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Weighted Decomposition in High-Performance Lattice-Boltzmann Simulations: Are Some Lattice Sites More Equal than Others?

机译:高性能格子-Boltzmann模拟中的加权分解:一些格子网站比其他格子更多吗?

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Obtaining a good load balance is a significant challenge in scaling up lattice-Boltzmann simulations of realistic sparse problems to the exascale. Here we analyze the effect of weighted decomposition on the performance of the HemeLB lattice-Boltzmann simulation environment, when applied to sparse domains. Prior to domain decomposition, we assign wall and in/outlet sites with increased weights which reflect their increased computational cost. We combine our weighted decomposition with a second optimization, which is to sort the lattice sites according to a space filling curve. We tested these strategies on a sparse bifurcation and very sparse aneurysm geometry, and find that using weights reduces calculation load imbalance by up to 85%, although the overall communication overhead is higher than some of our runs.
机译:获得良好的负载余额是将Lattice-Boltzmann模拟对ExaScale的现实稀疏问题的绘制模拟是一个重大挑战。在这里,我们在应用于稀疏域时分析加权分解对Hemelb Lattice-Boltzmann模拟环境的性能的影响。在域分解之前,我们将墙壁和进出口分配增加的重量,反映其增加的计算成本。我们将加权分解与第二优化相结合,这是根据空间填充曲线对晶格站点进行分类。我们在稀疏分叉和非常稀疏的动脉瘤几何上测试了这些策略,并发现使用权重降低计算负荷不平衡高达85%,尽管整体通信开销高于我们的一些运行。

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