A Locality-aware Approach to Scalable Parallel Agent- based Models of Spatially Heterogeneous Interactions
نویسندگان
چکیده
Spatially explicit agent-based models have a great potential to mitigate their computational costs by taking advantage of parallel and high-performance computing. However, the spatial dependency and heterogeneity of interactions pose challenges for parallel SE-ABMs to achieve good scalability. This paper applies the principle of data locality to tackle these challenges by extending a theoretical approach to the representation of spatial computational domain. Using a graph-based approach, we minimize the interaction overhead in domain decomposition and maximize the efficiency of allocating computing resources. Our approach is illustrated by simulating agent-level spatial interaction models on different parallel platforms.
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