Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says, and all
Speaker 1: the world is biscuit shaped. It's just for me to
Speaker 1: feed my face. I'm Jonathan Strickland, I'm Lauren, and I'm
Speaker 1: Joe McCormick. No, Joe. The lyric this week does not
Speaker 1: die into the topic. It was just some words, just
Speaker 1: say in words. Words are good words here. Well, then
Speaker 1: what is the topic, Jonathan? Well, the topic is you know,
Speaker 1: you guys are putting so much pressure on me. There's
Speaker 1: way too much I'm having to carry the whole load
Speaker 1: of explaining what the topic is. How can we help you, Jonathan?
Speaker 1: I think we should all kind of, you know, share
Speaker 1: the responsibility. Okay, let's do that thing, the improv thing
Speaker 1: where each person Okay, so I'll start and then where's
Speaker 1: it going to? From you to to Lauren. Today's topic
Speaker 1: is about distributed computing guys. Yeah, all right, there we
Speaker 1: go that. I think that was probably the most successful
Speaker 1: word of the the time I've ever been part of. And
Speaker 1: nobody brought in like gussy anything about nazis. Yeah, there
Speaker 1: wasn't a head in a box, which is almost always
Speaker 1: what happens in an improv scene. Wait, what did we
Speaker 1: say distributed computing? Yes, yes, so Joe back in the day,
Speaker 1: which was a Tuesday, Uh, there was this issue where
Speaker 1: you would have a big computational problem, really really complex,
Speaker 1: and you essentially had one computer defeat it through and
Speaker 1: you really just had to wait until the computer was
Speaker 1: able to complete that problem, and you were completely reliant
Speaker 1: upon that computer and that computer's limitations. Right, that was it.
Speaker 1: That was that. So if it didn't have enough punch
Speaker 1: card slots, you were just out of luck. Yeah, that's
Speaker 1: pretty much it. Like if you if the program you
Speaker 1: wanted to run had a requirement that was so large
Speaker 1: that you didn't have a box big enough to hold
Speaker 1: all the punch cards. It was too complex for that machine. Yeah,
Speaker 1: which is you know also how stuff like algorithms started
Speaker 1: to be written because people said, oh, hey, if we
Speaker 1: can create a shortcut, that says you know, yeah, this
Speaker 1: is the birth of hackers, right where hackers would say,
Speaker 1: I have an outcome that I need to have happen.
Speaker 1: How do I make that happen? It doesn't have to
Speaker 1: be pretty, It just has to work. Well, you know,
Speaker 1: in science we actually have issues like this where scientists
Speaker 1: researchers are working on uh projects where you get massive,
Speaker 1: truly massive amounts of data and then you have to
Speaker 1: figure out, well, we've got all this information, but we
Speaker 1: can't really do anything meaningful with it. Yeah, you've got
Speaker 1: a problem that's computationally intensive, meaning you you sort of
Speaker 1: know how to solve it. You have a method to
Speaker 1: get there, but you just need the ability to do
Speaker 1: all that work. And if you're going to run it
Speaker 1: through one computer. What if you're trying to do a
Speaker 1: research project where you're sitting here in front of your
Speaker 1: computer and it gives you an estimate it says, okay,
Speaker 1: it will only take forty five years to do all
Speaker 1: the calculations on this data, right. Yeah. Yeah. It's sort
Speaker 1: of the equivalent of saying like, well, I have this
Speaker 1: room full of fun sized candy bars. I know I
Speaker 1: can count them, but I only have you know, ten
Speaker 1: fingers and ten toes. So once I get beyond that,
Speaker 1: it's going to start getting tricky. Well, here's the other
Speaker 1: way of doing this. I just want a room full
Speaker 1: of fun sized candy bars. I'm sorry, I'm gonna come
Speaker 1: back and run with that example interruption, what kind of
Speaker 1: candy bars? Oh, I'm going to say heath crunch bars.
Speaker 1: That's acceptable. I also would have accepted butterfinger. Okay, I
Speaker 1: shouldn't really have peanuts, So that's true. Yeah, you're having
Speaker 1: the issue with the peanuts. So let's say let's say
Speaker 1: that all we have that issue with fun heaths. Okay,
Speaker 1: Lauren needs has got a room full of fun sized
Speaker 1: heath bars, and uh. And if she were going in
Speaker 1: there on her own to count them all, and was
Speaker 1: using her fingers and toes as reference, she would very
Speaker 1: quickly run out of fingers and toes, and then she'd
Speaker 1: have to start trying to remember things. It would take
Speaker 1: her time to get through all of that. I'd have
Speaker 1: to make a little hash marks on paper individually. Right. So,
Speaker 1: if she ended up inviting a bunch of her friends over,
Speaker 1: and she had divided up the floor into a grid
Speaker 1: and assigned each person a part of that floor where
Speaker 1: they are not allowed to eat said candy bars, merely
Speaker 1: count them, and then together you all came up and
Speaker 1: wrote down the numbers you came up with, and then
Speaker 1: added all those numbers. You would then know how many
Speaker 1: candy bars were in that room you had divided the
Speaker 1: problem up. Yeah, take a lot less time that way.
Speaker 1: So this is kind of what distributed computing is all about.
Speaker 1: So you could do the approach of getting a supercomputer
Speaker 1: that has an incredibly fast processor, or maybe several processors,
Speaker 1: but one those are rare too. Because they're rare, their
Speaker 1: time is often spoken for. So your project will probably
Speaker 1: be competing with multiple other projects that they're very expensive
Speaker 1: to use. That's that's number three as well. They are
Speaker 1: very expensive to get that time and so and also
Speaker 1: you know they're going to tackle that problem. They might
Speaker 1: be really fast at it, but they're going to tackle
Speaker 1: that problem sequentially. Okay, but how about instead of one
Speaker 1: really powerful computer, you buy a whole bunch of less
Speaker 1: powerful computers and then you break the task up into
Speaker 1: small chunks like we were talking about. And so you've
Speaker 1: you've got a lot of sort of powerful computers computing
Speaker 1: little bits of your task. That's often referred to as
Speaker 1: grid computing. It's uh, grid computing and distributing computing are
Speaker 1: are sometimes interchangeable. It really depends upon who's doing the talking,
Speaker 1: is ultimately what it comes down to. Sure, but but
Speaker 1: what if you don't have the funding to buy a
Speaker 1: grid worth of computers, or what if even a reasonably
Speaker 1: sized grid it of computers isn't going to get the
Speaker 1: problem done faster, or if you don't have space to
Speaker 1: put a grid of computers because it's all taken up
Speaker 1: by fun sized heath bars. Yeah, so clearly this is
Speaker 1: not necessarily the best option either. There could be other
Speaker 1: options out there, right, So problem meat wasted potential. Your
Speaker 1: computer and I mean your computer, you, the listener, and
Speaker 1: the three of us. Actually yea, it was confusing me
Speaker 1: because Joe was clearly pointing at me or your well,
Speaker 1: I was pointing at my microphone, so I was pointing
Speaker 1: at the listener and Knowle's computer and everybody in the office.
Speaker 1: I mean, what if your smartphones and your tablets, etcetera. Yeah,
Speaker 1: things that have processors. We have tons of CPUs around
Speaker 1: the world and that are not Yeah, yeah, that are
Speaker 1: so they're they're capable of doing important processing work. They
Speaker 1: can do math, they can run simulations, they can catalog data,
Speaker 1: they can do all these computational tasks these big supercomputers
Speaker 1: do on a much smaller scale, and they're not helping.
Speaker 1: They're just sitting there when you're not using Yeah, unlike
Speaker 1: unlike our brains, which which genuinely do use the whole
Speaker 1: brain to do stuff, that ten percent rule does not apply.
Speaker 1: As we've spoken about before, your computer is really only
Speaker 1: using a small fraction of its potential processing power at
Speaker 1: any given time, and especially when you're, for example, away
Speaker 1: from home. Right. So here we now reach the idea
Speaker 1: of the popular distributed computing method also known as volunteer computing,
Speaker 1: where there's a project that says, hey, I wonder if
Speaker 1: we can get a bunch of people out in the
Speaker 1: world to donate some of their computer power to help
Speaker 1: us find a solution to our problem right now. Clearly,
Speaker 1: this only really works if the problems you are tackling
Speaker 1: can be divided, right. Not all computer problems are that way.
Speaker 1: So there. We kind of talked about this with quantum
Speaker 1: computers as well, which sort of take this concept and
Speaker 1: boil it down to a single machine. It's the fact
Speaker 1: that the cubits can act as either zeros or ones
Speaker 1: and all values in between technically that allow them to
Speaker 1: to do all the parallel processing simultaneously. At least, that's
Speaker 1: the concept. This is sort of the same thing, except
Speaker 1: instead of the conceptual doing them all simultaneously on one machine,
Speaker 1: you've divided that problem up across a network of machines,
Speaker 1: all of which are working on parts of it. If
Speaker 1: the problem can't be divided, then grid computing or distributed
Speaker 1: computing isn't going to work. You have to have that
Speaker 1: sequential approach. Think of it as if I have a
Speaker 1: a math problem that's completely distinct, then I can solve
Speaker 1: that uh. And if I have a whole series of
Speaker 1: math problems, each of which are distinct, I can solve
Speaker 1: them in whatever order I want. Right. If problem one
Speaker 1: seems like it's too tricky, I can skip the problem
Speaker 1: to and solve that one, then go back to problem one.
Speaker 1: But if I have a series of math problems, each
Speaker 1: of which which is dependent upon the answer of the
Speaker 1: previous problem, that's more of an issue where I need
Speaker 1: to take a more sequential approach. So it all depends
Speaker 1: on the nature of the problem. But assuming that it
Speaker 1: is one that can be divided up, this model is
Speaker 1: a fantastic solution, and it's really amazing how people have
Speaker 1: started to leverage it. Right, and so this is ongoing today.
Speaker 1: But the concept is not at all new it has been.
Speaker 1: The idea has existed for decades, and it's been employed
Speaker 1: in very powerful ways for at least like ten or
Speaker 1: fifteen years now. But I think we should go back
Speaker 1: to the beginning of the idea of distributed or grid
Speaker 1: computing and take a look at how we got where
Speaker 1: we are today. Okay, all right, So if you go
Speaker 1: to the very beginning, we're talking about the basic, major,
Speaker 1: huge mainframe computers that didn't have any connection with any
Speaker 1: other computational device. So they were completely standalone. And if
Speaker 1: you wanted to divide up work, well you might want
Speaker 1: a lot of things, but here's the way life works.
Speaker 1: It's not gonna all pan out for you. So that
Speaker 1: was the big and then you had the the early
Speaker 1: days of networking computers together, the first major one being
Speaker 1: our Pannette. Now that was in the sixties and seventies really,
Speaker 1: So in the sixties you get this team together that
Speaker 1: DARPA had wanted to get together, um, and they started
Speaker 1: trying to figure out ways to let computers talk to
Speaker 1: one another. Now that early communication didn't really facilitate distributed computing.
Speaker 1: You could share work, but you couldn't divide a problem
Speaker 1: up at that point. There was no infrastructure to allow
Speaker 1: that sort of thing to happen yet, So think of
Speaker 1: it as people who are able to communicate with one another,
Speaker 1: but they can't collaborate actively on things. Yet you get
Speaker 1: to the nineteen seventy one you get the first distributed
Speaker 1: computing problem, which was not a true distributed computing challenge.
Speaker 1: It was actually the first computer virus purposeful virus though
Speaker 1: I'm well, I mean, I guess all viruses areful viruses.
Speaker 1: It was an experiment. Really yeah, it's called Creeper. It's
Speaker 1: a self copying, self duplicating piece of code, which you
Speaker 1: can realize if you talk about self duplication, it could
Speaker 1: quickly get out of control and duplicate self in order
Speaker 1: to take up all remaining space. There was also Reaper,
Speaker 1: which was meant to diffuse and disarm and remove Creeper.
Speaker 1: Then you in the nineteen eighties you get to the
Speaker 1: birth of the Internet. So this is the true network
Speaker 1: of networks. Urban net is kind of the predecessor to
Speaker 1: the Internet. It's not really the um the earlier version
Speaker 1: of it. Internet itself was a different thing, right, But
Speaker 1: this is when you get to the kind of net
Speaker 1: that Sandra bullet gets caught up right exactly. It was
Speaker 1: very accurately portrayed in that movie. As I recall, Uh, well,
Speaker 1: distributed computing ends up becoming an area of research, even
Speaker 1: though no one has actually launched a distributed computing project
Speaker 1: the way we think of them today. All Right, Well,
Speaker 1: at this time, it was only government officials and university researchers,
Speaker 1: people on that level who had access to to this network,
Speaker 1: right right, You and I, assuming we were not employed
Speaker 1: by a government or military or university, didn't have access
Speaker 1: to it. Uh. The Internet was something that probably most
Speaker 1: of us were largely unaware of at the time. It
Speaker 1: was it was still so um so expensive to obtain
Speaker 1: the kind of equipment that you would need that it was.
Speaker 1: It was really left up to these large organizations. Most
Speaker 1: of us were dealing with local networks like like Bolton
Speaker 1: board systems and stuff that had some connectivity but weren't
Speaker 1: ultimately leading to the larger Internet. So by nineteen eighty
Speaker 1: two there was a Distributed Principles of Distributed Computing symposium,
Speaker 1: and then another one in International Symposium in nineteen five,
Speaker 1: So people were already thinking about the potential for computers
Speaker 1: to do this kind of thing and starting to lay
Speaker 1: the groundwork for the technology that would make it possible.
Speaker 1: By night we get the first Internet based distributing computing
Speaker 1: project at the d e C System Research Center. And
Speaker 1: the way it worked is through email. We get a
Speaker 1: task through email. So you would volunteer to be part
Speaker 1: of this? Yeah, well, I mean there wasn't an automated
Speaker 1: system here yet. Yeah, so you will? You would, you
Speaker 1: would first, you would volunteer. You'd see like, you know,
Speaker 1: you go to the coffee shop. You see a little
Speaker 1: piece of paper with strips on it saying, hey, are
Speaker 1: you willing to lend your computer to help science? Tear
Speaker 1: off a strip and send an email? And uh, and
Speaker 1: most people in the coffee shop and say, what's an email?
Speaker 1: But you knowing what an email is, You tear off
Speaker 1: a strip and you send off an email. They send
Speaker 1: you an email with a task to complete. You run
Speaker 1: it on your computer, complete the task, and you email
Speaker 1: the results back. So it's an It takes takes an
Speaker 1: active role for the volunteer in order for this system
Speaker 1: to work. And the purpose of this project was to
Speaker 1: factor large numbers, which is an important role part of cryptography,
Speaker 1: computer cryptography and UM It was a relatively modest project
Speaker 1: by There were about a hundred volunteers using this and
Speaker 1: it was fueled in part by challenges issued by the
Speaker 1: r S, a security incorporated company. H They wanted to
Speaker 1: do lots of research on factoring of large numbers as
Speaker 1: a security company, and and the fact that this has
Speaker 1: a lot to do with cryptography. It was very important
Speaker 1: to their business in order for them to make sure
Speaker 1: that their systems and other systems remain ahead of hackers.
Speaker 1: So if it turns out that a group of people
Speaker 1: can quickly factor a certain size prime number, or to
Speaker 1: find the largest primes of a certain number, then they
Speaker 1: wanted to make sure they could go to the next
Speaker 1: level in order to avoid issues with people immediately hacking
Speaker 1: through all sorts of security system Yeah. Yeah, greater encryption,
Speaker 1: for greater protection against people who wanted decrypt YEP. And
Speaker 1: in NT you get distributed dot net, which is really
Speaker 1: the first project to use the Internet to distribute data
Speaker 1: for calculation and collect the results automatically, so it did
Speaker 1: not require the volunteers to receive and send email. It
Speaker 1: had a software package that did all the handling of
Speaker 1: that automatically. Yeah, it's all in the background. So the
Speaker 1: user just had to install software onto his or her
Speaker 1: computer and then start up the software, have an Internet connection,
Speaker 1: and the program would would run itself. Yeah, essentially running
Speaker 1: in the background, taking you know, any unused cycles of
Speaker 1: processor and dedicating it to working on the problem. And
Speaker 1: that has pretty much been the model for distributed computing
Speaker 1: or volunteer computing ever since. You know, I thought you
Speaker 1: were going to say, the first distributed computing project was
Speaker 1: like an x files used net group that was sending
Speaker 1: out task to get different people to decode the messages
Speaker 1: and learn Molder secret. I wish. I'm sure some of
Speaker 1: that was going on. I think, I mean not Molder secret,
Speaker 1: the secret of his his his family has passed. I
Speaker 1: think I think the real secret was that everybody but
Speaker 1: Molder and Scully knew. Everybody everyone in that show knew
Speaker 1: more than the spoiler. Look, I think I think that's
Speaker 1: not a spoiler. That's saving you some time. Okay, okay,
Speaker 1: Well here we are in the present day. Yes, what
Speaker 1: are some of the big distributed computing projects or volunteer
Speaker 1: computing projects out there today? And how are they actually
Speaker 1: making a difference? Like is this just kind of a lark.
Speaker 1: You know, Oh, that's kind of fun, you can participate,
Speaker 1: or does this actually impact science in a meaningful way? Well,
Speaker 1: let me answer that second question first, because it's certainly
Speaker 1: impacts it in a meaningful way. You know, like we
Speaker 1: were saying, access to supercomputers and grid computers, that's something
Speaker 1: that if you're incredibly well funded, might not be an
Speaker 1: issue you. But there are a lot of science projects
Speaker 1: out there that are doing amazing science on very limited budgets.
Speaker 1: And so for this kind of model to come along
Speaker 1: for specific types of problems, particularly science projects where you're
Speaker 1: generating huge amounts of data and then you have to
Speaker 1: analyze it, it's an incredibly useful tool. And it also
Speaker 1: not only is it meaningful in that sense, it's meaningful
Speaker 1: in the sense it gets people involved in science, even
Speaker 1: in a passive way. You start to learn more about
Speaker 1: what your computer is being used for one the purpose
Speaker 1: of that scientific project is, and that encourages scientific literacy,
Speaker 1: which I think is incredibly valuable. Yeah, so it's partially outrage.
Speaker 1: It's it's not just getting stuff done. It's also saying, hey,
Speaker 1: you can be a part of this, and also look
Speaker 1: at this cool thing that we're learning about Yeah, like,
Speaker 1: you know, the universe is mysterious. You want to help
Speaker 1: but be less mysterious. Yeah, you know, it's kind of
Speaker 1: kind of the way it boils down. And a lot
Speaker 1: of these projects are all folded under the umbrella of
Speaker 1: the Berkeley Open Infrastructure for Network Computing, which has a
Speaker 1: great acronym. You want to say it, Joe, you know,
Speaker 1: you want to say it bow wink, blink blink. It
Speaker 1: doesn't actually it's not. The full thing isn't blink with
Speaker 1: a C, but it is blink that has a C
Speaker 1: at the end of it. Um. But it's just fun
Speaker 1: to say, and so I'll probably try and say it
Speaker 1: as frequently as I possibly can. Well, this is sort
Speaker 1: of an umbrella program. Yeah. Yeah. It actually started as
Speaker 1: a specific platform for one of the programs will talk
Speaker 1: about in the second But what happened was it was
Speaker 1: such a useful approach that got adopted for other programs
Speaker 1: and now it serves as this umbrella where you can
Speaker 1: go to the Blink website and see all sorts of
Speaker 1: different projects listed underneath. Yeah, there's a few of these
Speaker 1: kind of infrastructures out there, and and this one is
Speaker 1: really cool because it's definitely run by by Berkeley, who
Speaker 1: are good people and and don't really have much of
Speaker 1: a profit factor in it. This is more by night.
Speaker 1: It's it's definitely more about enabling people to do science
Speaker 1: right and less about we've made this incredible platform that
Speaker 1: you can use for one low price of five million
Speaker 1: dollars or whatever. UH. So it's it's really cool. I
Speaker 1: blogged onto it the day that we're recording this podcast
Speaker 1: November four, two thousand and fourteen, and saw that there
Speaker 1: were about forty projects listed under Blink. I know that
Speaker 1: there that changes. There are times when there are many
Speaker 1: more uh and some of them actually were offline when
Speaker 1: I looked into them, probably for actually maintenance for for
Speaker 1: several of them, because you're talking about a fairly complex system. Ultimately,
Speaker 1: the way Blink works is that you've got a master
Speaker 1: system that kind of determines what the job is, sends
Speaker 1: the various tasks out to the volunteer computers, uh, accepts
Speaker 1: the incoming tasks from computers that have completed whatever work
Speaker 1: you've given them, and then assimilates that into a meaningful
Speaker 1: way for researchers to look at. So all of this
Speaker 1: is going on behind the scenes. And meanwhile the volunteers
Speaker 1: computers are doing actual scientific work without really, you know,
Speaker 1: without necessarily you being aware of it. Some of them
Speaker 1: come with fun like screen savers, so you can kind
Speaker 1: of watch a graphical representation of what's going on with your,
Speaker 1: you know, the work you're doing. One of the ones
Speaker 1: we'll talk about has one of those. That's pretty cool. Well,
Speaker 1: let's get to the examples. Sure, so here's a fun one,
Speaker 1: steady at home. The search for extraterrestrial intelligence. Wait a minute,
Speaker 1: how can I search for extraterrestrial intelligence without a radio telescope.
Speaker 1: Well here's how, Joe. You can take data that was
Speaker 1: gathered by other radio telescopes and use your computers processor
Speaker 1: to analyze that information and look for anything that might
Speaker 1: be meaningful. Right, Because the thing is is that when
Speaker 1: you know these telescopes are searching space, there's no like
Speaker 1: big red light that starts flashing when when something, oh, alien,
Speaker 1: it's right there, it's right there. We just found the
Speaker 1: Venusian elvis. No, I guess what we end up with
Speaker 1: is a lot of numbers, a whole lot of numbers
Speaker 1: that need to be crunched, and so your computer can
Speaker 1: crunch part of them. And so it wouldn't have a
Speaker 1: red light, but your computer might be the one that says, hey,
Speaker 1: here is an anomaly in the data. Right, wonder what
Speaker 1: that means? Right, And that's it really is, looking for
Speaker 1: those kind of anomalies and trying to flag them so
Speaker 1: that other people can take a closer look and see
Speaker 1: if there's actually any signal inside the noise. Because as
Speaker 1: we know, there's lots of stuff out there in the
Speaker 1: universe that generates radio signals. So just because you pick
Speaker 1: up yeah, yeah, you might get up stuff from a pulsar,
Speaker 1: which is already super cool. The fact that you know
Speaker 1: your your computer is working on data generated by an
Speaker 1: extrasolar body that could be light years across from us.
Speaker 1: But obviously that's not the same as Venusian Elvis, which
Speaker 1: is what our real goal is here for CET. But
Speaker 1: that's the thing is that with all that kind of information,
Speaker 1: it's the same sort of thing that if you were
Speaker 1: to have a massive telescope look deep into space and
Speaker 1: slowly do uh pans across the night sky looking at
Speaker 1: every single teeny tiny bit of that space to look
Speaker 1: for meaningful things, would take forever. Same sort of thing. Um,
Speaker 1: it's really dividing up this massive task among lots of
Speaker 1: different computers to make it more manageable, because otherwise you
Speaker 1: would just keep gathering data and it would pile up,
Speaker 1: and you know, you would always be playing catch up
Speaker 1: and always falling further and further behind. So that's the
Speaker 1: purpose of SETI at home. Then you have other projects too,
Speaker 1: for other types of science, like like astronomy is a
Speaker 1: big one, but it's by no means the only one, right,
Speaker 1: And it's not always just that you will be analyzing
Speaker 1: data that comes in from some kind of collector. You
Speaker 1: might be running emulations of some kind or processing data
Speaker 1: from simulation, right, one of those being Atlas at Home,
Speaker 1: And Atlas is one of the big projects, one of
Speaker 1: the scientific research projects connected to the Large Hadron Collider. Now,
Speaker 1: is that the one that shrugged that I keep hearing about. No, no, uh,
Speaker 1: John Galt has nothing to do with it either, so
Speaker 1: uh well, very little to do with it at any rate,
Speaker 1: you know, I mean, who is John Galt really really?
Speaker 1: So No, the the Atlas at Home project, you're just
Speaker 1: staring daggers at me, Joe, You're giving me this superior
Speaker 1: grand I don't know what it means it's not superior
Speaker 1: at all. It's not superior at all. It's just it's
Speaker 1: just kind of shame. Actually, it's a shame grin. Now,
Speaker 1: Atlas at home is all about running these U simulations
Speaker 1: that you were talking about. So, uh, what Atlas is
Speaker 1: looking for. It's looking for the the outcomes of proton collisions,
Speaker 1: which is what happens at the Large Hadron Collider. Right.
Speaker 1: You're you're lighting streams of protons that near the speed
Speaker 1: of light and then looking to see what happens. And
Speaker 1: part of that is a search for lots of stuff
Speaker 1: that we just don't know if it's going to pan
Speaker 1: out or not. You know, the boson was one of
Speaker 1: those things, and then it turned out that we found it.
Speaker 1: But other things include extra dimensions of space, or dark
Speaker 1: matter or the unification of the various fundamental forces. Yeah,
Speaker 1: you know we're talking about like the big questions about
Speaker 1: the universe from a physics perspective, right, well, from any perspective.
Speaker 1: I mean, these are like the most fundamental unsolved questions
Speaker 1: about reality that exists. Yeah, or at least the ones
Speaker 1: that we know of. Who knows what other questions we're
Speaker 1: going to find in the future, which is also really explaining. Right,
Speaker 1: so you know what's your laptop doing right now? Right now,
Speaker 1: it's telling me that my battery is dying. But no,
Speaker 1: the the the idea is that we would use the
Speaker 1: Atlas at Home project. In fact, this is this is
Speaker 1: what's happening that to go through pedo bytes of data.
Speaker 1: A petabyte is one thousand terabytes. Uh So, in order
Speaker 1: for you to get a handle on how big that is,
Speaker 1: first of all, I remember when a kilobyte was a
Speaker 1: lot because home hold. But kilobite is a thousand bits.
Speaker 1: Then you've got megabyte which is a million, gigabyte is
Speaker 1: a billion, terabyte is a trillion, and then peta bite
Speaker 1: which is one quadrillion. So it needs to go through
Speaker 1: all this huge amount of data. The computers attached to
Speaker 1: Atlas at Home are using a computer program that run
Speaker 1: the simulations of the creation and decay of supersymmetric bosons
Speaker 1: and vermons, and it then uh sends that information back
Speaker 1: to the Atlas project. Now, the reason for this, the
Speaker 1: reason for running all these simulations is to look at
Speaker 1: which scenarios are the most likely to really happen in
Speaker 1: real life under the conditions of the pot proton collisions
Speaker 1: and the large Hadron collider. So you look at the
Speaker 1: ones that look the most likely based upon these computer simulations,
Speaker 1: and if you find any evidence of a similar uh
Speaker 1: reaction after a real proton collision, then that shows that
Speaker 1: you might be on the right track, right, You might
Speaker 1: actually have seen something that could be lead to evidence
Speaker 1: to something like dark matter, which to this day we
Speaker 1: think exists. It's kind of a kind of a placeholder name. Really,
Speaker 1: it's the to fill up the matter that must be
Speaker 1: out there for our our vision of the universe to
Speaker 1: be tracked. We observe its effects, but we can't detect
Speaker 1: it directly right exactly. So this could lead to evidence
Speaker 1: that would give us more of an idea of what
Speaker 1: dark matter actually is. And that's just one thing that
Speaker 1: Allison Home could help lead to. Now, it's not the
Speaker 1: type of program that you're super old computer is going
Speaker 1: to be able to run in the background, because the
Speaker 1: simulations actually do require a bit of processing power. So
Speaker 1: they have some well some of these well, I mean,
Speaker 1: we're simulating particle collisions, y'all. Yeah, but some of the
Speaker 1: some of these programs require just you know, some unused
Speaker 1: cycles of a CPU and that's it, right, It doesn't.
Speaker 1: It doesn't have to be particularly powerful. It may mean
Speaker 1: that you are a computer running this This program might
Speaker 1: take longer to solve a certain task than another person's computer,
Speaker 1: but it would still work. With the case of Atlas
Speaker 1: at home, there are actual system requirements. You need to
Speaker 1: have a sixty four bit computer with at least four
Speaker 1: gigabytes of memory, which is not unusual today. If you
Speaker 1: went out to buy a computer, more than likely fil
Speaker 1: fill those requirements. But if you're talking about this desktop
Speaker 1: computer that's just collecting dust, and you think, oh, I
Speaker 1: can dedicate this to science, it's not. No, it'll you know,
Speaker 1: maybe you'll be able to play some some some wicked
Speaker 1: games from the nineteen nineties, but that's about all you're
Speaker 1: gonna manage to do as opposed to helping figure out
Speaker 1: whether or not dark matter exists. Um So, anyway, it's
Speaker 1: it's a neat example. And then we come to a
Speaker 1: couple of brothers. Right. Yeah. One that I wanted to
Speaker 1: talk about is the Clean Energy Project, which is through
Speaker 1: IBM's World Community Grid, which is another one of these
Speaker 1: kind of infrastructures that allows different projects to come in
Speaker 1: and use this. Uh, you know, network of networks, so
Speaker 1: sort of sort of this similar role as boink right right,
Speaker 1: UM and and the Clean Energy Energy Project is looking
Speaker 1: for cheaper, more efficient, greener, more more flexible solar cell materials.
Speaker 1: But we've talked a lot on the show previously about
Speaker 1: the issues with solar cells and what makes them so
Speaker 1: clunky and expensive and difficult to manufacture UM, so the
Speaker 1: research that they're doing could be huge for the future
Speaker 1: of energy. UM. The project itself has been around since
Speaker 1: about two thousand four, and they teamed up with Harvard
Speaker 1: University to create this distributed UH computing program around UH
Speaker 1: twenty ten or eleven, which is when phase one kicked off.
Speaker 1: It just completed recently, and and during it, volunteers helped
Speaker 1: to sift through a database of over two million compounds
Speaker 1: that they were mathematically testing how these these molecules joined
Speaker 1: together to form solids, and then predicting whether those solids
Speaker 1: could have the right electrical properties to be useful as
Speaker 1: components and solar cells. UH. They isolated some thirty six
Speaker 1: thousand compounds that could be able to to double the
Speaker 1: current efficiency of your average solar cell so UM. According
Speaker 1: to the project website, we can put the work of
Speaker 1: this in perspective by saying that it would have taken
Speaker 1: a single average PC some seventeen thousand years to have
Speaker 1: done this work. The Volunteer Computers joint effort took about
Speaker 1: three seventeen thousand years. Yeah, I think we might have
Speaker 1: different problems by then some time. That's quite some time.
Speaker 1: It's a chunk. It's a chunk, um And the project
Speaker 1: has now moved on to phase two, in which there
Speaker 1: phase two. I know it does sound fair. We're moved
Speaker 1: on to final processing. I hear phase two. I just think,
Speaker 1: now what we we figured out the most efficient means
Speaker 1: for solar panels now the world domination. Yeah, it's very
Speaker 1: very pinky in the brain. Um but but but so,
Speaker 1: these these compounds, these thirty six thousand some compounds that
Speaker 1: they've identified UM as being potentially useful, are going to
Speaker 1: be explored more thoroughly. You know, all of their physical
Speaker 1: and electrochemical properties from from optical ability to squish ability
Speaker 1: are are going to be identified. Like going down as
Speaker 1: squish ability is important and material science it's the most
Speaker 1: it's the most important, um you know, going down to
Speaker 1: the quantum level of what's going on with these things.
Speaker 1: So the idea here is going to be to put
Speaker 1: together a database containing everything about these compounds and to
Speaker 1: provide um, you know, not only that, but also direct
Speaker 1: input to various researchers and inventors who are working on
Speaker 1: improving solar cell design. Cool. That's really awesome. Yeah, well, Joe,
Speaker 1: why don't you tell me a little bit of out proteins?
Speaker 1: Why do you ask, Jonathan, Because I'm pretty sure you're
Speaker 1: the one who wrote the notes on it, you'd be
Speaker 1: right now. I'm gonna talk about proteins because one of
Speaker 1: the most interesting projects in distributed computing I think is
Speaker 1: called folding at Home. And you might have heard of
Speaker 1: this before because it's been around for years. This has
Speaker 1: been operating since two thousand. Yeah. Yeah, it's still going strong.
Speaker 1: And I think I think we've talked a little bit
Speaker 1: about protein folding before on the show. It was either
Speaker 1: on this her tech stuff. They'll get a little scrambled sometimes.
Speaker 1: I think we've talked about folded before. I don't know.
Speaker 1: We did to fold it, which was the actual computer
Speaker 1: user program where you would video in order to to
Speaker 1: self self work out these problems self workout. That was
Speaker 1: so good grammar man, I need a good self work out.
Speaker 1: But yes, so proteins. Our cells are kind of made
Speaker 1: up of them. They do stuff in our bodies, right, Yeah, well,
Speaker 1: proteins are what make your body interesting. Well, what makes
Speaker 1: you more interesting than a rock or a mud puddle
Speaker 1: there in general, not like my body in particular, your
Speaker 1: body in particular, but also everybody else's body. They're they're
Speaker 1: they're sort of the animal workhorses that they do all
Speaker 1: the molecular work in your body. You've often heard of
Speaker 1: d n A being referred to as like plans or blueprints.
Speaker 1: The blueprints are for making proteins. They make amino acids,
Speaker 1: which chain up into proteins. So a protein is a
Speaker 1: huge long chain of amino acids in a particular sequence.
Speaker 1: So think of like a literal chain, except each link
Speaker 1: in the chain is of a different type that has
Speaker 1: different properties. Now, imagine you're building a machine, maybe a
Speaker 1: gigantic factory assembly line for making big mouth billy bass
Speaker 1: or terrible person for making heath bars. There you go, exact,
Speaker 1: bring it all back, but all you have to work
Speaker 1: with are these chains, and chains don't do much good
Speaker 1: as machine parts. It sounds like a floppy machine. But
Speaker 1: have you ever taken a chain and twisted it and
Speaker 1: keep twisting it kind of curls up, makes these these
Speaker 1: very like eventually uh immobile kind of shape. Yeah. And
Speaker 1: so if you can take a chain and twist it
Speaker 1: up into a tighter, rigid shape, you might be able
Speaker 1: to take those tight rigid shapes that are different because
Speaker 1: of the different amino acids and the sequences in the
Speaker 1: different ways that they twist up when you when you
Speaker 1: press them together, they might fit together like gears, or
Speaker 1: like wires and sockets, or like wheels and levers. And
Speaker 1: this is sort of what happens in your body. For
Speaker 1: protein chains inside your body to do their job, they
Speaker 1: need to have the right shape, and to assume the
Speaker 1: right shape, they fold up. This is called protein folding. Um. Now,
Speaker 1: most of the time a protein folds up without a problem, right,
Speaker 1: It just goes on to do its important role and
Speaker 1: whatever that might be. It could be making up a
Speaker 1: body tissue like an artery or a strand of hair
Speaker 1: or a little piece of muscle, or it might become
Speaker 1: an enzyme or who knows what I mean. They do
Speaker 1: call all kinds of stuff in your body. Yeah, these
Speaker 1: are these are the basic building blocks of like Joe said,
Speaker 1: of what make you electrically and chemically you right, But
Speaker 1: there's a downside because sometimes, in rare cases, a protein
Speaker 1: fails to fold correctly and the chain does not twist
Speaker 1: up into the correct shape for doing its job, and
Speaker 1: so a misfolded protein can be a really bad thing
Speaker 1: in the body. A lot of really bad diseases are
Speaker 1: now believed to be caused by either the accumulations or
Speaker 1: the effects of misfolded proteins. So the examples given by
Speaker 1: the Folding at Home project are Alzheimer's disease, cystic fibrosis
Speaker 1: uh b SC, also known as mad cow disease. Uh
Speaker 1: They cite an inherited form of emphysema and some cancers.
Speaker 1: So obviously, in order to cure these diseases, we need
Speaker 1: to know more about protein folding and about misfolding in particular,
Speaker 1: and unfortunately, there's a lot we don't know about how
Speaker 1: protein folding works because obviously, I mean, it's so tiny
Speaker 1: and it happened so fast you can't just like film
Speaker 1: it and run the game footage. Yeah, yeah, And there's
Speaker 1: so many potential combinations that they can they can mess
Speaker 1: up in a whole bunch of different exciting ways, exactly right.
Speaker 1: So how are you going to solve this problem? Well,
Speaker 1: for more than a decade now, we've been trying to
Speaker 1: learn more about the whole process of protein folding by
Speaker 1: running computer simulations on this distributed network. Now, there are
Speaker 1: different kinds of simulations you can run about protein folding.
Speaker 1: You could just try to look at what the amino
Speaker 1: acid chain is and then predict the final shape based
Speaker 1: on that. So that's one kind of simulation, but that's
Speaker 1: not what Folding at Home does. Folding at Home was
Speaker 1: designed specifically to study the whole process of folding, focusing
Speaker 1: on the intermediate states. So what's happening as this chain
Speaker 1: is curling up on it? So because they think that
Speaker 1: those intermediate states might be the source of the problem
Speaker 1: with misfolding based diseases. This is really I mean, I
Speaker 1: find the whole concept of protein folding so fascinating because
Speaker 1: if you look at it with just a couple of
Speaker 1: amino acids like a small string, you could kind of
Speaker 1: conceptualize how those rules all work together, that certain sequences
Speaker 1: are going to fold in very specific ways. It's just
Speaker 1: a set of rules. But then as you increase the
Speaker 1: length of that chain, it becomes more and more complicated,
Speaker 1: which rules end up being the ones that take priority,
Speaker 1: which ones are going to happen first, And then by
Speaker 1: the time you get to an actual protein length chain,
Speaker 1: it is almost unfathomably difficult for us to conceive this.
Speaker 1: This is truly what we were saying earlier, computationally intensive
Speaker 1: to simulate, especially because of the time length involved. So
Speaker 1: from our point of view that they point out, and
Speaker 1: I think it's a good point to make from our
Speaker 1: point of view, a protein folds almost immediately, you might
Speaker 1: take a millisecond or a microsecond. But to run this
Speaker 1: simulation simulating every single force at the molecular level, that
Speaker 1: that that long of a transformation takes forever to compute.
Speaker 1: You know, it might take a forever just to do
Speaker 1: a few nanoseconds of computation. And so this is a
Speaker 1: really serious computer problem. But they invited people to help out.
Speaker 1: In two thousand they launched this project, and since then
Speaker 1: it has really come a long way. In the past
Speaker 1: fourteen years, more than a hundred research papers have been
Speaker 1: published based on the simulations run by folding at home,
Speaker 1: and it's gotten obviously over time because computer, you know,
Speaker 1: because it has expanded in because computers have gotten faster,
Speaker 1: it's become a lot more powerful as a tool. And
Speaker 1: it's also been used in combination with other similar tools. So,
Speaker 1: like those other types of simulations I was talking about
Speaker 1: the the in sequence predictors, you can sort of pair
Speaker 1: these two different approaches together to get some interesting information.
Speaker 1: Or you can pair the simulations done on the distributed
Speaker 1: network with other types of simulations done on supercomputers. And
Speaker 1: so this really is really important and and useful knowledge
Speaker 1: in medical science that could cure these things. I just
Speaker 1: checked the stats on it today. Uh. They said around
Speaker 1: two pm on November fourteen that today we are one
Speaker 1: hundred and sixty three thousand, nine d and fifty eight
Speaker 1: computers strong, outputting thirty eight thousand, two hundred and twenty
Speaker 1: two TARA flops of computing power. That's a huge amount
Speaker 1: of computing. Yeah. Uh, it's it's pretty awesome. And flops
Speaker 1: is of floating point operations per second. So uh, and
Speaker 1: you're actually installed this on your computer. Oh yeah, I
Speaker 1: was right. This is one of the coolest things about it.
Speaker 1: Just from a user perspective, they give you a little
Speaker 1: readout of whatever project your CPU cycles are currently contributing to.
Speaker 1: So mine is right now, it's my computer in the
Speaker 1: background is working on project nine zero zero eight, which
Speaker 1: is targeted at Alzheimer's and it's it's studying some of
Speaker 1: the natural features of Brian Staton, which it's got a
Speaker 1: whole description I think is a little too complex to
Speaker 1: try to explain here. I don't really fully understand it,
Speaker 1: but uh, it's really cool that you can what would
Speaker 1: otherwise just be wasted potential. Just my computer sitting here
Speaker 1: while I'm talking into a microphone is actually contributing to
Speaker 1: something that could literally save lives in the future. Now,
Speaker 1: does your version of Folding at Home? Does that have
Speaker 1: the screen saver where it shows the the graphical depiction
Speaker 1: of the protein. I don't know. I haven't poked around
Speaker 1: it on it enough. See when I when I installed
Speaker 1: Folding at Home on a work computer many years ago,
Speaker 1: that was the default, Like whenever my computer would go
Speaker 1: into you know, the low energy mode or whatever, that
Speaker 1: was the screen saver. So it actually showed the protein
Speaker 1: folding a representation of the protein folding as my computer
Speaker 1: worked on it. And uh, I remember it changing colors
Speaker 1: as well. To indicate sections that had completed versus ones
Speaker 1: that were still working. But that was an early version
Speaker 1: of Folding at Home, and it may very well be
Speaker 1: that the current ones are very different because because just
Speaker 1: like any other software, this is software that gets uh patches,
Speaker 1: gets something. Yeah, it's continually updated, especially as the research
Speaker 1: moves forward. Now there are tons of other ones we
Speaker 1: could talk about. Some of the other ones that are
Speaker 1: in boink include Climate Prediction dot net, which, as he
Speaker 1: would guess, studies the climate, Cosmology at home, which studies
Speaker 1: astronomy and cosmology, Constellation which is aerospace engineering, malaria control
Speaker 1: dot net which is it all about epidemiology, mind modeling
Speaker 1: at home, which was not for people with very attractive brains.
Speaker 1: I want to get into the magazine. Yeah, I thought
Speaker 1: it was that too, like you just see brains going
Speaker 1: down the catwalk. But as it turns out, it's all
Speaker 1: about cognitive science. So I mean, still pretty cool, I guess. Yeah,
Speaker 1: there's skull. Yeah, I think we've got an idea for
Speaker 1: a reality show. Guys. Let's let's try and keep that
Speaker 1: on the d L till we can develop a full pitch.
Speaker 1: But now RNA World, which is all about molecular biology
Speaker 1: H and Quick Catcher Network which is all about seismology.
Speaker 1: And like I said, when I looked, there were around
Speaker 1: forty projects, So this is just a it's really just
Speaker 1: to show you that they go across all sorts of
Speaker 1: areas of science. Also, I shouldn't mention, and I didn't
Speaker 1: mention it earlier, that they use lots of different platforms.
Speaker 1: So depending on the project, you might be able to
Speaker 1: run this on a PC, a MAC, tablet, UH, of
Speaker 1: smartphone UH. Some are specifically designed so that specific types
Speaker 1: of graphic processing units can work on it. GPUs are
Speaker 1: fantastic because they are multi core processors, so they tend
Speaker 1: to be able to work on problems and divide those
Speaker 1: up into smaller sections that each core can work on independently.
Speaker 1: Who knew that your proclivity for violent video games could
Speaker 1: one day save the world. I certainly didn't. I did
Speaker 1: not know that until you know fairly recently. But if
Speaker 1: you would like to get involved in any of these,
Speaker 1: you can visit blink by going to b O I
Speaker 1: n C dot Berkeley dot E d U and and
Speaker 1: get the hook up from there, or you could search
Speaker 1: for Boink on your your local app store of choice.
Speaker 1: I I believe that if you search it an Android
Speaker 1: or I I Apple, I stuff, yeah that thing, you
Speaker 1: can come up with something. Um. Also, if you were
Speaker 1: interested in checking out World Community Grid, the four projects
Speaker 1: that they're featuring right now, um, well, so you've got
Speaker 1: the Cleaner Energy project. There's also geno mysteries, cancer and
Speaker 1: AIDS research up up up for for looking at, and
Speaker 1: so if you want to, if you want to check
Speaker 1: them out, you can go to World Community Grid dot org.
Speaker 1: So yeah, lots of ways to get involved, and it's
Speaker 1: so easy, right you Just all you have to do
Speaker 1: is install a little bit of software and whenever your
Speaker 1: computer is going to be idle and still connected to
Speaker 1: the network, to the Internet, you'll be able to contribute
Speaker 1: to these scientific projects. So if there's something in particular
Speaker 1: that that tickles your fancy and you think I want
Speaker 1: to be part of this, I want to contribute. And
Speaker 1: it may seem small, but it is significant, and it's
Speaker 1: definitely more significant than you saying that's a neat project
Speaker 1: but not doing anything. So there's that I recommend it.
Speaker 1: I haven't noticed really any performance difference on my computers
Speaker 1: since running these are these are designed so that they
Speaker 1: are only supposed to take up the idle cycles. Like,
Speaker 1: so if you are actually actively using your computer, it's
Speaker 1: supposed to fade into the background and not take up
Speaker 1: the resources. Yeah, so if you have an older computer
Speaker 1: where you're having some sluggish issues already, that's probably not
Speaker 1: going to be ideal because it's just that suggests that
Speaker 1: there's probably some things that need to be cleared up
Speaker 1: on your machine, just because the various processes that are running.
Speaker 1: But if you have a you know, a decent running
Speaker 1: computer and you think I wanna, you know, do my
Speaker 1: part and help science, and especially since it's going to
Speaker 1: be doing stuff when I'm not doing anything else, I
Speaker 1: recommend it too. I mean, it's a great way to
Speaker 1: get involved, so really exciting stuff. And who knows what
Speaker 1: we could see this used for in the future. Well, yeah,
Speaker 1: one of the things I was thinking about was just
Speaker 1: pairing this with the continuation of Morse law. Sure, we
Speaker 1: don't know how long MOR's Law is going to continue,
Speaker 1: but that's the thing we've talked about plenty of times
Speaker 1: on here, that computer processing power multiplies at a pretty
Speaker 1: predictable rate. It's continually getting better, and we haven't seen
Speaker 1: it stop yet. So the faster computers get, the more
Speaker 1: and more you can do with a hundred thousand of them,
Speaker 1: or two hundred thousand of them, or or I don't
Speaker 1: know how many thousands of them you can you can
Speaker 1: talk people into sharing their their computer power with you. Sure,
Speaker 1: and then also with multi core processors being able to
Speaker 1: divide problems up into smaller problems. As long as we
Speaker 1: continue that pathway where we're able to do that effectively,
Speaker 1: than this kind of approach could be useful in all
Speaker 1: areas of science. There's not there's not a specific one
Speaker 1: that it would be better for. I mean, anything that
Speaker 1: has huge amounts of data that needs to be analyzed
Speaker 1: is ripe for this kind of thing. So it's really
Speaker 1: exciting stuff. I'm glad that we covered this topic. And Joe,
Speaker 1: this was something that you had suggested, and so it
Speaker 1: was a great suggestion. Um, some of you guys out
Speaker 1: there have had some great suggestions too, and I think
Speaker 1: you should continue to suggest great things and the best
Speaker 1: way to do it is to get in touch with
Speaker 1: us and let us know what those great things are.
Speaker 1: Because of the as we have to guess. So if
Speaker 1: you want to send us a message, do so at
Speaker 1: our email address that's FW thinking at how Stuff Works
Speaker 1: dot com. Or drop us a line on Facebook, Twitter,
Speaker 1: or Google Plus. Our Twitter and Google Plus handle is
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Speaker 1: want us to talk about. If there's some topic that
Speaker 1: you want to hear more about about how that's going
Speaker 1: to be in the future. Tell us ask us your
Speaker 1: silliest questions. We read all of them, we really enjoy them,
Speaker 1: and we will talk to you again really soon. For
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