Speaker 1: Brought to you by the reinvented two thousand twelve camera.
Speaker 1: It's ready. Are you get in touch with technology? With
Speaker 1: tech Stuff from how stuff works dot com. Hello again, everyone,
Speaker 1: welcome to tech Stuff. My name is Chris Boulette and
Speaker 1: I am the tech editor here at how stuff works
Speaker 1: dot Com. Sitting across from me, as he always does,
Speaker 1: is senior writer Jonathan Strickland. Hey there, all right, So
Speaker 1: what do you want to talk about today? Well, I
Speaker 1: wanted to talk about something that comes comes to us
Speaker 1: courtesy of a little listener mail. List listener mail comes
Speaker 1: from Samuel, and Samuel says, Hi, First of all, thanks
Speaker 1: for the great info. I really like your show. I
Speaker 1: just started listening to you guys, and I am liking it.
Speaker 1: I would like to know more about distributed computing services
Speaker 1: and how would I be able to sell my computers
Speaker 1: idling time or CPU cycles, also if it would be
Speaker 1: a good idea to do it. Thanks again, Sam from Pasco, Washington,
Speaker 1: By bye, Sam. It's nice talking to you. Um so
Speaker 1: distributed computing. We're gonna go into what distributing distributed computing
Speaker 1: is and um and the whole idea of selling your
Speaker 1: CPUs cycles, which I don't just go ahead and get
Speaker 1: this out of the way. UM, As far as I
Speaker 1: can tell, there are no projects currently available where you
Speaker 1: can actually sell your CPUs cycles. In most cases, you're
Speaker 1: donating your cycles. UM. There was a proposed project at
Speaker 1: one point to compensate people for their computer time, but
Speaker 1: that one kind of fizzled out before it ever reached
Speaker 1: a point where payments could be made to participants. So UM.
Speaker 1: So as it stands the as the recording of this podcast,
Speaker 1: and as far as I am aware, the only way
Speaker 1: you can participate in distributed computing projects is by either
Speaker 1: working at a particular facility or donating your your computer
Speaker 1: CPUs cycles. Should we talk about what distributed computing is,
Speaker 1: how it wants. I just wanted to make sure that
Speaker 1: we answered Samuel's question and didn't forget about it. No,
Speaker 1: I'm absolutely okay with that, So distribute computing take it away.
Speaker 1: We Distributed computing UH is a way of basically crunching data. UM.
Speaker 1: And in a lot of cases there are massive, massive
Speaker 1: projects underway. A lot of the ones I'm sure we'll
Speaker 1: be talking about in a few minutes are science related,
Speaker 1: where UH scientists, for example, are collecting lots and lots
Speaker 1: and lots of information, but they don't have the computing
Speaker 1: power to crunch all those numbers in a handy fashion. So, um, well,
Speaker 1: I would guess about the mid nineties, that's when I
Speaker 1: started seeing information on undistributed computing. They started saying, well, hey,
Speaker 1: you guys all want to help us out with these projects,
Speaker 1: So why don't we find a way to let you
Speaker 1: contribute your computer processing cycles from when your machine is down,
Speaker 1: say overnight, If you leave your computer on, it's not
Speaker 1: doing a whole lot other than just sort of sitting
Speaker 1: there on sleep. So why don't you help us out
Speaker 1: by crunching some of these numbers for us and send
Speaker 1: them back and we can use this to get our
Speaker 1: experiment further down the road, right right. It speeds things
Speaker 1: up considerably, because really, when you're working on one of
Speaker 1: these huge projects, you only have a couple of options
Speaker 1: available to you, because, as Plett was mentioning, you get
Speaker 1: tons and tons of information. If you were to use
Speaker 1: a single regular computer to try and process that, it
Speaker 1: could take thousands or even millions of years to complete
Speaker 1: the calculations. Because we're talking about enormous figures here, um,
Speaker 1: and often lots of different kinds of information coming in
Speaker 1: that you have to analyze, and the computer may only
Speaker 1: be able to concentrate on one at a time. Your
Speaker 1: other option is you could try and book some time
Speaker 1: with a supercomputer, and some of these projects are doing
Speaker 1: that too. They're also using supercomputers, not just a distributed computing,
Speaker 1: but supercomputer time is precious. There are only so many
Speaker 1: supercomputers out there. Many of them were built with a
Speaker 1: specific task in mind, so there's not a whole lot
Speaker 1: of time for for them to to dedicate to other projects.
Speaker 1: So if your project does not have a supercomputer, it
Speaker 1: may be a struggle for you to get the money
Speaker 1: and the time available on one of those machines to
Speaker 1: do your number crunching. So what do you do? Well,
Speaker 1: if the problems that you're working on can be broken
Speaker 1: down into smaller problems, you can look into distributed computing. Now,
Speaker 1: some of you may be thinking this sounds a lot
Speaker 1: like what we talked about when we talk about multi
Speaker 1: core processors. It is not dissimilar. The two are are
Speaker 1: very close together in a way. You're talking about a
Speaker 1: big problem that you can break down into smaller problems,
Speaker 1: and each processor or in distributed computing. Each computer can
Speaker 1: work on a portion of that problem, solve it, send
Speaker 1: it back to a master computer, which then incorporates everything
Speaker 1: that has been gathered up to that point, and you
Speaker 1: end up solving the big problem much faster than you
Speaker 1: would if you tackled it with just one machine. Yeah. Actually,
Speaker 1: if you think about it, if you're a long time
Speaker 1: tech stuff fan, you you've heard us talk about this
Speaker 1: in a number of occasions, UM, including when we talked
Speaker 1: about the PlayStation threes being used for the military, and um,
Speaker 1: you know the many occasions on which we've mentioned Google. Um,
Speaker 1: they sort of do the same thing if you think
Speaker 1: about it, because they're using rather than buying lots and
Speaker 1: lots of really expensive hardware. UH, they used sort of
Speaker 1: run of the mill uh PCs using Linux. They're sort
Speaker 1: of custom built. But um, they're still not you know,
Speaker 1: high end, high end, super powerful computers. UM. And by
Speaker 1: using those uh smaller, less powerful computers, they're able to
Speaker 1: distribute the load of the entire network on UH and
Speaker 1: and spread their budget out a little bit more too. So, right,
Speaker 1: and I have to kind of fun. Definitions of fun
Speaker 1: might be a stretch. I have two definitions. Have distributed
Speaker 1: computing from two different individuals. So this first one comes
Speaker 1: from andrew S. Tannenbaum, and he says, distributed computing distributed
Speaker 1: communing system is a collection of independent computers that appears
Speaker 1: to its users as a single coherent system. So, in
Speaker 1: other words, from a macro point of view, if you
Speaker 1: if you get the bird's eye view on this, Uh, yeah,
Speaker 1: it's a system that's made up of lots and lots
Speaker 1: of computers, but they're all working to the same end,
Speaker 1: and they have, you know, a kind of a master
Speaker 1: that is coordinating everything. If you look at this one
Speaker 1: big system, then you just think, oh, each of these
Speaker 1: computers is really a processor. And another example of of
Speaker 1: distributing computing we've talked about in the face it applies
Speaker 1: to that definition a bot net. Yes, that's also a
Speaker 1: good example. Uh. And you could argue that the Internet
Speaker 1: itself is in a way a distributed computing system, although
Speaker 1: it's not really geared to a specific project. It's not
Speaker 1: solving a problem so much, but the computing power of
Speaker 1: moving all that information is distributed. Yes. So then the
Speaker 1: second definition I wanted to mention comes from Leslie Lamport,
Speaker 1: who says, you know, you know, you have of a
Speaker 1: distributed system when the crash of a computer you've never
Speaker 1: heard of halts all progress. So that brings us to
Speaker 1: some of the considerations you have to have in mind
Speaker 1: when you start to build a distributed computing system. Now,
Speaker 1: to build a distributed computing system, what you really need
Speaker 1: is an algorithm that which is of course a set
Speaker 1: of instructions. An algorithm is just really just a set
Speaker 1: of instructions in mathematical format usually UM that tells computers
Speaker 1: what they have to do. You know, it gives them
Speaker 1: a step by step procedure. With a distributed algorithm, what
Speaker 1: this is doing is it determines how to divide up
Speaker 1: a problem. It determines how to send that information out
Speaker 1: to the different nodes or clients um uh. The clients
Speaker 1: and nodes in this case would be the individual computers
Speaker 1: out there UH and to get also would have to
Speaker 1: have information on how long to wait until UH for
Speaker 1: a response from that computer from that client um. In
Speaker 1: other words, if you send out a problem to a computer,
Speaker 1: and let's say that you you are using your computer
Speaker 1: to be part of one of these distributed computing projects,
Speaker 1: but you happen to go on vacation for a full
Speaker 1: month and you've turned your computer off. Well, in that
Speaker 1: that month long vacation that you're on, while you're enjoying
Speaker 1: life and drinking tropical drinks and listening to Beach Boys songs,
Speaker 1: your computer is not working on the problem that was
Speaker 1: sent to it by the master computer. If you don't
Speaker 1: build into the algorithm the you know the fact that, hey,
Speaker 1: sometimes computers are not going to send you the information
Speaker 1: you expect back, then the whole project could come to
Speaker 1: a halt. Theoretically, um Or, you could build into the
Speaker 1: algorithm if you don't hear back from the client within
Speaker 1: X number of days, then send the problem to another client,
Speaker 1: so that way you have redundancy in there. If one
Speaker 1: computer fails, the whole project doesn't come to a crashing halt.
Speaker 1: As as Leslie had pointed out, um that's something you
Speaker 1: have to build into the algorithm. As well as the
Speaker 1: idea that none of these individual computers are completely infallible,
Speaker 1: they have to be You have to be planned for
Speaker 1: the fact that some of these are going to crash. Uh.
Speaker 1: Users may turn their machines off, they may they may
Speaker 1: abandoned the project, and you have to build that into
Speaker 1: your system. So that it doesn't bring the whole thing
Speaker 1: down in the process. UM, I'm sorry you were going
Speaker 1: to say, well, no, no, it was UM. I didn't
Speaker 1: know where you're going next because I had an example.
Speaker 1: But UM. One of the projects that has become so
Speaker 1: it's really sort of an overarching way of working on
Speaker 1: different kinds of distributing computing projects is UH Berkeley University
Speaker 1: of California Berkeley blink yes, pink, the Berkeley Open Infrastructure
Speaker 1: for Network Computing. I couldn't read my own handwriting there. UM.
Speaker 1: And basically what this is it's a client a piece
Speaker 1: of client software UM that enables somebody. You can download
Speaker 1: the software and it will enable you to participate in
Speaker 1: all kinds of projects. It's sometimes more than one. Um yeah. Basically,
Speaker 1: a scheduling server sends out the instructions to your computer
Speaker 1: and says, you know, okay, here, here's your here's your information.
Speaker 1: It judges your CPU and memory, so it says I'm
Speaker 1: not gonna give you any more and you can handle,
Speaker 1: you know, go ahead and work on this. And the
Speaker 1: computer is also able to download any necessary files it
Speaker 1: needs to execute. Basically, the if the server, if the
Speaker 1: software is not up to date it can update itself
Speaker 1: so that it can go ahead and process the information.
Speaker 1: Then your computer does the UH, does the calculations, and
Speaker 1: sends it back up to the data server and tells
Speaker 1: the scheduling server, hey, I'm free. And basically they give
Speaker 1: you credit for the amount of cycles your computer has
Speaker 1: participated in. And it does send out each problem to
Speaker 1: more than one computer, actually two in this case, and
Speaker 1: based you know, it to be fair rather than to
Speaker 1: give all the credits. Say, you know, Nathan and I
Speaker 1: both have boy and constalled in our computers, and both
Speaker 1: of us are working on steady at home, and uh,
Speaker 1: Jonathan turned his computer off one night when I left
Speaker 1: my non so his wasn't able to process the information
Speaker 1: quite as fast because he was, you know, about ten
Speaker 1: hours shorter than than mine. The press you know, the cycles.
Speaker 1: So basically what it does is to be fair. Because
Speaker 1: Jonathan's computer did do most of the work UH to
Speaker 1: get the unit done, it gives both of us credit
Speaker 1: for what how much time our CPUs spent working on
Speaker 1: the problem. So um it sort of it serves as
Speaker 1: a a dual purpose because in one hand, if if
Speaker 1: my computer been off the entire time, the unit still
Speaker 1: needed to get to be finished. But at the same
Speaker 1: time they want to give us credit for it. Because
Speaker 1: part of the the UH, the fun and participating in
Speaker 1: some of these projects as they give you credit and
Speaker 1: you get to rack up hours and you can join
Speaker 1: teams and the teams can compete with one another. So
Speaker 1: it's mostly for fun for bragging rights. It's not like
Speaker 1: it's not a compensation thing. But when you think about it,
Speaker 1: you're helping contribute to two humans expanding our knowledge about
Speaker 1: the universe in some way, depending on what the project is.
Speaker 1: There's lots of different versions out there as emergencies go
Speaker 1: on outside of our studio. UM. But the besides the
Speaker 1: fact of the whole algorithm saying the whole UH scheduling
Speaker 1: thing that you pointed out, UM, there are two different
Speaker 1: major UH categories of distributed computing. You have synchronous, where
Speaker 1: everything is as you would imagine from the name synchronized,
Speaker 1: so that everything is going on a very set schedule,
Speaker 1: and then there's asynchronous. Now, asynchronous is harder to set up.
Speaker 1: It is much more difficult to create an algorithm that
Speaker 1: that compensates for an asynchronous system, but it's also the
Speaker 1: one that allows people more people to join, because, as
Speaker 1: you pointed out, they're gonna be times where someone's computer
Speaker 1: is going to be off for perhaps the person is
Speaker 1: using the computer for a really process or heavy application.
Speaker 1: And so most of the uh, these programs have middleware
Speaker 1: that will ease back on your computer while you're using it. Um.
Speaker 1: It may always be running in the background, but it
Speaker 1: will start to consume less and less power as you
Speaker 1: demand more and more for whatever application you need. So
Speaker 1: let's say you're doing like a lot of video editing
Speaker 1: and you need a lot of process or power at
Speaker 1: that time. Uh, it'll back off, or you can turn
Speaker 1: it off entirely for the duration of your work, which
Speaker 1: I sometimes do depending on what I have to do. Right,
Speaker 1: especially if you're you start feeling like your computers acting
Speaker 1: slugg as you start looking at the processes you can
Speaker 1: you can halt and that's usually one of the one
Speaker 1: of the first to go. But uh that's an asynchronous
Speaker 1: system can compensate for that. Uh. You know, it doesn't
Speaker 1: matter if your computer goes offline for a while. UM.
Speaker 1: And UH so that's another one of those those things
Speaker 1: that you have to take into consideration along with the
Speaker 1: UM the fact that UH if a computer does go down,
Speaker 1: you don't want your your project to crash. Another thing
Speaker 1: you have to keep in mind is that for a
Speaker 1: lot of these these, actually, I would say, are pretty
Speaker 1: much any distributed computing system that was not a very
Speaker 1: tightly controlled grid computing system, you're gonna be working with
Speaker 1: lots and lots of different kinds of hardware. Definitely. I
Speaker 1: mean we work in the same company, Chris and I
Speaker 1: and and really yeah, oddly and I have so Chris
Speaker 1: and I work in the same company, but our computers
Speaker 1: are different. Yes, his computer and my computer are not.
Speaker 1: Are not exactly the same, you know, the specs are different. Um,
Speaker 1: his his better, His e key works and mine doesn't. Um,
Speaker 1: I'm really tired of typing the number three instead of
Speaker 1: ever and over. I think that's called the minor vowel shift. Yes, yes,
Speaker 1: I had a vowel movement. Um, minor English major joke there. Yeah,
Speaker 1: we're sorry. We apologize at any rate. They you have
Speaker 1: to be able to build your system so that it
Speaker 1: can it can come sate for the fact that people
Speaker 1: are not using the exact same machine. Now, for some
Speaker 1: of these these distributed computing models, if you were, say
Speaker 1: a research facility and you just decide, you know, instead
Speaker 1: of trying to spend several hundred million dollars to build
Speaker 1: a supercomputer, we're gonna spend several thousand dollars and buy up,
Speaker 1: you know, a dozen PCs. You could theoretically make sure
Speaker 1: that every single one of them matches exactly to a
Speaker 1: specific model. Now, in that case, a synchronous approach might
Speaker 1: even work, because if all you're doing is relying on
Speaker 1: computers that you yourself are maintaining, you can make sure
Speaker 1: that everything's working properly. It's not the same as if
Speaker 1: it's a distributed computing system where anyone can join UM.
Speaker 1: And you know, really it's the Internet Protocol that made
Speaker 1: all of this possible from you know, opening it up
Speaker 1: to the public. Before that, you would just have uh
Speaker 1: various standards of computer to computer uh communication to to
Speaker 1: try and create a distributed computing system. I'm saying distributed
Speaker 1: computing so much it's it's start to lose all meaning
Speaker 1: to me, a right. But there we should also mention
Speaker 1: there are different variations of distributed computing, and the definitions
Speaker 1: are not set in stone. They're not so well defined
Speaker 1: that UM, that's easy to differentiate one from the other. So,
Speaker 1: for example, grid computing, you might think of grid computing
Speaker 1: as being a little more structured than you know, the
Speaker 1: word grid sound to be right, right, It doesn't necessarily
Speaker 1: mean that. I mean, some people use grid computing to
Speaker 1: mean any kind of distributed computing system, but or utility computing,
Speaker 1: where that kind of suggests that there's a transaction involved,
Speaker 1: like you are paying a company in order to access
Speaker 1: a certain amount of processing power or certain applications. It's
Speaker 1: kind of a software as the service spin off. Um.
Speaker 1: Then there's cloud computing, which sounds like it's a little
Speaker 1: less structured than grid computing, and that's usually what I
Speaker 1: think of when I think of these big projects that
Speaker 1: allow the public to participate. Um. But again, these definitions
Speaker 1: are just kind of guidelines, they're not There's a lot
Speaker 1: of overlap, and some people use the terms interchangeably. Yeah,
Speaker 1: so don't get too confused because no one knows what
Speaker 1: they're talking about. Yeah. I always think of distributing distributed
Speaker 1: computing as being the participatory projects that you opt into
Speaker 1: for these things, right, I mean, that's usually the way
Speaker 1: I hear it referred. And you you mentioned study at home,
Speaker 1: which would be the search for extraterrestrial intelligence. Yes, um,
Speaker 1: it was the very first project I ever worked on
Speaker 1: in a distributed sense. Honestly, I think we just need
Speaker 1: to start searching for the terrestrial kind first and then
Speaker 1: work our way up a dumb human joke. I'm sorry. Yeah.
Speaker 1: Setting at Home was the first project that I UH
Speaker 1: ever participated in like this, and it um basically it
Speaker 1: takes information gathered by a radio telescope, UM and UH
Speaker 1: sends it out to different people. Now, they used to
Speaker 1: have their own individual client, which was really pretty because
Speaker 1: it had lots and lots of multicolored blocks on it,
Speaker 1: and it when it was up as a screensaver, it
Speaker 1: made it look like your computer was doing something really
Speaker 1: really cool. Um. And and what your computer really was doing,
Speaker 1: just so you know, was analyzing the data from this
Speaker 1: this radio telescope and looking for any kind of patterns
Speaker 1: or spikes that might indicate an extraterrestrial radio transmission. Yeah.
Speaker 1: So they were, we were. They were listening, and we
Speaker 1: were helping them figure out whether we were actually picking
Speaker 1: up anything other than I love Lucy reruns and um,
Speaker 1: which oddly come from Venus. That is not true. Um. Yeah. Actually,
Speaker 1: now they're part of blink, or they're not part part
Speaker 1: of blink, but their data can be configured through boink.
Speaker 1: So if you're down to the point of client, you
Speaker 1: can do that, and a lot of projects are are
Speaker 1: done that way too. But yeah, at the moment um,
Speaker 1: in the moment I'm participating in three Wow, what are
Speaker 1: the other two? Um? Well no, actually I was okay,
Speaker 1: So what happened was good. This is a good transition
Speaker 1: because I wanted to talk about the different kinds of
Speaker 1: things because we're not just talking uh, you know, strict
Speaker 1: lee science stuff. Although science and life sciences are two
Speaker 1: of the big categories the participatory distributed computing projects like this,
Speaker 1: there's also stuff you can do with cryptography. Um, there
Speaker 1: are other internet and finance projects. You could work on, mathematics, language, art,
Speaker 1: and even puzzles and games that you can participate in.
Speaker 1: See I got confused. I thought cryptography was when you
Speaker 1: sent people to the forbidden zone. No, right, So anyway, Um,
Speaker 1: that was a Superman joke. And I'm sorry, but two
Speaker 1: of them might participate in and I'm ignoring you through
Speaker 1: point um. One of them is the similar similarity matrix
Speaker 1: of proteins UM and Uh. The other is Rosetta at
Speaker 1: Home with both of which are our life sciences related.
Speaker 1: And then I use a third one on our work
Speaker 1: on our aforementioned work computers. Um, those two are on
Speaker 1: the on our test machine. The other one is a
Speaker 1: Stanford project that's not run through point called Folding at Home.
Speaker 1: That's proteins, right, Yes, it is also proteins. They're basically
Speaker 1: looking the proteins fold um in a way that helps
Speaker 1: them work. And we don't really understand what it takes
Speaker 1: for the proteins to actually activate, so we're looking at
Speaker 1: you know, we're looking at how the proteins fold and
Speaker 1: how that helps determine what the proteins do. Yeah, exactly exactly,
Speaker 1: And it's uh, it's the difference in this and the
Speaker 1: point stuff is, you know, the Folding its home has
Speaker 1: a screensaver components. So when my computer is you know,
Speaker 1: a sleep when I go to get coffee or something
Speaker 1: like that. Uh, it has all these funny looking look
Speaker 1: molecule looking things up on the screen and solely are
Speaker 1: are building the the supposedly building the protein. Yeah. And uh,
Speaker 1: I even convinced, uh stuff you should know as Josh
Speaker 1: Clark to participate in the process too. So we have
Speaker 1: a house stuff Works group. Yes, we do on on
Speaker 1: that group, so you know you can always join us there,
Speaker 1: and I um, I'm going to start groups too for
Speaker 1: the other two projects I'm working on in our house
Speaker 1: stuff works dot Com groups. Hopefully I can get ones
Speaker 1: for the other groups and we can really get a
Speaker 1: how stuff works dot Com movement behind distributed computing and
Speaker 1: have a have fun trying to take on the other
Speaker 1: groups with it. And will make sure that we include
Speaker 1: that when we do the blog post right up for
Speaker 1: this this episode. Yeah, because I haven't done them all yet,
Speaker 1: so I'm gonna have to put those together. So there
Speaker 1: are a couple of other ones that we can talk about.
Speaker 1: There's the Milky Way at Home projects. That's that one's
Speaker 1: mapping out the Sagittarius stream and analyzing the data from that.
Speaker 1: There's Einstein at Home, which is looking for gravitational waves,
Speaker 1: particularly from pulsars um. There's the amount of time you
Speaker 1: can put in on that one is sort of relative nice. Yeah,
Speaker 1: it breaks down the closer you get to the big bang. Uh.
Speaker 1: The these these projects, of course, like like the other
Speaker 1: ones we mentioned, just involve massive amounts of data that
Speaker 1: needs to be analyzed and uh, and that's why these
Speaker 1: these projects are so important and why they depend so
Speaker 1: heavily upon volunteers, because otherwise, if you just dedicated you know,
Speaker 1: a dozen machines to this, it would take forever. But
Speaker 1: other big projects used distributed computing as well, like the
Speaker 1: Large Hadron Collider is collecting well, when it's working, it's
Speaker 1: collecting terra bytes of data, and so they use they're
Speaker 1: using so they're using a distributed computing to analyze all
Speaker 1: of that. And talking about the Big Bang, I mean,
Speaker 1: that's really what the LHC is is doing. It's it's
Speaker 1: slamming these protons together at such magnificent speeds that the
Speaker 1: resulting explosion is sort of in miniature. And I'm talking
Speaker 1: like unimaginable. Maniagure the sort of effects that you would
Speaker 1: have witnessed at the at the Big Bang, had there
Speaker 1: been some way for you to actually witness them. Now,
Speaker 1: I haven't heard whether they're going to allow the public
Speaker 1: to participate in the crunching of those numbers. It's going
Speaker 1: to be just as sign scientific community. As far as
Speaker 1: I can tell. That is one of those closed grid
Speaker 1: computing systems where UM I haven't seen anything about a
Speaker 1: public uh public version. Cern is pretty tight with that.
Speaker 1: That also leads another to another issue. First of all,
Speaker 1: to to run any of these UM at home type projects,
Speaker 1: normally you have to download some software. I mean pretty much,
Speaker 1: you have to download some software. This is the software
Speaker 1: that allows your computer to communicate with the master computer
Speaker 1: and it allows your computer to accept assignments and then
Speaker 1: work on the assignments because he usually has whatever the
Speaker 1: program is that's need to analyze the data and then
Speaker 1: send the information back. So, um yeah, just getting the
Speaker 1: numbers that it isn't going to really help you much
Speaker 1: to crunch them through, right Well, that means you have
Speaker 1: to download some software on your computer. So right there,
Speaker 1: there's a security vulnerability anytime you have to download software.
Speaker 1: Um Now, most of these are coming from very trustworthy sources,
Speaker 1: and I would imagine that they have pretty good security
Speaker 1: on their their products so that someone's not gonna, you know,
Speaker 1: kind of do a man in the middle attack and
Speaker 1: and have you download malware instead of the middleware. But
Speaker 1: you may also worry about the fact that this is
Speaker 1: essentially giving another computer access to your computer's processing power.
Speaker 1: Hopefully there are no back doors built into that software
Speaker 1: so that they could say, get control administrative control of
Speaker 1: your machine, or spy on any of your data. Again,
Speaker 1: most of these projects are from very reputable sources, and
Speaker 1: it's a good bet that there's no malicious intent behind them.
Speaker 1: But doesn't mean there isn't in vulnerability, right, Yes, there
Speaker 1: could be vulnerabilities. I mean sometimes people just don't think
Speaker 1: of that when they're building a tool. You know, they're
Speaker 1: not because they're thinking how can I achieve this goal?
Speaker 1: They're not thinking how can I make sure someone doesn't
Speaker 1: take advantage of this tool to do something naughty? Right,
Speaker 1: So just just you know, buyer beware naughty naughty um.
Speaker 1: Another thing to consider if you're at all environmentally conscious
Speaker 1: is if your computer is not on I mean not
Speaker 1: being used, say overnight, and you're contributing to one of
Speaker 1: these distributed computing projects. Yes, you're helping out science, but
Speaker 1: you're also running your computer when you don't absolutely have
Speaker 1: to write. So it's kind of a trade off. You know,
Speaker 1: if you're thinking about doing good, you have two ways
Speaker 1: to do good and there's sort of at a huts
Speaker 1: with each other, so you have to decide which is
Speaker 1: more important to you. Turning your computer off or right.
Speaker 1: So you're talking about conserving electricity. I didn't actually say that,
Speaker 1: but there's there's another element to it besides that. So
Speaker 1: there's the conserving electricity concern where if you do participate
Speaker 1: in this project, you are going to be consuming more power.
Speaker 1: So not only is it not it's not only is
Speaker 1: it less green, but it also could impact your electricity bill,
Speaker 1: although probably not to a huge extent um, but it
Speaker 1: could still affect it. The other thing to remember is
Speaker 1: that if you have a cap on how much data
Speaker 1: you can access on the internet, because some some I
Speaker 1: s P s cap how much you can access. If
Speaker 1: you're running this, that's going to count toward that cap.
Speaker 1: So let's say that you like to watch lots of
Speaker 1: streaming movies and stuff. Well, that eats up a lot
Speaker 1: of bandwidth pretty quickly. And if you are running this
Speaker 1: in the background the whole time, you may start inching
Speaker 1: toward that cap faster than you would imagine, because you know,
Speaker 1: you're always having maintaining that connection with the master computer
Speaker 1: um or at least you're you're it's checking back and
Speaker 1: you know, receiving data and sending dat on a fairly
Speaker 1: regular basis. So if you do have a cap on
Speaker 1: your broadband usage, think about that before you sign up
Speaker 1: for one of these things too, because if you're anywhere
Speaker 1: close to that cap, this might put you over the edge,
Speaker 1: depending on you know, how often you're letting it run
Speaker 1: in the background. Uh, did you have anything else to add? No? Yeah,
Speaker 1: I really focused on the uh, the public aspect of
Speaker 1: this because I thought it would be more fun to
Speaker 1: talk about. And sure, and it's and it's the part
Speaker 1: that applies. Yeah, it's the part that applies to our
Speaker 1: listeners the most too, unless they're all working at certain
Speaker 1: in which case, hey y'all, Hey y'all, they probably have
Speaker 1: to have that translated since they're in Switzerland. That's true.
Speaker 1: I don't, I don't, I don't. I can't speak any
Speaker 1: of those languages. Bonjeur, y'all. Yeah, there you go. That's
Speaker 1: part of its in France. Um, but no, I mean,
Speaker 1: it's it's it's kind of fun to participate in these groups.
Speaker 1: And uh, certainly, Uh, you're welcome to join any of
Speaker 1: the house stuff works dot com distributed computing groups that
Speaker 1: will set up and and uh, you know, like I said,
Speaker 1: maybe we'll take out some of those other media companies
Speaker 1: with extreme prejudice indeed excellent. Well that was a really
Speaker 1: good discussion and it leads us into our second round
Speaker 1: of a listener mail. This listener mail comes from Cameron,
Speaker 1: and Cameron says, Hi, Jonathan and Chris, I look through
Speaker 1: your podcast episodes and couldn't find one covering the topic
Speaker 1: of daylight saving time. Please cover the history and why
Speaker 1: it was made, like you guys normally do. I love
Speaker 1: the podcast, keep up the great work. Well, thanks, Cameron,
Speaker 1: But you know what daylight saving time really isn't in
Speaker 1: our wheelhouse. It's not really tech, it's not really tech related.
Speaker 1: But what I suggest is that you send this request
Speaker 1: to either stuff you missed in history class which could
Speaker 1: give you the historical low down on daylight saving time,
Speaker 1: or stuff you should know. And if you write the
Speaker 1: stuff you should know and ask them if they can
Speaker 1: cover the topic, you can also tell them that they
Speaker 1: smell that they smell wars are heating up. Yeah, yeah,
Speaker 1: they smell. We We've already received emails that we smell,
Speaker 1: so they also smell. Actually, technically, never mind, I can't go.
Speaker 1: Thanks so much for your mail, Cameron, Please continue to listen. Sorry,
Speaker 1: we can't tackle that one. It's just not tech enough,
Speaker 1: I think. But if any of you have any questions
Speaker 1: or criticisms, concerns you just want to say hi, you
Speaker 1: can write us our email addresses tech stuff at how
Speaker 1: stuff works dot com. We have articles about grid computing,
Speaker 1: cloud computing, all sorts of distributed computing things, bombie computers.
Speaker 1: All that you can find at the website how suff
Speaker 1: works dot com. And we will talk to you again
Speaker 1: really soon for moralness and thousands of other topics. Does
Speaker 1: it how stuff works dot com And be sure to
Speaker 1: check out the new tech stuff blog now on the
Speaker 1: house stuff Works homepage, brought to you by the reinvented
Speaker 1: two thousand twelve camera. It's ready, are you