In the last decade, agent-based modeling and simulation (ABMS) has been applied to a variety of domains, demonstrating the potential of this technique to advance science, engineering, and policy analysis. However, realizing the full potential of ABMS to find breakthrough research results requires far greater computing capability than is available through current ABMS tools. The Repast for High Performance Computing (Repast HPC) project addresses this need by developing a useful and useable next-generation ABMS system explicitly focusing on larger-scale distributed computing platforms. Repast HPC is intended to smooth the path from small-scale simulations to large-scale distributed simulations through the use of a Logo-like system. This article’s contribution is its detailed presentation of the implementation of Repast HPC as a useful and usable framework, a complete ABMS platform developed explicitly for larger-scale distributed computing systems that leverages modern C++ techniques and the ReLogo language.
NorthMJMacalCM. Managing Business Complexity: Discovering Strategic Solutions with Agent-based Modeling and Simulation. Oxford: Oxford University Press, 2007.
2.
KoehlerMTTivnanBF. Clustered computing with NetLogo and Repast J: beyond chewing gum and duct tape. In: Proceedings of the Agent 2005 Conference on Generative Social Processes, Models, and Mechanisms, 2005, pp. 43–54.
3.
SISC. IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) - Framework and Rules, 2000.
4.
HouariNFarBH. Building Collaborative Intelligent Agents: Revealing Main Pillars. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering, 2005.
5.
BradshawJDutfieldSBenoitPWooleyJD. Kaos: Toward an Industrial Strength Open Agent Architecture. In: BradshawJ (ed.), Software Agents. Cambridge, MA: AAAI/MIT Press, 1997.
LaycockG. The Theory and Practice of Specification based Software Testing. University of Sheffield, 1993.
8.
ScheutzMSchermerhornPConnaughatonRDinglerA. SWAGES: an extendable distributed experimentation system for large-scale agent-based ALife simulations. In: Proceedings of the 10th International Conference on the Simulation and Synthesis of Living Systems, 2006.
9.
KwokYKChowKP. On load balancing for distributed multiagent computing. IEEE Trans Parallel Distrib Syst2002; 13: 787–801.
10.
OguaraTChenDTheodoropoulosGLoganBLeesM. An adaptive load management mechanism for distribution simulation of multi-agent systems. In: Proceedings of the 9th IEEE International Symposium on Distributed Simulation and Real-Time Applications, 2006.
11.
MassaioliFCastiglioneFBernashciM. OpenMP parallelization of agent-based models. Parallel Computing2005; 31: 1066–1081.
FujimotoRM. Parallel and Distributed Simulation Systems. New York: Wiley Interscience, 2000.
14.
PerumullaKS. Scaling time warp-based discrete event execution to 104 processors on a Blue Gene supercomputer. In: Proceedings of the 4th International Conference on Computing Frontiers, 2007, pp. 69–76.
15.
PerumullaKS. Parallel and distributed simulation: traditional techniques and recent advances. In: Proceedings of the Winter Simulation Conference (WSC), 2006.
16.
ChandyKMMisraJ. Asynchronous distributed simulation via a sequence of parallel computations. Commun ACM1981; 24: 198–205.
17.
ChandyKMMisraJ. Distributed simulation: a case-study in design and verification of distributed programs. IEEE Trans Software Engng1979; 5: 440–452.
JeffersonDR. Virtual time. ACM Trans Programming Languages Syst1985; 7: 404–425.
20.
PerumullaKS. μsik - a micro-kernel for parallel/distributed simulation systems. In: Workshop on Principles of Advanced and Distributed Simulations, 2005, pp. 59–68.
21.
JhaVBagrodiaR. A unified framework for conservative and optimistic distributed simulation. ACM SIGSIM Sim Digest1994; 24: 12–19.
22.
BagrodiaRLLiaoW. Parallel simulation of the Sharks World problem. In: Proceedings of the 22nd Winter Simulation Conference, 1990, pp. 191–198.
23.
PresleyMTReiherPLBellenotSF. A Time Warp implementation of the Sharks World. In: Proceedings of the 22nd Winter Simulation Conference, 1990.
24.
NicolDMRiffeSE. A ‘conservative’ approach to parallelizing the Sharks World simulation. In: Proceedings of the 22nd Winter Simulation Conference, 1990.
25.
BauerDWCarothersCDHolderA. Scalable Time Warp on Blue Gene supercomputers. In: 2009 ACM/IEEE/SCS 23rd Workshop on Principles of Advanced and Distributed Simulation, 2009, pp. 35–44.
26.
CarothersCDBauerDWPearceSO. ROSS: a high-performance low memory, modular Time Warp system. J Parallel Distrib Comput2002; 62: 1648–1669.
27.
KarimabadiHDriscollJDaveJ. Parallel Discrete Event Simulation of Grid-based Models: Asynchronous Electromagnetic Hybrid Code (Lecture Notes in Computer Science, vol. 3732). Berlin: Springer-Verlag, 2006, pp. 573–582.
28.
HudakP. Conception, evolution, and application of functional programming languages. ACM Comput Surveys1989; 21: 359–411.
29.
D’SouzaRMLysenkoMRahmaniK. SugarScape on Steroids: simulating over a million agents at interactive rates. In: Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence, 2007.
30.
LysenkoMD’SouzaRM. A framework for megascale agent-based model simulations on graphical processing units. J Artificial Societies Social Sim2008; 11(4).
NorthMJTataraECollierNTOzikJ. Visual agent-based model development with Repast Simphony. In: Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence, 2007.
33.
HoweTRCollierNTNorthMJParkerMTVosJR. Containing agents: contexts, projections and agents. In: Proceedings of the Agent 2006 Conference on Social Agents: Results and Prospects, 2006.
MatsumotoMNishimuraT. Mersenne Twister: a 623-dimensionality equidistributed uniform psuedo-random number generator. ACM Trans Model Comput Sim1998; 8: 3–30.
37.
ZurellDBergerUCabralJS. The virtual ecologist approach: simulating data and observers. OIKOS2010; 119: 622–635.
