Run Simcenter STAR CCM+ workloads

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This tutorial shows you how to deploy an HPC cluster and run a Simcenter STAR CCM+ workload. The HPC cluster deployment is done by using Cluster Toolkit and this tutorial assumes that you've already set up Cluster Toolkit in your environment.

Objectives

In this tutorial, you will learn how to complete the following task:

1. Use Cluster Toolkit to create a cluster that's suitable for running Simcenter STAR-CCM+. Two blueprints are provided for the deployment of the cluster as follows:

   • tutorial-starccm.yaml: this blueprint uses four VMs without the use of a scheduler.
   • tutorial-starccm-slurm.yaml: this blueprint uses Slurm which is a cluster management and job scheduling system that is used in many HPC centers. This is the recommended option.

   

   Figure 1. Architecture diagram for an HPC cluster that uses Slurm

2. Install Simcenter STAR-CCM+

3. Run Simcenter STAR-CCM+

Costs

Important: In this document, you use the following billable components of Google Cloud:

• Compute Engine
• Filestore
• Cloud Storage

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

Before you begin

• Set up Cluster Toolkit.

• Get an installation file and input file for Simcenter STAR-CCM+. These files must be obtained directly from Siemens.

  • For the installation file, Version 17.02 or later is recommended. If using the version 17.02 install package it will have the following package name: STAR-CCM+17.02.007_01_linux-x86_64.tar.gz.

  • For the input file, we recommend the automobile simulation workload: lemans_poly_17m.amg.sim.

• Review the best practices.

Open your CLI

In the Google Cloud console, activate Cloud Shell.

Activate Cloud Shell

At the bottom of the Google Cloud console, a Cloud Shell session starts and displays a command-line prompt. Cloud Shell is a shell environment with the Google Cloud CLI already installed and with values already set for your current project. It can take a few seconds for the session to initialize.

Deploy the HPC cluster

The following steps must be completed from the CLI.

1. Set a default region and zone in which to deploy your compute nodes.

   gcloud config set compute/region REGION
   gcloud config set compute/zone ZONE
   

   Replace the following:

   • REGION: your preferred region
   • ZONE: a zone within your preferred region

2. Define environment variables.

   export DEPLOYMENT_NAME=DEPLOYMENT_NAME
   export GOOGLE_CLOUD_PROJECT=`gcloud config list --format 'value(core.project)'`
   export CLUSTER_REGION=`gcloud config list --format 'value(compute.region)'`
   export CLUSTER_ZONE=`gcloud config list --format 'value(compute.zone)'`
   

   Replace DEPLOYMENT_NAME with a name for your deployment.

3. Create the cluster deployment folder. This step varies depending on whether or not you are using Slurm in your cluster. Complete one of the following options:

   With Slurm

   This tutorial uses the tutorial-starccm-slurm.yaml cluster blueprint that is located in the Cluster Toolkit GitHub repository.

   To create a deployment folder from the cluster blueprint, run the following command from Cloud Shell:

   ./gcluster create community/examples/tutorial-starccm-slurm.yaml \
     --vars "deployment_name=${DEPLOYMENT_NAME}" \
     --vars "project_id=${GOOGLE_CLOUD_PROJECT}" \
     --vars "region=${CLUSTER_REGION}" \
     --vars "zone=${CLUSTER_ZONE}"
   

   This command creates the DEPLOYMENT_NAME/ deployment folder, which contains the Terraform needed to deploy your cluster.

   Without Slurm

   This tutorial uses the tutorial-starccm.yaml cluster blueprint that is located in the Cluster Toolkit GitHub repository.

   To create a deployment folder from the cluster blueprint, run the following command from Cloud Shell:

   ./gcluster create /community/examples/tutorial-starccm.yaml \
     --vars "deployment_name=${DEPLOYMENT_NAME}" \
     --vars "project_id=${GOOGLE_CLOUD_PROJECT}" \
     --vars "region=${CLUSTER_REGION}" \
     --vars "zone=${CLUSTER_ZONE}"
   

   This command creates the DEPLOYMENT_NAME/ deployment folder, which contains the Terraform needed to deploy your cluster.

4. Use the ghpc deploy command to begin automatic deployment of your cluster:

   ./gcluster deploy  ${DEPLOYMENT_NAME}

5. ghpc reports the changes that Terraform is proposing to make for your cluster. Optionally, you may review them by typing d and pressing enter. To deploy the cluster, accept the proposed changes by typing a and pressing enter.

   Summary of proposed changes: Plan: 33 to add, 0 to change, 0 to destroy.
   (D)isplay full proposed changes,
   (A)pply proposed changes,
   (S)top and exit,
   (C)ontinue without applying
   Please select an option [d,a,s,c]:
   

6. After accepting the changes, ghpc runs terraform apply automatically. This takes approximately 5 minutes while it displays progress. If the run is successful, the output is similar to the following:

   Apply complete! Resources: 38 added, 0 changed, 0 destroyed.
   

   You are now ready to submit jobs to your HPC cluster.

Install Simcenter STAR-CCM+

From the CLI, upload both the installation file for Simcenter STAR-CCM+ and the input file, that you got from Siemens, to Cloud Storage. After you upload the files, the files can then be copied to the VMs in your cluster. Replace BUCKET_NAME with the name of your Cloud Storage bucket. This bucket must be globally unique, not just for your project.

1. Create a Cloud Storage bucket by using the Google Cloud CLI.

   gcloud storage buckets create gs://BUCKET_NAME
   

2. Upload the install file to the Cloud Storage bucket from your local storage.

   gcloud storage cp STAR-CCM+17.02.007_01_linux-x86_64.tar.gz gs://BUCKET_NAME
   

3. Upload the input file.

   gcloud storage cp lemans_poly_17m.amg.sim gs://BUCKET_NAME
   

Connect to a VM

This step varies depending on whether or not you are using Slurm in your cluster. Complete one of the following options:

With Slurm

From the CLI, connect to the login node in your cluster. To connect, run the gcloud compute ssh command.

gcloud compute ssh VM_NAME

Replace VM_NAME with the name of the VM that has login in the name. The name is similar to the following: DEPLOYMENT_NAME-login-xxxxx-001

Without Slurm

1. From the CLI, connect to any one of the VMs in your cluster. To connect, run the gcloud compute ssh command.

   gcloud compute ssh VM_NAME

   Replace VM_NAME with the name of the VM that you want to connect to.

2. On the VM, create an hosts.txt file.

    pdsh -N -w DEPLOYMENT_NAME-[0-3] hostname > ~/hosts.txt
    

Configure the VM

From the VM instance, complete the following steps:

1. Copy the install and input files from Cloud Storage to the VM by using the gcloud CLI.

   gcloud storage cp gs://BUCKET_NAME/* .

2. After the transfer completes use tar to extract the contents of the files. For example, if the install file is named STAR-CCM+17.02.007_01_linux-x86_64.tar.gz, run the following command:

   tar xvf STAR-CCM+17.02.007_01_linux-x86_64.tar.gz

   In this example, the extracted archive creates a directory calledstar-ccm+_17.02.007.

3. Assign an environment variable with the install script name. The exact path depends on the version of Simcenter STAR-CCM+.

   export STAR_CCM_INSTALL=starccm+_17.02.007/STAR-CCM+17.02.007_01_linux-x86_64-2.17_gnu9.2.sh
   

4. Run the installer as follows:

   sudo $STAR_CCM_INSTALL -i silent \
   -DPRODUCTEXCELLENCEPROGRAM=0 \
   -DINSTALLDIR=$HOME/apps \
   -DINSTALLFLEX=false \
   -DADDSYSTEMPATH=true \
   -DNODOC=true
   

   This runs silently and installs the code in your home directory ~/apps.

Prepare to run Simcenter STAR-CCM+

This step varies depending on whether or not you are using Slurm in your cluster. Complete one of the following options:

With Slurm

From the login VM instance, create a Slurm batch file by pasting the following into the shell.

cat << EOF > starccm.sh
#!/bin/sh
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=30
#SBATCH --mem-per-cpu=3G
#SBATCH --job-name=starccm

#SBATCH --error=job_err_%J.txt
#SBATCH --output=job_out_%J.txt
#SBATCH --partition=compute

export LICENSE_SERVER=1999@LICENSESERVER
export LICENSE_KEYS=LICENSEKEY
export LICENSE_FLAGS="-licpath \${LICENSE_SERVER} -power -podkey \${LICENSE_KEYS}"
export NUM_PROCESSES=120
export NUM_PRE_ITER=60
export NUM_ITER=20
export INPUT_FILE="\$HOME/lemans_poly_17m.amg.sim"
export STARCCM=~/apps/PATH_TO_CODE/star/bin/starccm+

cd \$HOME
export HOSTFILE="job_hosts_\${SLURM_JOB_ID}.txt"
scontrol show hostname \$SLURM_NODELIST > \${HOSTFILE}

\${STARCCM} \${LICENSE_FLAGS} -benchmark \
"-preits \${NUM_PRE_ITER} -nits \${NUM_ITER} -nps \${NUM_PROCESSES}" \
-mpi intel -mpiflags "-genv I_MPI_FABRICS shm:ofi -genv FI_PROVIDER tcp \
-genv I_MPI_PIN_PROCESSOR_LIST allcores" -cpubind -pio -np \${NUM_PROCESSES} \
-machinefile \${HOSTFILE} \${INPUT_FILE}
EOF

Replace the following:

• LICENSEKEY: the FlexLM key provided by Siemens
• LICENSESERVER: the server URL hosting your license
• PATH_TO_CODE: the path to version of the Simcenter StarCCM+ code you installed. For example: 17.02.007/STAR-CCM+17.02.007

Without Slurm

From the VM instance, complete the following steps:

1. Add the location of the Simcenter STAR-CCM+ executable to the PATH variable.

    export PATH="$HOME/apps/17.02.007/STAR-CCM+17.02.007/star/bin/:$PATH"
    

2. Set environment variables for the STAR-CCM+ license configurations. The license configuration is dependent on your installation and is provided by Siemens.

    export LICENSE_SERVER=[e-mail]
    export LICENSE_KEYS=YOUR_LICENSE_KEY
    export LICENSE_FLAGS="-licpath ${LICENSE_SERVER} -power -podkey ${LICENSE_KEYS}"
    

   Replace YOUR_LICENSE_KEY with your license key.

3. Configure the job parameters.

    export NUM_PROCESSES=120
    export NUM_PRE_ITER=60
    export NUM_ITER=20
    export MACHINEFILE="$HOME/hosts.txt"
    export RUNDIR=$HOME
    export INPUT_FILE="$HOME/lemans_poly_17m.amg.sim"
    

4. Verify that the host.txt file and the input file (lemans_poly_17m.amg.sim) are in your home directory.

Run Simcenter STAR-CCM+

This step varies depending on whether or not you are using Slurm in your cluster. Complete one of the following options:

With Slurm

From the login VM instance, submit the batch script to Slurm on the HPC cluster as follows:

sbatch starccm.sh

This will output something like:

Submitted batch job 4

To monitor the status of job, run the squeue command.

JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
4     compute    starccm drj_gcp CF       0:48      4 starccmslu-compute-ghpc-[0-3]

When the job no longer appears in the queue, it is complete. The output is generated in an job_out_#.txt file. Where # is the number of the JOBID. In that file, there should be output from the application.

Without Slurm

From the VM instance, run Simcenter STAR-CCM+ as follows:

starccm+ ${LICENSE_FLAGS} -benchmark \
"-preits ${NUM_PRE_ITER} -nits ${NUM_ITER} -nps ${NUM_PROCESSES}" \
-mpi intel -mpiflags "-genv I_MPI_FABRICS shm:ofi -genv FI_PROVIDER tcp \
-genv I_MPI_PIN_PROCESSOR_LIST allcores" -cpubind -pio -np ${NUM_PROCESSES} \
-machinefile ${MACHINEFILE} ${INPUT_FILE}

This generates a long output listing to indicate simulation progress.

Iteration     Continuity     X-momentum     Y-momentum     Z-momentum     Tke            Sdr          Cd             Cl     Vmag (m/s) Total Solver CPU Time (s) Total Solver Elapsed Time (s)
2             1.000000e+00   1.000000e+00   1.000000e+00   1.000000e+00   9.705605e-01   1.000000e+00 3.671660e+00    -5.423592e+00   1.074021e+03    2.970680e+03  3.103627e+01
3             2.331303e-01   1.000000e+00   7.333426e-01   8.118292e-01   1.000000e+00   6.399014e-01 8.217129e-01  -1.449110e+00   4.546574e+02              3.175950e+03                  3.274697e+01
4             1.752827e-01   3.067447e-01   3.516874e-01   3.793376e-01   4.971905e-01   4.102950e-01   3.66

When the job is complete the final line of output is displayed with the CURRENT_DATE replaced by today's date.

Benchmark::Output file name : lemans_poly_17m.amg-Intel_R_Xeon_R_CPU_3-10GHz-CURRENT_DATE.xml

Benchmark::HTML Report : lemans_poly_17m.amg-Intel_R_Xeon_R_CPU_3-10GHz-CURRENT_DATE_194930.html

The output files can then be downloaded from your cluster VMs to your local machine by using Google Cloud CLI.

gcloud compute scp your_login@your-vm-name:~/lemans_poly_17m.amg-Intel_R_Xeon_R_CPU_3-10GHz-CURRENT_DATE_194930.* .

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Destroy the HPC cluster

To delete the terraform cluster, from the CLI, run the following command:

./gcluster destroy ${DEPLOYMENT_NAME}

When complete you should see something like:

Destroy complete! Resources: xx destroyed.

Delete the Cloud Storage bucket

To delete the bucket, use gcloud storage rm with the --recursive flag:

gcloud storage rm gs://BUCKET_NAME --recursive

Replace BUCKET_NAME with the name of your Cloud Storage bucket.

If successful, the response looks like the following example:

Removing gs://my-bucket/...

Delete the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.