Multi-GPU implementation of the lattice Boltzmann method

The lattice Boltzmann method (LBM) is an increasingly popular approach for solving fluid flows in a wide range of applications. The LBM yields regular, data-parallel computations; hence, it is especially well fitted to massively parallel hardware such as graphics processing units (GPU). Up to now, though, single-GPU implementations of the LBM are of moderate practical interest since the on-board memory of GPU based computing devices is too scarce for large scale simulations. In this paper, we present a multi-GPU LBM solver based on the well-known D3Q19 MRT model. Using appropriate hardware, we managed to run our program on six Tesla C1060 computing devices in parallel. We observed up to 2.15× 10 node updates per second for the lid-driven cubic cavity test case. It is worth mentioning that such performance is comparable to the one obtained with large high performance clusters or massively parallel supercomputers. Our solver enabled us to perform high resolution simulations for large Reynolds numbers without facing numerical instabilities. Though, we could observe symmetry breaking effects for long-extended simulations of unsteady flows. We describe the different levels of precision we implemented, showing that these effects are due to round off errors, and we discuss their relative impact on performance.