Abstract
We present a software framework called CARAVAN, which was developed for comprehensive simulations on massive parallel computers. The framework runs user-developed simulators with various input parameters in parallel without requiring the knowledge of parallel programming. The framework is useful for exploring high-dimensional parameter spaces, for which sampling points must be dynamically determined based on the previous results. Possible use cases include optimization, data assimilation, and Markov-chain Monte Carlo sampling in parameter spaces. As a demonstration, we applied CARAVAN to an evacuation planning problem in an urban area. We formulated the problem as a multi-objective optimization problem, and searched for solutions using multi-agent simulations and a multi-objective evolutionary algorithm, which were developed as modules of the framework.
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Acknowledgement
Y.M. acknowledges support from MEXT as “Exploratory Challenges on Post-K computer (Studies of multi-level spatiotemporal simulation of socioeconomic phenomena)” and the Japan Society for the Promotion of Science (JSPS) (JSPS KAKENHI; grant no. 18H03621). This research used computational resources of the K computer provided by the RIKEN Center for Computational Science through the HPCI System Research project (Project ID: hp160264). We thank Maxine Garcia, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.
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Murase, Y., Matsushima, H., Noda, I., Kamada, T. (2019). CARAVAN: A Framework for Comprehensive Simulations on Massive Parallel Machines. In: Lin, D., Ishida, T., Zambonelli, F., Noda, I. (eds) Massively Multi-Agent Systems II. MMAS 2018. Lecture Notes in Computer Science(), vol 11422. Springer, Cham. https://doi.org/10.1007/978-3-030-20937-7_9
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DOI: https://doi.org/10.1007/978-3-030-20937-7_9
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