Simulating heterogeneous behaviours in complex systems on GPUs

作     者：Chimeh, Mozhgan K. Richmond, Paul 

作者机构：Univ Sheffield Sheffield S Yorkshire England 

出 版 物：《SIMULATION MODELLING PRACTICE AND THEORY》 (仿真模拟实践与理论)

年 卷 期：2018年第83卷

页      面：3-17页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：EPSRC [EP/N018869/1] EPSRC [EP/N018869/1] Funding Source: UKRI 

主　　题：Agent Based Modeling GPGPU Data parallel algorithms Simulation FLAME GPU 

摘      要：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.