Takaaki Miyajima
ABSTRACT
Moving Particle Semi-implicit (MPS) method is a particle method used in fields such as computational fluid dynamics. It is classified as a particle method. Target fluids and objects are divided up into particles, and each particle interacts with its neighbour-particle. The search for neighbour-particle is the main bottleneck of the MPS method. In this paper, we port and optimize "search for neighbour-particle" part in MPS method for GPU by using OpenACC. It accounted for 56% of all the processing time. We present three different optimizations and evaluated them with three different data sets; 25,704, 224,910 and 2,247,750 particles. We also use four different GPUs; NVIDIA K20c, GTX1080, P100(PCIe) and P100(NVlink). As a result, P100(NVlink) GPU achieves 41.5 times speed-up compared with 24 MPI process CPU version when the number of particles is 2,247,750.
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