Research And Implementation Of The Smoothed Particle Hydrodynamics Algorithm Based On Multi-core Architecture

The phenomenon of fluid motion is an important partition of theenvironment. How to simulate the fluid phenomenon fast on computer isurgently needed in aerospace, ocean ships and many other industry fields.Currently, Smoothed Particle Hydrodynamics(SPH) is an ideal method ofsimulation hybrid medium fluid motion. It is a meshless Lagrange methodand based on the particle theory. SPH method gets the law of fluid motion byintegrating all the particles’ attributes. But this method needs large quantitiesof computation, if being applied to simulation the speed will be slow.Until now, there already have some achievements on SPH parallelism.But most of them are either using simple model or just putting partition ofSPH algorithm on GPU. Thus they can’t take full advantages of GPU’scomputing capability.This paper analyses the code of the SPH serial program in detail tounderstand the whole process well. Meanwhile, this paper focus on two keyprocedures, namely building neighbor particle pairs and particles’ attributecalculation, concluding the implementation. Afterwards, in order to find theperformance bottleneck we make the hotspots analysis of the serial SPHprogram.In order to take full use of the powerful computing capability of GPU,this paper makes some contributions as following:Realize Whole SPH algorithm on GPU, include building neighborparticle pairs and calculating particle attribute values.In the process of neighbor particle pair storage, replacing the criticalarea with atomic operation to make the store procedure parallel.In the process of calculating particle attribute values, takingadvantage of shared memory to reduce the times of accessing the global memory.Combining the analysis to GPU resources and testing by differentthread dimensions to find the most proper parallel granularity.In the experiment, we use CUDA as GPU programming language. In thecomparative performance test, we use NVIDIA Tesla C2050and NVIDIATesla K20as GPU device to run parallel program separately and use IntelXRON CPU W3520to run serial program. By comparison, it is shown thatby using C2050GPU we get8X speedup and by using K20GPU we get20Xspeedup.