>> Voice-over: theCUBE presents HPE Discover 2022.
Brought to you by HPE.
>> Hey everyone, welcome back to the CUBE's coverage
of HPE Discover '22.
Live from the Sands Expo Center in Las Vegas.
Lisa Martin here with Dave Vellante.
We've an alumni back joining us
to talk about high performance computing and AI.
Justin Hotard, EVP and General Manager of HPC and AI at HPE.
That's a mouthful. Welcome back.
>> It is.
No, it's great to be back.
And wow, it's great to be back in person as well.
>> It's life changing to be back in person.
The keynote this morning was great.
Dave was saying, the energy that he's seen
is probably the most out of any Discover that you've been at
and we've been feeling that and it's only day one.
>> Yeah, I agree.
And I think it's a testament to the places in the market
that we're leading the innovation, we're driving.
I mean, obviously the leadership in HPE GreenLake
and enabling as a service for every customer,
not just those in the public cloud,
providing that capability.
And then obviously what we're doing at HPC and AI
breaking records and advancing the industry.
>> I just saw the Q2 numbers.
Nice revenue growth there for HPC and AI.
Talk to us about the lay of the land.
What's going on?
What are customers excited about?
>> It's a really fascinating time in this business
because we just delivered the world's first exascale system.
And that's a huge milestone for our industry,
a breakthrough.
13 years ago we did the first petascale system.
Now we're doing the first exascale system.
Huge advance forward.
But what's exciting too is these systems are enabling
new applications, new workloads, breakthroughs in AI,
the beginning of being able to do proper quantum simulations
which will lead us to a much brighter future with quantum
and then actually better and more granular models,
which have the ability to really change the world.
>> I was telling Lisa that during the pandemic,
we did exascale day.
It was like this co-produce event.
And we weren't quite at exascale yet,
but we could see it coming.
And so it was great to see the Frontier and the keynote,
you guys broke through that.
Is that a natural evolution of HPC,
or are we entering a new era?
>> I think it's a new era.
And I think it's a new era for a few reasons
because that breakthrough really,
it starts to enable a different class of use cases.
And it's combined with the fact that I think
you look at where the rest
of the enterprises dataset has gone.
We've got a lot more data, a lot more visibility to data,
but we don't know how to use it.
And now with this computing power,
we can start to create new insights and new applications.
And so I think this is going to be a path
to making HPC more broadly available.
And of course it introduces AI models at scale.
And that's really critical cause AI is a buzzword.
I mean, lots of people say they're doing AI.
But to build true intelligence,
not effectively a machine that learns data
and then can only handle that data,
but to build true intelligence where you've got something
that can continue to learn
and understand and grow and evolve,
you need this class of system.
And so I think we're at the forefront
of a lot of exciting innovation.
>> In terms of innovation,
how important is it that you're able to combine
as a service and HPC?
What does that mean for customers,
for experimentation and innovation?
>> A couple things I've actually been talking to customers
about that over the last day and a half.
And one is you think about these systems are,
they're very large they're pretty big bets
if you're a customer.
So getting early access to them is really key.
Making sure that they can migrate
their software, their applications.
Again, in our space, most of our applications
are custom-built, whether you're a government
or a private sector company
that's using these systems,
you doing these are pretty specialized.
So getting that early access is important.
And then actually what we're seeing is with the growth
and explosion of insight that we can enable.
And some of the diversity of new accelerator partners
and new processors that are on the market
is actually the attraction of diversity,
and so making things available where customers
can use multimodal systems.
And we've seen that in this era,
like our customer LUMI in Finland.
The number three fastest system in the world
actually has two sides to their system.
So there's a compute side,
dense compute side and a dense accelerator side.
>> So Oak Ridge National Labs was on stage
with Antonio this morning talking about Frontier,
the Frontier system.
I thought what a great name, very apropos.
But it was also just named,
the number one to the supercomputing Top500.
That's a pretty big accomplishment.
Talk about the impact of what that really means.
>> I think a couple things.
First of all, anytime you have
this breakthrough of number one,
you see a massive acceleration of applications.
And if you look at the applications that were built,
because when the US Department of Energy
funded these exascale products or platforms,
they also funded set of applications.
And so it's the ability to get more accurate Earth models
for long term climate science.
It's the ability to model the electrical grid
and understand better how to build resiliency
into that grid.
Dr. Tourassi talked about a progressing cancer research
and cancer breakthroughs.
There's so many benefits to the world
that we can bring with these systems.
That's one element.
The other big part of this breakthrough
is actually a lesser known list from the Top500
called the Green500.
And that's where we measure performance
over power consumption.
And what's a huge breakthrough in this system
is that not only did Frontier debut
at number one on the Top500,
it's actually got the top two spots
because it's got a small test system that also is up there,
but it's got the top two spots on the Green500.
And that's actually a real huge breakthrough
because now we're doing a ton more computation
at far lesser power.
And that's really important
'cause you think about these systems,
ultimately, you can't continue to consume power linearly
with scaling up performance.
There's a huge issue on impact on our environment,
but it's the impact to the power grid.
It's the impact to heat dissipation.
There's a lot of complexities.
So this breakthrough with Frontier also enables us,
no pun intended, to really accelerate the capacity
and scale of these systems and what we can deliver.
>> It feels like we're entering a New Renaissance of HPC.
I'm old enough to remember.
It was wasn't until recently my wife,
not recently, maybe five, six years ago,
my wife threw out my green thinking machines T-shirt
that Danny Hillis gave.
You guys probably both too young to remember,
but you had thinking machines, square research,
convex tried to build a mini computer HPC.
There was a lot of innovation going on around that time
and then it just became too expensive and other things.
The X86 happened.
But it feels like now we're entering a new era of HPC.
Is that valid or is it true?
What's that mean for HPC as an industry and for industry?
>> I think it's a breadth, it's a market that's opening,
and getting much more broader,
the number of applications you can run.
And we've traditionally had scientific applications,
obviously there's a ton in energy and physics
and some of the traditional areas
that obviously the department of energy sponsor,
but we saw this with even the COVID pandemic
where our supercomputers were used to identify
the spike protein to help and validate
and test these vaccines
and bring them to market in record time.
We saw some of the benefits of these breakthroughs.
And I think it's this combination
of that we actually have the data.
It's digital, it's captured.
We're capturing it at the edge.
We're capturing it and storing it obviously, more broadly.
So we have the access to the data
and now we have the compute power to run it.
And the other big thing is the techniques
around artificial intelligence.
What we're able to do with neural networks, computer vision,
large language models, natural language processing.
These are breakthroughs that,
one, require these large systems,
but two, as you give them a large systems,
you can actually really enable acceleration
of how sophisticated these applications can get.
>> Let's talk about the impact
of the convergence of HPC and AI.
What are some of the things that you're seeing now,
and what are some of the things that we're going to see?
>> So one thing I like to talk about is,
convergence I think,
sometimes it gets a little bit oversimplified.
It's actually traditional modeling and simulation.
Leveraging machine learning to refine the simulation.
And this is one of the things we talk about a lot in AI.
It's using machine learning to actually create code
in real time rather than humans doing it.
That ability to refine the model as you're running.
So we have an example.
We actually launched an open source solution
called SmartSim.
And the first application of that was climate science.
And what it's doing is it's actually learning the data
from the model as the simulation is running
to provide more accurate climate prediction.
But you think about that,
that could be run for anything that has a complex model.
You could run that for financial modeling.
You can use AI.
And so we're seeing things like that
and I think we'll continue to see that.
The other side of that is using modeling and simulation
to actually represent what you see in AI.
So we were talking about the grid.
This is one of the exascale compute projects.
You could actually use, once you actually get the data
and you can start modeling the behavior
of every electrical endpoint in a city.
The meter in your house, the substation, the transformers.
You can start measuring the effects of that.
You can then build equations.
Well, once you build those equations,
you can then take a model
'cause you've learned what actually happens
in the real world, build the equation,
and then you can provide that to someone
who doesn't need a exascale supercomputer to run it,
but your local energy company can better understand
what's happening and they'll know,
oh, there's a problem here.
We need to shift the grid or respond more dynamically.
And hopefully that avoids brownouts
or some of the catastrophic outages we've seen.
>> So they can deploy that model,
which inherently has that intelligence
on more cost effective systems
and then apply it to a much broader range.
Do any of those smart simulations on climate
suggest that it's all a hoax?
You don't have to answer that question.
(laughs)
>> The temperature outside Dave
might give you a little bit of an argument to that.
>> Tell us about quantum.
What's your point of view there?
Is it becoming more stable?
What's HPE doing there?
>> So look, I think there's two things
to understand with quantum.
There's quantum hardware.
Fundamentally, that runs very differently
than how we run traditional computers.
And then there's the applications.
And ultimately a quantum application on quantum hardware
will be far more efficient in the future than anything else.
We see the opportunity for much like we see with HPC and AI,
we just talked about, for quantum to be complimentary.
It runs really well with certain applications
that fabricate themselves as quantum problems.
And some great examples are the life sciences,
obviously quantum chemistry,
you see some opportunities in AI
and in other areas where quantum has a very,
it just lends itself more naturally
to the behavior of the problem.
And what we believe is that in the short term,
we can actually model quantum effectively
on these supercomputers.
Because there's not a perfect quantum hardware
replacement over time,
we would anticipate that will evolve
and we'll see quantum accelerators
much like we see AI accelerators today in this space.
So we think it's going to be a natural evolution
in progression, but there's certain applications
that are just going to be solved better by quantum.
And that's the future we'll we'll run into.
>> And you're suggesting, if I understood it correctly,
you can start building those applications
and at least modeling what those applications
look like today with today's technology.
That's interesting because I think it's something
rudimentary compared to quantum as flash storage.
When you got rid of the spinning disc,
it changed the way in which people thought
about writing applications.
So if I understand it,
new applications that can take advantage of quantum
are going to change the way in which developers write,
not one or a zero,
it's one and virtually infinite combinations.
>> And I think that's what's compelling
about the opportunity is that you can,
if you think about a lot of traditional computing industry,
you always had to kind of wait for the hardware to be there
to really write and test the application.
And we even see that with our customers in HPC and AI.
They build a model
and then they actually have to optimize it
across the hardware once they deploy it at scale.
And with quantum what's interesting
is you can actually model and make progress on the software.
And then as the hardware becomes available, optimize it.
That's why we see this, that we talk about this concept
of quantum accelerators as really interesting.
>> What are the customer conversations these days
as there's been so much evolution
in HPC and AI and the technology,
so much change in the world in the last couple of years?
Is it elevating up the C stack
in terms of your conversations
with customers wanting to become familiar
with exascale computing for example?
>> I think two things.
One is we see a real rise in digital sovereignty
and exascale and HPC as a core fundamental foundation.
So you see what Europe is doing
with the Euro HPC initiative as one example.
We see the same kind of leadership coming out of the UK
with the system we deployed with them in ARCHER2.
We've got many customers across the globe
deploying next generation weather forecasting systems.
But everybody feels, they understand the foundation
of having a strong supercomputing
and HPC capability and competence
and not just the hardware,
the software development, the scientific research,
the computational scientists
to enable them to remain competitive economically.
It's important for defense purposes.
It's important for helping their citizens
and providing services and betterment.
So I'd say that's one big theme.
The other one is something Dave touched on before around,
as a service and why we think HP GreenLake
will be a beautiful marriage
with our HPC and AI systems over time,
which is customers also are going to scale up
and build really complex models.
And then they'll simplify them
and deploy them in other places.
And so there's a number of examples.
We see them in places like oil and gas.
We see them in manufacturing
where I've got to build a really complex model,
figure out what it looks like.
Then I can reduce it to a certain equation
or application that I can then deploy.
So I understand what's happening and running
because, of course, as much as I would love it,
you're not going to have every enterprise around the world
or every endpoint have an exascale system.
So that ability to really provide an as a service element
with HP GreenLake, we think is really compelling.
>> HPE's move into HPC, the acquisitions you've made,
it really have become a differentiator for the company,
hasn't it?
>> Yeah, and I think what's unique about us today
if you look at the landscape
is we're really the only system provider globally.
There are local players that we compete with,
but we are the one true global system provider.
And we're also the only,
I would say the only holistic innovator at the system level
to credit my team on the work they're doing.
But we're also very committed to open standards.
We're investing in a number of places
where we contribute the software assets to open source.
We're doing work with standards bodies to progress
and accelerate the industry and enable the ecosystem.
And I think ultimately, the last thing I'd say
is we are so connected in through the legacy
or the legend of Hewlett Packard Labs,
which now also reports.
And we have these really tight ties into advanced research
and some of that advanced research,
which isn't just around kind of core processing silicon
is really critical to enabling better applications,
better use cases and accelerating the outcomes
we see in these systems going forward.
>> Can you double click on that?
I wasn't aware that it reported into your group.
The roots of HP are invent.
HP labs are renowned.
It kind of lost that formula for a while.
And now it's sounds like it's coming back.
What are some of the cool things
that you guys are working on?
>> Well, let me start with a little bit of recent history.
So we just talked about the exascale program.
That's a great example of where we had
a public private partnership with the department of energy
and it wasn't just that we built a system and delivered it,
but if you go back a decade ago or five years ago,
there were innovations that were built
to accelerate that system.
One is our Slingshot fabric as an example,
which is a core enable
of this accelerated computing environment.
But others in software applications and services
that allowed us to really deliver
a complete solution into the market.
Today, we're looking at things
around trustworthy and ethical AI.
So trustworthy AI in the sense that the models are accurate.
And that's a challenge on two dimensions
'cause one is the model's only as good
as the data's studying.
So you need to validate that the data's accurate
and then you need to really study how do I make sure
that even if the data's accurate,
I've got a model that then is going to predict the right things
and not call a dog, a cat
or a cat, a mouse or whatever that is.
So that's important.
So that's one area.
The other is future system architectures
because as we've talked about before, Dave,
you have this constant tension between the fabric,
the interconnect, the compute and the storage
and constantly balancing it.
And so we're really looking at that.
How do we do more shared memory access?
How do we do more direct rights?
Like looking at some future system architectures
and thinking about that.
And we think that's really really critical in this part
of the business because these heterogeneous systems,
and not saying I'm going to have one monolithic application,
but I'm going to have applications that need to take advantage
of different technologies at different times.
And being able to move that seamlessly
across the architecture.
We think is going to be a part of the hallmark
of the exascale era.
>> Including edge, which is a completely different animal.
I think that's where some disruption is going to bubble up here
in the next decade.
>> Yeah, and that's the last thing I'd say,
is we look at AI at scale, which is another core part
of the business that can run on these large clusters.
That means getting all the way down to the edge
and doing inference at scale.
And inference at scale is, about a month ago,
I was at the World Economic Forum.
We were talking about the space economy
and to me, it's the perfect example of inference
because if you get a set of data that is out at Mars,
it doesn't matter whether you want to push all that data back
to Earth for processing or not.
You don't really have a choice
'cause it's just going to take too long.
>> We don't have that time.
Justin, thank you so much for spending some of your time
with Dave and me talking about what's going on
with HPC and AI.
The Frontier just seems endless and very exciting.
We appreciate your time and your insights.
>> Great, thanks so much.
>> Dave: Thanks, Justin.
>> And don't call a dog a cat.
That I learned from you.
>> A dog at no.
>> Nope, nope.
For Justin and Dave Vellante, I'm Lisa Martin.
You're watching theCUBE's coverage of day one
from HPE Discover '22.
theCUBE is, guess what?
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The leader in live tech coverage.
We'll be right back with our next guest.
(gentle music)