GPU ACCELERATION OF NUMERICAL WEATHER PREDICTION

Abstract

Weather and climate prediction software has enjoyed the benefits of exponentially increasing processor power for almost 50 years. Even with the advent of large-scale parallelism in weather models, much of the performance increase has come from increasing processor speed rather than increased parallelism. This free ride is nearly over. Recent results also indicate that simply increasing the use of large-scale parallelism will prove ineffective for many scenarios where strong scaling is required. We present an alternative method of scaling model performance by exploiting emerging architectures using the fine-grain parallelism once used in vector machines. The paper shows the promise of this approach by demonstrating a nearly 10 × speedup for a computationally intensive portion of the Weather Research and Forecast (WRF) model on a variety of NVIDIA Graphics Processing Units (GPU). This change alone speeds up the whole weather model by 1.23×.