Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0

Abstract. The spectral wave model WAM (Cycle 6) is a commonly used code package for ocean wave forecasting. However, it is still a challenge to include it into the long-term Earth system modeling due to the huge computing requirement. In this study, we have successfully developed a GPU-accelerated version of the WAM model that can run all its computing-demanding components on GPUs, with a significant performance increase compared with its original CPU version. The power of GPU computing has been unleashed through substantial efforts of code refactoring, which reduces the computing time of a 7 d global 1/10° wave modeling to only 7.6 min in a single-node server installed with eight NVIDIA A100 GPUs. Speedup comparisons exhibit that running the WAM6 with eight cards can achieve the maximum speedup ratio of 37 over the dual-socket CPU node with two Intel Xeon 6236 CPUs. The study provides an approach to energy-efficient computing for ocean wave modeling. A preliminary evaluation suggests that approximately 90 % of power can be saved.

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Publication Details

Journal
Geoscientific model development
Published
2024-08-16
DOI
https://doi.org/10.5194/gmd-17-6123-2024
Citations
8
Primary Topic
Ocean Waves and Remote Sensing
Type
article
Field-Weighted Citation Impact
3.85

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article

Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0

Fujiang Yu, Renbo Pang, Fang Hou, Zhiyi Gao et al.
8 citations
Geoscientific model development
Ocean Waves and Remote Sensing
3.85
article

Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0

Fujiang Yu, Renbo Pang, Fang Hou, Zhiyi Gao, Ye Yuan, Yuanyong Gao, Xueding Li, Zhi Chen
article en
8 citations

Abstract

Abstract. The spectral wave model WAM (Cycle 6) is a commonly used code package for ocean wave forecasting. However, it is still a challenge to include it into the long-term Earth system modeling due to the huge computing requirement. In this study, we have successfully developed a GPU-accelerated version of the WAM model that can run all its computing-demanding components on GPUs, with a significant performance increase compared with its original CPU version. The power of GPU computing has been unleashed through substantial efforts of code refactoring, which reduces the computing time of a 7 d global 1/10° wave modeling to only 7.6 min in a single-node server installed with eight NVIDIA A100 GPUs. Speedup comparisons exhibit that running the WAM6 with eight cards can achieve the maximum speedup ratio of 37 over the dual-socket CPU node with two Intel Xeon 6236 CPUs. The study provides an approach to energy-efficient computing for ocean wave modeling. A preliminary evaluation suggests that approximately 90 % of power can be saved.

Geoscientific model developmentVol. 17(16)
Ministry of Natural Resources (CN), National Marine Environmental Forecasting Center (CN), Hainan Marine Monitoring and Forecasting Center (CN)
National Major Science and Technology Projects of China
Life below water
Openalex Percentile: Top 6%
Ocean Waves and Remote Sensing
3.85
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Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0 — Fujiang Yu, Renbo Pang, et al. · Geoscientific model development (2024) | TGRS Research Map | TGRS