In a preceding project, four lattice Boltzmann algorithms were implemented on the CPU using HPX. Building up on this project, the goal of this work is to implement the two most suitable algorithms on the GPU together with a framework permitting real-time visualization. In the propaedeuticum, fundamental concepts of GPU programming are elaborated, and based on the insights gained, the aptitude of the four algorithms from the project for a portation to the GPU is investigated. The two-lattice and the swap algorithm were identified as the most promising candidates. The visualization framework was designed using the Dear ImGui and ImPlot APIs. In the bachelor thesis, the swap algorithm and multiple variants of the two-lattice algorithm were realized using AdaptiveCpp, which is one of two major implementations of the SYCL standard. Kármán vortex streets were chosen as a scenario demonstrating the capabilities of the proposed simulations. Since all algorithms update the lattice faster than the frontend can accept new frames, all of them are suitable for fulfilling the objective under the limitations imposed by the visualization framework. Similarly to the project, a simple and mostly runtime-coordinated two-lattice variant was recognized as the most convenient and, at the same time, very competitive implementation. Out of the data layouts proposed by Mattila et al., the bundle layout is well suited for devices with small caches, while the stream layout uses the memory bandwidth more efficiently. The optimal work group size and subdomain shape also depend on the targeted hardware.
