# WRF with ParallelIO

WRF with ParallelIO provides steps and code samples to build and run WRF on AWS using [AWS ParallelCluster](<https://docs.aws.amazon.com/parallelcluster/>).
It is targeted for Amazon EC2 HPC optimice instances based AMD CPU Milan.

## WRF On AWS

### Architecture

![WRF ParallelCluster Architecture](<docs/images/wrf_architecture.png>)

## Deploying WRF on AWS

### AWS Cloud9 Environment

[AWS Cloud9](<https://aws.amazon.com/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](<https://samdengler.github.io/cloudformation-launch-stack-button-svg/images/us-east-2.svg>)](<https://us-east-2.console.aws.amazon.com/cloudformation/home?region=us-east-2#/stacks/create/template?stackName=hpcsolutions-cloud9&templateURL=https://awsome-hpc.s3.amazonaws.com/cf_hpc_solutions_cloud9.yaml>)


[![Launch Stack](<https://samdengler.github.io/cloudformation-launch-stack-button-svg/images/eu-north-1.svg>)](<https://us-east-1.console.aws.amazon.com/cloudformation/home?region=eu-north-1#/stacks/create/template?stackName=hpcsolutions-cloud9&templateURL=https://awsome-hpc.s3.amazonaws.com/cf_hpc_solutions_cloud9.yaml>)

1. Open the [AWS Cloud9 console](<https://console.aws.amazon.com/cloud9>).
1. Select **MyCloud9Env**.
1. Choose **Open IDE**.

#### Disable AWS managed temporary credentials:

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

```bash
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:

- [Python3 and pip](<https://pip.pypa.io/en/latest/installing/>).
- [Packer version 1.6.0 and above](<https://learn.hashicorp.com/tutorials/packer/getting-started-install?in=packer/getting-started>).
- [AWS CLI version 2](<https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html>).
- [Session Manager plugin](<https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.html>).

### Install AWS ParallelCluster

Create your Python3 virtual environment

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

Install AWS ParallelCluster

```bash
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.

```bash
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](<docs/packer_ami_variables.md>).

#### Build an AMI

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

```bash
# 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

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

. ./scripts/setup/create_parallelcluster_config.sh
```

Create the WRF Cluster

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

Connect to the cluster

```bash
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](<https://www2.mmm.ucar.edu/wrf/users/benchmark/benchdata_v422.html>) 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).

```bash
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

```bash
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

```bash
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

```bash
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.

```bash
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

```bash
. ./scripts/cleanup/cleanup_solution_components.sh
```

### Delete the AWS Cloud9 environment:

1. Open the [AWS CloudFormation](<https://console.aws.amazon.com/cloudformation>).
1. Select **hpcsolutions-cloud9**.
1. Choose **Delete**.
1. Choose **Delete** to confirm deletion.