Intel® Parallel Computing Center at First Institute of Oceanography, MNR, China

Published:06/01/2018   Last Updated:06/01/2018

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Principal Investigators:

Project Lead, Prof. Fangli Qiao

Professor Fangli QiaoFangli Qiao has been working on the development of new generation ocean and climate models. Working with his research team, he established the surface wave-induced mixing theory which dramatically improves the performance of different ocean circulation models and climate models. He revealed the key role of sea spray in air-sea heat flux and greatly reduced the decades-standing systematic error in the forecast of typhoon/hurricane intensity. He led a team to design a high performance parallel scheme and test with more than 10 million CPU cores. He served as editorial board member of Ocean Modelling and Journal of Marine Systems etc.

Co-Project Lead, Prof. Zhenya Song

Zhenya Song obtained the Ph.D. degree in Physical Oceanography of Ocean University of China in 2011. Currently, Dr. Song is the professor of FIO and has been working on the ocean and climate simulation, HPC, and the effects of the wave effects in the climate systems since 2004. He for the first time incorporated a surface wave model into the global CGCMs and then developed a new generation coupled model named FIO-ESM.

Co-Project Lead, Associate Prof. Xiaomeng Huang

Xiaomeng Huang obtained the Ph.D. degree in Computer Science of Tsinghua University in 2007. Currently, he is an associate professor of the Department of Earth System Science in Tsinghua University. He focuses on the crossing field combined ocean modelling and HPC. Research interests include ocean model, parallel computing and big data.


Ocean surface wave is crucially important to navigation safety and climate change. High-resolution global wave model plays a key role in accurate surface wave forecasting and simulation. The MASNUM wave model is one of three state-of-the-art wave models in the world, which is developed by FIO and now widely used in several research groups and operational ocean forecasting systems. This work will focus on implementing code on new Intel technologies like AEP/HBW Memory/CLX-AP and new algorithms like Deep Learning, to improve the computing performance and simulation ability of MASNUM wave model. Moreover, it will deliver an open source high resolution and large-scale new generation wave model and development experience to the worldwide ocean community, which will expand both FIO and Intel’s influence on HPC and ocean scientific research.


Zhenya Song. September 27, 2018, Optimization strategy for MASNUM surface wave model IXPUG Annual Fall Conference

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