(gentle music)
- Hello, everyone.
My name is Nidhi Chappell,
and I'm head of product for Azure HPC and AI.
Hope everybody's having a good server computing so far.
Today, I'm excited to talk to you about
how you can use the power of cloud
to actually unlock innovation
with your HPC and AI use cases.
HPC innovation spent many different industry verticals,
automotive, financial services, energy,
healthcare research, silicon or manufacturing.
Across all of these verticals,
as we looked through the use cases,
there are customers that want
to simulate all aspects of vehicle engineering
to financial services customers
that are constantly looking
to the latest and greatest risk modeling,
to energy customers that push the envelope of seismic
and reservoir simulation,
or we have genetics research going on on precision medicine.
Our HPC use cases from EDA customers
that are really looking to optimize for five nanometer,
three nanometer chip designs.
Across all of these industries
and across all of these use cases,
the need for compute
and need for compute at demand is always increasing.
These increased compute needs can really benefit
from the skill and elasticity of cloud,
especially, our Azure cloud.
We have built Azure cloud on four core fundamental pillars.
We wanna be able to provide optimized performance,
not only optimize performance,
but provide the control back to our customers
so you can manage what simulations cost you
and how many simulations you can afford.
We wanna make sure
that you can have an end-to-end workflow agility.
So your applications that are running on-premise,
can easily scale onto cloud.
We also wanna make sure that as you scale onto cloud,
we can provide a production class platform,
so you can have the reliability,
the security that you really need
for your state-of-the-art research.
And finally, we wanna make sure that
as your workflow evolves,
you can easily take advantage of AI use cases
and incorporate AI across the chain.
We have made investments in infrastructure and capabilities
that are highly optimized for HPC and AI workflows.
They start with our choice of CPUs and GPUs,
and then our investment in InfiniBand networks
and in high-performance storage.
Beyond just the infrastructure,
we've also made significant investments
in the workload orchestration layers,
making sure that you can connect not only
with the infrastructure that is on-premise,
but that you can easily incorporate some of the AI models
that are freely available from Microsoft.
The results of these investments really accrue benefit.
for our customers.
For MPI jobs, for example,
our customers can really scale up to 80,000 cores
in one tightly-coupled job.
We offer 12x better scale,
than any other cloud service provider.
Not only do we offer the scale,
we make sure that you have the latest
and greatest performance from all the best CPUs,
from Intel, Nvidia, AMD or Arm.
With our investment in performance,
you would be able to see 80% increase in performance,
for example, for Ansys Fluent.
Our unique investments in the GPU roadmap
and our deep collaboration with Nvidia,
allow us to have an AI platform that can scale
for various complex needs of our customers,
whether you are trying to train a small neural network
or developing the state-of-art NLP models.
At large scale,
when you are clustering together thousands of GPUs
for AI training, you're really looking at the HPC problem.
This is where our unique investments in InfiniBand allow us
to have unparalleled performance for AI training workloads.
A few of our AI offerings have actually showcased
on the supercomputing list.
What that means is, now, any of our customers,
any of the AI researchers worldwide,
can have supercomputing on demand.
We continue to push the envelope of performance
we can deliver.
We started off with Nvidia's A100 40 gig,
and now, we have the 80 gig in our fleet,
which allows us to get 15% better performance,
over state-of-the-art A100's infrastructure today.
A lot of our investment,
is built not just around the product,
but also around the people.
We have invested pretty significantly in expertise,
whether it comes in pre-sales, post-sales,
product teams, engineering team,
to make sure that
we can really understand your customer pain points,
your pain points, and develop a solution around that.
One of the key tenants of our strategy is to allow customers
to seamlessly move from on-premise onto cloud.
And to make that happen,
we have made a lot of investments
in having a rich set of ecosystem partners.
Whether these are partners
that develop applications like Ansys, Schlumberger,
or vendors like Intel, AMD, Nvidia,
or partners really across the value stack.
We wanna make sure that the middleware,
the libraries, the frameworks
that you use can actually be optimized for Azure,
so you don't have to see any performance difference,
between on-premise and cloud.
Under the hood, we work very closely
to make sure the MPI applications, compilers, debuggers,
all of these applications are optimized for Azure.
And we make different OS images available,
so you can take what exists on-premise,
and seamlessly move it to cloud.
I'm excited to talk to you about Vestas.
Vestas is one of our key customers
that worked with Azure HPC and AI,
and mines that AI to use the power and skill of cloud
to mitigate wake effect in wind farms.
IEEE Spectrum estimates
that 10% of potential wind power is lost to wake effect.
Wake effect is really the result
of one turbine negatively affecting the other one behind it.
Wake steering, which is needed
to actually minimize this requires advanced AI techniques,
intense stimulations and high performance computing.
With collaboration with Mines AI, Vestas and Azure HPC,
were able to create a reinforced learning engine
that could run complex simulations
at scale to rapidly build and deploy models
that could train a machine controller
with simplified instructions designed
to orchestrate turbines in the wind farms
to automatically react to wind directions.
Every percentage of improvement
in wake effect equates to millions of dollars.
And it was possible because we were able to work closely
with Azure Mines AI running on AML and running
on our HPC HBv3.
Jellyfish is a large VFX in animation house.
When COVID hit, they needed access
to immense computing capabilities, remotely and securely,
so they could allow their artists
to continue to render visual effects
and animation sequence.
Before COVID, all of their work was on-premise.
As COVID hit, all of these remote artists,
faced connection issues and security issues.
So we worked closely with Jellyfish
and give them access to 90,000 cores
in a single VM skillset, HBv2 and via VFX.
Not only were they able to reduce their latency,
using Azure ExpressRoute,
we worked deeply with them to reduce the rendering costs
by looking for spot VM optimizations.
Overall, they were able to boost their on-premise capacity
by 70% and allowed artists to have the freedom
to work from anywhere.
Now, they have hired 200 people in 20 countries,
and all of these artists can work
and access remote infrastructure.
Interesting use case is with our customer, Sensyne Health.
When COVID hit, a lot of governments wanted
to be able to evaluate test results
at scale quickly and efficiently.
But there was actually a lot of inefficiencies
in the process because there were human beings involved.
The UK National Health Services envisioned an app
that could read COVID-19 tests at scale accurately.
Sensyne Health developed a mobile app
that reads the lateral flawed results
to give positive or negative COVID conclusions accurately.
They were able to develop this modern application,
very quickly on Azure.
They were able to use container and through AKS,
running on top of Nvidia GPUs.
And we were able to work closely with them
to find cost-effective solutions
that could scale and deliver fast results
by looking at a combination of CPUs and GPUs.
They used Azure Machine Learning
to predictably spin up infrastructure,
and then use Azure Cosmos DB for test results and request.
In doing so, they have a service now
that can run thousands of tests a day
and can scale to 30 to 40 million per week.
Really exciting to see how our customers use the power
of Azure to invent with purpose.
Going forward, we remain committed to our customers.
We wanna make sure we continue to deliver a platform
that is future ready.
So tomorrow, when your application can benefit
from quantum computing, you can have it available on Azure.
We wanna continue to build a platform
that is built around your terms
and focused on your developers.
Well, we are very committed to a hybrid environment,
and we understand that some workloads will stay on-premise,
and some workloads can seamlessly move on to cloud.
When you are ready to move those workloads to cloud,
we wanna make sure that we continue to make investments,
so you can trust your cloud provider.
I'm sure you have more questions
or wanna learn more about our offerings,
here are some easy handy links.
Feel free to explore at your leisure.
If you wanna connect with one of us
or one of our partners,
you will find information to on these links too.
Once again, thank you so much for the time.
(gentle music)