Optimization and large scale computation of an entropy-based moment closure
Optimization and large scale computation of an entropy-based moment closure
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DOI：
10.1016/j.jcp.2015.09.008
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发表时间：
2015-12-01
影响因子：
4.1
通讯作者：
Hill, Judith
中科院分区：
文献类型：
--
作者：
Garrett, C. Kristopher;Hauck, Cory;Hill, Judith
在线性动力学方程的背景下，我们介绍了基于熵的力矩闭包MN的计算进展和结果，重点是异构和大规模的计算平台。众所周知，在一些情况下，基于熵的闭包比基于标准谱近似（如PN）的闭包产生更准确的结果，但计算成本通常要高得多，而且常常令人望而却步。引入了几个优化来改进基于熵的算法的性能。这些优化包括使用GPU加速和利用球面谐波的数学性质，它们被用作力矩公式中的测试函数。为了测试通信界模拟的新兴高性能计算范式，我们在目前可用的最大计算尺度上给出了时序结果。这些结果特别表明，在扩展MN算法时，负载平衡问题不会出现在PN算法中。我们还观察到，在弱结垢试验中，MN与PN的溶解时间比减小。(C) 2015爱思唯尔公司版权所有。
We present computational advances and results in the implementation of an entropy-based moment closure, MN, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as PN, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. These results show, in particular, load balancing issues in scaling the MN algorithm that do not appear for the PN algorithm. We also observe that in weak scaling tests, the ratio in time to solution of MN to PN decreases. (C) 2015 Elsevier Inc. All rights reserved.