Chimeh, M.K. and Richmond, P. orcid.org/0000-0002-4657-5518 (2018) Simulating heterogeneous behaviours in complex systems on GPUs. Simulation Modelling Practice and Theory, 83. pp. 3-17. ISSN 1569-190X
Abstract
Agent Based Modelling (ABM) is an approach for modelling dynamic systems and studying complex and emergent behaviour. ABMs have been widely applied in diverse disciplines including biology, economics, and social sciences. The scalability of ABM simulations is typically limited due to the computationally expensive nature of simulating a large number of individuals. As such, large scale ABM simulations are excellent candidates to apply parallel computing approaches such as Graphics Processing Units (GPUs). In this paper, we present an extension to the FLAME GPU 1 [1] framework which addresses the divergence problem, i.e. the challenge of executing the behaviour of non-homogeneous individuals on vectorised GPU processors. We do this by describing a modelling methodology which exposes inherent parallelism within the model which is exploited by novel additions to the software permitting higher levels of concurrent simulation execution. Moreover, we demonstrate how this extension can be applied to realistic cellular level tissue model by benchmarking the model to demonstrate a measured speedup of over 4x.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Agent Based Modeling; GPGPU; Data parallel algorithms; Simulation; FLAME GPU |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Mar 2018 13:58 |
Last Modified: | 21 Mar 2018 13:58 |
Published Version: | https://doi.org/10.1016/j.simpat.2018.02.002 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.simpat.2018.02.002 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128797 |