DreamWorks Animation Crafts its Next Era of Dynamic IT Infrastructure
DreamWorks Animation Crafts its
Next Era of Dynamic IT Infrastructure
Transcript of a discussion on how an innovative entertainment company leads the
charge for multi-purpose IT-as-a-service capabilities.
Listen to the podcast. Find it on iTunes. Get the mobile app. Download the
transcript. Sponsor: Hewlett Packard Enterprise.
Dana Gardner: Welcome to the next edition of the BriefingsDirect Voice of the
Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions,
your host and moderator for this ongoing discussion on digital transformation success
stories. Stay with us now to learn how agile businesses are fending off disruption -- in
favor of innovation.
Our next thought leader interview examines how DreamWorks Animation is building a
multipurpose, all-inclusive, and agile data center capability. We’ll now learn why a new
era of responsive and dynamic IT infrastructure is demanded, and how one high-
performance digital manufacturing leader aims to get there sooner rather than later.
Here to describe how an entertainment industry innovator leads the charge for bleeding-
edge IT-as-a-service capabilities is Jeff Wike, CTO of DreamWorks Animation in
Glendale, California. Welcome, Jeff.
Jeff Wike: Thank you for having me.
Gardner: Tell us why the older way of doing IT infrastructure
and hosting apps and data just doesn't cut it anymore. What
has made that run out of gas?
Wike: You have to continue to improve things. We are in a
world where technology is advancing at an unbelievable pace.
The amount of data, the capability of the hardware, the
intelligence of the infrastructure are coming. In order for any
business to stay ahead of the curve -- to really drive value into
the business – it has to continue to innovate.
Gardner: IT has become more pervasive in what we do. I have heard you refer to
yourselves as digital manufacturing. Are the demands of your industry also a factor in
making it difficult for IT to keep up?
Wike: When I say we are a digital manufacturer, it’s because we are a place that
manufactures content, whether it's animated films or TV shows; that content is all made
on the computer. An artist sits in front of a workstation or a monitor and is basically
building these digital assets that we put through simulations and rendering so in the end
it comes together to produce a movie.
That's all about manufacturing, and we actually have a pipeline, but it's really like an
assembly line. I was looking at a slide today about Henry Ford coming up with the first
assembly line; it's exactly what we are doing, except instead of adding a car part, we are
adding a character, we’re adding a hair to a character, we’re adding clothes, we’re
adding an environment, and we’re putting things into that environment.
We are manufacturing that image, that story, in a linear way, but also in an iterative way.
We are constantly adding more details as we embark on that process of three to four
years to create one animated film.
Gardner: Well, it also seems that we are now taking that analogy of the manufacturing
assembly line to a higher plane, because you want to have an assembly line that doesn't
just make cars, it can make cars and trains and submarines and helicopters, but you
don't have to change the assembly line, you have to adjust and you have to utilize it
So it seems to me that we are at perhaps a cusp in IT where the agility of the
infrastructure and its responsiveness to your workloads and demands is better than ever.
Greater creativity, increased efficiency
Wike: That's true. If you think about this animation process or any digital manufacturing
process, one issue that you have to account for is legacy workflows, legacy software,
and legacy data formats -- all these things are inhibitors to innovation. There are a lot of
tools. We actually write our own software, and we’re very involved in projects related to
computer science at the studio.
We’ll ask ourselves, “How do you innovate? How can you change your environment to
be able to move forward and innovate and still carry around some of those legacy
And one of the things we’ve done over the past couple of years is start to re-architect all
of our software tools in order to take advantage of massive multi-core processing to try
to give artists interactivity in their creative process. It’s about iterations. How many things
can I show a director, how quickly can I create the scene to get it approved so that I can
hand it off to the next person, because there's two things that you get out of that.
One, you can explore more and you can add more creativity. Two, you can drive
efficiency, because it's all about how much time, how many people are working on a
particular project and how long does it take, all of which drives up the costs. So you now
have these choices where you can add more creativity or -- because of the compute
infrastructure -- you can drive efficiency into the operation.
So where does the infrastructure fit into that, because we talk about tools and the ability
to make those tools quicker, faster, more real-time? We conducted a project where we
tried to create a middleware layer between running applications and the hardware, so
that we can start to do data abstraction. We can get more mobile as to where the data is,
where the processing is, and what the systems underneath it all are. Until we could
separate the applications through that layer, we weren’t really able to do anything down
at the core.
Core flexibility, fast
Now that we have done that, we are attacking the core.
When we look at our ability to replace that with new
compute, and add the new templates with all the security
-- we want that in our infrastructure. We want to be able to
change how we are using that infrastructure -- examine
usage patterns, the workflows -- and be able to optimize.
Before, if we wanted to do a new project, we’d say, “Well,
we know that this project takes x amount of infrastructure.
So if we want to add a project, we need 2x,” and that
makes a lot of sense. So we would build to peak. If at
some point in the last six months of a show, we are going
to need 30,000 cores to be able to finish it in six months, we say, “Well, we better have
30,000 cores available, even though there might be times when we are only using
12,000 cores.” So we were buying to peak, and that’s wasteful.
What we wanted was to be able to take advantage of those valleys, if you will, as an
opportunity -- the opportunity to do other types of projects. But because our
infrastructure was so homogenous, we really didn't have the ability to do a different type
of project. We could create another movie if it was very much the same as a previous
film from an infrastructure-usage standpoint.
By now having composable, or software-defined infrastructure, and being able to
understand what the requirements are for those particular projects, we can recompose
our infrastructure -- parts of it or all of it -- and we can vary that. We can horizontally
scale and redefine it to get maximum use of our infrastructure -- and do it quickly.
Gardner: It sounds like you have an assembly line that’s very agile, able to do different
things without ripping and replacing the whole thing. It also sounds like you gain
infrastructure agility to allow your business leaders to make decisions such as bringing in
new types of businesses. And in IT, you will be responsive, able to put in the apps, and
manage those peaks and troughs.
Does having that agility not only give you the ability to make more and better movies
with higher utilization, but also give more wings to your leaders to go and find the right
business models for the future?
Wike: That’s absolutely true. We certainly don't want to ever have a reason to turn down
some exciting project because our digital infrastructure can’t support it. I would feel really
bad if that were the case.
In fact, that was the case at one time, way back when we produced Spirit: Stallion of the
Cimarron. Because it was such a big movie from a consumer products standpoint, we
were asked to make another movie for direct-to-video. But we couldn't do it; we just
didn’t have the capacity, so we had to just say, “No.” We turned away a project because
We want to be able to
change how we are
workflows -- and be
able to optimize.
we weren’t capable of doing it. The time it would take us to spin up a project like that
would have been six months.
The world is great for us today, because people want content -- they want to consume it
on their phone, on their laptop, on the side of buildings and in theaters. People are
looking for more content everywhere.
Yet projects for varied content platforms require different amounts of compute and
infrastructure, so we want to be able to create content quickly and avoid building to
peak, which is too expensive. We want to be able to be flexible with infrastructure in
order to take advantage of those opportunities.
Gardner: How is the agility in your infrastructure helping you reach the right creative
balance? I suppose it’s similar to what we did 30 years ago with simultaneous
engineering, where we would design a physical product for manufacturing, knowing that
if it didn't work on the factory floor, then what's the point of the design? Are we doing that
with digital manufacturing now?
Artifact analytics improve usage, rendering
Wike: It’s interesting that you mention that. We always
look at budgets, and budgets can be money budgets,
they can be rendering budgets, they can be storage
budgets, and networking -- I mean all of those things are
commodities that are required to create a project.
Artists, managers, production managers, directors, and
producers are all really good at managing those projects
if they understand what the commodity is. Years ago we
used to complain about disk space: “You guys are using
too much disk space.” And our production department
would say, “Well, give me a tool to help me manage my
disk space, and then I can clean it up. Don’t just tell me
it's too much.”
One of the initiatives that we have incorporated in recent
years is in the area of data analytics. We re-architected
our software and we decided we would re-instrument everything. So we started
collecting artifacts about rendering and usage. Every night we ran every digital asset that
had been created through our rendering, and we also collected analytics about it. We
now collect 1.2 billion artifacts a night.
And we correlate that information to a specific asset, such as a character, basket, or
chair -- whatever it is that I am rendering -- as well as where it’s located, which shot it’s
in, which sequence it’s in, and which characters are connected to it. So, when an artist
wants to render a particular shot, we know what digital resources are required to be able
to do that.
We always look at
budgets can be
money budgets, they
can be rendering
budgets, they can be
storage budgets, and
networking -- all of
those things are
commodities that are
required to create a
One of the things that’s wasteful of digital resources is either having a job that doesn't fit
the allocation that you assign to it, or not knowing when a job is complete. Some of
these rendering jobs and simulations will take hours and hours -- it could take 10 hours
At what point is it stuck? At what point do you kill that job and restart it because
something got wedged and it was a dependency? And you don't really know, you are just
watching it run. Do I pull the plug now? Is it two minutes away from finishing, or is it
never going to finish?
Just the facts
Before, an artist would go in every night and conduct a test render. And they would say,
“I think this is going to take this much memory, and I think it's going to take this long.”
And then we would add a margin of error, because people are not great judges, as
opposed to a computer. This is where we talk about going from feeling to fact.
So now we don't have artists do that anymore, because we are collecting all that
information every night. We have machine learning that then goes in and determines
requirements. Even though a certain shot has never been run before, it is very similar to
another previous shot, and so we can predict what it is going to need to run.
Now, if a job is stuck, we can kill it with confidence. By
doing that machine learning and taking the guesswork
out of the allocation of resources, we were able to save
15 percent of our render time, which is huge.
I listened to a gentleman today talk about what a
difference of one percent improvement would be. So 15
percent is huge, that's 15 percent less money you have
to spend. It's 15 percent faster time for a director to be
able to see something. It's 15 percent more iterations.
So that was really huge for us.
Gardner: It sounds like you are in the digital manufacturing equivalent of working
smarter and not harder. With more intelligence, you can free up the art, because you
have nailed the science when it comes to creating something.
Creative intelligence, at the edge, having fun
Wike: It's interesting; we talk about intelligence at the edge and the Internet of Things,
and that sort of thing. In my world, the edge is actually an artist. If we can take
intelligence about their work, the computational requirements that they have, and if we
can push that data -- that intelligence -- to an artist, then they are actually really, really
good at managing their own work.
It's only a problem when they don't have any idea that six months from now it's going to
cause a huge increase in memory usage or render time. When they don't know that, it's
hard for them to be able to self-manage. But now we have artists who can access
Tableau reports everyday and see exactly what the memory usage was or the compute
usage of any of the assets they’ve created, and they can correct it immediately.
By doing that machine
learning and taking
the guesswork out of
the allocation of
resources, we were
able to save 15
percent of our render
time, which is huge.
On Megamind, a film DreamWorks Animation released several years ago, it was prior to
having the data analytics in place, and the studio encountered massive rendering spikes
on certain shots. We really didn't understand why.
After the movie was complete, when we could go back and get printouts of logs to
analyze, we determined that these peaks in rendering resources were caused by the
main character’s watch. Whenever his watch was in a frame, the render times went up.
We looked at the models, and well-intended artists had taken a model of a watch and
every gear was modeled, and it was just a huge, heavy asset to render.
But it was too late to do anything about it. But now, if an artist were to create that watch
today, they would quickly find out that they had really over-modeled that watch. We
would then need to go in and reduce that asset down, because it's really not a key
element of the story. And they can do that today, which is really great.
Gardner: I am a big fan of animated films, and I am so happy that my kids drag me to
see them because I enjoy them as much as they do. When you mention an artist at the
edge, it seems to me it’s more like an army at the edge, because I wait through the end
of the movie, and I look at the credits scroll -- hundreds and hundreds of people at work
putting this together.
So you are dealing with not just one artist making a decision, you have an army of
people. It's astounding that you can bring this level of data-driven efficiency to it.
Movie-making’s mobile workforce collaboration
Wike: It becomes so much more important, too, as we become a more mobile
Now it becomes imperative to be able to obtain the
information about what those artists are doing so that
they can collaborate. We know what value we are really
getting from that, and so much information is available
now. If you capture it, you can find so many things that
we can really understand better about our creative
process to be able to drive efficiency and value into the
Gardner: Before we close out, maybe a look into the
crystal ball. With things like auto-scaling and
composable infrastructure, where do we go next with
computing infrastructure? As you say, it's now all these
great screens in people's hands, handling high-definition, all the networks are able to
deliver that, clearly almost an unlimited opportunity to bring entertainment to people.
What can you now do with the flexible, efficient, optimized infrastructure? What should
If you capture
information, you can find
so many things that we
can really understand
better about our creative
process to be able to
drive efficiency and
value into the entire
Wike: There's an explosion in content and an explosion in delivery platforms. We are
exploring all kinds of different mediums. I mean, there’s really no limit to where and how
one can create great imagery. The ability to do that, the ability to not say “No” to any
project that comes along is going to be a great asset.
We always say that we don't know in the future how audiences are going to consume
our content. We just know that we want to be able to supply that content and ensure that
it’s the highest quality that we can deliver to audiences worldwide.
Gardner: It sounds like you feel confident that the infrastructure you have in place is
going to be able to accommodate whatever those demands are. The art and the
economics are the variables, but the infrastructure is not.
Wike: Having a software-defined environment is essential. I came from the software
side; I started as a programmer, so I am coming back into my element. I really believe
that now that you can compose infrastructure, you can change things with software
without having to have people go in and rewire or re-stack, but instead change on-
demand. And with machine learning, we’re able to learn what those demands are.
I want the computers to actually optimize and compose
themselves so that I can rest knowing that my
infrastructure is changing, scaling, and flexing in order
to meet the demands of whatever we throw at it.
Gardner: I’m afraid we’ll have to leave it there. We
have been examining how DreamWorks Animation is
building a multipurpose, all-inclusive and agile data
center capability for now -- and the future. And we have
learned how one high-performance digital
manufacturing innovator is leading the charge for
bleeding edge IT-as-a-service agility.
Please join me in thanking our guest, Jeff Wike, CTO of
DreamWorks Animation in Glendale, California.
Wike: Thanks, Dana.
Gardner: And a big thank you to our audience as well for joining this BriefingsDirect
Voice of the Customer digital transformation success story. I’m Dana Gardner, Principal
Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard
Thanks again for listening. Feel free to pass this on to your IT community, and do come
back next time.
Listen to the podcast. Find it on iTunes. Get the mobile app. Download the
transcript. Sponsor: Hewlett Packard Enterprise.
Transcript of a discussion on how an innovative entertainment company leads the
charge for multi-purpose IT-as-a-Service capabilities. Copyright Interarbor Solutions,
LLC, 2005-2017. All rights reserved.
I want the computers to
actually optimize and
so that I can rest
knowing that my
changing, scaling, and
flexing in order to meet
the demands of
whatever we throw at it.
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