The Weather Research and Forecasting (WRF) model is a numerical weather prediction (NWP) system designed to serve both atmospheric research and operational forecasting needs. The WRF model serves a wide range of meteorological applications across scales ranging from meters to thousands of kilometers. WRF is one of the most widely used NWP models both in academia and industry with over 48,000 registered users spanning over 160 countries.
With the release of Arm-based AWS Graviton2 Amazon Elastic Compute Cloud (EC2) instances, a common question has been how these instances perform on large-scale NWP workloads. In this blog, we will present results from a standard WRF benchmark simulation and compare across three different instance types.
AWS Graviton2 processors are custom built by AWS using the 64-bit Arm Neoverse cores to deliver great price performance for cloud workloads running in Amazon EC2. These instances are powered by 64 physical core AWS Graviton2 processors that use 64-bit Arm Neoverse N1 cores and custom silicon designed by AWS, built using advanced 7-nanometer manufacturing technology.
As NWP models often benefit from high-speed networking, we will evaluate the C6gn.16xlarge (64-core Graviton2-based instance) and the C5n.18xlarge (36-core Intel Skylake-based instance). Both of these instances have 100-Gbps networking bandwidth and have support for Elastic Fabric Adapter (EFA). To identify the performance achieved through the increased networking capabilities of C6gn, we also evaluate the C6g. The C6g instance has the same characteristics as the C6gn instance aside from the increased network capabilities.
The benchmark case used for this blog is the UCAR CONUS 2.5km dataset for WRFv4. We use the first 3 hours of this 6-hour simulation, 2.5 km resolution case covering the Continental U.S (CONUS) domain from November 2019 with a 15-second time step and total of around 90M grid points. Note that in the past, WRFv3 has commonly been benchmarked using a similar CONUS 2.5 km dataset, however, WRFv3 models are not compatible with WRFv4. Despite the similar name, this is a different model than that dataset.
To learn more about benchmarking WRF on Graviton2, read the full blog here.