The weather research and forecasting (WRF) model is popular in high performance computing (HPC) code used by the weather and climate community. WRF v4 typically performs well on traditional HPC architectures that support high floating-point processing, high memory bandwidth, and a low-latency network—for example, top500 supercomputers. Now you can find all these characteristics on the new HBv2 Azure Virtual Machines (VMs) for HPC. With some minor tuning, large WRF v4 models perform very well on Azure.
WRF is a mesoscale numerical weather prediction system designed for both atmospheric research and operation forecasting applications. WRF has a large worldwide community of registered users—more than 48,000 in over 160 countries).
In the past, you needed a supercomputer to run WRF models if you wanted to complete high-resolution weather predictions in a reasonable and practical timeframe. In this post, we explore building WRF v4 models on Azure. We ran performance tests to compare the VMs optimized for HPC workloads—that is, the HB-series, HC-series, and HBv2.
Automated scripts for building different variations of WRF v4, targeting Azure HPC VMs, are contained in the Azure HPC git repository:
git clone email@example.com:Azure/azurehpc.git
We use Spack to build WRF’s dependencies. See the apps/spack and apps/wrf directories for details. All WRF performance data were generated using the version built with Open MPI v4.0.2. The hybrid parallel (MPI + Open MP) and the MPI-only version were used.
WRF v3 models typically are not compatible with WRF v4. For example, the WRF v3 CONUS bench models (2.5 km and 12 km) are no longer supported and are not compatible with WRF v4. For our tests, a new CONUS 2.5 km model (very similar to the original WRF v3 CONUS 2.5 km model) and a larger model, Hurricane Maria 1 km, were generated using the WRF preprocessing system (WPS). See the azurehpc git repository for details on building and installing WPS v4 and the steps we followed to generate these models for W
|Model||Resolution km||e_we||e_sn||e_vert||Total gridpoints||Timstep (s)||Simulation hours|
This benchmark compares the performance of WRF v4 on HB-series, HC-series, and HVv2 VMs. No performance tuning was applied. The MPI-only version of WRF v4 was used, and all cores on each HPC VM type were used. We were primarily interested in comparing the performance of these VM types on a per-node basis.
The following tuning optimization were investigated:
We applied performance optimizations to WRF v4 running a large Hurricane Maria 1km model on HBv2 VMs. We wanted to see how well the large Hurricane Maria 1km model would scale on this VM type.
The new HBv2 VMs have the performance you need to run large WRF v4 models, and only minor tuning is needed. To summarize the our test results:
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