APPLE-MASNUM: Accelerating parallel processing for lightweight expansion of MASNUM on a single multi-GPU node

The Marine Science and Numerical Modeling (MASNUM) system, developed for oceanic wave forecasting, play an important role in marine disaster prevention and maritime activities. However, its application is hampered by the requirement of large computing resources. To overcome these barriers, we have i...

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Vydané v:Ocean modelling (Oxford) Ročník 196; s. 102557
Hlavní autori: Lou, Qi, Wu, Changmao, Dong, Changming, Feng, Xingru, Xia, Yuanyuan, Liu, Li, Xu, Zhengwei, Gao, Xu, Sun, Meng, Yin, Xunqiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.07.2025
Predmet:
Heterogeneous computing
Lightweight
MASNUM
MPI
Parallel
Parallel
Heterogeneous computing
Lightweight
MASNUM
MPI
ISSN:1463-5003
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Shrnutí:The Marine Science and Numerical Modeling (MASNUM) system, developed for oceanic wave forecasting, play an important role in marine disaster prevention and maritime activities. However, its application is hampered by the requirement of large computing resources. To overcome these barriers, we have implemented an accelerating parallel processing for lightweight expansion of MASNUM (APPLE-MASNUM) on a single compute node with multiple GPUs. In initiating our approach, the mathematical-physics equations of the MASNUM system are thoroughly analyzed to pinpoint the primary computational bottlenecks. This study then transforms MASNUM from a multi-process MPI program into a preliminary GPU-compatible algorithms. Subsequently, the paper proposes an optimization strategy for two-dimensional four-point stencil computations. Following this, an optimization method for overlapping computation with communication is introduced. Finally, a refined data layout scheme tailored for GPUs is designed and implemented. Three numerical experiments with five-day wave forecasts demonstrated that compared to single-core MASNUM, the acceleration ratios of the framework presented in this study are 49.29-fold, 62.58-fold, and 65.74-fold, respectively. This considerable performance boost highlights the efficiency of the lightweight APPLE-MASNUM framework introduced in this research. This signifies the first implementation and optimization of the MASNUM model on a GPU-based heterogeneous platform. •APPLE-MASNUM: A lightweight wave model for rapid forecasts on limited resources.•GPU-accelerated method for efficient wave forecasting on a single multi-GPU node.•APPLE-MASNUM achieves 5-day wave forecasts within 30 min in classical tests.
ISSN:1463-5003
DOI:10.1016/j.ocemod.2025.102557