Global Sensitivity Analysis of a Large Agent-Based Model of Spatial Opinion Exchange: A Heterogeneous Multi-GPU Acceleration Approach

Sensitivity analysis is an important step in agent-based modeling of complex adaptive spatial systems to evaluate the contribution of influential variables to model response. Sensitivity analysis of agent-based models is computationally demanding, however, and this analysis tends to be intractable for large agent-based modeling. This computational challenge greatly limits our ability to investigate complex spatial dynamics using large agent-based models. The objective of this study is to gain insight into this computational issue by focusing on the sensitivity analysis of large agent-based modeling of spatial opinion exchange, accelerated using multiple graphics processing units (GPUs). We present a heterogeneous parallel computing approach based on nested parallelism for the global sensitivity analysis of the model. The agent-based opinion model is parallelized using many-core GPUs for the simulation of a large number of spatially aware and interacting agents. These agents exchange opinions for developin...