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Performance optimization and evaluation for parallel processing of big data in earth system models

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Abstract

Big data and high performance computing in Earth System Models (ESMs) are receiving increased attention in earth science research. When scaling to large-scale multi-core computing, efficient parallelization of an ESM, which demands fast parallel computing for long-term integration or climate simulation, becomes extremely challenging because of time-consuming internal big data communication. In this paper, an optimization algorithm for the massive data communication between the Weather Research and Forecasting model and Coupler version 7 in the Chinese Academy of Sciences-Earth System Model (CAS-ESM) is proposed. The optimization strategy is to transmit data from a small packet into a larger packet. Through experiments on a multi-core cluster, the efficiency of the algorithm is confirmed. Then, the parallel performance of the CAS-ESM is evaluated fully. Results show that the parallel efficiency of the CAS-ESM on 1024 CPU cores reaches nearly 70%, indicating that the CAS-ESM has desirable parallel performance and strong scalability. In addition, a generic performance evaluation method for ESMs from perspectives of optimal load balance and efficiency is proposed. Results show that the computing speed is the fastest and computational efficiency is the highest when the CAS-ESM runs on a certain number of cores.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (No. 2016YFB0200800), National Natural Science Foundation of China (No. 61602477, No. 41401512), China Postdoctoral Science Foundation (No. 2016M601158), Youth Innovation Promotion Association of CAS (No. Y6YR0300QM), and the Fundamental Research Funds for the Central Universities (No. 2652017113).

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Authors and Affiliations

  1. School of Information Engineering, China University of Geosciences, Beijing, People’s Republic of China

    Yuzhu Wang

  2. Computer Network Information Center, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Huiqun Hao & Jinrong Jiang

  3. University of Chinese Academy of Sciences, Beijing, People’s Republic of China

    Huiqun Hao

  4. School of Computer Science, China University of Geosciences, Wuhan, People’s Republic of China

    Junqiang Zhang

  5. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Juanxiong He

  6. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Yan Ma

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  1. Yuzhu Wang
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Correspondence to Junqiang Zhang or Yan Ma.

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Wang, Y., Hao, H., Zhang, J. et al. Performance optimization and evaluation for parallel processing of big data in earth system models. Cluster Comput 22 (Suppl 1), 2371–2381 (2019). https://doi.org/10.1007/s10586-017-1477-0

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