Nonobtuse local tetrahedral refinements towards a polygonal face/interface
Today, we are living a growing demand of larger and more efficient computational resources from the scienti c community. On the other hand, the appearance of GPUs for general purpose computing supposed an important advance for covering such demand. These devices o er an impressive computational capacity at low cost and an efficient power consumption. However, the memory available in these devices is (sometimes) not enough, and so it is necessary computationally expensive memory transfers from (to) CPU to (from) GPU, causing a dramatic fall in performance. Recently, the Lattice-Boltzmann Method has positioned as an e cient methodology for fluid simulations. Although this method presents some interesting features particularly amenable to be efficiently exploited on parallel computers, it requires a considerable memory capacity, which can suppose an important drawback, in particular, on GPUs. In the present paper, it is proposed a new GPU-based implementation, which minimizes such requirements with respect to other state-of-the-art implementations. It allows us to execute almost 2 bigger problems without additional memory transfers, achieving faster executions when dealing with large problems.