Simulating heterogeneous behaviours in complex systems on GPUs

Authored by Paul Richmond, Mozhgan K Chimeh

Date Published: 2018

DOI: 10.1016/j.simpat.2018.02.002

Sponsors: United Kingdom Engineering and Physical Sciences Research Council (EPSRC)

Platforms: FLAME

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

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. (C) 2018 The Authors. Published by Elsevier B.V.

Tags

Simulation GPGPU Agent Based Modeling Optimization Data Parallel Algorithms Flame gpu Control flow