A Barotropic Solver for High-Resolution Ocean General Circulation Models
A Barotropic Solver for High-Resolution Ocean General Circulation Models
复制标题
高分辨率海洋环流模型的正压求解器
DOI：
10.3390/jmse9040421
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发表时间：
2021-04
影响因子：
2.9
通讯作者：
Weiguo Liu
中科院分区：
文献类型：
--
作者：
Xiaodan Yang;Shan Zhou;Shengchang Zhou;Zhenya Song;Weiguo Liu
高分辨率全球海洋环流模式在准确的海洋预报中起着关键作用。然而，业务预报系统的模型分辨率仍然不高，这是由于随后对大运算量的高要求以及低并行效率的障碍。良好的可扩展性是衡量并行效率的重要指标，也是OGCM面临的挑战。我们发现，由于全局约化的频率很高，预条件共轭梯度法(PCG)中的通信开销是影响可伸缩性的关键瓶颈。在这项工作中，我们开发了一种新的算法--带有PCG的避免通信的Krylov子空间方法(CA-PCG)--以提高可伸缩性，并以NEMO为例将其应用于欧洲海洋模拟(NEMO)。对于PCG，每次迭代都需要进行具有全局通信的内积运算，而对于CA-PCG，每8次迭代只需要进行一次内积运算。因此，使用CA-PCG，全局通信开销从占总执行时间的94.5%以上降低到不到63.4%。结果，正压模式的执行时间从17,000多个S减少到不到6,000个S的CA-PCG，总执行时间从18,000多个S减少到不到6,200个S。此外，加速比也可以从3.7提高到4.6。综上所述，CA-PCG的高进程数可伸缩性较PCG方法得到了有效的提高，为精确的海洋模拟提供了一种高效的解决方案。
High-resolution global ocean general circulation models (OGCMs) play a key role in accurate ocean forecasting. However, the models of the operational forecasting systems are still not in high resolution due to the subsequent high demand for large computation, as well as the low parallel efficiency barrier. Good scalability is an important index of parallel efficiency and is still a challenge for OGCMs. We found that the communication cost in a barotropic solver, namely, the preconditioned conjugate gradient (PCG) method, is the key bottleneck for scalability due to the high frequency of the global reductions. In this work, we developed a new algorithm—a communication-avoiding Krylov subspace method with a PCG (CA-PCG)—to improve scalability and then applied it to the Nucleus for European Modelling of the Ocean (NEMO) as an example. For PCG, inner product operations with global communication were needed in every iteration, while for CA-PCG, inner product operations were only needed every eight iterations. Therefore, the global communication cost decreased from more than 94.5% of the total execution time with PCG to less than 63.4% with CA-PCG. As a result, the execution time of the barotropic modes decreased from more than 17,000 s with PCG to less than 6000 s with CA-PCG, and the total execution time decreased from more than 18,000 s with PCG to less than 6200 s with CA-PCG. Besides, the ratio of the speedup can also be increased from 3.7 to 4.6. In summary, the high process count scalability when using CA-PCG was effectively improved from that using the PCG method, providing a highly effective solution for accurate ocean simulation.
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POM.gpu-v1.0：基于 GPU 的普林斯顿海洋模型
影响因子：
5.1
作者：
Xu S.;Huang X.;Oey L. -Y.;Xu F.;Fu H.;Zhang Y.;Yang G.
通讯作者：
Yang G.
DOI：
10.1137/0902001
发表时间：
1981-01-01
期刊：
SIAM JOURNAL ON SCIENTIFIC AND STATISTICAL COMPUTING
影响因子：
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作者：
EISENSTAT, SC
通讯作者：
EISENSTAT, SC
影响因子：
4.6
作者：
Large, W. G.;Yeager, S. G.
通讯作者：
Yeager, S. G.
DOI：
10.1145/2600212.2600217
发表时间：
2014-06
期刊：
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影响因子：
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作者：
A. Baker;Haiying Xu;J. Dennis;M. Levy;D. Nychka;S. Mickelson;Jim Edwards;M. Vertenstein;Al Wegener
通讯作者：
A. Baker;Haiying Xu;J. Dennis;M. Levy;D. Nychka;S. Mickelson;Jim Edwards;M. Vertenstein;Al Wegener
DOI：
10.1007/s11430-014-4842-3
发表时间：
2014-06
期刊：
Science China Earth Sciences
影响因子：
--
作者：
Wei Zhao;Zhenya Song;F. Qiao;Xunqiang Yin
通讯作者：
Wei Zhao;Zhenya Song;F. Qiao;Xunqiang Yin