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Large Scale Agent Based Social Simulations with High Resolution Raster Inputs in Distributed HPC Environments

In: Sustained Simulation Performance 2018 and 2019

Author

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  • Sergiy Gogolenko

    (High Performance Computing Center Stuttgart (HLRS))

Abstract

Agent-based modelling and simulation (ABMS) is an essential tool which allows to explore the role of social phenomena via computer simulation. Large scale social simulations of HPCs—also known as parallel and/or distributed agent-based simulation (PDABS)—play a key role in the emerging field of computational global systems sciences (GSS). Agent-based models (ABMs) in GSS are characterized by highly non-uniform spatial distribution of agents and importance of long distance social interactions. Over the last two decades, researchers proposed a number of approaches to effectively address these traits of ABMs. Such approaches are driven by data available for scientists. In many GSS applications, the data partially come in raster formats. Yet, case of raster inputs for GSS applications is barely studied in literature and not supported sufficiently in the state-of-the-art ABMS frameworks. In this paper, we propose a graph-based approach to represent ABMs with raster inputs on HPCs. This approach naturally leads to a space-relationships-based work partitioning strategy which allows to improve performance of PDABS.

Suggested Citation

  • Sergiy Gogolenko, 2020. "Large Scale Agent Based Social Simulations with High Resolution Raster Inputs in Distributed HPC Environments," Springer Books, in: Michael M. Resch & Yevgeniya Kovalenko & Wolfgang Bez & Erich Focht & Hiroaki Kobayashi (ed.), Sustained Simulation Performance 2018 and 2019, pages 205-214, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-39181-2_16
    DOI: 10.1007/978-3-030-39181-2_16
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