A Data-Driven Approach To Run Agent-Based Multi-Modal Traffic Simulations On Heterogeneous Cpu-Gpu Hardware

In order to keep short and acceptable run times of agent-based mobility simulators that are used for scenarios, which are of increasing complexity and scale, there is need for increased computational efficiency. While, this need may be addressed by the use of heterogeneous hardware, existing traffic models may be inefficient or not run on such hardware. To simplify the development of mobility simulators with support for heterogeneous hardware, we propose a novel data-driven approach in which the data layer is built such that multiple types of hardware can yield improved run time performance. Using this novel approach, we port an existing GPU-accelerated, large-scale, multi-modal, mobility simulator to modern many-core CPUs. While, a CPU backend runs 3.89 times slower compared to a GPU backend, the CPU backend is 11.64 times faster than another widely used agent-based simulator. Moreover, the run time of the CPU backend on ARM CPUs is comparable to the run time on x86 CPUs. (C) 2021 The Authors. Published by Elsevier B.V.