Determining the optical flow of a video is a compute-intensive task essential for computer vision. For achieving this processing in real-time, the whole algorithm deployment chain must be thought of for efficiency first. The development is usually divided into two parts: first, designing an algorithm that meets precision constraints, then, implementing and optimizing its execution on the targeted platform. We argue that unifying those operations enhances performance on the embedded processor. This paper is based on an industrial use case of computer vision. The objective is to determine dense optical flow in real-time on an embedded GPU platform: the Nvidia AGX Xavier. The CLG (Combined Local-Global) optical flow method, initially chosen, is analyzed to understand the convergence speed of its underlying optimization problem. The Jacobi solver is selected for implementation because of its parallel nature. The whole multi-level processing is then ported to the GPU, using several specific optimization strategies. In particular, we analyze the impact of fusing the solver's iterations with the roofline model. As a result, with a 30W power budget, our implementation runs at 60FPS, on 640 × 512 images, with a four-level processing. Hopefully, this example should provide feedback on the issues that arise when trying to port a method to a parallel platform and serve for further implementations of computer vision algorithms on specialized hardware.