文献类型：会议论文

英文题名：Data Analyses and Parallel Optimization of the Regional Marine Ecological Model

作者：Wang, Yanqiang[1,2]; Zheng, Jingjing[1]; Zhang, Tianyu[3,4]; Liang, Peng[3]; Lin, Bo[1]

机构：[1] National Marine Environmental Forecasting Center, Beijing, 100081, China; [2] College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China; [3] Key Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Guangdong Ocean University, Zhanjiang, 54008, China; [4] Southern Marine Science and Engineering Guangdong Laboratory [Zhuhai], Zhuhai, 519000, China

会议论文集：Data Science - 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023, Proceedings

会议日期：September 22, 2023 - September 24, 2023

会议地点：Harbin, China

语种：英文

外文关键词：Climate change - Ecology - Forecasting

外文摘要：Under the joint influence of high-intensity human activities and climate change, the coastal ecological environment is deteriorating, and the ecological environment security and the sustainable development of the marine economy are seriously threatened. Therefore, it is of great significance to establish a high-resolution ecological environment operational forecasting system. To meet the run time requirements of the ecological operational forecasting system, a variety of parallel optimization methods were proposed to improve the operation efficiency of the model. First, based on the National Marine Environmental Forecasting Center's Lenovo cluster, the ROMS benchmark experiment was expanded to the 4000 Processes scale. A good speedup was obtained by the experiment. The ROMS model was analysed with strong scalability. Second, in the hydrodynamic-ecological simulation experiment of the Bohai Sea - Yellow Sea - East China Sea, by optimizing Vector, InfiniBand, and Parallel I/O, the performance of the model can be improved by 270% while maintaining the same computing resources. That computing resources were more reasonably used lay the foundation for the operational forecast. ? 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.