Description:

This session will be a joint session between the 10th Symposium on High-Performance Computing for Weather, Water, and Climate (10HPC) and the 23rd Conference on Artificial Intelligence for Environmental Science (23AI). As datasets grow larger and artificial intelligence / machine learning (AI/ML) methods grow more complex, researchers have pushed the need for more powerful computing resources to make scientific advances. This session will explore how these AI/ML projects are enabled by researchers using new computing methods, greater levels of parallelism, faster storage systems, better optimized algorithms, and more to push artificial intelligence and machine learning forward. Example submissions may include (but are not limited to) using clusters of specialized hardware such as graphical processing units (GPUs) to enable shorter time to results or larger pools of results, optimized storage systems that can reduce the latency of delivering data to learning algorithms distributed over a high-performance system, or work creating or using advanced algorithms that improve scalability towards exascale computing.