格子玻尔兹曼方法矩表示的传播模式。
Propagation pattern for moment representation of the lattice Boltzmann method.
作者信息
Gounley John, Vardhan Madhurima, Draeger Erik W, Valero-Lara Pedro, Moore Shirley V, Randles Amanda
机构信息
Computational Sciences and Engineering Division at Oak Ridge National Laboratory.
Department of Biomedical Engineering at Duke University.
出版信息
IEEE Trans Parallel Distrib Syst. 2022 Mar;33(3):642-653. doi: 10.1109/tpds.2021.3098456. Epub 2021 Jul 21.
A propagation pattern for the moment representation of the regularized lattice Boltzmann method (LBM) in three dimensions is presented. Using effectively lossless compression, the simulation state is stored as a set of moments of the lattice Boltzmann distribution function, instead of the distribution function itself. An efficient cache-aware propagation pattern for this moment representation has the effect of substantially reducing both the storage and memory bandwidth required for LBM simulations. This paper extends recent work with the moment representation by expanding the performance analysis on central processing unit (CPU) architectures, considering how boundary conditions are implemented, and demonstrating the effectiveness of the moment representation on a graphics processing unit (GPU) architecture.
本文提出了一种三维正则化格子玻尔兹曼方法（LBM）矩表示的传播模式。通过有效无损压缩，模拟状态被存储为格子玻尔兹曼分布函数的一组矩，而非分布函数本身。针对这种矩表示的一种高效缓存感知传播模式，能大幅减少LBM模拟所需的存储和内存带宽。本文通过扩展对中央处理器（CPU）架构的性能分析、考虑边界条件的实现方式，并在图形处理器（GPU）架构上证明矩表示的有效性，对矩表示的近期工作进行了扩展。
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