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WRF with ParallelIO

WRF with ParallelIO provides steps and code samples to build and run WRF on AWS using AWS ParallelCluster. It is targeted for Amazon EC2 HPC optimice instances based AMD CPU Milan.

WRF On AWS

Architecture

WRF ParallelCluster Architecture

Deploying WRF on AWS

AWS Cloud9 Environment

AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser.

AWS Cloud9 contains a collection of tools that let you code, build, run, test, debug, and release software in the cloud using your internet browser. The IDE offers support for python, pip, AWS CLI, and provides easy access to AWS resources through Identity and Access Management (IAM) user credentials. The IDE includes a terminal with sudo privileges to the managed instance that is hosting your development environment. This makes it easy for you to quickly run commands and directly access AWS services.

Create an AWS Cloud9 environment:

The link below will create an AWS Cloud9 environment from which you will be able to create your cluster.

Launch Stack

Launch Stack

  1. Open the AWS Cloud9 console.
  2. Select MyCloud9Env.
  3. Choose Open IDE.

Disable AWS managed temporary credentials:

  1. Once the Cloud9 environment is created.
  2. Choose the gear icon in the top right to open the Prefences tab.
  3. In the Preferences tab, choose AWS SETTINGS.
  4. Turn off the AWS managed temporary credentials.
  5. Close the Preferences tab.

Prerequisites

Let start by downloading the WRF repository containing the Infrastructure as Code on your Cloud9 instance.

On the Cloud9 instance terminal, run the script below to install the prerequisited software:

wget https://github.com/aws-samples/awsome-hpc/archive/refs/heads/main.tar.gz
mkdir -p AWSome-hpc
tar -xvzf main.tar.gz -C AWSome-hpc --strip-components 1
cd AWSome-hpc/apps/wrf
bash ./scripts/setup/install_prerequisites.sh

The script will install the following on the Cloud9 instance:

Install AWS ParallelCluster

Create your Python3 virtual environment

python3 -m venv .env
source .env/bin/activate

Install AWS ParallelCluster

pip3 install aws-parallelcluster==3.4.1

Set AWS Region The command below will query the metadata of the AWS Cloud9 instance to determine in which region it has been created.

export AWS_REGION=`curl --silent http://169.254.169.254/latest/meta-data/placement/region`

Building WRF Image using Packer on AWS

The sample relies on packer to build an AWS Machine Image (AMI) containing an installation of WRF. It is compiled and optimized for Intel Xeon Scalable Processor using the following compiler and MPI combination:

  • Intel oneAPI compiler and Intel oneAPI MPI 2022.2.0

The packer scripts are located in the amis folder and are organized by Operating System (OS) such as \[OS\]-pc-wrf. The available OS for this solution are:

  • Amazon Linux 2

The AMI name is generated as [company_name]-[OS]-parallelcluster-[parallel_cluster_version]-[application_name]-[application_version]-[isotime YYYMMDDHHmmss].

Different variables are passed to packer to build the WRF AMI. For simplicity, they have preset values and you can find the list and description of the variables in this document.

Build an AMI

Navigate to the amis folder, choose the desired OS, build the AMI using packer. You can accomplish this by typing:

# Set OS for WRF to be amzn2
OS_TYPE=amzn2

# Go to the corresponding folder
cd amis/amzn2-pc-wrf

# Build the ami using packer
packer build \
    -var-file variables.json \
    -var aws_region=${AWS_REGION} \
    -var parallel_cluster_version=`pcluster version | jq -r '.version'` \
    -var company_name=[COMPANY_NAME] \
    amzn2-pc-wrf.json

Deploy AWS ParallelCluster with WRF

Create the AWS ParallelCluster Configuration file. Instances that will be used are hpc6a.48xlarge

# Going back from where you started
cd ../../

. ./scripts/setup/create_parallelcluster_config.sh

Create the WRF Cluster

CLUSTER_NAME="wrf-cluster"
pcluster create-cluster -n ${CLUSTER_NAME} -c config/wrf-x86-64.yaml --region ${AWS_REGION}

Connect to the cluster

pcluster ssh -n ${CLUSTER_NAME} -i ~/.ssh/${SSH_KEY_NAME} --region ${AWS_REGION}

Run WRF v4

In this section, you will go through the steps to run test case(s) provided by NCAR on AWS ParallelCluster.

Once you are connected to the WRF cluster, you should navigate to the /fsx directory. Here are the steps:

Retrieve CONUS 12KM

Input data used for simulating the Weather Research and Forecasting (WRF) model are 12-km CONUS input. These are used to run the WRF executable (wrf.exe) to simulate atmospheric events that took place during the Pre-Thanksgiving Winter Storm of 2019. The model domain includes the entire Continental United States (CONUS), using 12-km grid spacing, which means that each grid point is 12x12 km. The full domain contains 425 x 300 grid points. After running the WRF model, post-processing will allow visualization of atmospheric variables available in the output (e.g., temperature, wind speed, pressure).

cd /fsx
curl -O https://www2.mmm.ucar.edu/wrf/OnLineTutorial/wrf_cloud/wrf_simulation_CONUS12km.tar.gz
tar -xzf wrf_simulation_CONUS12km.tar.gz

Prepare the data

cd /fsx/conus_12km

cp /opt/wrf-omp/src/run/{\
        GENPARM.TBL,\
        HLC.TBL,\
        LANDUSE.TBL,\
        MPTABLE.TBL,\
        RRTM_DATA,\
        RRTM_DATA_DBL,\
        RRTMG_LW_DATA,\
        RRTMG_LW_DATA_DBL,\
        RRTMG_SW_DATA,\
        RRTMG_SW_DATA_DBL,\
        SOILPARM.TBL,\
        URBPARM.TBL,\
        URBPARM_UZE.TBL,\
        VEGPARM.TBL,\
        ozone.formatted,\
        ozone_lat.formatted,\
    ozone_plev.formatted} .

Run CONUS 12Km

Create a slurm submission script

cat > slurm-hpca-wrf-conus12km.sh << EOF
#!/bin/bash

#SBATCH --job-name=WRF-CONUS12km
#SBATCH --partition=hpc6a48large
#SBATCH --output=%x_%j.out
#SBATCH --error=%x_%j.err
#SBATCH --ntasks=192
#SBATCH --ntasks-per-node=96
#SBATCH --cpus-per-task=1


export I_MPI_OFI_LIBRARY_INTERNAL=0
export I_MPI_OFI_PROVIDER=efa

module purge
module load wrf-omp/4.2.2-intel-2022.2.0

mpirun wrf.exe
EOF

Run the CONUS 12Km test case on 2 x hpc6a.48xlarge instances

sbatch slurm-hpca-wrf-conus12km.sh

The job should complete in a couple of minutes with the output and error files located in the /fsx directory.

Cleanup your cluster

To avoid unexpected charges to your account relative to the WRF cluster, make sure you delete the cluster and associated resources.

Delete the cluster.

pcluster delete-cluster -n ${CLUSTER_NAME} --region ${AWS_REGION}

The steps below are optional if you plan to deploy a cluster with WRF in the future.

Delete remaining components of the WRF solution

. ./scripts/cleanup/cleanup_solution_components.sh

Delete the AWS Cloud9 environment:

  1. Open the AWS CloudFormation.
  2. Select hpcsolutions-cloud9.
  3. Choose Delete.
  4. Choose Delete to confirm deletion.