Advanced load balancing for SPH simulations on multi-GPU architectures

Smoothed Particle Hydrodynamics (SPH) is a numerical method for fluid flow modeling, in which the fluid is discretized by a set of particles. SPH allows to model complex scenarios, which are difficult or costly to measure in the real world. This method has several advantages compared to other approaches, but suffers from a huge numerical complexity. In order to simulate real life phenomena, up to several hundred millions of particles have to be considered. Hence, HPC methods need to be leveraged to make SPH applicable for industrial applications. Distributing the respective computations among different GPUs to exploit massive parallelism is thereby particularly suited. However, certain characteristics of SPH make it a non-trivial task to properly distribute the respective workload. In this work, we present a load balancing method for a CUDA-based industrial SPH implementation on multi-GPU architectures. To that end, dedicated memory handling schemes are introduced, which reduce the synchronization overhead. Experimental evaluations confirm the scalability and efficiency of the proposed methods.