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GTC ON-DEMAND

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Abstract:
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 this programs prospects of success. 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 is nearing completion, 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 parametizations. 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.
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 this programs prospects of success. 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 is nearing completion, 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 parametizations. 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.   Back
 
Topics:
HPC and Supercomputing
Type:
Talk
Event:
Supercomputing
Year:
2019
Session ID:
SC1920
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Abstract:

Rapid progress in atmospheric science has been fueled in part over the years by faster computers. However, progress has slowed over the last decade due to three factors: the plateauing of core speeds, the increasing complexity of atmospheric models, and the mushrooming of data volumes. Our team at the National Center for Atmospheric Research is pursuing a hybrid approach to surmounting these barriers that combines machine learning techniques and GPU-acceleration to produce, we hope, a new generation of ultra-fast models of enhanced fidelity with nature and increased value to society.

Rapid progress in atmospheric science has been fueled in part over the years by faster computers. However, progress has slowed over the last decade due to three factors: the plateauing of core speeds, the increasing complexity of atmospheric models, and the mushrooming of data volumes. Our team at the National Center for Atmospheric Research is pursuing a hybrid approach to surmounting these barriers that combines machine learning techniques and GPU-acceleration to produce, we hope, a new generation of ultra-fast models of enhanced fidelity with nature and increased value to society.

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Topics:
HPC and AI
Type:
Talk
Event:
Supercomputing
Year:
2018
Session ID:
SC1818
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