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HPABM: A Hierarchical Parallel Simulation Framework for Spatially-explicit Agent-based Models

Authors

  • Wenwu Tang,

    Corresponding author
    1. Department of Geography and National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
      Wenwu Tang, Department of Geography and National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, 324 Davenport Hall, 607 South Mathews Avenue, Urbana, IL 61801, USA. E-mail: wentang@illinois.edu
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  • Shaowen Wang

    1. Department of Geography and National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
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Wenwu Tang, Department of Geography and National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, 324 Davenport Hall, 607 South Mathews Avenue, Urbana, IL 61801, USA. E-mail: wentang@illinois.edu

Abstract

A Hierarchical Parallel simulation framework for spatially-explicit Agent-Based Models (HPABM) is developed to enable computationally intensive agent-based models for the investigation of large-scale geospatial problems. HPABM allows for the utilization of high-performance and parallel computing resources to address computational challenges in agent-based models. Within HPABM, an agent-based model is decomposed into a set of sub-models that function as computational units for parallel computing. Each sub-model is comprised of a sub-set of agents and their spatially-explicit environments. Sub-models are aggregated into a group of super-models that represent computing tasks. HPABM based on the design of super- and sub-models leads to the loose coupling of agent-based models and underlying parallel computing architectures. The utility of HPABM in enabling the development of parallel agent-based models was examined in a case study. Results of computational experiments indicate that HPABM is scalable for developing large-scale agent-based models and, thus, demonstrates efficient support for enhancing the capability of agent-based modeling for large-scale geospatial simulation.

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