Description:

The size and scale of AI models has grown quickly with available computing power and storage resources, and the beginning of the exascale era of supercomputing (massive HPC systems capable of 10¹⁸ floating point operations per second) further expands the boundaries for how large AI models can be. With resources and systems this large, the prospects of data movement, storage, and analysis at this scale provide both opportunities and challenges. This session will focus on AI development projects targeting, actively preparing to scale up to, or currently using these largest HPC systems for weather, water, or climate applications. Submissions are encouraged to address not only their scientific goals, methods, and achievements, but also the computational, data storage and management, and/or performance scaling challenges they encountered or expect to encounter when using exascale resources to achieve their scientific goals.