallel Distrib. Comput. 66(8), 1037–1051 (2006)CrossRefzbMATHGoogle Scholar
  50. 50.
    Shook, E., Wang, S., Tang, W.: A communication-aware framework for parallel spatially explicit agent-based models. Int. J. Geogr. Inf. Sci. 27(11), 2160–2181 (2013). doi: 10.1080/13658816.2013.771740 CrossRefGoogle Scholar
  51. 51.
    Srinivasan, A., Mascagni, M., Ceperley, D.: Testing parallel random number generators. Parallel Comput. 29(1), 69–94 (2003). doi: 10.1016/S0167-8191(02)00163-1 MathSciNetCrossRefGoogle Scholar
  52. 52.
    Steele, Jr., G.L., Lea, D., Flood, C.H.: Fast splittable pseudorandom number generators. In: OOPSLA ’14 Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages and Applications, pp. 453–472 (2014). doi: 10.1145/2660193.2660195
  53. 53.
    Takahashi, T., Mizuta, H.: Efficient agent-based simulation framework for multi-node supercomputers. In: Proceedings of the 38th conference on Winter simulation, pp. 919–925. Winter Simulation Conference (2006)Google Scholar
  54. 54.
    Tang, W., Bennett, D.: Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units. Ecol. Model. 222(19), 3605–3615 (2011). doi: 10.1016/j.ecolmodel.2011.08.016 CrossRefGoogle Scholar
  55. 55.
    Tang, W., Jia, M.: Global sensitivity analysis of a large agent-based model of spatial opinion exchange: a heterogeneous multi-GPU acceleration approach. Ann. Assoc. Am. Geogr. 104(3), 485–509 (2014). doi: 10.1080/00045608.2014.892342 CrossRefGoogle Scholar
  56. 56.
    Tang, W., Wang, S.: HPABM: a hierarchical parallel simulation framework for spatially-explicit agent-based models. Trans. GIS 13(3), 315–333 (2009). doi: 10.1111/j.1467-9671.2009.01161.x CrossRefGoogle Scholar
  57. 57.
    Tobias, R., Hofmann, C.: Evaluation of free Java-libraries for social-scientific agent based simulation. J. Artif. Soc. Soc. Simul. 7(1), 6 (2004). http://jasss.soc.surrey.ac.uk/7/1/6.html
  58. 58.
    The Power Challenge technical report: Technical report, Silicon Graphics, Inc., Mountain View (1994)Google Scholar
  59. 59.
    Valiant, L.G.: A bridging model for parallel computation. Commun. ACM 33(8), 103–111 (1990). doi: 10.1145/79173.79181 CrossRefGoogle Scholar
  60. 60.
    Vigueras, G., Orduña, J.M., Lozano, M., Cecilia, J.M., García, J.M.: Accelerating collision detection for large-scale crowd simulation on multi-core and many-core architectures. Int. J. High Perform. Comput. Appl. (2013). doi: 10.1177/1094342013476119
  61. 61.
    Voss, A., You, J.Y., Yen, E., Chen, H.Y., Lin, S., Turner, A., Lin, J.P.: Scalable social simulation: investigating population-scale phenomena using commodity computing. In: 2010 IEEE Sixth International Conference on e-Science (e-Science), pp. 1–8. Brisbane, QLD, Australia (2010). doi: 10.1109/eScience.2010.46
  62. 62.
    Wang, J., Rubin, N., Wu, H., Yalamanchili, S.: Accelerating simulation of agent-based models on heterogeneous architectures. In: Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units, GPGPU-6, pp. 108–119. ACM, Houston, Texas, USA (2013). doi: 10.1145/2458523.2458534
  63. 63.
    Wang, K., Shen, Z.: A GPU based trafficparallel simulation module of artificial transportation systems. In: 2012 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 160–165. Suzhou, China (2012). doi: 10.1109/SOLI.2012.6273523
  64. 64.
    Wei, G., Bogdan, P., Marculescu, R.: Efficient modeling and simulation of bacteria-based nanonetworks with BNSim. IEEE J. Select. Areas Commun. 31(12), 868–878 (2013). doi: 10.1109/JSAC.2013.SUP2.12130019 CrossRefGoogle Scholar
  65. 65.
    Wilensky, U.: NetLogo (1999). http://ccl.northwestern.edu/netlogo/
  66. 66.
    Wilensky, U.: NetLogo heatbugs model (2004). http://ccl.northwestern.edu/netlogo/models/Heatbugs
  67. 67.
    Wilensky, U., Rand, W.: Making models match: replicating an agent-based model. J. Artif. Soc. Soc. Simul. 10(4), 2 (2007). http://jasss.soc.surrey.ac.uk/10/4/2.html
  68. 68.
    Yang, X.J., Liao, X.K., Lu, K., Hu, Q.F., Song, J.Q., Su, J.S.: The TianHe-1A supercomputer: its hardware and software. J. Comput. Sci. Technol. 26(3), 344–351 (2011). doi: 10.1007/s02011-011-1137-8 CrossRefGoogle Scholar
  69. 69.
    Yokokawa, M., Shoji, F., Uno, A., Kurokawa, M., Watanabe, T.: The K computer: Japanese next-generation supercomputer development project. In: Proceedings of the 17th IEEE/ACM International Symposium on Low-Power Electronics and Design, pp. 371–372 (2011). doi: 10.1109/ISLPED.2011.5993668

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© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute for Systems and Robotics, LARSySInstituto Superior Técnico, Universidade de LisboaLisbonPortugal
  2. 2.Universidad de las Fuerzas Armadas-ESPESangolquíEcuador
  3. 3.Life and Health Sciences Research Institute, School of Health SciencesUniversity of MinhoBragaPortugal

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