Exploring Execution Schemes for Agent-Based Traffic Simulation on Heterogeneous Hardware

Microscopic traffic simulation is associated with substantial runtimes, limiting the feasibility of large-scale evaluation of traffic scenarios. Even though today heterogeneous hardware comprised of CPUs, graphics processing units (GPUs) and fused CPU-GPU devices is inexpensive and widely available, common traffic simulators still rely purely on CPU-based execution, leaving substantial acceleration potentials untapped. A number of existing works have considered the execution of traffic simulations on accelerators, but have relied on simplified models of road networks and driver behaviour tailored to the given hardware platform. Thus, the existing approaches cannot directly benefit from the vast body of research on the validity of common traffic simulation models. In this paper, we explore the performance gains achievable through the use of heterogeneous hardware when relying on typical traffic simulation models used in CPU-based simulators. We propose a partial offloading approach that relies either on a dedicated GPU or a fused CPU-GPU device. Further, we present a traffic simulation running fully on a many-core GPU and discuss the challenges of this approach. Our results show that a CPU-based parallelisation closely approaches the results of partial offloading, while full offloading substantially outperforms the other approaches. We achieve a speedup of up to 28.7x over the sequential execution on a CPU.