Computational fluid dynamics (CFD) requires a vast amount of compute cycles on contemporary large-scale parallel computers. Hence, performance optimization is a pivotal activity in this field of computational science. Not only does it reduce the time
to solution, but it also allows to minimize the energy consumption. In this work we study performance optimizations for an MPI-parallel lattice Boltzmann-based flow solver that uses a sparse lattice representation with indirect addressing. First we describe how this indirect addressing can be minimized in order to increase the single-core and chip-level performance. Second, the communication overhead is reduced via appropriate partitioning, but maintaining the single core performance improvements. Both optimizations allow to run the solver at an operating point with minimal energy consumption.
