Abstract
Road network microsimulation is computationally expensive, and existing state of the art commercial tools use task parallelism and coarse-grained data-parallelism for multi-core processors to achieve improved levels of performance. An alternative is to use Graphics Processing Units (GPUs) and fine-grained data parallelism. This paper describes a GPU accelerated agent based microsimulation model of a road network transport system. The performance for a procedurally generated grid network is evaluated against that of an equivalent multi-core CPU simulation. In order to utilise GPU architectures effectively the paper describes an approach for graph traversal of neighbouring information which is vital to providing high levels of computational performance. The graph traversal approach has been integrated within a GPU agent based simulation framework as a generalised message traversal technique for graph-based communication. Speed-ups of up to 43 × are demonstrated with increased performance scaling behaviour. Simulation of over half a million vehicles and nearly two million detectors at a rate of 25 × faster than real-time is obtained on a single GPU.
Highlights
Simulations of road networks are used during the development and management of transport networks around the globe
This paper presents two contributions:(i) a Graphics Processing Units (GPUs) accelerated data-parallel agent-based road network microsimulation model is evaluated against an equivalent model in a commercial multi-core Central Processing Units (CPUs) software tool, demonstrating considerable improvements to simulation performance and performance scalability; and (ii) a general-purpose graph-based communication strategy is presented for high performance agent communication for fine-grained data-parallel agent based simulations, implemented for the FLAME GPU Agent Based Modelling (ABM) framework which enables high performance agent based simulations of transport networks on GPUs
This section summarises the cross-validation of the FLAME GPU based simulation against the existing multi-core CPU road network microsimulation tool Aimsun
Summary
Simulations of road networks are used during the development and management of transport networks around the globe. Microscopic road network simulations are fine-grained simulations which simulate individual vehicles within the system, capturing low level behaviours. Agent Based Modelling (ABM) is one microscopic approach, where relatively simple individual behaviours are defined, which combined with interactions between agents and the environment, allows the emergence of complex behaviours. Microscopic simulations are much more computationally expensive than the more traditional higher-level macroscopic simulations, which use a higher level of abstraction consisting of network flows rather than individual vehicles. The level of detail captured by microscopic simulations is much greater than that of macroscopic and mesoscopic simulations, including the emergent behaviours enabled by the use of ABM
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