Exascale Climate: Can OpenACC and Machine Learning Deliver the Goods?

Many have speculated that combining GPU computational power with machine learning algorithms could radically improve weather and climate modeling. This talk will discuss an experimental project centered on the Model for Prediction Across Scales-Atmosphere (MPAS-A) to evaluate whether this idea can deliver the goods.  Initially, the project set out to determine whether CPU-GPU performance portability could be attained in a single MPAS-A source code by applying OpenACC directives. The initial porting project has been completed, and is showing scalability and throughput performance competitive with other the state-of-the-art models. At the same time, machine learning scientists at NCAR and elsewhere began looking at the piecemeal replacement of atmospheric parameterizations with machine-learning emulators. This talk will present results from efforts at NCAR to apply machine learning to emulate the atmospheric surface layer and cloud microphysics parameterizations. The talk will also discuss related efforts to tackle radiative transport and other physics components, and will conclude with our own future plans to emulate the complex chemistry of aerosol formation.