Journal
2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS
Volume 147, Issue -, Pages 283-287Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2019.01.265
Keywords
parallel computing; massive crowd; compute node; scene segmentation
Funding
- Shandong Key Research and Development Program [2017GSF20105]
- National Natural Science Foundation of China [61502505, 61472232, 61572299]
- Natural Science Foundation of Shandong Province [ZR2016FB13]
- Shandong Province Higher Educational Science and Technology Program [J16LN09]
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With the advent of multi-core and even many-core processors, massively parallel computing processing has presented a trend of low cost and popularity. It provides new technical means and feasible solutions for the rapid simulation calculation of large-scale group movements and the simulable problem of super-large group movements. In the process of parallel computing, the design of parallel architecture and parallel algorithm is closely related to the group motion simulation algorithm itself due to the original task needs to be decomposed. A parallel simulation algorithm for the continuum crowds has been proposed in this paper. We use the transition block-based scene segmentation algorithm on the management node to divide the simulated scene after initializing the virtual scene, and assign each block to a different computing node for processing. Then, the compute node gets information about the individual it is responsible for. Based on the method, a parallel simulation prototype system was developed on the Sugon high-performance computing platform. Experimental results show that our parallel simulation algorithm can increase the efficiency of scene-rendering and solve the bottleneck in group scale. (C) 2019 The Authors. Published by Elsevier B.V.
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