Model-Based Design (MBD) is widely used for the design and simulation of electric traction control systems in the railway industry. Moreover, similar to other transportation industries, railway is moving towards the consolidation of multiple computing systems on fewer and more powerful ones, aiming for the reduction of Size, Weight and Power (SWaP). In that regard, Graphics Processing Units (GPUs) are increasingly considered by critical systems engineers, seeking to satisfy their ever increasing performance requirements. Recently, MBD tools have been enhanced with GPU code generation capabilities for machine learning acceleration, however, there is no indication whether these tools are ready for the design of time-sensitive systems. In this paper we analyse the suitability of commercial MBD toolsets by designing and deploying a model-based parallel control case study on embedded GPU platforms. While our results show promising feasibility evidence, they also reveal shortcomings which should be addressed before these toolsets become fit for developing critical systems. We propose certain improvements that have to be incorporated in these tools to achieve this goal. By implementing our proposals in the generated code, we experimentally show their effectiveness on two NVIDIA-based embedded GPUs.