A Performance Study of Moving Particle Semi-Implicit Method for Incompressible Fluid Flow on GPU

A Performance Study of Moving Particle Semi-Implicit Method for Incompressible Fluid Flow on GPU

Kirankumar V. Kataraki, Satyadhyan Chickerur
Copyright: © 2020 |Pages: 12
DOI: 10.4018/IJDST.2020010107
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Abstract

The aim of moving particle semi-implicit (MPS) is to simulate the incompressible flow of fluids in free surface. MPS, when implemented, consumes a lot of time and thus, needs a very powerful computing system. Instead of using parallel computing system, the performance level of the MPS model can be improved by using graphics processing units (GPUs). The aim is to have a computing system that is capable of performing at high levels thereby enhancing the speed of processing the numerical computations required in MPS. The primary aim of the study is to build a GPU-accelerated MPS model using CUDA aimed at reducing the time taken to perform the search for neighboring particles. In order to increase the GPU processing speed, specific consideration is given towards the optimization of a neighboring particle search process. The numerical model of MPS is performed using the governing equations, notably the Navier-Stokes equation. The simulation model indicates that using GPU based MPS produce better performance compared to the traditional arrangement of using CPUs.
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Iribe et al. (2010) showed the simulation results of parallelized MPS with regard to a PC cluster. They identified that the main thing of the solver of parallelization for shared memory is the cost of computation of communication that is between the sub-domains. In minimizing this communication, one needs a sophisticated particle that is renumbering process and be based in packages where a communication is to be used. Using these techniques, communication process was accelerated.

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