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. 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 20× speedup for a computationally intensive portion of the Weather Research and Forecast (WRF) model on an NVIDIA 8800 GTX Graphics Processing Unit (GPU). We expect an overall 1.3× speedup from this change alone.