Abstract
High Performance Computing has been a critical tool in understanding predicting climate change, and exascale computing combined with advances in mathematical modeling and parallel algorithm will lead to new insights in climate impacts, including the prevalence of hurricanes, droughts, wildfires and more. But the role for HPC in climate science goes well beyond physical modeling to the development of mitigation and adaptation strategies, and the use of high performance data analytics based with both traditional algorithms and machine learning. An explosion of new data sources from satellite imagery and embedded environmental sensors to portable genome sequencers and personal mobile phone data will transform our ability to monitor, infer and manage responses to a changing climate. What is the role of machine learning and HPC in climate change and how do the data-intensive problems change the requirements on hardware, programming support, parallel algorithms, and architecture? I will give an overview of several high performance modeling and analysis problems, their potential impacts on climate change and opportunities for mapping these to exascale architectures.
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