An investigation into the feasibility and benefits of GPU/multicore acceleration of the weather research and forecasting model

Abstract

There is a growing need for ever more accurate climate and weather simulations to be delivered in shorter timescales. Hardware Acceleration using GPUs or FPGAs could potentially result in much reduced run times or higher accuracy simulations. We studied the Weather Research and Forecasting Model in order to assess if GPU acceleration of this type of Numerical Weather Prediction code is both feasible and worthwhile. We studied the performance of the original code and created a simple performance model for comparing multicore CPUs and GPUs. Based on the WRF profiling results, we focused on the acceleration of the scalar advection module. We show that our data-parallel kernel version of the scalar advection module runs up to 7× faster on the GPU compared to the original code on the CPU. However, as the data transfer cost between GPU and CPU is very high (as shown by our analysis), there is only a small speed-up (2×) for the fully integrated code. We also developed an extensible system for integrating OpenCL code into large Fortran code bases such as WRF. In conclusion, we have shown that GPU acceleration of WRF is both feasible and worthwhile. Our findings are generally applicable to multi-physics fluid dynamics code and not limited to NWP models.