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<p>The Yin-He Global Spectral Model (YHGSM) is one of the most representative numerical weather prediction (NWP) models in China and has already been operationally applied to global weather forecasting. At present, YHGSM has achieved satisfactory parallel performance on high-performance computing (HPC) platforms to meet the real-time requirements of operational forecasting. However, when using the parallel two-dimensional (2D) domain decomposition algorithm, communication overhead still significantly impacts the overall performance of YHGSM. To address this issue, we introduce a pipelined optimization scheme in the Inverse Legendre Transform stage, aiming to reduce communication overhead through computation–communication overlap. Specifically, computation and communication tasks are grouped along the vertical dimension, where data dependencies are relatively weak, enabling the communication of one group to be overlapped with the computation of another. Experiments conducted on HPC platforms demonstrate the effectiveness of this approach and highlight its potential for broader application in spectral models. Under the best-case configuration, the proposed optimization reduces communication time by up to 75% and the total runtime by about 25% in this stage.</p>

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Computation–communication overlapping based on vertical layer grouping in the inverse legendre transform stage of YHGSM

  • 期刊论文

  • 发表日期：

    2026年4月20日

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  • Yuntian Zheng,
  • Jianping Wu,
  • Tun Chen,
  • Zhaokai Song,
  • Jinghui Yang,
  • Fukang Yin

摘要

The Yin-He Global Spectral Model (YHGSM) is one of the most representative numerical weather prediction (NWP) models in China and has already been operationally applied to global weather forecasting. At present, YHGSM has achieved satisfactory parallel performance on high-performance computing (HPC) platforms to meet the real-time requirements of operational forecasting. However, when using the parallel two-dimensional (2D) domain decomposition algorithm, communication overhead still significantly impacts the overall performance of YHGSM. To address this issue, we introduce a pipelined optimization scheme in the Inverse Legendre Transform stage, aiming to reduce communication overhead through computation–communication overlap. Specifically, computation and communication tasks are grouped along the vertical dimension, where data dependencies are relatively weak, enabling the communication of one group to be overlapped with the computation of another. Experiments conducted on HPC platforms demonstrate the effectiveness of this approach and highlight its potential for broader application in spectral models. Under the best-case configuration, the proposed optimization reduces communication time by up to 75% and the total runtime by about 25% in this stage.