Accelerating an implicit ocean model using CUDA C
文献类型：期刊论文
| 作者 | Xie, Jianbin5,6,7; Feng, Xingru3,4,5,6,7; Gao, Tianhai3,6,7; Dong, Changming1; Yin, Baoshu3,4,5,6,7; Wu, Changmao2 |
| 刊名 | APPLIED OCEAN RESEARCH
 |
| 出版日期 | 2025-10-01 |
| 卷号 | 163页码:12 |
| 关键词 | GPU acceleration Ocean model Tide-storm surge interaction |
| ISSN号 | 0141-1187 |
| DOI | 10.1016/j.apor.2025.104740 |
| 通讯作者 | Feng, Xingru(fengxingru07@qdio.ac.cn) |
| 英文摘要 | In this study, we developed an ocean model named GPU-IOCASM (GPU-Implicit Ocean Current and Storm Surge Model), which employs the finite difference method with implicit iteration to ensure simulation stability. Additionally, it incorporates an online nesting for multi-layer computational grids, allowing localized grid refinement in critical regions to enhance simulation accuracy. To maximize GPU parallelism and minimize memory overhead, we optimized the residual update algorithm, applied a mask-based conditional computation method, and designed an adaptive iteration count prediction strategy. When the simulation reaches a designated output time, relevant variables are copied from GPU memory to host memory, while the GPU proceeds with the next computation without waiting for the I/O operation to complete. This process is designed to run asynchronously in most cases, ensuring that data transfer and CPU-side operations do not interfere with GPU-based computation. Verification results demonstrate that GPU-IOCASM's simulation results exhibit strong agreement with both observed data and SCHISM's results, confirming its reliability and precision. Furthermore, GPUIOCASM achieves a remarkable speedup of over 312 times compared with traditional CPU-based approaches. Unlike traditional GPU acceleration methods that require frequent data transfers between the CPU and GPU, GPU-IOCASM is designed to perform as much computation as possible on the GPU, thereby minimizing data transfer overhead and improving computational efficiency. |
| WOS关键词 | SURGE |
| 资助项目 | National Key Research and Development Program of China[2023YFC3008200] ; National Natural Science Foundation of China[42276028] ; Marine Science and Technology Fund of Shandong Province for the Pilot National Laboratory for Marine Science and Technology (Qingdao)[2021QNLM040001-5] ; Oceanographic Data Center, Institute of Oceanology, Chinese Academy of Sciences |
| WOS研究方向 | Engineering ; Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:001561349200001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/203248]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Feng, Xingru |
| 作者单位 | 1.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing, Peoples R China 2.Chinese Acad Sci, Inst Software, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, CAS Engn Lab Marine Ranching, Inst Oceanol, Qingdao, Peoples R China 5.Qingdao Marine Sci & Technol Ctr, Lab Ocean Dynam & Climate, Qingdao, Peoples R China 6.Chinese Acad Sci, Inst Oceanol, Lab Ocean Circulat & Waves, Qingdao, Peoples R China 7.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Qingdao, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Jianbin,Feng, Xingru,Gao, Tianhai,et al. Accelerating an implicit ocean model using CUDA C[J]. APPLIED OCEAN RESEARCH,2025,163:12. |
| APA | Xie, Jianbin,Feng, Xingru,Gao, Tianhai,Dong, Changming,Yin, Baoshu,&Wu, Changmao.(2025).Accelerating an implicit ocean model using CUDA C.APPLIED OCEAN RESEARCH,163,12. |
| MLA | Xie, Jianbin,et al."Accelerating an implicit ocean model using CUDA C".APPLIED OCEAN RESEARCH 163(2025):12. |
入库方式： OAI收割
来源：海洋研究所
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