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    Liu Zhixiang, Fang Yong, Song Anping, Xu Lei, Wang Xiaowei, Zhou Liping, Zhang Wu. Large-Scale Scalable Parallel Computing Based on LBM with Multiple-Relaxation-Time Model[J]. Journal of Computer Research and Development, 2016, 53(5): 1156-1165. DOI: 10.7544/issn1000-1239.2016.20148441
    Citation: Liu Zhixiang, Fang Yong, Song Anping, Xu Lei, Wang Xiaowei, Zhou Liping, Zhang Wu. Large-Scale Scalable Parallel Computing Based on LBM with Multiple-Relaxation-Time Model[J]. Journal of Computer Research and Development, 2016, 53(5): 1156-1165. DOI: 10.7544/issn1000-1239.2016.20148441

    Large-Scale Scalable Parallel Computing Based on LBM with Multiple-Relaxation-Time Model

    • In the large-scale numerical simulation of three-dimensional complex flows, the multiple-relaxation-time model (MRT) of lattice Boltzmann method (LBM) has better property of numerical stability than single-relaxation-time model. Based on the turbulence model of large eddy simulation (LES) and the interpolation scheme of surface boundary, three iteration calculations of grid generation, initialization of flow information and parallelism property are analyzed respectively under the discrete velocity model D3Q19. Distributed architecture and the communication between different compute nodes using message passing interface (MPI) are often used by current high performance computing clusters. By considering both the features of distributed clusters and the load balance of calculation and using MPI programming model, the grid generation, initialization of flow information and the parallel algorithm of iteration calculation suitable for large-scale distributed cluster are studied, respectively. The proposed parallel algorithm also can be suitable for D3Q15 discrete velocity model and D3Q27 discrete velocity model. Two different cases, solving problem with fixed total calculation and solving problem with fixed calculate amount in every computing cores, are considered in the process of numerical simulation. The performances of parallelism are analyzed for these two cases, respectively. Experimental results on Sunway Blue Light supercomputer show that the proposed parallel algorithm still has good speedup and scalability on the order of hundreds of thousands of computing cores.
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