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Fast Data-Obtaining Algorithm for Data Assimilation with Large Data Set

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Abstract

Data assimilation is an analysis technique which combines observations and the numerical results from theoretical models to deduce more realistic and accurate data. It is widely used in investigations of the atmosphere, ocean and land surface. Due to the complicated data structure of the inputs from dynamical models and the increase of the amount of model data, the parallelization of data assimilation suffers from high overhead on file reading and data communication. In this paper, we propose a flexible parallel data access approach for reading a large number of data from disks firstly. Using this approach, the data access conflict is avoided successfully, and the frequency of disk addressing operations is also decreased significantly. Next, we design a communication-avoiding strategy to reduce the communication volume at the cost of some additional computations. Furthermore, we present a “pipe-flow” scheme for data exchange to conduct conflict-free message passing. Consequently, a fast data-obtaining algorithm is developed for the data assimilation. Our experiments show that the fast data-obtaining algorithm gains a performance of \(5\times \) speedup compared with the baseline, which is excellent at data-obtaining for the parallel data assimilation. Due to the reduction of disk addressing operations, the new approach achieves \(6\times \) speedup on average for the file reading process. Since a large amount of data movement can be avoided, the new approach achieves \(2.7\times \) speedup on average for the communication between processors.

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Acknowledgements

The authors would like to thank all anonymous reviewers for their valuable comments and helpful suggestions.

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Authors and Affiliations

  1. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Junmin Xiao & Guangming Tan

  2. University of Chinese Academy of Sciences, Beijing, China

    Junmin Xiao & Guangming Tan

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Guizhao Zhang & Xuehai Hong

  4. Beijing Institute of Control Engineering, Beijing, China

    Yanan Gao

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  1. Junmin Xiao
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  2. Guizhao Zhang
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  3. Yanan Gao
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  4. Xuehai Hong
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  5. Guangming Tan
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Correspondence to Junmin Xiao.

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The work is supported by the National Key Research and Development Program of China under Grant No. 2016YFC1401706 and National Natural Science Foundation of China under Grant No. 61802369.

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Xiao, J., Zhang, G., Gao, Y. et al. Fast Data-Obtaining Algorithm for Data Assimilation with Large Data Set. Int J Parallel Prog 48, 750–770 (2020). https://doi.org/10.1007/s10766-019-00653-y

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