Abstract
The Multi-Agent Spatial Simulation (MASS) library, which we have developed at University of Washington Bothell, provides the efficiency of agent-based parallelization while abstracting the parallel environment for the agent-based model [1]. It provides a middle layer that only requires the user to contribute code relevant to their project without having to manage the details of parallelization.
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References
Fukuda, M.: MASS: a parallelizing library for multi-agent spatial simulation. http://depts.washington.edu/dslab/MASS
Lukasik, S., Kowalski, P.A., Charytanowicz, M., Kulczycki, P.: Data Clustering with Grasshopper Optimization Algorithm
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Gordon, C., Fukuda, M. (2018). Analysis of Agent-Based Parallelism for Use in Clustering and Classification Applications. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Lecture Notes in Computer Science(), vol 10978. Springer, Cham. https://doi.org/10.1007/978-3-319-94580-4_28
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DOI: https://doi.org/10.1007/978-3-319-94580-4_28
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