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

Sergiy Gogolenko ()
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Sergiy Gogolenko: High Performance Computing Center Stuttgart (HLRS)

A chapter in Sustained Simulation Performance 2018 and 2019, 2020, pp 205-214 from Springer

Abstract: 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-39181-2_16

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DOI: 10.1007/978-3-030-39181-2_16

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