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Updated: May 26, 2025

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Experiences in Developing a Distributed Agent-based Modeling Toolkit with Python.

Nicholson T Collier1, Jonathan Ozik1, Eric R Tatara1

  • 1Decision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL USA.

Proceedings of PYHPC 2020 : 9Th Workshop on Python for High-Performance and Scientific Computing. Workshop on Python for High-Performance and Scientific Computing (9Th : 2020 : Online)
|February 24, 2025
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Summary
This summary is machine-generated.

We developed Repast4Py, a Python-based toolkit for distributed agent-based modeling (ABM) on high-performance computing (HPC) resources. This toolkit aims to simplify the creation of complex system models for wider scientific adoption.

Keywords:
agent-based modelingcomputer simulationhigh performance computingparallel processing

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Area of Science:

  • Computational Science
  • Complex Systems Modeling
  • Agent-Based Modeling

Background:

  • Distributed agent-based modeling (ABM) offers advanced simulation capabilities for large-scale complex systems.
  • Specialized knowledge requirements currently limit the widespread adoption of distributed ABM.

Purpose of the Study:

  • To present the development of Repast4Py, a Python-based distributed ABM toolkit.
  • To identify key elements for a user-friendly distributed ABM toolkit based on prior experience.

Main Methods:

  • Leveraging Python's C-API, Numba, NumPy, and PyTorch for performance.
  • Building upon experience from developing Repast for High Performance Computing (Repast HPC).

Main Results:

  • An initial implementation of Repast4Py has been developed.
  • The toolkit is designed for scalability on large high-performance computing (HPC) resources.

Conclusions:

  • Repast4Py aims to lower barriers to entry for distributed ABM development.
  • The toolkit facilitates the utilization of advanced computing architectures for complex system simulations.