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LITAL LEVY: A lot of people know Google for its search tools
and software, like Android, but did
you know that Google also develops
its own dedicated hardware?
Google makes its own chips-- no, not the potato ones,
but computer chips that we use in our machine learning
supercomputers, Pixel phones, network infrastructure,
and even in YouTube as the video accelerators that
uploaded this video were designed and created in-house
at Google.
All of these chips, from servers to supercomputers,
rely on Google Cloud.
Hi, I'm Lital Levy, a product marketing manager here
at Google, and today, we are discussing
how Google's chip design and development
group leverages Google Cloud.
With me to tell the story, we have
Matt Dau and Arsalan Farooq.
Matt, Arsalan, please introduce yourselves.
MATT DAU: Hi, my name is Matt Dau.
I'm an engineering manager for the team that
builds the infrastructure that Google
uses to design and build chips.
This used to be tightly integrated
with Google's internal software development environment,
but we are now launching a system that is entirely
run on Google Cloud.
ARSALAN FAROOQ: Hello, I'm Arsalan Farooq.
I run Alphabet Cloud, which is a team within Google Cloud.
Our mission is to help teams with Alphabet onboard
onto Google Cloud in a safe, frictionless,
and cost-effective manner.
We regularly work with teams and businesses
across all of Alphabet, including Google, Acquisitions,
Bets, and other teams to help them
avail themselves of the value and innovation cloud
has to offer.
Over the past year, our team has been
working closely with Google's chip design and development
group to migrate them fully onto Google Cloud.
This transformation has been incredible and very
satisfying to watch as the organization has
more than doubled their performance and scale.
LITAL LEVY: Matt, what would you say
is the most challenging aspect of building chips in-house?
MATT DAU: Google has the leading software design environment,
but hardware design has some specific requirements.
We need to be able to run third-party tools
and manage multiple versions of IP.
We have very large workloads and workloads
that need to scale massively, which
can make the process quite challenging.
LITAL LEVY: What was the team using before Google Cloud?
MATT DAU: Before Google Cloud, we
were using a traditional, standard data center
implementation.
We began with a single rack of computers
when the hardware team first started over a decade ago,
which grew to our own data center inside of Google.
As you can imagine, this brought many issues.
It was incredibly expensive to maintain
and required constant costly upgrades
as our machine capacity needed to double every year.
We weren't able to quickly ramp up a new project
because each one required hiring new engineers
and adding new hardware.
We also had to hire people just to manage and repair
legacy machines.
So, for example, our network file system
had performance issues.
And when it broke, it would take large workloads a week just
to get it back up and running again.
As a result, this shifted our priority
away from growth and innovation towards manual infrastructure
management and oversight.
LITAL LEVY: Wow, that sounds incredibly frustrating.
What happened next?
MATT DAU: In the interim, before totally moving to the cloud,
we were using Google's internal software design environment
and would overflow into Cloud.
Although this method is reliable in the short-term,
it still wasn't a viable solution for us in the long run
and brought even more challenges.
It took time to transfer the workload over to cloud,
and engineers wouldn't have immediate access to the results
after execution.
Additionally, engineers also needed
to have two desktops constantly running,
one for their design environment and the other
for the results in cloud.
LITAL LEVY: Yikes, that does not sound fun.
I'm assuming that's when you found the Alphabet Cloud team
and reached out to Arsalan?
ARSALAN FAROOQ: Yes, exactly.
This is the exact sort of scenario
that our team helps with.
So when we get a call from Matt's team,
we knew our team would be able to assist them migrating fully
onto Google Cloud.
MATT DAU: With Alphabet Cloud, we
started with their intake form, and that
led to us doing a full-spectrum architectural needs
assessment, the main need being a reliable infrastructure
with features that allowed us to scale massively.
ARSALAN FAROOQ: Yeah, so with the assessment,
we were able to evaluate which Google Cloud services would
be needed and quickly had a path forward.
LITAL LEVY: Great.
So now with the plan ready, how did the actual migration go?
What services did the team need to make the project a reality?
ARSALAN FAROOQ: Yeah, so working alongside Matt and his team,
we confirmed they needed to use Google Kubernetes
Engine for the containers, Cloud Storage, Filestore, Cloud
Spanner, BigQuery, and Pub/Sub.
MATT DAU: Yeah, through this collaboration
between my team and Alphabet Cloud,
we were able to move our workloads to GKE.
Once we were in the cloud, we took the opportunity
to identify a better machine platform, N1 to C2,
and quickly moved the workloads within just a week.
As a result, we could run jobs two times faster,
faster than before.
ARSALAN FAROOQ: It really was remarkable to see
this quick transition.
In the old operating method, this would have been,
what, a two-year program?
MATT DAU: Exactly.
Or, rather, we wouldn't have been
able to execute a move like this because it's effectively
building a new data center.
Between the general cost of that,
along with the complexity and productivity
disruptions that it would create,
we haven't even thought about attempting it.
Yet, with Google Cloud, we can simply
flip a switch through a couple of Terraform switches,
and it works immediately.
LITAL LEVY: That's amazing.
So what are the business benefits
you've seen since moving to Google Cloud?
MATT DAU: It's been night and day.
For instance, the lead times to provision new hardware
has gone from six months to just a few days.
As an infrastructure team, we have a small team that can now
manage a much bigger footprint.
We're able to grow far more quickly.
And if any bugs are found, they are
debugged within hours, which was not previously possible.
ARSALAN FAROOQ: It really has been so gratifying
watching the team reap the benefits of the cloud.
Matt, what are some examples of the chips
your team has launched since you started using Google Cloud?
MATT DAU: Some examples include the last two generations
of the TPUs, the networking chip we just announced,
Aquila, and the Argos VCU chip, which
is used in YouTube for video compression, which
is 20 to 33 times more efficient than conventional CPUs.
LITAL LEVY: Wow, that's amazing.
Looking into the future, what's in store for the team?
ARSALAN FAROOQ: Well, we're continuing
to work closely with chip development infrastructure team
to drive improvements in Google Cloud-- scheduling,
resource allocation, scaling.
We can say now that all Google chip design projects
are using Google Cloud.
MATT DAU: Additionally, we plan to open source
our processes so other chip manufacturers can benefit
from all the services we use in Google Cloud,
specifically our Bazel build rules and regression systems.
LITAL LEVY: Any predictions for how
this will impact the chip design space moving forward?
MATT DAU: With Google Cloud's AI and data capabilities,
we'll be able to anticipate resource usage
and use less resources as a result.
Compute design and development will continue
to improve in its efficiency.
LITAL LEVY: Thanks, Matt and Arsalan, for joining us.
If you're interested in learning more about Google Cloud,
visit cloud.google.com.
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