基于QCT开发者云的天气预报提速——以骑士登陆平台为例

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI:10.1109/CSCloud.2017.48

Gong-Do Hwang, Stephen Chang

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引用次数: 0

摘要

我们介绍了两种流行的天气预报模型的直接性能测量，天气研究和预报模型(WRF)和英特尔骑士登陆平台(KNL)上的跨尺度预测模型(MPAS)。WRF在不同的平台上得到了广泛的评价，而MPAS的基准仍然很少。在这项研究中，我们测量了WRF和MPAS在QCT开发人员云上的运行时间，包括基于knl的节点和基于Xeon broadwell的节点。我们发现，对于WRF，其在单个KNL节点上的性能比Broadwell快1.55倍，而对于MPAS则快1.1倍。一般来说，两个模型在单个节点上的可伸缩性是线性的，而在跨多个节点时则下降。这两个模型可能需要进一步优化

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Speed Up Weather Prediction on QCT Developer Cloud: A Case Study on Knights Landing Platform

We present the direct performance measurements of two popular weather forecast models, Weather Research and Forecast Model (WRF) and Models for Predictions Across Scales (MPAS) on Intel's Knight Landing Platform (KNL). WRF is widely evaluated over different platforms while the benchmarks of MPAS are still scarce. In this study we measured the running time of WRF and MPAS on the QCT Developer Cloud, both on its KNL-based nodes and Xeon Broadwell-based nodes. We found that for WRF its performance on single KNL node is 1.55 times faster than Broadwell one, while for MPAS is 1.1 times faster. Generally the scalability of two models on a single node is linear, and drops when across multiple nodes. Further optimization might be needed for those two models

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2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)

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