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Multi-agent large-scale parallel crowd simulation with NVRAM-based distributed cache
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DOI：10.1016/j.jocs.2019.04.004.png)
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This paper presents the architecture, main components and performance results for a parallel and modular agent-based environment aimed at crowd simulation. The environment allows to simulate thousands or more agents on maps of square kilometers or more, features a modular design and incorporates nonvolatile RAM (NVRAM) with a fail-safe mode that can be activated to allow to continue computations from a recently analyzed state in case of a failure. We show results for an evacuation scenario for an area of up to 6 km(2) in a district of Gdansk, Poland, performed on two clusters, one with hardware simulation of NVRAM. We have shown a very small overhead of using NVRAM compared to the RAM only solution and an overhead of 20% with the fail-safe mode on using NVRAM, shown up to 30 000 agents and up to 25 000 iterations of the simulation. We also show the benefit of using NVRAM for file synchronization with a slow growth of the execution time while increasing the map size. We then present how the frequency of visualization affects execution time and very good scaling of the proposed solution in a cluster environment for more than 650 processes and 60 000 agents. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Parallel crowd simulation
Multi-agent large-scale simulation
Scalability
NVRAM distributed cache
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