GPU accelerated three-stage execution model for event-parallel simulation

  • Authors:

  • Xiaosong Li;Wentong Cai;Stephen John Turner

  • Affiliations:

  • School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

  • Venue:
  • Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
  • Year:
  • 2013

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Abstract

This paper introduces the concept of event-parallel discrete event simulation (DES) and its corresponding implementation on the GPU platform. Inspired by the typical spatial-parallel DES and time-parallel DES, the event-parallel approach on GPU uses each thread to process one of the N events, where N is the total number of events. By taking advantage of the high parallelism of GPU threads, this approach achieves greater speedup. The GPU architecture is adopted in the execution of the event-parallel approach, so as to take advantage of the parallel processing capability provided by the massively large number of GPU threads. A three-stage execution model composing of generating events, sorting events and processing events in parallel is proposed. This execution model achieves good speedup. Compared with the event scheduling approach on CPU, we achieve up to 22.80 speedup in our case study.