  The Lattice Boltzmann method (LBM) for solving fluid flow is naturally well
suited to an efficient implementation for massively parallel computing, due to
the prevalence of local operations in the algorithm. This paper presents and
analyses the performance of a 3D lattice Boltzmann solver, optimized for third
generation nVidia GPU hardware, also known as `Kepler'. We provide a review of
previous optimisation strategies and analyse data read/write times for
different memory types. In LBM, the time propagation step (known as streaming),
involves shifting data to adjacent locations and is central to parallel
performance; here we examine three approaches which make use of different
hardware options. Two of which make use of `performance enhancing' features of
the GPU; shared memory and the new shuffle instruction found in Kepler based
GPUs. These are compared to a standard transfer of data which relies instead on
optimised storage to increase coalesced access. It is shown that the more
simple approach is most efficient; since the need for large numbers of
registers per thread in LBM limits the block size and thus the efficiency of
these special features is reduced. Detailed results are obtained for a D3Q19
LBM solver, which is benchmarked on nVidia K5000M and K20C GPUs. In the latter
case the use of a read-only data cache is explored, and peak performance of
over 1036 Million Lattice Updates Per Second (MLUPS) is achieved. The
appearance of a periodic bottleneck in the solver performance is also reported,
believed to be hardware related; spikes in iteration-time occur with a
frequency of around 11Hz for both GPUs, independent of the size of the problem.

Distributed, Parallel, and Cluster Computing;Performance