Fan, L.; Zhang, Y.; Wang, C.; Zhang, T. GPU Accelerated Particle-based Computational Acoustics Solving Based on SPH. Preprints2017, 2017090153. https://doi.org/10.20944/preprints201709.0153.v1
Fan, L., Zhang, Y., Wang, C., & Zhang, T. (2017). GPU Accelerated Particle-based Computational Acoustics Solving Based on SPH. Preprints. https://doi.org/10.20944/preprints201709.0153.v1
Fan, L., Chizhong Wang and Tao Zhang. 2017 "GPU Accelerated Particle-based Computational Acoustics Solving Based on SPH" Preprints. https://doi.org/10.20944/preprints201709.0153.v1
Smoothed particle hydrodynamics (SPH) is regarded as a pure Lagrangian approach, which can solve fluid dynamics problems without the creation of mesh. In this paper, a paralleled SPH solver is developed to solve particle-based computational acoustics (PCA). The aim of this paper is to study the feasibility of using SPH to solve acoustic problems and to improve the efficiency of solving processes by paralleling some procedures on GPU during calculating. A stand SPH code running serially in a CPU is proposed to solve wave equation. This is a wave propagating in a two-dimensional domain. After finishing the computation, the results are compared with the theoretical solutions and they agree well. So its feasibility is verified. There are two main methods for searching neighbor particles: all-pair search method and linked-list search method. Both methods are used in different codes to simulate an identical problem and their runtimes are compared to investigate their searching efficiencies. The runtime results show that linked-list search method has a higher efficiency, which can save a lot of searching time when simulating problems with huge amounts of particles. Furthermore, the percentages of different procedures’ runtimes in a simulation are also discussed to find the most consuming one. Then, some codes are modified to run in different GPUs and their runtimes are compared with those of serial ones on a CPU. Runtime results show that the paralleled algorithm can be more than 80 times faster than the serial one. The result shows that GPU paralleled SPH computing can achieve desirable accuracy and speed in solving acoustic problems.
Computer Science and Mathematics, Computational Mathematics
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