Parallelizing continuum crowds

Tianlu Mao, Hao Jiang, Jian Li, Yanfeng Zhang, Shihong Xia, Zhaoqi Wang

Abstract: In this paper, we present a novel parallelizing method for crowd simulators constructed with a continuum model rather than an agent-based model. The basic idea is to partition a crowded virtual environment into some districts, each of which keeps its own dynamic continuum fields and has several transitional blocks to make individuals keep continuum motion from one district to another. Our method makes continuum models to be parallelizable while preserving their existing superiority of generating smooth motion. Moreover, for most of large-scale applications, our partitioning method effectively simplifies the complexity of simulation. Experiments show that our method has achieved super-linear speedup and could employ more than one hundred worker processors to simulate 1 million people in an area of 672,400m2.