Comparison of Road Traffic Simulation Speed on CPU and GPU In this paper, we describe a fair comparison of the performance of a microscopic road traffic simulation performed on a GPU and on a CPU. The aim of our work is to determine the speedup, which can be achieved if the GPU is used for the same simulation instead of the (multicore) CPU. A microscopic road traffic simulator capable of running on both platforms was created for this purpose with the aim to make the GPUbased and the CPUbased simulations as similar as possible. The performances of both the GPUbased and the CPUbased simulations were tested using two different road traffic models (a carfollowing model and a cellular automaton model), four road traffic networks (regular square grids of crossroads) of different sizes, and three different hardware configurations. The maximal achieved speedup using the GPU instead of the multicore CPU for the cellular automaton model was 12.4. For the carfollowing model, the maximal achieved speedup was 10.7.

Performance Gains in V2X Experiments Using Distributed Simulation in the Veins Framework The increasing precision in simulation models leads to a high computational effort. In general, parallel and distributed simulation is one technique to speed up a simulation to an acceptable runtime. Depending on the simulation model, the suitability of a parallel or distributed simulation varies. This applies also to coupled V2X simulations, where distributed computation may help to increase performance drastically. In this work, we identify opportunities and possibilities regarding distributed V2X simulations. Therefore, we describe and evaluate an approach to distribute multiple instances of the wellknown Veins framework that are interacting and running concurrently. The instances are coupled using a hybrid cosimulation framework based on the High Level Architecture. With increasing traffic load, a higher speedup can be measured.