Abstract
Parallel agent based simulation is popular used in artificial society. However, it brings great challenges to the execution efficiency when facing large scale artificial society where the number of agents in the simulation is up to millions. A simulation kernel with conservative synchronization strategy and multi-thread scheduling paradigm for large scale parallel agent based simulation is introduced. Based on the simulation kernel, the paper proposes two optimization strategies: a container based agent management scheme and an event based load balance strategy. The paper then design several experiments to evaluate the optimization performance, it shows that the optimization strategies can obtain up to 5x speedup compared to the basic simulation kernel.
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Acknowledgment
This work was supported by the Natural Science Foundation of China (Grant No. 9102403071303252, 61403402, 41201544 and 71343282) and the HPC project (13010502) funded by NUDT.
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Li, Z., Guo, G., Chen, B., Ma, L., Luo, Y., Qiu, X. (2016). Optimization for Accelerating Large Scale Agent Based Simulation. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_18
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DOI: https://doi.org/10.1007/978-981-10-2663-8_18
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