The FastEddy (R) Resident-GPU Accelerated Large-Eddy Simulation Framework: Model Formulation, Dynamical-Core Validation and Performance Benchmarks

This paper introduces a new large-eddy simulation model, FastEddy (R), purpose built for leveraging the accelerated and more power-efficient computing capacity of graphics processing units (GPUs) toward adopting microscale turbulence-resolving atmospheric boundary layer simulations into future numerical weather prediction activities. Here a basis for future endeavors with the FastEddy (R) model is provided by describing the model dry dynamics formulation and investigating several validation scenarios that establish a baseline of model predictive skill for canonical neutral, convective, and stable boundary layer regimes, along with boundary layer flow over heterogeneous terrain. The current FastEddy (R) GPU performance and efficiency gains versus similarly formulated, state-of-the-art CPU-based models is determined through scaling tests as 1 GPU to 256 CPU cores. At this ratio of GPUs to CPU cores, FastEddy (R) achieves 6 times faster prediction rate than commensurate CPU models under equivalent power consumption. Alternatively, FastEddy (R) uses 8 times less power at this ratio under equivalent CPU/GPU prediction rate. The accelerated performance and efficiency gains of the FastEddy (R) model permit more broad application of large-eddy simulation to emerging atmospheric boundary layer research topics through substantial reduction of computational resource requirements and increase in model prediction rate.