Reducing the Carbon Footprint of Ensemble Weather Forecasting with GPUs
Jeff Adie, Terry Yin, Stan Posey, Simon Chong Wee See · 2023
Climate and Weather Modelling is a highly complex and computationally intensive task which consumes substantial amounts of energy. A desire to improve forecast skill demands further advances in these forecasts, such as increased model fidelity and more comprehensive physical representations of the underlying processes. Another driver towards better forecasts is the goal of uncertainty quantification, with ensembles of forecasts a popular technique. But ensembles place much higher demands on the computational workload as N ensemble members require N times the compute cycles. This leads to an even higher energy demand. This study examines one approach to reducing the energy demands of ensembles by taking advantage of a hardware feature in modern NVIDIA GPUs known as Multi Instance GPU (MIG). This feature allows us to run multiple ensemble members on hardware-isolated GPU slices to maximize efficient use of the GPU resources and subsequently reduce the Carbon Footprint for an Ensemble forecast. We examine both small and large test cases across a range of setups to determine the optimal runtime configuration. Our study shows a 2.S-2.8x reduction in CO2 emissions across all cases which translates into a savings of between 141–171 tonnes of carbon emissions annually per GPU.