GPU Acceleration of Smoothed Particle Hydrodynamics for the Navier-Stokes Equations

摘要

Although there exist much work on GPU acceleration on the SPH method, the focus so far has been on the Euler equations in fluid mechanics. This paper presents GPU acceleration on the SPH method for the Navier-Stokes equations for both solid and fluid mechanics. We investigate and compare three CPU-GPU coupling models in terms of one large-scale parallel application code: (1) CPU?GPU (to only run hotspots on GPU), (2) GPU-alone (to run the whole of simulation on GPU), and (3) CPU||GPU (to treat CPU and GPU as equivalent processors). A common issue to the three models, "easy code transplant onto GPU", is emphasized. Optimizations on particle indexing and particle interaction on GPU, which are of unique importance to a SPH code, are addressed. Numerical experiments are finally performed and 4x, 10x, 16x speedups are observed for the three coupling models, respectively, with reference to single CPU core. Among the three, the fastest model -- Xthe "CPU||GPU" model -- Xfurther undergoes scalability tests on a cluster of 6 heterogeneous nodes and shows 90+% parallel efficiency.