GPGPU computation of microscopic pedestrian simulations has been largely restricted to Cellular Automata and differential equations models, leaving out most agent-based models that rely on sequential updates.We combine a linked-cell data structure to reduce neighborhood complexity with a massive parallel filtering technique to identify agents that can be updated in parallel, thus extending GPGPU computation to one such model, the Optimal Steps Model. We compare two different OpenCL implementations: a parallel event-driven update scheme and a parallel update scheme that violates the event order for the sake of parallelism.We achieve significant speed ups for both in two benchmark scenarios making faster than real-time simulations possible even for large-scale scenarios.
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