GTC 2020: Toward an Exascale Earth System Model with Machine Learning Components: An Update
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Toward an Exascale Earth System Model with Machine Learning Components: An Update
Richard Loft, Computational and Information Systems Laboratory, National Center for Atmospheric Research
Many have speculated that combining exascale GPU computational power with machine-learning algorithms could radically improve weather and climate modeling. We'll discuss the status of an ambitious project at the U.S. National Center for Atmospheric Research that's moving in that direction. Having achieved performance portability for a standalone version of the Model for Prediction Across Scales-Atmosphere (MPAS-A) on heterogeneous CPU/GPU architectures across thousands of GPUs using OpenACC, our project has begun looking at two new directions. First, we've launched an effort to port the MOM-6 Ocean Model. Second, machine-learning scientists at NCAR and elsewhere have begun evaluating replacing atmospheric parameterizations with machine-learned emulators, including the atmospheric surface layer, cloud microphysics, and aerosol parameterizations. We'll also discuss related efforts to apply machine-learning emulation to model physics.