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Indexed by:期刊论文

Date of Publication:2011-03-01

Journal:SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY

Included Journals:SCIE、EI

Volume:54

Issue:3

Page Number:523-532

ISSN No.:1674-7348

Key Words:moving particle semi-implicit method (MPS); graphics processing units (GPU); compute unified device architecture (CUDA); neighbouring particle searching; free surface flow

Abstract:The Moving Particle Semi-implicit (MPS) method performs well in simulating violent free surface flow and hence becomes popular in the area of fluid flow simulation. However, the implementations of searching neighbouring particles and solving the large sparse matrix equations (Poisson-type equation) are very time-consuming. In order to utilize the tremendous power of parallel computation of Graphics Processing Units (GPU), this study has developed a GPU-based MPS model employing the Compute Unified Device Architecture (CUDA) on NVIDIA GTX 280. The efficient neighbourhood particle searching is done through an indirect method and the Poisson-type pressure equation is solved by the Bi-Conjugate Gradient (BiCG) method. Four different optimization levels for the present general parallel GPU-based MPS model are demonstrated. In addition, the elaborate optimization of GPU code is also discussed. A benchmark problem of dam-breaking flow is simulated using both codes of the present GPU-based MPS and the original CPU-based MPS. The comparisons between them show that the GPU-based MPS model outperforms 26 times the traditional CPU model.