VINCENT BELTRANI: Have you ever wondered
what scientific research looks like in the cloud?
Researchers are using Google Cloud
to gain new insights into disease
and discover drugs and therapies to improve patients' lives.
Today, I will dive in to show how high-performance computing
using the HPC Toolkit can help.
Let's see how.
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Whether it's drug discovery, genomics,
or computational chemistry and biology,
Google Cloud's flexible and scalable High Performance
Computing for life science solutions
are ready to help you innovate.
Scientists are using our HPC platform today
to run large-scale, virtual library screenings,
simulate the behavior of target proteins
and promising, drug-like compounds,
and run powerful tools like free energy perturbation
calculations on fleets of the latest GPUs.
There are many advantages to running your workloads
on Google Cloud.
Configurable and repeatable HPC deployment options
help you efficiently run and manage scientific workloads.
Flexible pricing means you only pay for what you need,
meaning the cost savings for your organization.
And scalable architecture means reduced or no queue wait times
so you can iterate and innovate more quickly than ever before.
The Cloud HPC Toolkit is an open-source product
developed by Google that makes it easy to configure and deploy
HPC blueprints that define a complete Cloud HPC environment.
In this tutorial, I will show you
how to deploy the health care and life science
blueprint, which includes an autoscaling HPC
cluster for molecular simulations and a visualization
environment that enables you to interact with and visualize
the results of your experiments.
To learn more about the HPC Toolkit,
I recommend you watch the previous videos
in this series linked in the description below.
Let's start by taking a look at the architecture
of the cluster.
We will be deploying the components needed
for high-performance compute jobs,
including GPU-enabled compute nodes for accelerated
computing, a Filestore instance for NFS-shared storage,
and Cloud Storage buckets that are automatically
mounted to work with input and output data easily.
In addition, a dedicated builder VM
will compile and install molecular dynamics software
on the shared Filestore and install VMD visualization
software on the remote desktop.
This architecture uses a Slurm HPC cluster for running jobs.
The HPC Toolkit includes other options too,
like batch, a fully-managed batch service for running jobs
on Google's infrastructure.
Let's dive into the demo to see this in action.
I'll be working in the Cloud Shell, where I have already
installed the HPC Toolkit, which includes the HCLS blueprint
and the GHPC command-line tool.
This is the HCLS blueprint, a text
file describing our HPC environment.
You will notice similar high-level concepts
in the blueprint, as shown in the architecture
slide, like storage, software, remote desktop, and the Slurm
cluster.
Now we can create a deployment using the toolkit's GHPC
command-line tool.
Running ghpc create will print any additional commands
that you need to run to deploy this blueprint.
This is a multistage deployment that requires time
to build the software.
The tutorial linked in the description
has a complete set of instructions.
Once the deployment is complete, you
can navigate to the Cloud Console web
interface for Compute Engine to see
that you've deployed a remote desktop VM, a Slurm
controller, a login node, and the dedicated builder VM.
To connect to the remote desktop using Chrome Remote Desktop,
configure the connection using the steps
described in the tutorial, which I have already done.
To show how researchers can use this environment,
we'll simulate the dynamics of lysozyme using GROMACS,
a molecular dynamics package.
Let's take a look at the protein using VMD.
As part of the deployment, the PDB file for lysozyme
has been downloaded to the input directory, which
is a Google Cloud Storage bucket mounted to the remote desktop
and the cluster.
Lysozyme is an antimicrobial enzyme
that attacks the protective cell walls of bacteria.
Before running a GROMACS simulation,
several steps need to be performed first
that have been collected into a single submission
script in my home directory.
Let's take a look at the submission script now.
These steps include preparing the input,
generating the topology, solvating a system,
minimizing the potential energy, and equilibrating the system
before running the production run.
You can find the submission script
in the repository linked in the description.
Now let's SSH into the login node,
copy the PDB file in place, and submit the job to the cluster.
Once the job is submitted, the cluster
autoscales a GPU compute node to run the simulation.
Once the simulation finishes, we can
return to the remote desktop to view the simulation results.
Like the input directory, the output directory
is a Google Cloud Storage bucket mounted to the cluster
and the remote desktop.
This means the results are accessible
from the remote desktop without ever needing to export
any data from the cloud.
Let's take a look at the simulation results in VMD.
Here, you can see the dynamics of lysozyme
generated in our experiment.
Since the results are stored in the Cloud,
they are immediately available for use cases like AI and ML
in Vertex AI, data analytics in BigQuery,
and interactive analysis in Vertex Workbench.
In this tutorial, I demonstrated how
to use the HPC Toolkit to deploy the HCLS blueprint
and utilize a remote desktop to view the molecular dynamics
simulation results.
If you want to run it yourself, follow the steps
in the tutorial linked in the description below.
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