DUSTIN KIRKLAND: Hello.
My name's Dustin Kirkland.
I'm a product manager at Google, and I'm
joined here by Lukas Heinrich.
He's a physicist at CERN and has a pretty exciting experiment
that he reproduced using cloud technology.
And he's going to tell us a bit about that.
LUKAS HEINRICH: Yeah, so during the keynote,
we rediscovered the Higgs boson using a publicly available data
set.
And during a keynote, we launched 25,000 workloads
on the public cloud to analyze collision data
of the Large Hadron Collider.
And there, we selected events where
we had a pretty good chance that the Higgs boson was originally
there.
And during the keynote, you could see a chart building up
where, in the middle of the chart,
there was a small peak forming.
And that peak, more or less, proved the existence
of the Higgs boson.
DUSTIN KIRKLAND: That's fascinating.
So you work on the ATLAS project.
And with the ATLAS project, you're working on some data
from CERN.
And I understand this has some parallels to what we do
in the open source community.
LUKAS HEINRICH: Right.
So many of the experiments at CERN
have an open data policy, which means that data
that we record, we release it openly.
So I work with the ATLAS project,
but the data that we used in the demo
was from the CMS project, which is a project that
is very similar to mine.
And it kind of exemplifies that even
me, who is not a member of the CMS project,
I can analyze this data from them, because they released it
openly.
And in fact, thanks to these cloud resources,
anybody can do it.
One of the challenges in open data for these large scale
scientific collaborations, is always
that the scale of the data is very large.
And while we can release it, it was always
a bit of a question of who can practically analyze it
who doesn't have a data center.
DUSTIN KIRKLAND: And how large is practically large?
LUKAS HEINRICH: The data set we analyzed yesterday
in the span of a few minutes was 70 terabytes.
DUSTIN KIRKLAND: 70 terabytes.
It's not even practical for me to download that and try
to store that at home on a laptop in a university.
LUKAS HEINRICH: Exactly.
DUSTIN KIRKLAND: But this can be found now in the cloud.
LUKAS HEINRICH: Right.
So we have it available at CERN, the open data.
And then we transfer it, because we have high speed links
to the cloud providers.
We could transfer it into Google Cloud Storage.
And this is from where we read the data to analyze it.
And now anybody could, in principle, do that.
DUSTIN KIRKLAND: So a student, another university,
another organization, anyone can work on that data
without downloading it.
LUKAS HEINRICH: Right.
DUSTIN KIRKLAND: And where do you see this going next?
Give us some idea of how these methods and Google Cloud
can be used for your research.
LUKAS HEINRICH: So I think what's super exciting,
is that this open data originally oftentimes
was seen as something for outreach or educational
purposes that you have really small scale data sets,
but now researchers are starting to really use it
for real scientific applications outside of the experiments.
So people who have nothing to do with the Large Hadron Collider,
they can analyze this data that we release openly.
I think that is pretty exciting.
DUSTIN KIRKLAND: Cool.
And what's your next project?
LUKAS HEINRICH: So one thing that I really want to try out
in the cloud ecosystem is functions-as-a-service.
So this is something that kind of fits well onto our work
that we showed yesterday.
And so we'll maybe get back home and try that out.
DUSTIN KIRKLAND: Yeah, Cloud Run, Knative,
on top of Kubernetes.
LUKAS HEINRICH: Exactly.
DUSTIN KIRKLAND: Tell me a little bit
about your next project.
What are you working on next?
And how do you see using Google Cloud?
LUKAS HEINRICH: So I think what is
pretty exciting is the release of Cloud Run a couple of weeks
ago.
And so the workload that we showed yesterday during
the demo actually would fit nicely into this
functions-as-a-service paradigm.
And maybe when we get home, we'll
try this out and see whether we can run the demo in this way.
DUSTIN KIRKLAND: Yeah, that's fascinating.
I would imagine those batch-type services really match
to the work that you're doing.
LUKAS HEINRICH: It's all the data transformations
where, so the data that we download is in one form,
and then we process it and produce a summary format.
And so this is essentially a function.
DUSTIN KIRKLAND: Thank you, Lukas.
I appreciate your time.
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