A Multi-GPU PCISPH Implementation with Efficient Memory Transfers

Smoothed Particle Hydrodynamics (SPH) is a particle-based method for fluid flow modeling. One promising variant of SPH is Predictive-Corrective Incompressible SPH (PCISPH), which employs a dedicate prediction-correction scheme and, by this, outperforms other SPH variants by almost one order of magnitude. However, similar to other particle-based methods, it suffers from a huge numerical complexity. In order to simulate real world phenomena, several millions of particles need to be considered. To make SPH applicable to real world engineering problems, it is hence common to exploit massive parallelism of multi-GPU architectures. However, certain algorithmic characteristics of PCISPH make it a non-trivial task to efficiently parallelize this method on multi-GPUs. In this work, we are, for the first time, proposing a multi-GPU implementation for PCISPH. To this end, we are proposing a scheme which allows to overlap the memory transfers between GPUs by actual computations and, by this, avoids the drawbacks caused by the mentioned algorithmic characteristics of PCISPH. Experimental evaluations confirm the efficiency of the proposed methods.