Massively parallel Modelling & Simulation of large crowd with GPGPU

  • Authors:

  • Dan Chen;Lizhe Wang;Mingwei Tian;Jian Tian;Shuaiting Wang;Congcong Bian;Xiaoli Li

  • Affiliations:

  • School of Computer Science, China University of Geosciences, Wuhan, China 430074 and School of Computer Science, University of Birmingham, Edgbaston, Birmingham, UK B15 2TT;Pervasive Technology Institute, Indiana University, Bloomington, USA 47408;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;School of Economics, Huazhong University of Science & Technology, Wuhan, China 430074;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2013

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Abstract

In peacekeeping, domestic, or combat operations, unanticipated crowd confrontations can occur. As a highly dynamic social group, human crowd in confrontation is a fascinating phenomenon. This paper presents a novel method based on the concept of vector field to formulate the way in which external stimuli may affect the behaviours of individuals in a crowd. Furthermore, Modelling & Simulation (M&S) of large crowds at individual level has long been placed in the highly computation intensive world. This study adopts GPGPU to sustain massively parallel M&S of a confrontation operation involving a large crowd. This approach enables investigation of a crowd consisting of tens of thousands individuals whose size was prohibitively large for conventional M&S technique to support. Experimental results indicate that the approach is efficient in terms of both performance and energy consumption.