(upbeat music)
- Hi, my name's Tim Carroll.
I lead the Azure efforts to support the research community
around HPC, AI,
and the other sort of workloads
that the research community requires
in order to do the important work that they do.
When we talk about the research community,
I think it's important to point out
that we mean research scientists,
the computational scientists
who are supporting the domain scientists,
the institutions within which they work,
and then the organizations within those institutions
who have to support them.
And that's a broad variety of interests,
requirements, and business processes
that we need to work through
in order to make sure that the research community
gets what they need in order to continue doing
the important work that they do.
And that's our mission, and that's my job.
Along those lines,
what I'd like to do today,
no slides, no fancy PowerPoint.
I think what's important today is we take a few minutes
and just talk about what's the impact
that the research computing community is having on cloud,
not just what's the impact that cloud
is having on the research community.
And I'd like to cite two examples
that I think are very important
because one, they're at opposite ends
of the spectrum in terms of an individual research team
and how they've been able to take advantage of this,
but then a broader community.
And we can start to see some of the commonalities
and hopefully by giving a slightly different perspective,
maybe, than you've heard to this point,
that what we can do is at the end of this,
I can give you something that you can do
to go figure out how to get started in this
in a more meaningful way
other than just being in the place
that most people are in today,
which is, "Hey, I know cloud is going to be important
to what I'm doing,
whether I'm in the commercial space
or I'm on the public research side.
And I know it's going be great,
but I don't understand exactly how to get started,
given that we've done this a certain way
for the last 15 or 20 years and gotten very good at it,
but we're going to need to think of some
things differently."
So in terms of thinking of some things differently,
let me just give you a scenario.
So very early in the pandemic,
there was a researcher at Duke who understood that
a ventilator is going to be in short supply.
And one of those potential solutions was going to be
that people were going to split ventilators.
Bear in mind that a ventilator is a physical device.
It's a mechanical device.
It's not like sharing your cell phone charger.
There are two people that are attached to this
as air is being forced in and out of lungs
in order to help people breathe.
And there needs to be a way to optimize,
to ensure that both of those patients
are getting the optimal health outcomes.
And so the researcher at Duke set about figuring out
if there was a device that could help with that.
In order to get there,
there was going to have to be a fair amount of modeling
and simulation that went into place.
And it was going to require millions of simulations
in order to do so.
And those millions of simulations were going
to require probably
somewhere in the order of 800,000 to a million core hours.
The challenge is they had less than two weeks
to get all of that pulled together
in order to be able to submit to the FDA,
in order to get FDA emergency approval for their device
so that they could use this device
in order to help people more readily.
Duke went to their internal HPC infrastructure,
Charley Kneifel, and said,
"Hey, do we have the ability to do this?"
And the answer was going to be yes,
but it would be extraordinarily intrusive
to the rest of the organization,
who was also doing critical research.
And so Amanda Randles, who was the principal investigator,
submitted to the COVID-19 HPC Consortium,
which was a group of vendors that had gotten together
and were going to pool computational resources
in order to help efforts like this.
When the request came in,
we at Microsoft saw that this was an opportunity
that we knew we could do well with and help out.
So we took it on, reached out.
The submission was made on a Monday. By Wednesday night,
we were talking to the folks from Duke.
Thursday and Friday, we spent prepping the workflow,
and we were able to run 800,000 compute hours
across close to 25,000 VMs,
virtual machines, over the weekend
so that they had the results that they needed
by Monday morning in order to prepare
the submission for the FDA,
which they then submitted the following Thursday.
And so what was significant about that
was not that it was 800,000 cores or 25,000 VMs.
It is from the time that somebody said,
"Hey, there is this thing,
there is this important piece of work we could do,
but for having access to the infrastructure."
We were able to get them access to the infrastructure,
access to the people at Microsoft
that could help them very quickly
get that workflow optimized for cloud.
And then we were able to get the infrastructure
that was required to go execute about it.
So this was all inside of a 10-day period,
which is, as we start looking
at what's going to happen going forward,
those are the timeframes that people are going to need
in terms of turnaround.
And so it's almost,
as you start thinking about
HPC becoming real time or operational,
which is not necessarily the way that we had done it
for the last 15 or 20 years,
where we were very batch-oriented.
And we thought in terms of grant cycles,
which were 6 months or 18 months or whatever else it was.
So in the other end of the spectrum,
Duke was one team with a very specific problem
that we were able to move in and help.
On the other side, was a workflow
that we ourselves did.
We had somebody who's on the Microsoft team
who was not a meteorologist,
but was just interested in seeing how WRF
which is the,
probably the most ubiquitous numerical
weather prediction code in the industry
or in the research community,
see how that would scale on Azure.
You're typically used to seeing a lot of scaling exercises
in maybe the 20,000 or 30,000 core range,
maybe up to 50,000 cores
in some of the very largest centers.
And over the course of about five or six days,
we were able to scale it up to over 80,000 cores
and do so at about 76% efficiency,
which is near-bare-metal-like performance.
And so we were able to scale to a far greater extent
than anything that we're aware of
having been run internally.
But we're also able to do so at efficiencies
and performance points that looked very much
like the on-prem that it was used to.
And while that's cool,
if you do what I do for a living,
what's more impactful to the research community
is that the WRF community
and WRF's an open-source code,
is made up of 30,000 users spread across 150 countries.
And so where most of the reports that we went back and read
to figure out who had run WRF at that kind of scale,
were around the very largest centers,
which means people had to get in job queues
and the other things that are associated
with needing to do a very large run like that:
A, being able to get access, and then B,
how long it takes to get that access and get it executed.
The significant thing about what we did relative
to this benchmark is that every tool that we used
is available on azure.com.
The data set that we used is a publicly available data set
around Hurricane Maria, from several years ago.
So all of the meteorological data that was connected
is what we used to then simulate the WRF run.
So what that means is that we've given this capability
to run bigger than anybody's ever run before.
And we didn't give it to five people.
We gave it to the 30,000 people who use this tool
as part of their job.
And we gave it to people across 150 countries.
So that of that group,
anyone with an internet connection and an Azure subscription
has the ability to do that kind of work.
So think about the impact that that has
on individual researchers, institutions,
in terms of not being afraid to go after any piece of work,
knowing that the capability is there to do so,
whenever you need it,
wherever you need it.
And this has always been an industry, a community,
a group where the technology shifts have been driven
not by the vendors and not by the center directors.
They were always driven by the scientists,
people that needed more in order to solve problems,
and they are going to find their way to do it.
So if I have a suggestion
that anyone who's trying to figure out
where to start in this continuum,
do not start at "What is my cloud strategy?"
Don't start at the top and try
and figure out what the overarching pillars are
and then work down to the individual researchers.
Start at the science and work forward.
Take a workflow,
whether it's simulating split ventilators,
whether it's running a WRF model,
but take that workflow.
Work with the folks at Microsoft to see exactly
what that looks like, how it runs on Azure.
And when you get to the point where you've seen
how efficiently it's run,
you now know exactly how much it costs,
what it took to get there, how long it took to do.
And those are the pieces that will then inform your strategy
moving forward.
So my ask, if you have something that you would like to do,
something that would be valuable to do,
please reach out to your Microsoft team.
Your Microsoft team knows exactly
how to find me and my team.
And I look forward to helping you
or your organization moving forward in the future.
Best of luck with everything.
(upbeat music)