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An MPI-CUDA implementation of an improved Roe method for two-layer shallow water systems

机译:用于两层浅水系统的改进Roe方法的MPI-CUDA实现

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The numerical solution of two-layer shallow water systems is required to simulate accurately stratified fluids, which are ubiquitous in nature: they appear in atmospheric flows, ocean currents, oil spills, etc. Moreover, the implementation of the numerical schemes to solve these models in realistic scenarios imposes huge demands of computing power. In this paper, we tackle the acceleration of these simulations in triangular meshes by exploiting the combined power of several CUDA-enabled CPUs in a GPU cluster. For that purpose, an improvement of a path conservative Roe-type finite volume scheme which is specially suitable for GPU implementation is presented, and a distributed implementation of this scheme which uses CUDA and MP1 to exploit the potential of a GPU cluster is developed. This implementation overlaps MPI communication with CPU-GPU memory transfers and GPU computation to increase efficiency. Several numerical experiments, performed on a cluster of modern CUDA-enabled CPUs, show the efficiency of the distributed solver.
机译:需要使用两层浅水系统的数值解来精确地模拟自然界中普遍存在的分层流体:它们出现在大气流动,洋流,溢油等中。此外,为解决这些模型而采用了数值方案在现实情况下,对计算能力提出了巨大的要求。在本文中,我们通过利用GPU集群中几个支持CUDA的CPU的组合功能,解决了三角形网格中这些仿真的加速问题。为此,提出了一种特别适用于GPU实现的路径保守Roe型有限体积方案的改进,并开发了该方案的分布式实现,该方案使用CUDA和MP1来挖掘GPU集群的潜力。此实现将MPI通信与CPU-GPU内存传输和GPU计算重叠,以提高效率。在支持CUDA的现代CPU集群上进行的几个数值实验表明了分布式求解器的效率。

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