Large-Scale Simulations with FLAME

  • Simon Coakley
  • Paul RichmondEmail author
  • Marian Gheorghe
  • Shawn Chin
  • David Worth
  • Mike Holcombe
  • Chris Greenough
Part of the Studies in Big Data book series (SBD, volume 14)


This chapter presents the latest stage of the FLAME development—the high-performance environment FLAME-II and the parallel architecture designed for Graphics Processing Units, FLAMEGPU. The architecture and the performances of these two agent-based software environments are presented, together with illustrative large-scale simulations for systems from biology, economy, psychology and crowd behaviour applications.


Graphic Processing Unit Dependency Graph Agent Instance Speedup Ratio Message Board 
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.



This work has been funded by EPSRC Grants EP/I030654/1 and EP/I030301/1 and the University of Sheffield Vice Chancellors Fellowship Scheme.


  1. 1.
    Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: MASON: A multi-agent simulation environment. Simul: Trans. Soc. Model. Simul. Int. 82(7), 517–527 (2005)CrossRefGoogle Scholar
  2. 2.
    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). JanuaryCrossRefGoogle Scholar
  3. 3.
    Minar, N., Burkhart, R., Langton, C., Askenazi, M.: The Swarm simulation system: a toolkit for building multi-agent simulations. Working Paper 96-06-042, Santa Fe Institute (1996)Google Scholar
  4. 4.
    Center for Connected Learning and Computer-Based Modeling: Northwestern University. NetLogo, Evanston, IL (1999)Google Scholar
  5. 5.
    FLAME Website: (2013)
  6. 6.
    Heath, B., Hill, R., Ciarallo, F.: A survey of agent-based modelling practices. J. Artif. Soc. Soc. Simul. 12, 9 (2009)Google Scholar
  7. 7.
    Allan, R.: Survey of agent-based modelling and simulation tools. Technical Report DL-TR-2010-007, Science and Technology Facilities Council (2010)Google Scholar
  8. 8.
    Weidlich, A., Veit, D.: A critical survey of agent-based wholesale electricity. Energy Econ. 30, 1728–1759 (2008)CrossRefGoogle Scholar
  9. 9.
    Leitäo, P.: Agent-based distributed manufacturing control: a state-of-the-art survey. Eng. Appl. Artif. Intell. 22, 979–991 (2009)CrossRefGoogle Scholar
  10. 10.
    Friesen, M.R., McLeod, R.D.: A survey of agent-based modelling of hospital environments. IEEE Access 2, 227–233 (2014)CrossRefGoogle Scholar
  11. 11.
    Sun, T., McMinn, P., Coakley, S., Holcombe, M., Smallwood, R., MacNeil, S.: An integrated systems biology approach to understanding the rules of keratinocyte colony formation. J. R. Soc. Interface 4, 1077–1092 (2007)CrossRefGoogle Scholar
  12. 12.
    Adra, S., Sun, T., MacNeil, S., Holcombe, M., Smallwood, R.: Development of a three dimensional multiscale computational model of the human epidermis. PLoS ONE 5 (2010)Google Scholar
  13. 13.
    Li, X., Upadhyay, A.K., Bullock, A.J., Dicolandrea, T., Xu, J., Binder, R.L., Robinson, M.K., Finlay, D.R., Mills, K.J., Bascom, C.C., Kelling, C.K., Isfort, R.J., Haycock, J.W., MacNeil, S., Smallwood, R.H.: Skin stem cell hypotheses and long term clone survival—explored using agent-based modelling. Sci. Rep. 3 (2013)Google Scholar
  14. 14.
    Burkitt, M., Walker, D., Romano, D., Fazeli, A.: Modelling sperm behaviour in a 3D environment, pp. 141–149 (2011)Google Scholar
  15. 15.
    Dawid, H., Gemkow, S., Harting, P., Neugart, M.: On the effects of skill upgrading in the presence of spatial labor market frictions: an agent-based analysis of spatial policy design. J. Artif. Soc. Soc. Simul. 12, 334–347 (2009)Google Scholar
  16. 16.
    van der Hoog, S., Deissenberg, C.: Energy shocks and macroeconomic stabilization policies in an agent-based macro model. In: Dawid, H., Semmler, W. (eds.) Computational Methods of Economics Dynamic. Dynamic Modeling and Econometrics in Economics and Finance, vol. 13, pp. 159–181. Springer, Berlin Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Deissenberg, C., van der Hoog, S., Dawid, H.: EURACE: a massively parallel agent-based model of the European economy. Appl. Math. Comput. 204(2), 541–552 (2008)zbMATHMathSciNetCrossRefGoogle Scholar
  18. 18.
    Richmond, P., Walker, D., Coakley, S., Romano, D.: High performance cellular level agent-based simulation with FLAME for the GPU. Briefing Bioinf. 11, 334–347 (2010)CrossRefGoogle Scholar
  19. 19.
    Holcombe, M., Adra, S., Bicak, M., Chin, S., Coakley, S., Graham, A., Green, J., Greenough, C., Jackson, D., Kiran, M., MacNeil, S., Maleki-Dizaji, A., McMinn, P., Pogson, M., Poole, R., Qwarnstrom, E., Ratnieks, F., Rolfe, M., Smallwood, R., Sun, T., Worth, D.: Modelling complex biological systems using an agent-based approach. Integr. Biol. 4, 53–64 (2012)CrossRefGoogle Scholar
  20. 20.
    Eilenberg, S.: Automata, Languages and Machines, vol. A. Academic Press, London (1974)zbMATHGoogle Scholar
  21. 21.
    Holcombe, M.: Towards a formal description of intracellular biochemical organisation. Technical Report CS-86-1, Department of Computer Science, University of Sheffield, Sheffield, UK (1986)Google Scholar
  22. 22.
    Laycock, G.: The theory and practice of specification based software testing. PhD thesis, Department of Computer Science, University of Sheffield, Sheffield, UK (1993)Google Scholar
  23. 23.
    Holcombe, M., Ipate, F.: Correct Systems—Building a Business Process Solution. Springer, Berlin (1998)Google Scholar
  24. 24.
    Barnard, J., Whitworth, J., Woodward, M.: Communicating X-machines. Inf. Softw. Technol. 38(6), 401–407 (1996)CrossRefGoogle Scholar
  25. 25.
    Balanescu, T., Cowling, A., Georgescu, H., Gheorghe, M., Holcombe, M., Vertan, C.: Communicating stream X-machines are no more than X-machines. J. Univ. Comput. Sci. 5(9), 494–507 (1999)zbMATHMathSciNetGoogle Scholar
  26. 26.
    Kefalas, P., Eleftherakis, G., Kehris, E.: Communicating X-machines: a practical approach for formal and modular specification of large systems. Inf. Softw. Technol. 45(5), 15–30 (2003)CrossRefGoogle Scholar
  27. 27.
    Gheorghe, M., Holcombe, M., Kefalas, P.: Computational models of collective foraging. BioSyst. 61, 133–141 (2001)CrossRefGoogle Scholar
  28. 28.
    Jackson, D., Gheorghe, M., Holcombe, M., Bernardini, F.: An agent-based behavioural model of monomorium pharaonis colonies. In: Proceedings of the 4th International Workshop on Membrane Computing. Lecture Notes in Computer Science, vol. 2933, pp. 232–239 (2004)Google Scholar
  29. 29.
    Holcombe, M., Holcombe, L., Gheorghe, M., Talbot, N.: A hybrid machine model of rice blast fungus, manaporthe grisea. BioSyst. 68, 223–228 (2003)CrossRefGoogle Scholar
  30. 30.
    Coakley, S.: Formal software architecture for agent-based modelling in biology. PhD thesis, Department of Computer Science, University of Sheffield, Sheffield, UK (2007)Google Scholar
  31. 31.
    Sakellariou, I.: Agent based modelling and simulation using state machines. In: 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2012), pp. 270–279 (2012)Google Scholar
  32. 32.
    Sakellariou, I.: Turtles as state machines—agent programming in NetLogo using state machines. In: 4th International Conference on Agents and Artificial Intelligence (ICAART 2012), pp. 235–378 (2012)Google Scholar
  33. 33.
    Sakellariou, I., Kefalas, P., Stamatopoulou, I.: Evacuation simulation through formal emotional agent based modelling. In: Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), SciTePress, pp. 193–200 (2014)Google Scholar
  34. 34.
    Hoops, S., Sahle, S., Gauges, R., Lee, C., Nimus, M., Singhal, M., Xu, L., Mendes, P., Kummer, U.: Copasi—a complex pathway simulator. Bioinformatics 22, 3067–3074 (2006)CrossRefGoogle Scholar
  35. 35.
    Raymond, G.M., Butterworth, E.A., Bassingthwaighthe, J.B.: JSim: Mathematical modelling for organ systems, tissues, and cells. FASEB J 21, 736.5 (2007)Google Scholar
  36. 36.
  37. 37.
    Richmond, P., Romano, D.: Template driven agent based modelling and simulation with CUDA. In: Hwu W.M (ed.) GPU Computing Gems Emerald Edition, pp. 313–324, Morgan Kaufmann (2011)Google Scholar
  38. 38.
    Richmond, P., Coakley, S., Romano, D.: A high performance agent based modelling framework on graphics card hardware with CUDA (extended abstract), pp. 1125–1126 (2009)Google Scholar
  39. 39.
    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 (2012)Google Scholar
  40. 40.
    Karmakharm, T., Richmond, P., Romano, D.: Agent-based large scale simulation of pedestrians with adaptive realistic navigation vector fields, pp. 67–74 (2010)Google Scholar
  41. 41.
    Coakley, S., Smallwood, R., Holcombe, M.: From molecules to insect communities — how formal agent-based computational modelling is uncovering new biological facts. Mathematicae Japonicae Online e-2006: 765–778 (2006)Google Scholar
  42. 42.
    Pogson, M., Smallwood, R., Qwarnstrom, E., Holcombe, M.: Formal agent-based modelling of intracellular chemical interactions. BioSyst. 85, 37–45 (2006)CrossRefGoogle Scholar
  43. 43.
    Pogson, M., Holcombe, M., Smallwood, R., Qwarnstrom, E.: Introducing spatial information into predictive NF-kB modelling—an agent-based approach. PLoS ONE 3, e2367 (2008)CrossRefGoogle Scholar
  44. 44.
    Maleki-Dizaji, S., Rolfe, M., Fisher, P., Holcombe, M.: A systematic approach to understanding bacterial responses to oxygen using Taverna and Webservices. In: Proceedings of 13th International Conference on Biomedical Engineering, pp. 77–80 (2009)Google Scholar
  45. 45.
    Walker, D., Wood, S., Southgate, J., Holcombe, M., Smallwood, R.: An integrated agent-mathematical model of the effect of intercellular signalling via the epidermal growth factor receptor on cell proliferation. J. Theor. Biol. 242, 774–789 (2006)MathSciNetCrossRefGoogle Scholar
  46. 46.
    Sun, T., McMinn, P., Holcombe, M., Smallwood, R., MacNeil, S.: Agent based modelling helps in understanding the rules by which fibroblasts support keratinocyte colony formation. PLoS ONE 3, e2129 (2008)CrossRefGoogle Scholar
  47. 47.
    Sun, T., Adra, S., MacNeil, S., Holcombe, M., Smallwood, R.: Exploring hypotheses of the actions of TGF-\(\beta \)1 in epidermal wound healing using a 3d computational multiscale model of the human epidermis. PLoS ONE 4, e8515 (2009)CrossRefGoogle Scholar
  48. 48.
    Jackson, D.E., Holcombe, M., Ratnieks, F.L.W.: Trail geometry gives polarity to ant foraging networks. Nature 432, 907–909 (2004)CrossRefGoogle Scholar
  49. 49.
    Jackson, D.E., Martin, S.J., Ratnieks, F.L.W., Holcombe, M.: Spatial and temporal variation in pheromone composition of ant foraging trails. Behav. Ecol. 18, 444–450 (2007)CrossRefGoogle Scholar
  50. 50.
    Holcombe, M., Coakley, S., Kiran, M., Chin, S., Greenough, C., Worth, D., Cincotti, S., Raberto, M., Teglio, A., Deissenberg, C., van der Hog, S., Dawid, H., Gemkow, S., Harting, P., Neugart, M.: Large-scale modelling of economic systems. Complex Syst. 22, 175–191 (2013)Google Scholar
  51. 51.
    Raberto, M., Teglio, A., Cincotti, S.: Credit money and macroeconomic instability in the agent-based model and simulator EURACE. Economics (2010).
  52. 52.
    Corbett, A.: Agent-based modelling of transactive memory systems and knowledge processes in agile versus traditional software development teams. Ph.D. thesis, Department of Computer Science, University of Sheffield, Sheffield, UK (2012)Google Scholar
  53. 53.
    Corbett, A., Wood, S., Holcombe, M.: It’s the people stupid!—Formal models for social interaction in agile software development teams. J. Adv. Soc. Sci. Res. 2(2):70–85 (2015)Google Scholar
  54. 54.
    Bakir, M.E., Ipate, F., Konur, S., Mierla, L., Niculescu, I.: Extended simulation and verification platform for kernel P systems, pp. 135–152 (2014)Google Scholar
  55. 55.
    Ţurcanu, A., Mierlă, L., Ipate, F., Ştefănescu, A., Bai, H., Holcombe, M., Coakley, S.: Modelling and analysis of E. coli respiratory chain. In: Frisco, P., Gheorghe, M., Pérez-Jiménez, M.J. (eds.) Applications of Membrane Computing in Systems and Synthetic Biology. Emergence, vol. 7, pp. 247–267. Complexity and Computation. Springer, Berlin Heidelberg (2014)Google Scholar
  56. 56.
    Baqueiro, O., Wang, Y.J., McBurney, P., Coenen, F.: Integrating data mining and agent based modeling and simulation. In: Advances in Data Mining. Applications and Theoretical Aspects. Springer, pp. 220–231 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Simon Coakley
    • 1
  • Paul Richmond
    • 1
    Email author
  • Marian Gheorghe
    • 1
  • Shawn Chin
    • 2
  • David Worth
    • 2
  • Mike Holcombe
    • 1
  • Chris Greenough
    • 2
  1. 1.University of SheffieldSheffieldUK
  2. 2.Software Engineering Group STFC Rutherford Apple LabsDidcotUK

Personalised recommendations