*music*
Herald: Good morning and welcome back to stage one. It's kind of going to be the
second talk about physics on this day already and it's about big data and
science and big data became something like Uber in science. It's everywhere every
discipline has it. Axel Naumann's working for CERN, the accelerator in Switzerland
and he talks about how physics and computing bridge in this area and he works
a lot with ROOT, a program that helps transform data into knowledge. A warm
welcome.
Axel Naumann: Thank you.
*applause*
AN: Thanks a lot. So, well you know, when, when I was discussing this abstract with
the science track people they tell me: "Well, you know about three hundred people
might be in the audience." But well, hey, you are huge that's much more than three
hundred people. So thank you so much for inviting me over it's a real honor. And of
course originally when talking to 300 people are all science interested I
thought you know I pick something fairly narrow focuswise but then I learned I'm
going to be in Saal one and that's different, so I decided to make the scope
a little bit wider and that's what I ended up with. I'll talk a little bit about
CERN in society as well if you so choose, you'll see what that means in a minute. So
the things I'll cover here is obviously CERN just a little bit of an introduction
how we do physics, how we do computing, what data means to us and I can tell you
it means everything, you heard about that already, right? How we do data analysis in
high energy physics and just because we've been doing it for a while and
because I've been doing it for more than ten years, I'm one of the guys who's
providing the software to do data analysis in high energy physics, so, you
know, because we know what we are doing and we have some experience, I thought
maybe you might be interested in hearing what my forecast is for data analysis in
general, in the future. So let's start with CERN. And so if you wonder what CERN
is, you've all heard about CERN, about the fantastic funds we love to use, then
you've probably also heard that we are doing science. We were founded right after
the Second World War or soon after the Second World War, basically as a way to
entertain those freaky scientists. You know that was the idea: peace europewide.
And damn, that's working out really well and so well there's not just Europe
anymore these days. We are located near Geneva, we are doing only fundamental
research, so we don't do any weapons, nuclear stuff you
know, these kind of things. The WWW was invented at CERN but that was just a, you
know, side effect happens sometimes, that we invent things. But usually we just do
science. So what we do is, we take money, lots off, and brains who like to discuss
and think and come up with ideas and from that we generate knowledge. It's really
all about curiosity. The things we try to answer is what is mass? Which is funny
question right? Like we all know what mass is but actually we don't. We know what
mass is in the universe. We understand that masses attract one another: gravity.
Which is beautifully correct. And in the small scale, our particles, we know that
mass is energy and we can't convert them. But we don't understand how these two
things go together. Like there is no bridge, they contradict one another. So we
are trying to understand what that bridge might be. Part of that mass thing is of
course also what's out there in the universe? That's a big question. We only
understand a few percent of that. 90 and some percent are completely unknown to
us, and that's scary right? I mean we know gravity really well, we can deal with
freaky things like black holes and yet we don't understand what's out there. Now to
do all these things we are probing nature at the smallest scale as we call it, so
that's particles, we are dealing with things like the Higgs particle and
supersymmetry. Here's a little bit of a fact sheet. We have about 12,000
physicists who are working with CERN. We are basically the workbench that you saw
in Andre's talk before. We are the table that physicists use, okay? And, so they
come to CERN and once a while about 10,000 physicists a year, or they work
remotely most of the time from about 120 nations. So you're seeing it's not
European anymore, this is a global thing. CERN in itself has about 2,500 employees,
you know those scrubbing the table, setting things up and so on. And our
table is right here. In the far end we have the Alps, it's in Switzerland
as I said, so the Alps are always close, with Mont Blanc, we have the
Lake Geneva we have the Jura, the French Mountains on the lower end here, it's just
beautiful. It's really nice, but we needed to stick a 30-kilometer ring in
there somewhere and people would have hated us had we put it like this. But
luckily people were smart back then in the 70s, and built a tunnel much better. So
now we have this huge tunnel, and we send particles through in both directions near
the speed of light and the tunnel is filled with magnets simply because if you
don't use a magnet the particles will fly straight but we need them to turn around.
Here you see what it's looking like, you also see these big halls there that have
access shafts from the top and that's where the experiments are. That's sort of
a sketch of one of the experiments. So the the LHC is one of the, no, is the biggest
particle accelerator at the moment, it's a ring with 27 kilometers circumference, 100
meters below Switzerland and France, it has four big experiments and several
small ones and we are expected to run until 2030. So you see that all of that
is large-scale simply because we're trying to make good use of the money we have.
Here, you see one of these caverns that are used by the experiments while it was
empty. The experiment was then lowered through this hole by the roof, piece by
piece, and these things are humongous. To give you an impression of how big it is, I
put Waldo in there, so your job for the next three slides is to find Waldo. You
know, that gives you the scale. He's friendlily waving at you, so it should be
easy to find him. So then we put a detector in there. Here it's pulled apart
a little bit, so it looks nicer, you can actually see something. You can for
example see the beam pipe, so that's where the particles are flying through, and then
they're coming from both directions and colliding in the center of the detector
and then things happen we try to understand what
is happening. That's yet another view, frontal view on one of the detectors and
now you have to imagine that, you know, you can't just open up Amazon and order an
LHC experiment, right, that's not how it works. We do this stuff ourselves, like
PhD students, postdocs, engineers. You know, that's all done by hand, just like
the microscope you saw before. Of course you order the parts, but you know the
design, the whole conception and actually screwing these things together, making
sure that all fits, is all done by hand. And I find that just beautiful, I mean
that's close to a miracle, right? That nations, like people no matter what
nation, people across the globe work together to build such a huge thing and
then you turn it on and it works. More or less, but you get it to work. That's not
my applause, that's your applause, because you make this possible. Really, but it's,
it's huge this is for me one of the things I love most about CERN: That is this
international thing that just works smoothly. Now the detectors are like a
massive camera. We have lots of pixels and we take many, many pictures a second. We
do this to identify particles and then sort of estimate what has happened during
the collision. Now, life at CERN is of course an important ingredient for
scientists as well, and if you live at CERN then actually it's just work at CERN
and that's what it's about. But it's not that bad, so we hang out together in our
control rooms, make sure that the experiments work correctly. We also, you
know, study the forces. *laughter*
We have scientific discourse, in the sun, view on the Mont Blanc, with a good
coffee. We have lectures and we are lectured and of course, as you, we have
more laptops than people. And, then we do stuff and so this presentation is going to
introduce you to some of the things we are doing, and more on the computing and the
society side as I said. But because I have so much to talk to about I decided that
you just build your own talk, you tell me what you want to hear. So let's do this,
you can choose between A, physics, and B, model simulation and data. You remember
these books like from the old days when we were all young? It's that kind of thing,
ok? You decide/design your own talk here. So, by applause, do you want to hear about
physics? *applause*
Okay. Or the model simulation data part? *louder applause*
Okay, there we go. So, this is what we skip. Model simulation data it is. You're
a strange crowd, first time I meet people who don't want to hear about physics... no
I'm kidding. *laughter*
Audience: *inaudible interjection* *laughter*
So model simulation data it is. So our theory is actually incredibly precise.
It's so precise that our basic job is really really boring, because we already
understand everything. Whenever there is a collision, we know what's going to happen.
Except for these very rare things. So we are trying to find these very rare things
out of this haystack of fairly boring things that we really understand well. And
the weird things are, for example, monopoles, supersymmetry, or black holes.
Now the theorists job is to tell us what we should be seeing in the detector, given
some fancy physics. Then we use simulation to see how our detector would respond to
that. Now, of course the question is: We are just counting, basically, when we do
experiments and the question is: How often do we need to see something to say: "Well,
that's not just the ordinary. That is something new, that's something that could
be explained by a weird theory. We use the detector simulation as I said to basically
predict how much we expect to see things. We use reconstruction software which
tells us what has happened, or might have happened in the detector to count how
often we saw something. And then we use statistics to compare these two and to say
whether something is expected or not. Now, that's fairly abstract but it's fairly
common, a fairly common approach. For example, if you look at climate versus
weather, right, I mean we always have temperature fluctuations because of
weather, and the question is: Is that rise in temperature because of a weather effect
or because of a climate effect? Is that large-scale or just a short-term
fluctuation. So there, we have a very similar problem and here what you do is
you measure temperatures, and you want to detect abnormal variations, and you can
improve that by measuring longer, like, for 300 years instead of 20 years. That
gives you a better prediction what you would expect in the future. Also, larger
deviations help, right?. If you look for something that
is just 0.1 degree, then you might not be able to find it. If there is a deviation
of 5 degrees, you will definitely find it. And for us it's very similar. So here we
have a plot, one of the first Higgs discovery plots, and you can see that we
have many ingredients there. So, the black dots are what we measure and they have
certain uncertainty, because when we measure, we count and we might have, you
know, not seen something, or we might have seen more than we we should have seen, so
there's always an uncertainty. And then we also have theory, which tells us you
should have seen so many and so for the red part that's something that we know
exists, it's nothing spectacular. It's simply what theory is telling us what we
should be seeing. And you can see the data follows the red part fairly well. But then
there is this other bump in our dots on the right-hand side or in the center and
that does not make sense, unless you take the Higgs into account, right, which is
the light blue part and so here you can see how this interplay between different
sources of physics and statistics works for us. Now just as for the climate, more
data helps. And there are two versions of more data more data: Either by having more
collisions, which is why we are running 24/7, or more data by combining different
analyses which is what's happening here. So here you see all these different
analyses. If you combine them, of course you get a much stronger prediction of, in
this case, the Higgs mass, then if you just take any single one of them. You see
how similar what we are doing is to, you know, any of the big data analyses out
there. Okay, so that was that part. Now comes the obligatory part again,
computering. When we were designing the LHC,not me, when people were designing the
LHC, they needed to project computing power from 1990 to 2000 2010 and so on.
And then they said: "Well, we need massive amount of computers" and for you
there's now "Ughhh - everybody has it, we have it as well, we have our racks of
computers". This is something that the big companies usually don't show: You you know
there is actually a ramp where the trucks arrive and they offload the things and
then someone needs to screw them together and then looks shiny. This is how we are
spending our CPU time: We have about 60,000 cores that are spinning all the
time for us, and they are distributed around the world. You can see that CERN,
for example, is the red part there near the bottom. Yeah, so we make good use of
that. We also monitor the efficiency, and because 100 percent efficient is for
beginners we are actually about 700 percent efficient. Don't ask why. They
decided if you are multi-threading, then we, you know, we multiply your efficiency
by the number of threads you have. Makes no sense to me. We also have storage,
currently we use about 0.7 exabytes. We also have available at one point seven
exabytes, so that's good, we make use of the storage we have. Where it's, you know,
tera- peta- exa-, so it's a lot, and here you can see on the right hand side you
see, for example, the tape usage on the bottom and you see this dip that was
before we were starting the accelerator again, we needed to make some space so we
monitor our hard disk usage all the time. Hey, here comes the next decision point:
So, do you want to hear about, 1, distributed computing or 2, measure
effects of bugs. So, 1, distributed computing
*applause* and 2, measure the effects of bugs
*similar amount of applause* Okay, so that's my call, and I would say
we do we do... Measure the effects of bugs, because it's shorter.
*laughter* So this is one of the views you can, you
know, electronic views you can get from a detector and you see how we trace the
particles that fly through the detector. Now, that software right, that's the
result of software, and you might not believe it, if you have bugs in there, in
that software.
And you know, these bugs are sometimes wrong coordinate transformations, so
things don't go this way but that way, it's kind of weird if you look at it, and
the result is that our particles don't go through the path that they should have
been going, but we are attributing them a different path. Now, the the nice thing
is that we are doing this a million times, right? So all of that is smeared. We are
not systematically doing this wrong it's just, we are always doing it a little bit
wrong. And so the net result is that if we measure our particles, we will not measure
the right thing but always a little bit wobbly left wobbly right you know? Things
are not as precise. That's simply an uncertainty. So for us just like counting
has an uncertainty and predictions have an uncertainty, software bugs introduced
another source of uncertainties. And here you can see how we are tracking
uncertainties for for all of our analyses. We are trying to understand the
different forces of uncertainties. And again, bugs are only one of the sources
here, so if we find the bug then we reduce our uncertainty and we can find new
physics earlier, instead of having to wait and collect more data. So for us
finding bugs is really key, we really love finding bugs because it brings
physics closer. I thought that was interesting. It's kind of rare that you're
in environment where you're able to measure the effect of bugs. Okay, so now
we are talking, we'll be talking about data. I talked, told you that we are
trying to find particle traces in our data and the way we do this is by using
reconstruction programs and there are multiple gigabytes of binaries in shared
libraries and stuff. They're huge, they're experiment specific and they are curated
by the experiments, open-source for some of them, and we want them to be correct
and efficient. The data format we use is not comma separated values, it's binary
and for some strange reason it's our own custom binary format. The reason is that
it's really targeted and the kind of data we are having. We have collisions
that are independent, so we only need one in memory at any time and we have nested
collections which makes the regular table layout a non-starter. We actually generate
them from C++ objects so from classes, class definitions, C++ class definitions
and we can read them back into C++ but also into JavaScript or Scala. Database
just didn't do it for us. They have the wrong model of data axis, they don't
scale, it's just not the kind of system that works for us. Also using a file
system as a storage back-end might sound really very traditional and boring but it
works amazingly well and seems to be future proof as well, so that's just the
way to go for us. There are many other structured data formats out there, many of
those did not exist when we started root our own data format. But they also miss
many things. For example, we wanted to make sure that we have schema evolution
support. We can change the class layout and still read back all data. We don't
want to throw away all data just because we're changing the class. Also we do not
trust people. That is a, you know, as a computer scientist or whatever you
probably know what I'm talking about right? If people have to write their own
streaming algorithm, there will be bugs and we will lose data.
We really don't want to do this, so we were trying to automate this, based on the
class definition. So, last decision point for the story. Do you want to hear about
cling, our C++ interpreter or about Open Data and Applied Science? Let's start with
option 1, the C++ interpreter *applause*
Okay and and Open Data and Applied Science?
*more applause than before* Yeah. I'm heading there. You miss a fish.
You can look at the slides later. Okay, so there we go. Really? No. The slide number
is wrong. Oh a bug! So, Open Data and Applied Science. Okay, you really wanted
to know about our budget, I understand that. So we get from you about 1 billion
year and the currency doesn't really matter anymore at this, at this point of
time. *laughter*
And that is a lot of money. And you know? We try to do really wonderful things, I
mean we really enjoy our job, we love it. It's fantastic to work in such an
environment. And thank you very much for making that possible. Really, I mean it.
But it also means that you decided as society to enable something like CERN.
Which I think really deserves my applause and yours probably as well. I think it's a
great decision to do something like this.
*applause*
So we realize this, right? We realized that we are basically, that we can do what
we do because of you, and we are trying to react to that by giving back what we do.
Software, research results, hardware and data. So the way we share research results
is through open access. We have it, finally. It took us a long time to fight
with publishers and, you know, the establishment, but now we have it. We
also, yes thank you.
*applause*
We also put a lot of effort in communicating our results and what we are
doing. And if you're in the region, it's definitely worth a visit. I mean the URL
is really easy to remember, it's visit.cern, and you know, works. And you
should go there by April, actually, if you can because then you can ask people how to
get on the ground, because the accelerator is off at the moment. We also do applied
research, for example we have this super cool experiment where we try to study how
clouds form, based on cosmic rays. So the the influence of cosmic rays and cloud
formation. Which is a key element in the uncertainty of climate models. We are
trying to, to think about, you know, how to make energy from nuclear waste. So
getting rid of nuclear waste while making energy from it. And we are trying to
repurpose detectors that we have and you know develop. We have something called
open hardware, for example White Rabbit: deterministic ethernet, we have Open Data,
and we have the LHC@home and some other programs, where either you can donate
compute power or your brain and help us get better results. We explicitly try to
use open source as much as possible, and also feed back, whenever we see issues.
But we also create open source. For example, we create Geant, which is a
program that allows you to simulate how particles fly through a matter, for
example used by the NASA. We have Indico, which allows us to schedule meetings,
upload slides, you know, these kind of things. Across the globe, lots of people,
with access protection, all these kind of things. And it's open source. We have
DaviX, the dimension we love HTTP. That's the next machine of Tim Berners-Lee. And
that's his futile effort in trying to prevent the cleaning personnel from
switching it off. They don't speak English, they did not back then at least.
So we use we used DaviX to transfer files over HTTP, with a high bandwidth. Or we
have CVM-FS, which allows us to distribute our binaries across the globe, and not
rely on admins downloading stuff and making sure it actually runs, and these
kind of things. That is a lifesaver, it's really fantastic, it's a great tool. But
nobody knows it. And we have ROOT, but that's coming up. So now, the last
official part of this, of this presentation, how do we do data analysis?
Not like that. *laughter*
*applause* We use, we use C++ and actually physicists
need to write their own analysis in C++. We have very few people who have an actual
education in programming. so that's sort of a clash. As I said, we need to keep one
collision in memory. And for what, you know, what matters to us is throughput. We
want to have, we want to analyze as many collisions as possible per second. What we
can do, is specialize our data format to match the analysis, because we don't want
to waste I/O cycles, if we can, you know, if we can make use of the CPU better. ROOT
allows us to do this since twenty years. It's really the workhorse for the analysis
in high energy physics. And it's also an interface to complex software. We have
serialization facilities, we have the statistical tools, that people need, and
we have graphics, because once you have done your analysis you need to communicate
that to your peers and convince people, and publish, and so on, so that's part of
the game. All of that is open source, and, of course, all of that is not just used by
high energy physics. So, to conclude: We are here, because you make it possible.
Thank you very much. It's fantastic to have you.
*applause* We want to share and we have great people
for science outreach, but we have nobody for software outreach, basically. So maybe
it's worth a look to see what what CERN is producing software-wise. Scientific
computing is nothing new, it existed since a long time, but we had to start fairly
early on a large scale. So when we were building it up, we had to take... we were
trying to take pieces that existed and did not found find much. So now we ended up
with C++ data serialization, efficient computing for non computer scientists
even... In the part that I skipped and, you know, one of the alternate tracks, you
would have seen that we have a Python binding as well for the whole software
stack in C++. And for us, what matters most is scale. Now we are seeing that we
are not the only ones. There are many more natural sciences arriving at a similar
challenge of having to analyze large amounts of data. Now I promised to you
that I'll be bold and I'll try to make a few statements of what will happen with
data analysis, not just in science. Because what we see is that we actually
educate the people who will do data analysis, not just in science. What we see
is that in the past, data volume mattered most. So more data meant more power. Now
that's not the complete truth anymore. It's a lot about finding correlations. So
even with the amount of data not growing anymore, because it's already humongous,
we try to squeeze more knowledge out of it. And for that, I/O becomes important
and CPU limitations is the crucial factor. We see that multivariate techniques are
still rising and they will just be part of the toolchain of the statistical tools;
except for generative parts, which, I believe, will change the way we model.
Now, based on what I just described, this is not a big surprise anymore. As we need
throughput, we need to have a language for the core analysis part, that is close to
metal, so something like C++. On the other hand writing analyses is
still complex, so you need a higher-level language and for that people could, for
example, use Python. So, now language binding becomes relevant all of a sudden.
It's much more important in the future. And we need to tailor I/O to the actual
analysis to not waste CPU cycles. So throughput is the king and, in my point of
view, also in the future we will see much more effort in increasing the throughput.
Okay, so that was it. In case you want to discuss anything with me, like "That's
just wrong!", that's fine. I'm probably have several bugs in there. I'm still here
until tomorrow. I don't know where yet, so I'll wander around and you can contact
me by email or Twitter. Thank you very much for your attention. Thank you.
*applause*
*music*
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