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Large-Scale Agent-Based Modeling with Repast HPC: A Case Study in Parallelizing an Agent-Based Model

  • Nicholson Collier
  • Jonathan Ozik
  • Charles M. Macal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9523)

Abstract

We present a case study for parallelizing a large-scale epidemiologic ABM developed with Repast HPC, the Chicago Social Interaction Model (chiSIM). The original serial model is a CA-MRSA model which tracks CA-MRSA transmission dynamics and infection in Chicago, and represents the spread of CA-MRSA in the population of Chicago. We utilize both within compute node parallelization using the OpenMP toolkit and distributed parallelization across multiple processes using MPI. The combined approach yields a 1350 % increase in run time performance utilizing 128 compute nodes.

Keywords

Agent-based modeling ABMS Repast HPC Parallel simulation Distributed simulation High performance computing 

Notes

Acknowledgements

This work is supported by the U.S. Department of Energy under contract number DE-AC02-06CH11357 and National Science Foundation (NSF) RAPID Award DEB-1516428.This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility.The Chicago MRSA ABM was developed with support from the MIDAS Program, NIH/National Institute of General Medical Sciences, grant U01GM087729.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nicholson Collier
    • 1
    • 2
  • Jonathan Ozik
    • 1
    • 2
  • Charles M. Macal
    • 1
  1. 1.Global Security SciencesArgonne National LaboratoryArgonneUSA
  2. 2.Computation InstituteUniversity of ChicagoChicagoUSA

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