A GPU-accelerated simulation of rapid intensification of a tropical cyclone with observed heating
By: Soonpil Kang, Francis X. Giraldo, Seth Camp
Potential Business Impact:
Makes storm predictions faster and more accurate.
This paper presents a limited-area atmospheric simulation of a tropical cyclone accelerated using GPUs. The OpenACC directive-based programming model is used to port the atmospheric model to the GPU. The GPU implementation of the main functions and kernels is discussed. The GPU-accelerated code produces high-fidelity simulations of a realistic tropical cyclone forced by observational latent heating. Performance tests show that the GPU-accelerated code yields energy-efficient simulations and scales well in both the strong and weak limit.
Similar Papers
Advancing Seasonal Prediction of Tropical Cyclone Activity with a Hybrid AI-Physics Climate Model
Atmospheric and Oceanic Physics
Predicts hurricanes months in advance.
Accelerating Gaussian beam tracing method with dynamic parallelism on graphics processing units
Performance
Makes sound simulations run much, much faster.
Spatiotemporal deep learning models for detection of rapid intensification in cyclones
Machine Learning (CS)
Predicts storms that get dangerously strong fast.